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The captcha solver made by and for japanese high school girls!
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JKCS/JKCS.user.js

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// ==UserScript==
// @name Janny Skiller's Captcha Solver
// @namespace /cumg/
// @match https://boards.4channel.org/*
// @match https://boards.4chan.org/*
// @grant none
// @version 1.1
// @author /cumg/, formerly AUTOMATIC
// @description The Janny Skillers Captcha Solver of choice
// ==/UserScript==
const _DOMParser = DOMParser;
(() => {
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __getProtoOf = Object.getPrototypeOf;
var __hasOwnProp = Object.prototype.hasOwnProperty;
var __esm = (fn, res) => function __init() {
return fn && (res = (0, fn[__getOwnPropNames(fn)[0]])(fn = 0)), res;
};
var __commonJS = (cb, mod4) => function __require() {
return mod4 || (0, cb[__getOwnPropNames(cb)[0]])((mod4 = { exports: {} }).exports, mod4), mod4.exports;
};
var __export = (target, all4) => {
for (var name in all4)
__defProp(target, name, { get: all4[name], enumerable: true });
};
var __copyProps = (to, from, except, desc) => {
if (from && typeof from === "object" || typeof from === "function") {
for (let key of __getOwnPropNames(from))
if (!__hasOwnProp.call(to, key) && key !== except)
__defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
}
return to;
};
var __toESM = (mod4, isNodeMode, target) => (target = mod4 != null ? __create(__getProtoOf(mod4)) : {}, __copyProps(
isNodeMode || !mod4 || !mod4.__esModule ? __defProp(target, "default", { value: mod4, enumerable: true }) : target,
mod4
));
// <define:BUILD_VERSION>
var init_define_BUILD_VERSION = __esm({
"<define:BUILD_VERSION>"() {
}
});
// node_modules/.pnpm/[email protected]/node_modules/long/src/long.js
var require_long = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/long/src/long.js"(exports, module) {
init_define_BUILD_VERSION();
module.exports = Long2;
var wasm = null;
try {
wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([
0,
97,
115,
109,
1,
0,
0,
0,
1,
13,
2,
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1,
6,
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0,
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0,
11,
36,
1,
1,
126,
32,
0,
173,
32,
1,
173,
66,
32,
134,
132,
32,
2,
173,
32,
3,
173,
66,
32,
134,
132,
126,
34,
4,
66,
32,
135,
167,
36,
0,
32,
4,
167,
11,
36,
1,
1,
126,
32,
0,
173,
32,
1,
173,
66,
32,
134,
132,
32,
2,
173,
32,
3,
173,
66,
32,
134,
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127,
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167,
36,
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66,
32,
134,
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32,
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3,
173,
66,
32,
134,
132,
128,
34,
4,
66,
32,
135,
167,
36,
0,
32,
4,
167,
11,
36,
1,
1,
126,
32,
0,
173,
32,
1,
173,
66,
32,
134,
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32,
2,
173,
32,
3,
173,
66,
32,
134,
132,
129,
34,
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66,
32,
135,
167,
36,
0,
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4,
167,
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36,
1,
1,
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32,
0,
173,
32,
1,
173,
66,
32,
134,
132,
32,
2,
173,
32,
3,
173,
66,
32,
134,
132,
130,
34,
4,
66,
32,
135,
167,
36,
0,
32,
4,
167,
11
])), {}).exports;
} catch (e) {
}
function Long2(low, high, unsigned) {
this.low = low | 0;
this.high = high | 0;
this.unsigned = !!unsigned;
}
Long2.prototype.__isLong__;
Object.defineProperty(Long2.prototype, "__isLong__", { value: true });
function isLong(obj) {
return (obj && obj["__isLong__"]) === true;
}
Long2.isLong = isLong;
var INT_CACHE = {};
var UINT_CACHE = {};
function fromInt(value, unsigned) {
var obj, cachedObj, cache;
if (unsigned) {
value >>>= 0;
if (cache = 0 <= value && value < 256) {
cachedObj = UINT_CACHE[value];
if (cachedObj)
return cachedObj;
}
obj = fromBits(value, (value | 0) < 0 ? -1 : 0, true);
if (cache)
UINT_CACHE[value] = obj;
return obj;
} else {
value |= 0;
if (cache = -128 <= value && value < 128) {
cachedObj = INT_CACHE[value];
if (cachedObj)
return cachedObj;
}
obj = fromBits(value, value < 0 ? -1 : 0, false);
if (cache)
INT_CACHE[value] = obj;
return obj;
}
}
Long2.fromInt = fromInt;
function fromNumber(value, unsigned) {
if (isNaN(value))
return unsigned ? UZERO : ZERO;
if (unsigned) {
if (value < 0)
return UZERO;
if (value >= TWO_PWR_64_DBL)
return MAX_UNSIGNED_VALUE;
} else {
if (value <= -TWO_PWR_63_DBL)
return MIN_VALUE;
if (value + 1 >= TWO_PWR_63_DBL)
return MAX_VALUE;
}
if (value < 0)
return fromNumber(-value, unsigned).neg();
return fromBits(value % TWO_PWR_32_DBL | 0, value / TWO_PWR_32_DBL | 0, unsigned);
}
Long2.fromNumber = fromNumber;
function fromBits(lowBits, highBits, unsigned) {
return new Long2(lowBits, highBits, unsigned);
}
Long2.fromBits = fromBits;
var pow_dbl = Math.pow;
function fromString(str, unsigned, radix) {
if (str.length === 0)
throw Error("empty string");
if (str === "NaN" || str === "Infinity" || str === "+Infinity" || str === "-Infinity")
return ZERO;
if (typeof unsigned === "number") {
radix = unsigned, unsigned = false;
} else {
unsigned = !!unsigned;
}
radix = radix || 10;
if (radix < 2 || 36 < radix)
throw RangeError("radix");
var p2;
if ((p2 = str.indexOf("-")) > 0)
throw Error("interior hyphen");
else if (p2 === 0) {
return fromString(str.substring(1), unsigned, radix).neg();
}
var radixToPower = fromNumber(pow_dbl(radix, 8));
var result = ZERO;
for (var i = 0; i < str.length; i += 8) {
var size = Math.min(8, str.length - i), value = parseInt(str.substring(i, i + size), radix);
if (size < 8) {
var power = fromNumber(pow_dbl(radix, size));
result = result.mul(power).add(fromNumber(value));
} else {
result = result.mul(radixToPower);
result = result.add(fromNumber(value));
}
}
result.unsigned = unsigned;
return result;
}
Long2.fromString = fromString;
function fromValue(val, unsigned) {
if (typeof val === "number")
return fromNumber(val, unsigned);
if (typeof val === "string")
return fromString(val, unsigned);
return fromBits(val.low, val.high, typeof unsigned === "boolean" ? unsigned : val.unsigned);
}
Long2.fromValue = fromValue;
var TWO_PWR_16_DBL = 1 << 16;
var TWO_PWR_24_DBL = 1 << 24;
var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL;
var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL;
var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2;
var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL);
var ZERO = fromInt(0);
Long2.ZERO = ZERO;
var UZERO = fromInt(0, true);
Long2.UZERO = UZERO;
var ONE = fromInt(1);
Long2.ONE = ONE;
var UONE = fromInt(1, true);
Long2.UONE = UONE;
var NEG_ONE = fromInt(-1);
Long2.NEG_ONE = NEG_ONE;
var MAX_VALUE = fromBits(4294967295 | 0, 2147483647 | 0, false);
Long2.MAX_VALUE = MAX_VALUE;
var MAX_UNSIGNED_VALUE = fromBits(4294967295 | 0, 4294967295 | 0, true);
Long2.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE;
var MIN_VALUE = fromBits(0, 2147483648 | 0, false);
Long2.MIN_VALUE = MIN_VALUE;
var LongPrototype = Long2.prototype;
LongPrototype.toInt = function toInt() {
return this.unsigned ? this.low >>> 0 : this.low;
};
LongPrototype.toNumber = function toNumber() {
if (this.unsigned)
return (this.high >>> 0) * TWO_PWR_32_DBL + (this.low >>> 0);
return this.high * TWO_PWR_32_DBL + (this.low >>> 0);
};
LongPrototype.toString = function toString(radix) {
radix = radix || 10;
if (radix < 2 || 36 < radix)
throw RangeError("radix");
if (this.isZero())
return "0";
if (this.isNegative()) {
if (this.eq(MIN_VALUE)) {
var radixLong = fromNumber(radix), div3 = this.div(radixLong), rem1 = div3.mul(radixLong).sub(this);
return div3.toString(radix) + rem1.toInt().toString(radix);
} else
return "-" + this.neg().toString(radix);
}
var radixToPower = fromNumber(pow_dbl(radix, 6), this.unsigned), rem = this;
var result = "";
while (true) {
var remDiv = rem.div(radixToPower), intval = rem.sub(remDiv.mul(radixToPower)).toInt() >>> 0, digits = intval.toString(radix);
rem = remDiv;
if (rem.isZero())
return digits + result;
else {
while (digits.length < 6)
digits = "0" + digits;
result = "" + digits + result;
}
}
};
LongPrototype.getHighBits = function getHighBits() {
return this.high;
};
LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() {
return this.high >>> 0;
};
LongPrototype.getLowBits = function getLowBits() {
return this.low;
};
LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() {
return this.low >>> 0;
};
LongPrototype.getNumBitsAbs = function getNumBitsAbs() {
if (this.isNegative())
return this.eq(MIN_VALUE) ? 64 : this.neg().getNumBitsAbs();
var val = this.high != 0 ? this.high : this.low;
for (var bit = 31; bit > 0; bit--)
if ((val & 1 << bit) != 0)
break;
return this.high != 0 ? bit + 33 : bit + 1;
};
LongPrototype.isZero = function isZero() {
return this.high === 0 && this.low === 0;
};
LongPrototype.eqz = LongPrototype.isZero;
LongPrototype.isNegative = function isNegative() {
return !this.unsigned && this.high < 0;
};
LongPrototype.isPositive = function isPositive() {
return this.unsigned || this.high >= 0;
};
LongPrototype.isOdd = function isOdd() {
return (this.low & 1) === 1;
};
LongPrototype.isEven = function isEven2() {
return (this.low & 1) === 0;
};
LongPrototype.equals = function equals(other) {
if (!isLong(other))
other = fromValue(other);
if (this.unsigned !== other.unsigned && this.high >>> 31 === 1 && other.high >>> 31 === 1)
return false;
return this.high === other.high && this.low === other.low;
};
LongPrototype.eq = LongPrototype.equals;
LongPrototype.notEquals = function notEquals(other) {
return !this.eq(other);
};
LongPrototype.neq = LongPrototype.notEquals;
LongPrototype.ne = LongPrototype.notEquals;
LongPrototype.lessThan = function lessThan(other) {
return this.comp(other) < 0;
};
LongPrototype.lt = LongPrototype.lessThan;
LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) {
return this.comp(other) <= 0;
};
LongPrototype.lte = LongPrototype.lessThanOrEqual;
LongPrototype.le = LongPrototype.lessThanOrEqual;
LongPrototype.greaterThan = function greaterThan(other) {
return this.comp(other) > 0;
};
LongPrototype.gt = LongPrototype.greaterThan;
LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) {
return this.comp(other) >= 0;
};
LongPrototype.gte = LongPrototype.greaterThanOrEqual;
LongPrototype.ge = LongPrototype.greaterThanOrEqual;
LongPrototype.compare = function compare(other) {
if (!isLong(other))
other = fromValue(other);
if (this.eq(other))
return 0;
var thisNeg = this.isNegative(), otherNeg = other.isNegative();
if (thisNeg && !otherNeg)
return -1;
if (!thisNeg && otherNeg)
return 1;
if (!this.unsigned)
return this.sub(other).isNegative() ? -1 : 1;
return other.high >>> 0 > this.high >>> 0 || other.high === this.high && other.low >>> 0 > this.low >>> 0 ? -1 : 1;
};
LongPrototype.comp = LongPrototype.compare;
LongPrototype.negate = function negate() {
if (!this.unsigned && this.eq(MIN_VALUE))
return MIN_VALUE;
return this.not().add(ONE);
};
LongPrototype.neg = LongPrototype.negate;
LongPrototype.add = function add4(addend) {
if (!isLong(addend))
addend = fromValue(addend);
var a48 = this.high >>> 16;
var a32 = this.high & 65535;
var a16 = this.low >>> 16;
var a00 = this.low & 65535;
var b48 = addend.high >>> 16;
var b32 = addend.high & 65535;
var b16 = addend.low >>> 16;
var b00 = addend.low & 65535;
var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
c00 += a00 + b00;
c16 += c00 >>> 16;
c00 &= 65535;
c16 += a16 + b16;
c32 += c16 >>> 16;
c16 &= 65535;
c32 += a32 + b32;
c48 += c32 >>> 16;
c32 &= 65535;
c48 += a48 + b48;
c48 &= 65535;
return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
};
LongPrototype.subtract = function subtract(subtrahend) {
if (!isLong(subtrahend))
subtrahend = fromValue(subtrahend);
return this.add(subtrahend.neg());
};
LongPrototype.sub = LongPrototype.subtract;
LongPrototype.multiply = function multiply3(multiplier) {
if (this.isZero())
return ZERO;
if (!isLong(multiplier))
multiplier = fromValue(multiplier);
if (wasm) {
var low = wasm.mul(
this.low,
this.high,
multiplier.low,
multiplier.high
);
return fromBits(low, wasm.get_high(), this.unsigned);
}
if (multiplier.isZero())
return ZERO;
if (this.eq(MIN_VALUE))
return multiplier.isOdd() ? MIN_VALUE : ZERO;
if (multiplier.eq(MIN_VALUE))
return this.isOdd() ? MIN_VALUE : ZERO;
if (this.isNegative()) {
if (multiplier.isNegative())
return this.neg().mul(multiplier.neg());
else
return this.neg().mul(multiplier).neg();
} else if (multiplier.isNegative())
return this.mul(multiplier.neg()).neg();
if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24))
return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned);
var a48 = this.high >>> 16;
var a32 = this.high & 65535;
var a16 = this.low >>> 16;
var a00 = this.low & 65535;
var b48 = multiplier.high >>> 16;
var b32 = multiplier.high & 65535;
var b16 = multiplier.low >>> 16;
var b00 = multiplier.low & 65535;
var c48 = 0, c32 = 0, c16 = 0, c00 = 0;
c00 += a00 * b00;
c16 += c00 >>> 16;
c00 &= 65535;
c16 += a16 * b00;
c32 += c16 >>> 16;
c16 &= 65535;
c16 += a00 * b16;
c32 += c16 >>> 16;
c16 &= 65535;
c32 += a32 * b00;
c48 += c32 >>> 16;
c32 &= 65535;
c32 += a16 * b16;
c48 += c32 >>> 16;
c32 &= 65535;
c32 += a00 * b32;
c48 += c32 >>> 16;
c32 &= 65535;
c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48;
c48 &= 65535;
return fromBits(c16 << 16 | c00, c48 << 16 | c32, this.unsigned);
};
LongPrototype.mul = LongPrototype.multiply;
LongPrototype.divide = function divide(divisor) {
if (!isLong(divisor))
divisor = fromValue(divisor);
if (divisor.isZero())
throw Error("division by zero");
if (wasm) {
if (!this.unsigned && this.high === -2147483648 && divisor.low === -1 && divisor.high === -1) {
return this;
}
var low = (this.unsigned ? wasm.div_u : wasm.div_s)(
this.low,
this.high,
divisor.low,
divisor.high
);
return fromBits(low, wasm.get_high(), this.unsigned);
}
if (this.isZero())
return this.unsigned ? UZERO : ZERO;
var approx, rem, res;
if (!this.unsigned) {
if (this.eq(MIN_VALUE)) {
if (divisor.eq(ONE) || divisor.eq(NEG_ONE))
return MIN_VALUE;
else if (divisor.eq(MIN_VALUE))
return ONE;
else {
var halfThis = this.shr(1);
approx = halfThis.div(divisor).shl(1);
if (approx.eq(ZERO)) {
return divisor.isNegative() ? ONE : NEG_ONE;
} else {
rem = this.sub(divisor.mul(approx));
res = approx.add(rem.div(divisor));
return res;
}
}
} else if (divisor.eq(MIN_VALUE))
return this.unsigned ? UZERO : ZERO;
if (this.isNegative()) {
if (divisor.isNegative())
return this.neg().div(divisor.neg());
return this.neg().div(divisor).neg();
} else if (divisor.isNegative())
return this.div(divisor.neg()).neg();
res = ZERO;
} else {
if (!divisor.unsigned)
divisor = divisor.toUnsigned();
if (divisor.gt(this))
return UZERO;
if (divisor.gt(this.shru(1)))
return UONE;
res = UZERO;
}
rem = this;
while (rem.gte(divisor)) {
approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber()));
var log22 = Math.ceil(Math.log(approx) / Math.LN2), delta = log22 <= 48 ? 1 : pow_dbl(2, log22 - 48), approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor);
while (approxRem.isNegative() || approxRem.gt(rem)) {
approx -= delta;
approxRes = fromNumber(approx, this.unsigned);
approxRem = approxRes.mul(divisor);
}
if (approxRes.isZero())
approxRes = ONE;
res = res.add(approxRes);
rem = rem.sub(approxRem);
}
return res;
};
LongPrototype.div = LongPrototype.divide;
LongPrototype.modulo = function modulo(divisor) {
if (!isLong(divisor))
divisor = fromValue(divisor);
if (wasm) {
var low = (this.unsigned ? wasm.rem_u : wasm.rem_s)(
this.low,
this.high,
divisor.low,
divisor.high
);
return fromBits(low, wasm.get_high(), this.unsigned);
}
return this.sub(this.div(divisor).mul(divisor));
};
LongPrototype.mod = LongPrototype.modulo;
LongPrototype.rem = LongPrototype.modulo;
LongPrototype.not = function not() {
return fromBits(~this.low, ~this.high, this.unsigned);
};
LongPrototype.and = function and(other) {
if (!isLong(other))
other = fromValue(other);
return fromBits(this.low & other.low, this.high & other.high, this.unsigned);
};
LongPrototype.or = function or(other) {
if (!isLong(other))
other = fromValue(other);
return fromBits(this.low | other.low, this.high | other.high, this.unsigned);
};
LongPrototype.xor = function xor(other) {
if (!isLong(other))
other = fromValue(other);
return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned);
};
LongPrototype.shiftLeft = function shiftLeft(numBits) {
if (isLong(numBits))
numBits = numBits.toInt();
if ((numBits &= 63) === 0)
return this;
else if (numBits < 32)
return fromBits(this.low << numBits, this.high << numBits | this.low >>> 32 - numBits, this.unsigned);
else
return fromBits(0, this.low << numBits - 32, this.unsigned);
};
LongPrototype.shl = LongPrototype.shiftLeft;
LongPrototype.shiftRight = function shiftRight(numBits) {
if (isLong(numBits))
numBits = numBits.toInt();
if ((numBits &= 63) === 0)
return this;
else if (numBits < 32)
return fromBits(this.low >>> numBits | this.high << 32 - numBits, this.high >> numBits, this.unsigned);
else
return fromBits(this.high >> numBits - 32, this.high >= 0 ? 0 : -1, this.unsigned);
};
LongPrototype.shr = LongPrototype.shiftRight;
LongPrototype.shiftRightUnsigned = function shiftRightUnsigned(numBits) {
if (isLong(numBits))
numBits = numBits.toInt();
numBits &= 63;
if (numBits === 0)
return this;
else {
var high = this.high;
if (numBits < 32) {
var low = this.low;
return fromBits(low >>> numBits | high << 32 - numBits, high >>> numBits, this.unsigned);
} else if (numBits === 32)
return fromBits(high, 0, this.unsigned);
else
return fromBits(high >>> numBits - 32, 0, this.unsigned);
}
};
LongPrototype.shru = LongPrototype.shiftRightUnsigned;
LongPrototype.shr_u = LongPrototype.shiftRightUnsigned;
LongPrototype.toSigned = function toSigned() {
if (!this.unsigned)
return this;
return fromBits(this.low, this.high, false);
};
LongPrototype.toUnsigned = function toUnsigned() {
if (this.unsigned)
return this;
return fromBits(this.low, this.high, true);
};
LongPrototype.toBytes = function toBytes(le) {
return le ? this.toBytesLE() : this.toBytesBE();
};
LongPrototype.toBytesLE = function toBytesLE() {
var hi = this.high, lo = this.low;
return [
lo & 255,
lo >>> 8 & 255,
lo >>> 16 & 255,
lo >>> 24,
hi & 255,
hi >>> 8 & 255,
hi >>> 16 & 255,
hi >>> 24
];
};
LongPrototype.toBytesBE = function toBytesBE() {
var hi = this.high, lo = this.low;
return [
hi >>> 24,
hi >>> 16 & 255,
hi >>> 8 & 255,
hi & 255,
lo >>> 24,
lo >>> 16 & 255,
lo >>> 8 & 255,
lo & 255
];
};
Long2.fromBytes = function fromBytes(bytes, unsigned, le) {
return le ? Long2.fromBytesLE(bytes, unsigned) : Long2.fromBytesBE(bytes, unsigned);
};
Long2.fromBytesLE = function fromBytesLE(bytes, unsigned) {
return new Long2(
bytes[0] | bytes[1] << 8 | bytes[2] << 16 | bytes[3] << 24,
bytes[4] | bytes[5] << 8 | bytes[6] << 16 | bytes[7] << 24,
unsigned
);
};
Long2.fromBytesBE = function fromBytesBE(bytes, unsigned) {
return new Long2(
bytes[4] << 24 | bytes[5] << 16 | bytes[6] << 8 | bytes[7],
bytes[0] << 24 | bytes[1] << 16 | bytes[2] << 8 | bytes[3],
unsigned
);
};
}
});
// (disabled):node_modules/.pnpm/[email protected]/node_modules/node-fetch/browser.js
var require_browser = __commonJS({
"(disabled):node_modules/.pnpm/[email protected]/node_modules/node-fetch/browser.js"() {
init_define_BUILD_VERSION();
}
});
// (disabled):util
var require_util = __commonJS({
"(disabled):util"() {
init_define_BUILD_VERSION();
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/alea.js
var require_alea = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/alea.js"(exports, module) {
init_define_BUILD_VERSION();
(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), state = 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 (state) {
if (typeof state == "object")
copy(state, xg);
prng.state = function() {
return copy(xg, {});
};
}
return prng;
}
function Mash() {
var n = 4022871197;
var mash = function(data) {
data = String(data);
for (var i = 0; i < data.length; i++) {
n += data.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;
}
})(
exports,
typeof module == "object" && module,
typeof define == "function" && define
);
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xor128.js
var require_xor128 = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xor128.js"(exports, module) {
init_define_BUILD_VERSION();
(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), state = 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 (state) {
if (typeof state == "object")
copy(state, 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;
}
})(
exports,
typeof module == "object" && module,
typeof define == "function" && define
);
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xorwow.js
var require_xorwow = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xorwow.js"(exports, module) {
init_define_BUILD_VERSION();
(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), state = 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 (state) {
if (typeof state == "object")
copy(state, 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;
}
})(
exports,
typeof module == "object" && module,
typeof define == "function" && define
);
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xorshift7.js
var require_xorshift7 = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xorshift7.js"(exports, module) {
init_define_BUILD_VERSION();
(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 init(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();
}
}
init(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), state = 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 (state) {
if (state.x)
copy(state, 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;
}
})(
exports,
typeof module == "object" && module,
typeof define == "function" && define
);
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xor4096.js
var require_xor4096 = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/xor4096.js"(exports, module) {
init_define_BUILD_VERSION();
(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 init(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 = 0 == t ? 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;
}
init(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), state = 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 (state) {
if (state.X)
copy(state, 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;
}
})(
exports,
typeof module == "object" && module,
typeof define == "function" && define
);
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/tychei.js
var require_tychei = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/lib/tychei.js"(exports, module) {
init_define_BUILD_VERSION();
(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), state = 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 (state) {
if (typeof state == "object")
copy(state, 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;
}
})(
exports,
typeof module == "object" && module,
typeof define == "function" && define
);
}
});
// (disabled):crypto
var require_crypto = __commonJS({
"(disabled):crypto"() {
init_define_BUILD_VERSION();
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/seedrandom.js
var require_seedrandom = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/seedrandom.js"(exports, module) {
init_define_BUILD_VERSION();
(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 seedrandom5(seed, options, callback) {
var key = [];
options = options == true ? { entropy: true } : options || {};
var shortseed = mixkey(flatten3(
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, state) {
if (state) {
if (state.S) {
copy(state, 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 flatten3(obj, depth) {
var result = [], typ = typeof obj, prop;
if (depth && typ == "object") {
for (prop in obj) {
try {
result.push(flatten3(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 = seedrandom5;
try {
nodecrypto = require_crypto();
} catch (ex) {
}
} else if (typeof define == "function" && define.amd) {
define(function() {
return seedrandom5;
});
} else {
math["seed" + rngname] = seedrandom5;
}
})(
typeof self !== "undefined" ? self : exports,
[],
Math
);
}
});
// node_modules/.pnpm/[email protected]/node_modules/seedrandom/index.js
var require_seedrandom2 = __commonJS({
"node_modules/.pnpm/[email protected]/node_modules/seedrandom/index.js"(exports, module) {
init_define_BUILD_VERSION();
var alea5 = 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 = alea5;
sr.xor128 = xor128;
sr.xorwow = xorwow;
sr.xorshift7 = xorshift7;
sr.xor4096 = xor4096;
sr.tychei = tychei;
module.exports = sr;
}
});
// (disabled):node_modules/.pnpm/[email protected]/node_modules/string_decoder/lib/string_decoder.js
var require_string_decoder = __commonJS({
"(disabled):node_modules/.pnpm/[email protected]/node_modules/string_decoder/lib/string_decoder.js"() {
init_define_BUILD_VERSION();
}
});
// src/main.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected][email protected]/node_modules/@tensorflow/tfjs/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/engine.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/backend.js
init_define_BUILD_VERSION();
var EPSILON_FLOAT32 = 1e-7;
var EPSILON_FLOAT16 = 1e-4;
var DataStorage = class {
constructor(backend2, dataMover) {
this.backend = backend2;
this.dataMover = dataMover;
this.data = /* @__PURE__ */ 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;
}
};
var KernelBackend = class {
refCount(dataId) {
return notYetImplemented("refCount");
}
incRef(dataId) {
return notYetImplemented("incRef");
}
timerAvailable() {
return true;
}
time(f) {
return notYetImplemented("time");
}
read(dataId) {
return notYetImplemented("read");
}
readSync(dataId) {
return notYetImplemented("readSync");
}
readToGPU(dataId, options) {
return notYetImplemented("readToGPU");
}
numDataIds() {
return notYetImplemented("numDataIds");
}
disposeData(dataId, force) {
return notYetImplemented("disposeData");
}
write(values, shape, dtype) {
return notYetImplemented("write");
}
move(dataId, values, shape, dtype, refCount) {
return notYetImplemented("move");
}
memory() {
return notYetImplemented("memory");
}
floatPrecision() {
return notYetImplemented("floatPrecision");
}
epsilon() {
return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16;
}
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/environment.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/util_base.js
init_define_BUILD_VERSION();
function shuffle(array2) {
let counter = array2.length;
let index = 0;
while (counter > 0) {
index = Math.random() * counter | 0;
counter--;
swap(array2, counter, index);
}
}
function shuffleCombo(array2, array22) {
if (array2.length !== array22.length) {
throw new Error(`Array sizes must match to be shuffled together First array length was ${array2.length}Second array length was ${array22.length}`);
}
let counter = array2.length;
let index = 0;
while (counter > 0) {
index = Math.random() * counter | 0;
counter--;
swap(array2, counter, index);
swap(array22, counter, index);
}
}
function clamp(min5, x, max5) {
return Math.max(min5, Math.min(x, max5));
}
function nearestLargerEven(val) {
return val % 2 === 0 ? val : val + 1;
}
function swap(object, left, right) {
const temp = object[left];
object[left] = object[right];
object[right] = temp;
}
function sum(arr) {
let sum5 = 0;
for (let i = 0; i < arr.length; i++) {
sum5 += arr[i];
}
return sum5;
}
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 || a instanceof Uint8ClampedArray;
}
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 ${dtype}`);
}
}
function bytesFromStringArray(arr) {
if (arr == null) {
return 0;
}
let bytes = 0;
arr.forEach((x) => bytes += x.length);
return bytes;
}
function isString(value) {
return typeof value === "string" || value instanceof String;
}
function isBoolean(value) {
return typeof value === "boolean";
}
function isNumber(value) {
return typeof value === "number";
}
function inferDtype(values) {
if (Array.isArray(values)) {
return inferDtype(values[0]);
}
if (values instanceof Float32Array) {
return "float32";
} else if (values instanceof Int32Array || values instanceof Uint8Array || values instanceof Uint8ClampedArray) {
return "int32";
} else if (isNumber(values)) {
return "float32";
} else if (isString(values)) {
return "string";
} else if (isBoolean(values)) {
return "bool";
}
return "float32";
}
function isFunction(f) {
return !!(f && f.constructor && f.call && f.apply);
}
function nearestDivisor(size, start) {
for (let i = start; i < size; ++i) {
if (size % i === 0) {
return i;
}
}
return size;
}
function computeStrides(shape) {
const rank = shape.length;
if (rank < 2) {
return [];
}
const strides = new Array(rank - 1);
strides[rank - 2] = shape[rank - 1];
for (let i = rank - 3; i >= 0; --i) {
strides[i] = strides[i + 1] * shape[i + 1];
}
return strides;
}
function createNestedArray(offset, shape, a, isComplex = false) {
const ret = new Array();
if (shape.length === 1) {
const d = shape[0] * (isComplex ? 2 : 1);
for (let i = 0; i < d; i++) {
ret[i] = a[offset + i];
}
} else {
const d = shape[0];
const rest = shape.slice(1);
const len = rest.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
for (let i = 0; i < d; i++) {
ret[i] = createNestedArray(offset + i * len, rest, a, isComplex);
}
}
return ret;
}
function toNestedArray(shape, a, isComplex = false) {
if (shape.length === 0) {
return a[0];
}
const size = shape.reduce((acc, c) => acc * c) * (isComplex ? 2 : 1);
if (size === 0) {
return [];
}
if (size !== a.length) {
throw new Error(`[${shape}] does not match the input size ${a.length}${isComplex ? " for a complex tensor" : ""}.`);
}
return createNestedArray(0, shape, a, isComplex);
}
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/environment.js
var TENSORFLOWJS_FLAGS_PREFIX = "tfjsflags";
var Environment = class {
constructor(global2) {
this.global = global2;
this.flags = {};
this.flagRegistry = {};
this.urlFlags = {};
this.getQueryParams = getQueryParams;
this.populateURLFlags();
}
setPlatform(platformName, platform) {
if (this.platform != null) {
if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
console.warn(`Platform ${this.platformName} has already been set. Overwriting the platform with ${platformName}.`);
}
}
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];
if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
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(flags) {
this.flags = Object.assign({}, flags);
}
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 = this.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;
}
var ENV = null;
function setEnvironmentGlobal(environment) {
ENV = environment;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/global_util.js
init_define_BUILD_VERSION();
var globalNameSpace;
function getGlobalNamespace() {
if (globalNameSpace == null) {
let ns;
if (typeof window !== "undefined") {
ns = window;
} else if (typeof window !== "undefined") {
ns = window;
} 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 = /* @__PURE__ */ new Map();
}
return ns._tfGlobals;
}
function getGlobal(key, init) {
const globalMap = getGlobalMap();
if (globalMap.has(key)) {
return globalMap.get(key);
} else {
const singleton = init();
globalMap.set(key, singleton);
return globalMap.get(key);
}
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/kernel_names.js
init_define_BUILD_VERSION();
var Abs = "Abs";
var Acos = "Acos";
var Acosh = "Acosh";
var Add = "Add";
var AddN = "AddN";
var All = "All";
var Any = "Any";
var ArgMax = "ArgMax";
var ArgMin = "ArgMin";
var Asin = "Asin";
var Asinh = "Asinh";
var Atan = "Atan";
var Atanh = "Atanh";
var Atan2 = "Atan2";
var AvgPool = "AvgPool";
var AvgPoolGrad = "AvgPoolGrad";
var AvgPool3D = "AvgPool3D";
var AvgPool3DGrad = "AvgPool3DGrad";
var BatchMatMul = "BatchMatMul";
var BatchToSpaceND = "BatchToSpaceND";
var Bincount = "Bincount";
var BroadcastTo = "BroadcastTo";
var BroadcastArgs = "BroadcastArgs";
var Cast = "Cast";
var Ceil = "Ceil";
var ClipByValue = "ClipByValue";
var Complex = "Complex";
var ComplexAbs = "ComplexAbs";
var Concat = "Concat";
var Conv2D = "Conv2D";
var Conv2DBackpropFilter = "Conv2DBackpropFilter";
var Conv2DBackpropInput = "Conv2DBackpropInput";
var Conv3D = "Conv3D";
var Conv3DBackpropFilterV2 = "Conv3DBackpropFilterV2";
var Conv3DBackpropInputV2 = "Conv3DBackpropInputV2";
var Cos = "Cos";
var Cosh = "Cosh";
var Cumprod = "Cumprod";
var Cumsum = "Cumsum";
var CropAndResize = "CropAndResize";
var DenseBincount = "DenseBincount";
var DepthToSpace = "DepthToSpace";
var DepthwiseConv2dNative = "DepthwiseConv2dNative";
var DepthwiseConv2dNativeBackpropFilter = "DepthwiseConv2dNativeBackpropFilter";
var DepthwiseConv2dNativeBackpropInput = "DepthwiseConv2dNativeBackpropInput";
var Diag = "Diag";
var Dilation2D = "Dilation2D";
var Dilation2DBackpropInput = "Dilation2DBackpropInput";
var Dilation2DBackpropFilter = "Dilation2DBackpropFilter";
var RealDiv = "RealDiv";
var Einsum = "Einsum";
var Elu = "Elu";
var EluGrad = "EluGrad";
var Erf = "Erf";
var Equal = "Equal";
var Exp = "Exp";
var ExpandDims = "ExpandDims";
var Expm1 = "Expm1";
var FFT = "FFT";
var Fill = "Fill";
var FlipLeftRight = "FlipLeftRight";
var Floor = "Floor";
var FloorDiv = "FloorDiv";
var FusedBatchNorm = "FusedBatchNorm";
var GatherV2 = "GatherV2";
var GatherNd = "GatherNd";
var Greater = "Greater";
var GreaterEqual = "GreaterEqual";
var Identity = "Identity";
var IFFT = "IFFT";
var Imag = "Imag";
var IsFinite = "IsFinite";
var IsInf = "IsInf";
var IsNan = "IsNan";
var LeakyRelu = "LeakyRelu";
var Less = "Less";
var LessEqual = "LessEqual";
var LinSpace = "LinSpace";
var Log = "Log";
var Log1p = "Log1p";
var LogicalAnd = "LogicalAnd";
var LogicalNot = "LogicalNot";
var LogicalOr = "LogicalOr";
var LogSoftmax = "LogSoftmax";
var LRN = "LRN";
var LRNGrad = "LRNGrad";
var Max = "Max";
var Maximum = "Maximum";
var MaxPool = "MaxPool";
var MaxPoolGrad = "MaxPoolGrad";
var MaxPool3D = "MaxPool3D";
var MaxPool3DGrad = "MaxPool3DGrad";
var MaxPoolWithArgmax = "MaxPoolWithArgmax";
var Mean = "Mean";
var Min = "Min";
var Minimum = "Minimum";
var MirrorPad = "MirrorPad";
var Mod = "Mod";
var Multinomial = "Multinomial";
var Multiply = "Multiply";
var Neg = "Neg";
var NotEqual = "NotEqual";
var NonMaxSuppressionV3 = "NonMaxSuppressionV3";
var NonMaxSuppressionV4 = "NonMaxSuppressionV4";
var NonMaxSuppressionV5 = "NonMaxSuppressionV5";
var OnesLike = "OnesLike";
var OneHot = "OneHot";
var Pack = "Pack";
var PadV2 = "PadV2";
var Pow = "Pow";
var Prelu = "Prelu";
var Prod = "Prod";
var Range = "Range";
var Real = "Real";
var Reciprocal = "Reciprocal";
var Relu = "Relu";
var Reshape = "Reshape";
var ResizeNearestNeighbor = "ResizeNearestNeighbor";
var ResizeNearestNeighborGrad = "ResizeNearestNeighborGrad";
var ResizeBilinear = "ResizeBilinear";
var ResizeBilinearGrad = "ResizeBilinearGrad";
var Relu6 = "Relu6";
var Reverse = "Reverse";
var Round = "Round";
var Rsqrt = "Rsqrt";
var ScatterNd = "ScatterNd";
var SearchSorted = "SearchSorted";
var Select = "Select";
var Selu = "Selu";
var Slice = "Slice";
var Sin = "Sin";
var Sinh = "Sinh";
var Sign = "Sign";
var Sigmoid = "Sigmoid";
var Softplus = "Softplus";
var Sqrt = "Sqrt";
var Sum = "Sum";
var SpaceToBatchND = "SpaceToBatchND";
var SplitV = "SplitV";
var Softmax = "Softmax";
var SparseFillEmptyRows = "SparseFillEmptyRows";
var SparseReshape = "SparseReshape";
var SparseSegmentMean = "SparseSegmentMean";
var SparseSegmentSum = "SparseSegmentSum";
var SparseToDense = "SparseToDense";
var SquaredDifference = "SquaredDifference";
var Square = "Square";
var StridedSlice = "StridedSlice";
var StringNGrams = "StringNGrams";
var StringSplit = "StringSplit";
var StringToHashBucketFast = "StringToHashBucketFast";
var Sub = "Sub";
var Tan = "Tan";
var Tanh = "Tanh";
var Tile = "Tile";
var TopK = "TopK";
var Transform = "Transform";
var Transpose = "Transpose";
var Unique = "Unique";
var Unpack = "Unpack";
var UnsortedSegmentSum = "UnsortedSegmentSum";
var ZerosLike = "ZerosLike";
var Step = "Step";
var FromPixels = "FromPixels";
var RotateWithOffset = "RotateWithOffset";
var _FusedMatMul = "_FusedMatMul";
var FusedConv2D = "FusedConv2D";
var FusedDepthwiseConv2D = "FusedDepthwiseConv2D";
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/log.js
init_define_BUILD_VERSION();
function warn(...msg) {
if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
console.warn(...msg);
}
}
function log(...msg) {
if (!(env().getBool("IS_TEST") || env().getBool("PROD"))) {
console.log(...msg);
}
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/kernel_registry.js
var kernelRegistry = getGlobal("kernelRegistry", () => /* @__PURE__ */ new Map());
var gradRegistry = getGlobal("gradRegistry", () => /* @__PURE__ */ 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, config] = value;
const [backend2] = key.split("_");
if (backend2 === backendName) {
result.push(config);
}
}
return result;
}
function registerKernel(config) {
const { kernelName, backendName } = config;
const key = makeKey(kernelName, backendName);
if (kernelRegistry.has(key)) {
warn(`The kernel '${kernelName}' for backend '${backendName}' is already registered`);
}
kernelRegistry.set(key, config);
}
function registerGradient(config) {
const { kernelName } = config;
if (gradRegistry.has(kernelName)) {
if (env().getBool("DEBUG")) {
warn(`Overriding the gradient for '${kernelName}'`);
}
}
gradRegistry.set(kernelName, config);
}
function makeKey(kernelName, backendName) {
return `${backendName}_${kernelName}`;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/profiler.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/util.js
var 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: () => fetch3,
fingerPrint64: () => fingerPrint64,
flatten: () => flatten,
getArrayFromDType: () => getArrayFromDType,
getTypedArrayFromDType: () => getTypedArrayFromDType,
hasEncodingLoss: () => hasEncodingLoss,
hexToLong: () => hexToLong,
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,
shuffleCombo: () => shuffleCombo,
sizeFromShape: () => sizeFromShape,
sizeToSquarishShape: () => sizeToSquarishShape,
squeezeShape: () => squeezeShape,
sum: () => sum,
swap: () => swap,
tanh: () => tanh,
toNestedArray: () => toNestedArray,
toTypedArray: () => toTypedArray
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/hash_util.js
init_define_BUILD_VERSION();
var LongExports = __toESM(require_long());
var Long = LongExports.default || LongExports;
function hexToLong(hex) {
return Long.fromString(hex, true, 16);
}
var k0 = hexToLong("c3a5c85c97cb3127");
var k1 = hexToLong("b492b66fbe98f273");
var k2 = hexToLong("9ae16a3b2f90404f");
function shiftMix(val) {
return val.xor(val.shru(47));
}
function fetch2(s, offset, numBytes) {
const bytes = s.slice(offset, offset + numBytes);
return Long.fromBytes(Array.from(bytes), true, true);
}
function fetch64(s, offset) {
return fetch2(s, offset, 8);
}
function fetch32(s, offset) {
return fetch2(s, offset, 4);
}
function rotate64(val, shift) {
return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift));
}
function hashLen16(u, v, mul2 = hexToLong("9ddfea08eb382d69")) {
let a = u.xor(v).mul(mul2);
a = a.xor(a.shru(47));
let b = v.xor(a).mul(mul2);
b = b.xor(b.shru(47));
b = b.mul(mul2);
return b;
}
function weakHashLen32WithSeeds(w, x, y, z, a, b) {
a = a.add(w);
b = rotate64(b.add(a).add(z), 21);
const c = a;
a = a.add(x);
a = a.add(y);
b = b.add(rotate64(a, 44));
return [a.add(z), b.add(c)];
}
function weakHashLen32WithSeedsStr(s, offset, a, b) {
return weakHashLen32WithSeeds(fetch64(s, offset), fetch64(s, offset + 8), fetch64(s, offset + 16), fetch64(s, offset + 24), a, b);
}
function hashLen0to16(s, len = s.length) {
if (len >= 8) {
const mul2 = k2.add(len * 2);
const a = fetch64(s, 0).add(k2);
const b = fetch64(s, len - 8);
const c = rotate64(b, 37).mul(mul2).add(a);
const d = rotate64(a, 25).add(b).mul(mul2);
return hashLen16(c, d, mul2);
}
if (len >= 4) {
const mul2 = k2.add(len * 2);
const a = fetch32(s, 0);
return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul2);
}
if (len > 0) {
const a = s[0];
const b = s[len >> 1];
const c = s[len - 1];
const y = a + (b << 8);
const z = len + (c << 2);
return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2);
}
return k2;
}
function hashLen17to32(s, len = s.length) {
const mul2 = k2.add(len * 2);
const a = fetch64(s, 0).mul(k1);
const b = fetch64(s, 8);
const c = fetch64(s, len - 8).mul(mul2);
const d = fetch64(s, len - 16).mul(k2);
return hashLen16(rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d), a.add(rotate64(b.add(k2), 18)).add(c), mul2);
}
function hashLen33to64(s, len = s.length) {
const mul2 = k2.add(len * 2);
const a = fetch64(s, 0).mul(k2);
const b = fetch64(s, 8);
const c = fetch64(s, len - 8).mul(mul2);
const d = fetch64(s, len - 16).mul(k2);
const y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d);
const z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul2);
const e = fetch64(s, 16).mul(mul2);
const f = fetch64(s, 24);
const g = y.add(fetch64(s, len - 32)).mul(mul2);
const h = z.add(fetch64(s, len - 24)).mul(mul2);
return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul2);
}
function fingerPrint64(s, len = s.length) {
const seed = Long.fromNumber(81, true);
if (len <= 32) {
if (len <= 16) {
return hashLen0to16(s, len);
} else {
return hashLen17to32(s, len);
}
} else if (len <= 64) {
return hashLen33to64(s, len);
}
let x = seed;
let y = seed.mul(k1).add(113);
let z = shiftMix(y.mul(k2).add(113)).mul(k2);
let v = [Long.UZERO, Long.UZERO];
let w = [Long.UZERO, Long.UZERO];
x = x.mul(k2).add(fetch64(s, 0));
let offset = 0;
const end = (len - 1 >> 6) * 64;
const last64 = end + (len - 1 & 63) - 63;
do {
x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(k1);
y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(k1);
x = x.xor(w[1]);
y = y.add(v[0]).add(fetch64(s, offset + 40));
z = rotate64(z.add(w[0]), 33).mul(k1);
v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(k1), x.add(w[0]));
w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
[z, x] = [x, z];
offset += 64;
} while (offset !== end);
const mul2 = k1.add(z.and(255).shl(1));
offset = last64;
w[0] = w[0].add(len - 1 & 63);
v[0] = v[0].add(w[0]);
w[0] = w[0].add(v[0]);
x = rotate64(x.add(y).add(v[0]).add(fetch64(s, offset + 8)), 37).mul(mul2);
y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul2);
x = x.xor(w[1].mul(9));
y = y.add(v[0].mul(9).add(fetch64(s, offset + 40)));
z = rotate64(z.add(w[0]), 33).mul(mul2);
v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul2), x.add(w[0]));
w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16)));
[z, x] = [x, z];
return hashLen16(hashLen16(v[0], w[0], mul2).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul2).add(x), mul2);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/util.js
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 fetch3(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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/profiler.js
var Profiler = class {
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();
};
let timer;
const start = now();
if (this.backendTimer.timerAvailable()) {
timer = this.backendTimer.time(holdResultWrapperFn);
} else {
holdResultWrapperFn();
for (const output of outputs) {
output.dataSync();
}
timer = Promise.resolve({ kernelMs: now() - start });
}
if (env().getBool("CHECK_COMPUTATION_FOR_ERRORS")) {
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;
}
var Logger = class {
logKernelProfile(name, result, vals, timeMs, inputs, extraInfo) {
const time = 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${time} %c${rank}D ${shape} %c${size} %c${inputShapesDescription} %c${extraInfo}`, "font-weight:bold", "color:red", "color:blue", "color: orange", "color: green", "color: steelblue");
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tape.js
init_define_BUILD_VERSION();
function getFilteredNodesXToY(tape, xs, y) {
const tensorsFromX = {};
const nodesFromX = {};
for (let i = 0; i < xs.length; i++) {
tensorsFromX[xs[i].id] = true;
}
for (let i = 0; i < tape.length; i++) {
const node = tape[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 = tape.length - 1; i >= 0; i--) {
const node = tape[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 < tape.length; i++) {
const node = tape[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, add4) {
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] = add4(curGradient, dx);
curGradient.dispose();
}
}
}
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tensor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
init_define_BUILD_VERSION();
var FORMAT_LIMIT_NUM_VALS = 20;
var FORMAT_NUM_FIRST_LAST_VALS = 3;
var 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, pad2, 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, pad2);
}
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tensor.js
var TensorBuffer = class {
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);
}
};
var trackerFn = null;
var opHandler = null;
var deprecationWarningFn = null;
function setTensorTracker(fn) {
trackerFn = fn;
}
function setOpHandler(handler) {
opHandler = handler;
}
function setDeprecationWarningFn(fn) {
deprecationWarningFn = fn;
}
var Tensor = class {
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, this.dtype === "complex64");
}
arraySync() {
return toNestedArray(this.shape, this.dataSync(), this.dtype === "complex64");
}
async data() {
this.throwIfDisposed();
const data = trackerFn().read(this.dataId);
if (this.dtype === "string") {
const bytes = await data;
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 data;
}
dataToGPU(options) {
this.throwIfDisposed();
return trackerFn().readToGPU(this.dataId, options);
}
dataSync() {
this.throwIfDisposed();
const data = trackerFn().readSync(this.dataId);
if (this.dtype === "string") {
try {
return data.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 data;
}
async bytes() {
this.throwIfDisposed();
const data = await trackerFn().read(this.dataId);
if (this.dtype === "string") {
return data;
} else {
return new Uint8Array(data.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;
}
});
function getGlobalTensorClass() {
return getGlobal("Tensor", () => {
return Tensor;
});
}
getGlobalTensorClass();
var Variable = class 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
var tensor_util_exports = {};
__export(tensor_util_exports, {
assertTypesMatch: () => assertTypesMatch,
getTensorsInContainer: () => getTensorsInContainer,
isTensorInList: () => isTensorInList,
makeTypesMatch: () => makeTypesMatch
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/types.js
init_define_BUILD_VERSION();
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 = {}));
var 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 ${typeB}`);
}
return upcastTypeMap[typeA][typeB];
}
function sumOutType(type) {
return upcastType(type, "int32");
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
function makeTypesMatch(a, b) {
if (a.dtype === b.dtype) {
return [a, b];
}
const dtype = upcastType(a.dtype, b.dtype);
return [a.cast(dtype), b.cast(dtype)];
}
function assertTypesMatch(a, b) {
assert(a.dtype === b.dtype, () => `The dtypes of the first(${a.dtype}) and second(${b.dtype}) input must match`);
}
function isTensorInList(tensor3, tensorList) {
return tensorList.some((x) => x.id === tensor3.id);
}
function getTensorsInContainer(result) {
const list = [];
const seen = /* @__PURE__ */ new Set();
walkTensorContainer(result, list, seen);
return list;
}
function walkTensorContainer(container, list, seen) {
if (container == null) {
return;
}
if (container instanceof Tensor) {
list.push(container);
return;
}
if (!isIterable(container)) {
return;
}
const iterable = container;
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/engine.js
function isRegisteredKernelInvocation(kernelInvocation) {
return kernelInvocation.kernelName != null;
}
var EngineState = class {
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 = /* @__PURE__ */ new WeakMap();
this.profiling = false;
this.activeProfile = {
newBytes: 0,
newTensors: 0,
peakBytes: 0,
kernels: [],
result: null,
get kernelNames() {
return Array.from(new Set(this.kernels.map((k) => k.name)));
}
};
}
dispose() {
for (const variableName in this.registeredVariables) {
this.registeredVariables[variableName].dispose();
}
}
};
var Engine = class {
constructor(ENV6) {
this.ENV = ENV6;
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 '${name}' has not yet been initialized. Make sure to await tf.ready() or await tf.setBackend() before calling other methods`);
}
this.setBackend(name);
}
return this.backendInstance;
}
backendNames() {
return Object.keys(this.registryFactory);
}
findBackend(backendName) {
if (!(backendName in this.registry)) {
if (backendName in this.registryFactory) {
const { asyncInit } = this.initializeBackend(backendName);
if (asyncInit) {
return null;
}
} else {
return null;
}
}
return this.registry[backendName];
}
findBackendFactory(backendName) {
if (!(backendName in this.registryFactory)) {
return null;
}
return this.registryFactory[backendName].factory;
}
registerBackend(backendName, factory, priority = 1) {
if (backendName in this.registryFactory) {
warn(`${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 backend2 = registryFactoryEntry.factory();
if (backend2 && !(backend2 instanceof KernelBackend) && typeof backend2.then === "function") {
const promiseId = ++this.pendingBackendInitId;
const success = backend2.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;
warn(`Initialization of backend ${backendName} failed`);
warn(err.stack || err.message);
return false;
});
this.pendingBackendInit = success;
return { success, asyncInit: true };
} else {
this.registry[backendName] = backend2;
return { success: true, asyncInit: false };
}
} catch (err) {
warn(`Initialization of backend ${backendName} failed`);
warn(err.stack || err.message);
return { success: false, asyncInit: false };
}
}
removeBackend(backendName) {
if (!(backendName in this.registryFactory)) {
throw new Error(`${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(backend2, dataId) {
const info = this.state.tensorInfo.get(dataId);
const srcBackend = info.backend;
const values = this.readSync(dataId);
const refCount = srcBackend.refCount(dataId);
srcBackend.disposeData(dataId, true);
info.backend = backend2;
backend2.move(dataId, values, info.shape, info.dtype, refCount);
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 = ENGINE.runKernel(Identity, { x });
const inputs = { x };
const grad = (dy) => ({
x: () => {
const dtype = "float32";
const gradInputs = { x: dy };
const attrs = { dtype };
return ENGINE.runKernel(
Cast,
gradInputs,
attrs
);
}
});
const saved = [];
this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {});
return y;
}
runKernel(kernelName, inputs, attrs) {
if (this.backendName == null) {
this.backend;
}
const hasKernel = getKernel(kernelName, this.backendName) != null;
if (!hasKernel) {
throw new Error(`Kernel '${kernelName}' not registered for backend '${this.backendName}'`);
}
return this.runKernelFunc({ kernelName, inputs, attrs });
}
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 '${kernelName}'`);
}
}
runKernelFunc(kernelParams) {
let outputs;
let saved = [];
const isTapeOn = this.isTapeOn();
const startingBytecount = this.state.numBytes;
const startingNumTensors = this.state.numTensors;
if (this.shouldCheckForMemLeaks()) {
this.state.numDataMovesStack.push(0);
}
let kernelFunc;
if (this.backendName == null) {
this.backend;
}
let out;
const kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? kernelParams.kernelName : this.state.activeScope != null ? this.state.activeScope.name : "";
if (isRegisteredKernelInvocation(kernelParams)) {
const { kernelName, inputs: inputs2, attrs: attrs2 } = kernelParams;
if (this.backendName == null) {
this.backend;
}
const kernel = getKernel(kernelName, this.backendName);
assert(kernel != null, () => `Cannot find registered kernel '${kernelName}' for backend '${this.backendName}'`);
kernelFunc = () => {
const numDataIdsBefore = this.backend.numDataIds();
out = kernel.kernelFunc({ inputs: inputs2, attrs: attrs2, backend: this.backend });
const outInfos = Array.isArray(out) ? out : [out];
if (this.shouldCheckForMemLeaks()) {
this.checkKernelForMemLeak(kernelName, numDataIdsBefore, outInfos);
}
const outTensors = outInfos.map((outInfo) => {
if (outInfo.rank != null) {
return outInfo;
}
return this.makeTensorFromTensorInfo(outInfo);
});
if (isTapeOn) {
const tensorsToSave = this.getTensorsForGradient(kernelName, inputs2, outTensors);
saved = this.saveTensorsForBackwardMode(tensorsToSave);
}
return outTensors;
};
} else {
const { forwardFunc } = kernelParams;
const saveFunc = (tensors) => {
if (!isTapeOn) {
return;
}
saved = tensors.map((tensor3) => this.keep(this.clone(tensor3)));
};
kernelFunc = () => {
const numDataIdsBefore = this.backend.numDataIds();
out = this.tidy(() => forwardFunc(this.backend, saveFunc));
const outs = Array.isArray(out) ? out : [out];
if (this.shouldCheckForMemLeaks()) {
this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs);
}
return outs;
};
}
const { inputs, attrs } = kernelParams;
const backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? null : kernelParams.backwardsFunc;
let kernelProfile;
this.scopedRun(
() => this.state.kernelDepth++,
() => this.state.kernelDepth--,
() => {
if (!this.ENV.getBool("DEBUG") && !this.state.profiling) {
outputs = kernelFunc();
} else {
kernelProfile = this.profiler.profileKernel(kernelOrScopeName, inputs, () => kernelFunc());
if (this.ENV.getBool("DEBUG")) {
this.profiler.logKernelProfile(kernelProfile);
}
outputs = kernelProfile.outputs;
}
}
);
if (isTapeOn) {
this.addTapeNode(kernelOrScopeName, inputs, outputs, backwardsFunc, saved, attrs);
}
if (this.state.profiling) {
this.state.activeProfile.kernels.push({
name: kernelOrScopeName,
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((tensor3) => this.keep(this.clone(tensor3)));
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 [];
}
makeTensor(values, shape, dtype, backend2) {
if (values == null) {
throw new Error("Values passed to engine.makeTensor() are null");
}
dtype = dtype || "float32";
backend2 = backend2 || this.backend;
let backendVals = values;
if (dtype === "string" && isString(values[0])) {
backendVals = values.map((d) => encodeString(d));
}
const dataId = backend2.write(backendVals, shape, dtype);
const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
this.trackTensor(t, backend2);
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, backend2) {
dtype = dtype || "float32";
const tensorInfo = { dataId, shape, dtype };
return this.makeTensorFromTensorInfo(tensorInfo, backend2);
}
makeTensorFromTensorInfo(tensorInfo, backend2) {
const { dataId, shape, dtype } = tensorInfo;
const t = new Tensor(shape, dtype, dataId, this.nextTensorId());
this.trackTensor(t, backend2);
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;
}
trackTensor(a, backend2) {
this.state.numTensors++;
if (a.dtype === "string") {
this.state.numStringTensors++;
}
let bytes = 0;
if (a.dtype !== "complex64" && a.dtype !== "string") {
bytes = a.size * bytesPerElement(a.dtype);
}
this.state.numBytes += bytes;
if (!this.state.tensorInfo.has(a.dataId)) {
this.state.numDataBuffers++;
this.state.tensorInfo.set(a.dataId, {
backend: backend2 || this.backend,
dtype: a.dtype,
shape: a.shape,
bytes
});
}
if (!(a instanceof Variable)) {
this.track(a);
}
}
incRef(a, backend2) {
this.trackTensor(a, backend2);
this.backend.incRef(a.dataId);
}
removeDataId(dataId, backend2) {
if (this.state.tensorInfo.has(dataId) && this.state.tensorInfo.get(dataId).backend === backend2) {
this.state.tensorInfo.delete(dataId);
this.state.numDataBuffers--;
}
}
disposeTensor(a) {
if (!this.state.tensorInfo.has(a.dataId)) {
return;
}
const info = this.state.tensorInfo.get(a.dataId);
this.state.numTensors--;
if (a.dtype === "string") {
this.state.numStringTensors--;
this.state.numBytes -= info.bytes;
}
if (a.dtype !== "complex64" && a.dtype !== "string") {
const bytes = a.size * bytesPerElement(a.dtype);
this.state.numBytes -= bytes;
}
if (info.backend.disposeData(a.dataId)) {
this.removeDataId(a.dataId, info.backend);
}
}
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 tensor3 = this.state.activeScope.track[i];
if (!tensor3.kept && !tensorsToTrackInParentSet.has(tensor3.id)) {
tensor3.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((tensor3) => {
if (!tensor3.kept && tensor3.scopeId === oldScope.id) {
this.track(tensor3);
}
});
}
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 grads = xs.map((x) => accumulatedGradientMap[x.id]);
if (this.state.gradientDepth === 0) {
this.state.activeTape.forEach((node) => {
for (const tensor3 of node.saved) {
tensor3.dispose();
}
});
this.state.activeTape = null;
}
return { value: y, grads };
});
}
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;
});
const forwardFunc = (_, 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;
};
const backwardsFunc = (dy, saved) => {
const gradRes = res.gradFunc(dy, saved);
const grads = Array.isArray(gradRes) ? gradRes : [gradRes];
assert(grads.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(grads.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 = {};
grads.forEach((grad, i) => {
gradMap[i] = () => grad;
});
return gradMap;
};
return this.runKernelFunc({
forwardFunc,
backwardsFunc,
inputs: inputMap
});
};
}
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);
}
readToGPU(dataId, options) {
const info = this.state.tensorInfo.get(dataId);
return info.backend.readToGPU(dataId, options);
}
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 environment = new Environment(ns);
ns._tfengine = new Engine(environment);
}
setEnvironmentGlobal(ns._tfengine.ENV);
setTensorTracker(() => ns._tfengine);
return ns._tfengine;
}
var ENGINE = getOrMakeEngine();
function add(a, b) {
const inputs = { a, b };
return ENGINE.runKernel(Add, inputs);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/flags.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/device_util.js
var device_util_exports = {};
__export(device_util_exports, {
isBrowser: () => isBrowser,
isMobile: () => isMobile,
mockIsMobile: () => mockIsMobile
});
init_define_BUILD_VERSION();
function _isNavigatorDefined() {
return typeof navigator !== "undefined" && navigator != null;
}
var isMobileMockValue;
function mockIsMobile(value) {
isMobileMockValue = value;
}
function isMobile(nav) {
if (isMobileMockValue !== void 0) {
return isMobileMockValue;
}
if (nav || _isNavigatorDefined()) {
if (!nav) {
nav = navigator;
}
if (nav.product === "ReactNative") {
return true;
}
const a = nav.userAgent || nav.vendor || (typeof window !== "undefined" ? window.opera : "");
if (!a) {
const navAny = nav;
return navAny.userAgentData && navAny.userAgentData.mobile;
}
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/flags.js
var 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);
ENV2.registerFlag("CHECK_COMPUTATION_FOR_ERRORS", () => true);
ENV2.registerFlag("WRAP_TO_IMAGEBITMAP", () => false);
ENV2.registerFlag("ENGINE_COMPILE_ONLY", () => false);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
init_define_BUILD_VERSION();
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 ${val.length} elements`);
const subShape = shape.slice(1);
for (let i = 0; i < val.length; ++i) {
deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
}
}
function assertDtype(expectedDtype, actualDType, argName, functionName) {
if (expectedDtype === "string_or_numeric") {
return;
}
if (expectedDtype == null) {
throw new Error(`Expected dtype cannot be null.`);
}
if (expectedDtype !== "numeric" && expectedDtype !== actualDType || expectedDtype === "numeric" && actualDType === "string") {
throw new Error(`Argument '${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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
init_define_BUILD_VERSION();
var 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/complex.js
function complex_(real4, imag4) {
const $real = convertToTensor(real4, "real", "complex");
const $imag = convertToTensor(imag4, "imag", "complex");
assertShapesMatch($real.shape, $imag.shape, `real and imag shapes, ${$real.shape} and ${$imag.shape}, must match in call to tf.complex().`);
const inputs = { real: $real, imag: $imag };
return ENGINE.runKernel(Complex, inputs);
}
var complex = op({ complex_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops_util.js
init_define_BUILD_VERSION();
function makeTensor(values, shape, inferredShape, dtype) {
if (dtype == null) {
dtype = inferDtype(values);
}
if (dtype === "complex64") {
throw new Error(`Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).`);
}
if (!isTypedArray(values) && !Array.isArray(values) && typeof values !== "number" && typeof values !== "boolean" && typeof values !== "string") {
throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");
}
if (shape != null) {
assertNonNegativeIntegerDimensions(shape);
const providedSize = sizeFromShape(shape);
const inferredSize = sizeFromShape(inferredShape);
assert(providedSize === inferredSize, () => `Based on the provided shape, [${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 (${shape}). `);
}
}
if (!isTypedArray(values) && !Array.isArray(values)) {
values = [values];
}
shape = shape || inferredShape;
values = dtype !== "string" ? toTypedArray(values, dtype) : flatten(values, [], true);
return ENGINE.makeTensor(values, shape, dtype);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tensor.js
function tensor2(values, shape, dtype) {
const inferredShape = inferShape(values, dtype);
return makeTensor(values, shape, inferredShape, dtype);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/types.js
init_define_BUILD_VERSION();
var DTYPE_VALUE_SIZE_MAP = {
"float32": 4,
"float16": 2,
"int32": 4,
"uint16": 2,
"uint8": 1,
"bool": 1,
"complex64": 8
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
var NUM_BYTES_STRING_LENGTH = 4;
async function encodeWeights(tensors, group) {
const specs = [];
const dataPromises = [];
const names = Array.isArray(tensors) ? tensors.map((tensor3) => tensor3.name) : Object.keys(tensors);
for (let i = 0; i < names.length; ++i) {
const name = names[i];
const t = Array.isArray(tensors) ? tensors[i].tensor : tensors[name];
if (t.dtype !== "float32" && t.dtype !== "int32" && t.dtype !== "bool" && t.dtype !== "string" && t.dtype !== "complex64") {
throw new Error(`Unsupported dtype in weight '${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((p2, c) => p2 + 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(buffer2, 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 = buffer2.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}': ${dtype}`);
}
offset += size * quantizationSizeFactor;
} else if (dtype === "string") {
const size2 = sizeFromShape(spec.shape);
values = [];
for (let i = 0; i < size2; i++) {
const byteLength = new Uint32Array(buffer2.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0];
offset += NUM_BYTES_STRING_LENGTH;
const bytes = new Uint8Array(buffer2.slice(offset, offset + byteLength));
values.push(bytes);
offset += byteLength;
}
} else {
const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
const byteBuffer = buffer2.slice(offset, offset + size * dtypeFactor);
if (dtype === "float32") {
values = new Float32Array(byteBuffer);
} else if (dtype === "int32") {
values = new Int32Array(byteBuffer);
} else if (dtype === "bool") {
values = new Uint8Array(byteBuffer);
} else if (dtype === "complex64") {
values = new Float32Array(byteBuffer);
const real4 = new Float32Array(values.length / 2);
const image3 = new Float32Array(values.length / 2);
for (let i = 0; i < real4.length; i++) {
real4[i] = values[i * 2];
image3[i] = values[i * 2 + 1];
}
const realTensor = tensor2(real4, shape, "float32");
const imageTensor = tensor2(image3, shape, "float32");
out[name] = complex(realTensor, imageTensor);
realTensor.dispose();
imageTensor.dispose();
} else {
throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);
}
offset += size * dtypeFactor;
}
if (dtype !== "complex64") {
out[name] = tensor2(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;
}
var 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 arrayBufferToBase64String(buffer2) {
if (useNodeBuffer) {
return Buffer.from(buffer2).toString("base64");
}
const buf = new Uint8Array(buffer2);
let s = "";
for (let i = 0, l = buf.length; i < l; i++) {
s += String.fromCharCode(buf[i]);
}
return btoa(s);
}
function base64StringToArrayBuffer(str) {
if (useNodeBuffer) {
const buf = Buffer.from(str, "base64");
return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
}
const s = atob(str);
const buffer2 = new Uint8Array(s.length);
for (let i = 0; i < s.length; ++i) {
buffer2.set([s.charCodeAt(i)], i);
}
return buffer2.buffer;
}
function concatenateArrayBuffers(buffers) {
if (buffers.length === 1) {
return buffers[0];
}
let totalByteLength = 0;
buffers.forEach((buffer2) => {
totalByteLength += buffer2.byteLength;
});
const temp = new Uint8Array(totalByteLength);
let offset = 0;
buffers.forEach((buffer2) => {
temp.set(new Uint8Array(buffer2), offset);
offset += buffer2.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 getModelJSONForModelArtifacts(artifacts, manifest) {
const result = {
modelTopology: artifacts.modelTopology,
format: artifacts.format,
generatedBy: artifacts.generatedBy,
convertedBy: artifacts.convertedBy,
weightsManifest: manifest
};
if (artifacts.signature != null) {
result.signature = artifacts.signature;
}
if (artifacts.userDefinedMetadata != null) {
result.userDefinedMetadata = artifacts.userDefinedMetadata;
}
if (artifacts.modelInitializer != null) {
result.modelInitializer = artifacts.modelInitializer;
}
if (artifacts.trainingConfig != null) {
result.trainingConfig = artifacts.trainingConfig;
}
return result;
}
async function getModelArtifactsForJSON(modelJSON, loadWeights2) {
const modelArtifacts = {
modelTopology: modelJSON.modelTopology,
format: modelJSON.format,
generatedBy: modelJSON.generatedBy,
convertedBy: modelJSON.convertedBy
};
if (modelJSON.trainingConfig != null) {
modelArtifacts.trainingConfig = modelJSON.trainingConfig;
}
if (modelJSON.weightsManifest != null) {
const [weightSpecs, weightData] = await loadWeights2(modelJSON.weightsManifest);
modelArtifacts.weightSpecs = weightSpecs;
modelArtifacts.weightData = weightData;
}
if (modelJSON.signature != null) {
modelArtifacts.signature = modelJSON.signature;
}
if (modelJSON.userDefinedMetadata != null) {
modelArtifacts.userDefinedMetadata = modelJSON.userDefinedMetadata;
}
if (modelJSON.modelInitializer != null) {
modelArtifacts.modelInitializer = modelJSON.modelInitializer;
}
return modelArtifacts;
}
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 buffer2 = new ArrayBuffer(4 * quantizedArray.length);
const bufferUint32View = new Uint32Array(buffer2);
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(buffer2);
};
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
init_define_BUILD_VERSION();
var IORouterRegistry = class {
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;
}
};
var registerSaveRouter = (loudRouter) => IORouterRegistry.registerSaveRouter(loudRouter);
var registerLoadRouter = (loudRouter) => IORouterRegistry.registerLoadRouter(loudRouter);
var getSaveHandlers = (url) => IORouterRegistry.getSaveHandlers(url);
var getLoadHandlers = (url, loadOptions) => IORouterRegistry.getLoadHandlers(url, loadOptions);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
var DATABASE_NAME = "tensorflowjs";
var DATABASE_VERSION = 1;
var MODEL_STORE_NAME = "models_store";
var INFO_STORE_NAME = "model_info_store";
function getIndexedDBFactory() {
if (!env().getBool("IS_BROWSER")) {
throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");
}
const theWindow = typeof window === "undefined" ? self : window;
const factory = theWindow.indexedDB || theWindow.mozIndexedDB || theWindow.webkitIndexedDB || theWindow.msIndexedDB || theWindow.shimIndexedDB;
if (factory == null) {
throw new Error("The current browser does not appear to support IndexedDB.");
}
return factory;
}
function setUpDatabase(openRequest) {
const db = openRequest.result;
db.createObjectStore(MODEL_STORE_NAME, { keyPath: "modelPath" });
db.createObjectStore(INFO_STORE_NAME, { keyPath: "modelPath" });
}
var BrowserIndexedDB = class {
constructor(modelPath) {
this.indexedDB = getIndexedDBFactory();
if (modelPath == null || !modelPath) {
throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");
}
this.modelPath = modelPath;
}
async save(modelArtifacts) {
if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
}
return this.databaseAction(this.modelPath, modelArtifacts);
}
async load() {
return this.databaseAction(this.modelPath);
}
databaseAction(modelPath, modelArtifacts) {
return new Promise((resolve, reject) => {
const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
openRequest.onsuccess = () => {
const db = openRequest.result;
if (modelArtifacts == null) {
const modelTx = db.transaction(MODEL_STORE_NAME, "readonly");
const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
const getRequest = modelStore.get(this.modelPath);
getRequest.onsuccess = () => {
if (getRequest.result == null) {
db.close();
return reject(new Error(`Cannot find model with path '${this.modelPath}' in IndexedDB.`));
} else {
resolve(getRequest.result.modelArtifacts);
}
};
getRequest.onerror = (error) => {
db.close();
return reject(getRequest.error);
};
modelTx.oncomplete = () => db.close();
} else {
const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
let infoStore = infoTx.objectStore(INFO_STORE_NAME);
const putInfoRequest = infoStore.put({ modelPath: this.modelPath, modelArtifactsInfo });
let modelTx;
putInfoRequest.onsuccess = () => {
modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
const putModelRequest = modelStore.put({
modelPath: this.modelPath,
modelArtifacts,
modelArtifactsInfo
});
putModelRequest.onsuccess = () => resolve({ modelArtifactsInfo });
putModelRequest.onerror = (error) => {
infoStore = infoTx.objectStore(INFO_STORE_NAME);
const deleteInfoRequest = infoStore.delete(this.modelPath);
deleteInfoRequest.onsuccess = () => {
db.close();
return reject(putModelRequest.error);
};
deleteInfoRequest.onerror = (error2) => {
db.close();
return reject(putModelRequest.error);
};
};
};
putInfoRequest.onerror = (error) => {
db.close();
return reject(putInfoRequest.error);
};
infoTx.oncomplete = () => {
if (modelTx == null) {
db.close();
} else {
modelTx.oncomplete = () => db.close();
}
};
}
};
openRequest.onerror = (error) => reject(openRequest.error);
});
}
};
BrowserIndexedDB.URL_SCHEME = "indexeddb://";
var indexedDBRouter = (url) => {
if (!env().getBool("IS_BROWSER")) {
return null;
} else {
if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
} else {
return null;
}
}
};
IORouterRegistry.registerSaveRouter(indexedDBRouter);
IORouterRegistry.registerLoadRouter(indexedDBRouter);
function browserIndexedDB(modelPath) {
return new BrowserIndexedDB(modelPath);
}
function maybeStripScheme(key) {
return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? key.slice(BrowserIndexedDB.URL_SCHEME.length) : key;
}
var BrowserIndexedDBManager = class {
constructor() {
this.indexedDB = getIndexedDBFactory();
}
async listModels() {
return new Promise((resolve, reject) => {
const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
openRequest.onsuccess = () => {
const db = openRequest.result;
const tx = db.transaction(INFO_STORE_NAME, "readonly");
const store = tx.objectStore(INFO_STORE_NAME);
const getAllInfoRequest = store.getAll();
getAllInfoRequest.onsuccess = () => {
const out = {};
for (const item of getAllInfoRequest.result) {
out[item.modelPath] = item.modelArtifactsInfo;
}
resolve(out);
};
getAllInfoRequest.onerror = (error) => {
db.close();
return reject(getAllInfoRequest.error);
};
tx.oncomplete = () => db.close();
};
openRequest.onerror = (error) => reject(openRequest.error);
});
}
async removeModel(path) {
path = maybeStripScheme(path);
return new Promise((resolve, reject) => {
const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
openRequest.onupgradeneeded = () => setUpDatabase(openRequest);
openRequest.onsuccess = () => {
const db = openRequest.result;
const infoTx = db.transaction(INFO_STORE_NAME, "readwrite");
const infoStore = infoTx.objectStore(INFO_STORE_NAME);
const getInfoRequest = infoStore.get(path);
let modelTx;
getInfoRequest.onsuccess = () => {
if (getInfoRequest.result == null) {
db.close();
return reject(new Error(`Cannot find model with path '${path}' in IndexedDB.`));
} else {
const deleteInfoRequest = infoStore.delete(path);
const deleteModelData = () => {
modelTx = db.transaction(MODEL_STORE_NAME, "readwrite");
const modelStore = modelTx.objectStore(MODEL_STORE_NAME);
const deleteModelRequest = modelStore.delete(path);
deleteModelRequest.onsuccess = () => resolve(getInfoRequest.result.modelArtifactsInfo);
deleteModelRequest.onerror = (error) => reject(getInfoRequest.error);
};
deleteInfoRequest.onsuccess = deleteModelData;
deleteInfoRequest.onerror = (error) => {
deleteModelData();
db.close();
return reject(getInfoRequest.error);
};
}
};
getInfoRequest.onerror = (error) => {
db.close();
return reject(getInfoRequest.error);
};
infoTx.oncomplete = () => {
if (modelTx == null) {
db.close();
} else {
modelTx.oncomplete = () => db.close();
}
};
};
openRequest.onerror = (error) => reject(openRequest.error);
});
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js
init_define_BUILD_VERSION();
var PATH_SEPARATOR = "/";
var PATH_PREFIX = "tensorflowjs_models";
var INFO_SUFFIX = "info";
var MODEL_TOPOLOGY_SUFFIX = "model_topology";
var WEIGHT_SPECS_SUFFIX = "weight_specs";
var WEIGHT_DATA_SUFFIX = "weight_data";
var MODEL_METADATA_SUFFIX = "model_metadata";
function getModelKeys(path) {
return {
info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR),
modelMetadata: [PATH_PREFIX, path, MODEL_METADATA_SUFFIX].join(PATH_SEPARATOR)
};
}
function removeItems(keys) {
for (const key of Object.values(keys)) {
window.localStorage.removeItem(key);
}
}
function getModelPathFromKey(key) {
const items = key.split(PATH_SEPARATOR);
if (items.length < 3) {
throw new Error(`Invalid key format: ${key}`);
}
return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
}
function maybeStripScheme2(key) {
return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? key.slice(BrowserLocalStorage.URL_SCHEME.length) : key;
}
var BrowserLocalStorage = class {
constructor(modelPath) {
if (!env().getBool("IS_BROWSER") || typeof window === "undefined" || typeof window.localStorage === "undefined") {
throw new Error("The current environment does not support local storage.");
}
this.LS = window.localStorage;
if (modelPath == null || !modelPath) {
throw new Error("For local storage, modelPath must not be null, undefined or empty.");
}
this.modelPath = modelPath;
this.keys = getModelKeys(this.modelPath);
}
async save(modelArtifacts) {
if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");
} else {
const topology = JSON.stringify(modelArtifacts.modelTopology);
const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
const modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
try {
this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
this.LS.setItem(this.keys.topology, topology);
this.LS.setItem(this.keys.weightSpecs, weightSpecs);
this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));
const metadata = {
format: modelArtifacts.format,
generatedBy: modelArtifacts.generatedBy,
convertedBy: modelArtifacts.convertedBy,
signature: modelArtifacts.signature != null ? modelArtifacts.signature : void 0,
userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? modelArtifacts.userDefinedMetadata : void 0,
modelInitializer: modelArtifacts.modelInitializer != null ? modelArtifacts.modelInitializer : void 0,
trainingConfig: modelArtifacts.trainingConfig != null ? modelArtifacts.trainingConfig : void 0
};
this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata));
return { modelArtifactsInfo };
} catch (err) {
removeItems(this.keys);
throw new Error(`Failed to save model '${this.modelPath}' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);
}
}
}
async load() {
const info = JSON.parse(this.LS.getItem(this.keys.info));
if (info == null) {
throw new Error(`In local storage, there is no model with name '${this.modelPath}'`);
}
if (info.modelTopologyType !== "JSON") {
throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");
}
const out = {};
const topology = JSON.parse(this.LS.getItem(this.keys.topology));
if (topology == null) {
throw new Error(`In local storage, the topology of model '${this.modelPath}' is missing.`);
}
out.modelTopology = topology;
const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
if (weightSpecs == null) {
throw new Error(`In local storage, the weight specs of model '${this.modelPath}' are missing.`);
}
out.weightSpecs = weightSpecs;
const metadataString = this.LS.getItem(this.keys.modelMetadata);
if (metadataString != null) {
const metadata = JSON.parse(metadataString);
out.format = metadata.format;
out.generatedBy = metadata.generatedBy;
out.convertedBy = metadata.convertedBy;
if (metadata.signature != null) {
out.signature = metadata.signature;
}
if (metadata.userDefinedMetadata != null) {
out.userDefinedMetadata = metadata.userDefinedMetadata;
}
if (metadata.modelInitializer != null) {
out.modelInitializer = metadata.modelInitializer;
}
if (metadata.trainingConfig != null) {
out.trainingConfig = metadata.trainingConfig;
}
}
const weightDataBase64 = this.LS.getItem(this.keys.weightData);
if (weightDataBase64 == null) {
throw new Error(`In local storage, the binary weight values of model '${this.modelPath}' are missing.`);
}
out.weightData = base64StringToArrayBuffer(weightDataBase64);
return out;
}
};
BrowserLocalStorage.URL_SCHEME = "localstorage://";
var localStorageRouter = (url) => {
if (!env().getBool("IS_BROWSER")) {
return null;
} else {
if (!Array.isArray(url) && url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
} else {
return null;
}
}
};
IORouterRegistry.registerSaveRouter(localStorageRouter);
IORouterRegistry.registerLoadRouter(localStorageRouter);
function browserLocalStorage(modelPath) {
return new BrowserLocalStorage(modelPath);
}
var BrowserLocalStorageManager = class {
constructor() {
assert(env().getBool("IS_BROWSER"), () => "Current environment is not a web browser");
assert(typeof window === "undefined" || typeof window.localStorage !== "undefined", () => "Current browser does not appear to support localStorage");
this.LS = window.localStorage;
}
async listModels() {
const out = {};
const prefix = PATH_PREFIX + PATH_SEPARATOR;
const suffix = PATH_SEPARATOR + INFO_SUFFIX;
for (let i = 0; i < this.LS.length; ++i) {
const key = this.LS.key(i);
if (key.startsWith(prefix) && key.endsWith(suffix)) {
const modelPath = getModelPathFromKey(key);
out[modelPath] = JSON.parse(this.LS.getItem(key));
}
}
return out;
}
async removeModel(path) {
path = maybeStripScheme2(path);
const keys = getModelKeys(path);
if (this.LS.getItem(keys.info) == null) {
throw new Error(`Cannot find model at path '${path}'`);
}
const info = JSON.parse(this.LS.getItem(keys.info));
removeItems(keys);
return info;
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
init_define_BUILD_VERSION();
var URL_SCHEME_SUFFIX = "://";
var ModelStoreManagerRegistry = class {
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 '${scheme}'.`);
registry.managers[scheme] = manager;
}
static getManager(scheme) {
const manager = ModelStoreManagerRegistry.getInstance().managers[scheme];
if (manager == null) {
throw new Error(`Cannot find model manager for scheme '${scheme}'`);
}
return manager;
}
static getSchemes() {
return Object.keys(ModelStoreManagerRegistry.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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_browser.js
var PlatformBrowser = class {
fetch(path, init) {
return fetch(path, init);
}
now() {
return performance.now();
}
encode(text, encoding) {
if (encoding !== "utf-8" && encoding !== "utf8") {
throw new Error(`Browser's encoder only supports utf-8, but got ${encoding}`);
}
if (this.textEncoder == null) {
this.textEncoder = new TextEncoder();
}
return this.textEncoder.encode(text);
}
decode(bytes, encoding) {
return new TextDecoder(encoding).decode(bytes);
}
};
if (env().get("IS_BROWSER")) {
env().setPlatform("browser", new PlatformBrowser());
try {
ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
} catch (err) {
}
try {
ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
} catch (err) {
}
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/platforms/platform_node.js
init_define_BUILD_VERSION();
var getNodeFetch = {
importFetch: () => require_browser()
};
var systemFetch;
var PlatformNode = class {
constructor() {
this.util = require_util();
this.textEncoder = new this.util.TextEncoder();
}
fetch(path, requestInits) {
if (env().global.fetch != null) {
return env().global.fetch(path, requestInits);
}
if (systemFetch == null) {
systemFetch = getNodeFetch.importFetch();
}
return systemFetch(path, requestInits);
}
now() {
const time = process.hrtime();
return time[0] * 1e3 + time[1] / 1e6;
}
encode(text, encoding) {
if (encoding !== "utf-8" && encoding !== "utf8") {
throw new Error(`Node built-in encoder only supports utf-8, but got ${encoding}`);
}
return this.textEncoder.encode(text);
}
decode(bytes, encoding) {
if (bytes.length === 0) {
return "";
}
return new this.util.TextDecoder(encoding).decode(bytes);
}
};
if (env().get("IS_NODE") && !env().get("IS_BROWSER")) {
env().setPlatform("node", new PlatformNode());
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/buffer.js
init_define_BUILD_VERSION();
function buffer(shape, dtype = "float32", values) {
dtype = dtype || "float32";
assertNonNegativeIntegerDimensions(shape);
return new TensorBuffer(shape, dtype, values);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/cast.js
init_define_BUILD_VERSION();
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.runKernel(Cast, inputs, attrs);
}
var cast = op({ cast_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/clone.js
init_define_BUILD_VERSION();
function clone_(x) {
const $x = convertToTensor(x, "x", "clone", "string_or_numeric");
const inputs = { x: $x };
return ENGINE.runKernel(Identity, inputs);
}
var clone = op({ clone_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/print.js
init_define_BUILD_VERSION();
function print(x, verbose = false) {
console.log(x.toString(verbose));
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/base_side_effects.js
getOrMakeEngine();
var opHandler2 = {
buffer,
cast,
clone,
print
};
setOpHandler(opHandler2);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/base.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/io.js
var io_exports = {};
__export(io_exports, {
browserFiles: () => browserFiles,
browserHTTPRequest: () => browserHTTPRequest,
concatenateArrayBuffers: () => concatenateArrayBuffers,
copyModel: () => copyModel,
decodeWeights: () => decodeWeights,
encodeWeights: () => encodeWeights,
fromMemory: () => fromMemory,
fromMemorySync: () => fromMemorySync,
getLoadHandlers: () => getLoadHandlers,
getModelArtifactsForJSON: () => getModelArtifactsForJSON,
getModelArtifactsInfoForJSON: () => getModelArtifactsInfoForJSON,
getSaveHandlers: () => getSaveHandlers,
http: () => http,
isHTTPScheme: () => isHTTPScheme,
listModels: () => listModels,
loadWeights: () => loadWeights,
moveModel: () => moveModel,
registerLoadRouter: () => registerLoadRouter,
registerSaveRouter: () => registerSaveRouter,
removeModel: () => removeModel,
weightsLoaderFactory: () => weightsLoaderFactory,
withSaveHandler: () => withSaveHandler,
withSaveHandlerSync: () => withSaveHandlerSync
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
init_define_BUILD_VERSION();
var DEFAULT_FILE_NAME_PREFIX = "model";
var DEFAULT_JSON_EXTENSION_NAME = ".json";
var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = ".weights.bin";
function defer(f) {
return new Promise((resolve) => setTimeout(resolve)).then(f);
}
var BrowserDownloads = class {
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.modelJsonFileName = 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 modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
const modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: "application/json" }));
const jsonAnchor = this.modelJsonAnchor == null ? document.createElement("a") : this.modelJsonAnchor;
jsonAnchor.download = this.modelJsonFileName;
jsonAnchor.href = modelJsonURL;
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://";
var BrowserFiles = class {
constructor(files) {
if (files == null || files.length < 1) {
throw new Error(`When calling browserFiles, at least 1 file is required, but received ${files}`);
}
this.jsonFile = files[0];
this.weightsFiles = files.slice(1);
}
async load() {
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 ${this.jsonFile.name}`));
return;
}
const weightsManifest = modelJSON.weightsManifest;
if (weightsManifest == null) {
reject(new Error(`weightManifest field is missing from file ${this.jsonFile.name}`));
return;
}
if (this.weightsFiles.length === 0) {
resolve({ modelTopology });
return;
}
const modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));
resolve(modelArtifactsPromise);
};
jsonReader.onerror = (error) => reject(`Failed to read model topology and weights manifest JSON from file '${this.jsonFile.name}'. BrowserFiles supports loading Keras-style tf.Model artifacts only.`);
jsonReader.readAsText(this.jsonFile);
});
}
loadWeights(weightsManifest) {
const weightSpecs = [];
const paths = [];
for (const entry of weightsManifest) {
weightSpecs.push(...entry.weights);
paths.push(...entry.paths);
}
const pathToFile = this.checkManifestAndWeightFiles(weightsManifest);
const promises = paths.map((path) => this.loadWeightsFile(path, pathToFile[path]));
return Promise.all(promises).then((buffers) => [weightSpecs, concatenateArrayBuffers(buffers)]);
}
loadWeightsFile(path, file) {
return new Promise((resolve, reject) => {
const weightFileReader = new FileReader();
weightFileReader.onload = (event) => {
const weightData = event.target.result;
resolve(weightData);
};
weightFileReader.onerror = (error) => reject(`Failed to weights data from file of path '${path}'.`);
weightFileReader.readAsArrayBuffer(file);
});
}
checkManifestAndWeightFiles(manifest) {
const basenames = [];
const fileNames = this.weightsFiles.map((file) => basename(file.name));
const pathToFile = {};
for (const group of manifest) {
group.paths.forEach((path) => {
const pathBasename = basename(path);
if (basenames.indexOf(pathBasename) !== -1) {
throw new Error(`Duplicate file basename found in weights manifest: '${pathBasename}'`);
}
basenames.push(pathBasename);
if (fileNames.indexOf(pathBasename) === -1) {
throw new Error(`Weight file with basename '${pathBasename}' is not provided.`);
} else {
pathToFile[path] = this.weightsFiles[fileNames.indexOf(pathBasename)];
}
});
}
if (basenames.length !== this.weightsFiles.length) {
throw new Error(`Mismatch in the number of files in weights manifest (${basenames.length}) and the number of weight files provided (${this.weightsFiles.length}).`);
}
return pathToFile;
}
};
var 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/http.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/progress.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
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 buffer2 = new Uint8Array(buffers[bufferIndexOffset + i2]);
groupByteBuffer.set(buffer2, groupBufferOffset);
groupBufferOffset += buffer2.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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/http.js
var OCTET_STREAM_MIME_TYPE = "application/octet-stream";
var JSON_TYPE = "application/json";
var HTTPRequest = class {
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 init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
init.body = new FormData();
const weightsManifest = [{
paths: ["./model.weights.bin"],
weights: modelArtifacts.weightSpecs
}];
const modelTopologyAndWeightManifest = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest);
init.body.append("model.json", new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), "model.json");
if (modelArtifacts.weightData != null) {
init.body.append("model.weights.bin", new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), "model.weights.bin");
}
const response = await this.fetch(this.path, init);
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 modelJSON;
try {
modelJSON = 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 = modelJSON.modelTopology;
const weightsManifest = modelJSON.weightsManifest;
if (modelTopology == null && weightsManifest == null) {
throw new Error(`The JSON from HTTP path ${this.path} contains neither model topology or manifest for weights.`);
}
return getModelArtifactsForJSON(modelJSON, (weightsManifest2) => this.loadWeights(weightsManifest2));
}
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;
}
var 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
init_define_BUILD_VERSION();
var PassthroughLoader = class {
constructor(modelArtifacts) {
this.modelArtifacts = modelArtifacts;
}
load() {
return this.modelArtifacts;
}
};
var PassthroughSaver = class {
constructor(saveHandler) {
this.saveHandler = saveHandler;
}
save(modelArtifacts) {
return this.saveHandler(modelArtifacts);
}
};
var PassthroughAsync = class {
constructor(handler) {
if (handler.load) {
this.load = () => Promise.resolve(handler.load());
}
if (handler.save) {
this.save = (modelArtifacts) => Promise.resolve(handler.save(modelArtifacts));
}
}
};
function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) {
const args = arguments;
return new PassthroughAsync(fromMemorySync(...args));
}
function fromMemorySync(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);
}
function withSaveHandlerSync(saveHandler) {
return new PassthroughSaver(saveHandler);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/mat_mul.js
init_define_BUILD_VERSION();
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 inputs = { a: $a, b: $b };
const attrs = { transposeA, transposeB };
return ENGINE.runKernel(BatchMatMul, inputs, attrs);
}
var matMul = op({ matMul_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/one_hot.js
init_define_BUILD_VERSION();
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 inputs = { indices: $indices };
const attrs = { depth, onValue, offValue };
return ENGINE.runKernel(OneHot, inputs, attrs);
}
var oneHot = op({ oneHot_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/globals.js
init_define_BUILD_VERSION();
function deprecationWarn(msg) {
if (env().getBool("DEPRECATION_WARNINGS_ENABLED")) {
console.warn(msg + " You can disable deprecation warnings with tf.disableDeprecationWarnings().");
}
}
setDeprecationWarningFn(deprecationWarn);
function engine() {
return ENGINE;
}
function memory() {
return ENGINE.memory();
}
function tidy(nameOrFn, fn) {
return ENGINE.tidy(nameOrFn, fn);
}
function dispose(container) {
const tensors = getTensorsInContainer(container);
tensors.forEach((tensor3) => tensor3.dispose());
}
function keep(result) {
return ENGINE.keep(result);
}
function setBackend(backendName) {
return ENGINE.setBackend(backendName);
}
function registerBackend(name, factory, priority = 1) {
return ENGINE.registerBackend(name, factory, priority);
}
function backend() {
return ENGINE.backend;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/imag.js
init_define_BUILD_VERSION();
function imag_(input2) {
const $input = convertToTensor(input2, "input", "imag");
const inputs = { input: $input };
return ENGINE.runKernel(Imag, inputs);
}
var imag = op({ imag_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/neg.js
init_define_BUILD_VERSION();
function neg_(x) {
const $x = convertToTensor(x, "x", "neg");
const inputs = { x: $x };
return ENGINE.runKernel(Neg, inputs);
}
var neg = op({ neg_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/real.js
init_define_BUILD_VERSION();
function real_(input2) {
const $input = convertToTensor(input2, "input", "real");
const inputs = { input: $input };
return ENGINE.runKernel(Real, inputs);
}
var real = op({ real_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
function transpose_(x, perm, conjugate) {
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: $x };
const attrs = { perm };
if ($x.dtype === "complex64") {
return tidy(() => {
let $real = real($x);
let $imag = imag($x);
$real = ENGINE.runKernel(Transpose, { x: $real }, attrs);
$imag = ENGINE.runKernel(Transpose, { x: $imag }, attrs);
if (conjugate) {
$imag = neg($imag);
}
return complex($real, $imag);
});
}
return ENGINE.runKernel(Transpose, inputs, attrs);
}
var transpose = op({ transpose_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
var broadcast_util_exports = {};
__export(broadcast_util_exports, {
assertAndGetBroadcastShape: () => assertAndGetBroadcastShape,
getBroadcastDims: () => getBroadcastDims,
getReductionAxes: () => getReductionAxes
});
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
var browser_exports = {};
__export(browser_exports, {
fromPixels: () => fromPixels,
fromPixelsAsync: () => fromPixelsAsync,
toPixels: () => toPixels
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tensor3d.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/browser.js
var 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 isPixelData2 = false;
let isImageData = false;
let isVideo = false;
let isImage = false;
let isCanvasLike = false;
let isImageBitmap = false;
if (pixels.data instanceof Uint8Array) {
isPixelData2 = 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 if (typeof ImageBitmap !== "undefined" && pixels instanceof ImageBitmap) {
isImageBitmap = 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 || isPixelData2) {
vals = pixels.data;
} else if (isImage || isVideo || isImageBitmap) {
if (fromPixels2DContext == null) {
if (typeof document === "undefined") {
if (typeof OffscreenCanvas !== "undefined" && typeof OffscreenCanvasRenderingContext2D !== "undefined") {
fromPixels2DContext = new OffscreenCanvas(1, 1).getContext("2d");
} else {
throw new Error("Cannot parse input in current context. Reason: OffscreenCanvas Context2D rendering is not supported.");
}
} else {
fromPixels2DContext = document.createElement("canvas").getContext("2d", { willReadFrequently: true });
}
}
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");
}
function isPixelData(pixels) {
return pixels != null && pixels.data instanceof Uint8Array;
}
function isImageBitmapFullySupported() {
return typeof window !== "undefined" && typeof ImageBitmap !== "undefined" && window.hasOwnProperty("createImageBitmap");
}
function isNonEmptyPixels(pixels) {
return pixels != null && pixels.width !== 0 && pixels.height !== 0;
}
function canWrapPixelsToImageBitmap(pixels) {
return isImageBitmapFullySupported() && !(pixels instanceof ImageBitmap) && isNonEmptyPixels(pixels) && !isPixelData(pixels);
}
async function fromPixelsAsync(pixels, numChannels = 3) {
let inputs = null;
if (env().getBool("WRAP_TO_IMAGEBITMAP") && canWrapPixelsToImageBitmap(pixels)) {
let imageBitmap;
try {
imageBitmap = await createImageBitmap(pixels, { premultiplyAlpha: "none" });
} catch (e) {
imageBitmap = null;
}
if (imageBitmap != null && imageBitmap.width === pixels.width && imageBitmap.height === pixels.height) {
inputs = imageBitmap;
} else {
inputs = pixels;
}
} else {
inputs = pixels;
}
return fromPixels_(inputs, numChannels);
}
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 data = await $img.data();
const multiplier = $img.dtype === "float32" ? 255 : 1;
const bytes = new Uint8ClampedArray(width * height * 4);
for (let i = 0; i < height * width; ++i) {
const rgba = [0, 0, 0, 255];
for (let d = 0; d < depth; d++) {
const value = data[i * depth + d];
if ($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;
}
var fromPixels = op({ fromPixels_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
init_define_BUILD_VERSION();
function prepareAndValidate(tensor3, indices) {
const tensorRank = tensor3.shape.length;
const indicesRank = indices.shape.length;
if (tensorRank < 1) {
throw new Error(`tf.gatherND() expects the input to be rank 1 or higher, but the rank was ${tensorRank}.`);
}
if (indicesRank < 1) {
throw new Error(`tf.gatherND() expects the indices to be rank 1 or higher, but the rank was ${indicesRank}.`);
}
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[indicesRank - 1] > tensorRank) {
throw new Error(`index innermost dimension length must be <= tensor rank; saw: ${indices.shape[indicesRank - 1]} vs. ${tensorRank}`);
}
if (sizeFromShape(tensor3.shape) === 0) {
throw new Error(`Requested more than 0 entries, but input is empty. Input shape: ${tensor3.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 = tensor3.shape;
const resultShape = indicesShape.slice();
resultShape.pop();
let sliceSize = 1;
for (let i = sliceRank; i < tensorRank; ++i) {
sliceSize *= inputShape[i];
resultShape.push(inputShape[i]);
}
const strides = [
...computeStrides(tensor3.shape).map((stride) => stride / sliceSize),
1
].slice(0, sliceRank);
return [resultShape, nResult, sliceSize, strides];
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
init_define_BUILD_VERSION();
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: ${updates.shape}`);
}
}
validateUpdateShape(shape, indices, updates);
}
function calculateShapes(updates, indices, shape) {
const indicesRank = indices.shape.length;
const sliceRank = indicesRank > 1 ? indices.shape[indicesRank - 1] : 1;
const totalNd = shape.length;
let sliceSize = 1;
for (let i = sliceRank; i < totalNd; ++i) {
sliceSize *= shape[i];
}
const safeSliceDim = sliceRank < 1 ? 1 : sliceRank;
const numUpdates = sizeFromShape(indices.shape) / safeSliceDim;
const strides = [...computeStrides(shape.slice(0, sliceRank)), 1];
const outputSize = sizeFromShape(shape);
return { sliceRank, numUpdates, sliceSize, strides, outputSize };
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
var slice_util_exports = {};
__export(slice_util_exports, {
assertParamsValid: () => assertParamsValid,
computeFlatOffset: () => computeFlatOffset,
computeOutShape: () => computeOutShape,
getNormalizedAxes: () => getNormalizedAxes,
isSliceContinous: () => isSliceContinous,
maskToAxes: () => maskToAxes,
parseSliceParams: () => parseSliceParams,
sliceInfo: () => sliceInfo,
startForAxis: () => startForAxis,
startIndicesWithElidedDims: () => startIndicesWithElidedDims,
stopForAxis: () => stopForAxis,
stopIndicesWithElidedDims: () => stopIndicesWithElidedDims,
stridesForAxis: () => stridesForAxis,
stridesWithElidedDims: () => stridesWithElidedDims
});
init_define_BUILD_VERSION();
var NEW_AXIS = -2;
var SHRINK_AXIS = -1;
function assertParamsValid(input2, begin, size) {
const inputRank = input2.shape.length;
assert(inputRank === begin.length, () => `Error in slice${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 ${i}.`);
return x.shape[i] - begin_[i];
}
});
return [begin_, size_];
}
function sliceInfo(xShape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
let stridesNonNull;
if (strides == null) {
stridesNonNull = new Array(begin.length);
stridesNonNull.fill(1);
} else {
stridesNonNull = strides;
}
if (ellipsisMask != null && (ellipsisMask & ellipsisMask - 1) !== 0) {
throw new Error("Multiple ellipses in slice is not allowed.");
}
let ellipsisSeen = false;
const sparseSpec = {
dims: stridesNonNull.length,
numAddAxisAfterEllipsis: 0,
begin: begin.slice(),
end: end.slice(),
strides: stridesNonNull.slice(),
beginMask,
endMask,
ellipsisMask,
newAxisMask,
shrinkAxisMask
};
for (let i = 0; i < sparseSpec.dims; i++) {
if (ellipsisSeen && (1 << i & newAxisMask) !== 0) {
sparseSpec.numAddAxisAfterEllipsis++;
}
if (1 << i & ellipsisMask) {
ellipsisSeen = true;
}
}
if (!ellipsisSeen) {
sparseSpec.ellipsisMask |= 1 << sparseSpec.dims;
sparseSpec.dims++;
}
const denseSpec = {
dims: xShape.length,
beginMask: 0,
endMask: 0,
beginValid: false,
endValid: false
};
buildDenseSpec(sparseSpec, denseSpec);
let isIdentity = true;
let sliceDim0 = true;
let isSimpleSlice = true;
const processingShape = [];
const finalShape = [];
for (let i = 0; i < xShape.length; ++i) {
if (denseSpec.strides[i] === 0) {
throw Error(`strides[${i}] must be non-zero`);
}
const shrinkI = !!(denseSpec.shrinkAxisMask & 1 << i);
const dimI = xShape[i];
if (dimI === -1) {
processingShape.push(shrinkI ? 1 : -1);
continue;
}
const masks = [denseSpec.beginMask & 1 << i, denseSpec.endMask & 1 << i];
const validRange = [
denseSpec.strides[i] > 0 ? 0 : -1,
denseSpec.strides[i] > 0 ? dimI : dimI - 1
];
if (shrinkI && denseSpec.strides[i] <= 0) {
throw Error("only stride 1 allowed on non-range indexing.");
}
isSimpleSlice = isSimpleSlice && denseSpec.strides[i] === 1;
const beginAndEndMasked = !!(denseSpec.beginMask & 1 << i && denseSpec.endMask & 1 << i);
if (denseSpec.beginValid && denseSpec.endValid) {
if (shrinkI) {
const xFwd = denseSpec.begin[i] < 0 ? dimI + denseSpec.begin[i] : denseSpec.begin[i];
denseSpec.begin[i] = xFwd;
denseSpec.end[i] = denseSpec.begin[i] + 1;
if (xFwd < 0 || xFwd >= dimI) {
throw Error(`slice index ${denseSpec.begin[i]} of dimension ${i} out of bounds.`);
}
} else {
denseSpec.begin[i] = canonical(denseSpec.begin[i], 0, denseSpec.strides[i], dimI, masks, validRange);
denseSpec.end[i] = canonical(denseSpec.end[i], 1, denseSpec.strides[i], dimI, masks, validRange);
}
const takeAllInDimension = denseSpec.strides[i] === 1 && denseSpec.begin[i] === 0 && denseSpec.end[i] === dimI;
isIdentity = isIdentity && takeAllInDimension;
sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || takeAllInDimension);
} else {
isIdentity = isIdentity && (denseSpec.strides[i] === 1 && beginAndEndMasked);
sliceDim0 = sliceDim0 && (i === 0 && denseSpec.strides[i] === 1 || beginAndEndMasked);
}
let intervalLength;
let knownInterval = false;
if (denseSpec.beginValid && denseSpec.endValid) {
intervalLength = denseSpec.end[i] - denseSpec.begin[i];
knownInterval = true;
} else if (shrinkI) {
intervalLength = 1;
knownInterval = true;
} else if (beginAndEndMasked) {
if (dimI >= 0) {
if (denseSpec.strides[i] < 0) {
intervalLength = -dimI;
} else {
intervalLength = dimI;
}
knownInterval = true;
}
}
if (knownInterval) {
let sizeI;
if (intervalLength === 0 || intervalLength < 0 !== denseSpec.strides[i] < 0) {
sizeI = 0;
} else {
sizeI = Math.trunc(intervalLength / denseSpec.strides[i]) + (intervalLength % denseSpec.strides[i] !== 0 ? 1 : 0);
}
processingShape.push(sizeI);
} else {
processingShape.push(-1);
}
}
for (let denseDim = 0; denseDim < denseSpec.finalShapeGatherIndices.length; ++denseDim) {
const gatherIndex = denseSpec.finalShapeGatherIndices[denseDim];
if (gatherIndex >= 0) {
finalShape.push(processingShape[gatherIndex]);
} else if (gatherIndex === NEW_AXIS) {
finalShape.push(1);
}
}
const finalShapeSparse = finalShape.filter((dim, i) => denseSpec.finalShapeGatherIndices[i] !== NEW_AXIS);
return {
finalShapeSparse,
finalShape,
isIdentity,
sliceDim0,
isSimpleSlice,
begin: denseSpec.begin,
end: denseSpec.end,
strides: denseSpec.strides
};
}
function buildDenseSpec(sparse, dense) {
dense.beginMask = 0;
dense.endMask = 0;
dense.shrinkAxisMask = 0;
let fullIndex = 0;
dense.beginValid = sparse.begin != null;
dense.endValid = sparse.end != null;
dense.begin = new Array(dense.dims);
dense.end = new Array(dense.dims);
dense.strides = new Array(dense.dims);
dense.finalShapeGatherIndices = [];
dense.finalShapeGatherIndicesSparse = [];
dense.inputShapeGatherIndicesSparse = new Array(dense.dims);
for (let i = 0; i < sparse.dims; i++) {
if (1 << i & sparse.ellipsisMask) {
const nextIndex = Math.min(dense.dims - (sparse.dims - i) + 1 + sparse.numAddAxisAfterEllipsis, dense.dims);
for (; fullIndex < nextIndex; fullIndex++) {
dense.begin[fullIndex] = 0;
dense.end[fullIndex] = 0;
dense.strides[fullIndex] = 1;
dense.beginMask |= 1 << fullIndex;
dense.endMask |= 1 << fullIndex;
dense.finalShapeGatherIndices.push(fullIndex);
dense.finalShapeGatherIndicesSparse.push(-1);
dense.inputShapeGatherIndicesSparse[fullIndex] = i;
}
} else if (1 << i & sparse.newAxisMask) {
dense.finalShapeGatherIndices.push(NEW_AXIS);
dense.finalShapeGatherIndicesSparse.push(-1);
} else {
if (fullIndex === dense.begin.length) {
throw Error(`Index out of range using input dim ${fullIndex}; input has only ${dense.dims} dims, ${dense.begin.length}.`);
}
if (sparse.begin != null) {
dense.begin[fullIndex] = sparse.begin[i];
}
if (sparse.end != null) {
dense.end[fullIndex] = sparse.end[i];
}
dense.strides[fullIndex] = sparse.strides[i];
if (sparse.beginMask & 1 << i) {
dense.beginMask |= 1 << fullIndex;
}
if (sparse.endMask & 1 << i) {
dense.endMask |= 1 << fullIndex;
}
if (sparse.shrinkAxisMask & 1 << i) {
dense.finalShapeGatherIndices.push(SHRINK_AXIS);
dense.finalShapeGatherIndicesSparse.push(-1);
dense.shrinkAxisMask |= 1 << fullIndex;
} else {
dense.finalShapeGatherIndices.push(fullIndex);
dense.finalShapeGatherIndicesSparse.push(i);
}
dense.inputShapeGatherIndicesSparse[fullIndex] = i;
fullIndex++;
}
}
}
function canonical(x, c, strideI, dimI, masks, validRange) {
if (masks[c]) {
return strideI > 0 ? validRange[c] : validRange[c + 1 & 1];
} else {
const xFwd = x < 0 ? dimI + x : x;
return xFwd < validRange[0] ? validRange[0] : xFwd > validRange[1] ? validRange[1] : xFwd;
}
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/serialization.js
var serialization_exports = {};
__export(serialization_exports, {
Serializable: () => Serializable,
SerializationMap: () => SerializationMap,
registerClass: () => registerClass
});
init_define_BUILD_VERSION();
var Serializable = class {
getClassName() {
return this.constructor.className;
}
static fromConfig(cls, config) {
return new cls(config);
}
};
var SerializationMap = class {
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/add.js
init_define_BUILD_VERSION();
function add_(a, b) {
let $a = convertToTensor(a, "a", "add");
let $b = convertToTensor(b, "b", "add");
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Add, inputs);
}
var add2 = op({ add_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/div.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/floorDiv.js
init_define_BUILD_VERSION();
function floorDiv_(a, b) {
let $a = convertToTensor(a, "a", "floorDiv");
let $b = convertToTensor(b, "b", "floorDiv");
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(FloorDiv, inputs);
}
var floorDiv = op({ floorDiv_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/div.js
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 inputs = { a: $a, b: $b };
const attrs = {};
return ENGINE.runKernel(RealDiv, inputs, attrs);
}
var div = op({ div_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/mul.js
init_define_BUILD_VERSION();
function mul_(a, b) {
let $a = convertToTensor(a, "a", "mul");
let $b = convertToTensor(b, "b", "mul");
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Multiply, inputs);
}
var mul = op({ mul_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/abs.js
init_define_BUILD_VERSION();
function abs_(x) {
const $x = convertToTensor(x, "x", "abs");
if ($x.dtype === "complex64") {
const inputs = { x: $x };
return ENGINE.runKernel(ComplexAbs, inputs);
} else {
const inputs = { x: $x };
return ENGINE.runKernel(Abs, inputs);
}
}
var abs = op({ abs_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/acos.js
init_define_BUILD_VERSION();
function acos_(x) {
const $x = convertToTensor(x, "x", "acos");
const inputs = { x: $x };
return ENGINE.runKernel(Acos, inputs);
}
var acos = op({ acos_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/acosh.js
init_define_BUILD_VERSION();
function acosh_(x) {
const $x = convertToTensor(x, "x", "acosh");
const inputs = { x: $x };
return ENGINE.runKernel(Acosh, inputs);
}
var acosh = op({ acosh_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/all.js
init_define_BUILD_VERSION();
function all_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "all", "bool");
const inputs = { x: $x };
const attrs = { axis, keepDims };
return ENGINE.runKernel(All, inputs, attrs);
}
var all = op({ all_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/any.js
init_define_BUILD_VERSION();
function any_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "any", "bool");
const inputs = { x: $x };
const attrs = { axis, keepDims };
return ENGINE.runKernel(Any, inputs, attrs);
}
var any = op({ any_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/arg_max.js
init_define_BUILD_VERSION();
function argMax_(x, axis = 0) {
const $x = convertToTensor(x, "x", "argMax");
const inputs = { x: $x };
const attrs = { axis };
return ENGINE.runKernel(ArgMax, inputs, attrs);
}
var argMax = op({ argMax_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/arg_min.js
init_define_BUILD_VERSION();
function argMin_(x, axis = 0) {
const $x = convertToTensor(x, "x", "argMin");
const inputs = { x: $x };
const attrs = { axis };
return ENGINE.runKernel(ArgMin, inputs, attrs);
}
var argMin = op({ argMin_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/asin.js
init_define_BUILD_VERSION();
function asin_(x) {
const $x = convertToTensor(x, "x", "asin");
const inputs = { x: $x };
return ENGINE.runKernel(Asin, inputs);
}
var asin = op({ asin_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/asinh.js
init_define_BUILD_VERSION();
function asinh_(x) {
const $x = convertToTensor(x, "x", "asinh");
const inputs = { x: $x };
return ENGINE.runKernel(Asinh, inputs);
}
var asinh = op({ asinh_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/atan.js
init_define_BUILD_VERSION();
function atan_(x) {
const $x = convertToTensor(x, "x", "atan");
const inputs = { x: $x };
return ENGINE.runKernel(Atan, inputs);
}
var atan = op({ atan_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/atan2.js
init_define_BUILD_VERSION();
function atan2_(a, b) {
let $a = convertToTensor(a, "a", "atan2");
let $b = convertToTensor(b, "b", "atan2");
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Atan2, inputs);
}
var atan2 = op({ atan2_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/atanh.js
init_define_BUILD_VERSION();
function atanh_(x) {
const $x = convertToTensor(x, "x", "atanh");
const inputs = { x: $x };
return ENGINE.runKernel(Atanh, inputs);
}
var atanh = op({ atanh_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
init_define_BUILD_VERSION();
function computeDilation2DInfo(inputShape, filterShape, strides, pad2, dataFormat = "NHWC", dilations) {
const inputChannels = inputShape[3];
const $filterShape = [...filterShape, inputChannels];
const $dataFormat = convertConv2DDataFormat(dataFormat);
return computeConv2DInfo(inputShape, $filterShape, strides, dilations, pad2, null, null, $dataFormat);
}
function computePool2DInfo(inShape, filterSize, strides, dilations, pad2, 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, pad2, roundingMode, false, dataFormat);
}
function computePool3DInfo(inShape, filterSize, strides, dilations, pad2, 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, pad2, false, $dataFormat, roundingMode);
}
function computeConv2DInfo(inShape, filterShape, strides, dilations, pad2, 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(pad2, 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, pad2, 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(pad2, 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 = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
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 = round((inputDepth - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
const outputRows = round((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
const outputCols = round((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
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(pad2, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) {
let padInfo;
let outHeight;
let outWidth;
if (typeof pad2 === "number") {
const padType = pad2 === 0 ? "VALID" : "NUMBER";
padInfo = { top: pad2, bottom: pad2, left: pad2, right: pad2, type: padType };
const outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad2, roundingMode);
outHeight = outShape[0];
outWidth = outShape[1];
} else if (pad2 === "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 (pad2 === "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 pad2 === "object") {
const top = dataFormat === "channelsLast" ? pad2[1][0] : pad2[2][0];
const bottom = dataFormat === "channelsLast" ? pad2[1][1] : pad2[2][1];
const left = dataFormat === "channelsLast" ? pad2[2][0] : pad2[3][0];
const right = dataFormat === "channelsLast" ? pad2[2][1] : pad2[3][1];
const padType = top === 0 && bottom === 0 && left === 0 && right === 0 ? "VALID" : "EXPLICIT";
padInfo = { top, bottom, left, right, type: padType };
outHeight = round((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode);
outWidth = round((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode);
} else {
throw Error(`Unknown padding parameter: ${pad2}`);
}
return { padInfo, outHeight, outWidth };
}
function get3DPadAndOutInfo(pad2, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth, roundingMode) {
let padInfo;
let outDepth;
let outHeight;
let outWidth;
if (typeof pad2 === "number") {
const padType = pad2 === 0 ? "VALID" : "NUMBER";
padInfo = {
top: pad2,
bottom: pad2,
left: pad2,
right: pad2,
front: pad2,
back: pad2,
type: padType
};
const outShape = computeOutputShape4D([inDepth, inHeight, inWidth, 1], filterDepth, 1, strideDepth, pad2, roundingMode);
outDepth = outShape[0];
outHeight = outShape[1];
outWidth = outShape[2];
} else if (pad2 === "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 (pad2 === "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: ${pad2}`);
}
return { padInfo, outDepth, outHeight, outWidth };
}
function round(value, roundingMode) {
if (!roundingMode) {
return Math.trunc(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}`);
}
}
function checkPadOnDimRoundingMode(opDesc, pad2, dimRoundingMode) {
if (dimRoundingMode != null) {
if (typeof pad2 === "string") {
throw Error(`Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad2}.`);
} else if (typeof pad2 === "number") {
assert(isInt(pad2), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${pad2}.`);
} else if (typeof pad2 === "object") {
pad2.forEach((p2) => {
p2.forEach((v) => {
assert(isInt(v), () => `Error in ${opDesc}: pad must be an integer when using dimRoundingMode ${dimRoundingMode} but got pad ${v}.`);
});
});
} else {
throw Error(`Error in ${opDesc}: Unknown padding parameter: ${pad2}`);
}
}
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/reshape.js
init_define_BUILD_VERSION();
function reshape_(x, shape) {
const $x = convertToTensor(x, "x", "reshape", "string_or_numeric");
const inputs = { x: $x };
const attrs = { shape };
return ENGINE.runKernel(Reshape, inputs, attrs);
}
var reshape = op({ reshape_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool.js
function avgPool_(x, filterSize, strides, pad2, 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}.`);
checkPadOnDimRoundingMode("avgPool", pad2, dimRoundingMode);
const inputs = { x: x4D };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode };
let res = ENGINE.runKernel(AvgPool, inputs, attrs);
res = cast(res, $x.dtype);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var avgPool = op({ avgPool_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d.js
init_define_BUILD_VERSION();
function avgPool3d_(x, filterSize, strides, pad2, dimRoundingMode, dataFormat = "NDHWC") {
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}`);
checkPadOnDimRoundingMode("avgPool3d", pad2, dimRoundingMode);
const inputs = { x: x5D };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode, dataFormat };
let res = ENGINE.runKernel(AvgPool3D, inputs, 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;
}
var avgPool3d = op({ avgPool3d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/concat.js
init_define_BUILD_VERSION();
function concat_(tensors, axis = 0) {
assert(tensors.length >= 1, () => "Pass at least one tensor to concat");
const $tensors = convertToTensorArray(tensors, "tensors", "concat", "string_or_numeric");
if ($tensors[0].dtype === "complex64") {
$tensors.forEach((tensor3) => {
if (tensor3.dtype !== "complex64") {
throw new Error(`Cannot concatenate complex64 tensors with a tensor
with dtype ${tensor3.dtype}. `);
}
});
}
if ($tensors.length === 1) {
return clone($tensors[0]);
}
const inputs = $tensors;
const attr = { axis };
return ENGINE.runKernel(Concat, inputs, attr);
}
var concat = op({ concat_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sigmoid.js
init_define_BUILD_VERSION();
function sigmoid_(x) {
const $x = convertToTensor(x, "x", "sigmoid", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Sigmoid, inputs);
}
var sigmoid = op({ sigmoid_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
init_define_BUILD_VERSION();
function slice_(x, begin, size) {
const $x = convertToTensor(x, "x", "slice", "string_or_numeric");
if ($x.rank === 0) {
throw new Error("Slicing scalar is not possible");
}
const inputs = { x: $x };
const attrs = { begin, size };
return ENGINE.runKernel(Slice, inputs, attrs);
}
var slice = op({ slice_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tanh.js
init_define_BUILD_VERSION();
function tanh_(x) {
const $x = convertToTensor(x, "x", "tanh", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Tanh, inputs);
}
var tanh2 = op({ tanh_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batch_to_space_nd.js
init_define_BUILD_VERSION();
function batchToSpaceND_(x, blockShape, crops) {
const $x = convertToTensor(x, "x", "batchToSpaceND");
const prod4 = blockShape.reduce((a, b) => a * b);
assert($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] % prod4 === 0, () => `input tensor batch is ${$x.shape[0]} but is not divisible by the product of the elements of blockShape ${blockShape.join(" * ")} === ${prod4}`);
const inputs = { x: $x };
const attrs = { blockShape, crops };
return ENGINE.runKernel(BatchToSpaceND, inputs, attrs);
}
var batchToSpaceND = op({ batchToSpaceND_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_util.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
function batchNorm_(x, mean3, variance, offset, scale2, varianceEpsilon) {
if (varianceEpsilon == null) {
varianceEpsilon = 1e-3;
}
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean3, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale2 != null) {
$scale = convertToTensor(scale2, "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 inputs = {
x: x4D,
scale: $scale,
offset: $offset,
mean: $mean,
variance: $variance
};
const attrs = { varianceEpsilon };
const res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs);
return reshape(res, $x.shape);
}
var batchNorm = op({ batchNorm_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm2d.js
init_define_BUILD_VERSION();
function batchNorm2d_(x, mean3, variance, offset, scale2, varianceEpsilon) {
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean3, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale2 != null) {
$scale = convertToTensor(scale2, "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);
}
var batchNorm2d = op({ batchNorm2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm3d.js
init_define_BUILD_VERSION();
function batchNorm3d_(x, mean3, variance, offset, scale2, varianceEpsilon) {
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean3, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale2 != null) {
$scale = convertToTensor(scale2, "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);
}
var batchNorm3d = op({ batchNorm3d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm4d.js
init_define_BUILD_VERSION();
function batchNorm4d_(x, mean3, variance, offset, scale2, varianceEpsilon) {
const $x = convertToTensor(x, "x", "batchNorm");
const $mean = convertToTensor(mean3, "mean", "batchNorm");
const $variance = convertToTensor(variance, "variance", "batchNorm");
let $scale;
if (scale2 != null) {
$scale = convertToTensor(scale2, "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);
}
var batchNorm4d = op({ batchNorm4d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/bincount.js
init_define_BUILD_VERSION();
function bincount_(x, weights, size) {
const $x = convertToTensor(x, "x", "bincount");
const $weights = convertToTensor(weights, "weights", "bincount");
assert($x.dtype === "int32", () => `Error in bincount: input dtype must be int32, but got ${$x.dtype}`);
assert(size >= 0, () => `size must be non-negative, but got ${size}.`);
assert($weights.size === $x.size || $weights.size === 0, () => `Error in bincount: weights must have the same size as input or0-length, but got input shape: ${$x.shape}, weights shape: ${$weights.shape}.`);
const inputs = { x: $x, weights: $weights };
const attrs = { size };
return ENGINE.runKernel(Bincount, inputs, attrs);
}
var bincount = op({ bincount_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_to.js
init_define_BUILD_VERSION();
function broadcastTo_(x, shape) {
let input2 = convertToTensor(x, "broadcastTo", "x");
const xShape = input2.shape;
if (shape.some((d) => !(d > 0) || d % 1 !== 0)) {
throw new Error(`broadcastTo(): Invalid broadcast shape [${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 inputs = { x: input2 };
const attrs = { reps };
return ENGINE.runKernel(Tile, inputs, attrs);
}
var broadcastTo = op({ broadcastTo_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/ceil.js
init_define_BUILD_VERSION();
function ceil_(x) {
const $x = convertToTensor(x, "x", "ceil", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Ceil, inputs);
}
var ceil = op({ ceil_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/clip_by_value.js
init_define_BUILD_VERSION();
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.runKernel(ClipByValue, inputs, attrs);
}
var clipByValue = op({ clipByValue_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/concat_1d.js
init_define_BUILD_VERSION();
function concat1d_(tensors) {
return concat(tensors, 0);
}
var concat1d = op({ concat1d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/concat_2d.js
init_define_BUILD_VERSION();
function concat2d_(tensors, axis) {
return concat(tensors, axis);
}
var concat2d = op({ concat2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/concat_3d.js
init_define_BUILD_VERSION();
function concat3d_(tensors, axis) {
return concat(tensors, axis);
}
var concat3d = op({ concat3d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/concat_4d.js
init_define_BUILD_VERSION();
function concat4d_(tensors, axis) {
return concat(tensors, axis);
}
var concat4d = op({ concat4d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d.js
init_define_BUILD_VERSION();
function conv2d_(x, filter, strides, pad2, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
const $x = convertToTensor(x, "x", "conv2d", "float32");
const $filter = convertToTensor(filter, "filter", "conv2d", "float32");
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}.`);
checkPadOnDimRoundingMode("conv2d", pad2, dimRoundingMode);
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 inputs = { x: x4D, filter: $filter };
const attrs = { strides, pad: pad2, dataFormat, dilations, dimRoundingMode };
const res = ENGINE.runKernel(Conv2D, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var conv2d = op({ conv2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d.js
function conv1d_(x, filter, stride, pad2, 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}.`);
checkPadOnDimRoundingMode("conv1d", pad2, dimRoundingMode);
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], $filter.shape[2]]);
const input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]);
const strides = [1, stride];
const dilations = [1, dilation];
const conv2dDataFormat = "NHWC";
const res = conv2d(input4D, filter4D, strides, pad2, conv2dDataFormat, dilations, dimRoundingMode);
if (reshapedTo3D) {
return reshape(res, [res.shape[2], res.shape[3]]);
}
return reshape(res, [res.shape[0], res.shape[2], res.shape[3]]);
}
var conv1d = op({ conv1d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_input.js
init_define_BUILD_VERSION();
function conv2DBackpropInput_(xShape, dy, filter, strides, pad2, dataFormat = "NHWC", dimRoundingMode) {
assert(xShape.length === dy.rank, () => `Length of inShape (${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]}.`);
checkPadOnDimRoundingMode("conv2dDerInput", pad2, dimRoundingMode);
const inputs = { dy: dy4D, filter };
const attrs = { strides, pad: pad2, dataFormat, dimRoundingMode, inputShape: xShape4D };
const res = ENGINE.runKernel(Conv2DBackpropInput, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var conv2DBackpropInput = op({ conv2DBackpropInput_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose.js
function conv2dTranspose_(x, filter, outputShape, strides, pad2, dimRoundingMode) {
const $x = convertToTensor(x, "x", "conv2dTranspose");
const $filter = convertToTensor(filter, "filter", "conv2dTranspose");
return conv2DBackpropInput(outputShape, $x, $filter, strides, pad2, "NHWC", dimRoundingMode);
}
var conv2dTranspose = op({ conv2dTranspose_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d.js
init_define_BUILD_VERSION();
function conv3d_(x, filter, strides, pad2, 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 inputs = { x: x5D, filter: $filter };
const attrs = { strides, pad: pad2, dataFormat, dilations };
const res = ENGINE.runKernel(Conv3D, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
var conv3d = op({ conv3d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_input.js
init_define_BUILD_VERSION();
function conv3DBackpropInput_(xShape, dy, filter, strides, pad2) {
assert(xShape.length === dy.rank, () => `Length of inShape (${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 inputs = { dy: dy5D, filter };
const attrs = { pad: pad2, strides, inputShape: xShape5D };
const res = ENGINE.runKernel(Conv3DBackpropInputV2, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
var conv3DBackpropInput = op({ conv3DBackpropInput_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_transpose.js
function conv3dTranspose_(x, filter, outputShape, strides, pad2) {
const $x = convertToTensor(x, "x", "conv3dTranspose");
const $filter = convertToTensor(filter, "filter", "conv3dTranspose");
return conv3DBackpropInput(outputShape, $x, $filter, strides, pad2);
}
var conv3dTranspose = op({ conv3dTranspose_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/cos.js
init_define_BUILD_VERSION();
function cos_(x) {
const $x = convertToTensor(x, "x", "cos", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Cos, inputs);
}
var cos = op({ cos_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/cosh.js
init_define_BUILD_VERSION();
function cosh_(x) {
const $x = convertToTensor(x, "x", "cosh", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Cosh, inputs);
}
var cosh = op({ cosh_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/cumprod.js
init_define_BUILD_VERSION();
function cumprod_(x, axis = 0, exclusive = false, reverse4 = false) {
const $x = convertToTensor(x, "x", "cumprod");
const inputs = { x: $x };
const attrs = { axis, exclusive, reverse: reverse4 };
return ENGINE.runKernel(Cumprod, inputs, attrs);
}
var cumprod = op({ cumprod_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/cumsum.js
init_define_BUILD_VERSION();
function cumsum_(x, axis = 0, exclusive = false, reverse4 = false) {
const $x = convertToTensor(x, "x", "cumsum");
const inputs = { x: $x };
const attrs = { axis, exclusive, reverse: reverse4 };
return ENGINE.runKernel(Cumsum, inputs, attrs);
}
var cumsum = op({ cumsum_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/depth_to_space.js
init_define_BUILD_VERSION();
function depthToSpace_(x, blockSize, dataFormat = "NHWC") {
const $x = convertToTensor(x, "x", "depthToSpace", "float32");
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(blockSize > 1, () => `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);
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 inputs = { x: $x };
const attrs = { blockSize, dataFormat };
return ENGINE.runKernel(DepthToSpace, inputs, attrs);
}
var depthToSpace = op({ depthToSpace_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d.js
init_define_BUILD_VERSION();
function depthwiseConv2d_(x, filter, strides, pad2, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode) {
const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32");
const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32");
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}.`);
const inChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
assert(inChannels === $filter.shape[2], () => `Error in depthwiseConv2d: number of input channels (${inChannels}) must match the inChannels dimension in filter ${$filter.shape[2]}.`);
checkPadOnDimRoundingMode("depthwiseConv2d", pad2, dimRoundingMode);
const inputs = { x: x4D, filter: $filter };
const attrs = { strides, pad: pad2, dataFormat, dilations, dimRoundingMode };
const res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var depthwiseConv2d = op({ depthwiseConv2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/dilation2d.js
init_define_BUILD_VERSION();
function dilation2d_(x, filter, strides, pad2, 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: pad2, dilations };
const res = ENGINE.runKernel(Dilation2D, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var dilation2d = op({ dilation2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/equal.js
init_define_BUILD_VERSION();
function equal_(a, b) {
let $a = convertToTensor(a, "a", "equal", "string_or_numeric");
let $b = convertToTensor(b, "b", "equal", "string_or_numeric");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Equal, inputs);
}
var equal = op({ equal_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/where.js
init_define_BUILD_VERSION();
function where_(condition, a, b) {
const $a = convertToTensor(a, "a", "where");
const $b = convertToTensor(b, "b", "where");
const $condition = convertToTensor(condition, "condition", "where", "bool");
const broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape);
const $broadcastedCondition = broadcastTo($condition, broadcastShape);
const $broadcastedA = broadcastTo($a, broadcastShape);
const $broadcastedB = broadcastTo($b, broadcastShape);
const inputs = {
condition: $broadcastedCondition,
t: $broadcastedA,
e: $broadcastedB
};
return ENGINE.runKernel(Select, inputs);
}
var where = op({ where_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/zeros_like.js
init_define_BUILD_VERSION();
function zerosLike_(x) {
const $x = convertToTensor(x, "x", "zerosLike");
const inputs = { x: $x };
return ENGINE.runKernel(ZerosLike, inputs);
}
var zerosLike = op({ zerosLike_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/div_no_nan.js
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, $b);
const zeros3 = zerosLike(divResult);
const bEqualsZero = equal($b, zeros3);
return where(bEqualsZero, zeros3, divResult);
}
var divNoNan = op({ divNoNan_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/dot.js
init_define_BUILD_VERSION();
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;
}
}
var dot = op({ dot_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/elu.js
init_define_BUILD_VERSION();
function elu_(x) {
const $x = convertToTensor(x, "x", "elu", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Elu, inputs);
}
var elu = op({ elu_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/erf.js
init_define_BUILD_VERSION();
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: $x };
return ENGINE.runKernel(Erf, inputs);
}
var erf = op({ erf_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
init_define_BUILD_VERSION();
function axesAreInnerMostDims(axes, rank) {
for (let i = 0; i < axes.length; ++i) {
if (axes[axes.length - i - 1] !== rank - 1 - i) {
return false;
}
}
return true;
}
function combineLocations(outputLoc, reduceLoc, axes) {
const rank = outputLoc.length + reduceLoc.length;
const loc = [];
let outIdx = 0;
let reduceIdx = 0;
for (let dim = 0; dim < rank; dim++) {
if (axes.indexOf(dim) === -1) {
loc.push(outputLoc[outIdx++]);
} else {
loc.push(reduceLoc[reduceIdx++]);
}
}
return loc;
}
function computeOutAndReduceShapes(aShape, axes) {
const outShape = [];
const rank = aShape.length;
for (let dim = 0; dim < rank; dim++) {
if (axes.indexOf(dim) === -1) {
outShape.push(aShape[dim]);
}
}
const reduceShape = axes.map((dim) => aShape[dim]);
return [outShape, reduceShape];
}
function expandShapeToKeepDim(shape, axes) {
const reduceSubShape = axes.map((x) => 1);
return combineLocations(shape, reduceSubShape, axes);
}
function assertAxesAreInnerMostDims(msg, axes, rank) {
assert(axesAreInnerMostDims(axes, rank), () => `${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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/max.js
init_define_BUILD_VERSION();
function max_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "max");
const inputs = { x: $x };
const attrs = { reductionIndices: axis, keepDims };
return ENGINE.runKernel(Max, inputs, attrs);
}
var max = op({ max_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/min.js
init_define_BUILD_VERSION();
function min_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "min");
const inputs = { x: $x };
const attrs = { axis, keepDims };
return ENGINE.runKernel(Min, inputs, attrs);
}
var min = op({ min_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/pow.js
init_define_BUILD_VERSION();
function pow_(base, exp4) {
let $base = convertToTensor(base, "base", "pow");
let $exp = convertToTensor(exp4, "exp", "pow");
[$base, $exp] = makeTypesMatch($base, $exp);
const inputs = { a: $base, b: $exp };
return ENGINE.runKernel(Pow, inputs);
}
var pow = op({ pow_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/scalar.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sqrt.js
init_define_BUILD_VERSION();
function sqrt_(x) {
const $x = convertToTensor(x, "x", "sqrt", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Sqrt, inputs);
}
var sqrt = op({ sqrt_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/square.js
init_define_BUILD_VERSION();
function square_(x) {
const $x = convertToTensor(x, "x", "square");
const attrs = {};
return ENGINE.runKernel("Square", { x: $x }, attrs);
}
var square = op({ square_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sum.js
init_define_BUILD_VERSION();
function sum_(x, axis = null, keepDims = false) {
let $x = convertToTensor(x, "x", "sum");
if ($x.dtype === "bool") {
$x = cast($x, "int32");
}
const inputs = { x: $x };
const attrs = { axis, keepDims };
return ENGINE.runKernel(Sum, inputs, attrs);
}
var sum2 = op({ sum_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
function norm_(x, ord = "euclidean", axis = null, keepDims = false) {
x = convertToTensor(x, "x", "norm");
const norm2 = normImpl(x, ord, axis);
let keepDimsShape = norm2.shape;
if (keepDims) {
const axes = parseAxisParam(axis, x.shape);
keepDimsShape = expandShapeToKeepDim(norm2.shape, axes);
}
return reshape(norm2, keepDimsShape);
}
function normImpl(x, p2, axis = null) {
if (x.rank === 0) {
return abs(x);
}
if (x.rank !== 1 && axis === null) {
return normImpl(reshape(x, [-1]), p2, axis);
}
if (x.rank === 1 || typeof axis === "number" || Array.isArray(axis) && axis.length === 1) {
if (p2 === 1) {
return sum2(abs(x), axis);
}
if (p2 === Infinity) {
return max(abs(x), axis);
}
if (p2 === -Infinity) {
return min(abs(x), axis);
}
if (p2 === "euclidean" || p2 === 2) {
return sqrt(sum2(pow(abs(x), scalar(2, "int32")), axis));
}
throw new Error(`Error in norm: invalid ord value: ${p2}`);
}
if (Array.isArray(axis) && axis.length === 2) {
if (p2 === 1) {
return max(sum2(abs(x), axis[0]), axis[1] - 1);
}
if (p2 === Infinity) {
return max(sum2(abs(x), axis[1]), axis[0]);
}
if (p2 === -Infinity) {
return min(sum2(abs(x), axis[1]), axis[0]);
}
if (p2 === "fro" || p2 === "euclidean") {
return sqrt(sum2(square(x), axis));
}
throw new Error(`Error in norm: invalid ord value: ${p2}`);
}
throw new Error(`Error in norm: invalid axis: ${axis}`);
}
var norm = op({ norm_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/euclidean_norm.js
function euclideanNorm_(x, axis = null, keepDims = false) {
return norm(x, "euclidean", axis, keepDims);
}
var euclideanNorm = op({ euclideanNorm_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/exp.js
init_define_BUILD_VERSION();
function exp_(x) {
const $x = convertToTensor(x, "x", "exp");
const inputs = { x: $x };
return ENGINE.runKernel(Exp, inputs);
}
var exp = op({ exp_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/expand_dims.js
init_define_BUILD_VERSION();
function expandDims_(x, axis = 0) {
const $x = convertToTensor(x, "x", "expandDims", "string_or_numeric");
assert(axis <= $x.rank, () => "Axis must be <= rank of the tensor");
const inputs = { input: $x };
const attrs = { dim: axis };
return ENGINE.runKernel(ExpandDims, inputs, attrs);
}
var expandDims = op({ expandDims_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/expm1.js
init_define_BUILD_VERSION();
function expm1_(x) {
const $x = convertToTensor(x, "x", "expm1");
const inputs = { x: $x };
return ENGINE.runKernel(Expm1, inputs);
}
var expm1 = op({ expm1_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tile.js
init_define_BUILD_VERSION();
function tile_(x, reps) {
const $x = convertToTensor(x, "x", "tile", "string_or_numeric");
assert($x.rank === reps.length, () => `Error in transpose: rank of input ${$x.rank} must match length of reps ${reps}.`);
const inputs = { x: $x };
const attrs = { reps };
return ENGINE.runKernel(Tile, inputs, attrs);
}
var tile = op({ tile_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/eye.js
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.`);
}
}
}
var eye = op({ eye_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fill.js
init_define_BUILD_VERSION();
function fill(shape, value, dtype) {
const attrs = { shape, value, dtype };
return ENGINE.runKernel(Fill, {}, attrs);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/floor.js
init_define_BUILD_VERSION();
function floor_(x) {
const $x = convertToTensor(x, "x", "floor", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Floor, inputs);
}
var floor = op({ floor_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/gather.js
init_define_BUILD_VERSION();
function gather_(x, indices, axis = 0, batchDims = 0) {
const $x = convertToTensor(x, "x", "gather");
const $indices = convertToTensor(indices, "indices", "gather", "int32");
const inputs = { x: $x, indices: $indices };
const attrs = { axis, batchDims };
return ENGINE.runKernel(GatherV2, inputs, attrs);
}
var gather = op({ gather_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/greater.js
init_define_BUILD_VERSION();
function greater_(a, b) {
let $a = convertToTensor(a, "a", "greater", "string_or_numeric");
let $b = convertToTensor(b, "b", "greater", "string_or_numeric");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Greater, inputs);
}
var greater = op({ greater_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/greater_equal.js
init_define_BUILD_VERSION();
function greaterEqual_(a, b) {
let $a = convertToTensor(a, "a", "greaterEqual", "string_or_numeric");
let $b = convertToTensor(b, "b", "greaterEqual", "string_or_numeric");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(GreaterEqual, inputs);
}
var greaterEqual = op({ greaterEqual_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/is_finite.js
init_define_BUILD_VERSION();
function isFinite_(x) {
const $x = convertToTensor(x, "x", "isFinite");
const inputs = { x: $x };
return ENGINE.runKernel(IsFinite, inputs);
}
var isFinite2 = op({ isFinite_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/is_inf.js
init_define_BUILD_VERSION();
function isInf_(x) {
const $x = convertToTensor(x, "x", "isInf");
const inputs = { x: $x };
return ENGINE.runKernel(IsInf, inputs);
}
var isInf = op({ isInf_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/is_nan.js
init_define_BUILD_VERSION();
function isNaN_(x) {
const $x = convertToTensor(x, "x", "isNaN");
const inputs = { x: $x };
return ENGINE.runKernel(IsNan, inputs);
}
var isNaN2 = op({ isNaN_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/leaky_relu.js
init_define_BUILD_VERSION();
function leakyRelu_(x, alpha = 0.2) {
const $x = convertToTensor(x, "x", "leakyRelu");
const inputs = { x: $x };
const attrs = { alpha };
return ENGINE.runKernel(LeakyRelu, inputs, attrs);
}
var leakyRelu = op({ leakyRelu_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/less.js
init_define_BUILD_VERSION();
function less_(a, b) {
let $a = convertToTensor(a, "a", "less", "string_or_numeric");
let $b = convertToTensor(b, "b", "less", "string_or_numeric");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Less, inputs);
}
var less = op({ less_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/less_equal.js
init_define_BUILD_VERSION();
function lessEqual_(a, b) {
let $a = convertToTensor(a, "a", "lessEqual", "string_or_numeric");
let $b = convertToTensor(b, "b", "lessEqual", "string_or_numeric");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(LessEqual, inputs);
}
var lessEqual = op({ lessEqual_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization.js
init_define_BUILD_VERSION();
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 inputs = { x: x4D };
const attrs = { depthRadius, bias, alpha, beta };
const res = ENGINE.runKernel(LRN, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
} else {
return res;
}
}
var localResponseNormalization = op({ localResponseNormalization_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log.js
init_define_BUILD_VERSION();
function log_(x) {
const $x = convertToTensor(x, "x", "log", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Log, inputs);
}
var log2 = op({ log_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log1p.js
init_define_BUILD_VERSION();
function log1p_(x) {
const $x = convertToTensor(x, "x", "log1p");
const inputs = { x: $x };
return ENGINE.runKernel(Log1p, inputs);
}
var log1p = op({ log1p_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients.js
init_define_BUILD_VERSION();
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((variable2) => !variable2.trainable) : null;
const originalVarCount = varList.length;
varList = varList.filter((variable2) => variable2.trainable);
assert(varList.length > 0, () => `variableGrads() expects at least one of the input variables to be trainable, but none of the ${originalVarCount} variables is trainable.`);
const allowNoGradients = true;
const { value, grads } = ENGINE.gradients(f, varList, null, allowNoGradients);
assert(grads.some((g) => g != null), () => "Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().");
assert(value.rank === 0, () => `The f passed in variableGrads(f) must return a scalar, but it returned a rank-${value.rank} tensor`);
const namedGrads = {};
varList.forEach((v, i) => {
if (grads[i] != null) {
namedGrads[v.name] = grads[i];
}
});
if (specifiedNonTrainable != null) {
specifiedNonTrainable.forEach((v) => namedGrads[v.name] = null);
}
return { value, grads: namedGrads };
}
function customGrad(f) {
return ENGINE.customGrad(f);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/softplus.js
init_define_BUILD_VERSION();
function softplus_(x) {
const $x = convertToTensor(x, "x", "softplus");
const inputs = { x: $x };
return ENGINE.runKernel(Softplus, inputs);
}
var softplus = op({ softplus_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log_sigmoid.js
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);
}
var logSigmoid = op({ logSigmoid_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sub.js
init_define_BUILD_VERSION();
function sub_(a, b) {
let $a = convertToTensor(a, "a", "sub");
let $b = convertToTensor(b, "b", "sub");
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Sub, inputs);
}
var sub = op({ sub_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log_softmax.js
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 ${axis}`);
}
const customOp = customGrad((logits2, save) => {
const keepDims = true;
const xMax = max(logits2, axis, true);
const shifted = sub(logits2, xMax);
const value = sub(cast(shifted, "float32"), log2(sum2(exp(shifted), axis, keepDims)));
save([value]);
const gradFunc = (dy, saved) => {
const [value2] = saved;
const keepDims2 = true;
const softmax4 = exp(value2);
return sub(dy, mul(sum2(dy, axis, keepDims2), softmax4));
};
return { value, gradFunc };
});
return customOp($logits);
}
var logSoftmax = op({ logSoftmax_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/log_sum_exp.js
init_define_BUILD_VERSION();
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 = log2(c);
const res = add2(reshape(xMax, d.shape), d);
if (keepDims) {
const newShape = expandShapeToKeepDim(res.shape, axes);
return reshape(res, newShape);
}
return res;
}
var logSumExp = op({ logSumExp_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/logical_and.js
init_define_BUILD_VERSION();
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.runKernel(LogicalAnd, inputs);
}
var logicalAnd = op({ logicalAnd_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/logical_not.js
init_define_BUILD_VERSION();
function logicalNot_(x) {
const $x = convertToTensor(x, "x", "logicalNot", "bool");
const inputs = { x: $x };
return ENGINE.runKernel(LogicalNot, inputs);
}
var logicalNot = op({ logicalNot_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/logical_or.js
init_define_BUILD_VERSION();
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.runKernel(LogicalOr, inputs);
}
var logicalOr = op({ logicalOr_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/logical_xor.js
init_define_BUILD_VERSION();
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)));
}
var logicalXor = op({ logicalXor_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool.js
init_define_BUILD_VERSION();
function maxPool_(x, filterSize, strides, pad2, 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}'`);
checkPadOnDimRoundingMode("maxPool", pad2, dimRoundingMode);
const inputs = { x: x4D };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode };
const res = ENGINE.runKernel(MaxPool, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var maxPool = op({ maxPool_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d.js
init_define_BUILD_VERSION();
function maxPool3d_(x, filterSize = [1, 1, 1], strides, pad2, dimRoundingMode, dataFormat = "NDHWC") {
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}`);
checkPadOnDimRoundingMode("maxPool3d", pad2, dimRoundingMode);
const inputs = { x: x5D };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode, dataFormat };
const res = ENGINE.runKernel(MaxPool3D, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
var maxPool3d = op({ maxPool3d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/maximum.js
init_define_BUILD_VERSION();
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 inputs = { a: $a, b: $b };
return ENGINE.runKernel(Maximum, inputs);
}
var maximum = op({ maximum_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/mean.js
init_define_BUILD_VERSION();
function mean_(x, axis = null, keepDims = false) {
const $x = convertToTensor(x, "x", "mean");
const inputs = { x: $x };
const attrs = { axis, keepDims };
return ENGINE.runKernel(Mean, inputs, attrs);
}
var mean = op({ mean_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/zeros.js
init_define_BUILD_VERSION();
function zeros(shape, dtype = "float32") {
if (dtype === "complex64") {
const real4 = zeros(shape, "float32");
const imag4 = zeros(shape, "float32");
return complex(real4, imag4);
}
const values = makeZerosTypedArray(sizeFromShape(shape), dtype);
return ENGINE.makeTensor(values, shape, dtype);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/ones.js
function ones2(shape, dtype = "float32") {
if (dtype === "complex64") {
const real4 = ones2(shape, "float32");
const imag4 = zeros(shape, "float32");
return complex(real4, imag4);
}
const values = makeOnesTypedArray(sizeFromShape(shape), dtype);
return ENGINE.makeTensor(values, shape, dtype);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/minimum.js
init_define_BUILD_VERSION();
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 inputs = { a: $a, b: $b };
return ENGINE.runKernel(Minimum, inputs);
}
var minimum = op({ minimum_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/mirror_pad.js
init_define_BUILD_VERSION();
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);
}
var mirrorPad = op({ mirrorPad_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/mod.js
init_define_BUILD_VERSION();
function mod_(a, b) {
let $a = convertToTensor(a, "a", "mod");
let $b = convertToTensor(b, "b", "mod");
[$a, $b] = makeTypesMatch($a, $b);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(Mod, inputs);
}
var mod = op({ mod_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/moments.js
init_define_BUILD_VERSION();
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 };
}
var moments = op({ moments_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/not_equal.js
init_define_BUILD_VERSION();
function notEqual_(a, b) {
let $a = convertToTensor(a, "a", "notEqual", "string_or_numeric");
let $b = convertToTensor(b, "b", "notEqual", "string_or_numeric");
[$a, $b] = makeTypesMatch($a, $b);
assertAndGetBroadcastShape($a.shape, $b.shape);
const inputs = { a: $a, b: $b };
return ENGINE.runKernel(NotEqual, inputs);
}
var notEqual = op({ notEqual_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/ones_like.js
init_define_BUILD_VERSION();
function onesLike_(x) {
const $x = convertToTensor(x, "x", "onesLike");
const inputs = { x: $x };
return ENGINE.runKernel(OnesLike, inputs);
}
var onesLike = op({ onesLike_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/pad.js
init_define_BUILD_VERSION();
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 attrs = { paddings, constantValue };
const inputs = { x: $x };
return ENGINE.runKernel(PadV2, inputs, attrs);
}
var pad = op({ pad_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/space_to_batch_nd.js
init_define_BUILD_VERSION();
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 inputs = { x: $x };
const attrs = { blockShape, paddings };
return ENGINE.runKernel(SpaceToBatchND, inputs, attrs);
}
var spaceToBatchND = op({ spaceToBatchND_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
function pool_(input2, windowShape, poolingType, pad2, dilations, strides, dimRoundingMode) {
if (dilations == null) {
dilations = [1, 1];
}
if (strides == null) {
strides = 1;
}
if (pad2 === 0) {
pad2 = "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, pad2);
const dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
let basePadding;
if (pad2 === "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 ? pad2 : "valid";
const convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
const forwardOp = poolingType === "avg" ? () => avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode) : () => maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode);
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]];
});
}
var pool = op({ pool_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/prelu.js
init_define_BUILD_VERSION();
function prelu_(x, alpha) {
const $x = convertToTensor(x, "x", "prelu");
const $alpha = convertToTensor(alpha, "alpha", "prelu");
const inputs = { x: $x, alpha: $alpha };
return ENGINE.runKernel(Prelu, inputs);
}
var prelu = op({ prelu_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/prod.js
init_define_BUILD_VERSION();
function prod_(x, axis = null, keepDims = false) {
let $x = convertToTensor(x, "x", "prod");
if ($x.dtype === "bool") {
$x = cast($x, "int32");
}
const inputs = { x: $x };
const attrs = { axis, keepDims };
return ENGINE.runKernel(Prod, inputs, attrs);
}
var prod = op({ prod_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
init_define_BUILD_VERSION();
var seedrandom = __toESM(require_seedrandom2());
var MPRandGauss = class {
constructor(mean3, stdDeviation, dtype, truncated, seed) {
this.mean = mean3;
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 mul2 = Math.sqrt(-2 * Math.log(s) / s);
resultX = this.mean + this.stdDev * v1 * mul2;
resultY = this.mean + this.stdDev * v2 * mul2;
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;
}
};
var UniformRandom = class {
constructor(min5 = 0, max5 = 1, dtype, seed) {
this.canReturnFloat = () => this.dtype == null || this.dtype === "float32";
this.min = min5;
this.range = max5 - min5;
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 ${min5} - ${max5} <= 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/random_normal.js
init_define_BUILD_VERSION();
function randomNormal_(shape, mean3 = 0, stdDev = 1, dtype, seed) {
if (dtype != null && dtype === "bool") {
throw new Error(`Unsupported data type ${dtype}`);
}
const randGauss = new MPRandGauss(mean3, 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();
}
var randomNormal = op({ randomNormal_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/random_uniform.js
init_define_BUILD_VERSION();
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();
}
var randomUniform = op({ randomUniform_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/range.js
init_define_BUILD_VERSION();
function range(start, stop, step4 = 1, dtype = "float32") {
if (step4 === 0) {
throw new Error("Cannot have a step of zero");
}
const attrs = { start, stop, step: step4, dtype };
return ENGINE.runKernel(Range, {}, attrs);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/reciprocal.js
init_define_BUILD_VERSION();
function reciprocal_(x) {
const $x = convertToTensor(x, "x", "reciprocal");
const inputs = { x: $x };
return ENGINE.runKernel(Reciprocal, inputs);
}
var reciprocal = op({ reciprocal_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/relu.js
init_define_BUILD_VERSION();
function relu_(x) {
const $x = convertToTensor(x, "x", "relu");
const inputs = { x: $x };
return ENGINE.runKernel(Relu, inputs);
}
var relu = op({ relu_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/relu6.js
init_define_BUILD_VERSION();
function relu6_(x) {
const $x = convertToTensor(x, "x", "relu6");
const inputs = { x: $x };
return ENGINE.runKernel(Relu6, inputs);
}
var relu6 = op({ relu6_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
init_define_BUILD_VERSION();
function reverse_(x, axis) {
const $x = convertToTensor(x, "x", "reverse");
const inputs = { x: $x };
const attrs = { dims: axis };
return ENGINE.runKernel(Reverse, inputs, attrs);
}
var reverse = op({ reverse_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/round.js
init_define_BUILD_VERSION();
function round_(x) {
const $x = convertToTensor(x, "x", "round");
const inputs = { x: $x };
return ENGINE.runKernel(Round, inputs);
}
var round2 = op({ round_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/rsqrt.js
init_define_BUILD_VERSION();
function rsqrt_(x) {
const $x = convertToTensor(x, "x", "rsqrt", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Rsqrt, inputs);
}
var rsqrt = op({ rsqrt_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/selu.js
init_define_BUILD_VERSION();
function selu_(x) {
const $x = convertToTensor(x, "x", "selu");
const inputs = { x: $x };
return ENGINE.runKernel(Selu, inputs);
}
var selu = op({ selu_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/separable_conv2d.js
init_define_BUILD_VERSION();
function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad2, 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, pad2, 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;
}
var separableConv2d = op({ separableConv2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sign.js
init_define_BUILD_VERSION();
function sign_(x) {
const $x = convertToTensor(x, "x", "sign");
const inputs = { x: $x };
return ENGINE.runKernel(Sign, inputs);
}
var sign = op({ sign_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sin.js
init_define_BUILD_VERSION();
function sin_(x) {
const $x = convertToTensor(x, "x", "sin", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Sin, inputs);
}
var sin = op({ sin_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sinh.js
init_define_BUILD_VERSION();
function sinh_(x) {
const $x = convertToTensor(x, "x", "sinh");
const inputs = { x: $x };
return ENGINE.runKernel(Sinh, inputs);
}
var sinh = op({ sinh_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/slice1d.js
init_define_BUILD_VERSION();
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]);
}
var slice1d = op({ slice1d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/slice2d.js
init_define_BUILD_VERSION();
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);
}
var slice2d = op({ slice2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/slice3d.js
init_define_BUILD_VERSION();
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);
}
var slice3d = op({ slice3d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/slice4d.js
init_define_BUILD_VERSION();
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);
}
var slice4d = op({ slice4d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
init_define_BUILD_VERSION();
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.runKernel(Softmax, inputs, attrs);
}
var softmax = op({ softmax_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/fft.js
init_define_BUILD_VERSION();
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.runKernel(FFT, inputs);
}
var fft = op({ fft_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/ifft.js
init_define_BUILD_VERSION();
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.runKernel(IFFT, inputs);
}
var ifft = op({ ifft_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/irfft.js
init_define_BUILD_VERSION();
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;
}
var irfft = op({ irfft_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/split.js
init_define_BUILD_VERSION();
function split_(x, numOrSizeSplits, axis = 0) {
const $x = convertToTensor(x, "x", "split");
const inputs = { x: $x };
const attr = { numOrSizeSplits, axis };
return ENGINE.runKernel(SplitV, inputs, attr);
}
var split = op({ split_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/spectral/rfft.js
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);
}
var rfft = op({ rfft_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/squared_difference.js
init_define_BUILD_VERSION();
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 inputs = { a: $a, b: $b };
const attrs = {};
return ENGINE.runKernel(SquaredDifference, inputs, attrs);
}
var squaredDifference = op({ squaredDifference_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/squeeze.js
init_define_BUILD_VERSION();
function squeeze_(x, axis) {
const $x = convertToTensor(x, "x", "squeeze", "string_or_numeric");
return reshape($x, squeezeShape($x.shape, axis).newShape);
}
var squeeze = op({ squeeze_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/stack.js
init_define_BUILD_VERSION();
function stack_(tensors, axis = 0) {
const $tensors = convertToTensorArray(tensors, "tensors", "stack", "string_or_numeric");
assert($tensors.length >= 1, () => "Pass at least one tensor to tf.stack");
if ($tensors.length > 0) {
assert(axis <= $tensors[0].rank, () => "Axis must be <= rank of the tensor");
}
const inputs = $tensors;
const attrs = { axis };
return ENGINE.runKernel(Pack, inputs, attrs);
}
var stack = op({ stack_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/step.js
init_define_BUILD_VERSION();
function step_(x, alpha = 0) {
const $x = convertToTensor(x, "x", "step");
const inputs = { x: $x };
const attrs = { alpha };
return ENGINE.runKernel(Step, inputs, attrs);
}
var step = op({ step_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
init_define_BUILD_VERSION();
function stridedSlice_(x, begin, end, strides, beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0, shrinkAxisMask = 0) {
const $x = convertToTensor(x, "x", "stridedSlice", "string_or_numeric");
const inputs = { x: $x };
const attrs = {
begin,
end,
strides,
beginMask,
endMask,
ellipsisMask,
newAxisMask,
shrinkAxisMask
};
return ENGINE.runKernel(StridedSlice, inputs, attrs);
}
var stridedSlice = op({ stridedSlice_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tan.js
init_define_BUILD_VERSION();
function tan_(x) {
const $x = convertToTensor(x, "x", "tan", "float32");
const inputs = { x: $x };
return ENGINE.runKernel(Tan, inputs);
}
var tan = op({ tan_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tensor1d.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/tensor2d.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
init_define_BUILD_VERSION();
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 < 0) {
throw new Error(`'k' passed to topk() must be >= 0 but got ${k}`);
}
if (k > lastDim) {
throw new Error(`'k' passed to topk() must be <= the last dimension (${lastDim}) but got ${k}`);
}
const inputs = { x: $x };
const attrs = { k, sorted };
const [values, indices] = ENGINE.runKernel(TopK, inputs, attrs);
return { values, indices };
}
var topk = op({ topk_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/truncated_normal.js
init_define_BUILD_VERSION();
function truncatedNormal_(shape, mean3 = 0, stdDev = 1, dtype, seed) {
if (dtype != null && dtype === "bool") {
throw new Error(`Unsupported data type $ { dtype }`);
}
const randGauss = new MPRandGauss(mean3, 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();
}
var truncatedNormal = op({ truncatedNormal_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/unique.js
init_define_BUILD_VERSION();
function unique_(x, axis = 0) {
const $x = convertToTensor(x, "x", "unique", "string_or_numeric");
assert($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 };
}
var unique = op({ unique_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/unsorted_segment_sum.js
init_define_BUILD_VERSION();
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 };
return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs);
}
var unsortedSegmentSum = op({ unsortedSegmentSum_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/unstack.js
init_define_BUILD_VERSION();
function unstack_(x, axis = 0) {
const $x = convertToTensor(x, "x", "unstack", "string_or_numeric");
assert(axis >= -$x.shape.length && axis < $x.shape.length, () => `Axis = ${axis} is not in [-${$x.shape.length}, ${$x.shape.length})`);
const inputs = { value: $x };
const attrs = { axis };
return ENGINE.runKernel(Unpack, inputs, attrs);
}
var unstack = op({ unstack_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/variable.js
init_define_BUILD_VERSION();
function variable(initialValue, trainable = true, name, dtype) {
return ENGINE.makeVariable(initialValue, trainable, name, dtype);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/where_impl.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/dropout_util.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/dropout.js
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);
}
var dropout = op({ dropout_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused_ops.js
var fused_ops_exports = {};
__export(fused_ops_exports, {
conv2d: () => conv2d2,
depthwiseConv2d: () => depthwiseConv2d2,
matMul: () => matMul2
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_backprop_filter.js
init_define_BUILD_VERSION();
function conv2DBackpropFilter_(x, dy, filterShape, strides, pad2, 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]}).`);
checkPadOnDimRoundingMode("conv2dDerFilter", pad2, dimRoundingMode);
const inputs = { x: x4D, dy: dy4D };
const attrs = { strides, pad: pad2, dataFormat, dimRoundingMode, filterShape };
return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs);
}
var conv2DBackpropFilter = op({ conv2DBackpropFilter_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused_util.js
init_define_BUILD_VERSION();
function getFusedDyActivation(dy, y, activation) {
if (activation == null || activation === "linear") {
return dy;
}
if (activation === "relu") {
return mul(dy, step(y));
}
throw new Error(`Cannot compute gradient for fused activation ${activation}.`);
}
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, activation, preluActivationWeights, leakyreluAlpha) {
if (activation === "linear") {
return x;
} else if (activation === "relu") {
return relu(x);
} else if (activation === "elu") {
return elu(x);
} else if (activation === "relu6") {
return relu6(x);
} else if (activation === "prelu") {
return prelu(x, preluActivationWeights);
} else if (activation === "leakyrelu") {
return leakyRelu(x, leakyreluAlpha);
} else if (activation === "sigmoid") {
return sigmoid(x);
}
throw new Error(`Unknown fused activation ${activation}.`);
}
var shouldFuse = (gradientDepth, activation) => {
const gradientMode = gradientDepth > 0;
return !gradientMode || activation === "linear";
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused/conv2d.js
function fusedConv2d_({ x, filter, strides, pad: pad2, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights, leakyreluAlpha }) {
activation = activation || "linear";
if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
assert(dataFormat === "NHWC", () => `Error in fused conv2d: got dataFormat of ${dataFormat} but only NHWC is currently supported for the case of gradient depth is 0 and the activation is not linear.`);
let result = conv2d(x, filter, strides, pad2, dataFormat, dilations, dimRoundingMode);
if (bias != null) {
result = add2(result, bias);
}
return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
}
const $x = convertToTensor(x, "x", "conv2d", "float32");
const $filter = convertToTensor(filter, "filter", "conv2d", "float32");
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}.`);
checkPadOnDimRoundingMode("fused conv2d", pad2, dimRoundingMode);
const inputChannels = dataFormat === "NHWC" ? x4D.shape[3] : x4D.shape[1];
assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) 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 convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad2, dimRoundingMode);
let $bias;
if (bias != null) {
$bias = convertToTensor(bias, "bias", "fused conv2d");
[$bias] = makeTypesMatch($bias, $x);
if (dataFormat === "NHWC") {
assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
} else {
assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor bias for NCHW format but got the bias of rank-${$bias.shape.length}.`);
assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels || $bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not compatible with the number of output channels (${convInfo.outChannels})`);
}
}
let $preluActivationWeights;
if (preluActivationWeights != null) {
const alphaShape = preluActivationWeights.shape;
assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or 3-D Tensor PReLU activation weights but got a tensor of rank-${alphaShape.length}.`);
if (alphaShape.length === 1) {
assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the number of output channels (${convInfo.outChannels}).`);
} else if (alphaShape.length === 3) {
try {
assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
} catch (e) {
const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) is not compatible with the output shape of the conv2d (${convInfo.outShape}).`;
throw Error(errMsg);
}
}
$preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused conv2d");
}
const grad = (dy, saved) => {
assert(dataFormat === "NHWC", () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
const [$filter2, x4D2, y, $bias2] = saved;
const dyActivation = getFusedDyActivation(dy, y, activation);
assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
const xDer = conv2DBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad2);
const filterDer = conv2DBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad2);
const der = [xDer, filterDer];
if ($bias2 != null) {
const biasDer = getFusedBiasGradient($bias2, dyActivation);
der.push(biasDer);
}
return der;
};
const inputs = {
x: x4D,
filter: $filter,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = {
strides,
pad: pad2,
dataFormat,
dilations,
dimRoundingMode,
activation,
leakyreluAlpha
};
if (bias == null) {
const customOp = customGrad((x4D2, filter2, save) => {
let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
save([filter2, x4D2, res]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return { value: res, gradFunc: grad };
});
return customOp(x4D, $filter);
} else {
const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
save([filter2, x4D2, res, bias2]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return { value: res, gradFunc: grad };
});
return customOpWithBias(x4D, $filter, $bias);
}
}
var conv2d2 = op({ fusedConv2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_filter.js
init_define_BUILD_VERSION();
function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad2, 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 inputs = { x: x4D, dy: dy4D };
const attrs = { strides, pad: pad2, dimRoundingMode, dilations, filterShape };
return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs);
}
var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/depthwise_conv2d_native_backprop_input.js
init_define_BUILD_VERSION();
function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad2, 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 inputs = { dy: dy4D, filter };
const attrs = { strides, pad: pad2, dimRoundingMode, dilations, inputShape: xShape };
const res = ENGINE.runKernel(DepthwiseConv2dNativeBackpropInput, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var depthwiseConv2dNativeBackpropInput = op({ depthwiseConv2dNativeBackpropInput_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.js
function fusedDepthwiseConv2d_({ x, filter, strides, pad: pad2, dataFormat = "NHWC", dilations = [1, 1], dimRoundingMode, bias, activation = "linear", preluActivationWeights, leakyreluAlpha }) {
if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
let result = depthwiseConv2d(x, filter, strides, pad2, dataFormat, dilations, dimRoundingMode);
if (bias != null) {
result = add2(result, bias);
}
return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
}
const $x = convertToTensor(x, "x", "depthwiseConv2d", "float32");
const $filter = convertToTensor(filter, "filter", "depthwiseConv2d", "float32");
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}'`);
checkPadOnDimRoundingMode("fused depthwiseConv2d", pad2, dimRoundingMode);
const convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad2, 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) {
$preluActivationWeights = convertToTensor(preluActivationWeights, "prelu weights", "fused depthwiseConv2d");
}
const grad = (dy, saved) => {
assert(tupleValuesAreOne(dilations), () => `Error in gradient of fused depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '${dilations}'`);
const [$filter2, x4D2, y, bias2] = saved;
const dyActivation = getFusedDyActivation(dy, y, activation);
const xDer = depthwiseConv2dNativeBackpropInput(x4D2.shape, dyActivation, $filter2, strides, pad2, dilations, dimRoundingMode);
const filterDer = depthwiseConv2dNativeBackpropFilter(x4D2, dyActivation, $filter2.shape, strides, pad2, dilations, dimRoundingMode);
if (bias2 != null) {
const biasDer = getFusedBiasGradient($bias, dyActivation);
return [xDer, filterDer, biasDer];
}
return [xDer, filterDer];
};
const inputs = {
x: x4D,
filter: $filter,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = {
strides,
pad: pad2,
dataFormat,
dilations,
dimRoundingMode,
activation,
leakyreluAlpha
};
if (bias == null) {
const customOp = customGrad((x4D2, filter2, save) => {
let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
save([filter2, x4D2, res]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return { value: res, gradFunc: grad };
});
return customOp(x4D, $filter);
} else {
const customOpWithBias = customGrad((x4D2, filter2, bias2, save) => {
let res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs);
save([filter2, x4D2, res, bias2]);
if (reshapedTo4D) {
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return { value: res, gradFunc: grad };
});
return customOpWithBias(x4D, $filter, $bias);
}
}
var depthwiseConv2d2 = op({ fusedDepthwiseConv2d_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/fused/mat_mul.js
init_define_BUILD_VERSION();
function fusedMatMul_({ a, b, transposeA = false, transposeB = false, bias, activation = "linear", preluActivationWeights, leakyreluAlpha = 0.2 }) {
if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
let result = matMul(a, b, transposeA, transposeB);
if (bias != null) {
result = add2(result, bias);
}
return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
}
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 = $b.shape.slice(0, -2);
const batchDimA = sizeFromShape(outerDimsA);
const batchDimB = sizeFromShape(outerDimsB);
assert(innerShapeA === innerShapeB, () => `Error in fused matMul: inner shapes (${innerShapeA}) and (${innerShapeB}) of Tensors with shapes ${$a.shape} and ${$b.shape} and transposeA=${transposeA} and transposeB=${transposeB} must match.`);
const outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2));
const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
const a3D = transposeA ? reshape($a, [batchDimA, innerShapeA, outerShapeA]) : reshape($a, [batchDimA, outerShapeA, innerShapeA]);
const b3D = transposeB ? reshape($b, [batchDimB, outerShapeB, innerShapeB]) : reshape($b, [batchDimB, innerShapeB, outerShapeB]);
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 grad = (dy, saved) => {
const [a3D2, b3D2, y, $bias2] = saved;
const dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation);
let aDer;
let bDer;
if (!transposeA && !transposeB) {
aDer = matMul(dyActivation, b3D2, false, true);
bDer = matMul(a3D2, dyActivation, true, false);
} else if (!transposeA && transposeB) {
aDer = matMul(dyActivation, b3D2, false, false);
bDer = matMul(dyActivation, a3D2, true, false);
} else if (transposeA && !transposeB) {
aDer = matMul(b3D2, dyActivation, false, true);
bDer = matMul(a3D2, dyActivation, false, false);
} else {
aDer = matMul(b3D2, dyActivation, true, true);
bDer = matMul(dyActivation, a3D2, true, true);
}
if (bias != null) {
const biasDer = getFusedBiasGradient($bias2, dyActivation);
return [aDer, bDer, biasDer];
} else {
return [aDer, bDer];
}
};
const inputs = {
a: a3D,
b: b3D,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = { transposeA, transposeB, activation, leakyreluAlpha };
if (bias == null) {
const customOp = customGrad((a3D2, b3D2, save) => {
const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
save([a3D2, b3D2, res]);
return { value: reshape(res, outShape), gradFunc: grad };
});
return customOp(a3D, b3D);
} else {
const customOpWithBias = customGrad((a3D2, b3D2, $bias2, save) => {
const res = ENGINE.runKernel(_FusedMatMul, inputs, attrs);
save([a3D2, b3D2, res, $bias2]);
return { value: reshape(res, outShape), gradFunc: grad };
});
return customOpWithBias(a3D, b3D, $bias);
}
}
var matMul2 = op({ fusedMatMul_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/crop_and_resize.js
init_define_BUILD_VERSION();
function cropAndResize_(image3, boxes, boxInd, cropSize, method = "bilinear", extrapolationValue = 0) {
const $image = convertToTensor(image3, "image", "cropAndResize");
const $boxes = convertToTensor(boxes, "boxes", "cropAndResize", "float32");
const $boxInd = convertToTensor(boxInd, "boxInd", "cropAndResize", "int32");
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 inputs = { image: $image, boxes: $boxes, boxInd: $boxInd };
const attrs = { method, extrapolationValue, cropSize };
const res = ENGINE.runKernel(CropAndResize, inputs, attrs);
return res;
}
var cropAndResize = op({ cropAndResize_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/flip_left_right.js
init_define_BUILD_VERSION();
function flipLeftRight_(image3) {
const $image = convertToTensor(image3, "image", "flipLeftRight", "float32");
assert($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;
}
var flipLeftRight = op({ flipLeftRight_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/grayscale_to_rgb.js
init_define_BUILD_VERSION();
function grayscaleToRGB_(image3) {
const $image = convertToTensor(image3, "image", "grayscaleToRGB");
const lastDimsIdx = $image.rank - 1;
const lastDims = $image.shape[lastDimsIdx];
assert($image.rank >= 2, () => `Error in grayscaleToRGB: images must be at least rank 2, but got rank ${$image.rank}.`);
assert(lastDims === 1, () => `Error in grayscaleToRGB: last dimension of a grayscale image should be size 1, but got size ${lastDims}.`);
const reps = new Array($image.rank);
reps.fill(1, 0, lastDimsIdx);
reps[lastDimsIdx] = 3;
return tile($image, reps);
}
var grayscaleToRGB = op({ grayscaleToRGB_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/rotate_with_offset.js
init_define_BUILD_VERSION();
function rotateWithOffset_(image3, radians, fillValue = 0, center = 0.5) {
const $image = convertToTensor(image3, "image", "rotateWithOffset", "float32");
assert($image.rank === 4, () => `Error in rotateWithOffset: image must be rank 4,but got rank ${$image.rank}.`);
const inputs = { image: $image };
const attrs = { radians, fillValue, center };
const res = ENGINE.runKernel(RotateWithOffset, inputs, attrs);
return res;
}
var rotateWithOffset = op({ rotateWithOffset_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/nonmax_util.js
init_define_BUILD_VERSION();
function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) {
if (iouThreshold == null) {
iouThreshold = 0.5;
}
if (scoreThreshold == null) {
scoreThreshold = Number.NEGATIVE_INFINITY;
}
if (softNmsSigma == null) {
softNmsSigma = 0;
}
const numBoxes = boxes.shape[0];
maxOutputSize = Math.min(maxOutputSize, numBoxes);
assert(0 <= iouThreshold && iouThreshold <= 1, () => `iouThreshold must be in [0, 1], but was '${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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression.js
function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold = 0.5, scoreThreshold = Number.NEGATIVE_INFINITY) {
const $boxes = convertToTensor(boxes, "boxes", "nonMaxSuppression", "float32");
const $scores = convertToTensor(scores, "scores", "nonMaxSuppression", "float32");
const inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
maxOutputSize = inputs.maxOutputSize;
iouThreshold = inputs.iouThreshold;
scoreThreshold = inputs.scoreThreshold;
const attrs = { maxOutputSize, iouThreshold, scoreThreshold };
return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs);
}
var nonMaxSuppression = op({ nonMaxSuppression_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_util.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/non_max_suppression_impl.js
function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0);
}
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 scale2 = 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, scale2, 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 };
if (returnScoresTensor) {
result["selectedScores"] = selectedScores;
}
if (returnValidOutputs) {
result["validOutputs"] = validOutputs;
}
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, scale2, iou) {
const weight = Math.exp(scale2 * 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_async.js
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(), $scores.data()]);
const boxesVals = boxesAndScores[0];
const scoresVals = boxesAndScores[1];
const { selectedIndices } = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return tensor1d(selectedIndices, "int32");
}
var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score.js
init_define_BUILD_VERSION();
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] };
}
var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_with_score_async.js
init_define_BUILD_VERSION();
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(), $scores.data()]);
const boxesVals = boxesAndScores[0];
const scoresVals = boxesAndScores[1];
const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return {
selectedIndices: tensor1d(selectedIndices, "int32"),
selectedScores: tensor1d(selectedScores)
};
}
var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_;
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded.js
init_define_BUILD_VERSION();
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] };
}
var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/non_max_suppression_padded_async.js
init_define_BUILD_VERSION();
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 { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize);
if ($boxes !== boxes) {
$boxes.dispose();
}
if ($scores !== scores) {
$scores.dispose();
}
return {
selectedIndices: tensor1d(selectedIndices, "int32"),
validOutputs: scalar(validOutputs, "int32")
};
}
var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_;
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_bilinear.js
init_define_BUILD_VERSION();
function resizeBilinear_(images, size, alignCorners = false, halfPixelCenters = 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}.`);
assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeBilinear: If halfPixelCenters is true, alignCorners must be false.`);
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 [] = size;
const inputs = { images: batchImages };
const attrs = { alignCorners, halfPixelCenters, size };
const res = ENGINE.runKernel(ResizeBilinear, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var resizeBilinear = op({ resizeBilinear_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/resize_nearest_neighbor.js
init_define_BUILD_VERSION();
function resizeNearestNeighbor_(images, size, alignCorners = false, halfPixelCenters = 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");
assert(halfPixelCenters === false || alignCorners === false, () => `Error in resizeNearestNeighbor: If halfPixelCenters is true, alignCorners must be false.`);
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 [] = size;
const inputs = { images: batchImages };
const attrs = { alignCorners, halfPixelCenters, size };
const res = ENGINE.runKernel(ResizeNearestNeighbor, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var resizeNearestNeighbor = op({ resizeNearestNeighbor_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/threshold.js
init_define_BUILD_VERSION();
function threshold_(image3, method = "binary", inverted = false, threshValue = 0.5) {
const $image = convertToTensor(image3, "image", "threshold");
const RED_INTENCITY_COEF = 0.2989;
const GREEN_INTENCITY_COEF = 0.587;
const BLUE_INTENCITY_COEF = 0.114;
const totalPixelsInImage = $image.shape[0] * $image.shape[1];
let $threshold = mul(tensor1d([threshValue]), 255);
let r, g, b, grayscale;
assert($image.rank === 3, () => `Error in threshold: image must be rank 3,but got rank ${$image.rank}.`);
assert($image.shape[2] === 3 || $image.shape[2] === 1, () => `Error in threshold: image color channel must be equal to 3 or 1but got ${$image.shape[2]}.`);
assert($image.dtype === "int32" || $image.dtype === "float32", () => `Error in dtype: image dtype must be int32 or float32,but got dtype ${$image.dtype}.`);
assert(method === "otsu" || method === "binary", () => `Method must be binary or otsu, but was ${method}`);
if ($image.shape[2] === 3) {
[r, g, b] = split($image, [1, 1, 1], -1);
const $r = mul(r, RED_INTENCITY_COEF);
const $g = mul(g, GREEN_INTENCITY_COEF);
const $b = mul(b, BLUE_INTENCITY_COEF);
grayscale = add2(add2($r, $g), $b);
} else {
grayscale = image3;
}
if (method === "otsu") {
const $histogram = bincount(cast(round2(grayscale), "int32"), tensor2([]), 256);
$threshold = otsu($histogram, totalPixelsInImage);
}
const invCondition = inverted ? lessEqual(grayscale, $threshold) : greater(grayscale, $threshold);
const result = cast(mul(invCondition, 255), "int32");
return result;
}
function otsu(histogram, total) {
let bestThresh = tensor1d([-1]);
let bestInBetVar = tensor1d([0]);
let cInBetVar = tensor1d([0]);
let classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack;
for (let index = 0; index < histogram.size - 1; index++) {
classFirst = slice(histogram, 0, index + 1);
classSecond = slice(histogram, index + 1);
weightForeground = div(sum2(classFirst), total);
weightBack = div(sum2(classSecond), total);
const meanFirstDivA = sum2(mul(classFirst, range(0, classFirst.size)));
meanFirst = div(meanFirstDivA, sum2(classFirst));
const meanSecFill = fill(classSecond.shape, classFirst.size);
const meanSecAdd = add2(range(0, classSecond.size), meanSecFill);
const meanSecMul = mul(classSecond, meanSecAdd);
meanSec = div(sum2(meanSecMul), sum2(classSecond));
const cInBetVarSubA = sub(meanFirst, meanSec);
const cInBetVarSubB = sub(meanFirst, meanSec);
const cInBetVarMul = mul(weightForeground, weightBack);
cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB);
const condition = greater(cInBetVar, bestInBetVar);
bestInBetVar = where(condition, cInBetVar, bestInBetVar);
bestThresh = where(condition, tensor1d([index]), bestThresh);
}
return bestThresh;
}
var threshold = op({ threshold_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/image/transform.js
init_define_BUILD_VERSION();
function transform_(image3, transforms, interpolation = "nearest", fillMode = "constant", fillValue = 0, outputShape) {
const $image = convertToTensor(image3, "image", "transform", "float32");
const $transforms = convertToTensor(transforms, "transforms", "transform", "float32");
assert($image.rank === 4, () => `Error in transform: image must be rank 4,but got rank ${$image.rank}.`);
assert($transforms.rank === 2 && ($transforms.shape[0] === $image.shape[0] || $transforms.shape[0] === 1) && $transforms.shape[1] === 8, () => `Error in transform: Input transform should be batch x 8 or 1 x 8`);
assert(outputShape == null || outputShape.length === 2, () => `Error in transform: outputShape must be [height, width] or null, but got ${outputShape}.`);
const inputs = { image: $image, transforms: $transforms };
const attrs = { interpolation, fillMode, fillValue, outputShape };
return ENGINE.runKernel(Transform, inputs, attrs);
}
var transform = op({ transform_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/band_part.js
init_define_BUILD_VERSION();
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);
}
var bandPart = op({ bandPart_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/gram_schmidt.js
init_define_BUILD_VERSION();
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;
}
}
var gramSchmidt = op({ gramSchmidt_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/linalg/qr.js
init_define_BUILD_VERSION();
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];
});
}
var qr = op({ qr_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
var image2 = {
flipLeftRight,
grayscaleToRGB,
resizeNearestNeighbor,
resizeBilinear,
rotateWithOffset,
cropAndResize,
nonMaxSuppression,
nonMaxSuppressionAsync,
nonMaxSuppressionWithScore,
nonMaxSuppressionWithScoreAsync,
nonMaxSuppressionPadded,
nonMaxSuppressionPaddedAsync,
threshold,
transform
};
var linalg = {
bandPart,
gramSchmidt,
qr
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
init_define_BUILD_VERSION();
var Optimizer = class extends Serializable {
minimize(f, returnCost = false, varList) {
const { value, grads } = this.computeGradients(f, varList);
if (varList != null) {
const gradArray = varList.map((v) => ({ name: v.name, tensor: grads[v.name] }));
this.applyGradients(gradArray);
} else {
this.applyGradients(grads);
}
dispose(grads);
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 ${this.getClassName()}`);
}
async extractIterations(weightValues) {
this.iterations_ = (await weightValues[0].tensor.data())[0];
return weightValues.slice(1);
}
};
Object.defineProperty(Optimizer, Symbol.hasInstance, {
value: (instance) => {
return instance.minimize != null && instance.computeGradients != null && instance.applyGradients != null;
}
});
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
var AdadeltaOptimizer = class extends Optimizer {
constructor(learningRate, rho, epsilon3 = null) {
super();
this.learningRate = learningRate;
this.rho = rho;
this.epsilon = epsilon3;
this.accumulatedGrads = [];
this.accumulatedUpdates = [];
if (epsilon3 == null) {
this.epsilon = ENGINE.backend.epsilon();
}
}
applyGradients(variableGradients) {
const variableNames = Array.isArray(variableGradients) ? variableGradients.map((item) => item.name) : Object.keys(variableGradients);
variableNames.forEach((name, i) => {
const value = ENGINE.registeredVariables[name];
const trainable = false;
if (this.accumulatedGrads[i] == null) {
this.accumulatedGrads[i] = {
originalName: `${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 variables = [...this.accumulatedGrads, ...this.accumulatedUpdates];
return [await this.saveIterations()].concat(variables.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, config) {
return new cls(config["learningRate"], config["rho"], config["epsilon"]);
}
};
AdadeltaOptimizer.className = "Adadelta";
registerClass(AdadeltaOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
init_define_BUILD_VERSION();
var AdagradOptimizer = class 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, config) {
return new cls(config["learningRate"], config["initialAccumulatorValue"]);
}
};
AdagradOptimizer.className = "Adagrad";
registerClass(AdagradOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
init_define_BUILD_VERSION();
var AdamOptimizer = class extends Optimizer {
constructor(learningRate, beta1, beta2, epsilon3 = null) {
super();
this.learningRate = learningRate;
this.beta1 = beta1;
this.beta2 = beta2;
this.epsilon = epsilon3;
this.accumulatedFirstMoment = [];
this.accumulatedSecondMoment = [];
tidy(() => {
this.accBeta1 = scalar(beta1).variable();
this.accBeta2 = scalar(beta2).variable();
});
if (epsilon3 == 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 variables = [...this.accumulatedFirstMoment, ...this.accumulatedSecondMoment];
return [await this.saveIterations()].concat(variables.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, config) {
return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"]);
}
};
AdamOptimizer.className = "Adam";
registerClass(AdamOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
init_define_BUILD_VERSION();
var AdamaxOptimizer = class extends Optimizer {
constructor(learningRate, beta1, beta2, epsilon3 = null, decay = 0) {
super();
this.learningRate = learningRate;
this.beta1 = beta1;
this.beta2 = beta2;
this.epsilon = epsilon3;
this.decay = decay;
this.accumulatedFirstMoment = [];
this.accumulatedWeightedInfNorm = [];
tidy(() => {
this.iteration = scalar(0).variable();
this.accBeta1 = scalar(beta1).variable();
});
if (epsilon3 == 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, config) {
return new cls(config["learningRate"], config["beta1"], config["beta2"], config["epsilon"], config["decay"]);
}
};
AdamaxOptimizer.className = "Adamax";
registerClass(AdamaxOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
init_define_BUILD_VERSION();
var SGDOptimizer = class 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, config) {
return new cls(config["learningRate"]);
}
};
SGDOptimizer.className = "SGD";
registerClass(SGDOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
var MomentumOptimizer = class 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, config) {
return new cls(config["learningRate"], config["momentum"], config["useNesterov"]);
}
};
MomentumOptimizer.className = "Momentum";
registerClass(MomentumOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
init_define_BUILD_VERSION();
var RMSPropOptimizer = class extends Optimizer {
constructor(learningRate, decay = 0.9, momentum = 0, epsilon3 = null, centered = false) {
super();
this.learningRate = learningRate;
this.decay = decay;
this.momentum = momentum;
this.epsilon = epsilon3;
this.accumulatedMeanSquares = [];
this.accumulatedMoments = [];
this.accumulatedMeanGrads = [];
this.centered = centered;
if (epsilon3 == 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 variables = [...this.accumulatedMeanSquares, ...this.accumulatedMoments];
if (this.centered) {
variables.push(...this.accumulatedMeanGrads);
}
return [await this.saveIterations()].concat(variables.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, config) {
return new cls(config["learningRate"], config["decay"], config["momentum"], config["epsilon"], config["centered"]);
}
};
RMSPropOptimizer.className = "RMSProp";
registerClass(RMSPropOptimizer);
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
var OptimizerConstructors = class {
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, epsilon3 = null, centered = false) {
return new RMSPropOptimizer(learningRate, decay, momentum, epsilon3, centered);
}
static adam(learningRate = 1e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null) {
return new AdamOptimizer(learningRate, beta1, beta2, epsilon3);
}
static adadelta(learningRate = 1e-3, rho = 0.95, epsilon3 = null) {
return new AdadeltaOptimizer(learningRate, rho, epsilon3);
}
static adamax(learningRate = 2e-3, beta1 = 0.9, beta2 = 0.999, epsilon3 = null, decay = 0) {
return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon3, decay);
}
static adagrad(learningRate, initialAccumulatorValue = 0.1) {
return new AdagradOptimizer(learningRate, initialAccumulatorValue);
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/train.js
init_define_BUILD_VERSION();
var train = {
sgd: OptimizerConstructors.sgd,
momentum: OptimizerConstructors.momentum,
adadelta: OptimizerConstructors.adadelta,
adagrad: OptimizerConstructors.adagrad,
rmsprop: OptimizerConstructors.rmsprop,
adamax: OptimizerConstructors.adamax,
adam: OptimizerConstructors.adam
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/browser_util.js
init_define_BUILD_VERSION();
var 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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
var 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,
checkEinsumDimSizes: () => checkEinsumDimSizes,
checkPadOnDimRoundingMode: () => checkPadOnDimRoundingMode,
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,
decodeEinsumEquation: () => decodeEinsumEquation,
eitherStridesOrDilationsAreOne: () => eitherStridesOrDilationsAreOne,
expandShapeToKeepDim: () => expandShapeToKeepDim,
exponent: () => exponent,
exponents: () => exponents,
fromStringArrayToUint8: () => fromStringArrayToUint8,
fromUint8ToStringArray: () => fromUint8ToStringArray,
getAxesPermutation: () => getAxesPermutation,
getBroadcastDims: () => getBroadcastDims,
getComplexWithIndex: () => getComplexWithIndex,
getEinsumComputePath: () => getEinsumComputePath,
getEinsumPermutation: () => getEinsumPermutation,
getFusedBiasGradient: () => getFusedBiasGradient,
getFusedDyActivation: () => getFusedDyActivation,
getImageCenter: () => getImageCenter,
getInnerMostAxes: () => getInnerMostAxes,
getPermuted: () => getPermuted,
getReductionAxes: () => getReductionAxes,
getReshaped: () => getReshaped,
getReshapedPermuted: () => getReshapedPermuted,
getSliceBeginCoords: () => getSliceBeginCoords,
getSliceSize: () => getSliceSize,
getSparseFillEmptyRowsIndicesDenseShapeMismatch: () => getSparseFillEmptyRowsIndicesDenseShapeMismatch,
getSparseFillEmptyRowsNegativeIndexErrorMessage: () => getSparseFillEmptyRowsNegativeIndexErrorMessage,
getSparseFillEmptyRowsOutOfRangeIndexErrorMessage: () => getSparseFillEmptyRowsOutOfRangeIndexErrorMessage,
getSparseReshapeEmptyTensorZeroOutputDimErrorMessage: () => getSparseReshapeEmptyTensorZeroOutputDimErrorMessage,
getSparseReshapeInputOutputMismatchErrorMessage: () => getSparseReshapeInputOutputMismatchErrorMessage,
getSparseReshapeInputOutputMultipleErrorMessage: () => getSparseReshapeInputOutputMultipleErrorMessage,
getSparseReshapeMultipleNegativeOneOutputDimErrorMessage: () => getSparseReshapeMultipleNegativeOneOutputDimErrorMessage,
getSparseReshapeNegativeOutputDimErrorMessage: () => getSparseReshapeNegativeOutputDimErrorMessage,
getSparseSegmentReductionIndicesOutOfRangeErrorMessage: () => getSparseSegmentReductionIndicesOutOfRangeErrorMessage,
getSparseSegmentReductionNegativeSegmentIdsErrorMessage: () => getSparseSegmentReductionNegativeSegmentIdsErrorMessage,
getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage: () => getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage,
getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage: () => getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage,
getUndoAxesPermutation: () => getUndoAxesPermutation,
isIdentityPermutation: () => isIdentityPermutation,
log: () => log,
mergeRealAndImagArrays: () => mergeRealAndImagArrays,
prepareAndValidate: () => prepareAndValidate,
prepareSplitSize: () => prepareSplitSize,
segment_util: () => segment_util_exports,
shouldFuse: () => shouldFuse,
slice_util: () => slice_util_exports,
splitRealAndImagArrays: () => splitRealAndImagArrays,
tupleValuesAreOne: () => tupleValuesAreOne,
upcastType: () => upcastType,
validateInput: () => validateInput,
validateUpdateShape: () => validateUpdateShape,
warn: () => warn
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
init_define_BUILD_VERSION();
var PARALLELIZE_THRESHOLD = 30;
function computeOptimalWindowSize(inSize) {
if (inSize <= PARALLELIZE_THRESHOLD) {
return inSize;
}
return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/rotate_util.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
init_define_BUILD_VERSION();
function getReshaped(inputShape, blockShape, prod4, batchToSpace = true) {
let reshaped = [];
if (batchToSpace) {
reshaped = reshaped.concat(blockShape.slice(0));
reshaped.push(inputShape[0] / prod4);
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, prod4, batchToSpace = true) {
const reshapedPermuted = [];
if (batchToSpace) {
reshapedPermuted.push(inputShape[0] / prod4);
} else {
reshapedPermuted.push(inputShape[0] * prod4);
}
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
init_define_BUILD_VERSION();
var SELU_SCALEALPHA = 1.7580993408473768;
var SELU_SCALE = 1.0507009873554805;
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
init_define_BUILD_VERSION();
var ERF_P = 0.3275911;
var ERF_A1 = 0.254829592;
var ERF_A2 = -0.284496736;
var ERF_A3 = 1.421413741;
var ERF_A4 = -1.453152027;
var ERF_A5 = 1.061405429;
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/complex_util.js
init_define_BUILD_VERSION();
function mergeRealAndImagArrays(real4, imag4) {
if (real4.length !== imag4.length) {
throw new Error(`Cannot merge real and imag arrays of different lengths. real:${real4.length}, imag: ${imag4.length}.`);
}
const result = new Float32Array(real4.length * 2);
for (let i = 0; i < result.length; i += 2) {
result[i] = real4[i / 2];
result[i + 1] = imag4[i / 2];
}
return result;
}
function splitRealAndImagArrays(complex4) {
const real4 = new Float32Array(complex4.length / 2);
const imag4 = new Float32Array(complex4.length / 2);
for (let i = 0; i < complex4.length; i += 2) {
real4[i / 2] = complex4[i];
imag4[i / 2] = complex4[i + 1];
}
return { real: real4, imag: imag4 };
}
function complexWithEvenIndex(complex4) {
const len = Math.ceil(complex4.length / 4);
const real4 = new Float32Array(len);
const imag4 = new Float32Array(len);
for (let i = 0; i < complex4.length; i += 4) {
real4[Math.floor(i / 4)] = complex4[i];
imag4[Math.floor(i / 4)] = complex4[i + 1];
}
return { real: real4, imag: imag4 };
}
function complexWithOddIndex(complex4) {
const len = Math.floor(complex4.length / 4);
const real4 = new Float32Array(len);
const imag4 = new Float32Array(len);
for (let i = 2; i < complex4.length; i += 4) {
real4[Math.floor(i / 4)] = complex4[i];
imag4[Math.floor(i / 4)] = complex4[i + 1];
}
return { real: real4, imag: imag4 };
}
function getComplexWithIndex(complex4, index) {
const real4 = complex4[index * 2];
const imag4 = complex4[index * 2 + 1];
return { real: real4, imag: imag4 };
}
function assignToTypedArray(data, real4, imag4, index) {
data[index * 2] = real4;
data[index * 2 + 1] = imag4;
}
function exponents(n, inverse) {
const real4 = new Float32Array(n / 2);
const imag4 = new Float32Array(n / 2);
for (let i = 0; i < Math.ceil(n / 2); i++) {
const x = (inverse ? 2 : -2) * Math.PI * (i / n);
real4[i] = Math.cos(x);
imag4[i] = Math.sin(x);
}
return { real: real4, imag: imag4 };
}
function exponent(k, n, inverse) {
const x = (inverse ? 2 : -2) * Math.PI * (k / n);
const real4 = Math.cos(x);
const imag4 = Math.sin(x);
return { real: real4, imag: imag4 };
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/einsum_util.js
init_define_BUILD_VERSION();
var ARROW = "->";
var ARROW_REGEX = /->/g;
var COMMA = ",";
var ELLIPSIS = "...";
function decodeEinsumEquation(equation, numTensors) {
equation = equation.replace(/\s/g, "");
const numArrows = (equation.length - equation.replace(ARROW_REGEX, "").length) / ARROW.length;
if (numArrows < 1) {
throw new Error("Equations without an arrow are not supported.");
} else if (numArrows > 1) {
throw new Error(`Equation must contain exactly one arrow ("${ARROW}").`);
}
const [inputString, outputString] = equation.split(ARROW);
assert(inputString.indexOf(ELLIPSIS) === -1, () => `The ellipsis notation ("${ELLIPSIS}") is not supported yet.`);
const inputTerms = inputString.split(COMMA);
const numInputs = inputTerms.length;
if (numTensors !== numInputs) {
throw new Error(`Expected ${numInputs} input tensors, received ${numTensors}`);
}
if (numInputs > 2) {
throw new Error("Support for more than 2 input tensors is not implemented yet.");
}
const allDims = [];
for (let i = 0; i < outputString.length; ++i) {
const dimName = outputString[i];
if (!inputTerms.some((inputTerm) => inputTerm.indexOf(dimName) !== -1)) {
throw new Error(`Output subscripts contain the label ${dimName} not present in the input subscripts.`);
}
if (allDims.indexOf(dimName) === -1) {
allDims.push(dimName);
}
}
for (let i = 0; i < inputString.length; ++i) {
const dimName = inputString[i];
if (allDims.indexOf(dimName) === -1 && dimName !== COMMA) {
allDims.push(dimName);
}
}
const idDims = new Array(inputTerms.length);
for (let i = 0; i < numInputs; ++i) {
if (new Set(inputTerms[i].split("")).size !== inputTerms[i].length) {
throw new Error(`Found duplicate axes in input component ${inputTerms[i]}. Support for duplicate axes in input is not implemented yet.`);
}
idDims[i] = [];
for (let j = 0; j < inputTerms[i].length; ++j) {
idDims[i].push(allDims.indexOf(inputTerms[i][j]));
}
}
const numDims = allDims.length;
const numOutDims = outputString.length;
const summedDims = [];
for (let i = numOutDims; i < numDims; ++i) {
summedDims.push(i);
}
return { allDims, summedDims, idDims };
}
function getEinsumPermutation(nDims, idDims) {
let permutationIndices = new Array(nDims);
permutationIndices.fill(-1);
for (let i = 0; i < idDims.length; ++i) {
permutationIndices[idDims[i]] = i;
}
const expandDims5 = [];
for (let i = 0; i < nDims; ++i) {
if (permutationIndices[i] === -1) {
expandDims5.push(i);
}
}
permutationIndices = permutationIndices.filter((d) => d !== -1);
return { permutationIndices, expandDims: expandDims5 };
}
function checkEinsumDimSizes(nDims, idDims, tensors) {
const dimSizes = new Array(nDims);
for (let i = 0; i < tensors.length; ++i) {
const shape = tensors[i].shape;
for (let j = 0; j < idDims[i].length; ++j) {
if (dimSizes[idDims[i][j]] === void 0) {
dimSizes[idDims[i][j]] = shape[j];
} else {
assert(dimSizes[idDims[i][j]] === shape[j], () => `Expected dimension ${dimSizes[idDims[i][j]]} at axis ${j} of input shaped ${JSON.stringify(shape)}, but got dimension ${shape[j]}`);
}
}
}
}
function getEinsumComputePath(summedDims, idDims) {
const path = summedDims;
const steps = [];
let nSteps = 0;
if (summedDims.length === 0) {
path.push(-1);
}
nSteps = summedDims.length + 1;
for (let i = 0; i < nSteps; ++i) {
steps.push([]);
}
const computedTermIndices = [];
for (let i = 0; i < path.length; ++i) {
const summedDim = path[i];
const termIndices = findTermsWithDim(idDims, summedDim);
for (const termIndex of termIndices) {
if (computedTermIndices.indexOf(termIndex) === -1) {
steps[i].push(termIndex);
computedTermIndices.push(termIndex);
}
}
}
return { path, steps };
}
function isIdentityPermutation(perm) {
return perm.every((dim, index) => dim === index);
}
function findTermsWithDim(idDims, dim) {
const termIndices = [];
for (let i = 0; i < idDims.length; ++i) {
if (idDims[i].length === 0 || idDims[i].indexOf(dim) !== -1 || dim === -1) {
termIndices.push(i);
}
}
return termIndices;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/split_util.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_fill_empty_rows_util.js
init_define_BUILD_VERSION();
function getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesLength) {
return `Received SparseTensor with denseShape[0] = 0 but
indices.shape[0] = ${indicesLength}`;
}
function getSparseFillEmptyRowsNegativeIndexErrorMessage(index, value) {
return `indices(${index}, 0) is invalid: ${value} < 0`;
}
function getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(index, value, limit) {
return `indices(${index}, 0) is invalid: ${value} >= ${limit}`;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape_util.js
init_define_BUILD_VERSION();
function getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(dim1, dim2) {
return `only one output dimension may be -1, not both ${dim1} and ${dim2}`;
}
function getSparseReshapeNegativeOutputDimErrorMessage(dim, value) {
return `size ${dim} must be non-negative, not ${value}`;
}
function getSparseReshapeEmptyTensorZeroOutputDimErrorMessage() {
return "reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero";
}
function getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape) {
const inputSize = sizeFromShape(inputShape);
const outputSize = sizeFromShape(outputShape);
return `Input to reshape is a SparseTensor with ${inputSize}
dense values, but the requested shape requires a multiple of ${outputSize}. inputShape=${inputShape} outputShape= ${outputShape}`;
}
function getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape) {
const inputSize = sizeFromShape(inputShape);
const outputSize = sizeFromShape(outputShape);
return `Input to reshape is a tensor with ${inputSize} dense values, but the requested shape has ${outputSize}. inputShape=${inputShape} outputShape=${outputShape}`;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/sparse/sparse_segment_reduction_util.js
init_define_BUILD_VERSION();
function getSparseSegmentReductionNegativeSegmentIdsErrorMessage() {
return `segment ids must be >= 0`;
}
function getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage() {
return `segment ids are not increasing`;
}
function getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(segmentId, outputRows) {
return `Segment id ${segmentId} out of range [0, ${outputRows}), possibly because segmentIds input is not sorted.`;
}
function getSparseSegmentReductionIndicesOutOfRangeErrorMessage(index, indexValue, inputRows) {
return `Bad: indices[${index}] == ${indexValue} out of range [0, ${inputRows})`;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
var segment_util_exports = {};
__export(segment_util_exports, {
collectGatherOpShapeInfo: () => collectGatherOpShapeInfo,
computeOutShape: () => computeOutShape3,
segOpComputeOptimalWindowSize: () => segOpComputeOptimalWindowSize
});
init_define_BUILD_VERSION();
function segOpComputeOptimalWindowSize(inSize, numSegments) {
let done = false;
let res;
if (inSize <= PARALLELIZE_THRESHOLD) {
res = inSize;
done = true;
} else {
res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
}
while (!done) {
if (res > numSegments || res === inSize) {
done = true;
} else {
res = nearestDivisor(inSize, res + 1);
}
}
return res;
}
function computeOutShape3(aShape, axis, numSegments) {
const outShape = [];
const rank = aShape.length;
for (let dim = 0; dim < rank; dim++) {
if (dim !== axis) {
outShape.push(aShape[dim]);
} else {
outShape.push(numSegments);
}
}
return outShape;
}
function collectGatherOpShapeInfo(x, indices, axis, batchDims) {
const indicesRank = indices.shape.length;
const xRank = x.shape.length;
if (batchDims !== 0) {
if (batchDims < -indicesRank || batchDims > indicesRank) {
throw new Error(`Expect batchDims in the range of [-${indicesRank}, ${indicesRank}], but got ${batchDims}`);
}
}
if (batchDims < 0) {
batchDims += indicesRank;
}
if (batchDims > xRank) {
throw new Error(`batchDims (${batchDims}) must be less than rank(x) (
${xRank}).`);
}
if (axis < batchDims) {
throw new Error(`batchDims (${batchDims}) must be less than or equal to axis (${axis}).`);
}
for (let i = 0; i < batchDims; ++i) {
if (x.shape[i] !== indices.shape[i]) {
throw new Error(`x.shape[${i}]: ${x.shape[i]} should be equal to indices.shape[${i}]: ${indices.shape[i]}.`);
}
}
const dimSize = x.shape[axis];
const outputShape = [];
let batchSize = 1;
let outerSize = 1;
let sliceSize = 1;
for (let i = 0; i < batchDims; ++i) {
outputShape.push(x.shape[i]);
batchSize *= x.shape[i];
}
for (let i = batchDims; i < axis; i++) {
outputShape.push(x.shape[i]);
outerSize *= x.shape[i];
}
for (let i = batchDims; i < indicesRank; i++) {
outputShape.push(indices.shape[i]);
}
for (let i = axis + 1; i < xRank; i++) {
outputShape.push(x.shape[i]);
sliceSize *= x.shape[i];
}
return { batchSize, sliceSize, outerSize, dimSize, outputShape };
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/backend_util.js
function fromUint8ToStringArray(vals) {
try {
return vals.map((val) => decodeString(val));
} catch (err) {
throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${err}`);
}
}
function fromStringArrayToUint8(strings) {
return strings.map((s) => encodeString(s));
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/backends/kernel_impls.js
var kernel_impls_exports = {};
__export(kernel_impls_exports, {
nonMaxSuppressionV3Impl: () => nonMaxSuppressionV3Impl,
nonMaxSuppressionV4Impl: () => nonMaxSuppressionV4Impl,
nonMaxSuppressionV5Impl: () => nonMaxSuppressionV5Impl,
whereImpl: () => whereImpl
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Abs_grad.js
init_define_BUILD_VERSION();
var absGradConfig = {
kernelName: Abs,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(dy, step(cast(x, "float32"), -1)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Acos_grad.js
init_define_BUILD_VERSION();
var acosGradConfig = {
kernelName: Acos,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return {
x: () => {
const a = square(cast(x, "float32"));
const b = sqrt(sub(scalar(1), a));
return neg(div(dy, b));
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Acosh_grad.js
init_define_BUILD_VERSION();
var acoshGradConfig = {
kernelName: Acosh,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return {
x: () => {
const a = sqrt(sub(square(cast(x, "float32")), 1));
return div(dy, a);
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Add_grad.js
init_define_BUILD_VERSION();
var addGradConfig = {
kernelName: Add,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
let res = dy;
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, a.shape);
};
const derB = () => {
let res = dy;
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, b.shape);
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/AddN_grad.js
init_define_BUILD_VERSION();
var addNGradConfig = {
kernelName: AddN,
saveAllInputs: true,
gradFunc: (dy, saved) => {
const ders = {};
saved.forEach((_, i) => {
ders[i] = () => dy.clone();
});
return ders;
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMax_grad.js
init_define_BUILD_VERSION();
var argMaxGradConfig = {
kernelName: ArgMax,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => zerosLike(x) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ArgMin_grad.js
init_define_BUILD_VERSION();
var argMinGradConfig = {
kernelName: ArgMin,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => zerosLike(x) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Asin_grad.js
init_define_BUILD_VERSION();
var asinGradConfig = {
kernelName: Asin,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, sqrt(sub(scalar(1), square(cast(x, "float32"))))) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Asinh_grad.js
init_define_BUILD_VERSION();
var asinhGradConfig = {
kernelName: Asinh,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return {
x: () => {
const a = sqrt(add2(scalar(1), square(cast(x, "float32"))));
return div(dy, a);
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan2_grad.js
init_define_BUILD_VERSION();
var atan2GradConfig = {
kernelName: Atan2,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
const d = add2(square(a), square(b));
let res = mul(dy, div(b, d));
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, a.shape);
};
const derB = () => {
const d = add2(square(a), square(b));
let res = neg(mul(dy, div(a, d)));
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, b.shape);
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Atan_grad.js
init_define_BUILD_VERSION();
var atanGradConfig = {
kernelName: Atan,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, add2(square(cast(x, "float32")), 1)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Atanh_grad.js
init_define_BUILD_VERSION();
var atanhGradConfig = {
kernelName: Atanh,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, sub(scalar(1), square(cast(x, "float32")))) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_3d_grad.js
init_define_BUILD_VERSION();
function avgPool3dGrad_(dy, input2, filterSize, strides, pad2, dimRoundingMode) {
const $dy = convertToTensor(dy, "dy", "avgPool3dGrad");
const $input = convertToTensor(input2, "input", "avgPool3dGrad");
let dy5D = $dy;
let input5D = $input;
let reshapedTo5D = false;
if ($input.rank === 4) {
reshapedTo5D = true;
dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
input5D = reshape($input, [
1,
$input.shape[0],
$input.shape[1],
$input.shape[2],
$input.shape[3]
]);
}
assert(dy5D.rank === 5, () => `Error in avgPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
assert(input5D.rank === 5, () => `Error in avgPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
checkPadOnDimRoundingMode("avgPool3dGrad", pad2, dimRoundingMode);
const inputs = { dy: dy5D, input: input5D };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode };
const res = ENGINE.runKernel(AvgPool3DGrad, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
var avgPool3dGrad = op({ avgPool3dGrad_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool3D_grad.js
var avgPool3DGradConfig = {
kernelName: AvgPool3D,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
return {
x: () => avgPool3dGrad(dy, x, filterSize, strides, pad2, dimRoundingMode)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/avg_pool_grad.js
init_define_BUILD_VERSION();
function avgPoolGrad_(dy, input2, filterSize, strides, pad2) {
const $dy = convertToTensor(dy, "dy", "avgPoolGrad");
const $input = convertToTensor(input2, "input", "avgPoolGrad");
assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
let input4D = $input;
let dy4D = $dy;
let reshapedTo4D = false;
if ($input.rank === 3) {
reshapedTo4D = true;
input4D = reshape($input, [1, $input.shape[0], $input.shape[1], $input.shape[2]]);
dy4D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2]]);
}
assert(dy4D.rank === 4, () => `Error in avgPoolGrad: dy must be rank 4 but got rank ${dy4D.rank}.`);
assert(input4D.rank === 4, () => `Error in avgPoolGrad: input must be rank 4 but got rank ${input4D.rank}.`);
const inputs = { dy: dy4D, input: input4D };
const attrs = { filterSize, strides, pad: pad2 };
const res = ENGINE.runKernel(AvgPoolGrad, inputs, attrs);
if (reshapedTo4D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return res;
}
var avgPoolGrad = op({ avgPoolGrad_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/AvgPool_grad.js
var avgPoolGradConfig = {
kernelName: AvgPool,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { filterSize, strides, pad: pad2 } = attrs;
return { x: () => avgPoolGrad(dy, x, filterSize, strides, pad2) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchMatMul_grad.js
init_define_BUILD_VERSION();
var batchMatMulGradConfig = {
kernelName: BatchMatMul,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved, attrs) => {
const [a, b] = saved;
const { transposeA, transposeB } = attrs;
if (!transposeA && !transposeB) {
return {
a: () => matMul(dy, b, false, true),
b: () => matMul(a, dy, true, false)
};
} else if (!transposeA && transposeB) {
return {
a: () => matMul(dy, b, false, false),
b: () => matMul(dy, a, true, false)
};
} else if (transposeA && !transposeB) {
return {
a: () => matMul(b, dy, false, true),
b: () => matMul(a, dy, false, false)
};
} else {
return {
a: () => matMul(b, dy, true, true),
b: () => matMul(dy, a, true, true)
};
}
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/BatchToSpaceND_grad.js
init_define_BUILD_VERSION();
var batchToSpaceNDGradConfig = {
kernelName: BatchToSpaceND,
gradFunc: (dy, saved, attrs) => {
const { blockShape, crops } = attrs;
return { x: () => spaceToBatchND(dy, blockShape, crops) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/BroadcastTo_grad.js
init_define_BUILD_VERSION();
var broadcastToGradConfig = {
kernelName: BroadcastTo,
gradFunc: (dy, saved, attrs) => {
const broadCastToAttrs = attrs;
const inputShape = broadCastToAttrs.inputShape;
const outputShape = broadCastToAttrs.shape;
const reps = Array.from(outputShape);
for (let i = inputShape.length - 1; i >= 0; i--) {
if (inputShape[i] === outputShape[i]) {
reps[i] = 1;
} else if (inputShape[i] !== 1) {
throw new Error(`broadcastTo(): [${inputShape}] cannot be broadcast to [${outputShape}].`);
}
}
const axes = [];
for (let i = 0; i < reps.length; i++) {
if (reps[i] > 1) {
axes.push(i);
}
}
return { x: () => sum2(dy, axes, true) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Cast_grad.js
init_define_BUILD_VERSION();
var castGradConfig = {
kernelName: Cast,
gradFunc: (dy) => {
return { x: () => dy.clone() };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Ceil_grad.js
init_define_BUILD_VERSION();
var ceilGradConfig = {
kernelName: Ceil,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ClipByValue_grad.js
init_define_BUILD_VERSION();
var clipByValueGradConfig = {
kernelName: ClipByValue,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { clipValueMin, clipValueMax } = attrs;
return {
x: () => where(logicalAnd(greaterEqual(x, clipValueMin), lessEqual(x, clipValueMax)), dy, zerosLike(dy))
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ComplexAbs_grad.js
init_define_BUILD_VERSION();
var complexAbsGradConfig = {
kernelName: ComplexAbs,
inputsToSave: ["x"],
gradFunc: absGradConfig.gradFunc
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Concat_grad.js
init_define_BUILD_VERSION();
var concatGradConfig = {
kernelName: Concat,
saveAllInputs: true,
gradFunc: (dy, saved, attrs) => {
const shapes = saved.map((t) => t.shape);
const { axis } = attrs;
const $axis = parseAxisParam(axis, saved[0].shape)[0];
const sizeSplits = shapes.map((s) => s[$axis]);
const derTensors = split(dy, sizeSplits, $axis);
return derTensors.map((t) => () => t);
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2D_grad.js
init_define_BUILD_VERSION();
var conv2DGradConfig = {
kernelName: Conv2D,
inputsToSave: ["x", "filter"],
gradFunc: (dy, saved, attrs) => {
const [x4D, $filter] = saved;
const { dilations, strides, pad: pad2, dataFormat } = attrs;
assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv2D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
return {
x: () => conv2DBackpropInput(x4D.shape, dy, $filter, strides, pad2, dataFormat),
filter: () => conv2DBackpropFilter(x4D, dy, $filter.shape, strides, pad2, dataFormat)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv2DBackpropInput_grad.js
init_define_BUILD_VERSION();
var conv2DBackpropInputGradConfig = {
kernelName: Conv2DBackpropInput,
inputsToSave: ["dy", "filter"],
gradFunc: (ddx, saved, attrs) => {
const [dy, filter] = saved;
const { strides, pad: pad2, dataFormat, dimRoundingMode } = attrs;
return {
dy: () => conv2d(ddx, filter, strides, pad2, dataFormat, 1, dimRoundingMode),
filter: () => conv2DBackpropFilter(ddx, dy, filter.shape, strides, pad2, dataFormat, dimRoundingMode)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_backprop_filter.js
init_define_BUILD_VERSION();
function conv3DBackpropFilter_(x, dy, filterShape, strides, pad2) {
let x5D = x;
if (x.rank === 4) {
x5D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]]);
}
let dy5D = dy;
if (dy5D.rank === 4) {
dy5D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]]);
}
assert(x5D.rank === 5, () => `Error in conv3dDerFilter: input must be rank 5, but got shape ${x5D.shape}.`);
assert(dy5D.rank === 5, () => `Error in conv3dDerFilter: dy must be rank 5, but got shape ${dy5D.shape}.`);
assert(filterShape.length === 5, () => `Error in conv3dDerFilter: filterShape must be length 5, but got ${filterShape}.`);
assert(x5D.shape[4] === filterShape[3], () => `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must match input depth in filter (${filterShape[3]}.`);
assert(dy5D.shape[4] === filterShape[4], () => `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must match output depth for filter (${filterShape[4]}).`);
const inputs = { x: x5D, dy: dy5D };
const attrs = { strides, pad: pad2, filterShape };
return ENGINE.runKernel(Conv3DBackpropFilterV2, inputs, attrs);
}
var conv3DBackpropFilter = op({ conv3DBackpropFilter_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Conv3D_grad.js
var conv3DGradConfig = {
kernelName: Conv3D,
inputsToSave: ["x", "filter"],
gradFunc: (dy, saved, attrs) => {
const { dilations, strides, pad: pad2 } = attrs;
assert(tupleValuesAreOne(dilations), () => `Error in gradient of conv3D: dilation rates greater than 1 are not yet supported in gradients. Got dilations '${dilations}'`);
const [x5D, $filter] = saved;
return {
x: () => conv3DBackpropInput(x5D.shape, dy, $filter, strides, pad2),
filter: () => conv3DBackpropFilter(x5D, dy, $filter.shape, strides, pad2)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Cos_grad.js
init_define_BUILD_VERSION();
var cosGradConfig = {
kernelName: Cos,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(neg(sin(cast(x, "float32"))), dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Cosh_grad.js
init_define_BUILD_VERSION();
var coshGradConfig = {
kernelName: Cosh,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(sinh(cast(x, "float32")), dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Cumsum_grad.js
init_define_BUILD_VERSION();
var cumsumGradConfig = {
kernelName: Cumsum,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { axis, exclusive, reverse: reverse4 } = attrs;
return {
x: () => {
const permutation = getAxesPermutation([axis], x.rank);
let out = cumsum(dy, axis, exclusive, !reverse4);
if (permutation != null) {
out = transpose(out, permutation);
}
return out;
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js
init_define_BUILD_VERSION();
var depthwiseConv2dNativeGradConfig = {
kernelName: DepthwiseConv2dNative,
inputsToSave: ["x", "filter"],
gradFunc: (dy, saved, attrs) => {
const { dilations, strides, pad: pad2, dimRoundingMode } = attrs;
const $dilations = dilations == null ? [1, 1] : dilations;
assert(tupleValuesAreOne($dilations), () => `Error in gradient of depthwiseConv2dNative: dilation rates greater than 1 are not yet supported. Got dilations '${$dilations}'`);
const [x, filter] = saved;
assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be rank 4, but got rank ${x.rank}.`);
assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be rank 4, but got rank ${filter.rank}.`);
assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input channels (${x.shape[3]}) must match the inChannels dimension in filter ${filter.shape[2]}.`);
assert(eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in gradient of depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'.`);
checkPadOnDimRoundingMode("depthwiseConv2d", pad2, dimRoundingMode);
return {
x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad2, $dilations, dimRoundingMode),
filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad2, $dilations, dimRoundingMode)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Dilation2D_grad.js
init_define_BUILD_VERSION();
var dilation2dGradConfig = {
kernelName: Dilation2D,
inputsToSave: ["x", "filter"],
gradFunc: (dy, saved, attrs) => {
const [x, filter] = saved;
const inputInputs = { x, filter, dy };
const filterInputs = { x, filter, dy };
return {
x: () => ENGINE.runKernel(Dilation2DBackpropInput, inputInputs, attrs),
filter: () => ENGINE.runKernel(Dilation2DBackpropFilter, filterInputs, attrs)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Elu_grad.js
init_define_BUILD_VERSION();
var eluGradConfig = {
kernelName: Elu,
outputsToSave: [true],
gradFunc: (dy, saved) => {
const [y] = saved;
const inputs = { dy, y };
return { x: () => ENGINE.runKernel(EluGrad, inputs) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Erf_grad.js
init_define_BUILD_VERSION();
var erfGradConfig = {
kernelName: Erf,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
const a = mul(exp(neg(square(x))), 2 / Math.sqrt(Math.PI));
return { x: () => mul(dy, a) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Exp_grad.js
init_define_BUILD_VERSION();
var expGradConfig = {
kernelName: Exp,
outputsToSave: [true],
gradFunc: (dy, saved) => {
const [y] = saved;
return { x: () => mul(dy, y) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ExpandDims_grad.js
init_define_BUILD_VERSION();
var expandDimsGradConfig = {
kernelName: ExpandDims,
inputsToSave: ["input"],
gradFunc: (dy, saved) => {
const [input2] = saved;
return { input: () => reshape(dy, input2.shape) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Expm1_grad.js
init_define_BUILD_VERSION();
var expm1GradConfig = {
kernelName: Expm1,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(dy, exp(x)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Floor_grad.js
init_define_BUILD_VERSION();
var floorGradConfig = {
kernelName: Floor,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/FloorDiv_grad.js
init_define_BUILD_VERSION();
var floorDivGradConfig = {
kernelName: FloorDiv,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
const res = div(dy, cast(b, "float32"));
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
return reshape(sum2(res, reduceAxes), a.shape);
}
return res;
};
const derB = () => {
let res = mul(dy, cast(a, "float32"));
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
res = reshape(sum2(res, reduceAxes), b.shape);
}
const tmp = square(b);
return neg(div(res, cast(tmp, "float32")));
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/FusedBatchNorm_grad.js
init_define_BUILD_VERSION();
var fusedBatchNormGradConfig = {
kernelName: FusedBatchNorm,
inputsToSave: ["x", "mean", "variance", "scale"],
gradFunc: (dy, saved, attrs) => {
const { varianceEpsilon } = attrs;
const [x, mean3, variance, scale2] = saved;
const scaleValue = scale2 == null ? scalar(1) : scale2;
const reductionAxes = getReductionAxes(mean3.shape, x.shape);
const tileShape = [];
if (mean3.rank === 1) {
for (let i = 0; i < x.shape.length - 1; ++i) {
tileShape.push(x.shape[i]);
}
tileShape.push(1);
}
const xMinusMean = sub(x, mean3);
const dyTimesScaleValue = mul(dy, scaleValue);
const oneOverSqrtVariance = rsqrt(add2(variance, scalar(varianceEpsilon)));
const minusHalfRCube = mul(mul(mul(oneOverSqrtVariance, oneOverSqrtVariance), oneOverSqrtVariance), scalar(-0.5));
const derX = () => {
if (mean3.rank === 1) {
return reshape(mul(mul(dy, tile(reshape(oneOverSqrtVariance, [1, 1, 1, mean3.shape[0]]), tileShape)), scaleValue), x.shape);
} else {
return reshape(mul(mul(dy, oneOverSqrtVariance), scaleValue), x.shape);
}
};
const derMean = () => {
let meanDer = mul(mul(oneOverSqrtVariance, scalar(-1)), dyTimesScaleValue);
if (mean3.rank === 1) {
meanDer = sum2(meanDer, reductionAxes);
}
return reshape(meanDer, mean3.shape);
};
const derVariance = () => {
let varianceDer = mul(mul(minusHalfRCube, xMinusMean), dyTimesScaleValue);
if (mean3.rank === 1) {
varianceDer = sum2(varianceDer, reductionAxes);
}
return reshape(varianceDer, mean3.shape);
};
const derScale = () => {
const xMinusMean2TimesRsqrt = mul(xMinusMean, oneOverSqrtVariance);
let scaleDer = mul(dy, xMinusMean2TimesRsqrt);
if (mean3.rank === 1) {
scaleDer = sum2(scaleDer, reductionAxes);
}
return reshape(scaleDer, mean3.shape);
};
const derOffset = () => {
let offsetDer = dy;
if (mean3.rank === 1) {
offsetDer = sum2(offsetDer, reductionAxes);
}
return reshape(offsetDer, mean3.shape);
};
return {
x: derX,
mean: derMean,
variance: derVariance,
scale: derScale,
offset: derOffset
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/GatherV2_grad.js
init_define_BUILD_VERSION();
var gatherGradConfig = {
kernelName: GatherV2,
inputsToSave: ["x", "indices"],
gradFunc: (dy, saved, attrs) => {
const [x, indices] = saved;
const { axis } = attrs;
const parsedAxis = parseAxisParam(axis, x.shape)[0];
const derX = () => {
const paramsShape = x.shape;
const indicesSize = indices.size;
const outerShape = paramsShape.slice(0, parsedAxis);
const outerDims = outerShape.length;
const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
const innerDims = innerShape.length;
const outerAxesIndices = arrayRange(0, outerDims);
const innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
const values = reshape(dy, valuesShape);
const reshapedIndices = reshape(indices, [indicesSize]);
const transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
const valuesTranspose = transpose(values, transposeDims);
let paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, x.shape[parsedAxis]);
const invertTransposeDims = getUndoAxesPermutation(transposeDims);
paramsGrad = transpose(paramsGrad, invertTransposeDims);
return paramsGrad;
};
return { x: derX, indices: () => indices };
}
};
function arrayRange(start, stop) {
const result = [];
for (let i = start; i < stop; ++i) {
result.push(i);
}
return result;
}
function arrayConcat(arrays) {
const result = [];
for (let i = 0; i < arrays.length; ++i) {
for (let j = 0; j < arrays[i].length; ++j) {
result.push(arrays[i][j]);
}
}
return result;
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/GreaterEqual_grad.js
init_define_BUILD_VERSION();
var greaterEqualGradConfig = {
kernelName: GreaterEqual,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
return { a: () => zerosLike(a), b: () => zerosLike(b) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Identity_grad.js
init_define_BUILD_VERSION();
var identityGradConfig = {
kernelName: Identity,
gradFunc: (dy) => {
return { x: () => cast(dy, "float32") };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/IsFinite_grad.js
init_define_BUILD_VERSION();
var isFiniteGradConfig = {
kernelName: IsFinite,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/IsInf_grad.js
init_define_BUILD_VERSION();
var isInfGradConfig = {
kernelName: IsInf,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/IsNan_grad.js
init_define_BUILD_VERSION();
var isNanGradConfig = {
kernelName: IsNan,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/LeakyRelu_grad.js
init_define_BUILD_VERSION();
var leakyReluGradConfig = {
kernelName: LeakyRelu,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { alpha } = attrs;
const mask = greater(x, 0);
return { x: () => where(mask, dy, mul(dy, alpha)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Log1p_grad.js
init_define_BUILD_VERSION();
var log1pGradConfig = {
kernelName: Log1p,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, add2(x, 1)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Log_grad.js
init_define_BUILD_VERSION();
var logGradConfig = {
kernelName: Log,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, cast(x, "float32")) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/LogSoftmax_grad.js
init_define_BUILD_VERSION();
var logSoftmaxGradConfig = {
kernelName: LogSoftmax,
inputsToSave: [],
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const [value] = saved;
const { axis } = attrs;
return {
logits: () => {
const keepDims = true;
const softmax4 = exp(value);
return sub(dy, mul(sum2(dy, axis, keepDims), softmax4));
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/local_response_normalization_backprop.js
init_define_BUILD_VERSION();
function localResponseNormalizationBackprop_(x, y, dy, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5) {
const inputs = { x, y, dy };
const attrs = { depthRadius, bias, alpha, beta };
return ENGINE.runKernel(LRNGrad, inputs, attrs);
}
var localResponseNormalizationBackprop = op({ localResponseNormalizationBackprop_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/LRN_grad.js
var lrnGradConfig = {
kernelName: LRN,
inputsToSave: ["x"],
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const [x, y] = saved;
const { depthRadius, bias, alpha, beta } = attrs;
return {
x: () => localResponseNormalizationBackprop(x, y, dy, depthRadius, bias, alpha, beta)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/min_max_grad_util.js
init_define_BUILD_VERSION();
function gradForMinAndMax(dy, y, xOrig, origAxes) {
if (y.rank < xOrig.rank) {
y = reshape(y, expandShapeToKeepDim(y.shape, origAxes));
}
if (dy.rank < xOrig.rank) {
dy = reshape(dy, expandShapeToKeepDim(dy.shape, origAxes));
}
return {
x: () => {
const dx = mul(dy, cast(equal(xOrig, y), dy.dtype));
return dx;
}
};
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Max_grad.js
var maxGradConfig = {
kernelName: Max,
inputsToSave: ["x"],
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const maxAttrs = attrs;
const { reductionIndices } = maxAttrs;
const x = saved[0];
const y = saved[1];
const origAxes = parseAxisParam(reductionIndices, x.shape);
const maxGrad = gradForMinAndMax(dy, y, x, origAxes);
return {
x: () => {
return maxGrad["x"]();
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Maximum_grad.js
init_define_BUILD_VERSION();
var maximumGradConfig = {
kernelName: Maximum,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const derA = () => mul(dy, cast(greaterEqual(a, b), "float32"));
const derB = () => mul(dy, cast(less(a, b), "float32"));
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_3d_grad.js
init_define_BUILD_VERSION();
function maxPool3dGrad_(dy, input2, output, filterSize, strides, pad2, dimRoundingMode) {
const $dy = convertToTensor(dy, "dy", "maxPool3dGrad");
const $input = convertToTensor(input2, "input", "maxPool3dGrad");
const $output = convertToTensor(output, "output", "maxPool3dGrad");
let dy5D = $dy;
let input5D = $input;
let output5D = $output;
let reshapedTo5D = false;
if ($input.rank === 4) {
reshapedTo5D = true;
dy5D = reshape($dy, [1, $dy.shape[0], $dy.shape[1], $dy.shape[2], $dy.shape[3]]);
input5D = reshape($input, [
1,
$input.shape[0],
$input.shape[1],
$input.shape[2],
$input.shape[3]
]);
output5D = reshape($output, [
1,
$output.shape[0],
$output.shape[1],
$output.shape[2],
$output.shape[3]
]);
}
assert(dy5D.rank === 5, () => `Error in maxPool3dGrad: dy must be rank 5 but got rank ${dy5D.rank}.`);
assert(input5D.rank === 5, () => `Error in maxPool3dGrad: input must be rank 5 but got rank ${input5D.rank}.`);
assert(output5D.rank === 5, () => `Error in maxPool3dGrad: output must be rank 5 but got rank ${output5D.rank}.`);
checkPadOnDimRoundingMode("maxPool3dGrad", pad2, dimRoundingMode);
const inputs = { dy: dy5D, input: input5D, output: output5D };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode };
const res = ENGINE.runKernel(MaxPool3DGrad, inputs, attrs);
if (reshapedTo5D) {
return reshape(res, [res.shape[1], res.shape[2], res.shape[3], res.shape[4]]);
}
return res;
}
var maxPool3dGrad = op({ maxPool3dGrad_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool3D_grad.js
var maxPool3DGradConfig = {
kernelName: MaxPool3D,
inputsToSave: ["x"],
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const [x, y] = saved;
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
return {
x: () => maxPool3dGrad(dy, x, y, filterSize, strides, pad2, dimRoundingMode)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/ops/max_pool_grad.js
init_define_BUILD_VERSION();
function maxPoolGrad_(dy, input2, output, filterSize, strides, pad2, dimRoundingMode) {
const $dy = convertToTensor(dy, "dy", "maxPoolGrad");
const $input = convertToTensor(input2, "input", "maxPoolGrad");
const $output = convertToTensor(output, "output", "maxPoolGrad");
assert($input.rank === $dy.rank, () => `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);
assert($dy.rank === 4, () => `Error in maxPoolGrad: dy must be rank 4 but got rank ${$dy.rank}.`);
assert($input.rank === 4, () => `Error in maxPoolGrad: input must be rank 4 but got rank ${$input.rank}.`);
checkPadOnDimRoundingMode("maxPoolGrad", pad2, dimRoundingMode);
const inputs = { dy: $dy, input: $input, output: $output };
const attrs = { filterSize, strides, pad: pad2, dimRoundingMode };
return ENGINE.runKernel(MaxPoolGrad, inputs, attrs);
}
var maxPoolGrad = op({ maxPoolGrad_ });
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/MaxPool_grad.js
var maxPoolGradConfig = {
kernelName: MaxPool,
inputsToSave: ["x"],
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const [x, y] = saved;
const { filterSize, strides, pad: pad2 } = attrs;
return {
x: () => maxPoolGrad(dy, x, y, filterSize, strides, pad2)
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Mean_grad.js
init_define_BUILD_VERSION();
var meanGradConfig = {
kernelName: Mean,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { axis } = attrs;
const axes = parseAxisParam(axis, x.shape);
const shapes = computeOutAndReduceShapes(x.shape, axes);
const reduceShape = shapes[1];
const reduceSize = sizeFromShape(reduceShape);
const derX = () => {
const expandedDyShape = x.shape.slice();
axes.forEach((axis2) => {
expandedDyShape[axis2] = 1;
});
const expandedDy = reshape(dy, expandedDyShape);
const res = div(mul(expandedDy, ones2(x.shape, "float32")), reduceSize);
return res;
};
return { x: derX };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Min_grad.js
init_define_BUILD_VERSION();
var minGradConfig = {
kernelName: Min,
inputsToSave: ["x"],
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const minAttrs = attrs;
const { axis } = minAttrs;
const [x, y] = saved;
const origAxes = parseAxisParam(axis, x.shape);
const minGrad = gradForMinAndMax(dy, y, x, origAxes);
return {
x: () => {
return minGrad["x"]();
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Minimum_grad.js
init_define_BUILD_VERSION();
var minimumGradConfig = {
kernelName: Minimum,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const derA = () => mul(dy, cast(lessEqual(a, b), "float32"));
const derB = () => mul(dy, cast(greater(a, b), "float32"));
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/MirrorPad_grad.js
init_define_BUILD_VERSION();
var mirrorPadGradConfig = {
kernelName: MirrorPad,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const x = saved[0];
const { paddings } = attrs;
const begin = paddings.map((p2) => p2[0]);
return { x: () => slice(dy, begin, x.shape) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Mod_grad.js
init_define_BUILD_VERSION();
var modGradConfig = {
kernelName: Mod,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
return reshape(sum2(dy, reduceAxes), a.shape);
}
return dy;
};
const derB = () => {
const res = mul(dy, neg(floor(div(a, b))));
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
return reshape(sum2(res, reduceAxes), b.shape);
}
return res;
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Multiply_grad.js
init_define_BUILD_VERSION();
var multiplyGradConfig = {
kernelName: Multiply,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
const res = mul(dy, cast(b, "float32"));
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
return reshape(sum2(res, reduceAxes), a.shape);
}
return res;
};
const derB = () => {
const res = mul(dy, cast(a, "float32"));
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
return reshape(sum2(res, reduceAxes), b.shape);
}
return res;
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Neg_grad.js
init_define_BUILD_VERSION();
var negGradConfig = {
kernelName: Neg,
gradFunc: (dy) => {
return { x: () => neg(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/OneHot_grad.js
init_define_BUILD_VERSION();
var oneHotGradConfig = {
kernelName: OneHot,
inputsToSave: ["indices"],
gradFunc: (dy, saved) => {
const indices = saved[0];
return { indices: () => zeros(indices.shape, "float32") };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/OnesLike_grad.js
init_define_BUILD_VERSION();
var onesLikeGradConfig = {
kernelName: OnesLike,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Pack_grad.js
init_define_BUILD_VERSION();
var packGradConfig = {
kernelName: Pack,
saveAllInputs: true,
gradFunc: (dy, saved, attrs) => {
const { axis } = attrs;
const derTensors = unstack(dy, axis);
return derTensors.map((t) => () => t);
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/PadV2_grad.js
init_define_BUILD_VERSION();
var padV2GradConfig = {
kernelName: PadV2,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const x = saved[0];
const { paddings } = attrs;
const begin = paddings.map((p2) => p2[0]);
return { x: () => slice(dy, begin, x.shape) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Pow_grad.js
init_define_BUILD_VERSION();
var powGradConfig = {
kernelName: Pow,
inputsToSave: ["a", "b"],
outputsToSave: [true],
gradFunc: (dy, saved) => {
const [a, b, y] = saved;
const base = a;
const exp4 = b;
const outShape = assertAndGetBroadcastShape(base.shape, exp4.shape);
const derBase = () => {
const expFloat = cast(exp4, "float32");
let res = mul(dy, mul(expFloat, pow(base, sub(expFloat, scalar(1)))));
const reduceAxes = getReductionAxes(base.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, base.shape);
};
const derExp = () => {
const condition = greater(base, 0);
const logBase = where(condition, log2(base), zerosLike(base));
let res = mul(dy, mul(y, logBase));
const reduceAxes = getReductionAxes(exp4.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, exp4.shape);
};
return { a: derBase, b: derExp };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Prelu_grad.js
init_define_BUILD_VERSION();
var preluGradConfig = {
kernelName: Prelu,
inputsToSave: ["x", "alpha"],
gradFunc: (dy, saved) => {
const [x, alpha] = saved;
const mask = greater(x, 0);
return {
x: () => where(mask, dy, mul(dy, alpha)),
alpha: () => {
let res = where(mask, zerosLike(dy), mul(dy, x));
const reduceAxes = getReductionAxes(alpha.shape, dy.shape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, alpha.shape);
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Prod_grad.js
init_define_BUILD_VERSION();
function prodGradFn_(x, dy, axis) {
const expandedYShape = x.shape.slice();
expandedYShape[axis] = 1;
const expandedDy = reshape(dy, expandedYShape);
const xCumProd = cumprod(x, axis, true, false);
const xCumRevProd = cumprod(x, axis, true, true);
const dx = mul(xCumProd, xCumRevProd);
return mul(expandedDy, dx);
}
function prodsGradFn_(x, dy, axis) {
const xRank = x.shape.length;
const finalProdAxis = xRank - axis.length;
const xPermutation = backend_util_exports.getAxesPermutation(axis, xRank);
let permutedX = x;
if (xPermutation != null) {
permutedX = transpose(x, xPermutation);
}
const newShape = permutedX.shape.slice();
const removedShape = newShape.splice(xRank - axis.length, axis.length);
const endPartShape = removedShape.reduce((p2, c) => p2 * c, 1);
newShape.push(endPartShape);
const reshapedPermutedX = permutedX.reshape(newShape);
let prodGrad = prodGradFn_(reshapedPermutedX, dy, finalProdAxis);
prodGrad = prodGrad.reshape(permutedX.shape);
if (xPermutation != null) {
const undoPermutation = backend_util_exports.getUndoAxesPermutation(xPermutation);
prodGrad = transpose(prodGrad, undoPermutation);
}
return prodGrad;
}
var prodGradConfig = {
kernelName: Prod,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { axis } = attrs;
let axisArr = [];
if (axis === void 0 || axis === null) {
axisArr = x.shape.map((_, i) => i);
} else if (typeof axis === "number") {
axisArr = [axis];
} else {
axisArr = axis;
}
return { x: () => prodsGradFn_(x, dy, axisArr) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/RealDiv_grad.js
init_define_BUILD_VERSION();
var divGradConfig = {
kernelName: RealDiv,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
const res = div(dy, cast(b, "float32"));
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
return reshape(sum2(res, reduceAxes), a.shape);
}
return res;
};
const derB = () => {
let res = mul(dy, cast(a, "float32"));
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
res = reshape(sum2(res, reduceAxes), b.shape);
}
const tmp = square(b);
return neg(div(res, cast(tmp, "float32")));
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Reciprocal_grad.js
init_define_BUILD_VERSION();
var reciprocalGradConfig = {
kernelName: Reciprocal,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, neg(square(x))) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu6_grad.js
init_define_BUILD_VERSION();
var relu6GradConfig = {
kernelName: Relu6,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
const mask = mul(lessEqual(x, 6), step(x));
return { x: () => mul(dy, cast(mask, "float32")) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Relu_grad.js
init_define_BUILD_VERSION();
var reluGradConfig = {
kernelName: Relu,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(dy, cast(step(x), "float32")) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Reshape_grad.js
init_define_BUILD_VERSION();
var reshapeGradConfig = {
kernelName: Reshape,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => reshape(dy, x.shape) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeBilinear_grad.js
init_define_BUILD_VERSION();
var resizeBilinearGradConfig = {
kernelName: ResizeBilinear,
inputsToSave: ["images"],
gradFunc: (dy, saved, attrs) => {
const [images] = saved;
const inputs = { dy, images };
const imagesDer = () => ENGINE.runKernel(ResizeBilinearGrad, inputs, attrs);
return { images: imagesDer };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ResizeNearestNeighbor_grad.js
init_define_BUILD_VERSION();
var resizeNearestNeighborGradConfig = {
kernelName: ResizeNearestNeighbor,
inputsToSave: ["images"],
gradFunc: (dy, saved, attrs) => {
const [images] = saved;
const inputs = { dy, images };
const imagesDer = () => ENGINE.runKernel(ResizeNearestNeighborGrad, inputs, attrs);
return { images: imagesDer };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Reverse_grad.js
init_define_BUILD_VERSION();
var reverseGradConfig = {
kernelName: Reverse,
gradFunc: (dy, saved, attrs) => {
const { dims } = attrs;
const axes = parseAxisParam(dims, dy.shape);
return { x: () => reverse(dy, axes) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Round_grad.js
init_define_BUILD_VERSION();
var roundGradConfig = {
kernelName: Round,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Rsqrt_grad.js
init_define_BUILD_VERSION();
var rsqrtGradConfig = {
kernelName: Rsqrt,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => neg(div(dy, mul(pow(x, 1.5), 2))) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Select_grad.js
init_define_BUILD_VERSION();
var selectGradConfig = {
kernelName: Select,
inputsToSave: ["condition"],
gradFunc: (dy, saved) => {
const [condition] = saved;
return {
condition: () => cast(zerosLike(condition), "float32"),
t: () => mul(dy, cast(condition, dy.dtype)),
e: () => mul(dy, cast(logicalNot(condition), dy.dtype))
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Selu_grad.js
init_define_BUILD_VERSION();
var seluGradConfig = {
kernelName: Selu,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return {
x: () => {
const mask = greater(x, scalar(0));
const scaleAlpha2 = scalar(SELU_SCALEALPHA);
const scale2 = scalar(SELU_SCALE);
const greaterThanZeroDer = mul(dy, scale2);
const lessEqualZeroDer = mul(mul(dy, scaleAlpha2), exp(cast(x, "float32")));
return where(mask, greaterThanZeroDer, lessEqualZeroDer);
}
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sigmoid_grad.js
init_define_BUILD_VERSION();
var sigmoidGradConfig = {
kernelName: Sigmoid,
outputsToSave: [true],
gradFunc: (dy, saved) => {
const [y] = saved;
return { x: () => mul(dy, mul(y, sub(scalar(1), y))) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sign_grad.js
init_define_BUILD_VERSION();
var signGradConfig = {
kernelName: Sign,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sin_grad.js
init_define_BUILD_VERSION();
var sinGradConfig = {
kernelName: Sin,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(cos(cast(x, "float32")), dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sinh_grad.js
init_define_BUILD_VERSION();
var sinhGradConfig = {
kernelName: Sinh,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(cosh(cast(x, "float32")), dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Slice_grad.js
init_define_BUILD_VERSION();
var sliceGradConfig = {
kernelName: Slice,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { begin, size } = attrs;
const inputShape = x.shape;
const [begin_, size_] = parseSliceParams(x, begin, size);
const paddings = [];
for (let i = 0; i < dy.rank; i++) {
paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
}
return { x: () => pad(dy, paddings) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Softmax_grad.js
init_define_BUILD_VERSION();
var softmaxGradConfig = {
kernelName: Softmax,
outputsToSave: [true],
gradFunc: (dy, saved, attrs) => {
const [y] = saved;
const { dim } = attrs;
const keepDims = true;
const dyTimesY = mul(dy, y);
return {
logits: () => sub(dyTimesY, mul(sum2(dyTimesY, [dim], keepDims), y))
};
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Softplus_grad.js
init_define_BUILD_VERSION();
var softplusGradConfig = {
kernelName: Softplus,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(dy, sigmoid(x)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/SpaceToBatchND_grad.js
init_define_BUILD_VERSION();
var spaceToBatchNDGradConfig = {
kernelName: SpaceToBatchND,
gradFunc: (dy, saved, attrs) => {
const { blockShape, paddings } = attrs;
return { x: () => batchToSpaceND(dy, blockShape, paddings) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/SplitV_grad.js
init_define_BUILD_VERSION();
var splitVGradConfig = {
kernelName: SplitV,
gradFunc: (dy, saved, attrs) => {
const { axis } = attrs;
return { x: () => concat(dy, axis) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sqrt_grad.js
init_define_BUILD_VERSION();
var sqrtGradConfig = {
kernelName: Sqrt,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, mul(sqrt(cast(x, "float32")), 2)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Square_grad.js
init_define_BUILD_VERSION();
var squareGradConfig = {
kernelName: Square,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => mul(dy, mul(cast(x, "float32"), 2)) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/SquaredDifference_grad.js
init_define_BUILD_VERSION();
var squaredDifferenceGradConfig = {
kernelName: SquaredDifference,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const two = scalar(2);
const derA = () => mul(dy, mul(two, sub(a, b)));
const derB = () => mul(dy, mul(two, sub(b, a)));
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Step_grad.js
init_define_BUILD_VERSION();
var stepGradConfig = {
kernelName: Step,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sub_grad.js
init_define_BUILD_VERSION();
var subGradConfig = {
kernelName: Sub,
inputsToSave: ["a", "b"],
gradFunc: (dy, saved) => {
const [a, b] = saved;
const outShape = assertAndGetBroadcastShape(a.shape, b.shape);
const derA = () => {
let res = dy;
const reduceAxes = getReductionAxes(a.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(res, a.shape);
};
const derB = () => {
let res = dy;
const reduceAxes = getReductionAxes(b.shape, outShape);
if (reduceAxes.length > 0) {
res = sum2(res, reduceAxes);
}
return reshape(neg(res), b.shape);
};
return { a: derA, b: derB };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Sum_grad.js
init_define_BUILD_VERSION();
var sumGradConfig = {
kernelName: Sum,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const expandedDyShape = x.shape.slice();
const { axis } = attrs;
const axes = parseAxisParam(axis, x.shape);
axes.forEach((axis2) => {
expandedDyShape[axis2] = 1;
});
const expandedDy = reshape(dy, expandedDyShape);
const derX = mul(expandedDy, ones2(x.shape, "float32"));
return { x: () => derX };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Tan_grad.js
init_define_BUILD_VERSION();
var tanGradConfig = {
kernelName: Tan,
inputsToSave: ["x"],
gradFunc: (dy, saved) => {
const [x] = saved;
return { x: () => div(dy, square(cos(x))) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Tanh_grad.js
init_define_BUILD_VERSION();
var tanhGradConfig = {
kernelName: Tanh,
outputsToSave: [true],
gradFunc: (dy, saved) => {
const [y] = saved;
return { x: () => mul(sub(scalar(1), square(y)), dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Tile_grad.js
init_define_BUILD_VERSION();
var tileGradConfig = {
kernelName: Tile,
inputsToSave: ["x"],
gradFunc: (dy, saved, attrs) => {
const [x] = saved;
const { reps } = attrs;
const derX = () => {
let xGrad = zerosLike(x);
if (x.rank === 1) {
for (let i = 0; i < reps[0]; ++i) {
xGrad = add2(xGrad, slice(dy, [i * x.shape[0]], [x.shape[0]]));
}
} else if (x.rank === 2) {
for (let i = 0; i < reps[0]; ++i) {
for (let j = 0; j < reps[1]; ++j) {
xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1]], [
x.shape[0],
x.shape[1]
]));
}
}
} else if (x.rank === 3) {
for (let i = 0; i < reps[0]; ++i) {
for (let j = 0; j < reps[1]; ++j) {
for (let k = 0; k < reps[2]; ++k) {
xGrad = add2(xGrad, slice(dy, [i * x.shape[0], j * x.shape[1], k * x.shape[2]], [x.shape[0], x.shape[1], x.shape[2]]));
}
}
}
} else if (x.rank === 4) {
for (let i = 0; i < reps[0]; ++i) {
for (let j = 0; j < reps[1]; ++j) {
for (let k = 0; k < reps[2]; ++k) {
for (let l = 0; l < reps[3]; ++l) {
xGrad = add2(xGrad, slice(dy, [
i * x.shape[0],
j * x.shape[1],
k * x.shape[2],
l * x.shape[3]
], [x.shape[0], x.shape[1], x.shape[2], x.shape[3]]));
}
}
}
}
} else {
throw new Error(`Gradient for tile operation is not implemented for rank-${x.rank} tensors yet.`);
}
return xGrad;
};
return { x: derX };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Transpose_grad.js
init_define_BUILD_VERSION();
var transposeGradConfig = {
kernelName: Transpose,
gradFunc: (dy, saved, attrs) => {
const transposeAttrs = attrs;
const { perm } = transposeAttrs;
const undoPerm = getUndoAxesPermutation(perm);
return { x: () => transpose(dy, undoPerm) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/Unpack_grad.js
init_define_BUILD_VERSION();
var unpackGradConfig = {
kernelName: Unpack,
gradFunc: (dy, saved, attrs) => {
const unpackAttrs = attrs;
const { axis } = unpackAttrs;
return { value: () => stack(dy, axis) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/UnsortedSegmentSum_grad.js
init_define_BUILD_VERSION();
var unsortedSegmentSumGradConfig = {
kernelName: UnsortedSegmentSum,
inputsToSave: ["segmentIds"],
gradFunc: (dy, saved) => {
const [segmentIds] = saved;
const derX = () => {
return gatherDropNegatives(dy, segmentIds);
};
return { x: derX };
}
};
function gatherDropNegatives(x, indices) {
const zeroClippedIndices = maximum(indices, zerosLike(indices));
const gathered = gather(x, zeroClippedIndices);
let isPositive = greaterEqual(indices, scalar(0, "int32"));
const numIters = gathered.rank - isPositive.rank;
for (let i = 0; i < numIters; ++i) {
isPositive = expandDims(isPositive, i + 1);
}
isPositive = logicalAnd(isPositive, ones2(gathered.shape, "bool"));
const zeroSlice = zerosLike(gathered);
return where(isPositive, gathered, zeroSlice);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/gradients/ZerosLike_grad.js
init_define_BUILD_VERSION();
var zerosLikeGradConfig = {
kernelName: ZerosLike,
gradFunc: (dy) => {
return { x: () => zerosLike(dy) };
}
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/register_all_gradients.js
var gradConfigs = [
absGradConfig,
acosGradConfig,
acoshGradConfig,
addGradConfig,
addNGradConfig,
argMaxGradConfig,
argMinGradConfig,
asinGradConfig,
asinhGradConfig,
atan2GradConfig,
atanGradConfig,
atanhGradConfig,
avgPool3DGradConfig,
avgPoolGradConfig,
batchMatMulGradConfig,
batchToSpaceNDGradConfig,
broadcastToGradConfig,
castGradConfig,
ceilGradConfig,
clipByValueGradConfig,
complexAbsGradConfig,
concatGradConfig,
conv2DBackpropInputGradConfig,
conv2DGradConfig,
conv3DGradConfig,
cosGradConfig,
coshGradConfig,
cumsumGradConfig,
depthwiseConv2dNativeGradConfig,
dilation2dGradConfig,
divGradConfig,
eluGradConfig,
erfGradConfig,
expGradConfig,
expandDimsGradConfig,
expm1GradConfig,
floorDivGradConfig,
floorGradConfig,
fusedBatchNormGradConfig,
gatherGradConfig,
greaterEqualGradConfig,
identityGradConfig,
isFiniteGradConfig,
isInfGradConfig,
isNanGradConfig,
leakyReluGradConfig,
log1pGradConfig,
logGradConfig,
logSoftmaxGradConfig,
lrnGradConfig,
maxGradConfig,
maxGradConfig,
maximumGradConfig,
maxPool3DGradConfig,
maxPoolGradConfig,
meanGradConfig,
minGradConfig,
minimumGradConfig,
mirrorPadGradConfig,
modGradConfig,
multiplyGradConfig,
negGradConfig,
oneHotGradConfig,
onesLikeGradConfig,
packGradConfig,
padV2GradConfig,
padV2GradConfig,
powGradConfig,
preluGradConfig,
prodGradConfig,
reciprocalGradConfig,
relu6GradConfig,
reluGradConfig,
reshapeGradConfig,
resizeBilinearGradConfig,
resizeNearestNeighborGradConfig,
reverseGradConfig,
roundGradConfig,
rsqrtGradConfig,
selectGradConfig,
seluGradConfig,
sigmoidGradConfig,
signGradConfig,
sinGradConfig,
sinhGradConfig,
sliceGradConfig,
softmaxGradConfig,
softplusGradConfig,
spaceToBatchNDGradConfig,
spaceToBatchNDGradConfig,
splitVGradConfig,
splitVGradConfig,
sqrtGradConfig,
squaredDifferenceGradConfig,
squareGradConfig,
stepGradConfig,
subGradConfig,
sumGradConfig,
tanGradConfig,
tanhGradConfig,
tileGradConfig,
transposeGradConfig,
unpackGradConfig,
unsortedSegmentSumGradConfig,
zerosLikeGradConfig
];
for (const gradientConfig of gradConfigs) {
registerGradient(gradientConfig);
}
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/register_all_chained_ops.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/abs.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.abs = function() {
this.throwIfDisposed();
return abs(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acos.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.acos = function() {
this.throwIfDisposed();
return acos(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/acosh.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.acosh = function() {
this.throwIfDisposed();
return acosh(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/add.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.add = function(b) {
this.throwIfDisposed();
return add2(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/all.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.all = function(axis, keepDims) {
this.throwIfDisposed();
return all(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/any.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.any = function(axis, keepDims) {
this.throwIfDisposed();
return any(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_max.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.argMax = function(axis) {
this.throwIfDisposed();
return argMax(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/arg_min.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.argMin = function(axis) {
this.throwIfDisposed();
return argMin(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_scalar.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.asScalar = function() {
this.throwIfDisposed();
assert(this.size === 1, () => "The array must have only 1 element.");
return reshape(this, []);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as_type.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.asType = function(dtype) {
this.throwIfDisposed();
return cast(this, dtype);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as1d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.as1D = function() {
this.throwIfDisposed();
return reshape(this, [this.size]);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as2d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.as2D = function(rows, columns) {
this.throwIfDisposed();
return reshape(this, [rows, columns]);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as3d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.as3D = function(rows, columns, depth) {
this.throwIfDisposed();
return reshape(this, [rows, columns, depth]);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as4d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.as4D = function(rows, columns, depth, depth2) {
this.throwIfDisposed();
return reshape(this, [rows, columns, depth, depth2]);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/as5d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.as5D = function(rows, columns, depth, depth2, depth3) {
this.throwIfDisposed();
return reshape(this, [rows, columns, depth, depth2, depth3]);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asin.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.asin = function() {
this.throwIfDisposed();
return asin(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/asinh.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.asinh = function() {
this.throwIfDisposed();
return asinh(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.atan = function() {
this.throwIfDisposed();
return atan(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atan2.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.atan2 = function(b) {
this.throwIfDisposed();
return atan2(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/atanh.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.atanh = function() {
this.throwIfDisposed();
return atanh(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/avg_pool.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.avgPool = function(filterSize, strides, pad2, dimRoundingMode) {
this.throwIfDisposed();
return avgPool(this, filterSize, strides, pad2, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batch_to_space_nd.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.batchToSpaceND = function(blockShape, crops) {
this.throwIfDisposed();
return batchToSpaceND(this, blockShape, crops);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/batchnorm.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.batchNorm = function(mean3, variance, offset, scale2, varianceEpsilon) {
this.throwIfDisposed();
return batchNorm(this, mean3, variance, offset, scale2, varianceEpsilon);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/broadcast_to.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.broadcastTo = function(shape) {
this.throwIfDisposed();
return broadcastTo(this, shape);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cast.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.cast = function(dtype) {
this.throwIfDisposed();
return cast(this, dtype);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ceil.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.ceil = function() {
this.throwIfDisposed();
return ceil(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/clip_by_value.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.clipByValue = function(min5, max5) {
this.throwIfDisposed();
return clipByValue(this, min5, max5);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/concat.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.concat = function(x, axis) {
this.throwIfDisposed();
if (x instanceof Tensor) {
x = [x];
}
return concat([this, ...x], axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv1d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.conv1d = function(filter, stride, pad2, dataFormat, dilation, dimRoundingMode) {
this.throwIfDisposed();
return conv1d(this, filter, stride, pad2, dataFormat, dilation, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d_transpose.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.conv2dTranspose = function(filter, outputShape, strides, pad2, dimRoundingMode) {
this.throwIfDisposed();
return conv2dTranspose(this, filter, outputShape, strides, pad2, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/conv2d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.conv2d = function(filter, strides, pad2, dataFormat, dilations, dimRoundingMode) {
this.throwIfDisposed();
return conv2d(this, filter, strides, pad2, dataFormat, dilations, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cos.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.cos = function() {
this.throwIfDisposed();
return cos(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cosh.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.cosh = function() {
this.throwIfDisposed();
return cosh(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumprod.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.cumprod = function(axis, exclusive, reverse4) {
this.throwIfDisposed();
return cumprod(this, axis, exclusive, reverse4);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/cumsum.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.cumsum = function(axis, exclusive, reverse4) {
this.throwIfDisposed();
return cumsum(this, axis, exclusive, reverse4);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depth_to_space.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.depthToSpace = function(blockSize, dataFormat) {
this.throwIfDisposed();
return depthToSpace(this, blockSize, dataFormat);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/depthwise_conv2d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.depthwiseConv2d = function(filter, strides, pad2, dataFormat, dilations, dimRoundingMode) {
this.throwIfDisposed();
return depthwiseConv2d(this, filter, strides, pad2, dataFormat, dilations, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dilation2d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.dilation2d = function(filter, strides, pad2, dilations, dataFormat) {
this.throwIfDisposed();
return dilation2d(this, filter, strides, pad2, dilations, dataFormat);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div_no_nan.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.divNoNan = function(b) {
this.throwIfDisposed();
return divNoNan(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/div.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.div = function(b) {
this.throwIfDisposed();
return div(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/dot.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.dot = function(b) {
this.throwIfDisposed();
return dot(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/elu.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.elu = function() {
this.throwIfDisposed();
return elu(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/equal.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.equal = function(b) {
this.throwIfDisposed();
return equal(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/erf.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.erf = function() {
this.throwIfDisposed();
return erf(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/euclidean_norm.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.euclideanNorm = function(axis, keepDims) {
this.throwIfDisposed();
return euclideanNorm(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/exp.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.exp = function() {
this.throwIfDisposed();
return exp(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expand_dims.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.expandDims = function(axis) {
this.throwIfDisposed();
return expandDims(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/expm1.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.expm1 = function() {
this.throwIfDisposed();
return expm1(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/fft.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.fft = function() {
this.throwIfDisposed();
return fft(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/flatten.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.flatten = function() {
this.throwIfDisposed();
return reshape(this, [this.size]);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floor.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.floor = function() {
this.throwIfDisposed();
return floor(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/floorDiv.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.floorDiv = function(b) {
this.throwIfDisposed();
return floorDiv(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/gather.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.gather = function(indices, axis) {
this.throwIfDisposed();
return gather(this, indices, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater_equal.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.greaterEqual = function(b) {
this.throwIfDisposed();
return greaterEqual(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/greater.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.greater = function(b) {
this.throwIfDisposed();
return greater(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ifft.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.ifft = function() {
this.throwIfDisposed();
return ifft(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/irfft.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.irfft = function() {
this.throwIfDisposed();
return irfft(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_finite.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.isFinite = function() {
this.throwIfDisposed();
return isFinite2(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_inf.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.isInf = function() {
this.throwIfDisposed();
return isInf(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/is_nan.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.isNaN = function() {
this.throwIfDisposed();
return isNaN2(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/leaky_relu.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.leakyRelu = function(alpha) {
this.throwIfDisposed();
return leakyRelu(this, alpha);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less_equal.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.lessEqual = function(b) {
this.throwIfDisposed();
return lessEqual(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/less.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.less = function(b) {
this.throwIfDisposed();
return less(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/local_response_normalization.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.localResponseNormalization = function(depthRadius, bias, alpha, beta) {
this.throwIfDisposed();
return localResponseNormalization(this, depthRadius, bias, alpha, beta);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sigmoid.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logSigmoid = function() {
this.throwIfDisposed();
return logSigmoid(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_softmax.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logSoftmax = function(axis) {
this.throwIfDisposed();
return logSoftmax(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log_sum_exp.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logSumExp = function(axis, keepDims) {
this.throwIfDisposed();
return logSumExp(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.log = function() {
this.throwIfDisposed();
return log2(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/log1p.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.log1p = function() {
this.throwIfDisposed();
return log1p(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_and.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logicalAnd = function(b) {
this.throwIfDisposed();
return logicalAnd(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_not.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logicalNot = function() {
this.throwIfDisposed();
return logicalNot(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_or.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logicalOr = function(b) {
this.throwIfDisposed();
return logicalOr(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/logical_xor.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.logicalXor = function(b) {
this.throwIfDisposed();
return logicalXor(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mat_mul.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.matMul = function(b, transposeA, transposeB) {
this.throwIfDisposed();
return matMul(this, b, transposeA, transposeB);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max_pool.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.maxPool = function(filterSize, strides, pad2, dimRoundingMode) {
this.throwIfDisposed();
return maxPool(this, filterSize, strides, pad2, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/max.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.max = function(axis, keepDims) {
this.throwIfDisposed();
return max(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/maximum.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.maximum = function(b) {
this.throwIfDisposed();
return maximum(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mean.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.mean = function(axis, keepDims) {
this.throwIfDisposed();
return mean(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/min.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.min = function(axis, keepDims) {
this.throwIfDisposed();
return min(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/minimum.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.minimum = function(b) {
this.throwIfDisposed();
return minimum(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mirror_pad.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.mirrorPad = function(paddings, mode) {
this.throwIfDisposed();
return mirrorPad(this, paddings, mode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mod.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.mod = function(b) {
this.throwIfDisposed();
return mod(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/mul.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.mul = function(b) {
this.throwIfDisposed();
return mul(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/neg.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.neg = function() {
this.throwIfDisposed();
return neg(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/norm.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.norm = function(ord, axis, keepDims) {
this.throwIfDisposed();
return norm(this, ord, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/not_equal.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.notEqual = function(b) {
this.throwIfDisposed();
return notEqual(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/one_hot.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.oneHot = function(depth, onValue = 1, offValue = 0) {
this.throwIfDisposed();
return oneHot(this, depth, onValue, offValue);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/ones_like.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.onesLike = function() {
this.throwIfDisposed();
return onesLike(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pad.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.pad = function(paddings, constantValue) {
this.throwIfDisposed();
return pad(this, paddings, constantValue);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pool.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.pool = function(windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode) {
this.throwIfDisposed();
return pool(this, windowShape, poolingType, padding, dilationRate, strides, dimRoundingMode);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/pow.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.pow = function(exp4) {
this.throwIfDisposed();
return pow(this, exp4);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prelu.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.prelu = function(alpha) {
this.throwIfDisposed();
return prelu(this, alpha);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/prod.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.prod = function(axis, keepDims) {
this.throwIfDisposed();
return prod(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reciprocal.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.reciprocal = function() {
this.throwIfDisposed();
return reciprocal(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.relu = function() {
this.throwIfDisposed();
return relu(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/relu6.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.relu6 = function() {
this.throwIfDisposed();
return relu6(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape_as.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.reshapeAs = function(x) {
this.throwIfDisposed();
return reshape(this, x.shape);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reshape.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.reshape = function(shape) {
this.throwIfDisposed();
return reshape(this, shape);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_bilinear.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.resizeBilinear = function(newShape2D, alignCorners, halfPixelCenters) {
this.throwIfDisposed();
return resizeBilinear(this, newShape2D, alignCorners, halfPixelCenters);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/resize_nearest_neighbor.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.resizeNearestNeighbor = function(newShape2D, alignCorners, halfFloatCenters) {
this.throwIfDisposed();
return resizeNearestNeighbor(this, newShape2D, alignCorners, halfFloatCenters);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/reverse.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.reverse = function(axis) {
this.throwIfDisposed();
return reverse(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rfft.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.rfft = function() {
this.throwIfDisposed();
return rfft(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/round.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.round = function() {
this.throwIfDisposed();
return round2(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/rsqrt.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.rsqrt = function() {
this.throwIfDisposed();
return rsqrt(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/selu.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.selu = function() {
this.throwIfDisposed();
return selu(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/separable_conv2d.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.separableConv2d = function(depthwiseFilter, pointwiseFilter, strides, pad2, dilation, dataFormat) {
this.throwIfDisposed();
return separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad2, dilation, dataFormat);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sigmoid.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sigmoid = function() {
this.throwIfDisposed();
return sigmoid(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sign.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sign = function() {
this.throwIfDisposed();
return sign(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sin.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sin = function() {
this.throwIfDisposed();
return sin(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sinh.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sinh = function() {
this.throwIfDisposed();
return sinh(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/slice.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.slice = function(begin, size) {
this.throwIfDisposed();
return slice(this, begin, size);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softmax.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.softmax = function(dim) {
this.throwIfDisposed();
return softmax(this, dim);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/softplus.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.softplus = function() {
this.throwIfDisposed();
return softplus(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/space_to_batch_nd.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.spaceToBatchND = function(blockShape, paddings) {
this.throwIfDisposed();
return spaceToBatchND(this, blockShape, paddings);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/split.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.split = function(numOrSizeSplits, axis) {
this.throwIfDisposed();
return split(this, numOrSizeSplits, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sqrt.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sqrt = function() {
this.throwIfDisposed();
return sqrt(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/square.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.square = function() {
this.throwIfDisposed();
return square(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squared_difference.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.squaredDifference = function(b) {
this.throwIfDisposed();
return squaredDifference(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/squeeze.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.squeeze = function(axis) {
this.throwIfDisposed();
return squeeze(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/stack.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.stack = function(x, axis) {
this.throwIfDisposed();
const tensorsToBeStacked = x instanceof Tensor ? [this, x] : [this, ...x];
return stack(tensorsToBeStacked, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/step.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.step = function(alpha) {
this.throwIfDisposed();
return step(this, alpha);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/strided_slice.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.stridedSlice = function(begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
this.throwIfDisposed();
return stridedSlice(this, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sub.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sub = function(b) {
this.throwIfDisposed();
return sub(this, b);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/sum.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.sum = function(axis, keepDims) {
this.throwIfDisposed();
return sum2(this, axis, keepDims);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tan.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.tan = function() {
this.throwIfDisposed();
return tan(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tanh.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.tanh = function() {
this.throwIfDisposed();
return tanh2(this);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/tile.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.tile = function(reps) {
this.throwIfDisposed();
return tile(this, reps);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_bool.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.toBool = function() {
this.throwIfDisposed();
return cast(this, "bool");
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_float.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.toFloat = function() {
this.throwIfDisposed();
return cast(this, "float32");
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/to_int.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.toInt = function() {
this.throwIfDisposed();
return cast(this, "int32");
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/topk.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.topk = function(k, sorted) {
this.throwIfDisposed();
return topk(this, k, sorted);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/transpose.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.transpose = function(perm) {
this.throwIfDisposed();
return transpose(this, perm);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unique.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.unique = function(axis) {
this.throwIfDisposed();
return unique(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unsorted_segment_sum.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.unsortedSegmentSum = function(segmentIds, numSegments) {
this.throwIfDisposed();
return unsortedSegmentSum(this, segmentIds, numSegments);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/unstack.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.unstack = function(axis) {
this.throwIfDisposed();
return unstack(this, axis);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/where.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.where = function(condition, x) {
this.throwIfDisposed();
return where(condition, this, x);
};
// node_modules/.pnpm/@[email protected]/node_modules/@tensorflow/tfjs-core/dist/public/chained_ops/zeros_like.js
init_define_BUILD_VERSION();
getGlobalTensorClass().prototype.zerosLike = function() {
this.throwIfDisposed();
return zerosLike(this);
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/errors.js
init_define_BUILD_VERSION();
var AttributeError = class extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, AttributeError.prototype);
}
};
var RuntimeError = class extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, RuntimeError.prototype);
}
};
var ValueError = class extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, ValueError.prototype);
}
};
var NotImplementedError = class extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, NotImplementedError.prototype);
}
};
var AssertionError = class extends Error {
constructor(message) {
super(message);
Object.setPrototypeOf(this, AssertionError.prototype);
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/executor_utils.js
init_define_BUILD_VERSION();
var LruCache = class {
constructor(maxEntries) {
this.maxEntries = maxEntries || 100;
this.cache = /* @__PURE__ */ new Map();
}
get(key) {
let entry;
if (this.cache.has(key)) {
entry = this.cache.get(key);
this.cache.delete(key);
this.cache.set(key, entry);
}
return entry;
}
put(key, value) {
if (this.cache.has(key)) {
this.cache.delete(key);
} else if (this.cache.size >= this.maxEntries) {
const keyToDelete = this.cache.keys().next().value;
this.cache.delete(keyToDelete);
}
this.cache.set(key, value);
}
getMaxEntries() {
return this.maxEntries;
}
setMaxEntries(maxEntries) {
if (maxEntries < 0) {
throw new Error(`The maxEntries of LRU caches must be at least 0, but got ${maxEntries}.`);
}
if (this.maxEntries > maxEntries) {
for (let i = 0; i < this.maxEntries - maxEntries; i++) {
const keyToDelete = this.cache.keys().next().value;
this.cache.delete(keyToDelete);
}
}
this.maxEntries = maxEntries;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/generic_utils.js
init_define_BUILD_VERSION();
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());
}
var _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(config) {
if (config == null || typeof config !== "object") {
return;
} else if (Array.isArray(config)) {
config.forEach((configItem) => convertNDArrayScalarsInConfig(configItem));
} else {
const fields = Object.keys(config);
for (const field of fields) {
const value = config[field];
if (value != null && typeof value === "object") {
if (!Array.isArray(value) && value["type"] === "ndarray" && typeof value["value"] === "number") {
config[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 ${printableModuleName} is defined in JavaScript, but is not registered properly with tf.serialization.registerClass().`);
}
}
return fn;
} else {
const config = identifier;
if (config["className"] == null || config["config"] == null) {
throw new ValueError(`${printableModuleName}: Improper config format: ${JSON.stringify(config)}.
'className' and 'config' must set.`);
}
const className = config["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 = config["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(config["config"]);
const returnObj = fromConfig(cls, config["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(config["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 unique2(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, nowFunc) {
let lastTime = nowFunc != null ? nowFunc() : util_exports.now();
let lastResult;
const f2 = (...args) => {
const now2 = nowFunc != null ? nowFunc() : util_exports.now();
if (now2 - lastTime < waitMs) {
return lastResult;
}
lastTime = now2;
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/backend/state.js
init_define_BUILD_VERSION();
var _nextUniqueTensorId = 0;
function getNextUniqueTensorId() {
return _nextUniqueTensorId++;
}
var _uidPrefixes = {};
function getUid(prefix = "") {
if (!(prefix in _uidPrefixes)) {
_uidPrefixes[prefix] = 0;
}
_uidPrefixes[prefix] += 1;
return prefix + _uidPrefixes[prefix].toString();
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/common.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/keras_format/common.js
init_define_BUILD_VERSION();
var VALID_DATA_FORMAT_VALUES = ["channelsFirst", "channelsLast"];
var VALID_INTERPOLATION_FORMAT_VALUES = ["nearest", "bilinear"];
var VALID_PADDING_MODE_VALUES = ["valid", "same", "causal"];
var VALID_POOL_MODE_VALUES = ["max", "avg"];
var VALID_BIDIRECTIONAL_MERGE_MODES = ["sum", "mul", "concat", "ave"];
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/common.js
var nameMap = /* @__PURE__ */ new Map();
function checkDataFormat(value) {
checkStringTypeUnionValue(VALID_DATA_FORMAT_VALUES, "DataFormat", value);
}
function checkInterpolationFormat(value) {
checkStringTypeUnionValue(VALID_INTERPOLATION_FORMAT_VALUES, "InterpolationFormat", value);
}
function checkPaddingMode(value) {
checkStringTypeUnionValue(VALID_PADDING_MODE_VALUES, "PaddingMode", value);
}
function checkPoolMode(value) {
checkStringTypeUnionValue(VALID_POOL_MODE_VALUES, "PoolMode", value);
}
var _nameScopeStack = [];
var _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}_${index}`;
nameMap.set(result, 1);
return result;
} else {
return scopedName;
}
}
var tensorNameRegex = new RegExp(/^[A-Za-z0-9][-A-Za-z0-9\._\/]*$/);
function isValidTensorName(name) {
return !!name.match(tensorNameRegex);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/initializers.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/math_utils.js
init_define_BUILD_VERSION();
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 prod4 = 1;
for (let i = begin; i < end; ++i) {
prod4 *= array2[i];
}
return prod4;
}
function min2(array2) {
if (array2.length === 0) {
return Number.NaN;
}
let min5 = Number.POSITIVE_INFINITY;
for (let i = 0; i < array2.length; i++) {
const value = array2[i];
if (value < min5) {
min5 = value;
}
}
return min5;
}
function max2(array2) {
if (array2.length === 0) {
return Number.NaN;
}
let max5 = Number.NEGATIVE_INFINITY;
for (let i = 0; i < array2.length; i++) {
const value = array2[i];
if (value > max5) {
max5 = value;
}
}
return max5;
}
function range2(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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/backend/common.js
init_define_BUILD_VERSION();
var _epsilon;
function epsilon() {
if (_epsilon == null) {
_epsilon = backend().epsilon();
}
return _epsilon;
}
function imageDataFormat() {
return "channelsLast";
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/backend/tfjs_backend.js
function cast2(x, dtype) {
return cast(x, 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 reshape(x, 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 tile2(y, [1, n, 1]);
});
}
function flatten2(x) {
const newShape = [arrayProd(x.shape)];
return reshape(x, 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 reshape(x, 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: ${a.rank}`);
}
}
function tile2(x, n) {
if (!Array.isArray(n)) {
n = [n];
}
if (x.rank !== n.length) {
throw new ValueError(`The length of input n (${n.length}) does not match the number of dimensions in input x (${x.rank})`);
}
return tile(x, n);
}
function randomNormal2(shape, mean3 = 0, stddev = 1, dtype, seed) {
return randomNormal(shape, mean3, stddev, dtype, seed);
}
function dot2(a, b, activation, 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
});
} else {
const aFirstDims = a.shape.slice();
const aLastDim = aFirstDims.pop();
a = reshape(a, [-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 = reshape(transpose(b, perm), [ySecondLastDim, -1]);
const outputShape = [...aFirstDims, ...yOtherDims];
const transposeA = false;
const transposeB = false;
return reshape(fused_ops_exports.matMul({
a,
b,
transposeA,
transposeB,
bias: bias ? reshapeBias(a.rank, bias, imageDataFormat()) : null,
activation
}), outputShape);
}
}
function gather2(reference, indices, axis) {
return tidy(() => {
if (Array.isArray(indices)) {
indices = tensor1d(indices, "int32");
} else {
indices = cast(indices, "int32");
}
return gather(reference, indices, axis);
});
}
function square2(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 reshape(bias, [1, biasShape[0], 1, 1, 1]);
} else {
return reshape(bias, [1, biasShape[3], biasShape[0], biasShape[1], biasShape[2]]);
}
} else if (dataFormat === "channelsLast") {
if (biasShape.length === 1) {
return reshape(bias, [1, 1, 1, 1, biasShape[0]]);
} else {
return reshape(bias, [1].concat(biasShape));
}
}
} else if (xRank === 4) {
if (dataFormat === "channelsFirst") {
if (biasShape.length === 1) {
return reshape(bias, [1, biasShape[0], 1, 1]);
} else {
return reshape(bias, [1, biasShape[2], biasShape[0], biasShape[1]]);
}
} else if (dataFormat === "channelsLast") {
if (biasShape.length === 1) {
return reshape(bias, [1, 1, 1, biasShape[0]]);
} else {
return reshape(bias, [1].concat(biasShape));
}
}
} else if (xRank === 3) {
if (dataFormat === "channelsFirst") {
if (biasShape.length === 1) {
return reshape(bias, [1, biasShape[0], 1]);
} else {
return reshape(bias, [1, biasShape[1], biasShape[0]]);
}
} else if (dataFormat === "channelsLast") {
if (biasShape.length === 1) {
return reshape(bias, [1, 1, biasShape[0]]);
} else {
return reshape(bias, [1].concat(biasShape));
}
}
} else if (xRank < 3) {
return bias;
}
throw new ValueError(`Unsupported input rank by biasAdd: ${bias.rank}`);
}
function biasAdd(x, bias, dataFormat) {
return tidy(() => {
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
checkDataFormat(dataFormat);
return add2(x, reshapeBias(x.rank, bias, dataFormat));
});
}
function elu2(x, alpha = 1) {
if (alpha !== 1) {
throw new NotImplementedError(`Support for alpha values other than 1 (${alpha}) is not implemented yet.`);
}
return elu(x);
}
function softsign(x) {
return tidy(() => div(x, add2(abs(x), 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, training = false) {
return training ? x() : alt();
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/keras_format/initializer_config.js
init_define_BUILD_VERSION();
var VALID_FAN_MODE_VALUES = ["fanIn", "fanOut", "fanAvg"];
var VALID_DISTRIBUTION_VALUES = ["normal", "uniform", "truncatedNormal"];
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/initializers.js
function checkFanMode(value) {
checkStringTypeUnionValue(VALID_FAN_MODE_VALUES, "FanMode", value);
}
function checkDistribution(value) {
checkStringTypeUnionValue(VALID_DISTRIBUTION_VALUES, "Distribution", value);
}
var Initializer = class extends serialization_exports.Serializable {
fromConfigUsesCustomObjects() {
return false;
}
getConfig() {
return {};
}
};
var Zeros = class extends Initializer {
apply(shape, dtype) {
return zeros(shape, dtype);
}
};
Zeros.className = "Zeros";
serialization_exports.registerClass(Zeros);
var Ones = class extends Initializer {
apply(shape, dtype) {
return ones2(shape, dtype);
}
};
Ones.className = "Ones";
serialization_exports.registerClass(Ones);
var Constant = class 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);
var RandomUniform = class 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);
var RandomNormal = class 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);
var TruncatedNormal = class 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);
var Identity2 = class 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];
}
var VarianceScaling = class extends Initializer {
constructor(args) {
super();
if (args.scale < 0) {
throw new ValueError(`scale must be a positive float. Got: ${args.scale}`);
}
this.scale = args.scale == null ? 1 : args.scale;
this.mode = args.mode == null ? "fanIn" : args.mode;
checkFanMode(this.mode);
this.distribution = args.distribution == null ? "normal" : args.distribution;
checkDistribution(this.distribution);
this.seed = args.seed;
}
apply(shape, dtype) {
const fans = computeFans(shape);
const fanIn = fans[0];
const fanOut = fans[1];
let scale2 = this.scale;
if (this.mode === "fanIn") {
scale2 /= Math.max(1, fanIn);
} else if (this.mode === "fanOut") {
scale2 /= Math.max(1, fanOut);
} else {
scale2 /= Math.max(1, (fanIn + fanOut) / 2);
}
if (this.distribution === "normal") {
const stddev = Math.sqrt(scale2);
dtype = dtype || "float32";
if (dtype !== "float32" && dtype !== "int32") {
throw new NotImplementedError(`${this.getClassName()} does not support dType ${dtype}.`);
}
return truncatedNormal(shape, 0, stddev, dtype, this.seed);
} else {
const limit = Math.sqrt(3 * scale2);
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);
var GlorotUniform = class 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);
var GlorotNormal = class 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);
var HeNormal = class 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);
var HeUniform = class 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);
var LeCunNormal = class 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);
var LeCunUniform = class 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);
var Orthogonal = class 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 = transpose(q);
}
return mul(this.gain, q);
});
}
getConfig() {
return {
gain: this.gain,
seed: this.seed
};
}
};
Orthogonal.className = "Orthogonal";
serialization_exports.registerClass(Orthogonal);
var 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(config, customObjects = {}) {
return deserializeKerasObject(config, 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 config = {};
config["className"] = className;
config["config"] = {};
return deserializeInitializer(config);
}
} else if (identifier instanceof Initializer) {
return identifier;
} else {
return deserializeInitializer(identifier);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/types_utils.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/variable_utils.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/variables.js
init_define_BUILD_VERSION();
var DEFAULT_VARIABLE_NAME_PREFIX = "Variable";
var LayerVariable = class {
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 variable2 = variableAndValue[0];
variable2.write(variableAndValue[1]);
});
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/topology.js
var InputSpec = class {
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 || {};
}
};
var SymbolicTensor = class {
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;
}
};
var _nextNodeID = 0;
var Node = class {
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
};
}
};
var _nextLayerID = 0;
var Layer = class 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 p2 = 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([p2, w]);
}
batchSetValue(weightValueTuples);
});
}
addWeight(name, shape, dtype, initializer, regularizer, trainable, constraint, getInitializerFunc) {
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 = getInitializerFunc != null ? getInitializerFunc() : 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(losses) {
if (losses == null || Array.isArray(losses) && losses.length === 0) {
return;
}
losses = toList(losses);
if (this._losses !== void 0 && this._losses !== null) {
this.losses.push(...losses);
}
}
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 ${this.name} does not support masking, but was passed an inputMask.`);
}
}
return null;
}
return mask;
}
addInboundNode(inputTensors, outputTensors, inputMasks, outputMasks, inputShapes, outputShapes, kwargs = null) {
const inputTensorList = toList(inputTensors);
outputTensors = toList(outputTensors);
inputMasks = toList(inputMasks);
outputMasks = toList(outputMasks);
inputShapes = normalizeShapeList(inputShapes);
outputShapes = normalizeShapeList(outputShapes);
const inboundLayers = [];
const nodeIndices = [];
const tensorIndices = [];
for (const x of inputTensorList) {
inboundLayers.push(x.sourceLayer);
nodeIndices.push(x.nodeIndex);
tensorIndices.push(x.tensorIndex);
}
new Node({
outboundLayer: this,
inboundLayers,
nodeIndices,
tensorIndices,
inputTensors: inputTensorList,
outputTensors,
inputMasks,
outputMasks,
inputShapes,
outputShapes
}, kwargs);
for (let i = 0; i < outputTensors.length; i++) {
outputTensors[i].sourceLayer = this;
outputTensors[i].nodeIndex = this.inboundNodes.length - 1;
outputTensors[i].tensorIndex = i;
}
}
getConfig() {
const config = { name: this.name, trainable: this.trainable };
if (this.batchInputShape != null) {
config["batchInputShape"] = this.batchInputShape;
}
if (this.dtype != null) {
config["dtype"] = this.dtype;
}
return config;
}
disposeWeights() {
this.weights.forEach((weight) => weight.dispose());
return this.weights.length;
}
assertNotDisposed() {
if (this._refCount === 0) {
throw new Error(`Layer '${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(tensor3, layer, nodeIndex) {
if (layer == null || nodeIndex != null && nodeIndex > 0) {
layer = tensor3.sourceLayer;
nodeIndex = tensor3.nodeIndex;
}
if (layer.inboundNodes.length === 0) {
return [tensor3];
} 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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/input_layer.js
var InputLayer = class 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: ${this.name}`);
}
dispose() {
return { refCountAfterDispose: this._refCount, numDisposedVariables: 0 };
}
getConfig() {
return {
batchInputShape: this.batchInputShape,
dtype: this.dtype,
sparse: this.sparse,
name: this.name
};
}
};
InputLayer.className = "InputLayer";
serialization_exports.registerClass(InputLayer);
function Input(config) {
if (config.batchShape == null && config.shape == null) {
throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");
}
if (config.batchShape != null && config.shape != null) {
throw new ValueError("Please provide either a `shape` or `batchShape` argument to Input, but not both.");
}
let batchShape = config.batchShape;
if (config.shape != null && batchShape == null) {
batchShape = [null].concat(config.shape);
}
let dtype = config.dtype;
if (dtype == null) {
dtype = "float32";
}
const inputLayer = new InputLayer({
batchInputShape: batchShape,
name: config.name,
dtype,
sparse: config.sparse
});
const outputs = inputLayer.inboundNodes[0].outputTensors;
return outputs[0];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/executor.js
function assertFeedCompatibility(key, val) {
if (key.dtype == null || key.dtype === val.dtype) {
return val;
}
try {
return cast(val, key.dtype);
} catch (err) {
throw new ValueError(`The dtype of the feed (${val.dtype}) can not be cast to the dtype of the key '${key.name}' (${key.dtype}).`);
}
}
var FeedDict = class {
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);
}
}
};
var cachedSorted = new LruCache();
var cachedRecipientCounts = new LruCache();
function updateCacheMaxEntries(maxEntries) {
if (cachedSorted != null) {
cachedSorted.setMaxEntries(maxEntries);
}
if (cachedRecipientCounts != null) {
cachedRecipientCounts.setMaxEntries(maxEntries);
}
}
function execute(fetches, feedDict, kwargs, probe) {
const training = 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().sort().join(",");
let sorted = cachedSorted.get(fetchAndFeedKey);
let recipientCounts;
if (sorted == null) {
const out = getTopologicalSortAndRecipientCounts(fetchArray, feedDict);
sorted = out.sorted;
recipientCounts = out.recipientCounts;
cachedSorted.put(fetchAndFeedKey, sorted);
cachedRecipientCounts.put(fetchAndFeedKey, recipientCounts);
}
recipientCounts = {};
if (!training) {
Object.assign(recipientCounts, cachedRecipientCounts.get(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 (!training) {
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 (!training) {
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 = /* @__PURE__ */ new Set();
for (const fetch4 of fetches) {
const { sorted, recipientMap } = getTopologicalSortAndRecipientCountsForOneFetch(fetch4, 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] = /* @__PURE__ */ 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(fetch4, feedDict) {
const visited = /* @__PURE__ */ new Set();
const sorted = [];
const recipientMap = {};
for (const key of feedDict.names()) {
visited.add(key);
}
const stack2 = [];
const marks = [];
stack2.push(fetch4);
while (stack2.length > 0) {
const top = stack2[stack2.length - 1];
if (visited.has(top.name)) {
stack2.pop();
continue;
}
const topIsMarked = marks[marks.length - 1] === stack2.length - 1;
if (top.inputs.length === 0 || topIsMarked) {
stack2.pop();
sorted.push(top);
visited.add(top.name);
if (topIsMarked) {
marks.pop();
}
} else {
marks.push(stack2.length - 1);
for (const input2 of top.inputs) {
if (recipientMap[input2.name] == null) {
recipientMap[input2.name] = /* @__PURE__ */ new Set();
}
recipientMap[input2.name].add(top.name);
if (visited.has(input2.name)) {
continue;
}
stack2.push(input2);
}
}
}
return { sorted, recipientMap };
}
function getNodeOutputs(fetch4) {
let layerOutputs;
if (fetch4.sourceLayer.inboundNodes.length === 1) {
layerOutputs = fetch4.sourceLayer.output;
} else {
let nodeIndex = null;
for (let i = 0; i < fetch4.sourceLayer.inboundNodes.length; ++i) {
for (const outputTensor of fetch4.sourceLayer.inboundNodes[i].outputTensors) {
if (outputTensor.id === fetch4.id) {
nodeIndex = i;
break;
}
}
}
layerOutputs = fetch4.sourceLayer.getOutputAt(nodeIndex);
}
return layerOutputs;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/flags_layers.js
var ENV3 = env();
ENV3.registerFlag("TOPOLOGICAL_SORT_CACHE_MAX_ENTRIES", () => 100, updateCacheMaxEntries);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports_constraints.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/constraints.js
init_define_BUILD_VERSION();
function calcL2Norms(w, axis) {
return tidy(() => sqrt(sum2(mul(w, w), axis, true)));
}
var Constraint = class extends serialization_exports.Serializable {
getConfig() {
return {};
}
};
var MaxNorm = class 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);
var UnitNorm = class 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);
var NonNeg = class extends Constraint {
apply(w) {
return relu(w);
}
};
NonNeg.className = "NonNeg";
serialization_exports.registerClass(NonNeg);
var MinMaxNorm = class 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);
var CONSTRAINT_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
"maxNorm": "MaxNorm",
"minMaxNorm": "MinMaxNorm",
"nonNeg": "NonNeg",
"unitNorm": "UnitNorm"
};
function serializeConstraint(constraint) {
return serializeKerasObject(constraint);
}
function deserializeConstraint(config, customObjects = {}) {
return deserializeKerasObject(config, 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 config = { className, config: {} };
return deserializeConstraint(config);
} else if (identifier instanceof Constraint) {
return identifier;
} else {
return deserializeConstraint(identifier);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports_initializers.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports_layers.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/logs.js
init_define_BUILD_VERSION();
async function resolveScalarsInLogs(logs) {
if (logs == null) {
return;
}
const promises = [];
const keys = [];
const scalarsToDispose = [];
for (const key in logs) {
const value = logs[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) {
logs[keys[i]] = values[i][0];
}
dispose(scalarsToDispose);
}
}
function disposeTensorsInLogs(logs) {
if (logs == null) {
return;
}
for (const key in logs) {
const value = logs[key];
if (typeof value !== "number") {
value.dispose();
}
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/base_callbacks.js
var ModelLoggingVerbosity;
(function(ModelLoggingVerbosity2) {
ModelLoggingVerbosity2[ModelLoggingVerbosity2["SILENT"] = 0] = "SILENT";
ModelLoggingVerbosity2[ModelLoggingVerbosity2["VERBOSE"] = 1] = "VERBOSE";
})(ModelLoggingVerbosity || (ModelLoggingVerbosity = {}));
var DEFAULT_YIELD_EVERY_MS = 125;
var BaseCallback = class {
constructor() {
this.validationData = null;
}
setParams(params) {
this.params = params;
}
async onEpochBegin(epoch, logs) {
}
async onEpochEnd(epoch, logs) {
}
async onBatchBegin(batch, logs) {
}
async onBatchEnd(batch, logs) {
}
async onTrainBegin(logs) {
}
async onTrainEnd(logs) {
}
setModel(model3) {
}
};
var CallbackList = class {
constructor(callbacks2, queueLength = 10) {
if (callbacks2 == null) {
callbacks2 = [];
}
this.callbacks = callbacks2;
this.queueLength = queueLength;
}
append(callback) {
this.callbacks.push(callback);
}
setParams(params) {
for (const callback of this.callbacks) {
callback.setParams(params);
}
}
setModel(model3) {
for (const callback of this.callbacks) {
callback.setModel(model3);
}
}
async onEpochBegin(epoch, logs) {
if (logs == null) {
logs = {};
}
for (const callback of this.callbacks) {
await callback.onEpochBegin(epoch, logs);
}
}
async onEpochEnd(epoch, logs) {
if (logs == null) {
logs = {};
}
for (const callback of this.callbacks) {
await callback.onEpochEnd(epoch, logs);
}
}
async onBatchBegin(batch, logs) {
if (logs == null) {
logs = {};
}
for (const callback of this.callbacks) {
await callback.onBatchBegin(batch, logs);
}
}
async onBatchEnd(batch, logs) {
if (logs == null) {
logs = {};
}
for (const callback of this.callbacks) {
await callback.onBatchEnd(batch, logs);
}
}
async onTrainBegin(logs) {
if (logs == null) {
logs = {};
}
for (const callback of this.callbacks) {
await callback.onTrainBegin(logs);
}
}
async onTrainEnd(logs) {
if (logs == null) {
logs = {};
}
for (const callback of this.callbacks) {
await callback.onTrainEnd(logs);
}
}
};
var BaseLogger = class extends BaseCallback {
constructor() {
super();
}
async onEpochBegin(epoch) {
this.seen = 0;
this.totals = {};
}
async onBatchEnd(batch, logs) {
if (logs == null) {
logs = {};
}
const batchSize = logs["size"] == null ? 0 : logs["size"];
this.seen += batchSize;
for (const key in logs) {
const value = logs[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, logs) {
if (logs != null) {
for (const key of this.params["metrics"]) {
if (this.totals[key] == null) {
continue;
}
if (typeof this.totals[key] === "number") {
logs[key] = this.totals[key] / this.seen;
} else {
tidy(() => {
const log5 = mul(div(1, this.seen), this.totals[key]);
logs[key] = log5;
this.totals[key].dispose();
keep(logs[key]);
});
}
}
}
}
};
var History = class extends BaseCallback {
async onTrainBegin(logs) {
this.epoch = [];
this.history = {};
}
async onEpochEnd(epoch, logs) {
if (logs == null) {
logs = {};
}
this.epoch.push(epoch);
for (const key in logs) {
if (this.history[key] == null) {
this.history[key] = [];
}
this.history[key].push(logs[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];
}
}
};
var CustomCallback = class extends BaseCallback {
constructor(args, yieldEvery) {
super();
this.currentEpoch = 0;
this.nowFunc = args.nowFunc;
this.nextFrameFunc = args.nextFrameFunc || nextFrame;
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.nowFunc);
}
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, logs) {
const ps = [];
if (this.yield != null) {
await resolveScalarsInLogs(logs);
ps.push(this.yield(epoch, batch, logs));
}
ps.push(this.nextFrameFunc());
await Promise.all(ps);
}
async onEpochBegin(epoch, logs) {
this.currentEpoch = epoch;
if (this.epochBegin != null) {
await resolveScalarsInLogs(logs);
await this.epochBegin(epoch, logs);
}
}
async onEpochEnd(epoch, logs) {
const ps = [];
if (this.epochEnd != null) {
await resolveScalarsInLogs(logs);
ps.push(this.epochEnd(epoch, logs));
}
if (this.yieldEvery === "epoch") {
ps.push(this.nextFrameFunc());
}
await Promise.all(ps);
}
async onBatchBegin(batch, logs) {
if (this.batchBegin != null) {
await resolveScalarsInLogs(logs);
await this.batchBegin(batch, logs);
}
}
async onBatchEnd(batch, logs) {
const ps = [];
if (this.batchEnd != null) {
await resolveScalarsInLogs(logs);
ps.push(this.batchEnd(batch, logs));
}
if (this.yieldEvery === "batch") {
ps.push(this.nextFrameFunc());
} else if (util_exports.isNumber(this.yieldEvery)) {
ps.push(this.maybeWait(this.currentEpoch, batch, logs));
}
await Promise.all(ps);
}
async onTrainBegin(logs) {
if (this.trainBegin != null) {
await resolveScalarsInLogs(logs);
await this.trainBegin(logs);
}
}
async onTrainEnd(logs) {
if (this.trainEnd != null) {
await resolveScalarsInLogs(logs);
await this.trainEnd(logs);
}
}
};
function standardizeCallbacks(callbacks2, yieldEvery) {
if (callbacks2 == null) {
callbacks2 = {};
}
if (callbacks2 instanceof BaseCallback) {
return [callbacks2];
}
if (Array.isArray(callbacks2) && callbacks2[0] instanceof BaseCallback) {
return callbacks2;
}
const callbackConfigs = toList(callbacks2);
return callbackConfigs.map((callbackConfig) => new CustomCallback(callbackConfig, yieldEvery));
}
var CallbackConstructorRegistry = class {
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(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics) {
const history = new History();
const actualCallbacks = [
new BaseLogger(),
...CallbackConstructorRegistry.createCallbacks(verbose)
];
if (callbacks2 != null) {
actualCallbacks.push(...callbacks2);
}
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/serialization.js
init_define_BUILD_VERSION();
function deserialize(config, customObjects = {}, fastWeightInit = false) {
return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "layer", fastWeightInit);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/losses.js
init_define_BUILD_VERSION();
function l2Normalize(x, axis) {
return tidy(() => {
if (x.dtype !== "float32") {
x = cast(x, "float32");
}
const squareSum = sum2(square2(x), axis, true);
const epsilonTensor = fill(squareSum.shape, epsilon());
const norm2 = sqrt(maximum(squareSum, epsilonTensor));
return div(x, norm2);
});
}
function meanSquaredError(yTrue, yPred) {
return tidy(() => mean(square2(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 = log2(add2(1, clippedPred));
const clippedTrue = clipByValue(yTrue, epsilon(), Number.MAX_VALUE);
const secondLog = log2(add2(1, clippedTrue));
return mean(square2(sub(firstLog, secondLog)), -1);
});
}
function squaredHinge(yTrue, yPred) {
return tidy(() => {
const maxResult = maximum(0, sub(1, mul(yTrue, yPred)));
return mean(square2(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 neg4 = max(mul(sub(1, yTrue), yPred), -1);
return maximum(0, add2(1, sub(neg4, 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(cast(target, "float32"), log2(output)), output.shape.length - 1));
});
}
function sparseCategoricalCrossentropy(target, output, fromLogits = false) {
return tidy(() => {
const flatTarget = cast(floor(flatten2(target)), "int32");
output = clipByValue(output, epsilon(), 1 - epsilon());
const outputShape = output.shape;
const oneHotTarget = reshape(oneHot(flatTarget, outputShape[outputShape.length - 1]), 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 = relu(logits);
const negAbsLogits = neg(abs(logits));
return add2(sub(reluLogits, mul(logits, labels)), log1p(exp(negAbsLogits)));
});
}
function binaryCrossentropy(yTrue, yPred) {
return tidy(() => {
let y;
y = clipByValue(yPred, epsilon(), 1 - epsilon());
y = log2(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, log2(div(clippedTrue, clippedPred))), -1);
});
}
function poisson(yTrue, yPred) {
return tidy(() => {
const logPred = log2(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));
});
}
var lossesMap = {
meanSquaredError,
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/metrics.js
init_define_BUILD_VERSION();
function binaryAccuracy(yTrue, yPred) {
return tidy(() => {
const threshold3 = mul(0.5, onesLike(yPred));
const yPredThresholded = cast2(greater(yPred, threshold3), yTrue.dtype);
return mean(equal(yTrue, yPredThresholded), -1);
});
}
function categoricalAccuracy(yTrue, yPred) {
return tidy(() => cast2(equal(argMax(yTrue, -1), argMax(yPred, -1)), "float32"));
}
function truePositives(yTrue, yPred) {
return tidy(() => {
return cast(sum2(logicalAnd(equal(yTrue, 1), equal(yPred, 1))), "float32");
});
}
function falsePositives(yTrue, yPred) {
return tidy(() => {
return cast(sum2(logicalAnd(equal(yTrue, 0), equal(yPred, 1))), "float32");
});
}
function precision(yTrue, yPred) {
return tidy(() => {
const tp = truePositives(yTrue, yPred);
const fp = falsePositives(yTrue, yPred);
const denominator = add2(tp, fp);
return cast(where(greater(denominator, 0), div(tp, denominator), 0), "float32");
});
}
function binaryCrossentropy2(yTrue, yPred) {
return binaryCrossentropy(yTrue, yPred);
}
function sparseCategoricalAccuracy(yTrue, yPred) {
if (yTrue.rank === yPred.rank) {
yTrue = squeeze(yTrue, [yTrue.rank - 1]);
}
yPred = argMax(yPred, -1);
if (yPred.dtype !== yTrue.dtype) {
yPred = cast(yPred, yTrue.dtype);
}
return cast(equal(yTrue, yPred), "float32");
}
var mse = meanSquaredError;
var MSE = meanSquaredError;
var mae = meanAbsoluteError;
var MAE = meanAbsoluteError;
var mape = meanAbsolutePercentageError;
var MAPE = meanAbsolutePercentageError;
var categoricalCrossentropy2 = categoricalCrossentropy;
var cosine = cosineProximity;
var sparseCategoricalCrossentropy2 = sparseCategoricalCrossentropy;
var 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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/optimizers.js
init_define_BUILD_VERSION();
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 ${identifier}`);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/user_defined_metadata.js
init_define_BUILD_VERSION();
var MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH = 1 * 1024 * 1024;
function checkUserDefinedMetadata(userDefinedMetadata, modelName, checkSize = false) {
if (userDefinedMetadata == null || typeof userDefinedMetadata !== "object" || Object.getPrototypeOf(userDefinedMetadata) !== Object.prototype || !plainObjectCheck(userDefinedMetadata)) {
throw new Error("User-defined metadata is expected to be a JSON object, but is not.");
}
if (checkSize) {
const out = JSON.stringify(userDefinedMetadata);
if (out.length > MAX_USER_DEFINED_METADATA_SERIALIZED_LENGTH) {
console.warn(`User-defined metadata of model "${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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/layer_utils.js
init_define_BUILD_VERSION();
function printSummary(model3, lineLength, positions, printFn = console.log) {
const sequentialLike = isModelSequentialLike(model3);
const toDisplay = ["Layer (type)", "Input Shape", "Output shape", "Param #"];
if (sequentialLike) {
lineLength = lineLength || 90;
positions = positions || [0.32, 0.61, 0.89, 1];
} else {
lineLength = lineLength || 115;
positions = positions || [0.24, 0.48, 0.7, 0.8, 1];
}
if (positions[positions.length - 1] <= 1) {
positions = positions.map((p2) => Math.floor(lineLength * p2));
}
let relevantNodes;
if (!sequentialLike) {
toDisplay.push("Receives inputs");
relevantNodes = [];
for (const depth in model3.nodesByDepth) {
relevantNodes.push(...model3.nodesByDepth[depth]);
}
}
printFn("_".repeat(lineLength));
printRow(toDisplay, positions, printFn);
printFn("=".repeat(lineLength));
const layers = model3.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));
}
model3.checkTrainableWeightsConsistency();
const trainableCount = countTrainableParams(model3);
const nonTrainableCount = countParamsInWeights(model3.nonTrainableWeights);
printFn(`Total params: ${trainableCount + nonTrainableCount}`);
printFn(`Trainable params: ${trainableCount}`);
printFn(`Non-trainable params: ${nonTrainableCount}`);
printFn("_".repeat(lineLength));
}
function countTrainableParams(model3) {
let trainableCount;
if (model3.collectedTrainableWeights != null) {
trainableCount = countParamsInWeights(model3.collectedTrainableWeights);
} else {
trainableCount = countParamsInWeights(model3.trainableWeights);
}
return trainableCount;
}
function isModelSequentialLike(model3) {
let sequentialLike = true;
const nodesByDepth = [];
const nodes = [];
for (const depth in model3.nodesByDepth) {
nodesByDepth.push(model3.nodesByDepth[depth]);
}
for (const depthNodes of nodesByDepth) {
if (depthNodes.length > 1 || depthNodes.length === 1 && depthNodes[0].inboundLayers.length > 1) {
sequentialLike = false;
break;
}
nodes.push(...depthNodes);
}
if (sequentialLike) {
for (const layer of model3.layers) {
let flag = false;
for (const node of layer.inboundNodes) {
if (nodes.indexOf(node) !== -1) {
if (flag) {
sequentialLike = false;
break;
} else {
flag = true;
}
}
}
if (!sequentialLike) {
break;
}
}
}
return sequentialLike;
}
function printRow(fields, positions, printFn = console.log) {
let line = "";
for (let i = 0; i < fields.length; ++i) {
if (i > 0) {
line = line.slice(0, line.length - 1) + " ";
}
line += fields[i];
line = line.slice(0, positions[i]);
line += " ".repeat(positions[i] - line.length);
}
printFn(line);
}
function printLayerSummary(layer, positions, printFn) {
let outputShape;
let inputShape;
try {
inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(",");
} catch (err) {
inputShape = "multiple";
}
try {
outputShape = JSON.stringify(layer.outputShape);
} catch (err) {
outputShape = "multiple";
}
const name = layer.name;
const className = layer.getClassName();
const fields = [
`${name} (${className})`,
inputShape,
outputShape,
layer.countParams().toString()
];
printRow(fields, positions, printFn);
}
function printLayerSummaryWithConnections(layer, positions, relevantNodes, printFn) {
let outputShape;
let inputShape;
try {
inputShape = layer.inboundNodes.map((x) => JSON.stringify(x.inputShapes)).join(",");
} catch (err) {
inputShape = "multiple";
}
try {
outputShape = JSON.stringify(layer.outputShape);
} catch (err) {
outputShape = "multiple";
}
const connections = [];
for (const node of layer.inboundNodes) {
if (relevantNodes != null && relevantNodes.length > 0 && relevantNodes.indexOf(node) === -1) {
continue;
}
for (let i = 0; i < node.inboundLayers.length; ++i) {
const inboundLayer = node.inboundLayers[i].name;
const inboundLayerIndex = node.nodeIndices[i];
const inboundTensorIndex = node.tensorIndices[i];
connections.push(`${inboundLayer}[${inboundLayerIndex}][${inboundTensorIndex}]`);
}
}
const name = layer.name;
const className = layer.getClassName();
const firstConnection = connections.length === 0 ? "" : connections[0];
const fields = [
`${name} (${className})`,
inputShape,
outputShape,
layer.countParams().toString(),
firstConnection
];
printRow(fields, positions, printFn);
for (let i = 1; i < connections.length; ++i) {
printRow(["", "", "", "", connections[i]], positions, printFn);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/serialization_utils.js
init_define_BUILD_VERSION();
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/version.js
init_define_BUILD_VERSION();
var version = "3.19.0";
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/container.js
init_define_BUILD_VERSION();
var Container = class extends Layer {
constructor(args) {
super({});
this.containerNodes = /* @__PURE__ */ 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 (unique2(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 (unique2(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 = (tensor3, finishedNodes2, nodesInProgress2, layer, nodeIndex, tensorIndex) => {
if (layer == null || nodeIndex == null || tensorIndex == null) {
layer = tensor3.sourceLayer;
nodeIndex = tensor3.nodeIndex;
tensorIndex = tensor3.tensorIndex;
}
const node = layer.inboundNodes[nodeIndex];
if (nodesInProgress2.indexOf(node) !== -1) {
throw new RuntimeError(`The tensor ${tensor3.name} at layer "${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 container of this.internalContainerRefs) {
result.numDisposedVariables += container.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 ${version}`;
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 [tensor3, mask] = tensorMap[x.id];
outputShapes.push(tensor3.shape);
outputTensors.push(tensor3);
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 losses = [];
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)) {
losses.push(...layer.calculateLosses());
}
}
}
return losses;
});
}
getConfig() {
const config = { 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);
}
config["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]);
}
config["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]);
}
config["outputLayers"] = modelOutputs;
return config;
}
static fromConfig(cls, config, 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, config["customObjects"] != null ? config["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 = config["name"];
const layersFromConfig = config["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 = config["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 = config["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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/training_utils.js
init_define_BUILD_VERSION();
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: ${JSON.stringify(xWeight)}`);
}
}
function standardizeClassWeights(classWeight, outputNames) {
return standardizeSampleOrClassWeights(classWeight, outputNames, "classWeight");
}
async function standardizeWeights(y, sampleWeight, classWeight, sampleWeightMode) {
if (sampleWeight != null || sampleWeightMode != null) {
throw new Error("Support sampleWeight is not implemented yet");
}
if (classWeight != null) {
const yClasses = tidy(() => {
if (y.shape.length === 1) {
return clone(y);
} else if (y.shape.length === 2) {
if (y.shape[1] > 1) {
const axis = 1;
return argMax(y, axis);
} else if (y.shape[1] === 1) {
return reshape(y, [y.shape[0]]);
} else {
throw new Error(`Encountered unexpected last-dimension size (${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 computeWeightedLoss(losses, sampleWeights) {
return mul(losses, sampleWeights);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/training_dataset.js
var DEFAULT_VALIDATION_BATCH_SIZE = 32;
function standardizeDataIteratorOutput(model3, 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", model3.inputNames, xs);
const flattenedYs = flattenTensorOrArrayOrMap("output", model3.outputNames, ys);
const batchSize = flattenedXs[0].shape[0];
util_exports.assert(flattenedXs.length === model3.inputs.length, () => `LayersModel has ${model3.inputs.length} inputs, but the dataset provides ${flattenedXs.length} inputs. (Expected input keys: ${JSON.stringify(model3.inputNames)})`);
util_exports.assert(flattenedYs.length === model3.outputs.length, () => `LayersModel has ${model3.outputs.length} outputs, but the dataset provides ${flattenedYs.length} outputs. (Expected output keys: ${JSON.stringify(model3.outputNames)})`);
for (let xIndex = 0; xIndex < flattenedXs.length; xIndex++) {
util_exports.assert(flattenedXs[xIndex].shape[0] === batchSize, () => `Batch size mismatch: input ${model3.inputNames[xIndex]} has ${flattenedXs[xIndex].shape[0]}; expected ${batchSize} based on input ${model3.inputNames[0]}.`);
}
for (let yIndex = 0; yIndex < flattenedYs.length; yIndex++) {
util_exports.assert(flattenedYs[yIndex].shape[0] === batchSize, () => `Batch size mismatch: output ${model3.outputNames[yIndex]} has ${flattenedYs[yIndex].shape[0]}; expected ${batchSize} based on input ${model3.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(data) {
if (data.length === 3) {
throw new NotImplementedError("Validation with sample weights is not implemented yet.");
}
return { xs: data[0], ys: data[1] };
}
async function fitDataset(model3, dataset, args) {
const hasBatchesPerEpoch = args.batchesPerEpoch != null;
util_exports.assert(model3.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 (model3.isTraining) {
throw new Error("Cannot start training because another fit() call is ongoing.");
}
model3.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 = model3.makeTrainFunction();
const outLabels = model3.getDedupedMetricsNames();
let callbackMetrics;
if (doValidation) {
callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
} else {
callbackMetrics = outLabels.slice();
}
const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
const verbose = args.verbose == null ? 1 : args.verbose;
const { callbackList, history } = configureCallbacks(
callbacks2,
verbose,
args.epochs,
null,
null,
getStepsPerEpoch(dataset, args),
null,
doValidation,
callbackMetrics
);
callbackList.setModel(model3);
model3.history = history;
await callbackList.onTrainBegin();
model3.stopTraining_ = false;
let epoch = args.initialEpoch == null ? 0 : args.initialEpoch;
let dataIterator = await dataset.iterator();
while (epoch < args.epochs) {
const epochLogs = {};
await callbackList.onEpochBegin(epoch);
let stepsDone = 0;
let batchIndex = 0;
if (!hasBatchesPerEpoch) {
dataIterator = await dataset.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, ${args.batchesPerEpoch * args.epochs} batches). You may need to use the repeat() function when building your dataset.`);
break;
}
if (iteratorOut.value != null) {
const { xs, ys } = standardizeDataIteratorOutput(model3, iteratorOut.value);
const batchLogs = {};
batchLogs["batch"] = batchIndex;
batchLogs["size"] = xs[0].shape[0];
await callbackList.onBatchBegin(batchIndex, batchLogs);
const sampleWeights = [];
if (args.classWeight != null) {
const standardClassWeights = standardizeClassWeights(args.classWeight, model3.outputNames);
for (let i = 0; i < standardClassWeights.length; ++i) {
sampleWeights.push(await standardizeWeights(ys[i], null, standardClassWeights[i]));
}
}
const ins = xs.concat(ys).concat(sampleWeights);
const outs = trainFunction(ins);
dispose(ins);
for (let i = 0; i < outLabels.length; ++i) {
const label = outLabels[i];
const out = outs[i];
batchLogs[label] = out;
keep(out);
}
await callbackList.onBatchEnd(batchIndex, batchLogs);
disposeTensorsInLogs(batchLogs);
batchIndex++;
stepsDone++;
}
if (hasBatchesPerEpoch ? stepsDone >= args.batchesPerEpoch : iteratorOut.done) {
if (doValidation) {
let valOuts;
if (isDatasetObject(args.validationData)) {
valOuts = toList(await model3.evaluateDataset(args.validationData, { batches: args.validationBatches }));
} else {
valOuts = toList(model3.evaluate(valXs, valYs, {
batchSize: args.validationBatchSize == null ? DEFAULT_VALIDATION_BATCH_SIZE : args.validationBatchSize,
verbose: 0
}));
}
for (let i = 0; i < model3.metricsNames.length; ++i) {
epochLogs[`val_${model3.metricsNames[i]}`] = valOuts[i];
}
}
break;
}
if (model3.stopTraining_) {
break;
}
}
await callbackList.onEpochEnd(epoch, epochLogs);
epoch++;
if (model3.stopTraining_) {
break;
}
}
await callbackList.onTrainEnd();
await model3.history.syncData();
return model3.history;
} finally {
model3.isTraining = false;
}
}
function getStepsPerEpoch(dataset, args) {
let stepsPerEpoch = null;
if (args.batchesPerEpoch != null) {
stepsPerEpoch = args.batchesPerEpoch;
} else if (Number.isFinite(dataset.size)) {
stepsPerEpoch = dataset.size;
}
return stepsPerEpoch;
}
function isDatasetObject(dataset) {
return typeof dataset.iterator === "function";
}
function isLazyIteratorObject(iterator) {
return typeof iterator.next === "function";
}
async function evaluateDataset(model3, dataset, args) {
args = args || {};
const hasBatches = args.batches != null;
const f = model3.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(dataset) ? dataset : await dataset.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(model3, 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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/training_tensors.js
init_define_BUILD_VERSION();
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 gather2(arrays, indices.dtype === "int32" ? indices : cast(indices, "int32"));
}
});
}
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(model3, f, ins, outLabels, batchSize, epochs, verbose, callbacks2, 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 = model3.checkNumSamples(ins, batchSize, stepsPerEpoch, "steps_per_epoch");
let indexArray;
if (numTrainSamples != null) {
indexArray = range2(0, numTrainSamples);
}
if (verbose == null) {
verbose = 1;
}
const { callbackList, history } = configureCallbacks(callbacks2, verbose, epochs, initialEpoch, numTrainSamples, stepsPerEpoch, batchSize, doValidation, callbackMetrics);
callbackList.setModel(model3);
model3.history = history;
await callbackList.onTrainBegin();
model3.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 = model3.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 (model3.stopTraining_) {
break;
}
}
epochIndexArray1D.dispose();
}
await callbackList.onEpochEnd(epoch, epochLogs);
if (model3.stopTraining_) {
break;
}
}
await callbackList.onTrainEnd();
await model3.history.syncData();
return model3.history;
}
async function fitTensors(model3, x, y, args = {}) {
if (model3.isTraining) {
throw new Error("Cannot start training because another fit() call is ongoing.");
}
model3.isTraining = true;
let inputs;
let targets;
let originalInputs;
let originalTargets;
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 model3.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 model3.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);
originalInputs = inputs;
inputs = sliceArrays(inputs, 0, splitAt);
valY = sliceArrays(targets, splitAt, originalBatchSize);
originalTargets = targets;
targets = sliceArrays(targets, 0, splitAt);
valIns = valX.concat(valY);
} else if (args.validationSteps != null) {
doValidation = true;
}
const ins = inputs.concat(targets).concat(sampleWeights);
model3.checkTrainableWeightsConsistency();
const trainFunction = model3.makeTrainFunction();
const outLabels = model3.getDedupedMetricsNames();
let valFunction;
let callbackMetrics;
if (doValidation) {
model3.makeTestFunction();
valFunction = model3.testFunction;
callbackMetrics = outLabels.slice().concat(outLabels.map((n) => "val_" + n));
} else {
valFunction = null;
valIns = [];
callbackMetrics = outLabels.slice();
}
const callbacks2 = standardizeCallbacks(args.callbacks, args.yieldEvery);
const out = await fitLoop(model3, trainFunction, ins, outLabels, batchSize, args.epochs, args.verbose, callbacks2, valFunction, valIns, args.shuffle, callbackMetrics, args.initialEpoch, null, null);
return out;
} finally {
model3.isTraining = false;
disposeNewTensors(inputs, x);
disposeNewTensors(targets, y);
disposeNewTensors(originalInputs, x);
disposeNewTensors(originalTargets, 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 tensor3 = tensors[i];
if (tensor3.rank === 1) {
outs.push(expandDims2(tensor3, 1));
} else if (tensor3.rank === 0) {
throw new Error("Expected tensor to be at least 1D, but received a 0D tensor (scalar).");
} else {
outs.push(tensor3);
}
}
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 tensor3 = tensors[name];
if (oldTensorIds.indexOf(tensor3.id) === -1) {
tensorsToDispose.push(tensor3);
}
}
}
tensorsToDispose.forEach((t) => {
if (!t.isDisposed) {
t.dispose();
}
});
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/engine/training.js
function isDataTensor(x) {
return x instanceof Tensor;
}
function isDataArray(x) {
return Array.isArray(x);
}
function isDataDict(x) {
return !isDataTensor(x) && !isDataArray(x);
}
function standardizeInputData(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
if (names == null || names.length === 0) {
if (data != null) {
let gotUnexpectedData = false;
if (isDataArray(data) && data.length > 0) {
gotUnexpectedData = true;
} else if (isDataDict(data)) {
for (const key in data) {
if (data.hasOwnProperty(key)) {
gotUnexpectedData = true;
break;
}
}
} else {
gotUnexpectedData = true;
}
if (gotUnexpectedData) {
throw new ValueError(`Error when checking model ${exceptionPrefix} expected no data, but got ${data}`);
}
}
return [];
}
if (data == null) {
return names.map((name) => null);
}
let arrays;
if (isDataDict(data)) {
data = data;
arrays = [];
for (const name of names) {
if (data[name] == null) {
throw new ValueError(`No data provided for "${name}". Need data for each key in: ${names}`);
}
arrays.push(data[name]);
}
} else if (isDataArray(data)) {
data = data;
if (data.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): ${data}`);
}
arrays = data;
} else {
data = data;
if (names.length > 1) {
throw new ValueError(`The model ${exceptionPrefix} expects ${names.length} Tensor(s), but only received one Tensor. Found: Tensor with shape ${data.shape}`);
}
arrays = [data];
}
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 ${array2.shape}`);
}
for (let j = 0; j < shapes[i].length; ++j) {
if (j === 0 && !checkBatchAxis) {
continue;
}
const dim = array2.shape[j];
const refDim = shapes[i][j];
if (refDim != null && refDim >= 0 && dim !== refDim) {
throw new ValueError(`${exceptionPrefix} expected a batch of elements where each example has shape [${shapes[i].slice(1, shapes[i].length)}] (i.e.,tensor shape [*,${shapes[i].slice(1, shapes[i].length)}]) but the ${exceptionPrefix} received an input with ${array2.shape[0]} examples, each with shape [${array2.shape.slice(1, array2.shape.length)}] (tensor shape [${array2.shape}])`);
}
}
}
}
return arrays;
}
function checkArrayLengths(inputs, targets, weights) {
const setX = unique2(inputs.map((input2) => input2.shape[0]));
setX.sort();
const setY = unique2(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 = [
meanSquaredError,
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(data, names, shapes, checkBatchAxis = true, exceptionPrefix = "") {
let arrays;
if (Array.isArray(data)) {
if (data.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 ${data.length} Tensors(s).`);
}
arrays = data;
} 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(data.shape)}.`);
}
arrays = [data];
}
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(metrics, outputNames) {
if (metrics == null || Array.isArray(metrics) && metrics.length === 0) {
return outputNames.map((name) => []);
}
let wrappedMetrics;
if (typeof metrics === "string" || typeof metrics === "function") {
wrappedMetrics = [metrics];
} else if (Array.isArray(metrics) || typeof metrics === "object") {
wrappedMetrics = metrics;
} else {
throw new TypeError(`Type of metrics argument not understood. Expected an string,function, Array, or Object, found: ${metrics}`);
}
if (Array.isArray(wrappedMetrics)) {
return outputNames.map((name) => wrappedMetrics);
} else {
const nestedMetrics = [];
for (const name of outputNames) {
let outputMetrics = wrappedMetrics.hasOwnProperty(name) ? wrappedMetrics[name] : [];
if (!Array.isArray(outputMetrics)) {
outputMetrics = [outputMetrics];
}
nestedMetrics.push(outputMetrics);
}
return nestedMetrics;
}
}
var LAYERS_MODEL_FORMAT_NAME = "layers-model";
var LayersModel = class extends Container {
constructor(args) {
super(args);
this.isTraining = false;
}
summary(lineLength, positions, printFn = console.log) {
if (!this.built) {
throw new ValueError(`This model has never been called, thus its weights have not been created yet. So no summary can be displayed. Build the model first (e.g., by calling it on some test data).`);
}
printSummary(this, lineLength, positions, printFn);
}
compile(args) {
if (args.loss == null) {
args.loss = [];
}
this.loss = args.loss;
if (typeof args.optimizer === "string") {
this.optimizer_ = getOptimizer(args.optimizer);
this.isOptimizerOwned = true;
} else {
if (!(args.optimizer instanceof Optimizer)) {
throw new ValueError(`User-defined optimizer must be an instance of tf.Optimizer.`);
}
this.optimizer_ = args.optimizer;
this.isOptimizerOwned = false;
}
let lossFunctions = [];
if (!Array.isArray(args.loss) && typeof args.loss !== "string" && typeof args.loss !== "function") {
args.loss = args.loss;
for (const name in args.loss) {
if (this.outputNames.indexOf(name) === -1) {
throw new ValueError(`Unknown entry in loss dictionary: "${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 = (metrics) => {
const metricNamePrefix = "";
let metricName;
let accFn;
let weightedMetricFn;
for (const metric of metrics) {
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(dataset, args) {
this.makeTestFunction();
return evaluateDataset(this, dataset, 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((tensor3, i) => {
if (tensor3 == 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(range2(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 (data) => {
const lossValues = [];
const inputs = data.slice(0, this.inputs.length);
const targets = data.slice(this.inputs.length, this.inputs.length + this.outputs.length);
const sampleWeights = data.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 = computeWeightedLoss(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 variables = this.collectedTrainableWeights.map((param) => param.read());
const returnCost = true;
const totalLossValue = this.optimizer_.minimize(totalLossFunction, returnCost, variables);
return [totalLossValue].concat(metricsValues);
};
}
makeTestFunction() {
this.testFunction = (data) => {
return tidy(() => {
const valOutputs = [];
let totalLoss;
const inputs = data.slice(0, this.inputs.length);
const targets = data.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(dataset, args) {
return fitDataset(this, dataset, 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 losses = trainFunction(inputs.concat(targets));
const lossValues = [];
for (const loss of losses) {
const v = await loss.data();
lossValues.push(v[0]);
}
dispose(losses);
disposeNewTensors(standardizeOut[0], x);
disposeNewTensors(standardizeOut[1], y);
return singletonOrArray(lossValues);
}
getNamedWeights(config) {
const namedWeights = [];
const trainableOnly = config != null && config.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(optimizer) {
if (this.optimizer_ !== optimizer) {
this.optimizer_ = optimizer;
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 losses = this.loss;
for (const outputName of outputNames) {
if (typeof losses[outputName] === "string") {
lossNames[outputName] = toSnakeCase(losses[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 optimizer = 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 metrics;
if (Array.isArray(trainingConfig.metrics)) {
metrics = trainingConfig.metrics.map((metric) => toCamelCase(metric));
} else if (trainingConfig.metrics != null) {
metrics = {};
for (const key in trainingConfig.metrics) {
metrics[key] = toCamelCase(trainingConfig.metrics[key]);
}
}
this.compile({ loss, metrics, optimizer });
}
async save(handlerOrURL, config) {
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 '${handlerOrURL}'`);
}
handlerOrURL = handlers[0];
}
if (handlerOrURL.save == null) {
throw new ValueError("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");
}
const weightDataAndSpecs = await io_exports.encodeWeights(this.getNamedWeights(config));
const returnString = false;
const unusedArg = null;
const modelConfig = this.toJSON(unusedArg, returnString);
const modelArtifacts = {
modelTopology: modelConfig,
format: LAYERS_MODEL_FORMAT_NAME,
generatedBy: `TensorFlow.js tfjs-layers v${version}`,
convertedBy: null
};
const includeOptimizer = config == null ? false : config.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);
var Functional = class extends LayersModel {
};
Functional.className = "Functional";
serialization_exports.registerClass(Functional);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/models.js
init_define_BUILD_VERSION();
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 model3 = deserialize(convertPythonicToTs(modelTopology), customObjects, fastWeightInit);
const trainingConfig = artifacts.trainingConfig;
if (trainingConfig != null) {
model3.loadTrainingConfig(trainingConfig);
}
if (artifacts.userDefinedMetadata != null) {
model3.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);
model3.loadWeights(modelWeights, strict);
if (model3.optimizer != null && optimizerWeights.length > 0) {
await model3.optimizer.setWeights(optimizerWeights);
}
dispose(modelWeights);
dispose(optimizerWeights.map((w) => w.tensor));
}
return model3;
}
function decodeModelAndOptimizerWeights(buffer2, specs) {
const name2Tensor = io_exports.decodeWeights(buffer2, 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 };
}
var Sequential = class 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(dataset, args) {
if (!this.built) {
throw new RuntimeError("The model needs to be compiled before being used.");
}
return this.model.evaluateDataset(dataset, 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(optimizer) {
this.model.optimizer = optimizer;
}
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(dataset, args) {
if (!this.built) {
throw new RuntimeError("The model needs to be compiled before being used.");
}
return this.model.fitDataset(dataset, args);
}
async trainOnBatch(x, y) {
return this.model.trainOnBatch(x, y);
}
static fromConfig(cls, config, customObjects = {}, fastWeightInit = false) {
let configArray;
let extraModelConfig = {};
if (config instanceof Array) {
if (!(config[0].className != null) || config[0]["className"] === "Merge") {
throw new ValueError("Legacy serialization format not supported yet.");
}
configArray = config;
} else {
util_exports.assert(config["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 = config["layers"];
delete config["layers"];
extraModelConfig = config;
}
const model3 = new cls(extraModelConfig);
if (!(model3 instanceof Sequential)) {
throw new NotImplementedError(`Sequential.fromConfig called on non-Sequential input: ${model3}`);
}
for (const conf of configArray) {
const customObjects2 = void 0;
const layer = deserialize(conf, customObjects2, fastWeightInit);
if (fastWeightInit) {
layer.setFastWeightInitDuringBuild(true);
}
model3.add(layer);
}
return model3;
}
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports.js
function loadLayersModel(pathOrIOHandler, options) {
if (options == null) {
options = {};
}
return loadLayersModelInternal(pathOrIOHandler, options);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/activations.js
init_define_BUILD_VERSION();
var Activation = class extends serialization_exports.Serializable {
getConfig() {
return {};
}
};
var Elu2 = class extends Activation {
apply(x, alpha = 1) {
return elu2(x, alpha);
}
};
Elu2.className = "elu";
serialization_exports.registerClass(Elu2);
var Selu2 = class extends Activation {
apply(x) {
return selu(x);
}
};
Selu2.className = "selu";
serialization_exports.registerClass(Selu2);
var Relu2 = class extends Activation {
apply(x) {
return relu(x);
}
};
Relu2.className = "relu";
serialization_exports.registerClass(Relu2);
var Relu62 = class extends Activation {
apply(x) {
return tidy(() => minimum(6, relu(x)));
}
};
Relu62.className = "relu6";
serialization_exports.registerClass(Relu62);
var Linear = class extends Activation {
apply(x) {
return x;
}
};
Linear.className = "linear";
serialization_exports.registerClass(Linear);
var Sigmoid2 = class extends Activation {
apply(x) {
return sigmoid(x);
}
};
Sigmoid2.className = "sigmoid";
serialization_exports.registerClass(Sigmoid2);
var HardSigmoid = class extends Activation {
apply(x) {
return hardSigmoid(x);
}
};
HardSigmoid.className = "hardSigmoid";
serialization_exports.registerClass(HardSigmoid);
var Softplus2 = class extends Activation {
apply(x) {
return softplus(x);
}
};
Softplus2.className = "softplus";
serialization_exports.registerClass(Softplus2);
var Softsign = class extends Activation {
apply(x) {
return softsign(x);
}
};
Softsign.className = "softsign";
serialization_exports.registerClass(Softsign);
var Tanh2 = class extends Activation {
apply(x) {
return tanh2(x);
}
};
Tanh2.className = "tanh";
serialization_exports.registerClass(Tanh2);
var Softmax2 = class extends Activation {
apply(x, axis = -1) {
return softmax(x, axis);
}
};
Softmax2.className = "softmax";
serialization_exports.registerClass(Softmax2);
var LogSoftmax2 = class extends Activation {
apply(x, axis = -1) {
return logSoftmax(x, axis);
}
};
LogSoftmax2.className = "logSoftmax";
serialization_exports.registerClass(LogSoftmax2);
var Swish = class extends Activation {
apply(x, alpha = 1) {
return tidy(() => mul(sigmoid(mul(x, alpha)), x));
}
};
Swish.className = "swish";
serialization_exports.registerClass(Swish);
var Mish = class extends Activation {
apply(x) {
return tidy(() => mul(x, tanh2(softplus(x))));
}
};
Mish.className = "mish";
serialization_exports.registerClass(Mish);
function serializeActivation(activation) {
return activation.getClassName();
}
function deserializeActivation(config, customObjects = {}) {
return deserializeKerasObject(config, serialization_exports.SerializationMap.getMap().classNameMap, customObjects, "activation");
}
function getActivation(identifier) {
if (identifier == null) {
const config = {};
config["className"] = "linear";
config["config"] = {};
return deserializeActivation(config);
}
if (typeof identifier === "string") {
const config = {};
config["className"] = identifier;
config["config"] = {};
return deserializeActivation(config);
} else if (identifier instanceof Activation) {
return identifier;
} else {
return deserializeActivation(identifier);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/regularizers.js
init_define_BUILD_VERSION();
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}`);
}
}
var Regularizer = class extends serialization_exports.Serializable {
};
var L1L2 = class 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, square2(x))));
}
return reshape(regularization, []);
});
}
getConfig() {
return { "l1": this.l1, "l2": this.l2 };
}
static fromConfig(cls, config) {
return new cls({ l1: config["l1"], l2: config["l2"] });
}
};
L1L2.className = "L1L2";
serialization_exports.registerClass(L1L2);
var REGULARIZER_IDENTIFIER_REGISTRY_SYMBOL_MAP = {
"l1l2": "L1L2"
};
function serializeRegularizer(constraint) {
return serializeKerasObject(constraint);
}
function deserializeRegularizer(config, customObjects = {}) {
return deserializeKerasObject(config, 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 config = { className, config: {} };
return deserializeRegularizer(config);
} else if (identifier instanceof Regularizer) {
return identifier;
} else {
return deserializeRegularizer(identifier);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/advanced_activations.js
var ReLU = class 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 config = { maxValue: this.maxValue };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
ReLU.className = "ReLU";
serialization_exports.registerClass(ReLU);
var LeakyReLU = class 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 config = { alpha: this.alpha };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
LeakyReLU.className = "LeakyReLU";
serialization_exports.registerClass(LeakyReLU);
var PReLU = class 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 ${args.sharedAxes}`);
}
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
const paramShape = inputShape.slice(1);
if (this.sharedAxes != null) {
for (const i of this.sharedAxes) {
paramShape[i - 1] = 1;
}
}
this.alpha = this.addWeight("alpha", paramShape, "float32", this.alphaInitializer, this.alphaRegularizer, true, this.alphaConstraint);
const axes = {};
if (this.sharedAxes != null) {
for (let i = 1; i < inputShape.length; ++i) {
axes[i] = inputShape[i];
}
}
this.inputSpec = [new InputSpec({
ndim: inputShape.length,
axes
})];
this.built = true;
}
call(inputs, kwargs) {
inputs = getExactlyOneTensor(inputs);
return prelu(inputs, this.alpha.read());
}
getConfig() {
const config = {
alphaInitializer: serializeInitializer(this.alphaInitializer),
alphaRegularizer: serializeRegularizer(this.alphaRegularizer),
alphaConstraint: serializeConstraint(this.alphaConstraint),
sharedAxes: this.sharedAxes
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
PReLU.className = "PReLU";
serialization_exports.registerClass(PReLU);
var ELU = class extends Layer {
constructor(args) {
super(args == null ? {} : args);
this.DEFAULT_ALPHA = 1;
if (args == null) {
args = {};
}
if (args.alpha != null && args.alpha !== this.DEFAULT_ALPHA) {
throw new NotImplementedError(`Non-default alpha value (${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 config = { alpha: this.alpha };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
ELU.className = "ELU";
serialization_exports.registerClass(ELU);
var ThresholdedReLU = class 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 mul(x, cast(greater(x, this.theta), "float32"));
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const config = { theta: this.theta };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
ThresholdedReLU.className = "ThresholdedReLU";
serialization_exports.registerClass(ThresholdedReLU);
var Softmax3 = class 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 config = { axis: this.axis };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Softmax3.className = "Softmax";
serialization_exports.registerClass(Softmax3);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/utils/conv_utils.js
init_define_BUILD_VERSION();
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, padding, stride, dilation = 1) {
if (inputLength == null) {
return inputLength;
}
const dilatedFilterSize = filterSize + (filterSize - 1) * (dilation - 1);
let outputLength;
if (padding === "same") {
outputLength = inputLength;
} else {
outputLength = inputLength - dilatedFilterSize + 1;
}
return Math.floor((outputLength + stride - 1) / stride);
}
function deconvLength(dimSize, strideSize, kernelSize, padding) {
if (dimSize == null) {
return null;
}
if (padding === "valid") {
dimSize = dimSize * strideSize + max2([kernelSize - strideSize, 0]);
} else if (padding === "same") {
dimSize = dimSize * strideSize;
} else {
throw new ValueError(`Unsupport padding mode: ${padding}.`);
}
return dimSize;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional.js
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, padding = "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 (padding === "causal") {
throw new NotImplementedError("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");
}
let y = conv1d(x, kernel, strides, padding === "same" ? "same" : "valid", "NWC", dilationRate);
if (bias != null) {
y = biasAdd(y, bias);
}
return y;
});
}
function conv2dWithBiasActivation(x, kernel, bias, strides = [1, 1], padding = "valid", dataFormat, dilationRate, activation = 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 (padding === "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: padding === "same" ? "same" : "valid",
dilations: dilationRate,
dataFormat: "NHWC",
bias,
activation
});
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 3, 1, 2]);
}
return y;
});
}
function conv3dWithBias(x, kernel, bias, strides = [1, 1, 1], padding = "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 ${x.rank}.`);
}
let y = preprocessConv3DInput(x, dataFormat);
if (padding === "causal") {
throw new NotImplementedError("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");
}
y = conv3d(y, kernel, strides, padding === "same" ? "same" : "valid", "NDHWC", dilationRate);
if (bias != null) {
y = biasAdd(y, bias);
}
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 4, 1, 2, 3]);
}
return y;
});
}
var BaseConv = class extends Layer {
constructor(rank, args) {
super(args);
this.bias = null;
this.DEFAULT_KERNEL_INITIALIZER = "glorotNormal";
this.DEFAULT_BIAS_INITIALIZER = "zeros";
BaseConv.verifyArgs(args);
this.rank = rank;
assertPositiveInteger(this.rank, "rank");
if (this.rank !== 1 && this.rank !== 2 && this.rank !== 3) {
throw new NotImplementedError(`Convolution layer for rank other than 1, 2, or 3 (${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 config = {
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(config, baseConfig);
return config;
}
};
var Conv = class 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 config = {
filters: this.filters,
kernelInitializer: serializeInitializer(this.kernelInitializer),
kernelRegularizer: serializeRegularizer(this.kernelRegularizer),
kernelConstraint: serializeConstraint(this.kernelConstraint)
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
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)}`);
}
}
};
var Conv2D2 = class extends Conv {
constructor(args) {
super(2, args);
Conv2D2.verifyArgs(args);
}
getConfig() {
const config = super.getConfig();
delete config["rank"];
return config;
}
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);
var Conv3D2 = class extends Conv {
constructor(args) {
super(3, args);
Conv3D2.verifyArgs(args);
}
getConfig() {
const config = super.getConfig();
delete config["rank"];
return config;
}
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);
var Conv2DTranspose = class 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 config = super.getConfig();
delete config["dilationRate"];
return config;
}
};
Conv2DTranspose.className = "Conv2DTranspose";
serialization_exports.registerClass(Conv2DTranspose);
var Conv3DTranspose = class extends Conv3D2 {
constructor(args) {
super(args);
this.inputSpec = [new InputSpec({ ndim: 5 })];
if (this.padding !== "same" && this.padding !== "valid") {
throw new ValueError(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`);
}
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape.length !== 5) {
throw new ValueError("Input should have rank 5; Received input shape: " + JSON.stringify(inputShape));
}
const channelAxis = this.dataFormat === "channelsFirst" ? 1 : inputShape.length - 1;
if (inputShape[channelAxis] == null) {
throw new ValueError("The channel dimension of the inputs should be defined. Found `None`.");
}
const inputDim = inputShape[channelAxis];
const kernelShape = this.kernelSize.concat([this.filters, inputDim]);
this.kernel = this.addWeight("kernel", kernelShape, "float32", this.kernelInitializer, this.kernelRegularizer, true, this.kernelConstraint);
if (this.useBias) {
this.bias = this.addWeight("bias", [this.filters], "float32", this.biasInitializer, this.biasRegularizer, true, this.biasConstraint);
}
this.inputSpec = [new InputSpec({ ndim: 5, axes: { [channelAxis]: inputDim } })];
this.built = true;
}
call(inputs, kwargs) {
return tidy(() => {
let input2 = getExactlyOneTensor(inputs);
if (input2.shape.length !== 5) {
throw new ValueError(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${input2.shape.length}`);
}
const inputShape = input2.shape;
const batchSize = inputShape[0];
let hAxis;
let wAxis;
let dAxis;
if (this.dataFormat === "channelsFirst") {
dAxis = 2;
hAxis = 3;
wAxis = 4;
} else {
dAxis = 1;
hAxis = 2;
wAxis = 3;
}
const depth = inputShape[dAxis];
const height = inputShape[hAxis];
const width = inputShape[wAxis];
const kernelD = this.kernelSize[0];
const kernelH = this.kernelSize[1];
const kernelW = this.kernelSize[2];
const strideD = this.strides[0];
const strideH = this.strides[1];
const strideW = this.strides[2];
const outDepth = deconvLength(depth, strideD, kernelD, this.padding);
const outHeight = deconvLength(height, strideH, kernelH, this.padding);
const outWidth = deconvLength(width, strideW, kernelW, this.padding);
const outputShape = [batchSize, outDepth, outHeight, outWidth, this.filters];
if (this.dataFormat !== "channelsLast") {
input2 = transpose(input2, [0, 2, 3, 4, 1]);
}
let outputs = conv3dTranspose(input2, this.kernel.read(), outputShape, this.strides, this.padding);
if (this.dataFormat !== "channelsLast") {
outputs = transpose(outputs, [0, 4, 1, 2, 3]);
}
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 depthAxis;
let heightAxis;
let widthAxis;
if (this.dataFormat === "channelsFirst") {
channelAxis = 1;
depthAxis = 2;
heightAxis = 3;
widthAxis = 4;
} else {
channelAxis = 4;
depthAxis = 1;
heightAxis = 2;
widthAxis = 3;
}
const kernelD = this.kernelSize[0];
const kernelH = this.kernelSize[1];
const kernelW = this.kernelSize[2];
const strideD = this.strides[0];
const strideH = this.strides[1];
const strideW = this.strides[2];
outputShape[channelAxis] = this.filters;
outputShape[depthAxis] = deconvLength(outputShape[depthAxis], strideD, kernelD, this.padding);
outputShape[heightAxis] = deconvLength(outputShape[heightAxis], strideH, kernelH, this.padding);
outputShape[widthAxis] = deconvLength(outputShape[widthAxis], strideW, kernelW, this.padding);
return outputShape;
}
getConfig() {
const config = super.getConfig();
delete config["dilationRate"];
return config;
}
};
Conv3DTranspose.className = "Conv3DTranspose";
serialization_exports.registerClass(Conv3DTranspose);
var SeparableConv = class extends Conv {
constructor(rank, config) {
super(rank, config);
this.DEFAULT_DEPTHWISE_INITIALIZER = "glorotUniform";
this.DEFAULT_POINTWISE_INITIALIZER = "glorotUniform";
this.depthwiseKernel = null;
this.pointwiseKernel = null;
if (config.filters == null) {
throw new ValueError("The `filters` configuration field is required by SeparableConv, but is unspecified.");
}
if (config.kernelInitializer != null || config.kernelRegularizer != null || config.kernelConstraint != null) {
throw new ValueError("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");
}
if (config.padding != null && config.padding !== "same" && config.padding !== "valid") {
throw new ValueError(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(config.padding)}`);
}
this.depthMultiplier = config.depthMultiplier == null ? 1 : config.depthMultiplier;
this.depthwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_DEPTHWISE_INITIALIZER);
this.depthwiseRegularizer = getRegularizer(config.depthwiseRegularizer);
this.depthwiseConstraint = getConstraint(config.depthwiseConstraint);
this.pointwiseInitializer = getInitializer(config.depthwiseInitializer || this.DEFAULT_POINTWISE_INITIALIZER);
this.pointwiseRegularizer = getRegularizer(config.pointwiseRegularizer);
this.pointwiseConstraint = getConstraint(config.pointwiseConstraint);
}
build(inputShape) {
inputShape = getExactlyOneShape(inputShape);
if (inputShape.length < this.rank + 2) {
throw new ValueError(`Inputs to SeparableConv${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 config = super.getConfig();
delete config["rank"];
delete config["kernelInitializer"];
delete config["kernelRegularizer"];
delete config["kernelConstraint"];
config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
config["pointwiseInitializer"] = serializeInitializer(this.pointwiseInitializer);
config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
config["pointwiseRegularizer"] = serializeRegularizer(this.pointwiseRegularizer);
config["depthwiseConstraint"] = serializeConstraint(this.depthwiseConstraint);
config["pointwiseConstraint"] = serializeConstraint(this.pointwiseConstraint);
return config;
}
};
SeparableConv.className = "SeparableConv";
var SeparableConv2D = class extends SeparableConv {
constructor(args) {
super(2, args);
}
};
SeparableConv2D.className = "SeparableConv2D";
serialization_exports.registerClass(SeparableConv2D);
var Conv1D = class extends Conv {
constructor(args) {
super(1, args);
Conv1D.verifyArgs(args);
this.inputSpec = [{ ndim: 3 }];
}
getConfig() {
const config = super.getConfig();
delete config["rank"];
delete config["dataFormat"];
return config;
}
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);
var Cropping2D = class 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 config = { cropping: this.cropping, dataFormat: this.dataFormat };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Cropping2D.className = "Cropping2D";
serialization_exports.registerClass(Cropping2D);
var UpSampling2D = class 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;
checkDataFormat(this.dataFormat);
this.interpolation = args.interpolation == null ? "nearest" : args.interpolation;
checkInterpolationFormat(this.interpolation);
}
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 = this.interpolation === "nearest" ? image2.resizeNearestNeighbor(input2, [height, width]) : image2.resizeBilinear(input2, [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 this.interpolation === "nearest" ? image2.resizeNearestNeighbor(input2, [height, width]) : image2.resizeBilinear(input2, [height, width]);
}
});
}
getConfig() {
const config = {
size: this.size,
dataFormat: this.dataFormat,
interpolation: this.interpolation
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
UpSampling2D.className = "UpSampling2D";
serialization_exports.registerClass(UpSampling2D);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_depthwise.js
init_define_BUILD_VERSION();
function depthwiseConv2d3(x, depthwiseKernel, strides = [1, 1], padding = "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, padding === "same" ? "same" : "valid", "NHWC", dilationRate);
if (dataFormat === "channelsFirst") {
y = transpose(y, [0, 3, 1, 2]);
}
return y;
});
}
var DepthwiseConv2D = class 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 config = super.getConfig();
config["depthMultiplier"] = this.depthMultiplier;
config["depthwiseInitializer"] = serializeInitializer(this.depthwiseInitializer);
config["depthwiseRegularizer"] = serializeRegularizer(this.depthwiseRegularizer);
config["depthwiseConstraint"] = serializeConstraint(this.depthwiseRegularizer);
return config;
}
};
DepthwiseConv2D.className = "DepthwiseConv2D";
serialization_exports.registerClass(DepthwiseConv2D);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/recurrent.js
init_define_BUILD_VERSION();
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(range2(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 = cast(cast(mask, "bool"), "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 = sub(onesLike(stepMask), stepMask);
const output = add2(mul(stepOutputs[0], stepMask), mul(states[0], negStepMask));
const newStates = states.map((state, i) => {
return add2(mul(stepOutputs[1][i], stepMask), mul(state, 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];
});
}
var RNN = class 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 range2(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, training = 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 (training === 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((state) => keep(state.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 state of initialState) {
this.stateSpec.push(new InputSpec({ shape: state.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 training = 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 };
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, training);
}
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 ? tile2(initialState, [1, dim]) : initialState);
} else {
return this.cell.stateSize > 1 ? [tile2(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 config = {
returnSequences: this.returnSequences,
returnState: this.returnState,
goBackwards: this.goBackwards,
stateful: this.stateful,
unroll: this.unroll
};
if (this.numConstants != null) {
config["numConstants"] = this.numConstants;
}
const cellConfig = this.cell.getConfig();
if (this.getClassName() === RNN.className) {
config["cell"] = {
"className": this.cell.getClassName(),
"config": cellConfig
};
}
return Object.assign({}, cellConfig, baseConfig, config);
}
static fromConfig(cls, config, customObjects = {}) {
const cellConfig = config["cell"];
const cell = deserialize(cellConfig, customObjects);
return new cls(Object.assign(config, { cell }));
}
};
RNN.className = "RNN";
serialization_exports.registerClass(RNN);
var RNNCell = class extends Layer {
};
var SimpleRNNCell = class 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 = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
this.recurrentDropout = min2([
1,
max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
]);
this.dropoutFunc = args.dropoutFunc;
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 training = 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,
dropoutFunc: this.dropoutFunc
});
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(prevOutput),
rate: this.recurrentDropout,
training,
dropoutFunc: this.dropoutFunc
});
}
let h;
const dpMask = this.dropoutMask;
const recDpMask = this.recurrentDropoutMask;
if (dpMask != null) {
h = dot2(mul(inputs, dpMask), this.kernel.read());
} else {
h = dot2(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, dot2(prevOutput, this.recurrentKernel.read()));
if (this.activation != null) {
output = this.activation.apply(output);
}
return [output, output];
});
}
getConfig() {
const baseConfig = super.getConfig();
const config = {
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, config);
}
};
SimpleRNNCell.className = "SimpleRNNCell";
serialization_exports.registerClass(SimpleRNNCell);
var SimpleRNN = class 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 training = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, { mask, training, initialState });
});
}
static fromConfig(cls, config) {
return new cls(config);
}
};
SimpleRNN.className = "SimpleRNN";
serialization_exports.registerClass(SimpleRNN);
var GRUCell = class 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 = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
this.recurrentDropout = min2([
1,
max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
]);
this.dropoutFunc = args.dropoutFunc;
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 training = 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,
count: 3,
dropoutFunc: this.dropoutFunc
});
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(hTMinus1),
rate: this.recurrentDropout,
training,
count: 3,
dropoutFunc: this.dropoutFunc
});
}
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 = dot2(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 = dot2(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 = dot2(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 config = {
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, config);
}
};
GRUCell.className = "GRUCell";
serialization_exports.registerClass(GRUCell);
var GRU = class 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 training = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, { mask, training, initialState });
});
}
static fromConfig(cls, config) {
if (config["implmentation"] === 0) {
config["implementation"] = 1;
}
return new cls(config);
}
};
GRU.className = "GRU";
serialization_exports.registerClass(GRU);
var LSTMCell = class 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 = min2([1, max2([0, args.dropout == null ? 0 : args.dropout])]);
this.recurrentDropout = min2([
1,
max2([0, args.recurrentDropout == null ? 0 : args.recurrentDropout])
]);
this.dropoutFunc = args.dropoutFunc;
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 training = 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,
count: 4,
dropoutFunc: this.dropoutFunc
});
}
if (0 < this.recurrentDropout && this.recurrentDropout < 1 && this.recurrentDropoutMask == null) {
this.recurrentDropoutMask = generateDropoutMask({
ones: () => onesLike(hTMinus1),
rate: this.recurrentDropout,
training,
count: 4,
dropoutFunc: this.dropoutFunc
});
}
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 = dot2(inputs, this.kernel.read());
if (0 < this.recurrentDropout && this.recurrentDropout < 1) {
hTMinus1 = mul(hTMinus1, recDpMask[0]);
}
z = add2(z, dot2(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 config = {
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, config);
}
};
LSTMCell.className = "LSTMCell";
serialization_exports.registerClass(LSTMCell);
var LSTM = class 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 training = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, { mask, training, initialState });
});
}
static fromConfig(cls, config) {
if (config["implmentation"] === 0) {
config["implementation"] = 1;
}
return new cls(config);
}
};
LSTM.className = "LSTM";
serialization_exports.registerClass(LSTM);
var StackedRNNCells = class 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 config = { "cells": cellConfigs };
return Object.assign({}, baseConfig, config);
}
static fromConfig(cls, config, customObjects = {}) {
const cells = [];
for (const cellConfig of config["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: ones3, rate, training = false, count: count2 = 1, dropoutFunc } = args;
const droppedInputs = () => dropoutFunc != null ? dropoutFunc(ones3(), rate) : dropout2(ones3(), rate);
const createMask = () => inTrainPhase(droppedInputs, ones3, training);
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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/convolutional_recurrent.js
var __rest = function(s, e) {
var t = {};
for (var p2 in s)
if (Object.prototype.hasOwnProperty.call(s, p2) && e.indexOf(p2) < 0)
t[p2] = s[p2];
if (s != null && typeof Object.getOwnPropertySymbols === "function")
for (var i = 0, p2 = Object.getOwnPropertySymbols(s); i < p2.length; i++) {
if (e.indexOf(p2[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p2[i]))
t[p2[i]] = s[p2[i]];
}
return t;
};
var ConvRNN2D = class 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 training = kwargs == null ? null : kwargs["training"];
const initialState = kwargs == null ? null : kwargs["initialState"];
return super.call(inputs, { mask, training, 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, training = 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 (training) {
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((state) => keep(state.clone()));
});
}
computeSingleOutputShape(inputShape) {
const { dataFormat, filters, kernelSize, padding, 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], padding, strides[0], dilationRate[0]);
const wOut = convOutputLength(w, kernelSize[1], padding, strides[1], dilationRate[1]);
const outShape = [
...inputShape.slice(0, 2),
...isChannelsFirst ? [filters, hOut, wOut] : [hOut, wOut, filters]
];
return outShape;
}
};
ConvRNN2D.className = "ConvRNN2D";
var ConvLSTM2DCell = class extends LSTMCell {
constructor(args) {
const { filters, kernelSize, strides, padding, 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 = padding || "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 init = this.biasInitializer;
const filters = this.filters;
biasInitializer = new (_a = class CustomInit extends Initializer {
apply(shape, dtype) {
const biasI = init.apply([filters]);
const biasF = ones2([filters]);
const biasCAndO = init.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 training = 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,
count: numOfKernels,
dropoutFunc: this.dropoutFunc
});
}
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,
count: numOfKernels,
dropoutFunc: this.dropoutFunc
});
}
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 config = {
filters: this.filters,
kernelSize: this.kernelSize,
padding: this.padding,
dataFormat: this.dataFormat,
dilationRate: this.dilationRate,
strides: this.strides
};
return Object.assign({}, baseConfig, config);
}
inputConv(x, w, b, padding) {
const out = conv2d(x, w, this.strides, padding || "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);
var ConvLSTM2D = class extends ConvRNN2D {
constructor(args) {
const cell = new ConvLSTM2DCell(args);
super(Object.assign({}, args, { cell }));
}
static fromConfig(cls, config) {
return new cls(config);
}
};
ConvLSTM2D.className = "ConvLSTM2D";
serialization_exports.registerClass(ConvLSTM2D);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/core.js
init_define_BUILD_VERSION();
var Dropout = class 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 training = kwargs["training"] == null ? false : kwargs["training"];
const noiseShape = this.getNoiseShape(input2);
const output = inTrainPhase(() => dropout2(input2, this.rate, noiseShape, this.seed), () => input2, training);
return output;
}
return inputs;
});
}
getConfig() {
const config = {
rate: this.rate,
noiseShape: this.noiseShape,
seed: this.seed
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
dispose() {
return super.dispose();
}
};
Dropout.className = "Dropout";
serialization_exports.registerClass(Dropout);
var SpatialDropout1D = class 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);
var Dense = class 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 = dot2(input2, this.kernel.read(), fusedActivationName, this.bias ? this.bias.read() : null);
} else {
output = dot2(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 config = {
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(config, baseConfig);
return config;
}
};
Dense.className = "Dense";
serialization_exports.registerClass(Dense);
var Flatten = class 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 = transpose(input2, permutation);
}
return batchFlatten(input2);
});
}
getConfig() {
const config = {};
if (this.dataFormat != null) {
config["dataFormat"] = this.dataFormat;
}
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Flatten.className = "Flatten";
serialization_exports.registerClass(Flatten);
var Activation2 = class 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 config = { activation: serializeActivation(this.activation) };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Activation2.className = "Activation";
serialization_exports.registerClass(Activation2);
var RepeatVector = class 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 config = {
n: this.n
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
RepeatVector.className = "RepeatVector";
serialization_exports.registerClass(RepeatVector);
var Reshape2 = class 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 reshape(input2, outputShape);
});
}
getConfig() {
const config = {
targetShape: this.targetShape
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Reshape2.className = "Reshape";
serialization_exports.registerClass(Reshape2);
var Permute = class 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 = range2(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 config = {
dims: this.dims
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Permute.className = "Permute";
serialization_exports.registerClass(Permute);
var Masking = class 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 config = { maskValue: this.maskValue };
Object.assign(config, baseConfig);
return config;
}
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 = mul(input2, cast(booleanMask, input2.dtype));
return output;
});
}
};
Masking.className = "Masking";
serialization_exports.registerClass(Masking);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/embeddings.js
init_define_BUILD_VERSION();
var Embedding = class 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 = cast2(input2, "int32");
}
const output = gather2(this.embeddings.read(), reshape(input2, [input2.size]));
return reshape(output, getExactlyOneShape(this.computeOutputShape(input2.shape)));
});
}
getConfig() {
const config = {
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(config, baseConfig);
return config;
}
};
Embedding.className = "Embedding";
serialization_exports.registerClass(Embedding);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/merge.js
init_define_BUILD_VERSION();
var Merge = class 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 = unique2(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 && unique2(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 = max2(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 = reshape(x, [batchSize].concat(arrayProd(xShape.slice(1))));
xTransposed = transpose(xTransposed, [1, 0]);
xTransposed = reshape(xTransposed, newShape);
reshapedInputs.push(xTransposed);
transposed = true;
} else if (xNDim > 1) {
const dims = range2(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 = reshape(transpose(reshape(y, [-1, batchSize]), [1, 0]), newShape);
} else if (yNDim > 1) {
const dims = [yNDim - 1].concat(range2(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 = unique2(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;
});
}
};
var Add2 = class 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);
var Multiply2 = class 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);
var Average = class 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);
var Maximum2 = class 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);
var Minimum2 = class 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);
var Concatenate = class 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(cast(onesLike(inputs[i]), "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 config = {
"axis": this.axis
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
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 = reshape(y, y.shape.concat(diffShape));
} else if (yNDim > xNDim) {
diff = yNDim - xNDim;
const diffShape = [];
for (let i = 0; i < diff; ++i) {
diffShape.push(1);
}
x = reshape(x, x.shape.concat(diffShape));
} else {
diff = 0;
}
let out;
if (x.shape.length === 2 && y.shape.length === 2) {
if (axesArray[0] === axesArray[1]) {
out = sum2(mul(x, y), axesArray[0]);
} else {
out = sum2(mul(transpose(x, [1, 0]), y), axesArray[1]);
}
} else {
const adjX = axesArray[0] !== x.shape.length - 1;
const adjY = axesArray[1] === y.shape.length - 1;
out = matMul(x, 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 = squeeze(out, squeezeAxes);
}
if (out.shape.length === 1) {
out = expandDims(out, 1);
}
return out;
});
}
var Dot = class 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 config = {
"axes": this.axes,
"normalize": this.normalize
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
Dot.className = "Dot";
serialization_exports.registerClass(Dot);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/noise.js
init_define_BUILD_VERSION();
var GaussianNoise = class extends Layer {
constructor(args) {
super(args);
this.supportsMasking = true;
this.stddev = args.stddev;
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const baseConfig = super.getConfig();
const config = { stddev: this.stddev };
Object.assign(config, baseConfig);
return config;
}
call(inputs, kwargs) {
return tidy(() => {
this.invokeCallHook(inputs, kwargs);
const input2 = getExactlyOneTensor(inputs);
const noised = () => add2(randomNormal2(input2.shape, 0, this.stddev), input2);
const output = inTrainPhase(noised, () => input2, kwargs["training"] || false);
return output;
});
}
};
GaussianNoise.className = "GaussianNoise";
serialization_exports.registerClass(GaussianNoise);
var GaussianDropout = class extends Layer {
constructor(args) {
super(args);
this.supportsMasking = true;
this.rate = args.rate;
}
computeOutputShape(inputShape) {
return inputShape;
}
getConfig() {
const baseConfig = super.getConfig();
const config = { rate: this.rate };
Object.assign(config, baseConfig);
return config;
}
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 mul(input2, randomNormal2(input2.shape, 1, stddev));
};
return inTrainPhase(noised, () => input2, kwargs["training"] || false);
}
return input2;
});
}
};
GaussianDropout.className = "GaussianDropout";
serialization_exports.registerClass(GaussianDropout);
var AlphaDropout = class 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 config = { rate: this.rate };
Object.assign(config, baseConfig);
return config;
}
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 scale2 = 1.0507009873554805;
const alphaP = -alpha * scale2;
let keptIdx = greaterEqual(randomUniform(noiseShape), this.rate);
keptIdx = cast2(keptIdx, "float32");
const a = ((1 - this.rate) * (1 + this.rate * alphaP ** 2)) ** -0.5;
const b = -a * alphaP * this.rate;
const x = add2(mul(input2, keptIdx), mul(add2(keptIdx, -1), alphaP));
return add2(mul(x, a), b);
};
return inTrainPhase(droppedInputs, () => getExactlyOneTensor(inputs), kwargs["training"] || false);
}
return inputs;
});
}
};
AlphaDropout.className = "AlphaDropout";
serialization_exports.registerClass(AlphaDropout);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/normalization.js
init_define_BUILD_VERSION();
function batchNormalization(x, mean3, variance, beta, gamma, epsilon3 = 1e-3) {
let out;
if (x.rank === 2) {
out = batchNorm2d(x, mean3, variance, beta, gamma, epsilon3);
} else if (x.rank === 3) {
out = batchNorm3d(x, mean3, variance, beta, gamma, epsilon3);
} else if (x.rank === 4) {
out = batchNorm4d(x, mean3, variance, beta, gamma, epsilon3);
} else {
throw new NotImplementedError(`batchNormalization is not implemented for array of rank ${x.rank} yet`);
}
return out;
}
function regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
return tidy(() => {
const meanAndVariance = moments(x, reductionAxes);
const mean3 = meanAndVariance.mean;
const variance = meanAndVariance.variance;
const normed = batchNormalization(x, mean3, variance, beta, gamma, epsilon3);
return [normed, mean3, variance];
});
}
function broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
return tidy(() => {
const meanAndVariance = moments(x, reductionAxes);
const mean3 = meanAndVariance.mean;
const variance = meanAndVariance.variance;
const targetShape = [];
for (const axis of range2(0, x.rank)) {
if (reductionAxes.indexOf(axis) !== -1) {
targetShape.push(1);
} else {
targetShape.push(x.shape[axis]);
}
}
const broadcastMean = reshape(mean3, targetShape);
const broadcastVariance = reshape(variance, targetShape);
const broadcastGamma = gamma == null ? null : reshape(gamma, targetShape);
const broadcastBeta = beta == null ? null : reshape(beta, targetShape);
const normed = batchNormalization(x, broadcastMean, broadcastVariance, broadcastBeta, broadcastGamma, epsilon3);
return [normed, mean3, variance];
});
}
function normalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3 = 1e-3) {
if (util_exports.arraysEqual(reductionAxes.slice().sort(), range2(0, x.rank - 1))) {
return regularNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);
} else {
return broadcastNormalizeBatchInTraining(x, gamma, beta, reductionAxes, epsilon3);
}
}
var BatchNormalization = class 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 training = kwargs["training"] == null ? false : kwargs["training"];
const input2 = getExactlyOneTensor(inputs);
const inputShape = input2.shape;
const ndim = inputShape.length;
const reductionAxes = range2(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, range2(0, ndim).slice(0, ndim - 1));
const normalizeInference = () => {
if (needsBroadcasting) {
const broadcastMovingMean = reshape(this.movingMean.read(), broadcastShape);
const broadcastMovingVariance = reshape(this.movingVariance.read(), broadcastShape);
const broadcastBeta = this.center ? reshape(this.beta.read(), broadcastShape) : null;
const broadcastGamma = this.scale ? reshape(this.gamma.read(), 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 (!training) {
return normalizeInference();
}
const [normedTraining, mean3, variance] = normalizeBatchInTraining(input2, this.gamma.read(), this.beta.read(), reductionAxes, this.epsilon);
const doMovingAverage = (variable2, value, momentum) => {
tidy(() => {
const decay = 1 - momentum;
const origValue = variable2.read();
const updateDelta = mul(sub(origValue, value), decay);
variable2.write(sub(origValue, updateDelta));
});
};
const updateMovingMeanAndVariance = () => {
doMovingAverage(this.movingMean, mean3, this.momentum);
doMovingAverage(this.movingVariance, variance, this.momentum);
};
updateMovingMeanAndVariance();
return normedTraining;
});
}
getConfig() {
const config = {
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(config, baseConfig);
return config;
}
};
BatchNormalization.className = "BatchNormalization";
serialization_exports.registerClass(BatchNormalization);
var LayerNormalization = class 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 !== unique2(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: mean3, 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) {
return reshape(v, broadcastShape);
} else {
return v;
}
};
let scale2 = this.scale ? broadcast(this.gamma.read()) : null;
let offset = this.center ? broadcast(this.beta.read()) : null;
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]);
}
}
mean3 = tile(mean3, momentsTiling);
variance = tile(variance, momentsTiling);
if (scale2 != null) {
scale2 = tile(scale2, scaleOffsetTiling);
}
if (offset != null) {
offset = tile(offset, scaleOffsetTiling);
}
return batchNormalization(input2, mean3, variance, offset, scale2, this.epsilon);
});
}
getConfig() {
const config = {
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(config, baseConfig);
return config;
}
};
LayerNormalization.className = "LayerNormalization";
serialization_exports.registerClass(LayerNormalization);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/padding.js
init_define_BUILD_VERSION();
function spatial2dPadding(x, padding, 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 (padding == null) {
padding = [[1, 1], [1, 1]];
}
if (padding.length !== 2 || padding[0].length !== 2 || padding[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: ${dataFormat}. Supported data formats are 'channelsLast' and 'channelsFirst.`);
}
let pattern;
if (dataFormat === "channelsFirst") {
pattern = [[0, 0], [0, 0], padding[0], padding[1]];
} else {
pattern = [[0, 0], padding[0], padding[1], [0, 0]];
}
return pad(x, pattern);
});
}
var ZeroPadding2D = class extends Layer {
constructor(args) {
if (args == null) {
args = {};
}
super(args);
this.dataFormat = args.dataFormat == null ? imageDataFormat() : args.dataFormat;
if (args.padding == null) {
this.padding = [[1, 1], [1, 1]];
} else if (typeof args.padding === "number") {
this.padding = [[args.padding, args.padding], [args.padding, args.padding]];
} else {
args.padding = args.padding;
if (args.padding.length !== 2) {
throw new ValueError(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 config = {
padding: this.padding,
dataFormat: this.dataFormat
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
ZeroPadding2D.className = "ZeroPadding2D";
serialization_exports.registerClass(ZeroPadding2D);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/pooling.js
init_define_BUILD_VERSION();
function pool2d(x, poolSize, strides, padding, dataFormat, poolMode) {
return tidy(() => {
checkDataFormat(dataFormat);
checkPoolMode(poolMode);
checkPaddingMode(padding);
if (strides == null) {
strides = [1, 1];
}
if (padding == null) {
padding = "valid";
}
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
if (poolMode == null) {
poolMode = "max";
}
x = preprocessConv2DInput(x, dataFormat);
let y;
const paddingString = padding === "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, padding, dataFormat, poolMode) {
return tidy(() => {
checkDataFormat(dataFormat);
checkPoolMode(poolMode);
checkPaddingMode(padding);
if (strides == null) {
strides = [1, 1, 1];
}
if (padding == null) {
padding = "valid";
}
if (dataFormat == null) {
dataFormat = imageDataFormat();
}
if (poolMode == null) {
poolMode = "max";
}
x = preprocessConv3DInput(x, dataFormat);
let y;
const paddingString = padding === "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;
});
}
var Pooling1D = class 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 config = {
poolSize: this.poolSize,
padding: this.padding,
strides: this.strides
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
var MaxPooling1D = class extends Pooling1D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding);
return pool2d(inputs, poolSize, strides, padding, dataFormat, "max");
}
};
MaxPooling1D.className = "MaxPooling1D";
serialization_exports.registerClass(MaxPooling1D);
var AveragePooling1D = class extends Pooling1D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding);
return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg");
}
};
AveragePooling1D.className = "AveragePooling1D";
serialization_exports.registerClass(AveragePooling1D);
var Pooling2D = class 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 config = {
poolSize: this.poolSize,
padding: this.padding,
strides: this.strides,
dataFormat: this.dataFormat
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
var MaxPooling2D = class extends Pooling2D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding);
return pool2d(inputs, poolSize, strides, padding, dataFormat, "max");
}
};
MaxPooling2D.className = "MaxPooling2D";
serialization_exports.registerClass(MaxPooling2D);
var AveragePooling2D = class extends Pooling2D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding);
return pool2d(inputs, poolSize, strides, padding, dataFormat, "avg");
}
};
AveragePooling2D.className = "AveragePooling2D";
serialization_exports.registerClass(AveragePooling2D);
var Pooling3D = class 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 config = {
poolSize: this.poolSize,
padding: this.padding,
strides: this.strides,
dataFormat: this.dataFormat
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
var MaxPooling3D = class extends Pooling3D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding);
return pool3d(inputs, poolSize, strides, padding, dataFormat, "max");
}
};
MaxPooling3D.className = "MaxPooling3D";
serialization_exports.registerClass(MaxPooling3D);
var AveragePooling3D = class extends Pooling3D {
constructor(args) {
super(args);
}
poolingFunction(inputs, poolSize, strides, padding, dataFormat) {
checkDataFormat(dataFormat);
checkPaddingMode(padding);
return pool3d(inputs, poolSize, strides, padding, dataFormat, "avg");
}
};
AveragePooling3D.className = "AveragePooling3D";
serialization_exports.registerClass(AveragePooling3D);
var GlobalPooling1D = class 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();
}
};
var GlobalAveragePooling1D = class 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);
var GlobalMaxPooling1D = class 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);
var GlobalPooling2D = class 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 config = { dataFormat: this.dataFormat };
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
};
var GlobalAveragePooling2D = class 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);
var GlobalMaxPooling2D = class 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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/layers/wrappers.js
init_define_BUILD_VERSION();
var Wrapper = class 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 config = {
"layer": {
"className": this.layer.getClassName(),
"config": this.layer.getConfig()
}
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
setFastWeightInitDuringBuild(value) {
super.setFastWeightInitDuringBuild(value);
if (this.layer != null) {
this.layer.setFastWeightInitDuringBuild(value);
}
}
static fromConfig(cls, config, customObjects = {}) {
const layerConfig = config["layer"];
const layer = deserialize(layerConfig, customObjects);
delete config["layer"];
const newConfig = { layer };
Object.assign(newConfig, config);
return new cls(newConfig);
}
};
var TimeDistributed = class 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);
}
var DEFAULT_BIDIRECTIONAL_MERGE_MODE = "concat";
var Bidirectional = class 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((state) => new InputSpec({ shape: state.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 tensor3 of additionalInputs) {
if (tensor3 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((state) => 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 config = {
"mergeMode": this.mergeMode
};
const baseConfig = super.getConfig();
Object.assign(config, baseConfig);
return config;
}
static fromConfig(cls, config) {
const rnnLayer = deserialize(config["layer"]);
delete config["layer"];
if (config["numConstants"] != null) {
throw new NotImplementedError(`Deserialization of a Bidirectional layer with numConstants present is not supported yet.`);
}
const newConfig = config;
newConfig["layer"] = rnnLayer;
return new cls(newConfig);
}
};
Bidirectional.className = "Bidirectional";
serialization_exports.registerClass(Bidirectional);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports_metrics.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports_models.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/exports_regularizers.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-layers/dist/callbacks.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/flags.js
init_define_BUILD_VERSION();
var ENV4 = env();
ENV4.registerFlag("KEEP_INTERMEDIATE_TENSORS", () => false, (debugValue) => {
if (debugValue) {
console.warn("Keep intermediate tensors is ON. This will print the values of all intermediate tensors during model inference. Not all models support this mode. For details, check e2e/benchmarks/ model_config.js. This significantly impacts performance.");
}
});
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_model.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_mapper.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/data/compiled_api.js
init_define_BUILD_VERSION();
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_QINT16"] = 15] = "DT_QINT16";
DataType2[DataType2["DT_QUINT16"] = 16] = "DT_QUINT16";
DataType2[DataType2["DT_UINT16"] = 17] = "DT_UINT16";
DataType2[DataType2["DT_COMPLEX128"] = 18] = "DT_COMPLEX128";
DataType2[DataType2["DT_HALF"] = 19] = "DT_HALF";
DataType2[DataType2["DT_RESOURCE"] = 20] = "DT_RESOURCE";
DataType2[DataType2["DT_VARIANT"] = 21] = "DT_VARIANT";
DataType2[DataType2["DT_UINT32"] = 22] = "DT_UINT32";
DataType2[DataType2["DT_UINT64"] = 23] = "DT_UINT64";
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";
DataType2[DataType2["DT_QINT16_REF"] = 115] = "DT_QINT16_REF";
DataType2[DataType2["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF";
DataType2[DataType2["DT_UINT16_REF"] = 117] = "DT_UINT16_REF";
DataType2[DataType2["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF";
DataType2[DataType2["DT_HALF_REF"] = 119] = "DT_HALF_REF";
DataType2[DataType2["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF";
DataType2[DataType2["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF";
DataType2[DataType2["DT_UINT32_REF"] = 122] = "DT_UINT32_REF";
DataType2[DataType2["DT_UINT64_REF"] = 123] = "DT_UINT64_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/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/register.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/arithmetic.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/basic_math.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/control.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/convolution.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/creation.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/dynamic.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/evaluation.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/graph.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/hash_table.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/image.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/logical.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/matrices.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/normalization.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/reduction.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/slice_join.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/sparse.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/spectral.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/string.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/op_list/transformation.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/graph_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/operation_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/custom_op/node_value_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/arithmetic_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/basic_math_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/control_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_array.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/tensor_list.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/creation_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/dynamic_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/evaluation_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/graph_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/hash_table_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/hash_table.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/image_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/logical_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/matrices_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/normalization_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/reduction_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/slice_join_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/sparse_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/spectral_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/string_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/operations/executors/transformation_executor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/execution_context.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/model_analysis.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/executor/resource_manager.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-converter/dist/version.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/dataset.js
init_define_BUILD_VERSION();
var seedrandom3 = __toESM(require_seedrandom2());
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
init_define_BUILD_VERSION();
var seedrandom2 = __toESM(require_seedrandom2());
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/deep_map.js
init_define_BUILD_VERSION();
function deepMap(input2, mapFn) {
return deepMapInternal(input2, mapFn);
}
function deepMapInternal(input2, mapFn, seen = /* @__PURE__ */ new Map(), containedIn = /* @__PURE__ */ new Set()) {
if (input2 == null) {
return null;
}
if (typeof Blob === "function" && input2 instanceof Blob) {
return input2.slice();
}
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);
if (input2.__proto__) {
mappedIterable.__proto__ = input2.__proto__;
}
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 = /* @__PURE__ */ 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 };
}
}
function isIterable2(obj) {
let isTextDecoder = false;
if (env().get("IS_BROWSER")) {
isTextDecoder = obj instanceof TextDecoder;
} else {
const { StringDecoder } = require_string_decoder();
isTextDecoder = obj instanceof StringDecoder;
}
return obj != null && !ArrayBuffer.isView(obj) && (Array.isArray(obj) || typeof obj === "object" && !(obj instanceof Tensor) && !(obj instanceof Promise) && !isTextDecoder);
}
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/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/deep_clone.js
function deepClone(container) {
return deepMap(container, 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/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/ring_buffer.js
init_define_BUILD_VERSION();
var RingBuffer = class {
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/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/growing_ring_buffer.js
var GrowingRingBuffer = class 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/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/lazy_iterator.js
function iteratorFromItems(items) {
return new ArrayIterator(items);
}
function iteratorFromFunction(func2) {
return new FunctionCallIterator(func2);
}
function iteratorFromConcatenated(baseIterators, baseErrorHandler) {
return new ChainedIterator(baseIterators, baseErrorHandler);
}
var LazyIterator = class {
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(transform4) {
return new MapIterator(this, transform4);
}
mapAsync(transform4) {
return new AsyncMapIterator(this, transform4);
}
serialMapAsync(transform4) {
return new AsyncMapIterator(this, transform4).serial();
}
flatmap(transform4) {
return new FlatmapIterator(this, transform4);
}
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);
}
};
var ArrayIterator = class 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 };
}
};
var FunctionCallIterator = class 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: ${e.message}`;
throw e;
}
}
};
var SerialIterator = class extends LazyIterator {
constructor(upstream) {
super();
this.upstream = upstream;
this.lastRead = Promise.resolve({ value: null, done: false });
}
summary() {
return `${this.upstream.summary()} -> Serial`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
return this.upstream.next();
}
};
var SkipIterator = class 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 `${this.upstream.summary()} -> Skip`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
while (this.count++ < this.maxCount) {
const skipped = await this.upstream.next();
if (skipped.done) {
return skipped;
}
dispose(skipped.value);
}
return this.upstream.next();
}
};
var TakeIterator = class extends LazyIterator {
constructor(upstream, maxCount) {
super();
this.upstream = upstream;
this.maxCount = maxCount;
this.count = 0;
}
summary() {
return `${this.upstream.summary()} -> Take`;
}
async next() {
if (this.count++ >= this.maxCount) {
return { value: null, done: true };
}
return this.upstream.next();
}
};
var RowMajorBatchIterator = class 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 `${this.upstream.summary()} -> RowMajorBatch`;
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
const batch = [];
while (batch.length < this.batchSize) {
const item = await this.upstream.next();
if (item.done) {
if (this.enableSmallLastBatch && batch.length > 0) {
return { value: batch, done: false };
}
return { value: null, done: true };
}
batch.push(item.value);
}
return { value: batch, done: false };
}
};
var FilterIterator = class extends LazyIterator {
constructor(upstream, predicate) {
super();
this.upstream = upstream;
this.predicate = predicate;
this.lastRead = Promise.resolve({ value: null, done: false });
}
summary() {
return `${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);
}
}
};
var MapIterator = class extends LazyIterator {
constructor(upstream, transform4) {
super();
this.upstream = upstream;
this.transform = transform4;
}
summary() {
return `${this.upstream.summary()} -> Map`;
}
async next() {
const item = await this.upstream.next();
if (item.done) {
return { value: null, done: true };
}
const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
const mapped = this.transform(item.value);
const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
for (const t of inputTensors) {
if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
t.dispose();
}
}
return { value: mapped, done: false };
}
};
var ErrorHandlingLazyIterator = class extends LazyIterator {
constructor(upstream, handler) {
super();
this.upstream = upstream;
this.handler = handler;
this.count = 0;
this.lastRead = Promise.resolve({ value: null, done: false });
}
summary() {
return `${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 };
}
}
}
}
};
var AsyncMapIterator = class extends LazyIterator {
constructor(upstream, transform4) {
super();
this.upstream = upstream;
this.transform = transform4;
}
summary() {
return `${this.upstream.summary()} -> AsyncMap`;
}
async next() {
const item = await this.upstream.next();
if (item.done) {
return { value: null, done: true };
}
const inputTensors = tensor_util_exports.getTensorsInContainer(item.value);
const mapped = await this.transform(item.value);
const outputTensors = tensor_util_exports.getTensorsInContainer(mapped);
for (const t of inputTensors) {
if (!tensor_util_exports.isTensorInList(t, outputTensors)) {
t.dispose();
}
}
return { value: mapped, done: false };
}
};
var OneToManyIterator = class extends LazyIterator {
constructor() {
super();
this.outputQueue = new GrowingRingBuffer();
this.lastRead = Promise.resolve({ value: null, done: false });
}
async next() {
this.lastRead = this.lastRead.then(() => this.serialNext());
return this.lastRead;
}
async serialNext() {
while (this.outputQueue.length() === 0) {
if (!await this.pump()) {
return { value: null, done: true };
}
}
return { value: this.outputQueue.shift(), done: false };
}
};
var FlatmapIterator = class extends OneToManyIterator {
constructor(upstream, transform4) {
super();
this.upstream = upstream;
this.transform = transform4;
}
summary() {
return `${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;
}
};
var ChainedIterator = class 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 `${upstreamSummaries} -> Chained`;
}
async next() {
this.lastRead = this.readFromChain(this.lastRead);
return this.lastRead;
}
async readFromChain(lastRead) {
await lastRead;
if (this.iterator == null) {
const iteratorResult = await this.moreIterators.next();
if (iteratorResult.done) {
return { value: null, done: true };
}
this.iterator = iteratorResult.value;
if (this.baseErrorHandler != null) {
this.iterator = this.iterator.handleErrors(this.baseErrorHandler);
}
}
const itemResult = await this.iterator.next();
if (itemResult.done) {
this.iterator = null;
return this.readFromChain(lastRead);
}
return itemResult;
}
};
var ZipMismatchMode;
(function(ZipMismatchMode2) {
ZipMismatchMode2[ZipMismatchMode2["FAIL"] = 0] = "FAIL";
ZipMismatchMode2[ZipMismatchMode2["SHORTEST"] = 1] = "SHORTEST";
ZipMismatchMode2[ZipMismatchMode2["LONGEST"] = 2] = "LONGEST";
})(ZipMismatchMode || (ZipMismatchMode = {}));
var PrefetchIterator = class extends LazyIterator {
constructor(upstream, bufferSize) {
super();
this.upstream = upstream;
this.bufferSize = bufferSize;
this.buffer = new RingBuffer(bufferSize);
}
summary() {
return `${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();
}
};
var ShuffleIterator = class 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(max5) {
return Math.floor(this.random() * max5);
}
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/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/dataset.js
var Dataset = class {
constructor() {
this.size = null;
}
batch(batchSize, smallLastBatch = true) {
const base = 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 base.iterator()).columnMajorBatch(batchSize, smallLastBatch, deepBatchConcat);
}, size);
}
concatenate(dataset) {
const base = this;
let size;
if (this.size === Infinity || dataset.size === Infinity) {
size = Infinity;
} else if (this.size != null && dataset.size != null) {
size = this.size + dataset.size;
} else {
size = null;
}
return datasetFromIteratorFn(async () => (await base.iterator()).concatenate(await dataset.iterator()), size);
}
filter(predicate) {
const base = this;
let size;
if (this.size === Infinity) {
size = Infinity;
} else {
size = null;
}
return datasetFromIteratorFn(async () => {
return (await base.iterator()).filter((x) => tidy(() => predicate(x)));
}, size);
}
async forEachAsync(f) {
return (await this.iterator()).forEachAsync(f);
}
map(transform4) {
const base = this;
return datasetFromIteratorFn(async () => {
return (await base.iterator()).map((x) => tidy(() => transform4(x)));
}, this.size);
}
mapAsync(transform4) {
const base = this;
return datasetFromIteratorFn(async () => {
return (await base.iterator()).mapAsync(transform4);
}, this.size);
}
prefetch(bufferSize) {
if (bufferSize == null) {
throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");
}
const base = this;
return datasetFromIteratorFn(async () => (await base.iterator()).prefetch(bufferSize), this.size);
}
repeat(count2) {
const base = 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 base.iterator(), done: false }));
return iteratorFromConcatenated(iteratorIterator.take(count2));
}, size);
}
skip(count2) {
const base = 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 base.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 base = this;
const random = seedrandom3.alea(seed || util_exports.now().toString());
return datasetFromIteratorFn(async () => {
let seed2 = random.int32();
if (reshuffleEachIteration) {
seed2 += random.int32();
}
return (await base.iterator()).shuffle(bufferSize, seed2.toString());
}, this.size);
}
take(count2) {
const base = 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 base.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 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 tensor2(arrays);
}
}
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/datasets/text_line_dataset.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/datasets/csv_dataset.js
var STATE_OUT = Symbol("out");
var STATE_FIELD = Symbol("field");
var STATE_QUOTE = Symbol("quote");
var STATE_QUOTE_AFTER_QUOTE = Symbol("quoteafterquote");
var STATE_WITHIN_QUOTE_IN_QUOTE = Symbol("quoteinquote");
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/readers.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/microphone_iterator.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/webcam_iterator.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/sources/url_data_source.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/datasource.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/url_chunk_iterator.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/file_chunk_iterator.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/byte_chunk_iterator.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/iterators/string_iterator.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/util/source_util.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/sources/file_data_source.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_umkohei6j4f2ojerldjngreig4/node_modules/@tensorflow/tfjs-data/dist/version.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/cpu_util.js
init_define_BUILD_VERSION();
function assertNotComplex(tensor3, opName) {
if (!Array.isArray(tensor3)) {
tensor3 = [tensor3];
}
tensor3.forEach((t) => {
if (t != null) {
util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the CPU backend.`);
}
});
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/backend_cpu.js
var whereImpl2 = kernel_impls_exports.whereImpl;
var MathBackendCPU = class extends KernelBackend {
constructor() {
super();
this.blockSize = 48;
this.firstUse = true;
this.data = new DataStorage(this, engine());
}
nextDataId() {
return MathBackendCPU.nextDataId++;
}
write(values, shape, dtype) {
if (this.firstUse) {
this.firstUse = false;
if (env().get("IS_NODE")) {
backend_util_exports.warn("\n============================\nHi, looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, visit https://github.com/tensorflow/tfjs-node for more details. \n============================");
}
}
const dataId = { id: this.nextDataId() };
this.data.set(dataId, { values, dtype, refCount: 1 });
return dataId;
}
makeTensorInfo(shape, dtype, values) {
let outId;
if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
const encodedValues = values.map((d) => util_exports.encodeString(d));
outId = this.write(encodedValues, shape, dtype);
} else {
outId = this.write(values, shape, dtype);
}
return { dataId: outId, shape, dtype };
}
refCount(dataId) {
if (this.data.has(dataId)) {
const tensorData = this.data.get(dataId);
return tensorData.refCount;
}
return 0;
}
incRef(dataId) {
const tensorData = this.data.get(dataId);
tensorData.refCount++;
}
decRef(dataId) {
if (this.data.has(dataId)) {
const tensorData = this.data.get(dataId);
tensorData.refCount--;
}
}
move(dataId, values, shape, dtype, refCount) {
this.data.set(dataId, { values, dtype, refCount });
}
numDataIds() {
return this.data.numDataIds();
}
async read(dataId) {
return this.readSync(dataId);
}
readSync(dataId) {
const { dtype, complexTensorInfos } = this.data.get(dataId);
if (dtype === "complex64") {
const realValues = this.readSync(complexTensorInfos.real.dataId);
const imagValues = this.readSync(complexTensorInfos.imag.dataId);
return backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
}
return this.data.get(dataId).values;
}
bufferSync(t) {
const data = this.readSync(t.dataId);
if (t.dtype === "string") {
try {
const strings = data.map((d) => util_exports.decodeString(d));
return buffer(t.shape, t.dtype, strings);
} catch (_a) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
}
return buffer(t.shape, t.dtype, data);
}
makeOutput(values, shape, dtype) {
return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
}
disposeData(dataId, force = false) {
if (this.data.has(dataId)) {
this.data.get(dataId).refCount--;
if (!force && this.data.get(dataId).refCount > 0) {
return false;
}
const { complexTensorInfos } = this.data.get(dataId);
if (complexTensorInfos != null) {
this.disposeData(complexTensorInfos.real.dataId, true);
this.disposeData(complexTensorInfos.imag.dataId, true);
}
this.data.delete(dataId);
}
return true;
}
disposeIntermediateTensorInfo(tensorInfo) {
this.disposeData(tensorInfo.dataId);
}
async time(f) {
const start = util_exports.now();
f();
const kernelMs = util_exports.now() - start;
return { kernelMs };
}
memory() {
return {
unreliable: true,
reasons: ["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]
};
}
where(condition) {
assertNotComplex([condition], "where");
const condVals = this.readSync(condition.dataId);
return whereImpl2(condition.shape, condVals);
}
dispose() {
}
floatPrecision() {
return 32;
}
epsilon() {
return super.epsilon();
}
};
MathBackendCPU.nextDataId = 0;
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/shared.js
var shared_exports = {};
__export(shared_exports, {
addImpl: () => addImpl,
bincountImpl: () => bincountImpl,
bincountReduceImpl: () => bincountReduceImpl,
ceilImpl: () => ceilImpl,
concatImpl: () => concatImpl,
equalImpl: () => equalImpl,
expImpl: () => expImpl,
expm1Impl: () => expm1Impl,
floorImpl: () => floorImpl,
gatherNdImpl: () => gatherNdImpl,
gatherV2Impl: () => gatherV2Impl,
greaterEqualImpl: () => greaterEqualImpl,
greaterImpl: () => greaterImpl,
lessEqualImpl: () => lessEqualImpl,
lessImpl: () => lessImpl,
linSpaceImpl: () => linSpaceImpl,
logImpl: () => logImpl,
maxImpl: () => maxImpl,
maximumImpl: () => maximumImpl,
minimumImpl: () => minimumImpl,
multiplyImpl: () => multiplyImpl,
negImpl: () => negImpl,
notEqualImpl: () => notEqualImpl,
prodImpl: () => prodImpl,
rangeImpl: () => rangeImpl,
rsqrtImpl: () => rsqrtImpl,
scatterImpl: () => scatterImpl,
sigmoidImpl: () => sigmoidImpl,
simpleAbsImpl: () => simpleAbsImpl,
sliceImpl: () => sliceImpl,
sparseFillEmptyRowsImpl: () => sparseFillEmptyRowsImpl,
sparseReshapeImpl: () => sparseReshapeImpl,
sparseSegmentReductionImpl: () => sparseSegmentReductionImpl,
sqrtImpl: () => sqrtImpl,
squaredDifferenceImpl: () => squaredDifferenceImpl,
stridedSliceImpl: () => stridedSliceImpl,
stringNGramsImpl: () => stringNGramsImpl,
stringSplitImpl: () => stringSplitImpl,
stringToHashBucketFastImpl: () => stringToHashBucketFastImpl,
subImpl: () => subImpl,
tileImpl: () => tileImpl,
topKImpl: () => topKImpl,
transposeImpl: () => transposeImpl,
uniqueImpl: () => uniqueImpl
});
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Abs.js
init_define_BUILD_VERSION();
function simpleAbsImpl(vals) {
const resultValues = new Float32Array(vals.length);
for (let i = 0; i < vals.length; ++i) {
resultValues[i] = Math.abs(vals[i]);
}
return resultValues;
}
var abs2 = (args) => {
const { x } = args.inputs;
const cpuBackend = args.backend;
assertNotComplex(x, "abs");
let resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
const values = cpuBackend.data.get(x.dataId).values;
resultValues = simpleAbsImpl(values);
return cpuBackend.makeOutput(resultValues, x.shape, x.dtype);
};
var absConfig = {
kernelName: Abs,
backendName: "cpu",
kernelFunc: abs2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_impl.js
init_define_BUILD_VERSION();
function createSimpleBinaryKernelImpl(op2) {
return (aShape, bShape, aVals, bVals, dtype) => {
const newShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
const resultRank = newShape.length;
const resultStrides = util_exports.computeStrides(newShape);
const resultSize = util_exports.sizeFromShape(newShape);
const result = util_exports.getTypedArrayFromDType(dtype, resultSize);
const aRank = aShape.length;
const bRank = bShape.length;
const aStrides = util_exports.computeStrides(aShape);
const bStrides = util_exports.computeStrides(bShape);
const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, newShape);
const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, newShape);
if (aBroadcastDims.length + bBroadcastDims.length === 0) {
for (let i = 0; i < result.length; ++i) {
result[i] = op2(aVals[i % aVals.length], bVals[i % bVals.length]);
}
} else {
for (let i = 0; i < result.length; ++i) {
const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
const aLoc = loc.slice(-aRank);
aBroadcastDims.forEach((d) => aLoc[d] = 0);
const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
const bLoc = loc.slice(-bRank);
bBroadcastDims.forEach((d) => bLoc[d] = 0);
const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
result[i] = op2(aVals[aIndex], bVals[bIndex]);
}
}
return [result, newShape];
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Complex.js
init_define_BUILD_VERSION();
function complex2(args) {
const { inputs, backend: backend2 } = args;
const { real: real4, imag: imag4 } = inputs;
const realVals = backend2.data.get(real4.dataId).values;
const imagVals = backend2.data.get(imag4.dataId).values;
const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64");
const complex4 = backend2.data.get(complexInfo.dataId);
complex4.complexTensorInfos = {
real: backend2.makeTensorInfo(real4.shape, "float32", realVals),
imag: backend2.makeTensorInfo(imag4.shape, "float32", imagVals)
};
return complexInfo;
}
var complexConfig = {
kernelName: Complex,
backendName: "cpu",
kernelFunc: complex2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/zeros_impl.js
function zeros2(backend2, shape, dtype = "float32") {
if (dtype === "complex64") {
const real4 = zeros2(backend2, shape, "float32");
const imag4 = zeros2(backend2, shape, "float32");
return complex2({ inputs: { real: real4, imag: imag4 }, backend: backend2 });
}
const values = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(shape), dtype);
return backend2.makeTensorInfo(shape, dtype, values);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Identity.js
init_define_BUILD_VERSION();
function identity(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
backend2.incRef(x.dataId);
return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
}
var identityConfig = {
kernelName: Identity,
backendName: "cpu",
kernelFunc: identity
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Real.js
init_define_BUILD_VERSION();
function real2(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
const real4 = backend2.data.get(input2.dataId).complexTensorInfos.real;
const realVal = backend2.data.get(real4.dataId).values;
return backend2.makeTensorInfo(real4.shape, real4.dtype, realVal);
}
var realConfig = {
kernelName: Real,
backendName: "cpu",
kernelFunc: real2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cast.js
function cast3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { dtype } = attrs;
if (dtype === "complex64") {
if (x.dtype === "complex64") {
return identity({ inputs: { x }, backend: backend2 });
}
const zerosTensorInfo = zeros2(backend2, x.shape, x.dtype);
const floatX = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
const result = complex2({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
backend2.disposeIntermediateTensorInfo(floatX);
return result;
}
if (x.dtype === "complex64") {
const realPart = real2({ inputs: { input: x }, backend: backend2 });
const result = cast3({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
backend2.disposeIntermediateTensorInfo(realPart);
return result;
}
if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
const result = identity({ inputs: { x }, backend: backend2 });
return { dataId: result.dataId, shape: result.shape, dtype };
}
if (dtype === "int32") {
const values = backend2.data.get(x.dataId).values;
const resultValues = Int32Array.from(values);
return backend2.makeTensorInfo(x.shape, "int32", resultValues);
}
if (dtype === "bool") {
const xVals = backend2.data.get(x.dataId).values;
const zero = util_exports.toTypedArray([0], x.dtype);
const [resultData, resultShape] = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0)(x.shape, [], xVals, zero, "bool");
return backend2.makeTensorInfo(resultShape, "bool", resultData);
}
throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
}
var castConfig = {
kernelName: Cast,
backendName: "cpu",
kernelFunc: cast3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/binary_utils.js
function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) {
if (complexImpl == null) {
return ({ inputs, backend: backend2 }) => {
const { a, b } = inputs;
const cpuBackend = backend2;
assertNotComplex([a, b], name);
const aVals = cpuBackend.data.get(a.dataId).values;
const bVals = cpuBackend.data.get(b.dataId).values;
const decodedAVals = a.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;
const decodedBVals = a.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;
const $dtype = dtype || a.dtype;
const [resultData, resultShape] = simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
};
}
return ({ inputs, backend: backend2 }) => {
const { a, b } = inputs;
const cpuBackend = backend2;
if (a.dtype === "complex64" || b.dtype === "complex64") {
const $aComplex = cast3({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: "complex64" } });
const $aComplexVals = cpuBackend.data.get($aComplex.dataId);
const aReal = $aComplexVals.complexTensorInfos.real;
const aImag = $aComplexVals.complexTensorInfos.imag;
const aRealVals = cpuBackend.data.get(aReal.dataId).values;
const aImagVals = cpuBackend.data.get(aImag.dataId).values;
const $bComplex = cast3({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: "complex64" } });
const $bComplexVals = cpuBackend.data.get($bComplex.dataId);
const bReal = $bComplexVals.complexTensorInfos.real;
const bImag = $bComplexVals.complexTensorInfos.imag;
const bRealVals = cpuBackend.data.get(bReal.dataId).values;
const bImagVals = cpuBackend.data.get(bImag.dataId).values;
const [resultRealData, resultImagData, resultShape] = complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals);
const resultReal = cpuBackend.makeTensorInfo(resultShape, "float32", resultRealData);
const resultImag = cpuBackend.makeTensorInfo(resultShape, "float32", resultImagData);
const result = complex2({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend });
cpuBackend.disposeIntermediateTensorInfo($aComplex);
cpuBackend.disposeIntermediateTensorInfo($bComplex);
cpuBackend.disposeIntermediateTensorInfo(resultReal);
cpuBackend.disposeIntermediateTensorInfo(resultImag);
return result;
} else {
const aVals = cpuBackend.data.get(a.dataId).values;
const bVals = cpuBackend.data.get(b.dataId).values;
const $dtype = dtype || a.dtype;
const [resultData, resultShape] = simpleImpl(a.shape, b.shape, aVals, bVals, $dtype);
return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData);
}
};
}
function createComplexBinaryKernelImpl(op2) {
return (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) => {
const resultShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
const resultSize = util_exports.sizeFromShape(resultShape);
const resultRank = resultShape.length;
const resultStrides = util_exports.computeStrides(resultShape);
const resultRealVals = util_exports.getTypedArrayFromDType("float32", resultSize);
const resultImagVals = util_exports.getTypedArrayFromDType("float32", resultSize);
const aBroadcastDims = backend_util_exports.getBroadcastDims(aShape, resultShape);
const bBroadcastDims = backend_util_exports.getBroadcastDims(bShape, resultShape);
const aVals = backend_util_exports.mergeRealAndImagArrays(aRealVals, aImagVals);
const bVals = backend_util_exports.mergeRealAndImagArrays(bRealVals, bImagVals);
const aRank = aShape.length;
const aStrides = util_exports.computeStrides(aShape);
const bRank = bShape.length;
const bStrides = util_exports.computeStrides(bShape);
if (aBroadcastDims.length + bBroadcastDims.length === 0) {
for (let i = 0; i < resultRealVals.length; i++) {
const aIdx = i % aVals.length;
const bIdx = i % bVals.length;
const result = op2(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
resultRealVals[i] = result.real;
resultImagVals[i] = result.imag;
}
} else {
for (let i = 0; i < resultRealVals.length; i++) {
const loc = util_exports.indexToLoc(i, resultRank, resultStrides);
const aLoc = loc.slice(-aRank);
aBroadcastDims.forEach((d) => aLoc[d] = 0);
const aIndex = util_exports.locToIndex(aLoc, aRank, aStrides);
const bLoc = loc.slice(-bRank);
bBroadcastDims.forEach((d) => bLoc[d] = 0);
const bIndex = util_exports.locToIndex(bLoc, bRank, bStrides);
const opResult = op2(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
resultRealVals[i] = opResult.real;
resultImagVals[i] = opResult.imag;
}
}
return [resultRealVals, resultImagVals, resultShape];
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Add.js
var addImpl = createSimpleBinaryKernelImpl((a, b) => a + b);
var addComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
return { real: aReal + bReal, imag: aImag + bImag };
});
var add3 = binaryKernelFunc(Add, addImpl, addComplexImpl);
var addConfig = {
kernelName: Add,
backendName: "cpu",
kernelFunc: add3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount_impl.js
init_define_BUILD_VERSION();
function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) {
const weightsSize = util_exports.sizeFromShape(weightsShape);
const outVals = util_exports.makeZerosTypedArray(size, weightsDtype);
for (let i = 0; i < xVals.length; i++) {
const value = xVals[i];
if (value < 0) {
throw new Error("Input x must be non-negative!");
}
if (value >= size) {
continue;
}
if (weightsSize > 0) {
outVals[value] += weightsVals[i];
} else {
outVals[value] += 1;
}
}
return outVals;
}
function bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput = false) {
const numRows = xBuf.shape[0];
const numCols = xBuf.shape[1];
const outBuf = buffer([numRows, size], weightsBuf.dtype);
for (let i = 0; i < numRows; i++) {
for (let j = 0; j < numCols; j++) {
const value = xBuf.get(i, j);
if (value < 0) {
throw new Error("Input x must be non-negative!");
}
if (value >= size) {
continue;
}
if (binaryOutput) {
outBuf.set(1, i, value);
} else {
if (weightsBuf.size > 0) {
outBuf.set(outBuf.get(i, value) + weightsBuf.get(i, j), i, value);
} else {
outBuf.set(outBuf.get(i, value) + 1, i, value);
}
}
}
}
return outBuf;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_impl.js
init_define_BUILD_VERSION();
function createSimpleUnaryImpl(op2) {
return (values, dtype, attrs) => {
const newValues = util_exports.getTypedArrayFromDType(dtype, values.length);
for (let i = 0; i < values.length; ++i) {
newValues[i] = op2(values[i], attrs);
}
return newValues;
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/unary_utils.js
init_define_BUILD_VERSION();
function unaryKernelFunc(name, op2, dtype) {
return ({ inputs, attrs, backend: backend2 }) => {
const { x } = inputs;
assertNotComplex(x, name);
if (x.dtype === "string" || dtype === "string") {
throw new Error("unaryKernelFunc does not support string input/output");
}
const cpuBackend = backend2;
const values = cpuBackend.data.get(x.dataId).values;
const xSize = util_exports.sizeFromShape(x.shape);
const $dtype = dtype || x.dtype;
const newValues = util_exports.getArrayFromDType($dtype, xSize);
for (let i = 0; i < xSize; ++i) {
newValues[i] = op2(values[i], attrs);
}
return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
};
}
function unaryKernelFuncFromImpl(name, unaryImpl, dtype) {
return ({ inputs, attrs, backend: backend2 }) => {
const { x } = inputs;
assertNotComplex(x, name);
if (x.dtype === "string" || dtype === "string") {
throw new Error("unaryKernelFunc does not support string input/output");
}
const cpuBackend = backend2;
const values = cpuBackend.data.get(x.dataId).values;
const $dtype = dtype || x.dtype;
const newValues = unaryImpl(values, $dtype, attrs);
return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues);
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Ceil.js
var ceilImpl = createSimpleUnaryImpl((xi) => Math.ceil(xi));
var ceil2 = unaryKernelFuncFromImpl(Ceil, ceilImpl);
var ceilConfig = {
kernelName: Ceil,
backendName: "cpu",
kernelFunc: ceil2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat_impl.js
init_define_BUILD_VERSION();
function concatImpl(inputs, outShape, dtype, simplyConcat) {
const outVals = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
if (simplyConcat && dtype !== "string") {
let offset = 0;
inputs.forEach((input2) => {
const size = util_exports.sizeFromShape(input2.shape);
outVals.set(input2.vals, offset);
offset += size;
});
} else {
let colOffset = 0;
inputs.forEach((input2) => {
const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(input2.vals) : input2.vals;
let tIdx = 0;
for (let row = 0; row < input2.shape[0]; ++row) {
const resIdx = row * outShape[1] + colOffset;
for (let col = 0; col < input2.shape[1]; ++col) {
outVals[resIdx + col] = decodedData[tIdx++];
}
}
colOffset += input2.shape[1];
});
}
return outVals;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Equal.js
init_define_BUILD_VERSION();
var equalImpl = createSimpleBinaryKernelImpl((a, b) => a === b ? 1 : 0);
var equal2 = binaryKernelFunc(Equal, equalImpl, null, "bool");
var equalConfig = {
kernelName: Equal,
backendName: "cpu",
kernelFunc: equal2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Exp.js
init_define_BUILD_VERSION();
var expImpl = createSimpleUnaryImpl((xi) => Math.exp(xi));
var exp2 = unaryKernelFuncFromImpl(Exp, expImpl, "float32");
var expConfig = {
kernelName: Exp,
backendName: "cpu",
kernelFunc: exp2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Expm1.js
init_define_BUILD_VERSION();
var expm1Impl = createSimpleUnaryImpl((xi) => Math.expm1(xi));
var expm12 = unaryKernelFuncFromImpl(Expm1, expm1Impl);
var expm1Config = {
kernelName: Expm1,
backendName: "cpu",
kernelFunc: expm12
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Floor.js
init_define_BUILD_VERSION();
var floorImpl = createSimpleUnaryImpl((xi) => Math.floor(xi));
var floor2 = unaryKernelFuncFromImpl(Floor, floorImpl);
var floorConfig = {
kernelName: Floor,
backendName: "cpu",
kernelFunc: floor2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd_Impl.js
init_define_BUILD_VERSION();
function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) {
const outBuf = buffer([numSlices, sliceSize], dtype);
for (let i = 0; i < numSlices; i++) {
const index = [];
let flattenIndex = 0;
for (let j = 0; j < sliceRank; j++) {
const dim = indicesData[i * sliceRank + j];
flattenIndex += dim * strides[j];
index.push(dim);
}
if (flattenIndex < 0 || flattenIndex >= paramsSize / sliceSize) {
throw new Error(`Invalid indices: ${index} does not index into ${paramsShape}`);
}
for (let k = 0; k < sliceSize; k++) {
outBuf.values[i * sliceSize + k] = paramsBuf.get(...paramsBuf.indexToLoc(flattenIndex * sliceSize + k));
}
}
return outBuf;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2_impl.js
init_define_BUILD_VERSION();
function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) {
const outBuf = buffer(flattenOutputShape, xBuf.dtype);
for (let i = 0; i < outBuf.size; ++i) {
const newLoc = outBuf.indexToLoc(i);
const originalLoc = newLoc.slice();
const batchIdx = originalLoc[0];
const indicesIdx = originalLoc[2];
const indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]);
originalLoc[2] = indicesBuf.values[indicesIndex];
const originalIndex = xBuf.locToIndex(originalLoc);
if (0 <= originalIndex && originalIndex < xBuf.values.length) {
outBuf.values[i] = xBuf.values[originalIndex];
}
}
return outBuf;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Greater.js
init_define_BUILD_VERSION();
var greaterImpl = createSimpleBinaryKernelImpl((a, b) => a > b ? 1 : 0);
var greater2 = binaryKernelFunc(Greater, greaterImpl, null, "bool");
var greaterConfig = {
kernelName: Greater,
backendName: "cpu",
kernelFunc: greater2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GreaterEqual.js
init_define_BUILD_VERSION();
var greaterEqualImpl = createSimpleBinaryKernelImpl((a, b) => a >= b ? 1 : 0);
var greaterEqual2 = binaryKernelFunc(GreaterEqual, greaterEqualImpl, null, "bool");
var greaterEqualConfig = {
kernelName: GreaterEqual,
backendName: "cpu",
kernelFunc: greaterEqual2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Less.js
init_define_BUILD_VERSION();
var lessImpl = createSimpleBinaryKernelImpl((a, b) => a < b ? 1 : 0);
var less2 = binaryKernelFunc(Less, lessImpl, null, "bool");
var lessConfig = {
kernelName: Less,
backendName: "cpu",
kernelFunc: less2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LessEqual.js
init_define_BUILD_VERSION();
var lessEqualImpl = createSimpleBinaryKernelImpl((a, b) => a <= b ? 1 : 0);
var lessEqual2 = binaryKernelFunc(LessEqual, lessEqualImpl, null, "bool");
var lessEqualConfig = {
kernelName: LessEqual,
backendName: "cpu",
kernelFunc: lessEqual2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace_impl.js
init_define_BUILD_VERSION();
function linSpaceImpl(start, stop, num) {
const step4 = (stop - start) / (num - 1);
const values = util_exports.makeZerosTypedArray(num, "float32");
values[0] = start;
for (let i = 1; i < values.length; i++) {
values[i] = values[i - 1] + step4;
}
return values;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log.js
init_define_BUILD_VERSION();
var logImpl = createSimpleUnaryImpl((xi) => Math.log(xi));
var log3 = unaryKernelFuncFromImpl(Log, logImpl);
var logConfig = {
kernelName: Log,
backendName: "cpu",
kernelFunc: log3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max_impl.js
init_define_BUILD_VERSION();
function maxImpl(aVals, reduceSize, outShape, dtype) {
const vals = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(outShape));
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let max5 = aVals[offset];
for (let j = 0; j < reduceSize; ++j) {
const value = aVals[offset + j];
if (Number.isNaN(value) || value > max5) {
max5 = value;
}
}
vals[i] = max5;
}
return vals;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Maximum.js
init_define_BUILD_VERSION();
var maximumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.max(aValue, bValue));
var maximum2 = binaryKernelFunc(Maximum, maximumImpl);
var maximumConfig = {
kernelName: Maximum,
backendName: "cpu",
kernelFunc: maximum2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Minimum.js
init_define_BUILD_VERSION();
var minimumImpl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.min(aValue, bValue));
var minimum2 = binaryKernelFunc(Minimum, minimumImpl);
var minimumConfig = {
kernelName: Minimum,
backendName: "cpu",
kernelFunc: minimum2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multiply.js
init_define_BUILD_VERSION();
var multiplyImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue * bValue);
var multiplyComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
return {
real: aReal * bReal - aImag * bImag,
imag: aReal * bImag + aImag * bReal
};
});
var multiply = binaryKernelFunc(Multiply, multiplyImpl, multiplyComplexImpl);
var multiplyConfig = {
kernelName: Multiply,
backendName: "cpu",
kernelFunc: multiply
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Neg.js
init_define_BUILD_VERSION();
function negImpl(xVals, xShape, xDtype) {
const minusOne = util_exports.createScalarValue(-1, xDtype);
return multiplyImpl([], xShape, minusOne, xVals, xDtype);
}
function neg2(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
assertNotComplex(x, "neg");
const xVals = backend2.data.get(x.dataId).values;
const [res, newShape] = negImpl(xVals, x.shape, x.dtype);
return backend2.makeTensorInfo(newShape, x.dtype, res);
}
var negConfig = {
kernelName: Neg,
backendName: "cpu",
kernelFunc: neg2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NotEqual.js
init_define_BUILD_VERSION();
var notEqualImpl = createSimpleBinaryKernelImpl((a, b) => a !== b ? 1 : 0);
var notEqual2 = binaryKernelFunc(NotEqual, notEqualImpl, null, "bool");
var notEqualConfig = {
kernelName: NotEqual,
backendName: "cpu",
kernelFunc: notEqual2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose_impl.js
init_define_BUILD_VERSION();
function transposeImpl(xVals, xShape, dtype, perm, newShape) {
const xRank = xShape.length;
const xSize = util_exports.sizeFromShape(xShape);
const xStrides = util_exports.computeStrides(xShape);
const newStrides = util_exports.computeStrides(newShape);
const result = util_exports.getTypedArrayFromDType(dtype, util_exports.sizeFromShape(newShape));
for (let i = 0; i < xSize; ++i) {
const loc = util_exports.indexToLoc(i, xRank, xStrides);
const newLoc = new Array(loc.length);
for (let i2 = 0; i2 < newLoc.length; i2++) {
newLoc[i2] = loc[perm[i2]];
}
const newIndex = util_exports.locToIndex(newLoc, xRank, newStrides);
result[newIndex] = xVals[i];
}
return result;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transpose.js
function transpose2(args) {
const { inputs, attrs, backend: backend2 } = args;
const { x } = inputs;
const { perm } = attrs;
assertNotComplex(x, "transpose");
const xRank = x.shape.length;
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = x.shape[perm[i]];
}
const values = backend2.data.get(x.dataId).values;
const result = transposeImpl(values, x.shape, x.dtype, perm, newShape);
const dataId = backend2.write(result, newShape, x.dtype);
return { dataId, shape: newShape, dtype: x.dtype };
}
var transposeConfig = {
kernelName: Transpose,
backendName: "cpu",
kernelFunc: transpose2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prod.js
function prodImpl(xShape, xDtype, xVals, reductionAxes) {
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, reductionAxes);
const outDtype = upcastType(xDtype, "int32");
const outVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), outDtype);
const reduceSize = util_exports.sizeFromShape(reduceShape);
for (let i = 0; i < outVals.length; ++i) {
const offset = i * reduceSize;
let prod4 = 1;
for (let j = 0; j < reduceSize; ++j) {
prod4 *= xVals[offset + j];
}
outVals[i] = prod4;
}
return { outVals, outShape, outDtype };
}
function prod2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
assertNotComplex(x, "prod");
const xRank = x.shape.length;
const axes = util_exports.parseAxisParam(axis, x.shape);
const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
let reductionAxes = axes;
let permutedX = x;
const intermediateTensorInfos = [];
if (permutation != null) {
permutedX = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
intermediateTensorInfos.push(permutedX);
reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
}
const xVals = backend2.data.get(permutedX.dataId).values;
const { outVals, outShape, outDtype } = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes);
let resultShape = outShape;
if (keepDims) {
resultShape = backend_util_exports.expandShapeToKeepDim(outShape, axes);
}
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return backend2.makeTensorInfo(resultShape, outDtype, outVals);
}
var prodConfig = {
kernelName: Prod,
backendName: "cpu",
kernelFunc: prod2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range_impl.js
init_define_BUILD_VERSION();
function rangeImpl(start, stop, step4, dtype) {
const sameStartStop = start === stop;
const increasingRangeNegativeStep = start < stop && step4 < 0;
const decreasingRangePositiveStep = stop < start && step4 > 1;
if (sameStartStop || increasingRangeNegativeStep || decreasingRangePositiveStep) {
return util_exports.makeZerosTypedArray(0, dtype);
}
const numElements = Math.abs(Math.ceil((stop - start) / step4));
const values = util_exports.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 values;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Rsqrt.js
init_define_BUILD_VERSION();
var rsqrtImpl = createSimpleUnaryImpl((xi) => 1 / Math.sqrt(xi));
var rsqrt2 = unaryKernelFuncFromImpl(Rsqrt, rsqrtImpl);
var rsqrtConfig = {
kernelName: Rsqrt,
backendName: "cpu",
kernelFunc: rsqrt2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Scatter_impl.js
init_define_BUILD_VERSION();
function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
const flattenShape = [outputSize / sliceSize, sliceSize];
const indicesData = indices.values;
const updatesData = updates.values;
if (outputSize === 0) {
return buffer(shape, updates.dtype);
}
const outBuf = buffer(flattenShape, updates.dtype);
if (typeof defaultValue === "string") {
outBuf.values.fill(defaultValue);
} else if (typeof defaultValue === "number") {
outBuf.values.fill(defaultValue);
} else if (typeof defaultValue === "boolean") {
outBuf.values.fill(+defaultValue);
}
for (let i = 0; i < numUpdates; i++) {
const index = [];
let flattenIndex = 0;
for (let j = 0; j < sliceRank; j++) {
const dim = indicesData[i * sliceRank + j];
index.push(dim);
flattenIndex += dim * strides[j];
}
if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
throw new Error(`Invalid indices: ${index} does not index into ${shape}`);
}
for (let k = 0; k < sliceSize; k++) {
if (sumDupeIndices) {
outBuf.values[flattenIndex * sliceSize + k] += updatesData[i * sliceSize + k];
} else {
outBuf.values[flattenIndex * sliceSize + k] = updates.rank === 0 ? updatesData[0] : updatesData[i * sliceSize + k];
}
}
}
return outBuf;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sigmoid.js
init_define_BUILD_VERSION();
var sigmoidImpl = createSimpleUnaryImpl((xi) => 1 / (1 + Math.exp(-xi)));
var sigmoid2 = unaryKernelFunc(Sigmoid, (xi) => 1 / (1 + Math.exp(-xi)));
var sigmoidConfig = {
kernelName: Sigmoid,
backendName: "cpu",
kernelFunc: sigmoid2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Slice.js
init_define_BUILD_VERSION();
function sliceImpl(vals, begin, size, shape, dtype) {
const isContinous = slice_util_exports.isSliceContinous(shape, begin, size);
const length = util_exports.sizeFromShape(size);
const xStrides = util_exports.computeStrides(shape);
if (isContinous) {
const flatOffset = slice_util_exports.computeFlatOffset(begin, xStrides);
if (dtype === "string") {
return vals.slice(flatOffset, flatOffset + length);
}
return vals.subarray(flatOffset, flatOffset + length);
}
const decodedData = dtype === "string" ? backend_util_exports.fromUint8ToStringArray(vals) : vals;
const inBuf = buffer(shape, dtype, decodedData);
const outBuf = buffer(size, dtype);
for (let i = 0; i < outBuf.size; ++i) {
const outLoc = outBuf.indexToLoc(i);
const inLoc = outLoc.map((idx, j) => idx + begin[j]);
outBuf.set(inBuf.get(...inLoc), ...outLoc);
}
if (dtype === "string") {
return backend_util_exports.fromStringArrayToUint8(outBuf.values);
}
return outBuf.values;
}
function slice2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { begin, size } = attrs;
assertNotComplex(x, "slice");
const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
slice_util_exports.assertParamsValid(x, $begin, $size);
const vals = backend2.data.get(x.dataId).values;
const outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype);
return backend2.makeTensorInfo($size, x.dtype, outVals);
}
var sliceConfig = {
kernelName: Slice,
backendName: "cpu",
kernelFunc: slice2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows_impl.js
init_define_BUILD_VERSION();
function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) {
const indicesCount = indicesShape[0];
const denseRows = denseShape[0];
const emptyRowIndicator = new Array(denseRows);
const reverseIndexMap = new Array(indicesCount);
const rank = indicesShape[1];
if (denseRows === 0) {
if (indicesCount !== 0) {
throw new Error(backend_util_exports.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount));
}
const outputIndices = util_exports.getArrayFromDType(indicesDType, 0);
const outputValues = util_exports.getArrayFromDType(valuesDType, 0);
return [
outputIndices,
[0, rank],
outputValues,
emptyRowIndicator,
reverseIndexMap
];
}
let rowsAreOrdered = true;
let lastIndicesRow = 0;
const csrOffset = new Array(denseRows).fill(0);
for (let i = 0; i < indicesCount; ++i) {
const row = indices[i * rank];
if (row < 0) {
throw new Error(backend_util_exports.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row));
}
if (row >= denseRows) {
throw new Error(backend_util_exports.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows));
}
++csrOffset[row];
rowsAreOrdered = rowsAreOrdered && row >= lastIndicesRow;
lastIndicesRow = row;
}
let allRowsFull = true;
for (let row = 0; row < denseRows; ++row) {
const rowEmpty = csrOffset[row] === 0;
emptyRowIndicator[row] = rowEmpty;
allRowsFull = allRowsFull && !rowEmpty;
csrOffset[row] = Math.max(csrOffset[row], 1);
if (row > 0) {
csrOffset[row] += csrOffset[row - 1];
}
}
if (allRowsFull && rowsAreOrdered) {
const outputIndices = indices;
const outputValues = values;
for (let i = 0; i < indicesCount; ++i) {
reverseIndexMap[i] = i;
}
return [
outputIndices,
[indicesCount, rank],
outputValues,
emptyRowIndicator,
reverseIndexMap
];
} else {
const fullIndicesCount = csrOffset[denseRows - 1];
const outputIndices = util_exports.getArrayFromDType(indicesDType, fullIndicesCount * rank);
const outputValues = util_exports.getArrayFromDType(valuesDType, fullIndicesCount);
const filledCount = new Array(denseRows).fill(0);
for (let i = 0; i < indicesCount; ++i) {
const row = indices[i * rank];
const offset = filledCount[row];
const outputI = (row === 0 ? 0 : csrOffset[row - 1]) + offset;
filledCount[row]++;
for (let j = 0; j < rank; ++j) {
outputIndices[outputI * rank + j] = indices[i * rank + j];
}
outputValues[outputI] = values[i];
reverseIndexMap[i] = outputI;
}
for (let row = 0; row < denseRows; ++row) {
const rowCount = filledCount[row];
if (rowCount === 0) {
const startingIndex = row === 0 ? 0 : csrOffset[row - 1];
outputIndices[startingIndex * rank + 0] = row;
for (let col = 1; col < rank; ++col) {
outputIndices[startingIndex * rank + col] = 0;
}
outputValues[startingIndex] = defaultValue;
}
}
return [
outputIndices,
[fullIndicesCount, rank],
outputValues,
emptyRowIndicator,
reverseIndexMap
];
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape_impl.js
init_define_BUILD_VERSION();
function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) {
const denseSize = util_exports.sizeFromShape(inputShape);
const nnz = inputIndicesShape[0];
const outputRank = targetShape.length;
const outputShape = [];
let product = 1;
let unknownIndex = -1;
for (let d = 0; d < outputRank; ++d) {
const size = targetShape[d];
if (size === -1) {
if (unknownIndex !== -1) {
throw new Error(backend_util_exports.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d));
}
unknownIndex = d;
outputShape.push(1);
} else {
if (size < 0) {
throw new Error(backend_util_exports.getSparseReshapeNegativeOutputDimErrorMessage(d, size));
}
product *= size;
outputShape.push(size);
}
}
if (unknownIndex !== -1) {
if (product <= 0) {
throw new Error(backend_util_exports.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage());
}
const missing = Math.trunc(denseSize / product);
if (product * missing !== denseSize) {
throw new Error(backend_util_exports.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape));
}
outputShape[unknownIndex] = missing;
}
const outputSize = util_exports.sizeFromShape(outputShape);
if (outputSize !== denseSize) {
throw new Error(backend_util_exports.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape));
}
const inputRank = inputShape.length;
const inputStrides = [];
if (inputRank > 0) {
inputStrides[inputRank - 1] = 1;
for (let d = inputRank - 2; d >= 0; --d) {
inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1];
}
}
const outputStrides = [];
if (outputRank > 0) {
outputStrides[outputRank - 1] = 1;
for (let d = outputRank - 2; d >= 0; --d) {
outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1];
}
}
const newIndices = util_exports.getArrayFromDType(inputDType, nnz * outputRank);
for (let i = 0; i < nnz; ++i) {
let id = 0;
for (let j = 0; j < inputRank; ++j) {
id += inputIndices[i * inputRank + j] * inputStrides[j];
}
for (let j = 0; j < outputRank; ++j) {
newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]);
id %= outputStrides[j];
}
}
return [newIndices, [nnz, outputRank], outputShape];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentReduction_impl.js
init_define_BUILD_VERSION();
function sparseSegmentReductionImpl(input2, inputShape, inputDType, indices, segmentIds, isMean = false, defaultValue = 0) {
const numIndices = indices.length;
const inputFlat = [inputShape[0], input2.length / inputShape[0]];
const numCol = inputFlat[1];
const lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0;
const outputRows = lastSegmentIdPlusOne;
if (outputRows < 0) {
throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
}
const outputShape = inputShape.slice();
outputShape[0] = outputRows;
const outputLength = outputShape.reduce((product, value) => product * value, 1);
const output = util_exports.getArrayFromDType(inputDType, outputLength);
if (numIndices === 0) {
if (outputRows > 0) {
output.fill(defaultValue);
}
return [output, outputShape];
}
if (outputRows <= 0) {
throw new Error(backend_util_exports.getSparseSegmentReductionNegativeSegmentIdsErrorMessage());
}
let start = 0, end = 1;
let uninitializedIndex = 0;
let outIndex = segmentIds[start];
while (true) {
let nextIndex = 0;
if (end < numIndices) {
nextIndex = segmentIds[end];
if (outIndex === nextIndex) {
++end;
continue;
}
if (outIndex >= nextIndex) {
throw new Error(backend_util_exports.getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage());
}
}
if (outIndex < 0 || outIndex >= outputRows) {
throw new Error(backend_util_exports.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows));
}
if (outIndex > uninitializedIndex) {
output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol);
}
for (let i = start; i < end; ++i) {
const index = indices[i];
if (index < 0 || index >= inputFlat[0]) {
throw new Error(backend_util_exports.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0]));
}
for (let j = 0; j < numCol; j++) {
output[outIndex * numCol + j] += input2[index * numCol + j];
}
}
if (isMean) {
for (let j = 0; j < numCol; j++) {
output[outIndex * numCol + j] /= end - start;
}
}
start = end;
++end;
uninitializedIndex = outIndex + 1;
outIndex = nextIndex;
if (end > numIndices) {
break;
}
}
if (uninitializedIndex < outputRows) {
output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol);
}
return [output, outputShape];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sqrt.js
init_define_BUILD_VERSION();
var sqrtImpl = createSimpleUnaryImpl((xi) => Math.sqrt(xi));
var sqrt2 = unaryKernelFunc(Sqrt, (xi) => Math.sqrt(xi));
var sqrtConfig = {
kernelName: Sqrt,
backendName: "cpu",
kernelFunc: sqrt2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SquaredDifference.js
init_define_BUILD_VERSION();
var squaredDifferenceImpl = createSimpleBinaryKernelImpl((a, b) => {
const diff = a - b;
return diff * diff;
});
var squaredDifference2 = binaryKernelFunc(SquaredDifference, squaredDifferenceImpl);
var squaredDifferenceConfig = {
kernelName: SquaredDifference,
backendName: "cpu",
kernelFunc: squaredDifference2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice_impl.js
init_define_BUILD_VERSION();
function stridedSliceImpl(outShape, xBuf, strides, begin) {
const outBuf = buffer(outShape, xBuf.dtype);
for (let i = 0; i < outBuf.size; i++) {
const loc = outBuf.indexToLoc(i);
const newLoc = new Array(loc.length);
for (let j = 0; j < newLoc.length; j++) {
newLoc[j] = loc[j] * strides[j] + begin[j];
}
outBuf.set(xBuf.get(...newLoc), ...loc);
}
return outBuf;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams_impl.js
init_define_BUILD_VERSION();
var StringNGramsOp = class {
constructor(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
this.separator = util_exports.encodeString(separator);
this.nGramWidths = nGramWidths;
this.leftPad = util_exports.encodeString(leftPad);
this.rightPad = util_exports.encodeString(rightPad2);
this.padWidth = padWidth;
this.preserveShort = preserveShortSequences;
}
getPadWidth(nGramWidth) {
return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1);
}
getNumNGrams(length, nGramWidth) {
const padWidth = this.getPadWidth(nGramWidth);
return Math.max(0, length + 2 * padWidth - nGramWidth + 1);
}
createNGrams(data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) {
for (let nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) {
const padWidth = this.getPadWidth(nGramWidth);
const leftPadding = Math.max(0, padWidth - nGramIndex);
const rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1)));
const numTokens = nGramWidth - (leftPadding + rightPadding);
const dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth);
let nGramSize = 0;
nGramSize += leftPadding * this.leftPad.length;
for (let n = 0; n < numTokens; ++n) {
nGramSize += data[dataStartIndex + n].length;
}
nGramSize += rightPadding * this.rightPad.length;
const numSeparators = leftPadding + rightPadding + numTokens - 1;
nGramSize += numSeparators * this.separator.length;
output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize);
const nGram = output[outputStartIndex + nGramIndex];
let nextNGramIndex = 0;
const appendToNGram = (str) => str.forEach((value) => nGram[nextNGramIndex++] = value);
for (let n = 0; n < leftPadding; ++n) {
appendToNGram(this.leftPad);
appendToNGram(this.separator);
}
for (let n = 0; n < numTokens - 1; ++n) {
appendToNGram(data[dataStartIndex + n]);
appendToNGram(this.separator);
}
if (numTokens > 0) {
appendToNGram(data[dataStartIndex + numTokens - 1]);
for (let n = 0; n < rightPadding; ++n) {
appendToNGram(this.separator);
appendToNGram(this.rightPad);
}
} else {
for (let n = 0; n < rightPadding - 1; ++n) {
appendToNGram(this.rightPad);
appendToNGram(this.separator);
}
appendToNGram(this.rightPad);
}
}
}
compute(data, splits) {
const inputDataSize = data.length;
const splitsSize = splits.length;
if (splitsSize > 0) {
let prevSplit = splits[0];
if (prevSplit !== 0) {
throw new Error(`First split value must be 0, got ${prevSplit}`);
}
for (let i = 1; i < splitsSize; ++i) {
let validSplits = splits[i] >= prevSplit;
validSplits = validSplits && splits[i] <= inputDataSize;
if (!validSplits) {
throw new Error(`Invalid split value ${splits[i]}, must be in [${prevSplit}, ${inputDataSize}]`);
}
prevSplit = splits[i];
}
if (prevSplit !== inputDataSize) {
throw new Error(`Last split value must be data size. Expected ${inputDataSize}, got ${prevSplit}`);
}
}
const numBatchItems = splitsSize - 1;
const nGramsSplits = util_exports.getArrayFromDType("int32", splitsSize);
if (inputDataSize === 0 || splitsSize === 0) {
const empty = new Array(inputDataSize);
for (let i = 0; i <= numBatchItems; ++i) {
nGramsSplits[i] = 0;
}
return [empty, nGramsSplits];
}
nGramsSplits[0] = 0;
for (let i = 1; i <= numBatchItems; ++i) {
const length = splits[i] - splits[i - 1];
let numNGrams = 0;
this.nGramWidths.forEach((nGramWidth) => {
numNGrams += this.getNumNGrams(length, nGramWidth);
});
if (this.preserveShort && length > 0 && numNGrams === 0) {
numNGrams = 1;
}
nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams;
}
const nGrams = new Array(nGramsSplits[numBatchItems]);
for (let i = 0; i < numBatchItems; ++i) {
const splitIndex = splits[i];
let outputStartIdx = nGramsSplits[i];
this.nGramWidths.forEach((nGramWidth) => {
const length = splits[i + 1] - splits[i];
const numNGrams = this.getNumNGrams(length, nGramWidth);
this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
outputStartIdx += numNGrams;
});
if (this.preserveShort && outputStartIdx === nGramsSplits[i]) {
const dataLength = splits[i + 1] - splits[i];
if (dataLength === 0) {
continue;
}
const nGramWidth = dataLength + 2 * this.padWidth;
const numNGrams = 1;
this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth);
}
}
return [nGrams, nGramsSplits];
}
};
function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences) {
return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences).compute(data, dataSplits);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit_impl.js
init_define_BUILD_VERSION();
function split3(str, delimiters, skipEmpty, result) {
if (!str.length) {
return;
}
if (delimiters.length === 0) {
for (let i = 0; i < str.length; ++i) {
result.push(str.subarray(i, i + 1));
}
return;
}
if (delimiters.length === 1) {
const delimiter = delimiters[0];
let f = str.indexOf(delimiter);
while (f !== -1) {
const token = str.subarray(0, f);
if (!skipEmpty || token.length !== 0) {
result.push(token);
}
str = str.subarray(f + 1);
f = str.indexOf(delimiter);
}
if (!skipEmpty || str.length !== 0) {
result.push(str);
}
return;
}
let tokenStart = 0;
for (let i = 0; i < str.length + 1; i++) {
if (i === str.length || delimiters.indexOf(str[i]) !== -1) {
const token = str.subarray(tokenStart, i);
if (!skipEmpty || token.length !== 0) {
result.push(token);
}
tokenStart = i + 1;
}
}
}
function stringSplitImpl(input2, delimiter, skipEmpty) {
const batchSize = input2.length;
const tokens = [];
let outputSize = 0;
let maxNumEntries = 0;
const numIndices = new Array(batchSize);
for (let i = 0; i < batchSize; ++i) {
const prevTokensLength = tokens.length;
split3(input2[i], delimiter, skipEmpty, tokens);
const nEntries = tokens.length - prevTokensLength;
numIndices[i] = nEntries;
outputSize += nEntries;
maxNumEntries = Math.max(maxNumEntries, nEntries);
}
const indices = util_exports.getArrayFromDType("int32", outputSize * 2);
const values = new Array(outputSize);
const shape = [batchSize, maxNumEntries];
let c = 0;
for (let i = 0; i < batchSize; ++i) {
for (let j = 0; j < numIndices[i]; ++j) {
indices[c * 2] = i;
indices[c * 2 + 1] = j;
values[c] = tokens[c];
++c;
}
}
return [indices, values, shape];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast_impl.js
init_define_BUILD_VERSION();
function stringToHashBucketFastImpl(input2, numBuckets) {
const output = util_exports.getArrayFromDType("int32", input2.length);
for (let i = 0; i < input2.length; ++i) {
output[i] = util_exports.fingerPrint64(input2[i]).modulo(numBuckets).getLowBitsUnsigned();
}
return output;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sub.js
init_define_BUILD_VERSION();
var subImpl = createSimpleBinaryKernelImpl((aValue, bValue) => aValue - bValue);
var subComplexImpl = createComplexBinaryKernelImpl((aReal, aImag, bReal, bImag) => {
return { real: aReal - bReal, imag: aImag - bImag };
});
var sub2 = binaryKernelFunc(Sub, subImpl, subComplexImpl);
var subConfig = {
kernelName: Sub,
backendName: "cpu",
kernelFunc: sub2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile_impl.js
init_define_BUILD_VERSION();
function tileImpl(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;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK_impl.js
init_define_BUILD_VERSION();
var comparePair = (a, b) => {
const valueDiff = b.value - a.value;
return valueDiff === 0 ? a.index - b.index : valueDiff;
};
function select(array2, k, left = 0, right = array2.length - 1) {
while (right > left) {
if (right - left > 600) {
const n = right - left + 1;
const i2 = k - left + 1;
const z = Math.log(n);
const s = 0.5 * Math.exp(2 * z / 3);
const sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i2 - n / 2);
const newLeft = Math.max(left, Math.floor(k - i2 * s / n + sd));
const newRight = Math.min(right, Math.floor(k + (n - i2) * s / n + sd));
select(array2, k, newLeft, newRight);
}
const t = array2[k];
let i = left;
let j = right;
util_exports.swap(array2, left, k);
if (comparePair(array2[right], t) > 0) {
util_exports.swap(array2, left, right);
}
while (i < j) {
util_exports.swap(array2, i, j);
i++;
j--;
while (comparePair(array2[i], t) < 0) {
i = i + 1;
}
while (comparePair(array2[j], t) > 0) {
j = j - 1;
}
}
if (comparePair(array2[left], t) === 0) {
util_exports.swap(array2, left, j);
} else {
j = j + 1;
util_exports.swap(array2, j, right);
}
if (j <= k) {
left = j + 1;
}
if (k <= j) {
right = j - 1;
}
}
}
function topKImpl(x, xShape, xDtype, k, sorted) {
const lastDim = xShape[xShape.length - 1];
const [batch, size] = [x.length / lastDim, lastDim];
const allTopKVals = util_exports.getTypedArrayFromDType(xDtype, batch * k);
const allTopKIndices = util_exports.getTypedArrayFromDType("int32", batch * k);
for (let b = 0; b < batch; b++) {
const offset = b * size;
const vals = x.subarray(offset, offset + size);
let valAndInd = new Array(vals.length);
vals.forEach((value, index) => valAndInd[index] = { value, index });
if (k < valAndInd.length) {
select(valAndInd, k);
valAndInd = valAndInd.slice(0, k);
}
if (sorted) {
valAndInd.sort(comparePair);
}
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 [
buffer(outputShape, xDtype, allTopKVals),
buffer(outputShape, "int32", allTopKIndices)
];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique_impl.js
init_define_BUILD_VERSION();
function uniqueImpl(values, axis, shape, dtype) {
const $axis = util_exports.parseAxisParam(axis, shape)[0];
const newShape = [1, shape[0], 1];
for (let i = 0; i < $axis; i++) {
newShape[0] *= shape[i];
}
newShape[1] = shape[$axis];
for (let i = $axis + 1; i < shape.length; i++) {
newShape[2] *= shape[i];
}
const uniqueElements = {};
const indices = new Int32Array(shape[$axis]);
const inputBuffer = new TensorBuffer(newShape, dtype, values);
const uniqueIndices = [];
const is1DTensor = newShape[0] === 1 && newShape[2] === 1;
for (let i = 0; i < shape[$axis]; i++) {
let element;
if (is1DTensor) {
element = values[i].toString();
} else {
const axisValues = [];
for (let m = 0; m < newShape[0]; m++) {
for (let n = 0; n < newShape[2]; n++) {
axisValues.push(inputBuffer.get(m, i, n));
}
}
element = axisValues.join(",");
}
if (uniqueElements[element] !== void 0) {
indices[i] = uniqueElements[element];
} else {
const uniqueIndex = Object.keys(uniqueElements).length;
uniqueElements[element] = uniqueIndex;
indices[i] = uniqueIndex;
uniqueIndices.push(i);
}
}
const outputTmpShape = newShape.slice();
outputTmpShape[1] = Object.keys(uniqueElements).length;
const outputBuffer = new TensorBuffer(outputTmpShape, dtype);
uniqueIndices.forEach((uniqueElementIndex, i) => {
for (let m = 0; m < newShape[0]; m++) {
for (let n = 0; n < newShape[2]; n++) {
outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n);
}
}
});
const outputShape = shape.slice();
outputShape[$axis] = outputTmpShape[1];
return {
outputValues: outputBuffer.values,
outputShape,
indices
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/base.js
registerBackend("cpu", () => new MathBackendCPU(), 1);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Elu.js
init_define_BUILD_VERSION();
var elu3 = unaryKernelFunc(Elu, (xi) => xi >= 0 ? xi : Math.exp(xi) - 1);
var eluConfig = {
kernelName: Elu,
backendName: "cpu",
kernelFunc: elu3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LeakyRelu.js
init_define_BUILD_VERSION();
function leakyRelu2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { alpha } = attrs;
assertNotComplex([x], "leakyRelu");
const xSize = util_exports.sizeFromShape(x.shape);
const xVals = backend2.data.get(x.dataId).values;
const outVals = util_exports.getTypedArrayFromDType("float32", xSize);
for (let i = 0; i < xVals.length; i++) {
outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i];
}
return backend2.makeTensorInfo(x.shape, "float32", outVals);
}
var leakyReluConfig = {
kernelName: LeakyRelu,
backendName: "cpu",
kernelFunc: leakyRelu2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Prelu.js
init_define_BUILD_VERSION();
var preluImpl = createSimpleBinaryKernelImpl((xValue, aValue) => xValue < 0 ? aValue * xValue : xValue);
function prelu2(args) {
const { inputs, backend: backend2 } = args;
const { x, alpha } = inputs;
assertNotComplex([x, alpha], "prelu");
const aVals = backend2.data.get(x.dataId).values;
const bVals = backend2.data.get(alpha.dataId).values;
const [resultData, resultShape] = preluImpl(x.shape, alpha.shape, aVals, bVals, "float32");
return backend2.makeTensorInfo(resultShape, "float32", resultData);
}
var preluConfig = {
kernelName: Prelu,
backendName: "cpu",
kernelFunc: prelu2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu.js
init_define_BUILD_VERSION();
var relu2 = unaryKernelFunc(Relu, (xi) => Math.max(0, xi));
var reluConfig = {
kernelName: Relu,
backendName: "cpu",
kernelFunc: relu2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Relu6.js
init_define_BUILD_VERSION();
var relu62 = unaryKernelFunc(Relu6, (xi) => Math.min(Math.max(0, xi), 6));
var relu6Config = {
kernelName: Relu6,
backendName: "cpu",
kernelFunc: relu62
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fused_utils.js
function applyActivation2(backend2, x, activation, preluActivationWeights, leakyreluAlpha) {
if (activation === "linear") {
return identity({ inputs: { x }, backend: backend2 });
} else if (activation === "relu") {
return relu2({ inputs: { x }, backend: backend2 });
} else if (activation === "elu") {
return elu3({ inputs: { x }, backend: backend2 });
} else if (activation === "relu6") {
return relu62({ inputs: { x }, backend: backend2 });
} else if (activation === "prelu") {
return prelu2({ inputs: { x, alpha: preluActivationWeights }, backend: backend2 });
} else if (activation === "leakyrelu") {
return leakyRelu2({ inputs: { x }, backend: backend2, attrs: { alpha: leakyreluAlpha } });
} else if (activation === "sigmoid") {
return sigmoid2({ inputs: { x }, backend: backend2 });
}
throw new Error(`Activation ${activation} has not been implemented for the CPU backend.`);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reshape.js
init_define_BUILD_VERSION();
function reshape2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { shape } = attrs;
const xSize = util_exports.sizeFromShape(x.shape);
const $shape = util_exports.inferFromImplicitShape(shape, xSize);
const $xSize = util_exports.sizeFromShape($shape);
util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
backend2.incRef(x.dataId);
const xData = backend2.data.get(x.dataId);
if (xData.complexTensorInfos != null) {
const real4 = xData.complexTensorInfos.real;
const imag4 = xData.complexTensorInfos.imag;
real4.shape = $shape;
imag4.shape = $shape;
}
return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
}
var reshapeConfig = {
kernelName: Reshape,
backendName: "cpu",
kernelFunc: reshape2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchMatMul.js
function batchMatMul(args) {
const { inputs, backend: backend2, attrs } = args;
const { a, b } = inputs;
const { transposeA, transposeB } = attrs;
assertNotComplex([a, b], "matMul");
const aRank = a.shape.length;
const bRank = b.shape.length;
const innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1];
const innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2];
const outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2];
const outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1];
const outerDimsA = a.shape.slice(0, -2);
const outerDimsB = b.shape.slice(0, -2);
const batchDimA = util_exports.sizeFromShape(outerDimsA);
const batchDimB = util_exports.sizeFromShape(outerDimsB);
const outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2));
const outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]);
util_exports.assert(innerShapeA === innerShapeB, () => `Error in matMul: inner shapes (${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 = reshape2({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });
const b3d = reshape2({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
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 a3dValues = backend2.data.get(a3d.dataId).values;
const b3dValues = backend2.data.get(b3d.dataId).values;
const a3dStrides = util_exports.computeStrides(a3d.shape);
const b3dStrides = util_exports.computeStrides(b3d.shape);
const [aBatch, aOuterStep, aInnerStep] = transposeA ? [a3dStrides[0], 1, a3dStrides[1]] : [a3dStrides[0], a3dStrides[1], 1];
const [bInnerStep, bOuterStep, bBatch] = transposeB ? [1, b3dStrides[1], b3dStrides[0]] : [b3dStrides[1], 1, b3dStrides[0]];
const size = leftDim * rightDim;
const result = buffer([batchDim, leftDim, rightDim], a3d.dtype);
const resVals = result.values;
const blockSize = backend2.blockSize;
for (let bi = 0; bi < batchDim; bi++) {
for (let i0 = 0; i0 < leftDim; i0 += blockSize) {
for (let j0 = 0; j0 < rightDim; j0 += blockSize) {
for (let k02 = 0; k02 < sharedDim; k02 += blockSize) {
const iBlock = Math.min(i0 + blockSize, leftDim);
const jBlock = Math.min(j0 + blockSize, rightDim);
const kBlock = Math.min(k02 + blockSize, sharedDim);
for (let i = i0; i < iBlock; i++) {
for (let j = j0; j < jBlock; j++) {
let sum5 = 0;
for (let k = k02; k < kBlock; k++) {
const batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch;
const batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch;
const aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep];
const bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB];
sum5 += aVal * bVal;
}
resVals[bi * size + (i * rightDim + j)] += sum5;
}
}
}
}
}
}
backend2.disposeIntermediateTensorInfo(a3d);
backend2.disposeIntermediateTensorInfo(b3d);
return backend2.makeTensorInfo(outShape, result.dtype, result.values);
}
var batchMatMulConfig = {
kernelName: BatchMatMul,
backendName: "cpu",
kernelFunc: batchMatMul
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/_FusedMatMul.js
function _fusedMatMul(args) {
const { inputs, backend: backend2, attrs } = args;
const { a, b, bias, preluActivationWeights } = inputs;
const { transposeA, transposeB, activation, leakyreluAlpha } = attrs;
let current;
let addRes;
let activationRes;
const intermediates = [];
const matMulRes = batchMatMul({ inputs: { a, b }, attrs: { transposeA, transposeB }, backend: backend2 });
current = matMulRes;
if (bias) {
addRes = add3({ inputs: { a: current, b: bias }, backend: backend2 });
intermediates.push(current);
current = addRes;
}
if (activation) {
activationRes = applyActivation2(backend2, current, activation, preluActivationWeights, leakyreluAlpha);
intermediates.push(current);
current = activationRes;
}
for (const i of intermediates) {
backend2.disposeIntermediateTensorInfo(i);
}
return current;
}
var _fusedMatMulConfig = {
kernelName: _FusedMatMul,
backendName: "cpu",
kernelFunc: _fusedMatMul
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acos.js
init_define_BUILD_VERSION();
var acos2 = unaryKernelFunc(Acos, (xi) => Math.acos(xi));
var acosConfig = {
kernelName: Acos,
backendName: "cpu",
kernelFunc: acos2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Acosh.js
init_define_BUILD_VERSION();
var acosh2 = unaryKernelFunc(Acosh, (xi) => Math.acosh(xi));
var acoshConfig = {
kernelName: Acosh,
backendName: "cpu",
kernelFunc: acosh2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AddN.js
init_define_BUILD_VERSION();
function addN(args) {
const { inputs, backend: backend2 } = args;
const tensors = inputs;
assertNotComplex(inputs, "addN");
const vals = tensors.map((t) => backend2.data.get(t.dataId).values);
const outBuf = buffer(tensors[0].shape, tensors[0].dtype);
const outVals = outBuf.values;
for (let i = 0; i < tensors.length; i++) {
const currVals = vals[i];
for (let j = 0; j < outVals.length; j++) {
outVals[j] += currVals[j];
}
}
return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
var addNConfig = {
kernelName: AddN,
backendName: "cpu",
kernelFunc: addN
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/All.js
init_define_BUILD_VERSION();
function all2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
assertNotComplex(x, "all");
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
if (permutedAxes != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
}
backend_util_exports.assertAxesAreInnerMostDims("all", axes, $x.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
const aVals = backend2.data.get($x.dataId).values;
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let all4 = aVals[offset];
for (let j = 0; j < reduceSize; ++j) {
const value = aVals[offset + j];
all4 = all4 && value;
}
vals[i] = all4;
}
if (permutedAxes != null) {
backend2.disposeIntermediateTensorInfo($x);
}
const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
if (keepDims) {
const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
const reshapedResult = reshape2({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
backend2.disposeIntermediateTensorInfo(result);
return reshapedResult;
}
return result;
}
var allConfig = {
kernelName: All,
backendName: "cpu",
kernelFunc: all2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Any.js
init_define_BUILD_VERSION();
function any2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
assertNotComplex(x, "any");
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
if (permutedAxes != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
}
backend_util_exports.assertAxesAreInnerMostDims("any", axes, $x.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
const aVals = backend2.data.get($x.dataId).values;
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let anyVal = aVals[offset];
for (let j = 0; j < reduceSize; ++j) {
const value = aVals[offset + j];
anyVal = anyVal || value;
}
vals[i] = anyVal;
}
if (permutedAxes != null) {
backend2.disposeIntermediateTensorInfo($x);
}
const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
if (keepDims) {
const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
const reshapedResult = reshape2({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
backend2.disposeIntermediateTensorInfo(result);
return reshapedResult;
}
return result;
}
var anyConfig = {
kernelName: Any,
backendName: "cpu",
kernelFunc: any2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMax.js
init_define_BUILD_VERSION();
function argMax2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis } = attrs;
assertNotComplex(x, "argMax");
let axes = util_exports.parseAxisParam(axis, x.shape);
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
const intermediateTensorInfos = [];
if (permutedAxes != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
intermediateTensorInfos.push($x);
axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
}
axes = [axes[0]];
backend_util_exports.assertAxesAreInnerMostDims("argMax", axes, $x.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
const outSize = util_exports.sizeFromShape(outShape);
const vals = util_exports.makeZerosTypedArray(outSize, "int32");
const reduceSize = util_exports.sizeFromShape(reduceShape);
const aVals = backend2.data.get($x.dataId).values;
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let max5 = aVals[offset];
let maxIndex = 0;
for (let j = 0; j < reduceSize; ++j) {
const value = aVals[offset + j];
if (value > max5) {
max5 = value;
maxIndex = j;
}
}
vals[i] = maxIndex;
}
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return backend2.makeTensorInfo(outShape, "int32", vals);
}
var argMaxConfig = {
kernelName: ArgMax,
backendName: "cpu",
kernelFunc: argMax2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ArgMin.js
init_define_BUILD_VERSION();
function argMin2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis } = attrs;
assertNotComplex(x, "argMin");
let axes = util_exports.parseAxisParam(axis, x.shape);
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
const intermediateTensorInfos = [];
if (permutedAxes != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
intermediateTensorInfos.push($x);
axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
}
axes = [axes[0]];
backend_util_exports.assertAxesAreInnerMostDims("argMin", axes, $x.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
const outSize = util_exports.sizeFromShape(outShape);
const vals = util_exports.makeZerosTypedArray(outSize, "int32");
const reduceSize = util_exports.sizeFromShape(reduceShape);
const aVals = backend2.data.get($x.dataId).values;
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let min5 = aVals[offset];
let minIndex = 0;
for (let j = 0; j < reduceSize; ++j) {
const value = aVals[offset + j];
if (value < min5) {
min5 = value;
minIndex = j;
}
}
vals[i] = minIndex;
}
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return backend2.makeTensorInfo(outShape, "int32", vals);
}
var argMinConfig = {
kernelName: ArgMin,
backendName: "cpu",
kernelFunc: argMin2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asin.js
init_define_BUILD_VERSION();
var asin2 = unaryKernelFunc(Asin, (xi) => Math.asin(xi));
var asinConfig = {
kernelName: Asin,
backendName: "cpu",
kernelFunc: asin2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Asinh.js
init_define_BUILD_VERSION();
var asinh2 = unaryKernelFunc(Asinh, (xi) => Math.asinh(xi));
var asinhConfig = {
kernelName: Asinh,
backendName: "cpu",
kernelFunc: asinh2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan.js
init_define_BUILD_VERSION();
var atan3 = unaryKernelFunc(Atan, (xi) => Math.atan(xi));
var atanConfig = {
kernelName: Atan,
backendName: "cpu",
kernelFunc: atan3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atan2.js
init_define_BUILD_VERSION();
var atan2Impl = createSimpleBinaryKernelImpl((aValue, bValue) => Math.atan2(aValue, bValue));
var atan22 = binaryKernelFunc(Atan2, atan2Impl);
var atan2Config = {
kernelName: Atan2,
backendName: "cpu",
kernelFunc: atan22
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Atanh.js
init_define_BUILD_VERSION();
var atanh2 = unaryKernelFunc(Atanh, (xi) => Math.atanh(xi));
var atanhConfig = {
kernelName: Atanh,
backendName: "cpu",
kernelFunc: atanh2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/pool_utils.js
init_define_BUILD_VERSION();
function pool2(xValues, xShape, dtype, strides, convInfo, poolType) {
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
const output = buffer(convInfo.outShape, dtype);
const outputVals = output.values;
const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
const outputColStrides = convInfo.outShape[3];
for (let b = 0; b < convInfo.batchSize; ++b) {
const outputBatchOffset = b * outputBatchStrides;
const inputBatchOffset = b * strides[0];
for (let d = 0; d < convInfo.inChannels; ++d) {
for (let yR = 0; yR < convInfo.outHeight; ++yR) {
const xRCorner = yR * strideHeight - padTop;
const xRMin = Math.max(0, xRCorner);
const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
const outputRowOffset = outputBatchOffset + yR * outputRowStrides;
for (let yC = 0; yC < convInfo.outWidth; ++yC) {
const xCCorner = yC * strideWidth - padLeft;
const xCMin = Math.max(0, xCCorner);
const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
let minMaxValue = initialValue;
let avgValue = 0;
let count2 = 0;
for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
const xROffset = inputBatchOffset + xR * strides[1];
for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
const xCOffset = xROffset + xC * strides[2];
const pixel = xValues[xCOffset + d];
if (poolType === "max" && pixel > minMaxValue) {
minMaxValue = pixel;
} else if (poolType === "avg") {
avgValue += pixel;
count2++;
}
}
if (isNaN(minMaxValue)) {
break;
}
}
const outputOffset = outputRowOffset + yC * outputColStrides + d;
outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue;
}
}
}
}
return output;
}
function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions = false, includeBatchInIndex = false) {
const maxPositions = buffer(convInfo.outShape, "int32");
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const xBuf = buffer(xShape, dtype, xValues);
for (let b = 0; b < convInfo.batchSize; ++b) {
for (let d = 0; d < convInfo.inChannels; ++d) {
for (let yR = 0; yR < convInfo.outHeight; ++yR) {
const xRCorner = yR * strideHeight - padTop;
let xRMin = xRCorner;
while (xRMin < 0) {
xRMin += dilationHeight;
}
const xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
for (let yC = 0; yC < convInfo.outWidth; ++yC) {
const xCCorner = yC * strideWidth - padLeft;
let xCMin = xCCorner;
while (xCMin < 0) {
xCMin += dilationWidth;
}
const xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
let maxValue = Number.NEGATIVE_INFINITY;
let maxPosition = -1;
for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {
const wR = xR - xRCorner;
for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {
const wC = xC - xCCorner;
const pixel = xBuf.get(b, xR, xC, d);
if (pixel > maxValue) {
maxValue = pixel;
if (flattenPositions) {
maxPosition = includeBatchInIndex ? ((b * convInfo.inHeight + xR) * convInfo.inWidth + xC) * convInfo.inChannels + d : (xR * convInfo.inWidth + xC) * convInfo.inChannels + d;
} else {
maxPosition = wR * effectiveFilterWidth + wC;
}
}
}
}
maxPositions.set(maxPosition, b, yR, yC, d);
}
}
}
}
return maxPositions;
}
function pool3d2(xValues, xShape, dtype, strides, convInfo, poolType) {
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = convInfo.padInfo.front;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const initialValue = poolType === "max" ? Number.NEGATIVE_INFINITY : Number.POSITIVE_INFINITY;
const output = buffer(convInfo.outShape, dtype);
const outputVals = output.values;
const outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
const outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4];
const outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4];
const outputColStrides = convInfo.outShape[4];
for (let batch = 0; batch < convInfo.batchSize; ++batch) {
const outputBatchOffset = batch * outputBatchStrides;
const inputBatchOffset = batch * strides[0];
for (let channel = 0; channel < convInfo.inChannels; ++channel) {
for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
const xDepthCorner = yDepth * strideDepth - padFront;
let xDepthMin = xDepthCorner;
while (xDepthMin < 0) {
xDepthMin += dilationDepth;
}
const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
const outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides;
for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
const xRowCorner = yRow * strideHeight - padTop;
let xRowMin = xRowCorner;
while (xRowMin < 0) {
xRowMin += dilationHeight;
}
const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
const outputRowOffset = outputDepthOffset + yRow * outputRowStrides;
for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
const xColCorner = yCol * strideWidth - padLeft;
let xColMin = xColCorner;
while (xColMin < 0) {
xColMin += dilationWidth;
}
const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
const outputColOffset = outputRowOffset + yCol * outputColStrides;
let minMaxValue = initialValue;
let avgValue = 0;
let count2 = 0;
for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
const xDepthOffset = inputBatchOffset + xDepth * strides[1];
for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
const xRowOffset = xDepthOffset + xRow * strides[2];
for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
const xColOffset = xRowOffset + xCol * strides[3];
const pixel = xValues[xColOffset + channel];
if (poolType === "max" && pixel > minMaxValue) {
minMaxValue = pixel;
} else if (poolType === "avg") {
avgValue += pixel;
count2++;
}
if (isNaN(minMaxValue)) {
break;
}
}
if (isNaN(minMaxValue)) {
break;
}
}
if (isNaN(minMaxValue)) {
break;
}
}
const outputOffset = outputColOffset + channel;
outputVals[outputOffset] = poolType === "avg" ? avgValue / count2 : minMaxValue;
}
}
}
}
}
return output;
}
function maxPool3dPositions(xBuf, convInfo) {
const maxPositions = buffer(convInfo.outShape, "int32");
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = convInfo.padInfo.front;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
for (let batch = 0; batch < convInfo.batchSize; ++batch) {
for (let channel = 0; channel < convInfo.inChannels; ++channel) {
for (let yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) {
const xDepthCorner = yDepth * strideDepth - padFront;
let xDepthMin = xDepthCorner;
while (xDepthMin < 0) {
xDepthMin += dilationDepth;
}
const xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner);
for (let yRow = 0; yRow < convInfo.outHeight; ++yRow) {
const xRowCorner = yRow * strideHeight - padTop;
let xRowMin = xRowCorner;
while (xRowMin < 0) {
xRowMin += dilationHeight;
}
const xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner);
for (let yCol = 0; yCol < convInfo.outWidth; ++yCol) {
const xColCorner = yCol * strideWidth - padLeft;
let xColMin = xColCorner;
while (xColMin < 0) {
xColMin += dilationWidth;
}
const xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner);
let maxValue = Number.NEGATIVE_INFINITY;
let maxPosition = -1;
for (let xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) {
const wDepth = xDepth - xDepthCorner;
for (let xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) {
const wRow = xRow - xRowCorner;
for (let xCol = xColMin; xCol < xColMax; xCol += dilationWidth) {
const wCol = xCol - xColCorner;
const pixel = xBuf.get(batch, xDepth, xRow, xCol, channel);
if (pixel >= maxValue) {
maxValue = pixel;
maxPosition = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterHeight + wCol;
}
}
}
}
maxPositions.set(maxPosition, batch, yDepth, yRow, yCol, channel);
}
}
}
}
}
return maxPositions;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool.js
function avgPool2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
assertNotComplex(x, "avgPool");
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const dilations = 1;
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode);
let res;
if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
res = identity({ inputs: { x }, backend: backend2 });
} else {
const xValues = backend2.data.get(x.dataId).values;
const strides2 = util_exports.computeStrides(x.shape);
const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "avg");
res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
}
return res;
}
var avgPoolConfig = {
kernelName: AvgPool,
backendName: "cpu",
kernelFunc: avgPool2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3D.js
init_define_BUILD_VERSION();
function avgPool3D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { filterSize, strides, pad: pad2, dimRoundingMode, dataFormat } = attrs;
assertNotComplex(x, "avgPool3d");
const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad2, dimRoundingMode, dataFormat);
const xValues = backend2.data.get(x.dataId).values;
const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "avg");
return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
}
var avgPool3DConfig = {
kernelName: AvgPool3D,
backendName: "cpu",
kernelFunc: avgPool3D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPool3DGrad.js
init_define_BUILD_VERSION();
function avgPool3DGrad(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2 } = inputs;
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
assertNotComplex([dy, input2], "avgPool3DGrad");
const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad2, dimRoundingMode);
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const filterDepth = convInfo.filterDepth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const dx = buffer(input2.shape, "float32");
const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
const dyBuf = backend2.bufferSync(dy);
for (let batch = 0; batch < convInfo.batchSize; ++batch) {
for (let channel = 0; channel < convInfo.inChannels; ++channel) {
for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
const dyDepthCorner = dxDepth - padFront;
const dyRowCorner = dxRow - padTop;
const dyColCorner = dxCol - padLeft;
let dotProd = 0;
for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
continue;
}
for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
const dyRow = (dyRowCorner + wRow) / strideHeight;
if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
continue;
}
for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
const dyCol = (dyColCorner + wCol) / strideWidth;
if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
continue;
}
const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
dotProd += pixel;
}
}
}
dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel);
}
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var avgPool3DGradConfig2 = {
kernelName: AvgPool3DGrad,
backendName: "cpu",
kernelFunc: avgPool3DGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/AvgPoolGrad.js
init_define_BUILD_VERSION();
function avgPoolGrad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2 } = inputs;
const x = input2;
assertNotComplex([dy, input2], "avgPoolGrad");
const { filterSize, strides, pad: pad2 } = attrs;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad2);
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const dx = buffer(x.shape, "float32");
const avgMultiplier = 1 / (filterHeight * filterWidth);
const dyData = backend2.data.get(dy.dataId).values;
const dyBuf = buffer(dy.shape, "float32", dyData);
for (let b = 0; b < convInfo.batchSize; ++b) {
for (let d = 0; d < convInfo.inChannels; ++d) {
for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
const dyRCorner = dxR - padTop;
const dyCCorner = dxC - padLeft;
let dotProd = 0;
for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
const dyR = (dyRCorner + wR) / strideHeight;
if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
continue;
}
for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
const dyC = (dyCCorner + wC) / strideWidth;
if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
continue;
}
const pixel = dyBuf.get(b, dyR, dyC, d);
dotProd += pixel;
}
}
dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var avgPoolGradConfig2 = {
kernelName: AvgPoolGrad,
backendName: "cpu",
kernelFunc: avgPoolGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchNorm.js
init_define_BUILD_VERSION();
function batchNorm2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, scale: scale2, offset, mean: mean3, variance } = inputs;
util_exports.assert(mean3.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
util_exports.assert(offset == null || mean3.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
util_exports.assert(scale2 == null || mean3.shape.length === scale2.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
assertNotComplex([x, mean3, variance, scale2, offset], "batchNorm");
let { varianceEpsilon } = attrs;
if (varianceEpsilon == null) {
varianceEpsilon = 1e-3;
}
const xVals = backend2.data.get(x.dataId).values;
const mVals = backend2.data.get(mean3.dataId).values;
const varVals = backend2.data.get(variance.dataId).values;
const sVals = scale2 ? backend2.data.get(scale2.dataId).values : new Float32Array([1]);
const offVals = offset ? backend2.data.get(offset.dataId).values : new Float32Array([0]);
const outVals = new Float32Array(xVals.length);
const offValsLength = offVals.length;
const sValsLength = sVals.length;
const varValsLength = varVals.length;
const mValsLength = mVals.length;
let offi = 0;
let mi = 0;
let si = 0;
let vi = 0;
for (let i = 0; i < xVals.length; ++i) {
outVals[i] = offVals[offi++] + (xVals[i] - mVals[mi++]) * sVals[si++] / Math.sqrt(varVals[vi++] + varianceEpsilon);
if (offi >= offValsLength) {
offi = 0;
}
if (mi >= mValsLength) {
mi = 0;
}
if (si >= sValsLength) {
si = 0;
}
if (vi >= varValsLength) {
vi = 0;
}
}
return backend2.makeTensorInfo(x.shape, x.dtype, outVals);
}
var batchNormConfig = {
kernelName: FusedBatchNorm,
backendName: "cpu",
kernelFunc: batchNorm2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BatchToSpaceND.js
init_define_BUILD_VERSION();
function batchToSpaceND2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { blockShape, crops } = attrs;
assertNotComplex([x], "batchToSpaceND");
const prod4 = blockShape.reduce((a, b) => a * b);
const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod4);
const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod4);
const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
const xReshaped = reshape2({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
const xTransposed = transpose2({ inputs: { x: xReshaped }, backend: backend2, attrs: { perm: permuted } });
const xTransposedReshaped = reshape2({ inputs: { x: xTransposed }, backend: backend2, attrs: { shape: reshapedPermuted } });
const result = slice2({
inputs: { x: xTransposedReshaped },
backend: backend2,
attrs: { begin: sliceBeginCoords, size: sliceSize }
});
backend2.disposeIntermediateTensorInfo(xReshaped);
backend2.disposeIntermediateTensorInfo(xTransposed);
backend2.disposeIntermediateTensorInfo(xTransposedReshaped);
return result;
}
var batchToSpaceNDConfig = {
kernelName: BatchToSpaceND,
backendName: "cpu",
kernelFunc: batchToSpaceND2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Bincount.js
init_define_BUILD_VERSION();
function bincount2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, weights } = inputs;
const { size } = attrs;
const xVals = backend2.data.get(x.dataId).values;
const weightsVals = backend2.data.get(weights.dataId).values;
const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
return backend2.makeTensorInfo([size], weights.dtype, outVals);
}
var bincountConfig = {
kernelName: Bincount,
backendName: "cpu",
kernelFunc: bincount2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/BroadcastArgs.js
init_define_BUILD_VERSION();
function broadcastArgs(args) {
const { inputs, backend: backend2 } = args;
const { s0, s1 } = inputs;
const s0Vals = backend2.data.get(s0.dataId).values;
const s1Vals = backend2.data.get(s1.dataId).values;
const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape));
}
var broadcastArgsConfig = {
kernelName: BroadcastArgs,
backendName: "cpu",
kernelFunc: broadcastArgs
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ClipByValue.js
init_define_BUILD_VERSION();
var clipByValue2 = unaryKernelFunc(ClipByValue, (xi, attrs) => {
const clipAttrs = attrs;
if (xi > clipAttrs.clipValueMax) {
return clipAttrs.clipValueMax;
}
return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi;
});
var clipByValueConfig = {
kernelName: ClipByValue,
backendName: "cpu",
kernelFunc: clipByValue2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ComplexAbs.js
init_define_BUILD_VERSION();
var complexAbs = (args) => {
const { x } = args.inputs;
const cpuBackend = args.backend;
const resultValues = new Float32Array(util_exports.sizeFromShape(x.shape));
const complexVals = cpuBackend.data.get(x.dataId);
const real4 = complexVals.complexTensorInfos.real;
const imag4 = complexVals.complexTensorInfos.imag;
const realVals = cpuBackend.data.get(real4.dataId).values;
const imagVals = cpuBackend.data.get(imag4.dataId).values;
for (let i = 0; i < realVals.length; i++) {
const real5 = realVals[i];
const imag5 = imagVals[i];
resultValues[i] = Math.hypot(real5, imag5);
}
return cpuBackend.makeOutput(resultValues, x.shape, "float32");
};
var complexAbsConfig = {
kernelName: ComplexAbs,
backendName: "cpu",
kernelFunc: complexAbs
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Imag.js
init_define_BUILD_VERSION();
function imag2(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
const imag4 = backend2.data.get(input2.dataId).complexTensorInfos.imag;
const imagVal = backend2.data.get(imag4.dataId).values;
return backend2.makeTensorInfo(imag4.shape, imag4.dtype, imagVal);
}
var imagConfig = {
kernelName: Imag,
backendName: "cpu",
kernelFunc: imag2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Concat.js
function concat2(args) {
const { inputs, backend: backend2, attrs } = args;
const { axis } = attrs;
const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
let outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
if (util_exports.sizeFromShape(outShape) === 0) {
return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
}
const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
if ($inputs.length === 1) {
return identity({ inputs: { x: $inputs[0] }, backend: backend2 });
}
const shapes = $inputs.map((t) => t.shape);
backend_util_exports.assertParamsConsistent(shapes, $axis);
if ($inputs[0].dtype === "complex64") {
const reals = $inputs.map((t) => real2({ inputs: { input: t }, backend: backend2 }));
const imags = $inputs.map((t) => imag2({ inputs: { input: t }, backend: backend2 }));
const realConcated = concat2({ inputs: reals, backend: backend2, attrs: { axis: $axis } });
const imagConcated = concat2({ inputs: imags, backend: backend2, attrs: { axis: $axis } });
const result = complex2({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
backend2.disposeIntermediateTensorInfo(realConcated);
backend2.disposeIntermediateTensorInfo(imagConcated);
return result;
}
const inputs2D = $inputs.map((t) => {
const innerSize = util_exports.sizeFromShape(t.shape.slice($axis));
const shape = [-1, innerSize];
return reshape2({ inputs: { x: t }, backend: backend2, attrs: { shape } });
});
const inputsValShapes = inputs2D.map((t) => {
return { vals: backend2.data.get(t.dataId).values, shape: t.shape };
});
outShape = backend_util_exports.computeOutShape(inputs2D.map((t) => t.shape), 1);
const simplyConcat = inputs2D[0].shape[0] === 1;
const outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat);
const finalOutShape = backend_util_exports.computeOutShape($inputs.map((t) => t.shape), $axis);
const outInfo = backend2.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals);
inputs2D.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return outInfo;
}
var concatConfig = {
kernelName: Concat,
backendName: "cpu",
kernelFunc: concat2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2D.js
init_define_BUILD_VERSION();
function conv2D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dataFormat, dilations, dimRoundingMode } = attrs;
assertNotComplex([x, filter], "conv2d");
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad2, dimRoundingMode, false, $dataFormat);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const padLeft = convInfo.padInfo.left;
const padTop = convInfo.padInfo.top;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
const y = new TensorBuffer(convInfo.outShape, x.dtype);
const xStrides = util_exports.computeStrides(x.shape);
const filterStrides = util_exports.computeStrides(filter.shape);
const xBatchStride = xStrides[0];
const xRowStride = isChannelsLast ? xStrides[1] : xStrides[2];
const xColStride = isChannelsLast ? xStrides[2] : 1;
const xChannelStride = isChannelsLast ? 1 : xStrides[1];
const yBatchStride = y.strides[0];
const yRowStride = isChannelsLast ? y.strides[1] : y.strides[2];
const yColStride = isChannelsLast ? y.strides[2] : 1;
const yChannelStride = isChannelsLast ? 1 : y.strides[1];
const xVals = backend2.data.get(x.dataId).values;
const wVals = backend2.data.get(filter.dataId).values;
const yVals = y.values;
for (let b = 0; b < convInfo.batchSize; ++b) {
const xOffset1 = b * xBatchStride;
const yOffset1 = b * yBatchStride;
for (let yR = 0; yR < convInfo.outHeight; ++yR) {
const yOffset2 = yOffset1 + yR * yRowStride;
const xRCorner = yR * convInfo.strideHeight - padTop;
for (let wR = 0; wR < filterHeight; ++wR) {
const xR = xRCorner + wR * dilationHeight;
if (xR < 0 || xR >= convInfo.inHeight) {
continue;
}
const wOffset1 = wR * filterStrides[0];
const xOffset2 = xOffset1 + xR * xRowStride;
for (let yC = 0; yC < convInfo.outWidth; ++yC) {
const yOffset3 = yOffset2 + yC * yColStride;
const xCCorner = yC * convInfo.strideWidth - padLeft;
for (let wC = 0; wC < filterWidth; ++wC) {
const xC = xCCorner + wC * dilationWidth;
if (xC < 0 || xC >= convInfo.inWidth) {
continue;
}
const wOffset2 = wOffset1 + wC * filterStrides[1];
const xOffset3 = xOffset2 + xC * xColStride;
let wOffset3 = wOffset2;
for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
const xVal = xVals[xOffset3 + d1 * xChannelStride];
for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
yVals[yOffset3 + d2 * yChannelStride] += xVal * wVals[wOffset3 + d2];
}
wOffset3 += convInfo.outChannels;
}
}
}
}
}
}
return backend2.makeTensorInfo(y.shape, y.dtype, yVals);
}
var conv2DConfig = {
kernelName: Conv2D,
backendName: "cpu",
kernelFunc: conv2D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropFilter.js
init_define_BUILD_VERSION();
function conv2DBackpropFilter2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, dy } = inputs;
const { strides, pad: pad2, dataFormat, dimRoundingMode, filterShape } = attrs;
assertNotComplex([x, dy], "conv2dBackpropFilter");
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad2, dimRoundingMode, false, $dataFormat);
const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
const dW = new TensorBuffer(convInfo.filterShape, "float32");
const leftPad = convInfo.padInfo.left;
const topPad = convInfo.padInfo.top;
const xVals = backend2.data.get(x.dataId).values;
const dyVals = backend2.data.get(dy.dataId).values;
const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
for (let wR = 0; wR < filterHeight; ++wR) {
const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
for (let wC = 0; wC < filterWidth; ++wC) {
const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
let dotProd = 0;
for (let b = 0; b < convInfo.batchSize; ++b) {
for (let yR = yRMin; yR < yRMax; ++yR) {
const xR = wR + yR * strideHeight - topPad;
for (let yC = yCMin; yC < yCMax; ++yC) {
const xC = wC + yC * strideWidth - leftPad;
if (isChannelsLast) {
dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
} else {
dotProd += xBuf.get(b, d1, xR, xC) * dyBuf.get(b, d2, yR, yC);
}
}
}
}
dW.set(dotProd, wR, wC, d1, d2);
}
}
}
}
return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
}
var conv2DBackpropFilterConfig = {
kernelName: Conv2DBackpropFilter,
backendName: "cpu",
kernelFunc: conv2DBackpropFilter2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv2DBackpropInput.js
init_define_BUILD_VERSION();
function conv2DBackpropInput2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, filter } = inputs;
const { inputShape, strides, pad: pad2, dataFormat, dimRoundingMode } = attrs;
assertNotComplex([dy, filter], "conv2dBackpropInput");
const filterStrides = util_exports.computeStrides(filter.shape);
const dyStrides = util_exports.computeStrides(dy.shape);
let $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad2, dimRoundingMode, false, $dataFormat);
const dx = new TensorBuffer(convInfo.inShape, "float32");
const dxValues = dx.values;
const dyValues = backend2.data.get(dy.dataId).values;
const fltValues = backend2.data.get(filter.dataId).values;
const [fltS0, fltS1, fltS2] = filterStrides;
const { batchSize, filterHeight, filterWidth, inChannels, inHeight, inWidth, outChannels, outHeight, outWidth, strideHeight, strideWidth } = convInfo;
$dataFormat = convInfo.dataFormat;
const topPad = filterHeight - 1 - convInfo.padInfo.top;
const leftPad = filterWidth - 1 - convInfo.padInfo.left;
const isChannelsLast = $dataFormat === "channelsLast";
const xBatchStride = dx.strides[0];
const xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2];
const xColStride = isChannelsLast ? dx.strides[2] : 1;
const xChannelStride = isChannelsLast ? 1 : dx.strides[1];
const yBatchStride = dyStrides[0];
const yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2];
const yColStride = isChannelsLast ? dyStrides[2] : 1;
const yChannelStride = isChannelsLast ? 1 : dyStrides[1];
for (let b = 0; b < batchSize; ++b) {
for (let d1 = 0; d1 < inChannels; ++d1) {
for (let xR = 0; xR < inHeight; ++xR) {
const xRCorner = xR - topPad;
const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
for (let xC = 0; xC < inWidth; ++xC) {
const xCCorner = xC - leftPad;
const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
let dotProd = 0;
for (let yR = xRMin; yR < yRMax; ++yR) {
const wR = yR * strideHeight - xRCorner;
for (let yC = xCMin; yC < yCMax; ++yC) {
const wC = yC * strideWidth - xCCorner;
const dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC;
const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
for (let d2 = 0; d2 < outChannels; ++d2) {
const pixel = dyValues[dyOffset + yChannelStride * d2];
const weight = fltValues[fltOffset + d2];
dotProd += pixel * weight;
}
}
}
const dxOffset = xBatchStride * b + xRowStride * xR + xColStride * xC + xChannelStride * d1;
dxValues[dxOffset] = dotProd;
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var conv2DBackpropInputConfig = {
kernelName: Conv2DBackpropInput,
backendName: "cpu",
kernelFunc: conv2DBackpropInput2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3D.js
init_define_BUILD_VERSION();
function conv3D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dilations } = attrs;
assertNotComplex([x, filter], "conv3d");
const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad2);
const { filterDepth, filterHeight, filterWidth, dilationDepth, dilationHeight, dilationWidth, padInfo } = convInfo;
const padFront = padInfo.front;
const padLeft = padInfo.left;
const padTop = padInfo.top;
const y = new TensorBuffer(convInfo.outShape, x.dtype);
const xVals = backend2.data.get(x.dataId).values;
const wVals = backend2.data.get(filter.dataId).values;
const yVals = y.values;
const xStrides = util_exports.computeStrides(x.shape);
const filterStrides = util_exports.computeStrides(filter.shape);
for (let b = 0; b < convInfo.batchSize; ++b) {
const xOffset1 = b * xStrides[0];
const yOffset1 = b * y.strides[0];
for (let yF = 0; yF < convInfo.outDepth; ++yF) {
const yOffset2 = yOffset1 + yF * y.strides[1];
const xFCorner = yF * convInfo.strideDepth - padFront;
for (let wF = 0; wF < filterDepth; ++wF) {
const xF = xFCorner + wF * dilationDepth;
if (xF < 0 || xF >= convInfo.inDepth) {
continue;
}
const wOffset1 = wF * filterStrides[0];
const xOffset2 = xOffset1 + xF * xStrides[1];
for (let yR = 0; yR < convInfo.outHeight; ++yR) {
const yOffset3 = yOffset2 + yR * y.strides[2];
const xRCorner = yR * convInfo.strideHeight - padTop;
for (let wR = 0; wR < filterHeight; ++wR) {
const xR = xRCorner + wR * dilationHeight;
if (xR < 0 || xR >= convInfo.inHeight) {
continue;
}
const wOffset2 = wOffset1 + wR * filterStrides[1];
const xOffset3 = xOffset2 + xR * xStrides[2];
for (let yC = 0; yC < convInfo.outWidth; ++yC) {
const yOffset4 = yOffset3 + yC * convInfo.outChannels;
const xCCorner = yC * convInfo.strideWidth - padLeft;
for (let wC = 0; wC < filterWidth; ++wC) {
const xC = xCCorner + wC * dilationWidth;
if (xC < 0 || xC >= convInfo.inWidth) {
continue;
}
const wOffset3 = wOffset2 + wC * filterStrides[2];
const xOffset4 = xOffset3 + xC * convInfo.inChannels;
let wOffset4 = wOffset3;
for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
const xVal = xVals[xOffset4 + d1];
for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
}
wOffset4 += convInfo.outChannels;
}
}
}
}
}
}
}
}
return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
}
var conv3DConfig = {
kernelName: Conv3D,
backendName: "cpu",
kernelFunc: conv3D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropFilterV2.js
init_define_BUILD_VERSION();
function conv3DBackpropFilterV2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, dy } = inputs;
const { strides, pad: pad2, filterShape } = attrs;
assertNotComplex([x, dy], "conv3dBackpropFilterV2");
const xStrides = util_exports.computeStrides(x.shape);
const dyStrides = util_exports.computeStrides(dy.shape);
const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad2);
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const filterDepth = convInfo.filterDepth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const dw = new TensorBuffer(convInfo.filterShape, "float32");
const dwValues = dw.values;
const [dwS0, dwS1, dwS2, dwS3] = dw.strides;
const dyValues = backend2.data.get(dy.dataId).values;
const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
const xValues = backend2.data.get(x.dataId).values;
const [xS0, xS1, xS2, xS3] = xStrides;
const frontPad = convInfo.padInfo.front;
const leftPad = convInfo.padInfo.left;
const topPad = convInfo.padInfo.top;
for (let wF = 0; wF < filterDepth; ++wF) {
const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
const yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
const wOffset1 = wF * dwS0;
for (let wR = 0; wR < filterHeight; ++wR) {
const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
const wOffset2 = wR * dwS1 + wOffset1;
for (let wC = 0; wC < filterWidth; ++wC) {
const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
const wOffset3 = wC * dwS2 + wOffset2;
for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
const wOffset4 = d1 * dwS3 + wOffset3;
for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
let dotProd = 0;
for (let b = 0; b < convInfo.batchSize; ++b) {
const xOffset1 = b * xS0;
const yOffset1 = b * dyS0;
for (let yF = yFMin; yF < yFMax; ++yF) {
const xF = wF + yF * strideDepth - frontPad;
const xOffset2 = xF * xS1 + xOffset1;
const yOffset2 = yF * dyS1 + yOffset1;
for (let yR = yRMin; yR < yRMax; ++yR) {
const xR = wR + yR * strideHeight - topPad;
const xOffset3 = xR * xS2 + xOffset2;
const yOffset3 = yR * dyS2 + yOffset2;
for (let yC = yCMin; yC < yCMax; ++yC) {
const xC = wC + yC * strideWidth - leftPad;
const xOffset4 = xC * xS3 + xOffset3;
const yOffset4 = yC * dyS3 + yOffset3;
dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
}
}
}
}
dwValues[wOffset4 + d2] = dotProd;
}
}
}
}
}
return backend2.makeTensorInfo(dw.shape, dw.dtype, dw.values);
}
var conv3DBackpropFilterV2Config = {
kernelName: Conv3DBackpropFilterV2,
backendName: "cpu",
kernelFunc: conv3DBackpropFilterV2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Conv3DBackpropInputV2.js
init_define_BUILD_VERSION();
function conv3DBackpropInputV2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, filter } = inputs;
const { pad: pad2, strides, inputShape } = attrs;
assertNotComplex([dy], "conv3dBackpropInputV2");
const dyStrides = util_exports.computeStrides(dy.shape);
const filterStrides = util_exports.computeStrides(filter.shape);
const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad2);
const dx = new TensorBuffer(convInfo.inShape, "float32");
const dxValues = dx.values;
const [dxS0, dxS1, dxS2, dxS3] = dx.strides;
const dyValues = backend2.data.get(dy.dataId).values;
const [dyS0, dyS1, dyS2, dyS3] = dyStrides;
const fltValues = backend2.data.get(filter.dataId).values;
const [fltS0, fltS1, fltS2, fltS3] = filterStrides;
const { batchSize, filterDepth, filterHeight, filterWidth, inChannels, inDepth, inHeight, inWidth, outChannels, outDepth, outHeight, outWidth, strideDepth, strideHeight, strideWidth } = convInfo;
const frontPad = filterDepth - 1 - convInfo.padInfo.front;
const topPad = filterHeight - 1 - convInfo.padInfo.top;
const leftPad = filterWidth - 1 - convInfo.padInfo.left;
for (let b = 0; b < batchSize; ++b) {
for (let d1 = 0; d1 < inChannels; ++d1) {
for (let xF = 0; xF < inDepth; ++xF) {
const xFCorner = xF - frontPad;
const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
const yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
for (let xR = 0; xR < inHeight; ++xR) {
const xRCorner = xR - topPad;
const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
for (let xC = 0; xC < inWidth; ++xC) {
const xCCorner = xC - leftPad;
const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
let dotProd = 0;
for (let yF = xFMin; yF < yFMax; ++yF) {
const wF = yF * strideDepth - xFCorner;
for (let yR = xRMin; yR < yRMax; ++yR) {
const wR = yR * strideHeight - xRCorner;
for (let yC = xCMin; yC < yCMax; ++yC) {
const wC = yC * strideWidth - xCCorner;
const dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
const fltOffset = fltS0 * (filterDepth - 1 - wF) + fltS1 * (filterHeight - 1 - wR) + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
for (let d2 = 0; d2 < outChannels; ++d2) {
const pixel = dyValues[dyOffset + d2];
const weight = fltValues[fltOffset + d2];
dotProd += pixel * weight;
}
}
}
}
dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = dotProd;
}
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var conv3DBackpropInputV2Config = {
kernelName: Conv3DBackpropInputV2,
backendName: "cpu",
kernelFunc: conv3DBackpropInputV2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cos.js
init_define_BUILD_VERSION();
var cos2 = unaryKernelFunc(Cos, (xi) => Math.cos(xi));
var cosConfig = {
kernelName: Cos,
backendName: "cpu",
kernelFunc: cos2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cosh.js
init_define_BUILD_VERSION();
var cosh2 = unaryKernelFunc(Cosh, (xi) => Math.cosh(xi));
var coshConfig = {
kernelName: Cosh,
backendName: "cpu",
kernelFunc: cosh2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/CropAndResize.js
init_define_BUILD_VERSION();
function cropAndResize2(args) {
const { inputs, backend: backend2, attrs } = args;
const { image: image3, boxes, boxInd } = inputs;
const { cropSize, method, extrapolationValue } = attrs;
const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
const numBoxes = boxes.shape[0];
const [cropHeight, cropWidth] = cropSize;
const output = buffer([numBoxes, cropHeight, cropWidth, numChannels], "float32");
const boxVals = backend2.data.get(boxes.dataId).values;
const boxIndVals = backend2.data.get(boxInd.dataId).values;
const imageVals = backend2.data.get(image3.dataId).values;
const inStride = util_exports.computeStrides(image3.shape);
const outStride = util_exports.computeStrides(output.shape);
for (let b = 0; b < numBoxes; b++) {
const startInd = b * 4;
const y1 = boxVals[startInd];
const x1 = boxVals[startInd + 1];
const y2 = boxVals[startInd + 2];
const x2 = boxVals[startInd + 3];
const bInd = boxIndVals[b];
if (bInd >= batch) {
continue;
}
const heightScale = cropHeight > 1 ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0;
const widthScale = cropWidth > 1 ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
for (let y = 0; y < cropHeight; y++) {
const yInd = cropHeight > 1 ? y1 * (imageHeight - 1) + y * heightScale : 0.5 * (y1 + y2) * (imageHeight - 1);
if (yInd < 0 || yInd > imageHeight - 1) {
for (let x = 0; x < cropWidth; x++) {
for (let c = 0; c < numChannels; c++) {
const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
output.values[ind] = extrapolationValue;
}
}
continue;
}
if (method === "bilinear") {
const topInd = Math.floor(yInd);
const bottomInd = Math.ceil(yInd);
const yLerp = yInd - topInd;
for (let x = 0; x < cropWidth; x++) {
const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
if (xInd < 0 || xInd > imageWidth - 1) {
for (let c = 0; c < numChannels; c++) {
const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
output.values[ind] = extrapolationValue;
}
continue;
}
const leftInd = Math.floor(xInd);
const rightInd = Math.ceil(xInd);
const xLerp = xInd - leftInd;
for (let c = 0; c < numChannels; c++) {
let ind = c + leftInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
const topLeft = imageVals[ind];
ind = c + rightInd * inStride[2] + topInd * inStride[1] + bInd * inStride[0];
const topRight = imageVals[ind];
ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
const bottomLeft = imageVals[ind];
ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + bInd * inStride[0];
const bottomRight = imageVals[ind];
const top = topLeft + (topRight - topLeft) * xLerp;
const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
output.values[ind] = top + (bottom - top) * yLerp;
}
}
} else {
for (let x = 0; x < cropWidth; ++x) {
const xInd = cropWidth > 1 ? x1 * (imageWidth - 1) + x * widthScale : 0.5 * (x1 + x2) * (imageWidth - 1);
if (xInd < 0 || xInd > imageWidth - 1) {
for (let c = 0; c < numChannels; c++) {
const ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
output.values[ind] = extrapolationValue;
}
continue;
}
const closestX = Math.round(xInd);
const closestY = Math.round(yInd);
for (let c = 0; c < numChannels; c++) {
const inInd = c + closestX * inStride[2] + closestY * inStride[1] + bInd * inStride[0];
const outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
output.values[outInd] = imageVals[inInd];
}
}
}
}
}
return backend2.makeTensorInfo(output.shape, output.dtype, output.values);
}
var cropAndResizeConfig = {
kernelName: CropAndResize,
backendName: "cpu",
kernelFunc: cropAndResize2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumprod.js
init_define_BUILD_VERSION();
function cumprod2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, exclusive, reverse: reverse4 } = attrs;
assertNotComplex(x, "cumprod");
const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
let $x = x;
if (permutation != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
}
const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
if (permutedAxis !== $x.shape.length - 1) {
throw new Error(`backend.cumprod in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
}
const resultDtype = upcastType($x.dtype, "int32");
const vals = util_exports.makeOnesTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
const aVals = backend2.data.get($x.dataId).values;
const finalDim = $x.shape[$x.shape.length - 1];
const indexAdjuster = reverse4 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
for (let i = 0; i < aVals.length; i += finalDim) {
for (let j = 0; j < finalDim; j++) {
const idx = indexAdjuster(i, j);
if (j === 0) {
vals[idx] = exclusive ? 1 : aVals[idx];
} else {
const prevIdx = indexAdjuster(i, j - 1);
vals[idx] = exclusive ? aVals[prevIdx] * vals[prevIdx] : aVals[idx] * vals[prevIdx];
}
}
}
const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
if (permutation != null) {
const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
backend2.disposeIntermediateTensorInfo(result);
backend2.disposeIntermediateTensorInfo($x);
return reverseTransposedResult;
}
return result;
}
var cumprodConfig = {
kernelName: Cumprod,
backendName: "cpu",
kernelFunc: cumprod2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Cumsum.js
init_define_BUILD_VERSION();
function cumsum2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, exclusive, reverse: reverse4 } = attrs;
assertNotComplex(x, "cumsum");
const permutation = backend_util_exports.getAxesPermutation([axis], x.shape.length);
let $x = x;
if (permutation != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
}
const permutedAxis = backend_util_exports.getInnerMostAxes(1, x.shape.length)[0];
if (permutedAxis !== $x.shape.length - 1) {
throw new Error(`backend.cumsum in CPU expects an inner-most axis=${$x.shape.length - 1} but got axis=${permutedAxis}`);
}
const resultDtype = upcastType($x.dtype, "int32");
const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape($x.shape), resultDtype);
const aVals = backend2.data.get($x.dataId).values;
const finalDim = $x.shape[$x.shape.length - 1];
const indexAdjuster = reverse4 ? (i, j) => i + finalDim - j - 1 : (i, j) => i + j;
for (let i = 0; i < aVals.length; i += finalDim) {
for (let j = 0; j < finalDim; j++) {
const idx = indexAdjuster(i, j);
if (j === 0) {
vals[idx] = exclusive ? 0 : aVals[idx];
} else {
const prevIdx = indexAdjuster(i, j - 1);
vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : aVals[idx] + vals[prevIdx];
}
}
}
const result = backend2.makeTensorInfo($x.shape, resultDtype, vals);
if (permutation != null) {
const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
const reverseTransposedResult = transpose2({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
backend2.disposeIntermediateTensorInfo(result);
backend2.disposeIntermediateTensorInfo($x);
return reverseTransposedResult;
}
return result;
}
var cumsumConfig = {
kernelName: Cumsum,
backendName: "cpu",
kernelFunc: cumsum2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DenseBincount.js
init_define_BUILD_VERSION();
function denseBincount(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, weights } = inputs;
const { size, binaryOutput } = attrs;
if (x.shape.length === 1) {
const xVals = backend2.data.get(x.dataId).values;
const weightsVals = backend2.data.get(weights.dataId).values;
const outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size);
return backend2.makeTensorInfo([size], weights.dtype, outVals);
} else if (x.shape.length === 2) {
const xBuf = backend2.bufferSync(x);
const weightsBuf = backend2.bufferSync(weights);
const outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput);
return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
}
throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
}
var denseBincountConfig = {
kernelName: DenseBincount,
backendName: "cpu",
kernelFunc: denseBincount
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthToSpace.js
init_define_BUILD_VERSION();
function depthToSpace2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { blockSize, dataFormat } = attrs;
util_exports.assert(dataFormat === "NHWC", () => `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${dataFormat}`);
const batchSize = x.shape[0];
const inputHeight = x.shape[1];
const inputWidth = x.shape[2];
const inputDepth = x.shape[3];
const outputHeight = inputHeight * blockSize;
const outputWidth = inputWidth * blockSize;
const outputDepth = inputDepth / (blockSize * blockSize);
const xValues = backend2.data.get(x.dataId).values;
const result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
let outputIdx = 0;
for (let b = 0; b < batchSize; ++b) {
for (let h = 0; h < outputHeight; ++h) {
const inH = Math.floor(h / blockSize);
const offsetH = h % blockSize;
for (let w = 0; w < outputWidth; ++w) {
const inW = Math.floor(w / blockSize);
const offsetW = w % blockSize;
const offsetD = (offsetH * blockSize + offsetW) * outputDepth;
for (let d = 0; d < outputDepth; ++d) {
const inD = d + offsetD;
const inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
result[outputIdx++] = xValues[inputIdx];
}
}
}
}
return backend2.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result);
}
var depthToSpaceConfig = {
kernelName: DepthToSpace,
backendName: "cpu",
kernelFunc: depthToSpace2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNative.js
init_define_BUILD_VERSION();
function depthwiseConv2dNative(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dilations, dimRoundingMode } = attrs;
assertNotComplex([x, filter], "depthwiseConv2DNative");
const xStrides = util_exports.computeStrides(x.shape);
const filterStrides = util_exports.computeStrides(filter.shape);
let $dilations = dilations;
if ($dilations == null) {
$dilations = [1, 1];
}
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad2, dimRoundingMode, true);
const { filterHeight, filterWidth, dilationHeight, dilationWidth, padInfo } = convInfo;
const padLeft = padInfo.left;
const padTop = padInfo.top;
const chMul = convInfo.outChannels / convInfo.inChannels;
const y = new TensorBuffer(convInfo.outShape, x.dtype);
const xVals = backend2.data.get(x.dataId).values;
const wVals = backend2.data.get(filter.dataId).values;
const yVals = y.values;
for (let b = 0; b < convInfo.batchSize; ++b) {
const xOffset1 = b * xStrides[0];
const yOffset1 = b * y.strides[0];
for (let yR = 0; yR < convInfo.outHeight; ++yR) {
const yOffset2 = yOffset1 + yR * y.strides[1];
const xRCorner = yR * convInfo.strideHeight - padTop;
for (let wR = 0; wR < filterHeight; ++wR) {
const xR = xRCorner + wR * dilationHeight;
if (xR < 0 || xR >= convInfo.inHeight) {
continue;
}
const wOffset1 = wR * filterStrides[0];
const xOffset2 = xOffset1 + xR * xStrides[1];
for (let yC = 0; yC < convInfo.outWidth; ++yC) {
const yOffset3 = yOffset2 + yC * y.strides[2];
const xCCorner = yC * convInfo.strideWidth - padLeft;
for (let wC = 0; wC < filterWidth; ++wC) {
const xC = xCCorner + wC * dilationWidth;
if (xC < 0 || xC >= convInfo.inWidth) {
continue;
}
const wOffset2 = wOffset1 + wC * filterStrides[1];
const xOffset3 = xOffset2 + xC * convInfo.inChannels;
let yOffset4 = yOffset3;
let wOffset3 = wOffset2;
for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {
const xVal = xVals[xOffset3 + d1];
for (let q = 0; q < chMul; ++q) {
yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
}
yOffset4 += chMul;
wOffset3 += chMul;
}
}
}
}
}
}
return backend2.makeTensorInfo(y.shape, y.dtype, y.values);
}
var depthwiseConv2dNativeConfig = {
kernelName: DepthwiseConv2dNative,
backendName: "cpu",
kernelFunc: depthwiseConv2dNative
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
init_define_BUILD_VERSION();
function depthwiseConv2dNativeBackpropFilter2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, dy } = inputs;
const { strides, dilations, pad: pad2, dimRoundingMode, filterShape } = attrs;
assertNotComplex([x, dy], "depthwiseConv2dNativeBackpropFilter");
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad2, dimRoundingMode, true);
const { strideHeight, strideWidth, filterHeight, filterWidth } = convInfo;
const dW = new TensorBuffer(convInfo.filterShape, "float32");
const leftPad = convInfo.padInfo.left;
const topPad = convInfo.padInfo.top;
const chMul = convInfo.outChannels / convInfo.inChannels;
const xVals = backend2.data.get(x.dataId).values;
const xBuf = new TensorBuffer(x.shape, x.dtype, xVals);
const dyVals = backend2.data.get(dy.dataId).values;
const dyBuf = new TensorBuffer(dy.shape, dy.dtype, dyVals);
for (let wR = 0; wR < filterHeight; ++wR) {
const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
const yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
for (let wC = 0; wC < filterWidth; ++wC) {
const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
const yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {
const d1 = Math.trunc(d2 / chMul);
const dm = d2 % chMul;
let dotProd = 0;
for (let b = 0; b < convInfo.batchSize; ++b) {
for (let yR = yRMin; yR < yRMax; ++yR) {
const xR = wR + yR * strideHeight - topPad;
for (let yC = yCMin; yC < yCMax; ++yC) {
const xC = wC + yC * strideWidth - leftPad;
dotProd += xBuf.get(b, xR, xC, d1) * dyBuf.get(b, yR, yC, d2);
}
}
}
dW.set(dotProd, wR, wC, d1, dm);
}
}
}
return backend2.makeTensorInfo(dW.shape, dW.dtype, dW.values);
}
var depthwiseConv2dNativeBackpropFilterConfig = {
kernelName: DepthwiseConv2dNativeBackpropFilter,
backendName: "cpu",
kernelFunc: depthwiseConv2dNativeBackpropFilter2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
init_define_BUILD_VERSION();
function depthwiseConv2dNativeBackpropInput2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, filter } = inputs;
const { strides, dilations, pad: pad2, dimRoundingMode, inputShape } = attrs;
assertNotComplex([dy, filter], "depthwiseConv2DNativeBackpropInput");
const dyStrides = util_exports.computeStrides(dy.shape);
const filterStrides = util_exports.computeStrides(filter.shape);
const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad2, dimRoundingMode, true);
const dx = new TensorBuffer(convInfo.inShape, "float32");
const dxValues = dx.values;
const [dxS0, dxS1, dxS2] = dx.strides;
const dyValues = backend2.data.get(dy.dataId).values;
const [dyS0, dyS1, dyS2] = dyStrides;
const fltValues = backend2.data.get(filter.dataId).values;
const [fltS0, fltS1, fltS2] = filterStrides;
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 chMul = outChannels / inChannels;
for (let b = 0; b < batchSize; ++b) {
for (let d1 = 0; d1 < inChannels; ++d1) {
for (let xR = 0; xR < inHeight; ++xR) {
const xRCorner = xR - topPad;
const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
const yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
for (let xC = 0; xC < inWidth; ++xC) {
const xCCorner = xC - leftPad;
const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
const yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
let dotProd = 0;
for (let yR = xRMin; yR < yRMax; ++yR) {
const wR = yR * strideHeight - xRCorner;
for (let yC = xCMin; yC < yCMax; ++yC) {
const wC = yC * strideWidth - xCCorner;
const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
const fltOffset = fltS0 * (filterHeight - 1 - wR) + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
for (let dm = 0; dm < chMul; ++dm) {
const d2 = d1 * chMul + dm;
const pixel = dyValues[dyOffset + d2];
const weight = fltValues[fltOffset + dm];
dotProd += pixel * weight;
}
}
}
dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var depthwiseConv2dNativeBackpropInputConfig = {
kernelName: DepthwiseConv2dNativeBackpropInput,
backendName: "cpu",
kernelFunc: depthwiseConv2dNativeBackpropInput2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Diag.js
init_define_BUILD_VERSION();
function diag(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
const xSize = util_exports.sizeFromShape(x.shape);
const xVals = backend2.data.get(x.dataId).values;
const outBuf = buffer([xSize, xSize], x.dtype);
const vals = outBuf.values;
for (let i = 0; i < xVals.length; i++) {
vals[i * xSize + i] = xVals[i];
}
const outShape = [...x.shape, ...x.shape];
return backend2.makeTensorInfo(outShape, outBuf.dtype, outBuf.values);
}
var diagConfig = {
kernelName: Diag,
backendName: "cpu",
kernelFunc: diag
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2D.js
init_define_BUILD_VERSION();
var dilation2DConfig = {
kernelName: Dilation2D,
backendName: "cpu",
kernelFunc: ({ inputs, backend: backend2, attrs }) => {
const { x, filter } = inputs;
const { strides, pad: pad2, dilations } = attrs;
const cpuBackend = backend2;
const xVals = cpuBackend.data.get(x.dataId).values;
const xRank = x.shape.length;
const filterVals = cpuBackend.data.get(filter.dataId).values;
const filterRank = filter.shape.length;
const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad2, "NHWC", dilations);
const outSize = util_exports.sizeFromShape(outShape);
const outRank = outShape.length;
const outputVals = util_exports.getArrayFromDType(x.dtype, outSize);
for (let b = 0; b < batchSize; ++b) {
for (let hOut = 0; hOut < outHeight; ++hOut) {
const hBeg = hOut * strideHeight - padInfo.top;
for (let wOut = 0; wOut < outWidth; ++wOut) {
const wBeg = wOut * strideWidth - padInfo.left;
for (let d = 0; d < inChannels; ++d) {
let curVal = Number.MIN_SAFE_INTEGER;
for (let h = 0; h < filterHeight; ++h) {
const hIn = hBeg + h * dilationHeight;
if (hIn >= 0 && hIn < inHeight) {
for (let w = 0; w < filterWidth; ++w) {
const wIn = wBeg + w * dilationWidth;
if (wIn >= 0 && wIn < inWidth) {
const xIndex = util_exports.locToIndex([b, hIn, wIn, d], xRank, util_exports.computeStrides(x.shape));
const filterIndex = util_exports.locToIndex([h, w, d], filterRank, util_exports.computeStrides(filter.shape));
const val = xVals[xIndex] + filterVals[filterIndex];
if (val > curVal) {
curVal = val;
}
}
}
}
}
const outputIndex = util_exports.locToIndex([b, hOut, wOut, d], outRank, util_exports.computeStrides(outShape));
outputVals[outputIndex] = curVal;
}
}
}
}
const dataId = cpuBackend.write(util_exports.toTypedArray(outputVals, x.dtype), outShape, x.dtype);
return { dataId, shape: outShape, dtype: x.dtype };
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropFilter.js
init_define_BUILD_VERSION();
var dilation2DBackpropFilterConfig = {
kernelName: Dilation2DBackpropFilter,
backendName: "cpu",
kernelFunc: ({ inputs, backend: backend2, attrs }) => {
const { x, filter, dy } = inputs;
const { strides, pad: pad2, dilations } = attrs;
const cpuBackend = backend2;
const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad2, "NHWC", dilations);
util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropFilter}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
const gradients = util_exports.makeZerosNestedTypedArray(filter.shape, filter.dtype);
for (let b = 0; b < batchSize; ++b) {
for (let hOut = 0; hOut < outHeight; ++hOut) {
const hBeg = hOut * strideHeight - padInfo.top;
for (let wOut = 0; wOut < outWidth; ++wOut) {
const wBeg = wOut * strideWidth - padInfo.left;
for (let d = 0; d < inChannels; ++d) {
let curVal = Number.MIN_SAFE_INTEGER;
let hMax = 0;
let wMax = 0;
for (let h = 0; h < filterHeight; ++h) {
const hIn = hBeg + h * dilationHeight;
if (hIn >= 0 && hIn < inHeight) {
for (let w = 0; w < filterWidth; ++w) {
const wIn = wBeg + w * dilationWidth;
if (wIn >= 0 && wIn < inWidth) {
const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
if (val > curVal) {
curVal = val;
hMax = h;
wMax = w;
}
}
}
}
}
gradients[hMax][wMax][d] += $dy[b][hOut][wOut][d];
}
}
}
}
const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype);
return { dataId, shape: filter.shape, dtype: filter.dtype };
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Dilation2DBackpropInput.js
init_define_BUILD_VERSION();
var dilation2DBackpropInputConfig = {
kernelName: Dilation2DBackpropInput,
backendName: "cpu",
kernelFunc: ({ inputs, backend: backend2, attrs }) => {
const { x, filter, dy } = inputs;
const { strides, pad: pad2, dilations } = attrs;
const cpuBackend = backend2;
const $x = util_exports.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values);
const $filter = util_exports.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values);
const { batchSize, inHeight, inWidth, inChannels, outHeight, outWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth, outShape } = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad2, "NHWC", dilations);
util_exports.assert(dy.rank === outShape.length, () => `Error in ${Dilation2DBackpropInput}, dy must have the same rank as output ${outShape.length}, but got ${dy.rank}`);
const $dy = util_exports.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values);
const gradients = util_exports.makeZerosNestedTypedArray(x.shape, x.dtype);
for (let b = 0; b < batchSize; ++b) {
for (let hOut = 0; hOut < outHeight; ++hOut) {
const hBeg = hOut * strideHeight - padInfo.top;
for (let wOut = 0; wOut < outWidth; ++wOut) {
const wBeg = wOut * strideWidth - padInfo.left;
for (let d = 0; d < inChannels; ++d) {
let curVal = Number.MIN_SAFE_INTEGER;
let hInMax = hBeg < 0 ? 0 : hBeg;
let wInMax = wBeg < 0 ? 0 : wBeg;
for (let h = 0; h < filterHeight; ++h) {
const hIn = hBeg + h * dilationHeight;
if (hIn >= 0 && hIn < inHeight) {
for (let w = 0; w < filterWidth; ++w) {
const wIn = wBeg + w * dilationWidth;
if (wIn >= 0 && wIn < inWidth) {
const val = $x[b][hIn][wIn][d] + $filter[h][w][d];
if (val > curVal) {
curVal = val;
hInMax = hIn;
wInMax = wIn;
}
}
}
}
}
gradients[b][hInMax][wInMax][d] += $dy[b][hOut][wOut][d];
}
}
}
}
const dataId = cpuBackend.write(util_exports.toTypedArray(gradients, x.dtype), x.shape, x.dtype);
return { dataId, shape: x.shape, dtype: x.dtype };
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sum.js
init_define_BUILD_VERSION();
function sum3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
assertNotComplex(x, "sum");
let $x;
if (x.dtype === "bool") {
$x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "int32" } });
} else {
$x = identity({ inputs: { x }, backend: backend2 });
}
const xRank = $x.shape.length;
const axes = util_exports.parseAxisParam(axis, $x.shape);
const permutation = backend_util_exports.getAxesPermutation(axes, xRank);
let reductionAxes = axes;
let permutedX = $x;
if (permutation != null) {
permutedX = transpose2({ inputs: { x: $x }, backend: backend2, attrs: { perm: permutation } });
reductionAxes = backend_util_exports.getInnerMostAxes(reductionAxes.length, xRank);
}
backend_util_exports.assertAxesAreInnerMostDims("sum", reductionAxes, permutedX.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, reductionAxes);
const resultDtype = backend_util_exports.upcastType(permutedX.dtype, "int32");
let result = zeros2(backend2, outShape, resultDtype);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const vals = backend2.data.get(result.dataId).values;
const aVals = backend2.data.get(permutedX.dataId).values;
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let sum5 = 0;
for (let j = 0; j < reduceSize; ++j) {
sum5 += aVals[offset + j];
}
vals[i] = sum5;
}
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(result.shape, axes);
const oldResult = result;
result = reshape2({ inputs: { x: result }, backend: backend2, attrs: { shape: newShape } });
backend2.disposeIntermediateTensorInfo(oldResult);
}
backend2.disposeIntermediateTensorInfo($x);
if (permutation != null) {
backend2.disposeIntermediateTensorInfo(permutedX);
}
return result;
}
var sumConfig = {
kernelName: Sum,
backendName: "cpu",
kernelFunc: sum3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Einsum.js
function einsum(args) {
const { inputs, backend: backend2, attrs } = args;
const { equation } = attrs;
const tensors = inputs;
const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
const nSteps = steps.length;
let out = null;
let numDimsRemaining = allDims.length;
const tensorsToDispose = [];
for (let i = 0; i < nSteps; ++i) {
for (const idTerm of steps[i]) {
const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
let x;
if (backend_util_exports.isIdentityPermutation(perm)) {
x = tensors[idTerm];
} else {
x = transpose2({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
tensorsToDispose.push(x);
}
const targetShape = x.shape.slice();
for (let k = 0; k < dimsToExpand.length; ++k) {
targetShape.splice(dimsToExpand[k], 0, 1);
}
if (!util_exports.arraysEqual(x.shape, targetShape)) {
x = reshape2({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
tensorsToDispose.push(x);
}
if (out === null) {
out = x;
} else {
out = multiply({ inputs: { a: x, b: out }, backend: backend2 });
tensorsToDispose.push(out);
}
}
if (i < nSteps - 1) {
if (path[i] >= 0) {
out = sum3({
inputs: { x: out },
backend: backend2,
attrs: {
axis: path[i] - (allDims.length - numDimsRemaining),
keepDims: false
}
});
tensorsToDispose.push(out);
}
numDimsRemaining--;
}
}
for (const tensorInfo of tensorsToDispose) {
if (tensorInfo === out) {
continue;
}
backend2.disposeIntermediateTensorInfo(tensorInfo);
}
return out;
}
var einsumConfig = {
kernelName: Einsum,
backendName: "cpu",
kernelFunc: einsum
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/EluGrad.js
init_define_BUILD_VERSION();
function eluGrad(args) {
const { inputs, backend: backend2 } = args;
const { dy, y } = inputs;
assertNotComplex([dy, y], "eluGrad");
const resultValues = new Float32Array(util_exports.sizeFromShape(y.shape));
const values = backend2.data.get(y.dataId).values;
const dyValues = backend2.data.get(dy.dataId).values;
for (let i = 0; i < values.length; ++i) {
const v = values[i];
if (v >= 1) {
resultValues[i] = dyValues[i];
} else {
resultValues[i] = dyValues[i] * (v + 1);
}
}
return backend2.makeTensorInfo(y.shape, "float32", resultValues);
}
var eluGradConfig2 = {
kernelName: EluGrad,
backendName: "cpu",
kernelFunc: eluGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Erf.js
init_define_BUILD_VERSION();
var p = backend_util_exports.ERF_P;
var a1 = backend_util_exports.ERF_A1;
var a2 = backend_util_exports.ERF_A2;
var a3 = backend_util_exports.ERF_A3;
var a4 = backend_util_exports.ERF_A4;
var a5 = backend_util_exports.ERF_A5;
var erf2 = unaryKernelFunc(Erf, (xi) => {
const sign4 = Math.sign(xi);
const v = Math.abs(xi);
const t = 1 / (1 + p * v);
return sign4 * (1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-v * v));
});
var erfConfig = {
kernelName: Erf,
backendName: "cpu",
kernelFunc: erf2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ExpandDims.js
init_define_BUILD_VERSION();
function expandDims3(args) {
const { inputs, backend: backend2, attrs } = args;
const { input: input2 } = inputs;
const { dim } = attrs;
const inputRank = input2.shape.length;
const newShape = input2.shape.slice();
let $dim = dim;
if (dim < 0) {
util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
$dim = inputRank + dim + 1;
}
newShape.splice($dim, 0, 1);
return reshape2({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
}
var expandDimsConfig = {
kernelName: ExpandDims,
backendName: "cpu",
kernelFunc: expandDims3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RealDiv.js
init_define_BUILD_VERSION();
var realDivImpl = createSimpleBinaryKernelImpl((a, b) => a / b);
var div2 = binaryKernelFunc(RealDiv, realDivImpl);
var realDivConfig = {
kernelName: RealDiv,
backendName: "cpu",
kernelFunc: div2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/utils/fft_utils.js
function fftBatch(input2, inverse, cpuBackend) {
const inputShape = input2.shape;
const batch = inputShape[0];
const innerDim = inputShape[1];
const inputVals = cpuBackend.data.get(input2.dataId);
const real2D = inputVals.complexTensorInfos.real;
const imag2D = inputVals.complexTensorInfos.imag;
const resultShape = [batch, innerDim];
const resultSize = util_exports.sizeFromShape(resultShape);
const resultReal = util_exports.getTypedArrayFromDType("float32", resultSize);
const resultImag = util_exports.getTypedArrayFromDType("float32", resultSize);
for (let b = 0; b < batch; b++) {
const r = slice2({
inputs: { x: real2D },
backend: cpuBackend,
attrs: { begin: [b, 0], size: [1, innerDim] }
});
const i = slice2({
inputs: { x: imag2D },
backend: cpuBackend,
attrs: { begin: [b, 0], size: [1, innerDim] }
});
const input3 = complex2({ inputs: { real: r, imag: i }, backend: cpuBackend });
const { real: real4, imag: imag4 } = fftImpl(input3, inverse, cpuBackend);
const res = backend_util_exports.mergeRealAndImagArrays(real4, imag4);
for (let d = 0; d < innerDim; d++) {
const c = backend_util_exports.getComplexWithIndex(res, d);
resultReal[b * innerDim + d] = c.real;
resultImag[b * innerDim + d] = c.imag;
}
cpuBackend.disposeIntermediateTensorInfo(r);
cpuBackend.disposeIntermediateTensorInfo(i);
cpuBackend.disposeIntermediateTensorInfo(input3);
}
const $realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultReal);
const $imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", resultImag);
const result = complex2({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend });
cpuBackend.disposeIntermediateTensorInfo($realInfo);
cpuBackend.disposeIntermediateTensorInfo($imagInfo);
return result;
}
function fftImpl(input2, inverse, cpuBackend) {
const inputSize = util_exports.sizeFromShape(input2.shape);
const inputVals = cpuBackend.data.get(input2.dataId);
const realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values;
const imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values;
if (isExponentOf2(inputSize)) {
const result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend);
const resultShape = [input2.shape[0], input2.shape[1]];
if (inverse) {
const realInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.real);
const imagInfo = cpuBackend.makeTensorInfo(resultShape, "float32", result.imag);
const sizeInfo = cpuBackend.makeTensorInfo([], "float32", util_exports.createScalarValue(inputSize, "float32"));
const sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend });
const divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend });
const divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend });
const divRealVals = cpuBackend.data.get(divRealInfo.dataId).values;
const divImagVals = cpuBackend.data.get(divImagInfo.dataId).values;
cpuBackend.disposeIntermediateTensorInfo(realInfo);
cpuBackend.disposeIntermediateTensorInfo(imagInfo);
cpuBackend.disposeIntermediateTensorInfo(sizeInfo);
cpuBackend.disposeIntermediateTensorInfo(sizeInfoCopy);
cpuBackend.disposeIntermediateTensorInfo(divRealInfo);
cpuBackend.disposeIntermediateTensorInfo(divImagInfo);
return { real: divRealVals, imag: divImagVals };
}
return result;
} else {
const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
const rawOutput = fourierTransformByMatmul(data, inputSize, inverse);
return backend_util_exports.splitRealAndImagArrays(rawOutput);
}
}
function isExponentOf2(size) {
return (size & size - 1) === 0;
}
function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) {
if (size === 1) {
return { real: realVals, imag: imagVals };
}
const data = backend_util_exports.mergeRealAndImagArrays(realVals, imagVals);
const half = size / 2;
const evenComplex = backend_util_exports.complexWithEvenIndex(data);
const evenRealVals = evenComplex.real;
const evenImagVals = evenComplex.imag;
const evenShape = [evenRealVals.length];
const evenRealInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenRealVals);
const evenImagInfo = cpuBackend.makeTensorInfo(evenShape, "float32", evenImagVals);
const evenTensorInfo = complex2({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend });
const oddComplex = backend_util_exports.complexWithOddIndex(data);
const oddRealVals = oddComplex.real;
const oddImagVals = oddComplex.imag;
const oddShape = [oddRealVals.length];
const oddRealInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddRealVals);
const oddImagInfo = cpuBackend.makeTensorInfo(oddShape, "float32", oddImagVals);
const oddTensorInfo = complex2({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend });
const $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend);
const $evenRealVals = $evenComplex.real;
const $evenImagVals = $evenComplex.imag;
const $evenShape = [$evenRealVals.length];
const $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenRealVals);
const $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, "float32", $evenImagVals);
const $evenTensorInfo = complex2({
inputs: { real: $evenRealInfo, imag: $evenImagInfo },
backend: cpuBackend
});
const $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend);
const $oddRealVals = $oddComplex.real;
const $oddImagVals = $oddComplex.imag;
const $oddShape = [$oddRealVals.length];
const $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddRealVals);
const $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, "float32", $oddImagVals);
const $oddTensorInfo = complex2({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend });
const e = backend_util_exports.exponents(size, inverse);
const eShape = [e.real.length];
const eRealInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.real);
const eImagInfo = cpuBackend.makeTensorInfo(eShape, "float32", e.imag);
const complexInfo = complex2({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend });
const exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend });
const addPart = add3({
inputs: { a: $evenTensorInfo, b: exponentInfo },
backend: cpuBackend
});
const subPart = sub2({
inputs: { a: $evenTensorInfo, b: exponentInfo },
backend: cpuBackend
});
const addPartReal = real2({ inputs: { input: addPart }, backend: cpuBackend });
const subPartReal = real2({ inputs: { input: subPart }, backend: cpuBackend });
const addPartImag = imag2({ inputs: { input: addPart }, backend: cpuBackend });
const subPartImag = imag2({ inputs: { input: subPart }, backend: cpuBackend });
const $real = concat2({
inputs: [addPartReal, subPartReal],
backend: cpuBackend,
attrs: { axis: 0 }
});
const $imag = concat2({
inputs: [addPartImag, subPartImag],
backend: cpuBackend,
attrs: { axis: 0 }
});
const $realVals = cpuBackend.data.get($real.dataId).values;
const $imagVals = cpuBackend.data.get($imag.dataId).values;
cpuBackend.disposeIntermediateTensorInfo(evenRealInfo);
cpuBackend.disposeIntermediateTensorInfo(evenImagInfo);
cpuBackend.disposeIntermediateTensorInfo(evenTensorInfo);
cpuBackend.disposeIntermediateTensorInfo(oddRealInfo);
cpuBackend.disposeIntermediateTensorInfo(oddImagInfo);
cpuBackend.disposeIntermediateTensorInfo(oddTensorInfo);
cpuBackend.disposeIntermediateTensorInfo($evenRealInfo);
cpuBackend.disposeIntermediateTensorInfo($evenImagInfo);
cpuBackend.disposeIntermediateTensorInfo($evenTensorInfo);
cpuBackend.disposeIntermediateTensorInfo($oddRealInfo);
cpuBackend.disposeIntermediateTensorInfo($oddImagInfo);
cpuBackend.disposeIntermediateTensorInfo($oddTensorInfo);
cpuBackend.disposeIntermediateTensorInfo(eRealInfo);
cpuBackend.disposeIntermediateTensorInfo(eImagInfo);
cpuBackend.disposeIntermediateTensorInfo(complexInfo);
cpuBackend.disposeIntermediateTensorInfo(exponentInfo);
cpuBackend.disposeIntermediateTensorInfo(addPart);
cpuBackend.disposeIntermediateTensorInfo(subPart);
cpuBackend.disposeIntermediateTensorInfo(addPartReal);
cpuBackend.disposeIntermediateTensorInfo(addPartImag);
cpuBackend.disposeIntermediateTensorInfo(subPartReal);
cpuBackend.disposeIntermediateTensorInfo(subPartImag);
cpuBackend.disposeIntermediateTensorInfo($real);
cpuBackend.disposeIntermediateTensorInfo($imag);
return { real: $realVals, imag: $imagVals };
}
function fourierTransformByMatmul(data, size, inverse) {
const ret = new Float32Array(size * 2);
for (let r = 0; r < size; r++) {
let real4 = 0;
let imag4 = 0;
for (let c = 0; c < size; c++) {
const e = backend_util_exports.exponent(r * c, size, inverse);
const term = backend_util_exports.getComplexWithIndex(data, c);
real4 += term.real * e.real - term.imag * e.imag;
imag4 += term.real * e.imag + term.imag * e.real;
}
if (inverse) {
real4 /= size;
imag4 /= size;
}
backend_util_exports.assignToTypedArray(ret, real4, imag4, r);
}
return ret;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FFT.js
function fft2(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
const inputSize = util_exports.sizeFromShape(input2.shape);
const innerDimensionSize = input2.shape[input2.shape.length - 1];
const batch = inputSize / innerDimensionSize;
const input2D = reshape2({
inputs: { x: input2 },
backend: backend2,
attrs: { shape: [batch, innerDimensionSize] }
});
const result = fftBatch(input2D, false, backend2);
const resultReshaped = reshape2({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });
backend2.disposeIntermediateTensorInfo(input2D);
backend2.disposeIntermediateTensorInfo(result);
return resultReshaped;
}
var fftConfig = {
kernelName: FFT,
backendName: "cpu",
kernelFunc: fft2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Fill.js
init_define_BUILD_VERSION();
function fill2(args) {
const { backend: backend2, attrs } = args;
const { shape, value, dtype } = attrs;
const $dtype = dtype || util_exports.inferDtype(value);
const values = util_exports.getArrayFromDType($dtype, util_exports.sizeFromShape(shape));
fillValues(values, value, $dtype);
return backend2.makeTensorInfo(shape, $dtype, values);
}
var fillConfig = {
kernelName: Fill,
backendName: "cpu",
kernelFunc: fill2
};
function fillValues(values, value, dtype) {
if (dtype === "string") {
values.fill(value);
} else {
values.fill(value);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FlipLeftRight.js
init_define_BUILD_VERSION();
var flipLeftRightConfig = {
kernelName: FlipLeftRight,
backendName: "cpu",
kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
const { image: image3 } = inputs;
const cpuBackend = backend2;
const output = util_exports.getTypedArrayFromDType(image3.dtype, util_exports.sizeFromShape(image3.shape));
const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
const imageVals = cpuBackend.data.get(image3.dataId).values;
for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
for (let row = 0; row < imageHeight; row++) {
const rowOffset = row * (imageWidth * numChannels);
for (let col = 0; col < imageWidth; col++) {
const colOffset = col * numChannels;
for (let channel = 0; channel < numChannels; channel++) {
const coordX = Math.round(imageWidth - col - 1);
const outIdx = batchOffset + rowOffset + colOffset + channel;
let outputValue = imageVals[outIdx];
if (coordX >= 0 && coordX < imageWidth) {
const rotatedColOffset = coordX * numChannels;
const imageIdx = batchOffset + rowOffset + rotatedColOffset + channel;
outputValue = imageVals[imageIdx];
}
output[outIdx] = outputValue;
}
}
}
}
const dataId = cpuBackend.write(output, image3.shape, image3.dtype);
return { dataId, shape: image3.shape, dtype: image3.dtype };
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FloorDiv.js
init_define_BUILD_VERSION();
var floorDivImpl = createSimpleBinaryKernelImpl((a, b) => Math.floor(a / b));
var floorDiv2 = binaryKernelFunc(FloorDiv, floorDivImpl, null, "int32");
var floorDivConfig = {
kernelName: FloorDiv,
backendName: "cpu",
kernelFunc: floorDiv2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedConv2D.js
init_define_BUILD_VERSION();
function fusedConv2D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter, bias, preluActivationWeights } = inputs;
const { strides, pad: pad2, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
let result = conv2D({
inputs: { x, filter },
backend: backend2,
attrs: { strides, pad: pad2, dataFormat, dilations, dimRoundingMode }
});
if (bias) {
const resultOld = result;
if (dataFormat === "NCHW" && bias.shape.length === 1 && bias.shape[0] !== 1) {
const reshapedBias = reshape2({ inputs: { x: bias }, backend: backend2, attrs: { shape: [bias.shape[0], 1, 1] } });
result = add3({ inputs: { a: result, b: reshapedBias }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(reshapedBias);
} else {
result = add3({ inputs: { a: result, b: bias }, backend: backend2 });
}
backend2.disposeIntermediateTensorInfo(resultOld);
}
if (activation) {
const resultOld = result;
if (dataFormat === "NCHW" && activation === "prelu" && preluActivationWeights.shape.length === 1 && preluActivationWeights.shape[0] !== 1) {
const reshapedAlpha = reshape2({
inputs: { x: preluActivationWeights },
backend: backend2,
attrs: { shape: [preluActivationWeights.shape[0], 1, 1] }
});
result = applyActivation2(backend2, result, activation, reshapedAlpha, leakyreluAlpha);
backend2.disposeIntermediateTensorInfo(reshapedAlpha);
} else {
result = applyActivation2(backend2, result, activation, preluActivationWeights, leakyreluAlpha);
}
backend2.disposeIntermediateTensorInfo(resultOld);
}
return result;
}
var fusedConv2DConfig = {
kernelName: FusedConv2D,
backendName: "cpu",
kernelFunc: fusedConv2D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/FusedDepthwiseConv2D.js
init_define_BUILD_VERSION();
function fusedDepthwiseConv2D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter, bias, preluActivationWeights } = inputs;
const { strides, pad: pad2, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
let result = depthwiseConv2dNative({
inputs: { x, filter },
backend: backend2,
attrs: { strides, pad: pad2, dataFormat, dilations, dimRoundingMode }
});
if (bias) {
const oldResult = result;
result = add3({ inputs: { a: result, b: bias }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(oldResult);
}
if (activation) {
const oldResult = result;
result = applyActivation2(backend2, result, activation, preluActivationWeights, leakyreluAlpha);
backend2.disposeIntermediateTensorInfo(oldResult);
}
return result;
}
var fusedDepthwiseConv2DConfig = {
kernelName: FusedDepthwiseConv2D,
backendName: "cpu",
kernelFunc: fusedDepthwiseConv2D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherNd.js
init_define_BUILD_VERSION();
function gatherNd(args) {
const { inputs, backend: backend2 } = args;
const { params, indices } = inputs;
const paramsSize = util_exports.sizeFromShape(params.shape);
const indicesShape = indices.shape;
const sliceRank = indicesShape[indicesShape.length - 1];
const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
if (numSlices === 0) {
return backend2.makeTensorInfo(resultShape, params.dtype, []);
}
const indicesData = backend2.data.get(indices.dataId).values;
const paramsBuf = backend2.bufferSync(params);
const outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
return backend2.makeTensorInfo(resultShape, params.dtype, outBuf.values);
}
var gatherNdConfig = {
kernelName: GatherNd,
backendName: "cpu",
kernelFunc: gatherNd
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/GatherV2.js
init_define_BUILD_VERSION();
function gatherV2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, indices } = inputs;
const { axis, batchDims } = attrs;
assertNotComplex([x, indices], "gatherV2");
const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
const indicesVals = backend2.data.get(indices.dataId).values;
const axisDim = x.shape[parsedAxis];
for (let i = 0; i < indicesVals.length; ++i) {
const index = indicesVals[i];
util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
}
let $batchDims = batchDims;
if (batchDims == null) {
$batchDims = 0;
}
const indicesSize = util_exports.sizeFromShape(indices.shape);
const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims);
const flattenX = reshape2({
inputs: { x },
backend: backend2,
attrs: {
shape: [
shapeInfo.batchSize,
shapeInfo.outerSize,
shapeInfo.dimSize,
shapeInfo.sliceSize
]
}
});
const flattenIndex = reshape2({
inputs: { x: indices },
backend: backend2,
attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
});
const flattenOutputShape = [
shapeInfo.batchSize,
shapeInfo.outerSize,
indicesSize / shapeInfo.batchSize,
shapeInfo.sliceSize
];
const indicesBuf = backend2.bufferSync(flattenIndex);
const xBuf = backend2.bufferSync(flattenX);
const outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape);
backend2.disposeIntermediateTensorInfo(flattenX);
backend2.disposeIntermediateTensorInfo(flattenIndex);
return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
}
var gatherV2Config = {
kernelName: GatherV2,
backendName: "cpu",
kernelFunc: gatherV2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IFFT.js
init_define_BUILD_VERSION();
function ifft2(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
const inputSize = util_exports.sizeFromShape(input2.shape);
const innerDimensionSize = input2.shape[input2.shape.length - 1];
const batch = inputSize / innerDimensionSize;
const input2D = reshape2({
inputs: { x: input2 },
backend: backend2,
attrs: { shape: [batch, innerDimensionSize] }
});
const result = fftBatch(input2D, true, backend2);
const resultReshaped = reshape2({ inputs: { x: result }, backend: backend2, attrs: { shape: input2.shape } });
backend2.disposeIntermediateTensorInfo(input2D);
backend2.disposeIntermediateTensorInfo(result);
return resultReshaped;
}
var ifftConfig = {
kernelName: IFFT,
backendName: "cpu",
kernelFunc: ifft2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsFinite.js
init_define_BUILD_VERSION();
var isFinite3 = unaryKernelFunc(IsFinite, (xi) => Number.isFinite(xi) ? 1 : 0, "bool");
var isFiniteConfig = {
kernelName: IsFinite,
backendName: "cpu",
kernelFunc: isFinite3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsInf.js
init_define_BUILD_VERSION();
var isInf2 = unaryKernelFunc(IsInf, (xi) => Math.abs(xi) === Infinity ? 1 : 0, "bool");
var isInfConfig = {
kernelName: IsInf,
backendName: "cpu",
kernelFunc: isInf2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/IsNaN.js
init_define_BUILD_VERSION();
var isNaN3 = unaryKernelFunc(IsNan, (xi) => Number.isNaN(xi) ? 1 : 0, "bool");
var isNaNConfig = {
kernelName: IsNan,
backendName: "cpu",
kernelFunc: isNaN3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LinSpace.js
init_define_BUILD_VERSION();
function linSpace(args) {
const { backend: backend2, attrs } = args;
const { start, stop, num } = attrs;
const outVals = linSpaceImpl(start, stop, num);
return backend2.makeTensorInfo([outVals.length], "float32", outVals);
}
var linSpaceConfig = {
kernelName: LinSpace,
backendName: "cpu",
kernelFunc: linSpace
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Log1p.js
init_define_BUILD_VERSION();
var log1p2 = unaryKernelFunc(Log1p, (xi) => Math.log1p(xi));
var log1pConfig = {
kernelName: Log1p,
backendName: "cpu",
kernelFunc: log1p2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalAnd.js
init_define_BUILD_VERSION();
var logicalAndImpl = createSimpleBinaryKernelImpl((a, b) => a && b);
var logicalAnd2 = binaryKernelFunc(LogicalAnd, logicalAndImpl, null, "bool");
var logicalAndConfig = {
kernelName: LogicalAnd,
backendName: "cpu",
kernelFunc: logicalAnd2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalNot.js
init_define_BUILD_VERSION();
var logicalNot2 = unaryKernelFunc(LogicalNot, (xi) => xi ? 0 : 1, "bool");
var logicalNotConfig = {
kernelName: LogicalNot,
backendName: "cpu",
kernelFunc: logicalNot2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LogicalOr.js
init_define_BUILD_VERSION();
var logicalOrImpl = createSimpleBinaryKernelImpl((a, b) => a || b);
var logicalOr2 = binaryKernelFunc(LogicalOr, logicalOrImpl, null, "bool");
var logicalOrConfig = {
kernelName: LogicalOr,
backendName: "cpu",
kernelFunc: logicalOr2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRN.js
init_define_BUILD_VERSION();
function lRN(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { depthRadius, bias, alpha, beta } = attrs;
assertNotComplex(x, "LRN");
const channels = x.shape[3];
const maxD = channels - 1;
const xValues = backend2.data.get(x.dataId).values;
const size = util_exports.sizeFromShape(x.shape);
const result = new Float32Array(size);
function sumAcrossChannels(offset) {
const currentChannel = offset % channels;
let beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
const endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD);
let sum5 = 0;
for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
const z = xValues[beginSumOffset];
sum5 += z * z;
}
return sum5;
}
for (let offset = 0; offset < size; offset++) {
const sum5 = sumAcrossChannels(offset);
const val = xValues[offset] * Math.pow(bias + alpha * sum5, -beta);
result[offset] = val;
}
return backend2.makeTensorInfo(x.shape, x.dtype, result);
}
var LRNConfig = {
kernelName: LRN,
backendName: "cpu",
kernelFunc: lRN
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/LRNGrad.js
init_define_BUILD_VERSION();
function lRNGrad(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, y, dy } = inputs;
const { depthRadius, bias, alpha, beta } = attrs;
assertNotComplex(dy, "LRNGrad");
const dySize = util_exports.sizeFromShape(dy.shape);
const channels = dy.shape[3];
const dyValues = backend2.data.get(dy.dataId).values;
const xValues = backend2.data.get(x.dataId).values;
const yValues = backend2.data.get(y.dataId).values;
const result = new Float32Array(dySize);
const size = dySize;
for (let offset = 0; offset < size; offset++) {
const currentChannel = offset % channels;
const depthBegin = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
const depthEnd = offset - currentChannel + Math.min(channels, currentChannel + depthRadius + 1);
let norm2 = 0;
for (let k = depthBegin; k < depthEnd; k++) {
norm2 += Math.pow(xValues[k], 2);
}
norm2 = alpha * norm2 + bias;
for (let k = depthBegin; k < depthEnd; k++) {
let dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm2;
if (offset === k) {
dyi += Math.pow(norm2, -beta);
}
dyi *= dyValues[offset];
result[k] += dyi;
}
}
return backend2.makeTensorInfo(dy.shape, x.dtype, result);
}
var LRNGradConfig = {
kernelName: LRNGrad,
backendName: "cpu",
kernelFunc: lRNGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Max.js
init_define_BUILD_VERSION();
function max3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { reductionIndices, keepDims } = attrs;
const cpuBackend = backend2;
let xShape = x.shape;
const xRank = xShape.length;
const origAxes = util_exports.parseAxisParam(reductionIndices, xShape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
let xVals = cpuBackend.data.get(x.dataId).values;
if (permutedAxes != null) {
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = xShape[permutedAxes[i]];
}
xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape);
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
xShape = newShape;
}
assertNotComplex(x, "max");
backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xShape, axes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype);
const dataId = cpuBackend.write(result, maxOutShape, x.dtype);
let outShape = maxOutShape;
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
outShape = newShape;
}
return { dataId, shape: outShape, dtype: x.dtype };
}
var maxConfig = {
kernelName: Max,
backendName: "cpu",
kernelFunc: max3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool.js
init_define_BUILD_VERSION();
function maxPool2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
assertNotComplex(x, "maxPool");
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const dilations = 1;
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode);
let res;
if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
res = identity({ inputs: { x }, backend: backend2 });
} else {
const xValues = backend2.data.get(x.dataId).values;
const strides2 = util_exports.computeStrides(x.shape);
const buffer2 = pool2(xValues, x.shape, x.dtype, strides2, convInfo, "max");
res = backend2.makeTensorInfo(convInfo.outShape, x.dtype, buffer2.values);
}
return res;
}
var maxPoolConfig = {
kernelName: MaxPool,
backendName: "cpu",
kernelFunc: maxPool2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3D.js
init_define_BUILD_VERSION();
function maxPool3D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { filterSize, strides, pad: pad2, dimRoundingMode, dataFormat } = attrs;
assertNotComplex(x, "maxPool3d");
const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, 1, pad2, dimRoundingMode, dataFormat);
const xValues = backend2.data.get(x.dataId).values;
const outBuf = pool3d2(xValues, x.shape, x.dtype, util_exports.computeStrides(x.shape), convInfo, "max");
return backend2.makeTensorInfo(outBuf.shape, "float32", outBuf.values);
}
var maxPool3DConfig = {
kernelName: MaxPool3D,
backendName: "cpu",
kernelFunc: maxPool3D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPool3DGrad.js
init_define_BUILD_VERSION();
function maxPool3DGrad(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2 } = inputs;
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
assertNotComplex([dy, input2], "maxPool3DGrad");
const convInfo = backend_util_exports.computePool3DInfo(input2.shape, filterSize, strides, 1, pad2, dimRoundingMode);
const inputBuf = backend2.bufferSync(input2);
const maxPosBuf = maxPool3dPositions(inputBuf, convInfo);
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const dx = buffer(input2.shape, "float32");
const dyBuf = backend2.bufferSync(dy);
for (let batch = 0; batch < convInfo.batchSize; ++batch) {
for (let channel = 0; channel < convInfo.inChannels; ++channel) {
for (let dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) {
for (let dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) {
for (let dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) {
const dyDepthCorner = dxDepth - padFront;
const dyRowCorner = dxRow - padTop;
const dyColCorner = dxCol - padLeft;
let dotProd = 0;
for (let wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) {
const dyDepth = (dyDepthCorner + wDepth) / strideDepth;
if (dyDepth < 0 || dyDepth >= convInfo.outDepth || Math.floor(dyDepth) !== dyDepth) {
continue;
}
for (let wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) {
const dyRow = (dyRowCorner + wRow) / strideHeight;
if (dyRow < 0 || dyRow >= convInfo.outHeight || Math.floor(dyRow) !== dyRow) {
continue;
}
for (let wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) {
const dyCol = (dyColCorner + wCol) / strideWidth;
if (dyCol < 0 || dyCol >= convInfo.outWidth || Math.floor(dyCol) !== dyCol) {
continue;
}
const maxPos = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel);
const curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + wRow * effectiveFilterWidth + wCol;
const mask = maxPos === curPos ? 1 : 0;
if (mask === 0) {
continue;
}
const pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel);
dotProd += pixel * mask;
}
}
}
dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel);
}
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var maxPool3DGradConfig2 = {
kernelName: MaxPool3DGrad,
backendName: "cpu",
kernelFunc: maxPool3DGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolGrad.js
init_define_BUILD_VERSION();
function maxPoolGrad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2, output } = inputs;
const x = input2;
assertNotComplex([input2, output], "maxPoolGrad");
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad2, dimRoundingMode);
const xValues = backend2.data.get(x.dataId).values;
const maxPosBuf = buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values);
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const dx = buffer(x.shape, "float32");
const dyData = backend2.data.get(dy.dataId).values;
const dyBuf = buffer(dy.shape, "float32", dyData);
for (let b = 0; b < convInfo.batchSize; ++b) {
for (let d = 0; d < convInfo.inChannels; ++d) {
for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {
for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {
const dyRCorner = dxR - padTop;
const dyCCorner = dxC - padLeft;
let dotProd = 0;
for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
const dyR = (dyRCorner + wR) / strideHeight;
if (dyR < 0 || dyR >= convInfo.outHeight || Math.floor(dyR) !== dyR) {
continue;
}
for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
const dyC = (dyCCorner + wC) / strideWidth;
if (dyC < 0 || dyC >= convInfo.outWidth || Math.floor(dyC) !== dyC) {
continue;
}
const maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - maxPosBuf.get(b, dyR, dyC, d);
const curPos = wR * effectiveFilterWidth + wC;
const mask = maxPos === curPos ? 1 : 0;
if (mask === 0) {
continue;
}
const pixel = dyBuf.get(b, dyR, dyC, d);
dotProd += pixel * mask;
}
}
dx.set(dotProd, b, dxR, dxC, d);
}
}
}
}
return backend2.makeTensorInfo(dx.shape, dx.dtype, dx.values);
}
var maxPoolGradConfig2 = {
kernelName: MaxPoolGrad,
backendName: "cpu",
kernelFunc: maxPoolGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax_impl.js
init_define_BUILD_VERSION();
function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) {
const strides = util_exports.computeStrides(xShape);
const maxPools = pool2(xValues, xShape, dtype, strides, convInfo, "max");
const maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex);
return [maxPools.values, maxPositions.values];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MaxPoolWithArgmax.js
var maxPoolWithArgmaxConfig = {
kernelName: MaxPoolWithArgmax,
backendName: "cpu",
kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
const { x } = inputs;
const { filterSize, strides, pad: pad2, includeBatchInIndex } = attrs;
const cpuBackend = backend2;
assertNotComplex(x, "MaxPoolWithArgmax");
const values = cpuBackend.data.get(x.dataId).values;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad2);
const [pooled, indexes] = maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo);
const pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype);
const indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype);
return [
{ dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype },
{ dataId: indexesDataId, shape: convInfo.outShape, dtype: "int32" }
];
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mean.js
init_define_BUILD_VERSION();
function mean2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
const axes = util_exports.parseAxisParam(axis, x.shape);
const shapes = backend_util_exports.computeOutAndReduceShapes(x.shape, axes);
const reduceShape = shapes[1];
const reduceSize = util_exports.sizeFromShape(reduceShape);
const toDispose = [];
const reduceSizeScalar = backend2.makeTensorInfo([], "float32", new Float32Array([reduceSize]));
toDispose.push(reduceSizeScalar);
const $x = cast3({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
toDispose.push($x);
const res = div2({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend2 });
toDispose.push(res);
const result = sum3({ inputs: { x: res }, backend: backend2, attrs: { axis, keepDims } });
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
}
var meanConfig = {
kernelName: Mean,
backendName: "cpu",
kernelFunc: mean2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Min.js
init_define_BUILD_VERSION();
function min3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
assertNotComplex(x, "min");
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
if (permutedAxes != null) {
$x = transpose2({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
}
backend_util_exports.assertAxesAreInnerMostDims("min", axes, $x.shape.length);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes($x.shape, axes);
const reduceSize = util_exports.sizeFromShape(reduceShape);
const vals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(outShape), $x.dtype);
const aVals = backend2.data.get($x.dataId).values;
for (let i = 0; i < vals.length; ++i) {
const offset = i * reduceSize;
let min5 = aVals[offset];
for (let j = 0; j < reduceSize; ++j) {
const value = aVals[offset + j];
if (Number.isNaN(value) || value < min5) {
min5 = value;
}
}
vals[i] = min5;
}
if (permutedAxes != null) {
backend2.disposeIntermediateTensorInfo($x);
}
const result = backend2.makeTensorInfo(outShape, $x.dtype, vals);
if (keepDims) {
const expandedShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
const reshapedResult = reshape2({ inputs: { x: result }, backend: backend2, attrs: { shape: expandedShape } });
backend2.disposeIntermediateTensorInfo(result);
return reshapedResult;
}
return result;
}
var minConfig = {
kernelName: Min,
backendName: "cpu",
kernelFunc: min3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/MirrorPad.js
init_define_BUILD_VERSION();
function mirrorPad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { paddings, mode } = attrs;
assertNotComplex(x, "mirrorPad");
const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
const start = paddings.map((p2) => p2[0]);
const end = paddings.map((p2, i) => p2[0] + x.shape[i]);
const offset = mode === "reflect" ? 0 : 1;
const xVals = backend2.data.get(x.dataId).values;
const xRank = x.shape.length;
const xStrides = util_exports.computeStrides(x.shape);
const resultSize = util_exports.sizeFromShape(outShape);
const resultRank = outShape.length;
const resultStrides = util_exports.computeStrides(outShape);
const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
for (let i = 0; i < resultSize; i++) {
let coords2 = util_exports.indexToLoc(i, resultRank, resultStrides);
for (let i2 = 0; i2 < resultRank; i2++) {
if (coords2[i2] < start[i2]) {
coords2[i2] = start[i2] * 2 - coords2[i2] - offset;
} else if (coords2[i2] >= end[i2]) {
coords2[i2] = (end[i2] - 1) * 2 - coords2[i2] + offset;
}
}
coords2 = coords2.map((c, i2) => c - start[i2]);
const inIndex = util_exports.locToIndex(coords2, xRank, xStrides);
resVals[i] = xVals[inIndex];
}
const outId = backend2.write(resVals, outShape, x.dtype);
return { dataId: outId, shape: outShape, dtype: x.dtype };
}
var mirrorPadConfig = {
kernelName: MirrorPad,
backendName: "cpu",
kernelFunc: mirrorPad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Mod.js
init_define_BUILD_VERSION();
var modImpl = createSimpleBinaryKernelImpl((aValue, bValue) => {
const rem = aValue % bValue;
if (aValue < 0 && bValue < 0 || aValue >= 0 && bValue >= 0) {
return rem;
} else {
return (rem + bValue) % bValue;
}
});
var mod2 = binaryKernelFunc(Mod, modImpl);
var modConfig = {
kernelName: Mod,
backendName: "cpu",
kernelFunc: mod2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
init_define_BUILD_VERSION();
var seedrandom4 = __toESM(require_seedrandom2());
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softmax.js
init_define_BUILD_VERSION();
function softmax2(args) {
const { inputs, backend: backend2, attrs } = args;
const { logits } = inputs;
const { dim } = attrs;
const logitsRank = logits.shape.length;
let $dim = dim;
if ($dim === -1) {
$dim = logitsRank - 1;
}
if ($dim !== logitsRank - 1) {
throw Error(`Softmax along a non-last dimension is not yet supported. Logits was rank ${logitsRank} and dim was ${$dim}`);
}
const axes = util_exports.parseAxisParam([$dim], logits.shape);
const maxLogit = max3({
inputs: { x: logits },
backend: backend2,
attrs: { reductionIndices: axes, keepDims: false }
});
const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
const maxLogitReshaped = reshape2({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
const a = sub2({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend2 });
const b = exp2({ inputs: { x: a }, backend: backend2 });
const sumExp = sum3({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
const sumReshaped = reshape2({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
const result = div2({ inputs: { a: b, b: sumReshaped }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(maxLogit);
backend2.disposeIntermediateTensorInfo(maxLogitReshaped);
backend2.disposeIntermediateTensorInfo(a);
backend2.disposeIntermediateTensorInfo(b);
backend2.disposeIntermediateTensorInfo(sumExp);
backend2.disposeIntermediateTensorInfo(sumReshaped);
return result;
}
var softmaxConfig = {
kernelName: Softmax,
backendName: "cpu",
kernelFunc: softmax2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Multinomial.js
function multinomial(args) {
const { inputs, backend: backend2, attrs } = args;
const { logits } = inputs;
const { numSamples, seed, normalized } = attrs;
assertNotComplex(logits, "multinomial");
const probabilities = normalized ? logits : softmax2({ inputs: { logits }, backend: backend2, attrs: { dim: -1 } });
const batchSize = probabilities.shape[0];
const numEvents = probabilities.shape[1];
const probVals = backend2.data.get(probabilities.dataId).values;
const resShape = [batchSize, numSamples];
const resVals = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(resShape), "int32");
for (let b = 0; b < batchSize; ++b) {
const offset = b * numEvents;
const cdf = new Float32Array(numEvents - 1);
cdf[0] = probVals[offset];
for (let event = 1; event < cdf.length; ++event) {
cdf[event] = cdf[event - 1] + probVals[offset + event];
}
const random = seedrandom4.alea(seed.toString());
const outOffset = b * numSamples;
for (let sampleId = 0; sampleId < numSamples; ++sampleId) {
const r = random();
resVals[outOffset + sampleId] = cdf.length;
for (let event = 0; event < cdf.length; event++) {
if (r < cdf[event]) {
resVals[outOffset + sampleId] = event;
break;
}
}
}
}
if (!normalized) {
backend2.disposeIntermediateTensorInfo(probabilities);
}
return backend2.makeTensorInfo(resShape, "int32", resVals);
}
var multinomialConfig = {
kernelName: Multinomial,
backendName: "cpu",
kernelFunc: multinomial
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV3.js
init_define_BUILD_VERSION();
var nonMaxSuppressionV3Impl2 = kernel_impls_exports.nonMaxSuppressionV3Impl;
function nonMaxSuppressionV3(args) {
const { inputs, backend: backend2, attrs } = args;
const { boxes, scores } = inputs;
const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
assertNotComplex(boxes, "NonMaxSuppression");
const boxesVals = backend2.data.get(boxes.dataId).values;
const scoresVals = backend2.data.get(scores.dataId).values;
const { selectedIndices } = nonMaxSuppressionV3Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
}
var nonMaxSuppressionV3Config = {
kernelName: NonMaxSuppressionV3,
backendName: "cpu",
kernelFunc: nonMaxSuppressionV3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV4.js
init_define_BUILD_VERSION();
var nonMaxSuppressionV4Impl2 = kernel_impls_exports.nonMaxSuppressionV4Impl;
function nonMaxSuppressionV4(args) {
const { inputs, backend: backend2, attrs } = args;
const { boxes, scores } = inputs;
const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
assertNotComplex(boxes, "NonMaxSuppressionPadded");
const boxesVals = backend2.data.get(boxes.dataId).values;
const scoresVals = backend2.data.get(scores.dataId).values;
const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl2(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
return [
backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
];
}
var nonMaxSuppressionV4Config = {
kernelName: NonMaxSuppressionV4,
backendName: "cpu",
kernelFunc: nonMaxSuppressionV4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/NonMaxSuppressionV5.js
init_define_BUILD_VERSION();
var nonMaxSuppressionV5Impl2 = kernel_impls_exports.nonMaxSuppressionV5Impl;
function nonMaxSuppressionV5(args) {
const { inputs, backend: backend2, attrs } = args;
const { boxes, scores } = inputs;
const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
assertNotComplex(boxes, "NonMaxSuppressionWithScore");
const boxesVals = backend2.data.get(boxes.dataId).values;
const scoresVals = backend2.data.get(scores.dataId).values;
const maxOutputSizeVal = maxOutputSize;
const iouThresholdVal = iouThreshold;
const scoreThresholdVal = scoreThreshold;
const softNmsSigmaVal = softNmsSigma;
const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl2(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
return [
backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
];
}
var nonMaxSuppressionV5Config = {
kernelName: NonMaxSuppressionV5,
backendName: "cpu",
kernelFunc: nonMaxSuppressionV5
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OneHot.js
init_define_BUILD_VERSION();
function oneHot2(args) {
const { inputs, backend: backend2, attrs } = args;
const { indices } = inputs;
const { depth, onValue, offValue } = attrs;
assertNotComplex(indices, "oneHot");
const indicesSize = util_exports.sizeFromShape(indices.shape);
const res = new Float32Array(indicesSize * depth);
res.fill(offValue);
const indicesVal = backend2.data.get(indices.dataId).values;
for (let event = 0; event < indicesSize; ++event) {
if (indicesVal[event] >= 0 && indicesVal[event] < depth) {
res[event * depth + indicesVal[event]] = onValue;
}
}
return backend2.makeTensorInfo([...indices.shape, depth], "int32", res);
}
var oneHotConfig = {
kernelName: OneHot,
backendName: "cpu",
kernelFunc: oneHot2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ZerosLike.js
init_define_BUILD_VERSION();
function zerosLike2(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
if (x.dtype === "string") {
throw new Error("zerosLike is not supported for string tensors");
} else if (x.dtype === "complex64") {
const realPart = real2({ inputs: { input: x }, backend: backend2 });
const r = zerosLike2({ inputs: { x: realPart }, backend: backend2 });
const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(realPart);
backend2.disposeIntermediateTensorInfo(r);
backend2.disposeIntermediateTensorInfo(imagPart);
backend2.disposeIntermediateTensorInfo(i);
return result;
} else {
return fill2({ backend: backend2, attrs: { shape: x.shape, value: 0, dtype: x.dtype } });
}
}
var zerosLikeConfig = {
kernelName: ZerosLike,
backendName: "cpu",
kernelFunc: zerosLike2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/OnesLike.js
function onesLike2(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
if (x.dtype === "string") {
throw new Error("onesLike is not supported for string tensors");
} else if (x.dtype === "complex64") {
const realPart = real2({ inputs: { input: x }, backend: backend2 });
const r = onesLike2({ inputs: { x: realPart }, backend: backend2 });
const imagPart = imag2({ inputs: { input: x }, backend: backend2 });
const i = zerosLike2({ inputs: { x: imagPart }, backend: backend2 });
const result = complex2({ inputs: { real: r, imag: i }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(realPart);
backend2.disposeIntermediateTensorInfo(r);
backend2.disposeIntermediateTensorInfo(imagPart);
backend2.disposeIntermediateTensorInfo(i);
return result;
} else {
return fill2({ backend: backend2, attrs: { shape: x.shape, value: 1, dtype: x.dtype } });
}
}
var onesLikeConfig = {
kernelName: OnesLike,
backendName: "cpu",
kernelFunc: onesLike2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pack.js
init_define_BUILD_VERSION();
function pack(args) {
const { inputs, backend: backend2, attrs } = args;
const { axis } = attrs;
if (inputs.length === 1) {
return expandDims3({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
}
const shape = inputs[0].shape;
const dtype = inputs[0].dtype;
inputs.forEach((t) => {
util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
});
const intermediateTensorInfos = [];
const expandedTensors = inputs.map((t) => {
const expandedT = expandDims3({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });
intermediateTensorInfos.push(expandedT);
return expandedT;
});
const result = concat2({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
}
var packConfig = {
kernelName: Pack,
backendName: "cpu",
kernelFunc: pack
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/PadV2.js
init_define_BUILD_VERSION();
function padV2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { paddings, constantValue } = attrs;
assertNotComplex(x, "pad");
const outShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
const start = paddings.map((p2) => p2[0]);
const xVals = backend2.data.get(x.dataId).values;
const xSize = util_exports.sizeFromShape(x.shape);
const xRank = x.shape.length;
const xStrides = util_exports.computeStrides(x.shape);
const resultSize = util_exports.sizeFromShape(outShape);
const resultRank = outShape.length;
const resultStrides = util_exports.computeStrides(outShape);
const resVals = util_exports.getTypedArrayFromDType(x.dtype, resultSize);
if (constantValue !== 0) {
resVals.fill(constantValue);
}
for (let i = 0; i < xSize; i++) {
const coords2 = util_exports.indexToLoc(i, xRank, xStrides);
const outCoords = coords2.map((c, i2) => c + start[i2]);
const outIndex = util_exports.locToIndex(outCoords, resultRank, resultStrides);
resVals[outIndex] = xVals[i];
}
const outId = backend2.write(resVals, outShape, x.dtype);
return { dataId: outId, shape: outShape, dtype: x.dtype };
}
var padV2Config = {
kernelName: PadV2,
backendName: "cpu",
kernelFunc: padV2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Pow.js
init_define_BUILD_VERSION();
var powImpl = createSimpleBinaryKernelImpl((a, b) => Math.pow(a, b));
var pow2 = binaryKernelFunc(Pow, powImpl);
var powConfig = {
kernelName: Pow,
backendName: "cpu",
kernelFunc: pow2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Range.js
init_define_BUILD_VERSION();
function range3(args) {
const { backend: backend2, attrs } = args;
const { start, stop, dtype, step: step4 } = attrs;
const values = rangeImpl(start, stop, step4, dtype);
return backend2.makeTensorInfo([values.length], dtype, values);
}
var rangeConfig = {
kernelName: Range,
backendName: "cpu",
kernelFunc: range3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reciprocal.js
init_define_BUILD_VERSION();
var reciprocal2 = unaryKernelFunc(Reciprocal, (xi) => 1 / xi);
var reciprocalConfig = {
kernelName: Reciprocal,
backendName: "cpu",
kernelFunc: reciprocal2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinear.js
init_define_BUILD_VERSION();
function resizeBilinear2(args) {
const { inputs, backend: backend2, attrs } = args;
const { images } = inputs;
const { alignCorners, halfPixelCenters, size } = attrs;
assertNotComplex(images, "resizeBilinear");
const imagesStrides = util_exports.computeStrides(images.shape);
const [newHeight, newWidth] = size;
const [batch, oldHeight, oldWidth, numChannels] = images.shape;
const xValues = backend2.data.get(images.dataId).values;
const result = new Float32Array(util_exports.sizeFromShape([batch, newHeight, newWidth, numChannels]));
const effectiveInputSize = [
alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
];
const effectiveOutputSize = [
alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
];
let outputIdx = 0;
const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
for (let b = 0; b < batch; b++) {
for (let r = 0; r < newHeight; r++) {
let sourceFracRow;
if (halfPixelCenters) {
sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5;
} else {
sourceFracRow = effectiveRowSizeRatio * r;
}
const sourceRowFloor = Math.max(0, Math.floor(sourceFracRow));
const rowFrac = sourceFracRow - sourceRowFloor;
const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
const topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1];
const botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1];
for (let c = 0; c < newWidth; c++) {
let sourceFracCol;
if (halfPixelCenters) {
sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5;
} else {
sourceFracCol = effectiveColSizeRatio * c;
}
const sourceColFloor = Math.max(0, Math.floor(sourceFracCol));
const colFrac = sourceFracCol - sourceColFloor;
const sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
const topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2];
const botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2];
const topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2];
const botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2];
for (let d = 0; d < numChannels; d++) {
const topLeft = xValues[topLeftOffest + d];
const bottomLeft = xValues[botLeftOffset + d];
const topRight = xValues[topRightOffset + d];
const bottomRight = xValues[botRightOffest + d];
const top = topLeft + (topRight - topLeft) * colFrac;
const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
const newValue = top + (bottom - top) * rowFrac;
result[outputIdx++] = newValue;
}
}
}
}
return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], "float32", result);
}
var resizeBilinearConfig = {
kernelName: ResizeBilinear,
backendName: "cpu",
kernelFunc: resizeBilinear2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeBilinearGrad.js
init_define_BUILD_VERSION();
function resizeBilinearGrad(args) {
const { inputs, backend: backend2, attrs } = args;
const { images, dy } = inputs;
const { alignCorners } = attrs;
assertNotComplex([dy, images], "resizeBilinearGrad");
const imagesStrides = util_exports.computeStrides(images.shape);
const [batch, xHeight, xWidth, depth] = images.shape;
const [, yHeight, yWidth] = dy.shape;
const output = new Float32Array(batch * xHeight * xWidth * depth);
const effectiveXSize = [
alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
];
const effectiveYSize = [
alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
];
const heightScale = effectiveXSize[0] / effectiveYSize[0];
const widthScale = effectiveXSize[1] / effectiveYSize[1];
const dyValues = backend2.data.get(dy.dataId).values;
let offset = 0;
for (let b = 0; b < batch; b++) {
const bOffset = b * imagesStrides[0];
for (let r = 0; r < yHeight; r++) {
const dxR = r * heightScale;
const topDxRIndex = Math.floor(dxR);
const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
const topDxROffset = bOffset + topDxRIndex * imagesStrides[1];
const bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1];
const dxRLerp = dxR - topDxRIndex;
const inverseDxRLerp = 1 - dxRLerp;
for (let c = 0; c < yWidth; c++) {
const dxC = c * widthScale;
const leftDxCIndex = Math.floor(dxC);
const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
const dxCLerp = dxC - leftDxCIndex;
const inverseDxCLerp = 1 - dxCLerp;
const topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2];
const topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2];
const bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2];
const bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2];
const inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
for (let d = 0; d < depth; d++) {
const dyVal = dyValues[offset++];
output[topLeftRCOffset + d] += dyVal * inverseDxRLerpTimesInverseDxCLerp;
output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp;
output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
}
}
}
}
return backend2.makeTensorInfo([batch, xWidth, xHeight, depth], "float32", output);
}
var resizeBilinearGradConfig2 = {
kernelName: ResizeBilinearGrad,
backendName: "cpu",
kernelFunc: resizeBilinearGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighbor.js
init_define_BUILD_VERSION();
function resizeNearestNeighbor2(args) {
const { inputs, backend: backend2, attrs } = args;
const { images } = inputs;
const { alignCorners, halfPixelCenters, size } = attrs;
assertNotComplex(images, "resizeNearestNeighbor");
const imagesStrides = util_exports.computeStrides(images.shape);
const [newHeight, newWidth] = size;
const [batch, oldHeight, oldWidth, numChannels] = images.shape;
const xValues = backend2.data.get(images.dataId).values;
const output = new Float32Array(batch * newHeight * newWidth * numChannels);
const effectiveInputSize = [
alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
];
const effectiveOutputSize = [
alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
];
const effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
const effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
let outputOffset = 0;
for (let b = 0; b < batch; b++) {
const batchOffset = b * imagesStrides[0];
for (let r = 0; r < newHeight; r++) {
const sourceFracRow = halfPixelCenters ? effectiveRowSizeRatio * (r + 0.5) : effectiveRowSizeRatio * r;
let sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
if (halfPixelCenters) {
sourceNearestRow = Math.max(0, sourceNearestRow);
}
const rowOffset = batchOffset + sourceNearestRow * imagesStrides[1];
for (let c = 0; c < newWidth; c++) {
const sourceFracCol = halfPixelCenters ? effectiveColSizeRatio * (c + 0.5) : effectiveColSizeRatio * c;
let sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
if (halfPixelCenters) {
sourceNearestCol = Math.max(0, sourceNearestCol);
}
const colOffset = rowOffset + sourceNearestCol * imagesStrides[2];
for (let d = 0; d < numChannels; d++) {
const newVal = xValues[colOffset + d];
output[outputOffset++] = newVal;
}
}
}
}
return backend2.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output);
}
var resizeNearestNeighborConfig = {
kernelName: ResizeNearestNeighbor,
backendName: "cpu",
kernelFunc: resizeNearestNeighbor2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ResizeNearestNeighborGrad.js
init_define_BUILD_VERSION();
function resizeNearestNeighborGrad(args) {
const { inputs, backend: backend2, attrs } = args;
const { images, dy } = inputs;
const { alignCorners } = attrs;
assertNotComplex([dy, images], "resizeNearestNeighborGrad");
const imagesStrides = util_exports.computeStrides(images.shape);
const dyStrides = util_exports.computeStrides(dy.shape);
const [batch, xHeight, xWidth, depth] = images.shape;
const [, yHeight, yWidth] = dy.shape;
const output = new Float32Array(batch * xHeight * xWidth * depth);
const dyValues = backend2.data.get(dy.dataId).values;
const effectiveXSize = [
alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
];
const effectiveYSize = [
alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
];
const heightScale = effectiveXSize[0] / effectiveYSize[0];
const widthScale = effectiveXSize[1] / effectiveYSize[1];
const invHeightScale = 1 / heightScale;
const invWidthScale = 1 / widthScale;
const winHeight = Math.ceil(invHeightScale) * 2 + 2;
const winWidth = Math.ceil(invWidthScale) * 2 + 2;
for (let b = 0; b < batch; b++) {
const batchOffset = b * imagesStrides[0];
for (let r = 0; r < xHeight; r++) {
const rowOffset = batchOffset + r * imagesStrides[1];
const startRLerp = Math.floor(r * invHeightScale);
const startDyR = Math.floor(startRLerp - winHeight / 2);
for (let c = 0; c < xWidth; c++) {
const colOffset = rowOffset + c * imagesStrides[2];
const startCLerp = Math.floor(c * invWidthScale);
const startDyC = Math.floor(startCLerp - winWidth / 2);
for (let d = 0; d < depth; d++) {
let accum = 0;
for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
const dyR = dyRIndex + startDyR;
if (dyR < 0 || dyR >= yHeight) {
continue;
}
const dyROffset = batchOffset + dyR * dyStrides[1];
const sourceFracRow = dyR * heightScale;
const sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow));
if (r !== sourceNearestRow) {
continue;
}
for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
const dyC = dyCIndex + startDyC;
if (dyC < 0 || dyC >= yWidth) {
continue;
}
const dyCOffset = dyROffset + dyC * dyStrides[2];
const sourceFracCol = dyC * widthScale;
const sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : Math.floor(sourceFracCol));
if (c === sourceNearestCol) {
accum += dyValues[dyCOffset + d];
}
}
}
output[colOffset + d] = accum;
}
}
}
}
return backend2.makeTensorInfo(images.shape, images.dtype, output);
}
var resizeNearestNeighborGradConfig2 = {
kernelName: ResizeNearestNeighborGrad,
backendName: "cpu",
kernelFunc: resizeNearestNeighborGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Reverse.js
init_define_BUILD_VERSION();
function reverse2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { dims } = attrs;
assertNotComplex(x, "reverse");
const xRank = x.shape.length;
const $dims = util_exports.parseAxisParam(dims, x.shape);
if (xRank === 0) {
return identity({ inputs: { x }, backend: backend2 });
}
const outBuf = new TensorBuffer(x.shape, x.dtype);
const xBuf = backend2.bufferSync(x);
for (let i = 0; i < outBuf.size; i++) {
const outLoc = outBuf.indexToLoc(i);
const inLoc = outLoc.slice();
$dims.forEach((d) => inLoc[d] = x.shape[d] - 1 - inLoc[d]);
outBuf.set(xBuf.get(...inLoc), ...outLoc);
}
return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
var reverseConfig = {
kernelName: Reverse,
backendName: "cpu",
kernelFunc: reverse2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/RotateWithOffset.js
init_define_BUILD_VERSION();
var rotateWithOffsetConfig = {
kernelName: RotateWithOffset,
backendName: "cpu",
kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
const { image: image3 } = inputs;
const { radians, fillValue, center } = attrs;
const cpuBackend = backend2;
const output = util_exports.getTypedArrayFromDType(image3.dtype, util_exports.sizeFromShape(image3.shape));
const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
const [centerX, centerY] = backend_util_exports.getImageCenter(center, imageHeight, imageWidth);
const fullOpacityValue = 255;
const sinFactor = Math.sin(radians);
const cosFactor = Math.cos(radians);
const imageVals = cpuBackend.data.get(image3.dataId).values;
for (let batchIdx = 0; batchIdx < batch; batchIdx++) {
const batchOffset = batchIdx * imageWidth * imageHeight * numChannels;
for (let row = 0; row < imageHeight; row++) {
const rowOffset = row * (imageWidth * numChannels);
for (let col = 0; col < imageWidth; col++) {
const colOffset = col * numChannels;
for (let channel = 0; channel < numChannels; channel++) {
const coords2 = [batch, row, col, channel];
const x = coords2[2];
const y = coords2[1];
let coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor;
let coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor;
coordX = Math.round(coordX + centerX);
coordY = Math.round(coordY + centerY);
let outputValue = fillValue;
if (typeof fillValue !== "number") {
if (channel === 3) {
outputValue = fullOpacityValue;
} else {
outputValue = fillValue[channel];
}
}
if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && coordY < imageHeight) {
const rotatedRowOffset = coordY * (imageWidth * numChannels);
const rotatedColOffset = coordX * numChannels;
const imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel;
outputValue = imageVals[imageIdx];
}
const outIdx = batchOffset + rowOffset + colOffset + channel;
output[outIdx] = outputValue;
}
}
}
}
const dataId = cpuBackend.write(output, image3.shape, image3.dtype);
return { dataId, shape: image3.shape, dtype: image3.dtype };
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Round.js
init_define_BUILD_VERSION();
var round3 = unaryKernelFunc(Round, (xi) => {
const base = Math.floor(xi);
if (xi - base < 0.5) {
return Math.floor(xi);
} else if (xi - base > 0.5) {
return Math.ceil(xi);
} else {
if (base % 2 === 0) {
return base;
} else {
return base + 1;
}
}
});
var roundConfig = {
kernelName: Round,
backendName: "cpu",
kernelFunc: round3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/ScatterNd.js
init_define_BUILD_VERSION();
function scatterNd(args) {
const { inputs, backend: backend2, attrs } = args;
const { indices, updates } = inputs;
const { shape } = attrs;
const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);
const sumDupeIndices = true;
const indicesBuf = backend2.bufferSync(indices);
const updatesBuf = backend2.bufferSync(updates);
const outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0, sumDupeIndices);
return backend2.makeTensorInfo(shape, outBuf.dtype, outBuf.values);
}
var scatterNdConfig = {
kernelName: ScatterNd,
backendName: "cpu",
kernelFunc: scatterNd
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted_impl.js
init_define_BUILD_VERSION();
function lowerBound(array2, value) {
let left = 0;
let right = array2.length;
let mid = 0;
while (left < right) {
mid = Math.floor((left + right) / 2);
if (array2[mid] < value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
function upperBound(array2, value) {
let left = 0;
let right = array2.length;
let mid = 0;
while (left < right) {
mid = Math.floor((left + right) / 2);
if (array2[mid] <= value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
function searchSortedImpl(sortedInputs, values, batchSize, numInputs, numValues, side) {
const output = util_exports.getArrayFromDType("int32", batchSize * numValues);
for (let b = 0; b < batchSize; ++b) {
const sortedInputsSlice = sortedInputs.slice(b * numInputs, (b + 1) * numInputs);
const outputOffset = b * numValues;
for (let i = 0; i < numValues; ++i) {
output[outputOffset + i] = side === "left" ? lowerBound(sortedInputsSlice, values[i + outputOffset]) : upperBound(sortedInputsSlice, values[i + outputOffset]);
}
}
return output;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SearchSorted.js
function searchSorted(args) {
const { inputs, backend: backend2, attrs } = args;
const { sortedSequence, values } = inputs;
const { side } = attrs;
const $sortedSequence = backend2.data.get(sortedSequence.dataId).values;
const $values = backend2.data.get(values.dataId).values;
const output = searchSortedImpl($sortedSequence, $values, sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
return backend2.makeTensorInfo(values.shape, "int32", output);
}
var searchSortedConfig = {
kernelName: SearchSorted,
backendName: "cpu",
kernelFunc: searchSorted
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Select.js
init_define_BUILD_VERSION();
function select2(args) {
const { inputs, backend: backend2 } = args;
const { condition, t, e } = inputs;
assertNotComplex([condition, t, e], "select");
const conditionRank = condition.shape.length;
const values = backend2.data.get(condition.dataId).values;
const tValues = backend2.data.get(t.dataId).values;
const eValues = backend2.data.get(e.dataId).values;
const resultDtype = upcastType(t.dtype, e.dtype);
const newValues = util_exports.makeZerosTypedArray(util_exports.sizeFromShape(t.shape), resultDtype);
let index = 0;
const offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? 1 : util_exports.sizeFromShape(t.shape.slice(1));
for (let i = 0; i < values.length; i++) {
for (let j = 0; j < offset; j++) {
if (values[i] === 1) {
newValues[index++] = tValues[i];
} else {
newValues[index++] = eValues[i];
}
}
}
return backend2.makeTensorInfo(t.shape, resultDtype, newValues);
}
var selectConfig = {
kernelName: Select,
backendName: "cpu",
kernelFunc: select2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Selu.js
init_define_BUILD_VERSION();
var scaleAlpha = backend_util_exports.SELU_SCALEALPHA;
var scale = backend_util_exports.SELU_SCALE;
var selu2 = unaryKernelFunc(Selu, (xi) => {
if (xi >= 0) {
return scale * xi;
} else {
return scaleAlpha * (Math.exp(xi) - 1);
}
});
var seluConfig = {
kernelName: Selu,
backendName: "cpu",
kernelFunc: selu2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sign.js
init_define_BUILD_VERSION();
var sign2 = unaryKernelFunc(Sign, (xi) => {
if (xi < 0) {
return -1;
} else if (xi > 0) {
return 1;
} else {
return 0;
}
});
var signConfig = {
kernelName: Sign,
backendName: "cpu",
kernelFunc: sign2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sin.js
init_define_BUILD_VERSION();
var sin2 = unaryKernelFunc(Sin, (xi) => Math.sin(xi));
var sinConfig = {
kernelName: Sin,
backendName: "cpu",
kernelFunc: sin2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Sinh.js
init_define_BUILD_VERSION();
var sinh2 = unaryKernelFunc(Sinh, (xi) => Math.sinh(xi));
var sinhConfig = {
kernelName: Sinh,
backendName: "cpu",
kernelFunc: sinh2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Softplus.js
init_define_BUILD_VERSION();
var epsilon2 = 11920928955078125e-23;
var threshold2 = Math.log(epsilon2) + 2;
var softplus2 = unaryKernelFunc(Softplus, (xi) => {
const tooLarge = xi > -threshold2;
const tooSmall = xi < threshold2;
const expX = Math.exp(xi);
let result;
if (tooSmall) {
result = expX;
} else if (tooLarge) {
result = xi;
} else {
result = Math.log(1 + expX);
}
return result;
});
var softplusConfig = {
kernelName: Softplus,
backendName: "cpu",
kernelFunc: softplus2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SpaceToBatchND.js
init_define_BUILD_VERSION();
function spaceToBatchND2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { blockShape, paddings } = attrs;
assertNotComplex([x], "spaceToBatchND");
const prod4 = util_exports.sizeFromShape(blockShape);
const completePaddings = [[0, 0]];
completePaddings.push(...paddings);
for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
completePaddings.push([0, 0]);
}
const paddedX = padV2Config.kernelFunc({
inputs: { x },
backend: backend2,
attrs: { paddings: completePaddings, constantValue: 0 }
});
const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod4, false);
const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod4, false);
const reshapeInputs = { x: paddedX };
const reshapeAttrs = { shape: reshapedPaddedShape };
const paddedXReshaped = reshape2({ inputs: reshapeInputs, backend: backend2, attrs: reshapeAttrs });
const transposeInputs = { x: paddedXReshaped };
const transposeAttrs = { perm: permutedReshapedPaddedPermutation };
const paddedXT = transpose2({ inputs: transposeInputs, backend: backend2, attrs: transposeAttrs });
const resultReshapeInputs = { x: paddedXT };
const resultReshapeAttrs = { shape: flattenShape };
const result = reshape2({ inputs: resultReshapeInputs, backend: backend2, attrs: resultReshapeAttrs });
backend2.disposeIntermediateTensorInfo(paddedX);
backend2.disposeIntermediateTensorInfo(paddedXReshaped);
backend2.disposeIntermediateTensorInfo(paddedXT);
return result;
}
var spaceToBatchNDConfig = {
kernelName: SpaceToBatchND,
backendName: "cpu",
kernelFunc: spaceToBatchND2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseFillEmptyRows.js
init_define_BUILD_VERSION();
function sparseFillEmptyRows(args) {
const { inputs, backend: backend2 } = args;
const { indices, values, denseShape, defaultValue } = inputs;
if (denseShape.shape.length !== 1) {
throw new Error(`Dense shape must be a vector, saw:
${denseShape.shape}`);
}
if (indices.shape.length !== 2) {
throw new Error(`Indices must be a matrix, saw:
${indices.shape}`);
}
if (values.shape.length !== 1) {
throw new Error(`Values must be a vector, saw:
${values.shape}`);
}
if (defaultValue.shape.length !== 0) {
throw new Error(`Default value must be a scalar, saw:
${defaultValue.shape}`);
}
const $indices = backend2.data.get(indices.dataId).values;
const $values = backend2.data.get(values.dataId).values;
const $denseShape = backend2.data.get(denseShape.dataId).values;
const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
return [
backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),
backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))
];
}
var sparseFillEmptyRowsConfig = {
kernelName: SparseFillEmptyRows,
backendName: "cpu",
kernelFunc: sparseFillEmptyRows
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseReshape.js
init_define_BUILD_VERSION();
function sparseReshape(args) {
const { inputs, backend: backend2 } = args;
const { inputIndices, inputShape, newShape } = inputs;
if (inputIndices.shape.length !== 2) {
throw new Error(`Input indices should be a matrix but received shape
${inputIndices.shape}`);
}
if (inputShape.shape.length !== 1) {
throw new Error(`Input shape should be a vector but received shape
${inputShape.shape}`);
}
if (newShape.shape.length !== 1) {
throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
}
const $inputShape = Array.from(backend2.data.get(inputShape.dataId).values);
const $inputIndices = backend2.data.get(inputIndices.dataId).values;
const targetShape = Array.from(backend2.data.get(newShape.dataId).values);
const [newIndices, indicesShape, outputShape] = sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
return [
backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))
];
}
var sparseReshapeConfig = {
kernelName: SparseReshape,
backendName: "cpu",
kernelFunc: sparseReshape
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentMean.js
init_define_BUILD_VERSION();
function sparseSegmentMean(args) {
const { inputs, backend: backend2 } = args;
const { data, indices, segmentIds } = inputs;
if (data.shape.length < 1) {
throw new Error(`Data should be at least 1 dimensional but received scalar`);
}
if (indices.shape.length !== 1) {
throw new Error(`Indices should be a vector but received shape
${indices.shape}`);
}
if (segmentIds.shape.length !== 1) {
throw new Error(`Segment ids should be a vector but received shape
${segmentIds.shape}`);
}
if (indices.shape[0] !== segmentIds.shape[0]) {
throw new Error(`segmentIds and indices should have same size.`);
}
const $data = backend2.data.get(data.dataId).values;
const $indices = backend2.data.get(indices.dataId).values;
const $segmentIds = backend2.data.get(segmentIds.dataId).values;
const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true);
return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
var sparseSegmentMeanConfig = {
kernelName: SparseSegmentMean,
backendName: "cpu",
kernelFunc: sparseSegmentMean
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseSegmentSum.js
init_define_BUILD_VERSION();
function sparseSegmentSum(args) {
const { inputs, backend: backend2 } = args;
const { data, indices, segmentIds } = inputs;
if (data.shape.length < 1) {
throw new Error(`Data should be at least 1 dimensional but received scalar`);
}
if (indices.shape.length !== 1) {
throw new Error(`Indices should be a vector but received shape
${indices.shape}`);
}
if (segmentIds.shape.length !== 1) {
throw new Error(`Segment ids should be a vector but received shape
${segmentIds.shape}`);
}
if (indices.shape[0] !== segmentIds.shape[0]) {
throw new Error(`segmentIds and indices should have same size.`);
}
const $data = backend2.data.get(data.dataId).values;
const $indices = backend2.data.get(indices.dataId).values;
const $segmentIds = backend2.data.get(segmentIds.dataId).values;
const [outputData, outputDataShape] = sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds);
return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
var sparseSegmentSumConfig = {
kernelName: SparseSegmentSum,
backendName: "cpu",
kernelFunc: sparseSegmentSum
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SparseToDense.js
init_define_BUILD_VERSION();
function sparseToDense(args) {
const { inputs, backend: backend2, attrs } = args;
const { sparseIndices, sparseValues, defaultValue } = inputs;
const { outputShape } = attrs;
const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
const sumDupeIndices = false;
const indicesBuf = backend2.bufferSync(sparseIndices);
let outBuf;
switch (sparseValues.dtype) {
case "bool": {
const updatesBuf = backend2.bufferSync(sparseValues);
const $defaultValue = Boolean(backend2.data.get(defaultValue.dataId).values[0]);
outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
break;
}
case "float32": {
const updatesBuf = backend2.bufferSync(sparseValues);
const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
break;
}
case "int32": {
const updatesBuf = backend2.bufferSync(sparseValues);
const $defaultValue = backend2.data.get(defaultValue.dataId).values[0];
outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
break;
}
case "string": {
const updatesBuf = backend2.bufferSync(sparseValues);
const $defaultValue = util_exports.decodeString(backend2.data.get(defaultValue.dataId).values[0]);
outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
break;
}
default:
throw new Error(`Unsupported type ${sparseValues.dtype}`);
}
return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
}
var sparseToDenseConfig = {
kernelName: SparseToDense,
backendName: "cpu",
kernelFunc: sparseToDense
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/SplitV.js
init_define_BUILD_VERSION();
function splitV(args) {
const { inputs, backend: backend2, attrs } = 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, $axis);
const begin = new Array(x.shape.length).fill(0);
const size = x.shape.slice();
return splitSizes.map((s) => {
const sliceSize = [...size];
sliceSize[$axis] = s;
const sliceT = slice2({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
begin[$axis] += s;
return sliceT;
});
}
var splitVConfig = {
kernelName: SplitV,
backendName: "cpu",
kernelFunc: splitV
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Square.js
init_define_BUILD_VERSION();
var squareConfig = {
kernelName: Square,
backendName: "cpu",
kernelFunc: ({ inputs, backend: backend2 }) => {
const { x } = inputs;
const cpuBackend = backend2;
assertNotComplex(x, "square");
const values = cpuBackend.data.get(x.dataId).values;
const newValues = new Float32Array(values.length);
for (let i = 0; i < values.length; ++i) {
const value = values[i];
newValues[i] = value * value;
}
const dataId = cpuBackend.write(newValues, x.shape, x.dtype);
return { dataId, shape: x.shape, dtype: x.dtype };
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Step.js
init_define_BUILD_VERSION();
var step2 = unaryKernelFunc(Step, (xi, attrs) => {
const stepAttrs = attrs;
if (isNaN(xi)) {
return NaN;
} else {
return xi > 0 ? 1 : stepAttrs.alpha;
}
});
var stepConfig = {
kernelName: Step,
backendName: "cpu",
kernelFunc: step2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StridedSlice.js
init_define_BUILD_VERSION();
function stridedSlice2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
assertNotComplex(x, "stridedSlice");
const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
let result;
if (isIdentity) {
result = reshape2({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
} else if (sliceDim0 || isSimpleSlice) {
util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
const size = slice_util_exports.computeOutShape($begin, $end, $strides);
const sliced = slice2({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
result = reshape2({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
backend2.disposeIntermediateTensorInfo(sliced);
} else {
const xBuf = backend2.bufferSync(x);
const outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin);
result = backend2.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values);
}
return result;
}
var stridedSliceConfig = {
kernelName: StridedSlice,
backendName: "cpu",
kernelFunc: stridedSlice2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringNGrams.js
init_define_BUILD_VERSION();
function stringNGrams(args) {
const { inputs, backend: backend2, attrs } = args;
const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
const { data, dataSplits } = inputs;
const $data = backend2.data.get(data.dataId).values;
const $dataSplits = backend2.data.get(dataSplits.dataId).values;
const [nGrams, nGramsSplits] = stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
return [
backend2.makeTensorInfo([nGrams.length], "string", nGrams),
backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
];
}
var stringNGramsConfig = {
kernelName: StringNGrams,
backendName: "cpu",
kernelFunc: stringNGrams
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringSplit.js
init_define_BUILD_VERSION();
function stringSplit(args) {
const { inputs, backend: backend2, attrs } = args;
const { skipEmpty } = attrs;
const { input: input2, delimiter } = inputs;
if (input2.dtype !== "string") {
throw new Error("Input must be of datatype string");
}
if (input2.shape.length !== 1) {
throw new Error(`Input must be a vector, got shape: ${input2.shape}`);
}
if (delimiter.shape.length !== 0) {
throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
}
const $input = backend2.data.get(input2.dataId).values;
const $delimiter = backend2.data.get(delimiter.dataId).values[0];
const [indices, values, shape] = stringSplitImpl($input, $delimiter, skipEmpty);
const outputSize = values.length;
return [
backend2.makeTensorInfo([outputSize, 2], "int32", indices),
backend2.makeTensorInfo([outputSize], "string", values),
backend2.makeTensorInfo([2], "int32", new Int32Array(shape))
];
}
var stringSplitConfig = {
kernelName: StringSplit,
backendName: "cpu",
kernelFunc: stringSplit
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/StringToHashBucketFast.js
init_define_BUILD_VERSION();
function stringToHashBucketFast(args) {
const { inputs, backend: backend2, attrs } = args;
const { numBuckets } = attrs;
const { input: input2 } = inputs;
if (input2.dtype !== "string") {
throw new Error("Input must be of datatype string");
}
if (numBuckets <= 0) {
throw new Error(`Number of buckets must be at least 1`);
}
const $input = backend2.data.get(input2.dataId).values;
const output = stringToHashBucketFastImpl($input, numBuckets);
return backend2.makeTensorInfo(input2.shape, "int32", output);
}
var stringToHashBucketFastConfig = {
kernelName: StringToHashBucketFast,
backendName: "cpu",
kernelFunc: stringToHashBucketFast
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tan.js
init_define_BUILD_VERSION();
var tan2 = unaryKernelFunc(Tan, (xi) => Math.tan(xi));
var tanConfig = {
kernelName: Tan,
backendName: "cpu",
kernelFunc: tan2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tanh.js
init_define_BUILD_VERSION();
var tanh3 = unaryKernelFunc(Tanh, (xi) => Math.tanh(xi));
var tanhConfig = {
kernelName: Tanh,
backendName: "cpu",
kernelFunc: tanh3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Tile.js
init_define_BUILD_VERSION();
function tile3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { reps } = attrs;
assertNotComplex(x, "tile");
const outBuf = tileImpl(backend2.bufferSync(x), reps);
return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
var tileConfig = {
kernelName: Tile,
backendName: "cpu",
kernelFunc: tile3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/TopK.js
init_define_BUILD_VERSION();
function topK(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { k, sorted } = attrs;
assertNotComplex(x, "topk");
const xVals = backend2.data.get(x.dataId).values;
const [allTopKVals, allTopKIndices] = topKImpl(xVals, x.shape, x.dtype, k, sorted);
return [
backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
];
}
var topKConfig = {
kernelName: TopK,
backendName: "cpu",
kernelFunc: topK
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Transform.js
init_define_BUILD_VERSION();
function transform2(args) {
const { inputs, attrs, backend: backend2 } = args;
const { image: image3, transforms } = inputs;
const { interpolation, fillMode, fillValue, outputShape } = attrs;
const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
const outShape = [batch, outHeight, outWidth, numChannels];
const strides = util_exports.computeStrides(image3.shape);
const batchStride = strides[0];
const rowStride = strides[1];
const colStride = strides[2];
const outVals = util_exports.getTypedArrayFromDType(image3.dtype, util_exports.sizeFromShape(outShape));
outVals.fill(fillValue);
const imageVals = backend2.data.get(image3.dataId).values;
const transformVals = backend2.data.get(transforms.dataId).values;
for (let b = 0; b < batch; ++b) {
const transform4 = transforms.shape[0] === 1 ? transformVals : transformVals.subarray(b * 8, b * 8 + 8);
for (let outY = 0; outY < outHeight; ++outY) {
for (let outX = 0; outX < outWidth; ++outX) {
for (let channel = 0; channel < numChannels; ++channel) {
let val;
const projection = transform4[6] * outX + transform4[7] * outY + 1;
if (projection === 0) {
continue;
}
const inX = (transform4[0] * outX + transform4[1] * outY + transform4[2]) / projection;
const inY = (transform4[3] * outX + transform4[4] * outY + transform4[5]) / projection;
const x = mapCoord(inX, imageWidth, fillMode);
const y = mapCoord(inY, imageHeight, fillMode);
switch (interpolation) {
case "nearest":
val = nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
break;
case "bilinear":
val = bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, b, y, x, channel, fillValue);
break;
default:
throw new Error(`Error in Transform: Expect 'nearest' or 'bilinear', but got ${interpolation}`);
}
const ind = b * batchStride + outY * rowStride + outX * colStride + channel;
outVals[ind] = val;
}
}
}
return backend2.makeTensorInfo(outShape, image3.dtype, outVals);
}
const dataId = backend2.write(outVals, outShape, image3.dtype);
return { dataId, shape: image3.shape, dtype: image3.dtype };
}
var transformConfig = {
kernelName: Transform,
backendName: "cpu",
kernelFunc: transform2
};
function mapCoord(outCoord, len, mode) {
switch (mode) {
case "reflect":
return mapCoordReflect(outCoord, len);
case "wrap":
return mapCoordWrap(outCoord, len);
case "nearest":
return mapCoordNearest(outCoord, len);
case "constant":
default:
return mapCoordConstant(outCoord, len);
}
}
function mapCoordReflect(outCoord, len) {
let inCoord = outCoord;
if (inCoord < 0) {
if (len <= 1) {
inCoord = 0;
} else {
const sz2 = 2 * len;
if (inCoord < sz2) {
inCoord = sz2 * Math.trunc(-inCoord / sz2) + inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1;
}
} else if (inCoord > len - 1) {
if (len <= 1) {
inCoord = 0;
} else {
const sz2 = 2 * len;
inCoord -= sz2 * Math.trunc(inCoord / sz2);
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1;
}
}
}
return util_exports.clamp(0, inCoord, len - 1);
}
function mapCoordWrap(outCoord, len) {
let inCoord = outCoord;
if (inCoord < 0) {
if (len <= 1) {
inCoord = 0;
} else {
const sz = len - 1;
inCoord += len * (Math.trunc(-inCoord / sz) + 1);
}
} else if (inCoord > len - 1) {
if (len <= 1) {
inCoord = 0;
} else {
const sz = len - 1;
inCoord -= len * Math.trunc(inCoord / sz);
}
}
return util_exports.clamp(0, inCoord, len - 1);
}
function mapCoordConstant(outCoord, len) {
return outCoord;
}
function mapCoordNearest(outCoord, len) {
return util_exports.clamp(0, outCoord, len - 1);
}
function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
const ind = batch * batchStride + y * rowStride + x * colStride + channel;
if (0 <= y && y < imageHeight && 0 <= x && x < imageWidth) {
return imageVals[ind];
} else {
return fillValue;
}
}
function nearestInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
const $y = Math.round(y);
const $x = Math.round(x);
return readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, $y, $x, channel, fillValue);
}
function bilinearInterpolation(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) {
const yFloor = Math.floor(y);
const xFloor = Math.floor(x);
const yCeil = yFloor + 1;
const xCeil = xFloor + 1;
const valueYFloor = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yFloor, xCeil, channel, fillValue);
const valueYCeil = (xCeil - x) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xFloor, channel, fillValue) + (x - xFloor) * readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, yCeil, xCeil, channel, fillValue);
return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unique.js
init_define_BUILD_VERSION();
function unique3(args) {
const { inputs, attrs, backend: backend2 } = args;
const { axis } = attrs;
const { x } = inputs;
assertNotComplex(x, "unique");
const values = backend2.data.get(x.dataId).values;
const { outputValues, outputShape, indices } = uniqueImpl(values, axis, x.shape, x.dtype);
return [
backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
backend2.makeTensorInfo([indices.length], "int32", indices)
];
}
var uniqueConfig = {
kernelName: Unique,
backendName: "cpu",
kernelFunc: unique3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/Unpack.js
init_define_BUILD_VERSION();
function unpack(args) {
const { inputs, backend: backend2, attrs } = args;
const { value } = inputs;
let { axis } = attrs;
if (axis < 0) {
axis += value.shape.length;
}
const valueRank = value.shape.length;
const num = value.shape[axis];
const outShape = new Array(valueRank - 1);
let outIndex = 0;
for (let i = 0; i < valueRank; i++) {
if (i !== axis) {
outShape[outIndex++] = value.shape[i];
}
}
const begin = new Array(valueRank).fill(0);
const size = value.shape.slice();
size[axis] = 1;
const res = new Array(num);
for (let i = 0; i < res.length; i++) {
begin[axis] = i;
const tempRes = slice2({ inputs: { x: value }, backend: backend2, attrs: { begin, size } });
res[i] = reshape2({ inputs: { x: tempRes }, backend: backend2, attrs: { shape: outShape } });
backend2.disposeIntermediateTensorInfo(tempRes);
}
return res;
}
var unpackConfig = {
kernelName: Unpack,
backendName: "cpu",
kernelFunc: unpack
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/kernels/UnsortedSegmentSum.js
init_define_BUILD_VERSION();
function unsortedSegmentSum2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, segmentIds } = inputs;
const { numSegments } = attrs;
assertNotComplex(x, "unsortedSegmentSum");
const xRank = x.shape.length;
const segmentIdsRank = segmentIds.shape.length;
const res = [];
const intermediates = [];
const numIters = xRank - segmentIdsRank;
let $segmentIds = segmentIds;
for (let i = 0; i < numIters; ++i) {
const expanded = expandDims3({ inputs: { input: $segmentIds }, backend: backend2, attrs: { dim: i + 1 } });
$segmentIds = expanded;
intermediates.push(expanded);
}
for (let i = 0; i < numSegments; ++i) {
const scalarValue = util_exports.createScalarValue(i, "int32");
const segmentId = backend2.makeTensorInfo([], "int32", scalarValue);
const mask = equal2({ inputs: { a: segmentId, b: $segmentIds }, backend: backend2 });
const maskCasted = cast3({ inputs: { x: mask }, backend: backend2, attrs: { dtype: "float32" } });
const mul2 = multiply({ inputs: { a: maskCasted, b: x }, backend: backend2 });
const sumTensorInfo = sum3({ inputs: { x: mul2 }, backend: backend2, attrs: { axis: 0, keepDims: false } });
res.push(sumTensorInfo);
intermediates.push(segmentId);
intermediates.push(mask);
intermediates.push(maskCasted);
intermediates.push(mul2);
intermediates.push(sumTensorInfo);
}
const result = pack({ inputs: res, backend: backend2, attrs: { axis: 0 } });
intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
}
var unsortedSegmentSumConfig = {
kernelName: UnsortedSegmentSum,
backendName: "cpu",
kernelFunc: unsortedSegmentSum2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-cpu/dist/register_all_kernels.js
var kernelConfigs = [
_fusedMatMulConfig,
absConfig,
acosConfig,
acoshConfig,
addConfig,
addNConfig,
allConfig,
anyConfig,
argMaxConfig,
argMinConfig,
asinConfig,
asinhConfig,
atanConfig,
atan2Config,
atanhConfig,
avgPoolConfig,
avgPool3DConfig,
avgPool3DGradConfig2,
avgPoolGradConfig2,
batchMatMulConfig,
batchNormConfig,
batchToSpaceNDConfig,
bincountConfig,
broadcastArgsConfig,
castConfig,
ceilConfig,
clipByValueConfig,
complexConfig,
complexAbsConfig,
concatConfig,
conv2DConfig,
conv2DBackpropFilterConfig,
conv2DBackpropInputConfig,
conv3DConfig,
conv3DBackpropFilterV2Config,
conv3DBackpropInputV2Config,
cosConfig,
coshConfig,
cropAndResizeConfig,
cumprodConfig,
cumsumConfig,
denseBincountConfig,
depthToSpaceConfig,
depthwiseConv2dNativeConfig,
depthwiseConv2dNativeBackpropFilterConfig,
depthwiseConv2dNativeBackpropInputConfig,
diagConfig,
dilation2DConfig,
dilation2DBackpropFilterConfig,
dilation2DBackpropInputConfig,
einsumConfig,
eluConfig,
eluGradConfig2,
equalConfig,
erfConfig,
expConfig,
expandDimsConfig,
expm1Config,
fftConfig,
fillConfig,
flipLeftRightConfig,
floorConfig,
floorDivConfig,
fusedConv2DConfig,
fusedDepthwiseConv2DConfig,
gatherNdConfig,
gatherV2Config,
greaterConfig,
greaterEqualConfig,
identityConfig,
ifftConfig,
imagConfig,
isFiniteConfig,
isInfConfig,
isNaNConfig,
leakyReluConfig,
lessConfig,
lessEqualConfig,
linSpaceConfig,
logConfig,
log1pConfig,
logicalAndConfig,
logicalNotConfig,
logicalOrConfig,
LRNConfig,
LRNGradConfig,
maxConfig,
maximumConfig,
maxPoolConfig,
maxPool3DConfig,
maxPool3DGradConfig2,
maxPoolGradConfig2,
maxPoolWithArgmaxConfig,
meanConfig,
minConfig,
minimumConfig,
mirrorPadConfig,
modConfig,
multinomialConfig,
multiplyConfig,
negConfig,
nonMaxSuppressionV3Config,
nonMaxSuppressionV4Config,
nonMaxSuppressionV5Config,
notEqualConfig,
oneHotConfig,
onesLikeConfig,
packConfig,
padV2Config,
powConfig,
preluConfig,
prodConfig,
rangeConfig,
realConfig,
realDivConfig,
reciprocalConfig,
reluConfig,
relu6Config,
reshapeConfig,
resizeBilinearConfig,
resizeBilinearGradConfig2,
resizeNearestNeighborConfig,
resizeNearestNeighborGradConfig2,
reverseConfig,
rotateWithOffsetConfig,
roundConfig,
rsqrtConfig,
scatterNdConfig,
searchSortedConfig,
selectConfig,
seluConfig,
sigmoidConfig,
signConfig,
sinConfig,
sinhConfig,
sliceConfig,
softmaxConfig,
softplusConfig,
spaceToBatchNDConfig,
sparseFillEmptyRowsConfig,
sparseReshapeConfig,
sparseSegmentMeanConfig,
sparseSegmentSumConfig,
sparseToDenseConfig,
splitVConfig,
sqrtConfig,
squareConfig,
squaredDifferenceConfig,
stepConfig,
stridedSliceConfig,
stringNGramsConfig,
stringSplitConfig,
stringToHashBucketFastConfig,
subConfig,
sumConfig,
tanConfig,
tanhConfig,
tileConfig,
topKConfig,
transformConfig,
transposeConfig,
uniqueConfig,
unpackConfig,
unsortedSegmentSumConfig,
zerosLikeConfig
];
for (const kernelConfig of kernelConfigs) {
registerKernel(kernelConfig);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/index.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/canvas_util.js
init_define_BUILD_VERSION();
var contexts = {};
var WEBGL_ATTRIBUTES = {
alpha: false,
antialias: false,
premultipliedAlpha: false,
preserveDrawingBuffer: false,
depth: false,
stencil: false,
failIfMajorPerformanceCaveat: true
};
function setWebGLContext(webGLVersion, gl) {
contexts[webGLVersion] = gl;
}
function getWebGLContext(webGLVersion, customCanvas) {
if (!(webGLVersion in contexts) || customCanvas != null) {
const newCtx = getWebGLRenderingContext(webGLVersion, customCanvas);
if (newCtx !== null) {
contexts[webGLVersion] = newCtx;
} else {
console.log("Could not get context for WebGL version", webGLVersion);
return null;
}
}
const gl = contexts[webGLVersion];
if (gl == null || gl.isContextLost()) {
delete contexts[webGLVersion];
return getWebGLContext(webGLVersion);
}
gl.disable(gl.DEPTH_TEST);
gl.disable(gl.STENCIL_TEST);
gl.disable(gl.BLEND);
gl.disable(gl.DITHER);
gl.disable(gl.POLYGON_OFFSET_FILL);
gl.disable(gl.SAMPLE_COVERAGE);
gl.enable(gl.SCISSOR_TEST);
gl.enable(gl.CULL_FACE);
gl.cullFace(gl.BACK);
return contexts[webGLVersion];
}
function createCanvas(webGLVersion) {
if (typeof OffscreenCanvas !== "undefined" && webGLVersion === 2) {
return new OffscreenCanvas(300, 150);
} else if (typeof document !== "undefined") {
return document.createElement("canvas");
} else {
throw new Error("Cannot create a canvas in this context");
}
}
function getWebGLRenderingContext(webGLVersion, customCanvas) {
if (webGLVersion !== 1 && webGLVersion !== 2) {
throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");
}
const canvas = customCanvas == null ? createCanvas(webGLVersion) : customCanvas;
canvas.addEventListener("webglcontextlost", (ev) => {
ev.preventDefault();
delete contexts[webGLVersion];
}, false);
if (webGLVersion === 1) {
return canvas.getContext("webgl", WEBGL_ATTRIBUTES) || canvas.getContext("experimental-webgl", WEBGL_ATTRIBUTES);
}
return canvas.getContext("webgl2", WEBGL_ATTRIBUTES);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/tex_util.js
init_define_BUILD_VERSION();
var PackingScheme;
(function(PackingScheme2) {
PackingScheme2[PackingScheme2["DENSE"] = 0] = "DENSE";
PackingScheme2[PackingScheme2["SHARED_BATCH"] = 1] = "SHARED_BATCH";
})(PackingScheme || (PackingScheme = {}));
var TextureUsage;
(function(TextureUsage2) {
TextureUsage2[TextureUsage2["RENDER"] = 0] = "RENDER";
TextureUsage2[TextureUsage2["UPLOAD"] = 1] = "UPLOAD";
TextureUsage2[TextureUsage2["PIXELS"] = 2] = "PIXELS";
TextureUsage2[TextureUsage2["DOWNLOAD"] = 3] = "DOWNLOAD";
})(TextureUsage || (TextureUsage = {}));
var PhysicalTextureType;
(function(PhysicalTextureType2) {
PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
PhysicalTextureType2[PhysicalTextureType2["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
PhysicalTextureType2[PhysicalTextureType2["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
PhysicalTextureType2[PhysicalTextureType2["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
})(PhysicalTextureType || (PhysicalTextureType = {}));
function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
return [columns, rows];
}
function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
return matrixSize * channelsPerTexture;
}
function getDenseTexShape(shape) {
const size = util_exports.sizeFromShape(shape);
const texelsNeeded = Math.ceil(size / 4);
return util_exports.sizeToSquarishShape(texelsNeeded);
}
function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
return [
Math.max(1, Math.ceil(columns / 2)),
Math.max(1, Math.ceil(rows / 2))
];
}
function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
return w * h * 4;
}
function getTextureConfig(gl, textureHalfFloatExtension) {
const glany = gl;
let internalFormatFloat;
let internalFormatHalfFloat;
let internalFormatPackedHalfFloat;
let internalFormatPackedFloat;
let textureFormatFloat;
let downloadTextureFormat;
let downloadUnpackNumChannels;
let defaultNumChannels;
let textureTypeHalfFloat;
let textureTypeFloat;
if (env().getNumber("WEBGL_VERSION") === 2) {
internalFormatFloat = glany.R32F;
internalFormatHalfFloat = glany.R16F;
internalFormatPackedHalfFloat = glany.RGBA16F;
internalFormatPackedFloat = glany.RGBA32F;
textureFormatFloat = glany.RED;
downloadUnpackNumChannels = 4;
defaultNumChannels = 1;
textureTypeHalfFloat = glany.HALF_FLOAT;
textureTypeFloat = glany.FLOAT;
downloadTextureFormat = glany.RGBA8;
} else {
internalFormatFloat = gl.RGBA;
internalFormatHalfFloat = gl.RGBA;
internalFormatPackedHalfFloat = gl.RGBA;
internalFormatPackedFloat = glany.RGBA;
textureFormatFloat = gl.RGBA;
downloadUnpackNumChannels = 4;
defaultNumChannels = 4;
textureTypeHalfFloat = textureHalfFloatExtension != null ? textureHalfFloatExtension.HALF_FLOAT_OES : null;
textureTypeFloat = gl.FLOAT;
downloadTextureFormat = gl.RGBA;
}
return {
internalFormatFloat,
internalFormatHalfFloat,
internalFormatPackedHalfFloat,
internalFormatPackedFloat,
textureFormatFloat,
downloadTextureFormat,
downloadUnpackNumChannels,
defaultNumChannels,
textureTypeHalfFloat,
textureTypeFloat
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/webgl_util.js
function callAndCheck(gl, func2) {
const returnValue = func2();
if (env().getBool("DEBUG")) {
checkWebGLError(gl);
}
return returnValue;
}
function checkWebGLError(gl) {
const error = gl.getError();
if (error !== gl.NO_ERROR) {
throw new Error("WebGL Error: " + getWebGLErrorMessage(gl, error));
}
}
var MIN_FLOAT16 = 596e-10;
var MAX_FLOAT16 = 65504;
function canBeRepresented(num) {
if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED") || num === 0 || MIN_FLOAT16 < Math.abs(num) && Math.abs(num) < MAX_FLOAT16) {
return true;
}
return false;
}
function getWebGLErrorMessage(gl, status) {
switch (status) {
case gl.NO_ERROR:
return "NO_ERROR";
case gl.INVALID_ENUM:
return "INVALID_ENUM";
case gl.INVALID_VALUE:
return "INVALID_VALUE";
case gl.INVALID_OPERATION:
return "INVALID_OPERATION";
case gl.INVALID_FRAMEBUFFER_OPERATION:
return "INVALID_FRAMEBUFFER_OPERATION";
case gl.OUT_OF_MEMORY:
return "OUT_OF_MEMORY";
case gl.CONTEXT_LOST_WEBGL:
return "CONTEXT_LOST_WEBGL";
default:
return `Unknown error code ${status}`;
}
}
function getExtensionOrThrow(gl, extensionName) {
return throwIfNull(gl, () => gl.getExtension(extensionName), 'Extension "' + extensionName + '" not supported on this browser.');
}
function createVertexShader(gl, vertexShaderSource) {
const vertexShader = throwIfNull(gl, () => gl.createShader(gl.VERTEX_SHADER), "Unable to create vertex WebGLShader.");
callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));
callAndCheck(gl, () => gl.compileShader(vertexShader));
if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
console.log(gl.getShaderInfoLog(vertexShader));
throw new Error("Failed to compile vertex shader.");
}
return vertexShader;
}
function createFragmentShader(gl, fragmentShaderSource) {
const fragmentShader = throwIfNull(gl, () => gl.createShader(gl.FRAGMENT_SHADER), "Unable to create fragment WebGLShader.");
callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));
callAndCheck(gl, () => gl.compileShader(fragmentShader));
if (env().get("ENGINE_COMPILE_ONLY")) {
return fragmentShader;
}
if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
throw new Error("Failed to compile fragment shader.");
}
return fragmentShader;
}
var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
if (lineNumberRegexResult == null) {
console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);
console.log(shaderSource);
return;
}
const lineNumber = +lineNumberRegexResult[1];
const shaderLines = shaderSource.split("\n");
const pad2 = shaderLines.length.toString().length + 2;
const linesWithLineNumbers = shaderLines.map((line, lineNumber2) => util_exports.rightPad((lineNumber2 + 1).toString(), pad2) + line);
let maxLineLength = 0;
for (let i = 0; i < linesWithLineNumbers.length; i++) {
maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
}
const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
const afterErrorLines = linesWithLineNumbers.slice(lineNumber);
console.log(beforeErrorLines.join("\n"));
console.log(shaderInfoLog.split("\n")[0]);
console.log(`%c ${util_exports.rightPad(errorLine[0], maxLineLength)}`, "border:1px solid red; background-color:#e3d2d2; color:#a61717");
console.log(afterErrorLines.join("\n"));
}
function createProgram(gl) {
return throwIfNull(gl, () => gl.createProgram(), "Unable to create WebGLProgram.");
}
function linkProgram(gl, program) {
callAndCheck(gl, () => gl.linkProgram(program));
if (env().get("ENGINE_COMPILE_ONLY")) {
return;
}
if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
console.log(gl.getProgramInfoLog(program));
throw new Error("Failed to link vertex and fragment shaders.");
}
}
function validateProgram(gl, program) {
callAndCheck(gl, () => gl.validateProgram(program));
if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
console.log(gl.getProgramInfoLog(program));
throw new Error("Shader program validation failed.");
}
}
function createStaticVertexBuffer(gl, data) {
const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));
return buffer2;
}
function createStaticIndexBuffer(gl, data) {
const buffer2 = throwIfNull(gl, () => gl.createBuffer(), "Unable to create WebGLBuffer");
callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer2));
callAndCheck(gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));
return buffer2;
}
function createTexture(gl) {
return throwIfNull(gl, () => gl.createTexture(), "Unable to create WebGLTexture.");
}
function validateTextureSize(width, height) {
const maxTextureSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
if (width <= 0 || height <= 0) {
const requested = `[${width}x${height}]`;
throw new Error("Requested texture size " + requested + " is invalid.");
}
if (width > maxTextureSize || height > maxTextureSize) {
const requested = `[${width}x${height}]`;
const max5 = `[${maxTextureSize}x${maxTextureSize}]`;
throw new Error("Requested texture size " + requested + " greater than WebGL maximum on this browser / GPU " + max5 + ".");
}
}
function createFramebuffer(gl) {
return throwIfNull(gl, () => gl.createFramebuffer(), "Unable to create WebGLFramebuffer.");
}
function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer2, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
const loc = gl.getAttribLocation(program, attribute);
if (loc === -1) {
return false;
}
callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer2));
callAndCheck(gl, () => gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes));
callAndCheck(gl, () => gl.enableVertexAttribArray(loc));
return true;
}
function bindTextureUnit(gl, texture, textureUnit) {
validateTextureUnit(gl, textureUnit);
callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));
callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
}
function getProgramUniformLocationOrThrow(gl, program, uniformName) {
return throwIfNull(gl, () => gl.getUniformLocation(program, uniformName), 'uniform "' + uniformName + '" not present in program.');
}
function getProgramUniformLocation(gl, program, uniformName) {
return gl.getUniformLocation(program, uniformName);
}
function bindTextureToProgramUniformSampler(gl, texture, uniformSamplerLocation, textureUnit) {
callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));
callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));
}
function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));
}
function unbindColorTextureFromFramebuffer(gl, framebuffer) {
callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));
callAndCheck(gl, () => gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));
}
function validateFramebuffer(gl) {
const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
if (status !== gl.FRAMEBUFFER_COMPLETE) {
throw new Error("Error binding framebuffer: " + getFramebufferErrorMessage(gl, status));
}
}
function getFramebufferErrorMessage(gl, status) {
switch (status) {
case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";
case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";
case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";
case gl.FRAMEBUFFER_UNSUPPORTED:
return "FRAMEBUFFER_UNSUPPORTED";
default:
return `unknown error ${status}`;
}
}
function throwIfNull(gl, returnTOrNull, failureMessage) {
const tOrNull = callAndCheck(gl, () => returnTOrNull());
if (tOrNull == null) {
throw new Error(failureMessage);
}
return tOrNull;
}
function validateTextureUnit(gl, textureUnit) {
const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
const glTextureUnit = textureUnit + gl.TEXTURE0;
if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;
throw new Error(`textureUnit must be in ${textureUnitRange}.`);
}
}
function getBatchDim(shape, dimsToSkip = 2) {
return util_exports.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
}
function getRowsCols(shape) {
if (shape.length === 0) {
throw Error("Cannot get rows and columns of an empty shape array.");
}
return [
shape.length > 1 ? shape[shape.length - 2] : 1,
shape[shape.length - 1]
];
}
function getShapeAs3D(shape) {
let shapeAs3D = [1, 1, 1];
const isScalar = shape.length === 0 || shape.length === 1 && shape[0] === 1;
if (!isScalar) {
shapeAs3D = [getBatchDim(shape), ...getRowsCols(shape)];
}
return shapeAs3D;
}
function getTextureShapeFromLogicalShape(logShape, isPacked = false) {
let maxTexSize = env().getNumber("WEBGL_MAX_TEXTURE_SIZE");
if (isPacked) {
maxTexSize = maxTexSize * 2;
logShape = logShape.map((d, i) => i >= logShape.length - 2 ? util_exports.nearestLargerEven(logShape[i]) : logShape[i]);
if (logShape.length === 1) {
logShape = [2, logShape[0]];
}
}
if (logShape.length !== 2) {
const squeezeResult = util_exports.squeezeShape(logShape);
logShape = squeezeResult.newShape;
}
let size = util_exports.sizeFromShape(logShape);
if (logShape.length <= 1 && size <= maxTexSize) {
return [1, size];
} else if (logShape.length === 2 && logShape[0] <= maxTexSize && logShape[1] <= maxTexSize) {
return logShape;
} else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize && logShape[2] <= maxTexSize) {
return [logShape[0] * logShape[1], logShape[2]];
} else if (logShape.length === 3 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] <= maxTexSize) {
return [logShape[0], logShape[1] * logShape[2]];
} else if (logShape.length === 4 && logShape[0] * logShape[1] * logShape[2] <= maxTexSize && logShape[3] <= maxTexSize) {
return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
} else if (logShape.length === 4 && logShape[0] <= maxTexSize && logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
} else {
if (isPacked) {
const batchDim = getBatchDim(logShape);
let rows = 2, cols = 2;
if (logShape.length) {
[rows, cols] = getRowsCols(logShape);
}
size = batchDim * (rows / 2) * (cols / 2);
return util_exports.sizeToSquarishShape(size).map((d) => d * 2);
}
return util_exports.sizeToSquarishShape(size);
}
}
function isEven(n) {
return n % 2 === 0;
}
function isReshapeFree(shape1, shape2) {
shape1 = shape1.slice(-2);
shape2 = shape2.slice(-2);
if (util_exports.arraysEqual(shape1, shape2)) {
return true;
}
if (!shape1.length || !shape2.length) {
return true;
}
if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 || shape2[1] === 0) {
return true;
}
if (shape1.length !== shape2.length) {
const shape1Cols = shape1.slice(-1)[0];
const shape2Cols = shape2.slice(-1)[0];
if (shape1Cols === shape2Cols) {
return true;
}
if (isEven(shape1Cols) && isEven(shape2Cols) && (shape1[0] === 1 || shape2[0] === 1)) {
return true;
}
}
return shape1[1] === shape2[1] && isEven(shape1[0]) && isEven(shape2[0]);
}
var MAX_TEXTURE_SIZE;
var MAX_TEXTURES_IN_SHADER;
function getWebGLMaxTextureSize(webGLVersion) {
if (MAX_TEXTURE_SIZE == null) {
const gl = getWebGLContext(webGLVersion);
MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
}
return MAX_TEXTURE_SIZE;
}
function getMaxTexturesInShader(webGLVersion) {
if (MAX_TEXTURES_IN_SHADER == null) {
const gl = getWebGLContext(webGLVersion);
MAX_TEXTURES_IN_SHADER = gl.getParameter(gl.MAX_TEXTURE_IMAGE_UNITS);
}
return Math.min(16, MAX_TEXTURES_IN_SHADER);
}
function getWebGLDisjointQueryTimerVersion(webGLVersion) {
if (webGLVersion === 0) {
return 0;
}
let queryTimerVersion;
const gl = getWebGLContext(webGLVersion);
if (hasExtension(gl, "EXT_disjoint_timer_query_webgl2") && webGLVersion === 2) {
queryTimerVersion = 2;
} else if (hasExtension(gl, "EXT_disjoint_timer_query")) {
queryTimerVersion = 1;
} else {
queryTimerVersion = 0;
}
return queryTimerVersion;
}
function hasExtension(gl, extensionName) {
const ext = gl.getExtension(extensionName);
return ext != null;
}
function isWebGLVersionEnabled(webGLVersion) {
try {
const gl = getWebGLContext(webGLVersion);
if (gl != null) {
return true;
}
} catch (e) {
console.log("Error when getting WebGL context: ", e);
return false;
}
return false;
}
function isCapableOfRenderingToFloatTexture(webGLVersion) {
if (webGLVersion === 0) {
return false;
}
const gl = getWebGLContext(webGLVersion);
if (webGLVersion === 1) {
if (!hasExtension(gl, "OES_texture_float")) {
return false;
}
} else {
if (!hasExtension(gl, "EXT_color_buffer_float")) {
return false;
}
}
const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
return isFrameBufferComplete;
}
function isDownloadFloatTextureEnabled(webGLVersion) {
if (webGLVersion === 0) {
return false;
}
const gl = getWebGLContext(webGLVersion);
if (webGLVersion === 1) {
if (!hasExtension(gl, "OES_texture_float")) {
return false;
}
if (!hasExtension(gl, "WEBGL_color_buffer_float")) {
return false;
}
} else {
if (hasExtension(gl, "EXT_color_buffer_float")) {
return createFloatTextureAndBindToFramebuffer(gl);
}
const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
if (hasExtension(gl, COLOR_BUFFER_HALF_FLOAT)) {
const textureHalfFloatExtension = gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
return createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension);
}
return false;
}
const isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl);
return isFrameBufferComplete;
}
function createFloatTextureAndBindToFramebuffer(gl) {
const texConfig = getTextureConfig(gl);
const texture = gl.createTexture();
gl.bindTexture(gl.TEXTURE_2D, texture);
const width = 1;
const height = 1;
gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeFloat, null);
const frameBuffer = gl.createFramebuffer();
gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
gl.bindTexture(gl.TEXTURE_2D, null);
gl.bindFramebuffer(gl.FRAMEBUFFER, null);
gl.deleteTexture(texture);
gl.deleteFramebuffer(frameBuffer);
return isFrameBufferComplete;
}
function createHalfFloatTextureAndBindToFramebuffer(gl, textureHalfFloatExtension) {
const texConfig = getTextureConfig(gl, textureHalfFloatExtension);
const texture = gl.createTexture();
gl.bindTexture(gl.TEXTURE_2D, texture);
const width = 1;
const height = 1;
gl.texImage2D(gl.TEXTURE_2D, 0, texConfig.internalFormatHalfFloat, width, height, 0, texConfig.textureFormatFloat, texConfig.textureTypeHalfFloat, null);
const frameBuffer = gl.createFramebuffer();
gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
const isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
gl.bindTexture(gl.TEXTURE_2D, null);
gl.bindFramebuffer(gl.FRAMEBUFFER, null);
gl.deleteTexture(texture);
gl.deleteFramebuffer(frameBuffer);
return isFrameBufferComplete;
}
function isWebGLFenceEnabled(webGLVersion) {
if (webGLVersion !== 2) {
return false;
}
const gl = getWebGLContext(webGLVersion);
const isEnabled = gl.fenceSync != null;
return isEnabled;
}
function assertNotComplex2(tensor3, opName) {
if (!Array.isArray(tensor3)) {
tensor3 = [tensor3];
}
tensor3.forEach((t) => {
if (t != null) {
util_exports.assert(t.dtype !== "complex64", () => `${opName} does not support complex64 tensors in the WebGL backend.`);
}
});
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/flags_webgl.js
var ENV5 = env();
ENV5.registerFlag("HAS_WEBGL", () => ENV5.getNumber("WEBGL_VERSION") > 0);
ENV5.registerFlag("WEBGL_VERSION", () => {
if (isWebGLVersionEnabled(2)) {
return 2;
} else if (isWebGLVersionEnabled(1)) {
return 1;
}
return 0;
});
ENV5.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS", () => false);
ENV5.registerFlag("WEBGL_BUFFER_SUPPORTED", () => ENV5.get("WEBGL_VERSION") === 2);
ENV5.registerFlag("WEBGL_CPU_FORWARD", () => true);
ENV5.registerFlag("WEBGL_FORCE_F16_TEXTURES", () => false);
ENV5.registerFlag("WEBGL_PACK", () => ENV5.getBool("HAS_WEBGL"));
ENV5.registerFlag("WEBGL_PACK_NORMALIZATION", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_CLIP", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_DEPTHWISECONV", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_BINARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_UNARY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_PACK_REDUCE", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_LAZILY_UNPACK", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_CONV_IM2COL", () => ENV5.getBool("WEBGL_PACK"));
ENV5.registerFlag("WEBGL_MAX_TEXTURE_SIZE", () => getWebGLMaxTextureSize(ENV5.getNumber("WEBGL_VERSION")));
ENV5.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER", () => getMaxTexturesInShader(ENV5.getNumber("WEBGL_VERSION")));
ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION", () => {
const webGLVersion = ENV5.getNumber("WEBGL_VERSION");
if (webGLVersion === 0) {
return 0;
}
return getWebGLDisjointQueryTimerVersion(webGLVersion);
});
ENV5.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE", () => ENV5.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0 && !device_util_exports.isMobile());
ENV5.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE", () => isCapableOfRenderingToFloatTexture(ENV5.getNumber("WEBGL_VERSION")));
ENV5.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED", () => {
return ENV5.getBool("WEBGL_FORCE_F16_TEXTURES") ? false : ENV5.getBool("WEBGL_RENDER_FLOAT32_CAPABLE");
});
ENV5.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED", () => isDownloadFloatTextureEnabled(ENV5.getNumber("WEBGL_VERSION")));
ENV5.registerFlag("WEBGL_FENCE_API_ENABLED", () => isWebGLFenceEnabled(ENV5.getNumber("WEBGL_VERSION")));
ENV5.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM", () => {
const useUniforms = ENV5.getBool("WEBGL_RENDER_FLOAT32_ENABLED");
return useUniforms ? 4 : 0;
});
ENV5.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD", () => {
return -1;
}, (threshold3) => {
if (threshold3 < 0 && threshold3 !== -1) {
throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${threshold3}.`);
}
});
ENV5.registerFlag("WEBGL_FLUSH_THRESHOLD", () => {
return device_util_exports.isMobile() ? 1 : -1;
}, (threshold3) => {
if (threshold3 < 0 && threshold3 !== -1) {
throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${threshold3}.`);
}
});
ENV5.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD", () => 128);
ENV5.registerFlag("WEBGL_USE_SHAPES_UNIFORMS", () => false);
ENV5.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD", () => 1e5);
ENV5.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD", () => 128);
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/glsl_version.js
init_define_BUILD_VERSION();
function getGlslDifferences() {
let version8;
let attribute;
let varyingVs;
let varyingFs;
let texture2D;
let output;
let defineOutput;
let defineSpecialNaN;
let defineSpecialInf;
let defineRound;
if (env().getNumber("WEBGL_VERSION") === 2) {
version8 = "#version 300 es";
attribute = "in";
varyingVs = "out";
varyingFs = "in";
texture2D = "texture";
output = "outputColor";
defineOutput = "out vec4 outputColor;";
defineSpecialNaN = `
bool isnan_custom(float val) {
uint floatToUint = floatBitsToUint(val);
return (floatToUint & 0x7fffffffu) > 0x7f800000u;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan_custom(val.x),
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
}
#define isnan(value) isnan_custom(value)
`;
defineSpecialInf = ``;
defineRound = `
#define round(value) newRound(value)
int newRound(float value) {
return int(floor(value + 0.5));
}
ivec4 newRound(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`;
} else {
version8 = "";
attribute = "attribute";
varyingVs = "varying";
varyingFs = "varying";
texture2D = "texture2D";
output = "gl_FragColor";
defineOutput = "";
defineSpecialNaN = `
#define isnan(value) isnan_custom(value)
bool isnan_custom(float val) {
return (val > 0. || val < 1. || val == 0.) ? false : true;
}
bvec4 isnan_custom(vec4 val) {
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
}
`;
defineSpecialInf = `
uniform float INFINITY;
bool isinf(float val) {
return abs(val) == INFINITY;
}
bvec4 isinf(vec4 val) {
return equal(abs(val), vec4(INFINITY));
}
`;
defineRound = `
int round(float value) {
return int(floor(value + 0.5));
}
ivec4 round(vec4 value) {
return ivec4(floor(value + vec4(0.5)));
}
`;
}
return {
version: version8,
attribute,
varyingVs,
varyingFs,
texture2D,
output,
defineOutput,
defineSpecialNaN,
defineSpecialInf,
defineRound
};
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler_util.js
init_define_BUILD_VERSION();
function getLogicalCoordinatesFromFlatIndex(coords2, shape, index = "index") {
const strides = util_exports.computeStrides(shape);
return strides.map((stride, i) => {
const line1 = `int ${coords2[i]} = ${index} / ${stride}`;
const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${stride}` : `index -= ${coords2[i]} * ${stride}`;
return `${line1}; ${line2};`;
}).join("");
}
function getOutputLogicalCoordinatesFromFlatIndexByUniform(coords2, shape, index = "index") {
const strides = util_exports.computeStrides(shape);
return strides.map((_, i) => {
const line1 = `int ${coords2[i]} = ${index} / outShapeStrides[${i}]`;
const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * outShapeStrides[${i}]` : `index -= ${coords2[i]} * outShapeStrides[${i}]`;
return `${line1}; ${line2};`;
}).join("");
}
function symbolicallyComputeStrides(indicesArr, variableName) {
const numCoords = indicesArr.length;
const shape = indicesArr.map((d) => `${variableName}[${d}]`);
const strides = new Array(numCoords - 1);
strides[numCoords - 2] = shape[numCoords - 1];
for (let i = numCoords - 3; i >= 0; --i) {
strides[i] = `(${strides[i + 1]} * ${shape[i + 1]})`;
}
return strides;
}
function getLogicalCoordinatesFromFlatIndexByUniform(coords2, variableName, index = "index") {
const indicesArray = coords2.map((_, i) => i);
const strides = symbolicallyComputeStrides(indicesArray, variableName);
return strides.map((_, i) => {
const line1 = `int ${coords2[i]} = ${index} / ${strides[i]}`;
const line2 = i === strides.length - 1 ? `int ${coords2[i + 1]} = ${index} - ${coords2[i]} * ${strides[i]}` : `index -= ${coords2[i]} * ${strides[i]}`;
return `${line1}; ${line2};`;
}).join("");
}
function getFlatIndexFrom3D(shape) {
const strides = util_exports.computeStrides(shape).map((d) => d.toString());
return `
int getFlatIndex(ivec3 coords) {
return coords.x * ${strides[0]} + coords.y * ${strides[1]} + coords.z;
}
`;
}
function getFlatIndexFrom3DOutput() {
return `
int getFlatIndex(ivec3 coords) {
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
}
`;
}
var ENCODE_FLOAT_SNIPPET = `
const float FLOAT_MAX = 1.70141184e38;
const float FLOAT_MIN = 1.17549435e-38;
lowp vec4 encode_float(highp float v) {
if (isnan(v)) {
return vec4(255, 255, 255, 255);
}
highp float av = abs(v);
if(av < FLOAT_MIN) {
return vec4(0.0, 0.0, 0.0, 0.0);
} else if(v > FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
} else if(v < -FLOAT_MAX) {
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
}
highp vec4 c = vec4(0,0,0,0);
highp float e = floor(log2(av));
highp float m = exp2(fract(log2(av))) - 1.0;
c[2] = floor(128.0 * m);
m -= c[2] / 128.0;
c[1] = floor(32768.0 * m);
m -= c[1] / 32768.0;
c[0] = floor(8388608.0 * m);
highp float ebias = e + 127.0;
c[3] = floor(ebias / 2.0);
ebias -= c[3] * 2.0;
c[2] += floor(ebias) * 128.0;
c[3] += 128.0 * step(0.0, -v);
return c / 255.0;
}
`;
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/shader_compiler.js
var { getBroadcastDims: getBroadcastDims2 } = backend_util_exports;
function makeShader(inputsInfo, outputShape, program) {
const prefixSnippets = [];
inputsInfo.forEach((x) => {
const size = util_exports.sizeFromShape(x.shapeInfo.logicalShape);
if (x.shapeInfo.isUniform) {
prefixSnippets.push(`uniform float ${x.name}${size > 1 ? `[${size}]` : ""};`);
} else {
prefixSnippets.push(`uniform sampler2D ${x.name};`);
prefixSnippets.push(`uniform int offset${x.name};`);
}
if (program.enableShapeUniforms) {
const { uniformShape } = getUniformInfoFromShape(program.packedInputs, x.shapeInfo.logicalShape, x.shapeInfo.texShape);
switch (uniformShape.length) {
case 1:
prefixSnippets.push(`uniform int ${x.name}Shape;`);
break;
case 2:
prefixSnippets.push(`uniform ivec2 ${x.name}Shape;`);
break;
case 3:
prefixSnippets.push(`uniform ivec3 ${x.name}Shape;`);
break;
case 4:
prefixSnippets.push(`uniform ivec4 ${x.name}Shape;`);
break;
default:
break;
}
prefixSnippets.push(`uniform ivec2 ${x.name}TexShape;`);
}
});
if (program.enableShapeUniforms) {
switch (outputShape.logicalShape.length) {
case 1:
prefixSnippets.push(`uniform int outShape;`);
break;
case 2:
prefixSnippets.push(`uniform ivec2 outShape;`);
prefixSnippets.push(`uniform int outShapeStrides;`);
break;
case 3:
prefixSnippets.push(`uniform ivec3 outShape;`);
prefixSnippets.push(`uniform ivec2 outShapeStrides;`);
break;
case 4:
prefixSnippets.push(`uniform ivec4 outShape;`);
prefixSnippets.push(`uniform ivec3 outShapeStrides;`);
break;
default:
break;
}
prefixSnippets.push(`uniform ivec2 outTexShape;`);
}
if (program.customUniforms) {
program.customUniforms.forEach((d) => {
prefixSnippets.push(`uniform ${d.type} ${d.name}${d.arrayIndex ? `[${d.arrayIndex}]` : ""};`);
});
}
const inputPrefixSnippet = prefixSnippets.join("\n");
const inputSamplingSnippet = inputsInfo.map((x) => getInputSamplingSnippet(x, outputShape, program.packedInputs, program.enableShapeUniforms)).join("\n");
const outTexShape = outputShape.texShape;
const glsl = getGlslDifferences();
const floatTextureSampleSnippet = getFloatTextureSampleSnippet(glsl);
let outputSamplingSnippet;
let floatTextureSetOutputSnippet;
let shaderPrefix = getShaderPrefix(glsl);
if (outputShape.isPacked) {
outputSamplingSnippet = getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
floatTextureSetOutputSnippet = getFloatTextureSetRGBASnippet(glsl);
} else {
outputSamplingSnippet = getOutputSamplingSnippet(outputShape.logicalShape, outTexShape, program.enableShapeUniforms);
floatTextureSetOutputSnippet = getFloatTextureSetRSnippet(glsl);
}
if (program.packedInputs) {
shaderPrefix += SHADER_PACKED_PREFIX;
}
const source = [
shaderPrefix,
floatTextureSampleSnippet,
floatTextureSetOutputSnippet,
inputPrefixSnippet,
outputSamplingSnippet,
inputSamplingSnippet,
program.userCode
].join("\n");
return source;
}
function getSamplerFromInInfo(inInfo, enableShapeUniforms = false) {
const shape = inInfo.shapeInfo.logicalShape;
switch (shape.length) {
case 0:
return getSamplerScalar(inInfo, enableShapeUniforms);
case 1:
return getSampler1D(inInfo, enableShapeUniforms);
case 2:
return getSampler2D(inInfo, enableShapeUniforms);
case 3:
return getSampler3D(inInfo, enableShapeUniforms);
case 4:
return getSampler4D(inInfo, enableShapeUniforms);
case 5:
return getSampler5D(inInfo);
case 6:
return getSampler6D(inInfo);
default:
throw new Error(`${shape.length}-D input sampling is not yet supported`);
}
}
function getPackedSamplerFromInInfo(inInfo, enableShapeUniforms) {
const shape = inInfo.shapeInfo.logicalShape;
switch (shape.length) {
case 0:
return getPackedSamplerScalar(inInfo);
case 1:
return getPackedSampler1D(inInfo, enableShapeUniforms);
case 2:
return getPackedSampler2D(inInfo, enableShapeUniforms);
case 3:
return getPackedSampler3D(inInfo, enableShapeUniforms);
default:
return getPackedSamplerND(inInfo, enableShapeUniforms);
}
}
function getInputSamplingSnippet(inInfo, outShapeInfo, usesPackedTextures = false, enableShapeUniforms) {
let res = "";
if (usesPackedTextures) {
res += getPackedSamplerFromInInfo(inInfo, enableShapeUniforms);
} else {
res += getSamplerFromInInfo(inInfo, enableShapeUniforms);
}
const inShape = inInfo.shapeInfo.logicalShape;
const outShape = outShapeInfo.logicalShape;
if (inShape.length <= outShape.length) {
if (usesPackedTextures) {
res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo);
} else {
res += getSamplerAtOutputCoords(inInfo, outShapeInfo);
}
}
return res;
}
function getPackedOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
switch (outShape.length) {
case 0:
return getOutputScalarCoords();
case 1:
return getOutputPacked1DCoords(outShape, outTexShape, enableShapeUniforms);
case 2:
return getOutputPacked2DCoords(outShape, outTexShape, enableShapeUniforms);
case 3:
return getOutputPacked3DCoords(outShape, outTexShape, enableShapeUniforms);
default:
return getOutputPackedNDCoords(outShape, outTexShape, enableShapeUniforms);
}
}
function getOutputSamplingSnippet(outShape, outTexShape, enableShapeUniforms) {
switch (outShape.length) {
case 0:
return getOutputScalarCoords();
case 1:
return getOutput1DCoords(outShape, outTexShape, enableShapeUniforms);
case 2:
return getOutput2DCoords(outShape, outTexShape, enableShapeUniforms);
case 3:
return getOutput3DCoords(outShape, outTexShape, enableShapeUniforms);
case 4:
return getOutput4DCoords(outShape, outTexShape, enableShapeUniforms);
case 5:
return getOutput5DCoords(outShape, outTexShape);
case 6:
return getOutput6DCoords(outShape, outTexShape);
default:
throw new Error(`${outShape.length}-D output sampling is not yet supported`);
}
}
function getFloatTextureSampleSnippet(glsl) {
return `
float sampleTexture(sampler2D textureSampler, vec2 uv) {
return ${glsl.texture2D}(textureSampler, uv).r;
}
`;
}
function getFloatTextureSetRSnippet(glsl) {
return `
void setOutput(float val) {
${glsl.output} = vec4(val, 0, 0, 0);
}
`;
}
function getFloatTextureSetRGBASnippet(glsl) {
return `
void setOutput(vec4 val) {
${glsl.output} = val;
}
`;
}
function getShaderPrefix(glsl) {
const SHADER_PREFIX = `${glsl.version}
precision highp float;
precision highp int;
precision highp sampler2D;
${glsl.varyingFs} vec2 resultUV;
${glsl.defineOutput}
const vec2 halfCR = vec2(0.5, 0.5);
struct ivec5
{
int x;
int y;
int z;
int w;
int u;
};
struct ivec6
{
int x;
int y;
int z;
int w;
int u;
int v;
};
uniform float NAN;
${glsl.defineSpecialNaN}
${glsl.defineSpecialInf}
${glsl.defineRound}
int imod(int x, int y) {
return x - y * (x / y);
}
int idiv(int a, int b, float sign) {
int res = a / b;
int mod = imod(a, b);
if (sign < 0. && mod != 0) {
res -= 1;
}
return res;
}
//Based on the work of Dave Hoskins
//https://www.shadertoy.com/view/4djSRW
#define HASHSCALE1 443.8975
float random(float seed){
vec2 p = resultUV * seed;
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
p3 += dot(p3, p3.yzx + 19.19);
return fract((p3.x + p3.y) * p3.z);
}
${SAMPLE_1D_SNIPPET}
${SAMPLE_2D_SNIPPET}
${SAMPLE_3D_SNIPPET}
`;
return SHADER_PREFIX;
}
var SAMPLE_1D_SNIPPET = `
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
int texelIndex = index / 2;
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var SAMPLE_2D_SNIPPET = `
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
int texNumC, int row, int col) {
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
int texR = texelIndex / texNumC;
int texC = texelIndex - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var SAMPLE_3D_SNIPPET = `
vec2 packedUVfrom3D(int texNumR, int texNumC,
int texelsInBatch, int texelsInLogicalRow, int b,
int row, int col) {
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
int texR = index / texNumC;
int texC = index - texR * texNumC;
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
}
`;
var SHADER_PACKED_PREFIX = `
float getChannel(vec4 frag, vec2 innerDims) {
vec2 modCoord = mod(innerDims, 2.);
return modCoord.x == 0. ?
(modCoord.y == 0. ? frag.r : frag.g) :
(modCoord.y == 0. ? frag.b : frag.a);
}
float getChannel(vec4 frag, int dim) {
float modCoord = mod(float(dim), 2.);
return modCoord == 0. ? frag.r : frag.g;
}
`;
function getOutputScalarCoords() {
return `
int getOutputCoords() {
return 0;
}
`;
}
function getOutputPacked1DCoords(shape, texShape, enableShapeUniforms) {
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
if (packedTexShape[0] === 1) {
if (enableShapeUniforms) {
return `
int getOutputCoords() {
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
}
`;
}
return `
int getOutputCoords() {
return 2 * int(resultUV.x * ${packedTexShape[1]}.0);
}
`;
}
if (packedTexShape[1] === 1) {
if (enableShapeUniforms) {
return `
int getOutputCoords() {
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
}
`;
}
return `
int getOutputCoords() {
return 2 * int(resultUV.y * ${packedTexShape[0]}.0);
}
`;
}
if (enableShapeUniforms) {
return `
int getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
}
`;
}
return `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
return 2 * (resTexRC.x * ${packedTexShape[1]} + resTexRC.y);
}
`;
}
function getOutput1DCoords(shape, texShape, enableShapeUniforms) {
if (texShape[0] === 1) {
if (enableShapeUniforms) {
return `
int getOutputCoords() {
return int(resultUV.x * float(outTexShape[1]));
}
`;
}
return `
int getOutputCoords() {
return int(resultUV.x * ${texShape[1]}.0);
}
`;
}
if (texShape[1] === 1) {
if (enableShapeUniforms) {
return `
int getOutputCoords() {
return int(resultUV.y * float(outTexShape[0]));
}
`;
}
return `
int getOutputCoords() {
return int(resultUV.y * ${texShape[0]}.0);
}
`;
}
if (enableShapeUniforms) {
return `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
return resTexRC.x * outTexShape[1] + resTexRC.y;
}
`;
}
return `
int getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
return resTexRC.x * ${texShape[1]} + resTexRC.y;
}
`;
}
function getOutputPacked3DCoords(shape, texShape, enableShapeUniforms) {
if (enableShapeUniforms) {
return `
ivec3 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec3(b, r, c);
}
`;
}
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
const texelsInLogicalRow = Math.ceil(shape[2] / 2);
const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
int b = index / ${texelsInBatch};
index -= b * ${texelsInBatch};
int r = 2 * (index / ${texelsInLogicalRow});
int c = imod(index, ${texelsInLogicalRow}) * 2;
return ivec3(b, r, c);
}
`;
}
function getOutput3DCoords(shape, texShape, enableShapeUniforms) {
if (enableShapeUniforms) {
const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], shape);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${coordsFromIndexSnippet2}
return ivec3(r, c, d);
}
`;
}
const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
return `
ivec3 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
${coordsFromIndexSnippet}
return ivec3(r, c, d);
}
`;
}
function getOutputPackedNDCoords(shape, texShape, enableShapeUniforms) {
if (enableShapeUniforms) {
return `
ivec4 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
int texelsInBatchN = texelsInBatch * outShape[1];
int b2 = index / texelsInBatchN;
index -= b2 * texelsInBatchN;
int b = index / texelsInBatch;
index -= b * texelsInBatch;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec4(b2, b, r, c);
}
`;
}
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
const texelsInLogicalRow = Math.ceil(shape[shape.length - 1] / 2);
const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[shape.length - 2] / 2);
let texelsInBatchN = texelsInBatch;
let batches = ``;
let coords2 = "b, r, c";
for (let b = 2; b < shape.length - 1; b++) {
texelsInBatchN *= shape[shape.length - b - 1];
batches = `
int b${b} = index / ${texelsInBatchN};
index -= b${b} * ${texelsInBatchN};
` + batches;
coords2 = `b${b}, ` + coords2;
}
return `
ivec${shape.length} getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
${batches}
int b = index / ${texelsInBatch};
index -= b * ${texelsInBatch};
int r = 2 * (index / ${texelsInLogicalRow});
int c = imod(index, ${texelsInLogicalRow}) * 2;
return ivec${shape.length}(${coords2});
}
`;
}
function getOutput4DCoords(shape, texShape, enableShapeUniforms) {
if (enableShapeUniforms) {
const coordsFromIndexSnippet2 = getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d", "d2"], shape);
return `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
${coordsFromIndexSnippet2}
return ivec4(r, c, d, d2);
}
`;
}
const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2"], shape);
return `
ivec4 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
${coordsFromIndexSnippet}
return ivec4(r, c, d, d2);
}
`;
}
function getOutput5DCoords(shape, texShape) {
const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3"], shape);
return `
ivec5 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},
${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
${coordsFromIndexSnippet}
ivec5 outShape = ivec5(r, c, d, d2, d3);
return outShape;
}
`;
}
function getOutput6DCoords(shape, texShape) {
const coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(["r", "c", "d", "d2", "d3", "d4"], shape);
return `
ivec6 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
${coordsFromIndexSnippet}
ivec6 result = ivec6(r, c, d, d2, d3, d4);
return result;
}
`;
}
function getOutputPacked2DCoords(shape, texShape, enableShapeUniforms) {
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
if (util_exports.arraysEqual(shape, texShape)) {
if (enableShapeUniforms) {
return `
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
}
`;
}
return `
ivec2 getOutputCoords() {
return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
}
`;
}
const texelsInLogicalRow = Math.ceil(shape[1] / 2);
if (enableShapeUniforms) {
return `
ivec2 getOutputCoords() {
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(packedTexShape[0], packedTexShape[1]));
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
int r = 2 * (index / texelsInLogicalRow);
int c = imod(index, texelsInLogicalRow) * 2;
return ivec2(r, c);
}
`;
}
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${packedTexShape[0]}, ${packedTexShape[1]}));
int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;
int r = 2 * (index / ${texelsInLogicalRow});
int c = imod(index, ${texelsInLogicalRow}) * 2;
return ivec2(r, c);
}
`;
}
function getOutput2DCoords(shape, texShape, enableShapeUniforms) {
if (util_exports.arraysEqual(shape, texShape)) {
if (enableShapeUniforms) {
return `
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
}
`;
}
return `
ivec2 getOutputCoords() {
return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));
}
`;
}
if (shape[1] === 1) {
if (enableShapeUniforms) {
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(index, 0);
}
`;
}
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
return ivec2(index, 0);
}
`;
}
if (shape[0] === 1) {
if (enableShapeUniforms) {
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
return ivec2(0, index);
}
`;
}
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
return ivec2(0, index);
}
`;
}
if (enableShapeUniforms) {
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(outTexShape[0], outTexShape[1]));
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
int r = index / outShape[1];
int c = index - r * outShape[1];
return ivec2(r, c);
}
`;
}
return `
ivec2 getOutputCoords() {
ivec2 resTexRC = ivec2(resultUV.yx *
vec2(${texShape[0]}, ${texShape[1]}));
int index = resTexRC.x * ${texShape[1]} + resTexRC.y;
int r = index / ${shape[1]};
int c = index - r * ${shape[1]};
return ivec2(r, c);
}
`;
}
function getFlatOffsetUniformName(texName) {
return `offset${texName}`;
}
function getPackedSamplerScalar(inputInfo) {
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const glsl = getGlslDifferences();
return `
vec4 ${funcName}() {
return ${glsl.texture2D}(${texName}, halfCR);
}
`;
}
function getSamplerScalar(inputInfo, enableShapeUniforms) {
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
if (inputInfo.shapeInfo.isUniform) {
return `float ${funcName}() {return ${texName};}`;
}
const [texNumR, texNumC] = inputInfo.shapeInfo.texShape;
if (texNumR === 1 && texNumC === 1) {
return `
float ${funcName}() {
return sampleTexture(${texName}, halfCR);
}
`;
}
const offset = getFlatOffsetUniformName(texName);
if (enableShapeUniforms) {
return `
float ${funcName}() {
vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], ${offset});
return sampleTexture(${texName}, uv);
}
`;
}
const [tNumR, tNumC] = inputInfo.shapeInfo.texShape;
return `
float ${funcName}() {
vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, ${offset});
return sampleTexture(${texName}, uv);
}
`;
}
function getPackedSampler1D(inputInfo, enableShapeUniforms) {
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const texShape = inputInfo.shapeInfo.texShape;
const glsl = getGlslDifferences();
if (enableShapeUniforms) {
return `
vec4 ${funcName}(int index) {
ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
vec2 uv = packedUVfrom1D(
packedTexShape[0], packedTexShape[1], index);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
return `
vec4 ${funcName}(int index) {
vec2 uv = packedUVfrom1D(
${packedTexShape[0]}, ${packedTexShape[1]}, index);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
function getSampler1D(inputInfo, enableShapeUniforms) {
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
if (inputInfo.shapeInfo.isUniform) {
return `
float ${funcName}(int index) {
${getUniformSampler(inputInfo)}
}
`;
}
const texShape = inputInfo.shapeInfo.texShape;
const tNumR = texShape[0];
const tNumC = texShape[1];
if (tNumC === 1 && tNumR === 1) {
return `
float ${funcName}(int index) {
return sampleTexture(${texName}, halfCR);
}
`;
}
const offset = getFlatOffsetUniformName(texName);
if (tNumC === 1) {
if (enableShapeUniforms) {
return `
float ${funcName}(int index) {
vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / float(${texName}TexShape[0]));
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int index) {
vec2 uv = vec2(0.5, (float(index + ${offset}) + 0.5) / ${tNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
if (tNumR === 1) {
if (enableShapeUniforms) {
return `
float ${funcName}(int index) {
vec2 uv = vec2((float(index + ${offset}) + 0.5) / float(${texName}TexShape[1]), 0.5);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int index) {
vec2 uv = vec2((float(index + ${offset}) + 0.5) / ${tNumC}.0, 0.5);
return sampleTexture(${texName}, uv);
}
`;
}
if (enableShapeUniforms) {
return `
float ${funcName}(int index) {
vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int index) {
vec2 uv = uvFromFlat(${tNumR}, ${tNumC}, index + ${offset});
return sampleTexture(${texName}, uv);
}
`;
}
function getPackedSampler2D(inputInfo, enableShapeUniforms) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const texShape = inputInfo.shapeInfo.texShape;
const texNumR = texShape[0];
const texNumC = texShape[1];
const glsl = getGlslDifferences();
if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
if (enableShapeUniforms) {
return `
vec4 ${funcName}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
return `
vec4 ${funcName}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
if (enableShapeUniforms) {
return `
vec4 ${funcName}(int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${texName}Shape[1]) / 2.0));
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
const valuesPerRow = Math.ceil(shape[1] / 2);
return `
vec4 ${funcName}(int row, int col) {
vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${packedTexShape[1]}, row, col);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
function getSampler2D(inputInfo, enableShapeUniforms) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const texShape = inputInfo.shapeInfo.texShape;
if (texShape != null && util_exports.arraysEqual(shape, texShape)) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
return sampleTexture(${texName}, uv);
}
`;
}
const texNumR2 = texShape[0];
const texNumC2 = texShape[1];
return `
float ${funcName}(int row, int col) {
vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC2}.0, ${texNumR2}.0);
return sampleTexture(${texName}, uv);
}
`;
}
const { newShape, keptDims } = util_exports.squeezeShape(shape);
const squeezedShape = newShape;
if (squeezedShape.length < shape.length) {
const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
const params = ["row", "col"];
return `
${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
float ${funcName}(int row, int col) {
return ${funcName}(${getSqueezedParams(params, keptDims)});
}
`;
}
if (inputInfo.shapeInfo.isUniform) {
return `
float ${funcName}(int row, int col) {
int index = round(dot(vec2(row, col), vec2(${shape[1]}, 1)));
${getUniformSampler(inputInfo)}
}
`;
}
const texNumR = texShape[0];
const texNumC = texShape[1];
const offset = getFlatOffsetUniformName(texName);
if (texNumC === 1) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col) {
float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / float(${texName}TexShape[0]));
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col) {
float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
if (texNumR === 1) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col) {
float index = dot(vec3(row, col, ${offset}), vec3(${texName}Shape[1], 1, 1));
vec2 uv = vec2((index + 0.5) / float(${texName}TexShape[1]), 0.5);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col) {
float index = dot(vec3(row, col, ${offset}), vec3(${shape[1]}, 1, 1));
vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);
return sampleTexture(${texName}, uv);
}
`;
}
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${texName}Shape[1] + col + ${offset};
vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${shape[1]} + col + ${offset};
vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
return sampleTexture(${texName}, uv);
}
`;
}
function getPackedSampler3D(inputInfo, enableShapeUniforms) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const texShape = inputInfo.shapeInfo.texShape;
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
if (shape[0] === 1) {
const squeezedShape = shape.slice(1);
const keptDims = [1, 2];
const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
const params = ["b", "row", "col"];
return `
${getPackedSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
vec4 ${funcName}(int b, int row, int col) {
return ${funcName}(${getSqueezedParams(params, keptDims)});
}
`;
}
const glsl = getGlslDifferences();
if (enableShapeUniforms) {
return `
vec4 ${funcName}(int b, int row, int col) {
ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
int valuesPerRow = int(ceil(float(${texName}Shape[2]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[1]) / 2.0));
vec2 uv = packedUVfrom3D(
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
const texNumR = packedTexShape[0];
const texNumC = packedTexShape[1];
const valuesPerRow = Math.ceil(shape[2] / 2);
const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
return `
vec4 ${funcName}(int b, int row, int col) {
vec2 uv = packedUVfrom3D(
${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
function getSampler3D(inputInfo, enableShapeUniforms) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const stride0 = shape[1] * shape[2];
const stride1 = shape[2];
const { newShape, keptDims } = util_exports.squeezeShape(shape);
const squeezedShape = newShape;
if (squeezedShape.length < shape.length) {
const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
const params = ["row", "col", "depth"];
return `
${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
float ${funcName}(int row, int col, int depth) {
return ${funcName}(${getSqueezedParams(params, keptDims)});
}
`;
}
if (inputInfo.shapeInfo.isUniform) {
return `
float ${funcName}(int row, int col, int depth) {
int index = round(dot(vec3(row, col, depth),
vec3(${stride0}, ${stride1}, 1)));
${getUniformSampler(inputInfo)}
}
`;
}
const texShape = inputInfo.shapeInfo.texShape;
const texNumR = texShape[0];
const texNumC = texShape[1];
const flatOffset = inputInfo.shapeInfo.flatOffset;
if (texNumC === stride0 && flatOffset == null) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col, int depth) {
int stride1 = ${texName}Shape[2];
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(stride1, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texName}TexShape[1], ${texName}TexShape[0]);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col, int depth) {
float texR = float(row);
float texC = dot(vec2(col, depth), vec2(${stride1}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
if (texNumC === stride1 && flatOffset == null) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${texName}Shape[1], 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texName}TexShape[1], ${texName}TexShape[0]);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col, int depth) {
float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));
float texC = float(depth);
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
const offset = getFlatOffsetUniformName(texName);
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int stride0 = ${texName}Shape[1] * ${texName}Shape[2];
int stride1 = ${texName}Shape[2];
int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col, int depth) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${stride0} + col * ${stride1} + depth + ${offset};
vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
return sampleTexture(${texName}, uv);
}
`;
}
function getPackedSamplerND(inputInfo, enableShapeUniforms) {
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const glsl = getGlslDifferences();
if (enableShapeUniforms) {
return `
vec4 ${funcName}(int b2, int b, int row, int col) {
int valuesPerRow = int(ceil(float(${texName}Shape[3]) / 2.0));
int texelsInBatch = valuesPerRow * int(ceil(float(${texName}Shape[2]) / 2.0));
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
texelsInBatch *= ${texName}Shape[1];
index = b2 * texelsInBatch + index;
ivec2 packedTexShape = ivec2(ceil(float(${texName}TexShape[0]) / 2.0), ceil(float(${texName}TexShape[1]) / 2.0));
int texR = index / packedTexShape[1];
int texC = index - texR * packedTexShape[1];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${glsl.texture2D}(${texName}, uv);
}
`;
}
const shape = inputInfo.shapeInfo.logicalShape;
const rank = shape.length;
const texShape = inputInfo.shapeInfo.texShape;
const packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
const texNumR = packedTexShape[0];
const texNumC = packedTexShape[1];
const valuesPerRow = Math.ceil(shape[rank - 1] / 2);
let texelsInBatch = valuesPerRow * Math.ceil(shape[rank - 2] / 2);
let params = `int b, int row, int col`;
let index = `b * ${texelsInBatch} + (row / 2) * ${valuesPerRow} + (col / 2)`;
for (let b = 2; b < rank - 1; b++) {
params = `int b${b}, ` + params;
texelsInBatch *= shape[rank - b - 1];
index = `b${b} * ${texelsInBatch} + ` + index;
}
return `
vec4 ${funcName}(${params}) {
int index = ${index};
int texR = index / ${texNumC};
int texC = index - texR * ${texNumC};
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});
return ${glsl.texture2D}(${texName}, uv);
}
`;
}
function getSampler4D(inputInfo, enableShapeUniforms) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const stride2 = shape[3];
const stride1 = shape[2] * stride2;
const stride0 = shape[1] * stride1;
const { newShape, keptDims } = util_exports.squeezeShape(shape);
if (newShape.length < shape.length) {
const newInputInfo = squeezeInputInfo(inputInfo, newShape);
const params = ["row", "col", "depth", "depth2"];
return `
${getSamplerFromInInfo(newInputInfo, enableShapeUniforms)}
float ${funcName}(int row, int col, int depth, int depth2) {
return ${funcName}(${getSqueezedParams(params, keptDims)});
}
`;
}
if (inputInfo.shapeInfo.isUniform) {
return `
float ${funcName}(int row, int col, int depth, int depth2) {
int index = round(dot(vec4(row, col, depth, depth2),
vec4(${stride0}, ${stride1}, ${stride2}, 1)));
${getUniformSampler(inputInfo)}
}
`;
}
const flatOffset = inputInfo.shapeInfo.flatOffset;
const texShape = inputInfo.shapeInfo.texShape;
const texNumR = texShape[0];
const texNumC = texShape[1];
const stride2Str = `int stride2 = ${texName}Shape[3];`;
const stride1Str = `int stride1 = ${texName}Shape[2] * stride2;`;
const stride0Str = `int stride0 = ${texName}Shape[1] * stride1;`;
if (texNumC === stride0 && flatOffset == null) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col, int depth, int depth2) {
${stride2Str}
${stride1Str}
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(stride1, stride2, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texName}TexShape[1], ${texName}TexShape[0]);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col, int depth, int depth2) {
float texR = float(row);
float texC =
dot(vec3(col, depth, depth2),
vec3(${stride1}, ${stride2}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
if (texNumC === stride2 && flatOffset == null) {
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${texName}Shape[1] * ${texName}Shape[2], ${texName}Shape[2], 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texName}TexShape[1], ${texName}TexShape[0]);
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col, int depth, int depth2) {
float texR = dot(vec3(row, col, depth),
vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));
float texC = float(depth2);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
const offset = getFlatOffsetUniformName(texName);
if (enableShapeUniforms) {
return `
float ${funcName}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
${stride2Str}
${stride1Str}
${stride0Str}
int index = row * stride0 + col * stride1 +
depth * stride2 + depth2;
vec2 uv = uvFromFlat(${texName}TexShape[0], ${texName}TexShape[1], index + ${offset});
return sampleTexture(${texName}, uv);
}
`;
}
return `
float ${funcName}(int row, int col, int depth, int depth2) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${stride0} + col * ${stride1} +
depth * ${stride2} + depth2;
vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index + ${offset});
return sampleTexture(${texName}, uv);
}
`;
}
function getSampler5D(inputInfo) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const stride3 = shape[4];
const stride2 = shape[3] * stride3;
const stride1 = shape[2] * stride2;
const stride0 = shape[1] * stride1;
const { newShape, keptDims } = util_exports.squeezeShape(shape);
if (newShape.length < shape.length) {
const newInputInfo = squeezeInputInfo(inputInfo, newShape);
const params = ["row", "col", "depth", "depth2", "depth3"];
return `
${getSamplerFromInInfo(newInputInfo)}
float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
return ${funcName}(${getSqueezedParams(params, keptDims)});
}
`;
}
if (inputInfo.shapeInfo.isUniform) {
return `
float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
float index = dot(
vec4(row, col, depth, depth2),
vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
depth3;
${getUniformSampler(inputInfo)}
}
`;
}
const flatOffset = inputInfo.shapeInfo.flatOffset;
const texShape = inputInfo.shapeInfo.texShape;
const texNumR = texShape[0];
const texNumC = texShape[1];
if (texNumC === stride0 && flatOffset == null) {
return `
float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${stride1}, ${stride2}, ${stride3}, 1));
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
if (texNumC === stride3 && flatOffset == null) {
return `
float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
float texR = dot(
vec4(row, col, depth, depth2),
vec4(${shape[1] * shape[2] * shape[3]},
${shape[2] * shape[3]}, ${shape[3]}, 1));
int texC = depth3;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
const offset = getFlatOffsetUniformName(texName);
return `
float ${funcName}(int row, int col, int depth, int depth2, int depth3) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
depth2 * ${stride3} + depth3 + ${offset};
vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
return sampleTexture(${texName}, uv);
}
`;
}
function getSampler6D(inputInfo) {
const shape = inputInfo.shapeInfo.logicalShape;
const texName = inputInfo.name;
const funcName = "get" + texName.charAt(0).toUpperCase() + texName.slice(1);
const { newShape, keptDims } = util_exports.squeezeShape(shape);
if (newShape.length < shape.length) {
const newInputInfo = squeezeInputInfo(inputInfo, newShape);
const params = ["row", "col", "depth", "depth2", "depth3", "depth4"];
return `
${getSamplerFromInInfo(newInputInfo)}
float ${funcName}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
return ${funcName}(${getSqueezedParams(params, keptDims)});
}
`;
}
const stride4 = shape[5];
const stride3 = shape[4] * stride4;
const stride2 = shape[3] * stride3;
const stride1 = shape[2] * stride2;
const stride0 = shape[1] * stride1;
if (inputInfo.shapeInfo.isUniform) {
return `
float ${funcName}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int index = round(dot(
vec4(row, col, depth, depth2),
vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +
dot(
vec2(depth3, depth4),
vec2(${stride4}, 1)));
${getUniformSampler(inputInfo)}
}
`;
}
const flatOffset = inputInfo.shapeInfo.flatOffset;
const texShape = inputInfo.shapeInfo.texShape;
const texNumR = texShape[0];
const texNumC = texShape[1];
if (texNumC === stride0 && flatOffset == null) {
return `
float ${funcName}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
int texR = row;
float texC = dot(vec4(col, depth, depth2, depth3),
vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) +
float(depth4);
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
if (texNumC === stride4 && flatOffset == null) {
return `
float ${funcName}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
float texR = dot(vec4(row, col, depth, depth2),
vec4(${shape[1] * shape[2] * shape[3] * shape[4]},
${shape[2] * shape[3] * shape[4]},
${shape[3] * shape[4]},
${shape[4]})) + float(depth3);
int texC = depth4;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${texNumC}.0, ${texNumR}.0);
return sampleTexture(${texName}, uv);
}
`;
}
const offset = getFlatOffsetUniformName(texName);
return `
float ${funcName}(int row, int col, int depth,
int depth2, int depth3, int depth4) {
// Explicitly use integer operations as dot() only works on floats.
int index = row * ${stride0} + col * ${stride1} + depth * ${stride2} +
depth2 * ${stride3} + depth3 * ${stride4} + depth4 + ${offset};
vec2 uv = uvFromFlat(${texNumR}, ${texNumC}, index);
return sampleTexture(${texName}, uv);
}
`;
}
function getUniformSampler(inputInfo) {
const texName = inputInfo.name;
const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
if (inSize < 2) {
return `return ${texName};`;
}
return `
for (int i = 0; i < ${inSize}; i++) {
if (i == index) {
return ${texName}[i];
}
}
`;
}
function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo) {
const texName = inputInfo.name;
const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
const funcName = "get" + texFuncSnippet + "AtOutCoords";
const inRank = inputInfo.shapeInfo.logicalShape.length;
const outRank = outShapeInfo.logicalShape.length;
const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
const type = getCoordsDataType(outRank);
const rankDiff = outRank - inRank;
let coordsSnippet;
const fields = ["x", "y", "z", "w", "u", "v"];
if (inRank === 0) {
coordsSnippet = "";
} else if (outRank < 2 && broadcastDims.length >= 1) {
coordsSnippet = "coords = 0;";
} else {
coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
}
let unpackedCoordsSnippet = "";
if (outRank < 2 && inRank > 0) {
unpackedCoordsSnippet = "coords";
} else {
unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
}
let output = `return outputValue;`;
const inSize = util_exports.sizeFromShape(inputInfo.shapeInfo.logicalShape);
const isInputScalar = inSize === 1;
const outSize = util_exports.sizeFromShape(outShapeInfo.logicalShape);
const isOutputScalar = outSize === 1;
if (inRank === 1 && !isInputScalar && !isOutputScalar) {
output = `
return vec4(outputValue.xy, outputValue.xy);
`;
} else if (isInputScalar && !isOutputScalar) {
if (outRank === 1) {
output = `
return vec4(outputValue.x, outputValue.x, 0., 0.);
`;
} else {
output = `
return vec4(outputValue.x);
`;
}
} else if (broadcastDims.length) {
const rows = inRank - 2;
const cols = inRank - 1;
if (broadcastDims.indexOf(rows) > -1 && broadcastDims.indexOf(cols) > -1) {
output = `return vec4(outputValue.x);`;
} else if (broadcastDims.indexOf(rows) > -1) {
output = `return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);`;
} else if (broadcastDims.indexOf(cols) > -1) {
output = `return vec4(outputValue.xx, outputValue.zz);`;
}
}
return `
vec4 ${funcName}() {
${type} coords = getOutputCoords();
${coordsSnippet}
vec4 outputValue = get${texFuncSnippet}(${unpackedCoordsSnippet});
${output}
}
`;
}
function getSamplerAtOutputCoords(inputInfo, outShapeInfo) {
const texName = inputInfo.name;
const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
const funcName = "get" + texFuncSnippet + "AtOutCoords";
const outTexShape = outShapeInfo.texShape;
const inTexShape = inputInfo.shapeInfo.texShape;
const inRank = inputInfo.shapeInfo.logicalShape.length;
const outRank = outShapeInfo.logicalShape.length;
if (!inputInfo.shapeInfo.isUniform && inRank === outRank && inputInfo.shapeInfo.flatOffset == null && util_exports.arraysEqual(inTexShape, outTexShape)) {
return `
float ${funcName}() {
return sampleTexture(${texName}, resultUV);
}
`;
}
const type = getCoordsDataType(outRank);
const broadcastDims = getBroadcastDims2(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
const rankDiff = outRank - inRank;
let coordsSnippet;
const fields = ["x", "y", "z", "w", "u", "v"];
if (inRank === 0) {
coordsSnippet = "";
} else if (outRank < 2 && broadcastDims.length >= 1) {
coordsSnippet = "coords = 0;";
} else {
coordsSnippet = broadcastDims.map((d) => `coords.${fields[d + rankDiff]} = 0;`).join("\n");
}
let unpackedCoordsSnippet = "";
if (outRank < 2 && inRank > 0) {
unpackedCoordsSnippet = "coords";
} else {
unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape.map((s, i) => `coords.${fields[i + rankDiff]}`).join(", ");
}
return `
float ${funcName}() {
${type} coords = getOutputCoords();
${coordsSnippet}
return get${texFuncSnippet}(${unpackedCoordsSnippet});
}
`;
}
function getCoordsDataType(rank) {
if (rank <= 1) {
return "int";
} else if (rank === 2) {
return "ivec2";
} else if (rank === 3) {
return "ivec3";
} else if (rank === 4) {
return "ivec4";
} else if (rank === 5) {
return "ivec5";
} else if (rank === 6) {
return "ivec6";
} else {
throw Error(`GPU for rank ${rank} is not yet supported`);
}
}
function getUniformInfoFromShape(isPacked, shape, texShape) {
const { newShape, keptDims } = util_exports.squeezeShape(shape);
const rank = shape.length;
const useSqueezePackedShape = isPacked && rank === 3 && shape[0] === 1;
const squeezeShape2 = useSqueezePackedShape ? shape.slice(1) : newShape;
const useSqueezeShape = !isPacked && rank > 1 && !util_exports.arraysEqual(shape, texShape) && newShape.length < rank || useSqueezePackedShape;
const uniformShape = useSqueezeShape ? squeezeShape2 : shape;
return { useSqueezeShape, uniformShape, keptDims };
}
function squeezeInputInfo(inInfo, squeezedShape) {
const newInputInfo = JSON.parse(JSON.stringify(inInfo));
newInputInfo.shapeInfo.logicalShape = squeezedShape;
return newInputInfo;
}
function getSqueezedParams(params, keptDims) {
return keptDims.map((d) => params[d]).join(", ");
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_math.js
function compileProgram(gpgpu, program, inputs, output) {
const inputInfos = inputs.map((input2, i) => {
const shapeInfo = {
logicalShape: input2.shape,
texShape: input2.isUniform ? null : input2.texData.texShape,
isUniform: input2.isUniform,
isPacked: input2.isUniform ? false : input2.texData.isPacked,
flatOffset: null
};
if (input2.texData != null && input2.texData.slice != null && input2.texData.slice.flatOffset > 0) {
shapeInfo.flatOffset = input2.texData.slice.flatOffset;
}
return { name: program.variableNames[i], shapeInfo };
});
const inShapeInfos = inputInfos.map((x) => x.shapeInfo);
const outShapeInfo = {
logicalShape: output.shape,
texShape: output.texData.texShape,
isUniform: false,
isPacked: output.texData.isPacked,
flatOffset: null
};
const source = makeShader(inputInfos, outShapeInfo, program);
const fragmentShader = createFragmentShader(gpgpu.gl, source);
const webGLProgram = gpgpu.createProgram(fragmentShader);
if (!env().get("ENGINE_COMPILE_ONLY")) {
return Object.assign({
program,
fragmentShader,
source,
webGLProgram,
inShapeInfos,
outShapeInfo
}, getUniformLocations(gpgpu, program, webGLProgram));
} else {
return {
program,
fragmentShader,
source,
webGLProgram,
inShapeInfos,
outShapeInfo,
uniformLocations: null,
customUniformLocations: null,
infLoc: null,
nanLoc: null,
inShapesLocations: null,
inTexShapesLocations: null,
outShapeLocation: null,
outShapeStridesLocation: null,
outTexShapeLocation: null
};
}
}
function getUniformLocations(gpgpu, program, webGLProgram) {
const uniformLocations = {};
const inShapesLocations = {};
const inTexShapesLocations = {};
const customUniformLocations = [];
let outShapeLocation;
let outTexShapeLocation;
let outShapeStridesLocation;
let infLoc = null;
let nanLoc = null;
nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
if (env().getNumber("WEBGL_VERSION") === 1) {
infLoc = gpgpu.getUniformLocation(webGLProgram, "INFINITY", false);
}
const shouldThrow = false;
for (let i = 0; i < program.variableNames.length; i++) {
const varName = program.variableNames[i];
uniformLocations[varName] = gpgpu.getUniformLocation(webGLProgram, varName, shouldThrow);
uniformLocations[`offset${varName}`] = gpgpu.getUniformLocation(webGLProgram, `offset${varName}`, shouldThrow);
if (program.enableShapeUniforms) {
inShapesLocations[`${varName}Shape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}Shape`, shouldThrow);
inTexShapesLocations[`${varName}TexShape`] = gpgpu.getUniformLocation(webGLProgram, `${varName}TexShape`, shouldThrow);
}
}
if (program.enableShapeUniforms) {
outShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outShape", shouldThrow);
outShapeStridesLocation = gpgpu.getUniformLocation(webGLProgram, "outShapeStrides", shouldThrow);
outTexShapeLocation = gpgpu.getUniformLocation(webGLProgram, "outTexShape", shouldThrow);
}
if (program.customUniforms) {
program.customUniforms.forEach((d, i) => {
customUniformLocations[i] = gpgpu.getUniformLocation(webGLProgram, d.name, shouldThrow);
});
}
return {
uniformLocations,
customUniformLocations,
infLoc,
nanLoc,
inShapesLocations,
inTexShapesLocations,
outShapeLocation,
outShapeStridesLocation,
outTexShapeLocation
};
}
function validateBinaryAndProgram(shapeInfos, inputs) {
if (shapeInfos.length !== inputs.length) {
throw Error(`Binary was compiled with ${shapeInfos.length} inputs, but was executed with ${inputs.length} inputs`);
}
shapeInfos.forEach((s, i) => {
const shapeA = s.logicalShape;
const input2 = inputs[i];
const shapeB = input2.shape;
if (!util_exports.arraysEqual(shapeA, shapeB)) {
throw Error(`Binary was compiled with different shapes than the current args. Shapes ${shapeA} and ${shapeB} must match`);
}
if (s.isUniform && input2.isUniform) {
return;
}
const texShapeA = s.texShape;
const texShapeB = input2.isUniform ? null : input2.texData.texShape;
if (!util_exports.arraysEqual(texShapeA, texShapeB)) {
throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${texShapeA} and ${texShapeB} must match`);
}
});
}
function runProgram(gpgpu, binary, inputs, output, customUniformValues) {
if (!binary.program.enableShapeUniforms) {
validateBinaryAndProgram(binary.inShapeInfos, inputs);
validateBinaryAndProgram([binary.outShapeInfo], [output]);
}
const outTex = output.texData.texture;
const outTexShape = output.texData.texShape;
if (output.texData.isPacked) {
gpgpu.setOutputPackedMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
} else {
gpgpu.setOutputMatrixTexture(outTex.texture, outTexShape[0], outTexShape[1]);
}
gpgpu.setProgram(binary.webGLProgram);
if (env().getNumber("WEBGL_VERSION") === 1) {
if (binary.infLoc !== null) {
gpgpu.gl.uniform1f(binary.infLoc, Infinity);
}
}
if (binary.nanLoc !== null) {
gpgpu.gl.uniform1f(binary.nanLoc, NaN);
}
inputs.forEach((input2, i) => {
const varName = binary.program.variableNames[i];
const varLoc = binary.uniformLocations[varName];
const varOffsetLoc = binary.uniformLocations[`offset${varName}`];
const varShapeLoc = binary.inShapesLocations[`${varName}Shape`];
const varTexShapeLoc = binary.inTexShapesLocations[`${varName}TexShape`];
if (varShapeLoc) {
const { uniformShape } = getUniformInfoFromShape(binary.program.packedInputs, input2.shape, input2.texData.texShape);
switch (uniformShape.length) {
case 1:
gpgpu.gl.uniform1iv(varShapeLoc, new Int32Array(uniformShape));
break;
case 2:
gpgpu.gl.uniform2iv(varShapeLoc, new Int32Array(uniformShape));
break;
case 3:
gpgpu.gl.uniform3iv(varShapeLoc, new Int32Array(uniformShape));
break;
case 4:
gpgpu.gl.uniform4iv(varShapeLoc, new Int32Array(uniformShape));
break;
default:
break;
}
}
if (varTexShapeLoc) {
gpgpu.gl.uniform2i(varTexShapeLoc, input2.texData.texShape[0], input2.texData.texShape[1]);
}
if (varLoc == null) {
return;
}
if (input2.isUniform) {
if (util_exports.sizeFromShape(input2.shape) < 2) {
gpgpu.gl.uniform1f(varLoc, input2.uniformValues[0]);
} else {
let vals = input2.uniformValues;
if (!(vals instanceof Float32Array)) {
vals = new Float32Array(vals);
}
gpgpu.gl.uniform1fv(varLoc, vals);
}
return;
}
if (input2.texData.slice != null && varOffsetLoc != null) {
gpgpu.gl.uniform1i(varOffsetLoc, input2.texData.slice.flatOffset);
}
gpgpu.setInputMatrixTexture(input2.texData.texture.texture, varLoc, i);
});
const outShapeLoc = binary.outShapeLocation;
if (outShapeLoc) {
switch (output.shape.length) {
case 1:
gpgpu.gl.uniform1iv(outShapeLoc, new Int32Array(output.shape));
break;
case 2:
gpgpu.gl.uniform2iv(outShapeLoc, new Int32Array(output.shape));
break;
case 3:
gpgpu.gl.uniform3iv(outShapeLoc, new Int32Array(output.shape));
break;
case 4:
gpgpu.gl.uniform4iv(outShapeLoc, new Int32Array(output.shape));
break;
default:
break;
}
}
if (binary.outShapeStridesLocation) {
const strides = util_exports.computeStrides(output.shape);
switch (output.shape.length) {
case 2:
gpgpu.gl.uniform1iv(binary.outShapeStridesLocation, new Int32Array(strides));
break;
case 3:
gpgpu.gl.uniform2iv(binary.outShapeStridesLocation, new Int32Array(strides));
break;
case 4:
gpgpu.gl.uniform3iv(binary.outShapeStridesLocation, new Int32Array(strides));
break;
default:
break;
}
}
if (binary.outTexShapeLocation) {
gpgpu.gl.uniform2i(binary.outTexShapeLocation, output.texData.texShape[0], output.texData.texShape[1]);
}
if (binary.program.customUniforms && customUniformValues) {
binary.program.customUniforms.forEach((d, i) => {
const customLoc = binary.customUniformLocations[i];
const customValue = customUniformValues[i];
if (d.type === "float") {
gpgpu.gl.uniform1fv(customLoc, customValue);
} else if (d.type === "vec2") {
gpgpu.gl.uniform2fv(customLoc, customValue);
} else if (d.type === "vec3") {
gpgpu.gl.uniform3fv(customLoc, customValue);
} else if (d.type === "vec4") {
gpgpu.gl.uniform4fv(customLoc, customValue);
} else if (d.type === "int") {
gpgpu.gl.uniform1iv(customLoc, customValue);
} else if (d.type === "ivec2") {
gpgpu.gl.uniform2iv(customLoc, customValue);
} else if (d.type === "ivec3") {
gpgpu.gl.uniform3iv(customLoc, customValue);
} else if (d.type === "ivec4") {
gpgpu.gl.uniform4iv(customLoc, customValue);
} else {
throw Error(`uniform type ${d.type} is not supported yet.`);
}
});
}
gpgpu.executeProgram();
}
function makeShaderKey(program, inputs, output) {
let keyInputs = "";
inputs.concat(output).forEach((x) => {
const hasOffset = x.texData != null && x.texData.slice != null && x.texData.slice.flatOffset > 0;
if (program.enableShapeUniforms && !x.isUniform) {
const xTexShape = x.texData.texShape;
const { useSqueezeShape, uniformShape, keptDims } = getUniformInfoFromShape(program.packedInputs, x.shape, xTexShape);
let rank1 = "", rank2 = "", rank34 = "";
if (uniformShape.length === 1 && program.packedInputs) {
const packedTexShape = [Math.ceil(xTexShape[0] / 2), Math.ceil(xTexShape[1] / 2)];
rank1 = `${packedTexShape[0] > 1}_${packedTexShape[1] > 1}`;
} else if (uniformShape.length === 2 && !program.packedInputs) {
rank2 = `${uniformShape[0] > 1}_${uniformShape[1] > 1}`;
} else if (uniformShape.length > 2 && !program.packedInputs) {
const strides = util_exports.computeStrides(uniformShape);
rank34 = `${strides[0] === xTexShape[1]}_${strides[strides.length - 1] === xTexShape[1]}`;
}
const xRank = x.shape.length;
const isLogicalShapTexShapeEqual = uniformShape.length === 2 && util_exports.arraysEqual(x.shape, xTexShape);
const isScalar = util_exports.sizeFromShape(x.shape) === 1;
const broadcastDims = backend_util_exports.getBroadcastDims(x.shape, output.shape);
const isInOutTexShapeEqual = !program.packedInputs && xRank === output.shape.length && util_exports.arraysEqual(xTexShape, output.texData.texShape);
const isTexShapeGreaterThanOne = program.packedInputs || uniformShape.length > 2 ? "" : `${xTexShape[0] > 1}_${xTexShape[1] > 1}`;
keyInputs += `${xRank}_${isInOutTexShapeEqual}_${useSqueezeShape ? keptDims : ""}_${uniformShape.length}_${isScalar}_${broadcastDims}_${isLogicalShapTexShapeEqual}_${rank1}_${rank2}_${rank34}_${isTexShapeGreaterThanOne}_${hasOffset}`;
} else {
const texShape = x.isUniform ? "uniform" : x.texData.texShape;
keyInputs += `${x.shape}_${texShape}_${hasOffset}`;
}
});
const keyUserCode = program.userCode;
let key = program.constructor.name;
key += "_" + keyInputs + "_" + keyUserCode + `${env().getNumber("WEBGL_VERSION")}`;
return key;
}
function useShapeUniforms(rank) {
return env().getBool("WEBGL_USE_SHAPES_UNIFORMS") && rank <= 4;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_gpu.js
var DecodeMatrixProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.packedInputs = false;
this.packedOutput = true;
this.outPackingScheme = PackingScheme.DENSE;
this.customUniforms = [{ name: "texShape", type: "ivec2" }];
const glsl = getGlslDifferences();
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
this.userCode = `
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getA(rc.x, rc.y, rc.z);
}
${glsl.output} = result;
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/decode_matrix_packed_gpu.js
init_define_BUILD_VERSION();
var DecodeMatrixPackedProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.outPackingScheme = PackingScheme.DENSE;
this.customUniforms = [{ name: "texShape", type: "ivec2" }];
const glsl = getGlslDifferences();
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
this.userCode = `
ivec3 outCoordsFromFlatIndex(int index) {
${this.enableShapeUniforms ? getOutputLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], outputShape) : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], outputShape)}
return ivec3(r, c, d);
}
void main() {
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
vec4 result = vec4(0.);
for (int i=0; i<4; i++) {
int flatIndex = index + i;
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
}
${glsl.output} = result;
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_gpu.js
init_define_BUILD_VERSION();
var EncodeFloatProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.outTexUsage = TextureUsage.DOWNLOAD;
const glsl = getGlslDifferences();
this.outputShape = outputShape;
this.userCode = `
${ENCODE_FLOAT_SNIPPET}
void main() {
float x = getAAtOutCoords();
${glsl.output} = encode_float(x);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_float_packed_gpu.js
init_define_BUILD_VERSION();
var EncodeFloatPackedProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = false;
this.outTexUsage = TextureUsage.DOWNLOAD;
const glsl = getGlslDifferences();
this.outputShape = outputShape;
this.userCode = `
${ENCODE_FLOAT_SNIPPET}
void main() {
ivec3 coords = getOutputCoords();
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
${glsl.output} = encode_float(x);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_gpu.js
init_define_BUILD_VERSION();
var EncodeMatrixProgram = class {
constructor(outputShape, inputIsUnsignedByte = false) {
this.variableNames = ["A"];
this.customUniforms = [{ name: "texShape", type: "ivec2" }];
const glsl = getGlslDifferences();
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
let output = `result`;
if (inputIsUnsignedByte) {
output = `floor(result * 255. + 0.5)`;
}
this.userCode = `
${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
void main() {
ivec3 coords = getOutputCoords();
int flatIndex = getFlatIndex(coords);
int offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
vec4 values = ${glsl.texture2D}(A, uv);
float result;
if(offset == 0) {
result = values[0];
} else if(offset == 1) {
result = values[1];
} else if(offset == 2) {
result = values[2];
} else {
result = values[3];
}
${glsl.output} = vec4(${output}, 0., 0., 0.);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/encode_matrix_packed_gpu.js
init_define_BUILD_VERSION();
var EncodeMatrixPackedProgram = class {
constructor(outputShape, inputIsUnsignedByte = false) {
this.variableNames = ["A"];
this.packedInputs = false;
this.packedOutput = true;
this.customUniforms = [{ name: "texShape", type: "ivec2" }];
const glsl = getGlslDifferences();
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
let mainLoop = "";
let output = "result";
if (inputIsUnsignedByte) {
output = "floor(result * 255. + 0.5)";
}
for (let row = 0; row <= 1; row++) {
for (let col = 0; col <= 1; col++) {
const channel = row * 2 + col;
mainLoop += `
localCoords = coords;
if(localCoords[2] + ${col} < ${this.enableShapeUniforms ? "outShape[2]" : `${outputShape[2]}`}) {
localCoords[2] += ${col};
if (localCoords[1] + ${row} < ${this.enableShapeUniforms ? "outShape[1]" : `${outputShape[1]}`}) {
localCoords[1] += ${row};
flatIndex = getFlatIndex(localCoords);
offset = imod(flatIndex, 4);
flatIndex = idiv(flatIndex, 4, 1.);
int r = flatIndex / texShape[1];
int c = imod(flatIndex, texShape[1]);
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
values = ${glsl.texture2D}(A, uv);
if (offset == 0) {
result[${channel}] = values[0];
} else if (offset == 1) {
result[${channel}] = values[1];
} else if (offset == 2) {
result[${channel}] = values[2];
} else {
result[${channel}] = values[3];
}
}
}
`;
}
}
this.userCode = `
${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
void main() {
ivec3 coords = getOutputCoords();
vec4 result = vec4(0.);
int flatIndex, r, c, offset;
ivec3 localCoords;
vec2 uv;
vec4 values;
${mainLoop}
${glsl.output} = ${output};
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_util.js
init_define_BUILD_VERSION();
function createVertexShader2(gl) {
const glsl = getGlslDifferences();
const vertexShaderSource = `${glsl.version}
precision highp float;
${glsl.attribute} vec3 clipSpacePos;
${glsl.attribute} vec2 uv;
${glsl.varyingVs} vec2 resultUV;
void main() {
gl_Position = vec4(clipSpacePos, 1);
resultUV = uv;
}`;
return createVertexShader(gl, vertexShaderSource);
}
function createVertexBuffer(gl) {
const vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
return createStaticVertexBuffer(gl, vertexArray);
}
function createIndexBuffer(gl) {
const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
return createStaticIndexBuffer(gl, triangleVertexIndices);
}
function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
validateTextureSize(width, height);
const texture = createTexture(gl);
const tex2d = gl.TEXTURE_2D;
callAndCheck(gl, () => gl.bindTexture(tex2d, texture));
callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));
callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));
callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));
callAndCheck(gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));
if (env().getNumber("WEBGL_VERSION") === 1) {
callAndCheck(gl, () => gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null));
} else {
callAndCheck(gl, () => gl.texStorage2D(tex2d, 1, internalFormat, width, height));
}
callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
return { texture, texShape: [height, width] };
}
function getInternalFormatForFloat32MatrixTexture(textureConfig) {
return textureConfig.internalFormatFloat;
}
function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat32MatrixTexture(textureConfig), textureConfig.textureFormatFloat, gl.FLOAT);
}
function getInternalFormatForFloat16MatrixTexture(textureConfig) {
return textureConfig.internalFormatHalfFloat;
}
function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16MatrixTexture(textureConfig), textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
}
function getInternalFormatForUnsignedBytesMatrixTexture(textureConfig) {
return textureConfig.downloadTextureFormat;
}
function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
return createAndConfigureTexture(gl, width, height, getInternalFormatForUnsignedBytesMatrixTexture(textureConfig), gl.RGBA, gl.UNSIGNED_BYTE);
}
function getInternalFormatForPackedMatrixTexture(textureConfig) {
return textureConfig.internalFormatPackedFloat;
}
function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
return createAndConfigureTexture(gl, width, height, getInternalFormatForPackedMatrixTexture(textureConfig), gl.RGBA, gl.FLOAT);
}
function getInternalFormatForFloat16PackedMatrixTexture(textureConfig) {
return textureConfig.internalFormatPackedHalfFloat;
}
function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
return createAndConfigureTexture(gl, width, height, getInternalFormatForFloat16PackedMatrixTexture(textureConfig), gl.RGBA, textureConfig.textureTypeHalfFloat);
}
function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
const posOffset = 0;
const uvOffset = 3 * 4;
const stride = 3 * 4 + 2 * 4;
callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));
const success = bindVertexBufferToProgramAttribute(gl, program, "clipSpacePos", vertexBuffer, 3, stride, posOffset);
return success && bindVertexBufferToProgramAttribute(gl, program, "uv", vertexBuffer, 2, stride, uvOffset);
}
function uploadDenseMatrixToTexture(gl, texture, width, height, data, textureConfig) {
callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
let dataForUpload, texelDataType, internalFormat;
if (data instanceof Uint8Array) {
dataForUpload = new Uint8Array(width * height * 4);
texelDataType = gl.UNSIGNED_BYTE;
internalFormat = gl.RGBA;
} else {
dataForUpload = new Float32Array(width * height * 4);
texelDataType = gl.FLOAT;
internalFormat = textureConfig.internalFormatPackedFloat;
}
dataForUpload.set(data);
if (env().getNumber("WEBGL_VERSION") === 2) {
callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, gl.RGBA, texelDataType, dataForUpload));
} else {
callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, width, height, 0, gl.RGBA, texelDataType, dataForUpload));
}
callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
}
function uploadPixelDataToTexture(gl, texture, pixels) {
callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));
if (pixels.data instanceof Uint8Array) {
if (env().getNumber("WEBGL_VERSION") === 2) {
callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, pixels.width, pixels.height, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
} else {
callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, pixels.width, pixels.height, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels.data));
}
} else {
if (env().getNumber("WEBGL_VERSION") === 2) {
callAndCheck(gl, () => gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
} else {
callAndCheck(gl, () => gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));
}
}
callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));
}
function createBufferFromOutputTexture(gl2, rows, columns, textureConfig) {
const buffer2 = gl2.createBuffer();
callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2));
const bytesPerFloat = 4;
const valuesPerTexel = 4;
const bufferSizeBytes = bytesPerFloat * valuesPerTexel * rows * columns;
callAndCheck(gl2, () => gl2.bufferData(gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl2.STREAM_READ));
callAndCheck(gl2, () => gl2.readPixels(0, 0, columns, rows, gl2.RGBA, gl2.FLOAT, 0));
callAndCheck(gl2, () => gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));
return buffer2;
}
function downloadFloat32MatrixFromBuffer(gl, buffer2, size) {
const gl2 = gl;
const downloadTarget = new Float32Array(size);
gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
return downloadTarget;
}
function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
const [w, h] = getUnpackedMatrixTextureShapeWidthHeight(rows, columns);
const numChannels = 4;
const downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
callAndCheck(gl, () => gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget));
return new Float32Array(downloadTarget.buffer);
}
function downloadPackedMatrixFromBuffer(gl, buffer2, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
const gl2 = gl;
const downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer2);
gl2.getBufferSubData(gl2.PIXEL_PACK_BUFFER, 0, downloadTarget);
gl2.bindBuffer(gl2.PIXEL_PACK_BUFFER, null);
return downloadTarget;
}
function downloadMatrixFromPackedOutputTexture(gl, physicalRows, physicalCols) {
const packedRGBA = new Float32Array(physicalRows * physicalCols * 4);
callAndCheck(gl, () => gl.readPixels(0, 0, physicalCols, physicalRows, gl.RGBA, gl.FLOAT, packedRGBA));
return packedRGBA;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gpgpu_context.js
var GPGPUContext = class {
constructor(gl) {
this.outputTexture = null;
this.program = null;
this.disposed = false;
this.vertexAttrsAreBound = false;
this.itemsToPoll = [];
const glVersion = env().getNumber("WEBGL_VERSION");
if (gl != null) {
this.gl = gl;
setWebGLContext(glVersion, gl);
} else {
this.gl = getWebGLContext(glVersion);
}
let COLOR_BUFFER_FLOAT = "WEBGL_color_buffer_float";
const COLOR_BUFFER_HALF_FLOAT = "EXT_color_buffer_half_float";
this.parallelCompilationExtension = this.gl.getExtension("KHR_parallel_shader_compile");
if (env().getNumber("WEBGL_VERSION") === 1) {
const TEXTURE_FLOAT = "OES_texture_float";
const TEXTURE_HALF_FLOAT = "OES_texture_half_float";
this.textureFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_FLOAT);
if (hasExtension(this.gl, TEXTURE_HALF_FLOAT)) {
this.textureHalfFloatExtension = getExtensionOrThrow(this.gl, TEXTURE_HALF_FLOAT);
} else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
}
this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
this.colorBufferHalfFloatExtension = getExtensionOrThrow(this.gl, COLOR_BUFFER_HALF_FLOAT);
} else if (env().get("WEBGL_FORCE_F16_TEXTURES")) {
throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");
}
} else {
COLOR_BUFFER_FLOAT = "EXT_color_buffer_float";
if (hasExtension(this.gl, COLOR_BUFFER_FLOAT)) {
this.colorBufferFloatExtension = this.gl.getExtension(COLOR_BUFFER_FLOAT);
} else if (hasExtension(this.gl, COLOR_BUFFER_HALF_FLOAT)) {
this.colorBufferHalfFloatExtension = this.gl.getExtension(COLOR_BUFFER_HALF_FLOAT);
} else {
throw new Error("GL context does not support color renderable floats");
}
}
this.vertexBuffer = createVertexBuffer(this.gl);
this.indexBuffer = createIndexBuffer(this.gl);
this.framebuffer = createFramebuffer(this.gl);
this.textureConfig = getTextureConfig(this.gl, this.textureHalfFloatExtension);
}
get debug() {
return env().getBool("DEBUG");
}
dispose() {
if (this.disposed) {
return;
}
if (this.program != null) {
console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.");
}
if (this.outputTexture != null) {
console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");
}
const gl = this.gl;
callAndCheck(gl, () => gl.finish());
callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));
callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));
callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));
callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));
callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));
this.disposed = true;
}
createFloat32MatrixTexture(rows, columns) {
this.throwIfDisposed();
return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
}
createFloat16MatrixTexture(rows, columns) {
this.throwIfDisposed();
return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
}
createUnsignedBytesMatrixTexture(rows, columns) {
this.throwIfDisposed();
return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
}
uploadPixelDataToTexture(texture, pixels) {
this.throwIfDisposed();
uploadPixelDataToTexture(this.gl, texture, pixels);
}
uploadDenseMatrixToTexture(texture, width, height, data) {
this.throwIfDisposed();
uploadDenseMatrixToTexture(this.gl, texture, width, height, data, this.textureConfig);
}
createFloat16PackedMatrixTexture(rows, columns) {
this.throwIfDisposed();
return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
}
createPackedMatrixTexture(rows, columns) {
this.throwIfDisposed();
return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
}
deleteMatrixTexture(texture) {
this.throwIfDisposed();
if (this.outputTexture === texture) {
unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
this.outputTexture = null;
}
callAndCheck(this.gl, () => this.gl.deleteTexture(texture));
}
downloadByteEncodedFloatMatrixFromOutputTexture(texture, rows, columns) {
return this.downloadMatrixDriver(texture, () => downloadByteEncodedFloatMatrixFromOutputTexture(this.gl, rows, columns, this.textureConfig));
}
downloadPackedMatrixFromBuffer(buffer2, batch, rows, columns, physicalRows, physicalCols) {
return downloadPackedMatrixFromBuffer(this.gl, buffer2, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
}
downloadFloat32MatrixFromBuffer(buffer2, size) {
return downloadFloat32MatrixFromBuffer(this.gl, buffer2, size);
}
createBufferFromTexture(texture, rows, columns) {
this.bindTextureToFrameBuffer(texture);
const result = createBufferFromOutputTexture(this.gl, rows, columns, this.textureConfig);
this.unbindTextureToFrameBuffer();
return result;
}
createAndWaitForFence() {
const fenceContext = this.createFence(this.gl);
return this.pollFence(fenceContext);
}
createFence(gl) {
let query;
let isFencePassed;
if (env().getBool("WEBGL_FENCE_API_ENABLED")) {
const gl2 = gl;
const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);
gl.flush();
isFencePassed = () => {
const status = gl2.clientWaitSync(sync, 0, 0);
return status === gl2.ALREADY_SIGNALED || status === gl2.CONDITION_SATISFIED;
};
query = sync;
} else if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") > 0) {
query = this.beginQuery();
this.endQuery();
isFencePassed = () => this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
} else {
isFencePassed = () => true;
}
return { query, isFencePassed };
}
downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
}
createProgram(fragmentShader) {
this.throwIfDisposed();
const gl = this.gl;
if (this.vertexShader == null) {
this.vertexShader = createVertexShader2(gl);
}
const program = createProgram(gl);
callAndCheck(gl, () => gl.attachShader(program, this.vertexShader));
callAndCheck(gl, () => gl.attachShader(program, fragmentShader));
linkProgram(gl, program);
if (this.debug) {
validateProgram(gl, program);
}
if (!this.vertexAttrsAreBound) {
this.setProgram(program);
this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
}
return program;
}
deleteProgram(program) {
this.throwIfDisposed();
if (program === this.program) {
this.program = null;
}
if (program != null) {
callAndCheck(this.gl, () => this.gl.deleteProgram(program));
}
}
setProgram(program) {
this.throwIfDisposed();
this.program = program;
if (this.program != null && this.debug) {
validateProgram(this.gl, this.program);
}
callAndCheck(this.gl, () => this.gl.useProgram(program));
}
getUniformLocation(program, uniformName, shouldThrow = true) {
this.throwIfDisposed();
if (shouldThrow) {
return getProgramUniformLocationOrThrow(this.gl, program, uniformName);
} else {
return getProgramUniformLocation(this.gl, program, uniformName);
}
}
getAttributeLocation(program, attribute) {
this.throwIfDisposed();
return callAndCheck(this.gl, () => this.gl.getAttribLocation(program, attribute));
}
getUniformLocationNoThrow(program, uniformName) {
this.throwIfDisposed();
return this.gl.getUniformLocation(program, uniformName);
}
setInputMatrixTexture(inputMatrixTexture, uniformLocation, textureUnit) {
this.throwIfDisposed();
this.throwIfNoProgram();
bindTextureToProgramUniformSampler(this.gl, inputMatrixTexture, uniformLocation, textureUnit);
}
setOutputMatrixTexture(outputMatrixTexture, rows, columns) {
this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
}
setOutputPackedMatrixTexture(outputPackedMatrixTexture, rows, columns) {
this.throwIfDisposed();
const [width, height] = getPackedMatrixTextureShapeWidthHeight(rows, columns);
this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
}
setOutputMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
}
setOutputPackedMatrixWriteRegion(startRow, numRows, startColumn, numColumns) {
throw new Error("setOutputPackedMatrixWriteRegion not implemented.");
}
debugValidate() {
if (this.program != null) {
validateProgram(this.gl, this.program);
}
validateFramebuffer(this.gl);
}
executeProgram() {
this.throwIfDisposed();
this.throwIfNoProgram();
const gl = this.gl;
if (this.debug) {
this.debugValidate();
}
callAndCheck(gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));
}
blockUntilAllProgramsCompleted() {
this.throwIfDisposed();
callAndCheck(this.gl, () => this.gl.finish());
}
getQueryTimerExtension() {
if (this.disjointQueryTimerExtension == null) {
this.disjointQueryTimerExtension = getExtensionOrThrow(this.gl, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2 ? "EXT_disjoint_timer_query_webgl2" : "EXT_disjoint_timer_query");
}
return this.disjointQueryTimerExtension;
}
getQueryTimerExtensionWebGL2() {
return this.getQueryTimerExtension();
}
getQueryTimerExtensionWebGL1() {
return this.getQueryTimerExtension();
}
beginQuery() {
if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
const gl2 = this.gl;
const ext2 = this.getQueryTimerExtensionWebGL2();
const query2 = gl2.createQuery();
gl2.beginQuery(ext2.TIME_ELAPSED_EXT, query2);
return query2;
}
const ext = this.getQueryTimerExtensionWebGL1();
const query = ext.createQueryEXT();
ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
return query;
}
endQuery() {
if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION") === 2) {
const gl2 = this.gl;
const ext2 = this.getQueryTimerExtensionWebGL2();
gl2.endQuery(ext2.TIME_ELAPSED_EXT);
return;
}
const ext = this.getQueryTimerExtensionWebGL1();
ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
}
async waitForQueryAndGetTime(query) {
await util_exports.repeatedTry(() => this.disposed || this.isQueryAvailable(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")));
return this.getQueryTime(query, env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"));
}
getQueryTime(query, queryTimerVersion) {
if (queryTimerVersion === 0) {
return null;
}
if (queryTimerVersion === 2) {
const gl2 = this.gl;
const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
return timeElapsedNanos / 1e6;
} else {
const ext = this.getQueryTimerExtensionWebGL1();
const timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
return timeElapsedNanos / 1e6;
}
}
isQueryAvailable(query, queryTimerVersion) {
if (queryTimerVersion === 0) {
return true;
}
if (queryTimerVersion === 2) {
const gl2 = this.gl;
const ext = this.getQueryTimerExtensionWebGL2();
const available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
if (this.disjoint == null) {
this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
}
return available && !this.disjoint;
} else {
const ext = this.getQueryTimerExtensionWebGL1();
const available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
if (this.disjoint == null) {
this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
}
return available && !this.disjoint;
}
}
pollFence(fenceContext) {
return new Promise((resolve) => {
this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());
});
}
pollItems() {
const index = linearSearchLastTrue(this.itemsToPoll.map((x) => x.isDoneFn));
for (let i = 0; i <= index; ++i) {
const { resolveFn } = this.itemsToPoll[i];
resolveFn();
}
this.itemsToPoll = this.itemsToPoll.slice(index + 1);
}
addItemToPoll(isDoneFn, resolveFn) {
this.itemsToPoll.push({ isDoneFn, resolveFn });
if (this.itemsToPoll.length > 1) {
return;
}
util_exports.repeatedTry(() => {
this.pollItems();
return this.itemsToPoll.length === 0;
});
}
bindTextureToFrameBuffer(texture) {
this.throwIfDisposed();
bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
if (this.debug) {
validateFramebuffer(this.gl);
}
}
unbindTextureToFrameBuffer() {
if (this.outputTexture != null) {
bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
if (this.debug) {
validateFramebuffer(this.gl);
}
} else {
unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
}
}
downloadMatrixDriver(texture, downloadAndDecode) {
this.bindTextureToFrameBuffer(texture);
const result = downloadAndDecode();
this.unbindTextureToFrameBuffer();
return result;
}
setOutputMatrixTextureDriver(outputMatrixTextureMaybePacked, width, height) {
this.throwIfDisposed();
const gl = this.gl;
bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
if (this.debug) {
validateFramebuffer(gl);
}
this.outputTexture = outputMatrixTextureMaybePacked;
callAndCheck(gl, () => gl.viewport(0, 0, width, height));
callAndCheck(gl, () => gl.scissor(0, 0, width, height));
}
setOutputMatrixWriteRegionDriver(x, y, width, height) {
this.throwIfDisposed();
callAndCheck(this.gl, () => this.gl.scissor(x, y, width, height));
}
throwIfDisposed() {
if (this.disposed) {
throw new Error("Attempted to use disposed GPGPUContext.");
}
}
throwIfNoProgram() {
if (this.program == null) {
throw new Error("No GPU program is currently set.");
}
}
};
function linearSearchLastTrue(arr) {
let i = 0;
for (; i < arr.length; ++i) {
const isDone = arr[i]();
if (!isDone) {
break;
}
}
return i - 1;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/shared.js
init_define_BUILD_VERSION();
var { addImpl: addImplCPU, bincountImpl: bincountImplCPU, bincountReduceImpl: bincountReduceImplCPU, ceilImpl: ceilImplCPU, concatImpl: concatImplCPU, equalImpl: equalImplCPU, expImpl: expImplCPU, expm1Impl: expm1ImplCPU, floorImpl: floorImplCPU, gatherNdImpl: gatherNdImplCPU, gatherV2Impl: gatherV2ImplCPU, greaterImpl: greaterImplCPU, greaterEqualImpl: greaterEqualImplCPU, lessImpl: lessImplCPU, lessEqualImpl: lessEqualImplCPU, linSpaceImpl: linSpaceImplCPU, logImpl: logImplCPU, maxImpl: maxImplCPU, maximumImpl: maximumImplCPU, minimumImpl: minimumImplCPU, multiplyImpl: multiplyImplCPU, negImpl: negImplCPU, notEqualImpl: notEqualImplCPU, prodImpl: prodImplCPU, rangeImpl: rangeImplCPU, rsqrtImpl: rsqrtImplCPU, scatterImpl: scatterImplCPU, sigmoidImpl: sigmoidImplCPU, simpleAbsImpl: simpleAbsImplCPU, sliceImpl: sliceImplCPU, sparseFillEmptyRowsImpl: sparseFillEmptyRowsImplCPU, sparseReshapeImpl: sparseReshapeImplCPU, sparseSegmentReductionImpl: sparseSegmentReductionImplCPU, sqrtImpl: sqrtImplCPU, stridedSliceImpl: stridedSliceImplCPU, stringNGramsImpl: stringNGramsImplCPU, stringSplitImpl: stringSplitImplCPU, stringToHashBucketFastImpl: stringToHashBucketFastImplCPU, subImpl: subImplCPU, tileImpl: tileImplCPU, topKImpl: topKImplCPU, transposeImpl: transposeImplCPU, uniqueImpl: uniqueImplCPU } = shared_exports;
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/packing_util.js
init_define_BUILD_VERSION();
function getVecChannels(name, rank) {
return ["x", "y", "z", "w", "u", "v"].slice(0, rank).map((d) => `${name}.${d}`);
}
function getChannels(name, rank) {
if (rank === 1) {
return [name];
}
return getVecChannels(name, rank);
}
function getSourceCoords(rank, dims) {
if (rank === 1) {
return "rc";
}
let coords2 = "";
for (let i = 0; i < rank; i++) {
coords2 += dims[i];
if (i < rank - 1) {
coords2 += ",";
}
}
return coords2;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/pack_gpu.js
var PackProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.packedInputs = false;
this.packedOutput = true;
this.outputShape = outputShape;
this.rank = outputShape.length;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
if (this.rank === 0) {
this.userCode = `
void main() {
setOutput(vec4(getA(), 0., 0., 0.));
}
`;
} else {
const channels = getChannels("rc", this.rank);
const dtype = getCoordsDataType(this.rank);
const outOfBoundsCondition = this.getOutOfBoundsCondition(channels);
const setup = this.getSetup(channels);
const output = this.getOutput(channels);
this.userCode = `
void main() {
${dtype} rc = getOutputCoords();
if(${outOfBoundsCondition}) {
setOutput(vec4(0));
} else {
${setup}
setOutput(vec4(${output}));
}
}
`;
}
}
getSourceCoordsArr(dims) {
const coords2 = [];
for (let row = 0; row <= 1; row++) {
for (let col = 0; col <= 1; col++) {
let coord = `${row === 0 ? "r" : "rp1"}, ${col === 0 ? "c" : "cp1"}`;
for (let d = 2; d < this.rank; d++) {
coord = `${dims[dims.length - 1 - d]},` + coord;
}
coords2.push(coord);
}
}
return coords2;
}
getOutOfBoundsCondition(dims) {
if (this.rank === 1) {
return `rc > ${this.enableShapeUniforms ? "outShape" : this.outputShape[0]}`;
}
let cond = "";
for (let i = this.rank - 2; i < this.rank; i++) {
cond += `${dims[i]} >= ${this.enableShapeUniforms ? `outShape[${i}]` : this.outputShape[i]}`;
if (i < this.rank - 1) {
cond += "||";
}
}
return cond;
}
getSetup(dims) {
if (this.rank === 1) {
return "";
}
const innerDims = dims.slice(-2);
const col = this.enableShapeUniforms ? `outShape[${this.rank} - 1]` : this.outputShape[this.rank - 1];
const row = this.enableShapeUniforms ? `outShape[${this.rank} - 2]` : this.outputShape[this.rank - 2];
return `
int r = ${innerDims[0]};
int c = ${innerDims[1]};
int rp1 = r + 1;
int cp1 = c + 1;
bool cEdge = cp1 >= ${col};
bool rEdge = rp1 >= ${row};
`;
}
getOutput(dims) {
const sourceCoords = this.getSourceCoordsArr(dims);
if (this.rank === 1) {
const outShape = this.enableShapeUniforms ? "outShape" : this.outputShape[0];
return `getA(rc), (rc + 1 >= ${outShape} ? 0. : getA(rc + 1)), 0, 0`;
}
return `getA(${sourceCoords[0]}),
cEdge ? 0. : getA(${sourceCoords[1]}),
rEdge ? 0. : getA(${sourceCoords[2]}),
rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/reshape_packed_gpu.js
init_define_BUILD_VERSION();
var ReshapePackedProgram = class {
constructor(outputShape, inputShape) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.customUniforms = [{ name: "inputShape", type: "ivec3" }];
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
let mainLoop = ``;
for (let i = 0; i < 4; i++) {
let thisRC = `thisRC = rc;`;
if (i % 2 === 1) {
thisRC += `thisRC.z += 1;`;
}
if (i > 1) {
thisRC += `thisRC.y += 1;`;
}
mainLoop += `
${thisRC}
${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ""}
int flatIndex = getFlatIndex(thisRC);
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
result[${i}] =
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
${i > 0 ? "}" : ""}
`;
}
this.userCode = `
${getReshapedInputCoords(inputShape, this.enableShapeUniforms)}
${this.enableShapeUniforms ? getFlatIndexFrom3DOutput() : getFlatIndexFrom3D(outputShape)}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0.);
ivec3 thisRC;
int rows = ${this.enableShapeUniforms ? "outShape[1]" : outputShape[1]};
int cols = ${this.enableShapeUniforms ? "outShape[2]" : outputShape[2]};
${mainLoop}
setOutput(result);
}
`;
}
};
function getReshapedInputCoords(shape, enableShapeUniforms) {
const coordsFromIndexSnippet = enableShapeUniforms ? getLogicalCoordinatesFromFlatIndexByUniform(["r", "c", "d"], "inputShape") : getLogicalCoordinatesFromFlatIndex(["r", "c", "d"], shape);
return `
ivec3 inputCoordsFromReshapedOutCoords(int index) {
${coordsFromIndexSnippet}
return ivec3(r, c, d);
}
`;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/texture_manager.js
init_define_BUILD_VERSION();
var TextureManager = class {
constructor(gpgpu) {
this.gpgpu = gpgpu;
this.numUsedTextures = 0;
this.numFreeTextures = 0;
this._numBytesAllocated = 0;
this._numBytesFree = 0;
this.freeTextures = {};
this.logEnabled = false;
this.usedTextures = {};
}
acquireTexture(shapeRC, usage, isPacked) {
const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
if (!(shapeKey in this.freeTextures)) {
this.freeTextures[shapeKey] = [];
}
if (!(shapeKey in this.usedTextures)) {
this.usedTextures[shapeKey] = [];
}
const texBytes = computeBytes(shapeRC, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
if (this.freeTextures[shapeKey].length > 0) {
this.numFreeTextures--;
this.numUsedTextures++;
this._numBytesFree -= texBytes;
this.log();
const newTexture2 = this.freeTextures[shapeKey].shift();
this.usedTextures[shapeKey].push(newTexture2);
return newTexture2;
}
let newTexture;
if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
} else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
newTexture = this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
} else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
newTexture = this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
} else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
newTexture = this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
} else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
newTexture = this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
}
this.usedTextures[shapeKey].push(newTexture);
this.numUsedTextures++;
this._numBytesAllocated += texBytes;
this.log();
return newTexture;
}
releaseTexture(texture, shape, logicalTexType, isPacked) {
if (this.freeTextures == null) {
return;
}
const physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
if (!(shapeKey in this.freeTextures)) {
this.freeTextures[shapeKey] = [];
}
const texBytes = computeBytes(shape, physicalTexType, this.gpgpu.gl, this.gpgpu.textureConfig, isPacked);
const deleteTexThreshold = env().get("WEBGL_DELETE_TEXTURE_THRESHOLD");
if (deleteTexThreshold !== -1 && this._numBytesAllocated > deleteTexThreshold) {
this.gpgpu.deleteMatrixTexture(texture.texture);
this._numBytesAllocated -= texBytes;
} else {
this.freeTextures[shapeKey].push(texture);
this.numFreeTextures++;
this._numBytesFree += texBytes;
}
this.numUsedTextures--;
const texList = this.usedTextures[shapeKey];
const texIndex = texList.indexOf(texture);
if (texIndex < 0) {
throw new Error("Cannot release a texture that was never provided by this texture manager");
}
texList.splice(texIndex, 1);
this.log();
}
log() {
if (!this.logEnabled) {
return;
}
const total = this.numFreeTextures + this.numUsedTextures;
console.log("Free/Used", `${this.numFreeTextures} / ${this.numUsedTextures}`, `(${total})`);
const freeRatio = this._numBytesFree / this._numBytesAllocated;
console.log(`Bytes allocated: ${this._numBytesAllocated}`);
console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100 * freeRatio)}%)`);
}
get numBytesAllocated() {
return this._numBytesAllocated;
}
get numBytesFree() {
return this._numBytesFree;
}
getNumUsedTextures() {
return this.numUsedTextures;
}
getNumFreeTextures() {
return this.numFreeTextures;
}
dispose() {
if (this.freeTextures == null) {
return;
}
for (const texShape in this.freeTextures) {
this.freeTextures[texShape].forEach((tex) => {
this.gpgpu.deleteMatrixTexture(tex.texture);
});
}
for (const texShape in this.usedTextures) {
this.usedTextures[texShape].forEach((tex) => {
this.gpgpu.deleteMatrixTexture(tex.texture);
});
}
this.freeTextures = null;
this.usedTextures = null;
this.numUsedTextures = 0;
this.numFreeTextures = 0;
this._numBytesAllocated = 0;
this._numBytesFree = 0;
}
};
function numBytesForInternalFormat(gl, internalFormat) {
const glany = gl;
if (internalFormat === glany.R32F) {
return 4;
} else if (internalFormat === glany.R16F) {
return 2;
} else if (internalFormat === glany.RGBA32F) {
return 16;
} else if (internalFormat === gl.RGBA) {
return 16;
} else if (internalFormat === glany.RGBA16F) {
return 8;
} else if (internalFormat === glany.RGBA8) {
return 4;
}
throw new Error(`Unknown internal format ${internalFormat}`);
}
function computeBytes(shape, physicalTexType, gl, textureConfig, isPacked) {
const internalFormat = internalFormatForPhysicalTexType(physicalTexType, textureConfig);
let numElements;
if (isPacked) {
const [packedWidth, packedHeight] = getPackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
numElements = packedWidth * packedHeight;
} else {
const [width, height] = getUnpackedMatrixTextureShapeWidthHeight(shape[0], shape[1]);
numElements = width * height;
}
const bytesPerElement2 = numBytesForInternalFormat(gl, internalFormat);
return numElements * bytesPerElement2;
}
function internalFormatForPhysicalTexType(physicalTexType, textureConfig) {
switch (physicalTexType) {
case PhysicalTextureType.PACKED_2X2_FLOAT32:
return getInternalFormatForPackedMatrixTexture(textureConfig);
case PhysicalTextureType.PACKED_2X2_FLOAT16:
return getInternalFormatForFloat16PackedMatrixTexture(textureConfig);
case PhysicalTextureType.UNPACKED_FLOAT32:
return getInternalFormatForFloat32MatrixTexture(textureConfig);
case PhysicalTextureType.UNPACKED_FLOAT16:
return getInternalFormatForFloat16MatrixTexture(textureConfig);
case PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE:
return getInternalFormatForUnsignedBytesMatrixTexture(textureConfig);
default:
throw new Error(`Unknown physical texture type ${physicalTexType}`);
}
}
function getPhysicalTextureForRendering(isPacked) {
if (env().getBool("WEBGL_RENDER_FLOAT32_ENABLED")) {
if (isPacked) {
return PhysicalTextureType.PACKED_2X2_FLOAT32;
}
return PhysicalTextureType.UNPACKED_FLOAT32;
}
if (isPacked) {
return PhysicalTextureType.PACKED_2X2_FLOAT16;
}
return PhysicalTextureType.UNPACKED_FLOAT16;
}
function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
if (logicalTexType === TextureUsage.UPLOAD) {
return PhysicalTextureType.PACKED_2X2_FLOAT32;
} else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
return getPhysicalTextureForRendering(isPacked);
} else if (logicalTexType === TextureUsage.DOWNLOAD || logicalTexType === TextureUsage.PIXELS) {
return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
}
throw new Error(`Unknown logical texture type ${logicalTexType}`);
}
function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_gpu.js
init_define_BUILD_VERSION();
var UnaryOpProgram = class {
constructor(aShape, opSnippet) {
this.variableNames = ["A"];
this.outputShape = aShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
this.userCode = `
float unaryOperation(float x) {
${opSnippet}
}
void main() {
float x = getAAtOutCoords();
float y = unaryOperation(x);
setOutput(y);
}
`;
}
};
var CHECK_NAN_SNIPPET = `if (isnan(x)) return x;`;
var LINEAR = `return x;`;
var ABS = `return abs(x);`;
var ELU2 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
var RELU = CHECK_NAN_SNIPPET + `
return (x < 0.0) ? 0.0 : x;
`;
var RELU6 = CHECK_NAN_SNIPPET + `
return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var CLONE = "return x;";
var SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/unaryop_packed_gpu.js
init_define_BUILD_VERSION();
var LINEAR2 = `return x;`;
var ELU3 = `
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`;
var RELU2 = `
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var RELU62 = `
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var SIGMOID2 = `return 1.0 / (1.0 + exp(-1.0 * x));`;
var UnaryOpPackedProgram = class {
constructor(aShape, opSnippet) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = aShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
this.userCode = `
vec4 unaryOperation(vec4 x) {
${opSnippet}
}
void main() {
vec4 x = getAAtOutCoords();
vec4 y = unaryOperation(x);
setOutput(y);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/unpack_gpu.js
init_define_BUILD_VERSION();
var UnpackProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = false;
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
const rank = outputShape.length;
const channels = getChannels("rc", rank);
const dtype = getCoordsDataType(rank);
const sourceCoords = getSourceCoords(rank, channels);
const innerDims = channels.slice(-2);
const coords2 = rank <= 1 ? "rc" : `vec2(${innerDims.join(",")})`;
this.userCode = `
void main() {
${dtype} rc = getOutputCoords();
vec4 packedInput = getA(${sourceCoords});
setOutput(getChannel(packedInput, ${coords2}));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/backend_webgl.js
var whereImpl3 = kernel_impls_exports.whereImpl;
var EPSILON_FLOAT322 = 1e-7;
var EPSILON_FLOAT162 = 1e-4;
var binaryCaches = {};
function getBinaryCache(webGLVersion) {
if (webGLVersion in binaryCaches) {
return binaryCaches[webGLVersion];
}
binaryCaches[webGLVersion] = {};
return binaryCaches[webGLVersion];
}
var CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("CPU_HANDOFF_SIZE_THRESHOLD");
var BEFORE_PAGING_CONSTANT = 600;
function numMBBeforeWarning() {
if (env().global.screen == null) {
return 1024;
}
return env().global.screen.height * env().global.screen.width * window.devicePixelRatio * BEFORE_PAGING_CONSTANT / 1024 / 1024;
}
var MathBackendWebGL = class extends KernelBackend {
constructor(gpuResource) {
super();
this.pendingRead = /* @__PURE__ */ new WeakMap();
this.pendingDisposal = /* @__PURE__ */ new WeakSet();
this.dataRefCount = /* @__PURE__ */ new WeakMap();
this.numBytesInGPU = 0;
this.uploadWaitMs = 0;
this.downloadWaitMs = 0;
this.lastGlFlushTime = 0;
this.warnedAboutMemory = false;
this.pendingDeletes = 0;
this.disposed = false;
if (!env().getBool("HAS_WEBGL")) {
throw new Error("WebGL is not supported on this device");
}
let newGPGPU;
if (gpuResource != null) {
if (gpuResource instanceof GPGPUContext) {
newGPGPU = gpuResource;
} else {
const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"), gpuResource);
newGPGPU = new GPGPUContext(gl);
}
this.binaryCache = {};
this.gpgpuCreatedLocally = false;
} else {
const gl = getWebGLContext(env().getNumber("WEBGL_VERSION"));
newGPGPU = new GPGPUContext(gl);
this.binaryCache = getBinaryCache(env().getNumber("WEBGL_VERSION"));
this.gpgpuCreatedLocally = true;
}
this.gpgpu = newGPGPU;
this.canvas = this.gpgpu.gl.canvas;
this.textureManager = new TextureManager(this.gpgpu);
this.numMBBeforeWarning = numMBBeforeWarning();
this.texData = new DataStorage(this, engine());
}
nextDataId() {
return MathBackendWebGL.nextDataId++;
}
numDataIds() {
return this.texData.numDataIds() - this.pendingDeletes;
}
write(values, shape, dtype) {
if (env().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS") || env().getBool("DEBUG")) {
this.checkNumericalProblems(values);
}
if (dtype === "complex64" && values != null) {
throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
}
const dataId = { id: this.nextDataId() };
this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount: 1 });
return dataId;
}
refCount(dataId) {
if (this.texData.has(dataId)) {
const tensorData = this.texData.get(dataId);
return tensorData.refCount;
}
return 0;
}
incRef(dataId) {
const texData = this.texData.get(dataId);
texData.refCount++;
}
decRef(dataId) {
if (this.texData.has(dataId)) {
const texData = this.texData.get(dataId);
texData.refCount--;
}
}
move(dataId, values, shape, dtype, refCount) {
if (env().getBool("DEBUG")) {
this.checkNumericalProblems(values);
}
if (dtype === "complex64") {
throw new Error(`Cannot write to a complex64 dtype. Please use tf.complex(real, imag).`);
}
this.texData.set(dataId, { shape, dtype, values, usage: TextureUsage.UPLOAD, refCount });
}
disposeIntermediateTensorInfo(tensorInfo) {
this.disposeData(tensorInfo.dataId);
}
readSync(dataId) {
const texData = this.texData.get(dataId);
const { values, dtype, complexTensorInfos, slice: slice4, shape, isPacked } = texData;
if (slice4 != null) {
let program;
if (isPacked) {
program = new UnaryOpPackedProgram(shape, CLONE);
} else {
program = new UnaryOpProgram(shape, CLONE);
}
const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
const data = this.readSync(res.dataId);
this.disposeIntermediateTensorInfo(res);
return data;
}
if (values != null) {
return this.convertAndCacheOnCPU(dataId);
}
if (dtype === "string") {
return values;
}
const shouldTimeProgram = this.activeTimers != null;
let start;
if (shouldTimeProgram) {
start = util_exports.now();
}
let result;
if (dtype === "complex64") {
const realValues = this.readSync(complexTensorInfos.real.dataId);
const imagValues = this.readSync(complexTensorInfos.imag.dataId);
result = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
} else {
result = this.getValuesFromTexture(dataId);
}
if (shouldTimeProgram) {
this.downloadWaitMs += util_exports.now() - start;
}
return this.convertAndCacheOnCPU(dataId, result);
}
async read(dataId) {
if (this.pendingRead.has(dataId)) {
const subscribers2 = this.pendingRead.get(dataId);
return new Promise((resolve) => subscribers2.push(resolve));
}
const texData = this.texData.get(dataId);
const { values, shape, slice: slice4, dtype, complexTensorInfos, isPacked } = texData;
if (slice4 != null) {
let program;
if (isPacked) {
program = new UnaryOpPackedProgram(shape, CLONE);
} else {
program = new UnaryOpProgram(shape, CLONE);
}
const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
const data = this.read(res.dataId);
this.disposeIntermediateTensorInfo(res);
return data;
}
if (values != null) {
return this.convertAndCacheOnCPU(dataId);
}
if (env().getBool("DEBUG")) {
if (!env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED") && env().getNumber("WEBGL_VERSION") === 2) {
throw new Error(`tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.`);
}
}
let buffer2 = null;
let tmpDownloadTarget;
if (dtype !== "complex64" && env().get("WEBGL_BUFFER_SUPPORTED")) {
tmpDownloadTarget = this.decode(dataId);
const tmpData = this.texData.get(tmpDownloadTarget.dataId);
buffer2 = this.gpgpu.createBufferFromTexture(tmpData.texture.texture, ...getDenseTexShape(shape));
}
this.pendingRead.set(dataId, []);
if (dtype !== "complex64") {
await this.gpgpu.createAndWaitForFence();
}
let vals;
if (dtype === "complex64") {
const ps = await Promise.all([
this.read(complexTensorInfos.real.dataId),
this.read(complexTensorInfos.imag.dataId)
]);
const realValues = ps[0];
const imagValues = ps[1];
vals = backend_util_exports.mergeRealAndImagArrays(realValues, imagValues);
} else if (buffer2 == null) {
vals = this.getValuesFromTexture(dataId);
} else {
const size = util_exports.sizeFromShape(shape);
vals = this.gpgpu.downloadFloat32MatrixFromBuffer(buffer2, size);
}
if (tmpDownloadTarget != null) {
this.disposeIntermediateTensorInfo(tmpDownloadTarget);
}
if (buffer2 != null) {
const gl = this.gpgpu.gl;
callAndCheck(gl, () => gl.deleteBuffer(buffer2));
}
const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
const subscribers = this.pendingRead.get(dataId);
this.pendingRead.delete(dataId);
subscribers.forEach((resolve) => resolve(dTypeVals));
if (this.pendingDisposal.has(dataId)) {
this.pendingDisposal.delete(dataId);
if (this.disposeData(dataId)) {
engine().removeDataId(dataId, this);
}
this.pendingDeletes--;
}
return dTypeVals;
}
readToGPU(dataId, options = {}) {
const texData = this.texData.get(dataId);
const { values, shape, slice: slice4, dtype, isPacked, texture } = texData;
if (dtype === "complex64") {
throw new Error("Does not support reading texture for complex64 dtype.");
}
if (slice4 != null) {
let program;
if (isPacked) {
program = new UnaryOpPackedProgram(shape, CLONE);
} else {
program = new UnaryOpProgram(shape, CLONE);
}
const res = this.runWebGLProgram(program, [{ dataId, shape, dtype }], dtype);
const gpuResouorce = this.readToGPU(res, options);
this.disposeIntermediateTensorInfo(res);
return gpuResouorce;
}
if (texture == null) {
if (values != null) {
throw new Error("Data is not on GPU but on CPU.");
} else {
throw new Error("There is no data on GPU or CPU.");
}
}
const tmpTarget = this.decode(dataId, options.customTexShape);
const tensorRef = engine().makeTensorFromTensorInfo(tmpTarget);
const tmpData = this.texData.get(tmpTarget.dataId);
return Object.assign({ tensorRef }, tmpData.texture);
}
bufferSync(t) {
const data = this.readSync(t.dataId);
if (t.dtype === "string") {
try {
const strings = data.map((d) => util_exports.decodeString(d));
return buffer(t.shape, t.dtype, strings);
} catch (_a) {
throw new Error("Failed to decode encoded string bytes into utf-8");
}
}
return buffer(t.shape, t.dtype, data);
}
checkNumericalProblems(values) {
if (values == null) {
return;
}
for (let i = 0; i < values.length; i++) {
const num = values[i];
if (!canBeRepresented(num)) {
if (env().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")) {
throw Error(`The value ${num} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`);
}
throw Error(`The value ${num} cannot be represented on this device.`);
}
}
}
getValuesFromTexture(dataId) {
const { shape, dtype, isPacked } = this.texData.get(dataId);
const size = util_exports.sizeFromShape(shape);
if (env().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")) {
const tmpTarget = this.decode(dataId);
const tmpData2 = this.texData.get(tmpTarget.dataId);
const vals2 = this.gpgpu.downloadMatrixFromPackedTexture(tmpData2.texture.texture, ...getDenseTexShape(shape)).subarray(0, size);
this.disposeIntermediateTensorInfo(tmpTarget);
return vals2;
}
const shouldUsePackedProgram = env().getBool("WEBGL_PACK") && isPacked === true;
const outputShape = shouldUsePackedProgram ? getShapeAs3D(shape) : shape;
const program = shouldUsePackedProgram ? new EncodeFloatPackedProgram(outputShape) : new EncodeFloatProgram(outputShape);
const output = this.runWebGLProgram(program, [{ shape: outputShape, dtype, dataId }], "float32");
const tmpData = this.texData.get(output.dataId);
const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture.texture, tmpData.texShape[0], tmpData.texShape[1]).subarray(0, size);
this.disposeIntermediateTensorInfo(output);
return vals;
}
timerAvailable() {
return env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0;
}
time(f) {
const oldActiveTimers = this.activeTimers;
const newActiveTimers = [];
let outerMostTime = false;
if (this.programTimersStack == null) {
this.programTimersStack = newActiveTimers;
outerMostTime = true;
} else {
this.activeTimers.push(newActiveTimers);
}
this.activeTimers = newActiveTimers;
f();
const flattenedActiveTimerQueries = util_exports.flatten(this.activeTimers.map((d) => d.query)).filter((d) => d != null);
const flattenedActiveTimerNames = util_exports.flatten(this.activeTimers.map((d) => d.name)).filter((d) => d != null);
this.activeTimers = oldActiveTimers;
if (outerMostTime) {
this.programTimersStack = null;
}
const res = {
uploadWaitMs: this.uploadWaitMs,
downloadWaitMs: this.downloadWaitMs,
kernelMs: null,
wallMs: null
};
return (async () => {
if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
const kernelMs = await Promise.all(flattenedActiveTimerQueries);
res["kernelMs"] = util_exports.sum(kernelMs);
res["getExtraProfileInfo"] = () => kernelMs.map((d, i) => ({ name: flattenedActiveTimerNames[i], ms: d })).map((d) => `${d.name}: ${d.ms}`).join(", ");
} else {
res["kernelMs"] = {
error: "WebGL query timers are not supported in this environment."
};
}
this.uploadWaitMs = 0;
this.downloadWaitMs = 0;
return res;
})();
}
memory() {
return {
unreliable: false,
numBytesInGPU: this.numBytesInGPU,
numBytesInGPUAllocated: this.textureManager.numBytesAllocated,
numBytesInGPUFree: this.textureManager.numBytesFree
};
}
startTimer() {
if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
return this.gpgpu.beginQuery();
}
return { startMs: util_exports.now(), endMs: null };
}
endTimer(query) {
if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
this.gpgpu.endQuery();
return query;
}
query.endMs = util_exports.now();
return query;
}
async getQueryTime(query) {
if (env().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE") > 0) {
return this.gpgpu.waitForQueryAndGetTime(query);
}
const timerQuery = query;
return timerQuery.endMs - timerQuery.startMs;
}
disposeData(dataId, force = false) {
if (this.pendingDisposal.has(dataId)) {
return false;
}
if (!this.texData.has(dataId)) {
return true;
}
if (force) {
this.texData.get(dataId).refCount = 0;
} else {
this.texData.get(dataId).refCount--;
}
if (!force && this.texData.get(dataId).refCount > 0) {
return false;
}
if (this.pendingRead.has(dataId)) {
this.pendingDisposal.add(dataId);
this.pendingDeletes++;
return false;
}
this.releaseGPUData(dataId);
const { complexTensorInfos } = this.texData.get(dataId);
if (complexTensorInfos != null) {
this.disposeData(complexTensorInfos.real.dataId, force);
this.disposeData(complexTensorInfos.imag.dataId, force);
}
this.texData.delete(dataId);
return true;
}
releaseGPUData(dataId) {
const { texture, dtype, texShape, usage, isPacked, slice: slice4 } = this.texData.get(dataId);
const key = slice4 && slice4.origDataId || dataId;
const refCount = this.dataRefCount.get(key);
if (refCount > 1) {
this.dataRefCount.set(key, refCount - 1);
} else {
this.dataRefCount.delete(key);
if (texture != null) {
this.numBytesInGPU -= this.computeBytes(texShape, dtype);
this.textureManager.releaseTexture(texture, texShape, usage, isPacked);
}
}
const texData = this.texData.get(dataId);
texData.texture = null;
texData.texShape = null;
texData.isPacked = false;
texData.slice = null;
}
getTexture(dataId) {
this.uploadToGPU(dataId);
return this.texData.get(dataId).texture.texture;
}
getDataInfo(dataId) {
return this.texData.get(dataId);
}
shouldExecuteOnCPU(inputs, sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD) {
return env().getBool("WEBGL_CPU_FORWARD") && inputs.every((input2) => this.texData.get(input2.dataId).texture == null && util_exports.sizeFromShape(input2.shape) < sizeThreshold);
}
getGPGPUContext() {
return this.gpgpu;
}
where(condition) {
backend_util_exports.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");
const condVals = condition.dataSync();
return whereImpl3(condition.shape, condVals);
}
packedUnaryOp(x, op2, dtype) {
const program = new UnaryOpPackedProgram(x.shape, op2);
const outInfo = this.compileAndRun(program, [x], dtype);
return engine().makeTensorFromTensorInfo(outInfo);
}
abs(x) {
if (this.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
const outValues = simpleAbsImplCPU(this.texData.get(x.dataId).values);
return this.makeOutput(x.shape, x.dtype, outValues);
}
if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
return this.packedUnaryOp(x, ABS, x.dtype);
}
const program = new UnaryOpProgram(x.shape, ABS);
const outInfo = this.compileAndRun(program, [x]);
return engine().makeTensorFromTensorInfo(outInfo);
}
makeTensorInfo(shape, dtype, values) {
let dataId;
if (dtype === "string" && values != null && values.length > 0 && util_exports.isString(values[0])) {
const encodedValues = values.map((d) => util_exports.encodeString(d));
dataId = this.write(encodedValues, shape, dtype);
} else {
dataId = this.write(values, shape, dtype);
}
this.texData.get(dataId).usage = null;
return { dataId, shape, dtype };
}
makeOutput(shape, dtype, values) {
return engine().makeTensorFromTensorInfo(this.makeTensorInfo(shape, dtype, values), this);
}
unpackTensor(input2) {
const program = new UnpackProgram(input2.shape);
return this.runWebGLProgram(program, [input2], input2.dtype);
}
packTensor(input2) {
const program = new PackProgram(input2.shape);
const preventEagerUnpackingOutput = true;
return this.runWebGLProgram(program, [input2], input2.dtype, null, preventEagerUnpackingOutput);
}
packedReshape(input2, afterShape) {
const input3DShape = [
getBatchDim(input2.shape),
...getRowsCols(input2.shape)
];
const input3D = {
dtype: input2.dtype,
shape: input3DShape,
dataId: input2.dataId
};
const afterShapeAs3D = [
getBatchDim(afterShape),
...getRowsCols(afterShape)
];
const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
const preventEagerUnpackingOfOutput = true;
const customValues = [input3DShape];
const output = this.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);
return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
}
decode(dataId, customTexShape) {
const texData = this.texData.get(dataId);
const { isPacked, shape, dtype } = texData;
if (customTexShape != null) {
const size = util_exports.sizeFromShape(shape);
const texSize = customTexShape[0] * customTexShape[1] * 4;
util_exports.assert(size <= texSize, () => "customTexShape is too small. Row * Column * 4 should be equal or larger than the size of the tensor data.");
}
const shapeAs3D = getShapeAs3D(shape);
let program;
if (isPacked) {
program = new DecodeMatrixPackedProgram(shapeAs3D);
} else {
program = new DecodeMatrixProgram(shapeAs3D);
}
const preventEagerUnpackingOfOutput = true;
const customValues = [customTexShape != null ? customTexShape : getDenseTexShape(shapeAs3D)];
const out = this.runWebGLProgram(program, [{ shape: shapeAs3D, dtype, dataId }], dtype, customValues, preventEagerUnpackingOfOutput, customTexShape);
return { dtype, shape, dataId: out.dataId };
}
runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false, customTexShape) {
const output = this.makeTensorInfo(program.outputShape, outputDtype);
const outData = this.texData.get(output.dataId);
if (program.packedOutput) {
outData.isPacked = true;
}
if (program.outPackingScheme === PackingScheme.DENSE) {
const texelShape = customTexShape != null ? customTexShape : getDenseTexShape(program.outputShape);
outData.texShape = texelShape.map((d) => d * 2);
}
if (program.outTexUsage != null) {
outData.usage = program.outTexUsage;
}
if (util_exports.sizeFromShape(output.shape) === 0) {
outData.values = util_exports.getTypedArrayFromDType(output.dtype, 0);
return output;
}
const dataToDispose = [];
const inputsData = inputs.map((input2) => {
if (input2.dtype === "complex64") {
throw new Error(`GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.`);
}
let texData = this.texData.get(input2.dataId);
if (texData.texture == null) {
if (!program.packedInputs && util_exports.sizeFromShape(input2.shape) <= env().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM")) {
return {
shape: input2.shape,
texData: null,
isUniform: true,
uniformValues: texData.values
};
}
if (program.packedInputs) {
texData.isPacked = true;
texData.shape = input2.shape;
}
}
this.uploadToGPU(input2.dataId);
if (!!texData.isPacked !== !!program.packedInputs) {
input2 = texData.isPacked ? this.unpackTensor(input2) : this.packTensor(input2);
dataToDispose.push(input2);
texData = this.texData.get(input2.dataId);
} else if (texData.isPacked && !isReshapeFree(texData.shape, input2.shape)) {
const savedInput = input2;
const targetShape = input2.shape;
input2.shape = texData.shape;
input2 = this.packedReshape(input2, targetShape);
dataToDispose.push(input2);
texData = this.texData.get(input2.dataId);
savedInput.shape = targetShape;
}
return { shape: input2.shape, texData, isUniform: false };
});
this.uploadToGPU(output.dataId);
const outputData = { shape: output.shape, texData: outData, isUniform: false };
const key = makeShaderKey(program, inputsData, outputData);
const binary = this.getAndSaveBinary(key, () => {
return compileProgram(this.gpgpu, program, inputsData, outputData);
});
const shouldTimeProgram = this.activeTimers != null;
let query;
if (shouldTimeProgram) {
query = this.startTimer();
}
if (!env().get("ENGINE_COMPILE_ONLY")) {
runProgram(this.gpgpu, binary, inputsData, outputData, customUniformValues);
}
dataToDispose.forEach((info) => this.disposeIntermediateTensorInfo(info));
if (shouldTimeProgram) {
query = this.endTimer(query);
this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
}
const glFlushThreshold = env().get("WEBGL_FLUSH_THRESHOLD");
if (glFlushThreshold > 0) {
const time = util_exports.now();
if (time - this.lastGlFlushTime > glFlushThreshold) {
this.gpgpu.gl.flush();
this.lastGlFlushTime = time;
}
}
if (!env().getBool("WEBGL_LAZILY_UNPACK") && outData.isPacked && preventEagerUnpackingOfOutput === false) {
const unpacked = this.unpackTensor(output);
this.disposeIntermediateTensorInfo(output);
return unpacked;
}
return output;
}
compileAndRun(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput = false) {
outputDtype = outputDtype || inputs[0].dtype;
const outInfo = this.runWebGLProgram(program, inputs, outputDtype, customUniformValues, preventEagerUnpackingOfOutput);
return outInfo;
}
getAndSaveBinary(key, getBinary) {
if (!(key in this.binaryCache)) {
this.binaryCache[key] = getBinary();
}
return this.binaryCache[key];
}
getTextureManager() {
return this.textureManager;
}
dispose() {
if (this.disposed) {
return;
}
if (!env().getBool("IS_TEST")) {
const allKeys = Object.keys(this.binaryCache);
allKeys.forEach((key) => {
this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
delete this.binaryCache[key];
});
}
this.textureManager.dispose();
if (this.canvas != null && (typeof HTMLCanvasElement !== "undefined" && this.canvas instanceof HTMLCanvasElement)) {
this.canvas.remove();
} else {
this.canvas = null;
}
if (this.gpgpuCreatedLocally) {
this.gpgpu.program = null;
this.gpgpu.dispose();
}
this.disposed = true;
}
floatPrecision() {
if (this.floatPrecisionValue == null) {
this.floatPrecisionValue = tidy(() => {
if (!env().get("WEBGL_RENDER_FLOAT32_ENABLED")) {
const debugFlag = env().getBool("DEBUG");
env().set("DEBUG", false);
const underflowCheckValue = this.abs(scalar(1e-8)).dataSync()[0];
env().set("DEBUG", debugFlag);
if (underflowCheckValue > 0) {
return 32;
}
}
return 16;
});
}
return this.floatPrecisionValue;
}
epsilon() {
return this.floatPrecision() === 32 ? EPSILON_FLOAT322 : EPSILON_FLOAT162;
}
uploadToGPU(dataId) {
const texData = this.texData.get(dataId);
const { shape, dtype, values, texture, usage, isPacked } = texData;
if (texture != null) {
return;
}
const shouldTimeProgram = this.activeTimers != null;
let start;
if (shouldTimeProgram) {
start = util_exports.now();
}
let texShape = texData.texShape;
if (texShape == null) {
texShape = getTextureShapeFromLogicalShape(shape, isPacked);
texData.texShape = texShape;
}
if (values != null) {
const shapeAs3D = getShapeAs3D(shape);
let program;
let width = texShape[1], height = texShape[0];
const isByteArray = values instanceof Uint8Array || values instanceof Uint8ClampedArray;
if (isPacked || !isByteArray) {
[width, height] = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]);
}
if (isPacked) {
program = new EncodeMatrixPackedProgram(shapeAs3D, isByteArray);
} else {
program = new EncodeMatrixProgram(shapeAs3D, isByteArray);
}
const tempDenseInputTexShape = isByteArray ? [height, width] : texShape;
const tempDenseInputHandle = this.makeTensorInfo(tempDenseInputTexShape, dtype);
const tempDenseInputTexData = this.texData.get(tempDenseInputHandle.dataId);
if (isByteArray) {
tempDenseInputTexData.usage = TextureUsage.PIXELS;
} else {
tempDenseInputTexData.usage = TextureUsage.UPLOAD;
}
tempDenseInputTexData.texShape = tempDenseInputTexShape;
this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(tempDenseInputHandle.dataId), width, height, values);
const customValues = [[height, width]];
const preventEagerUnpacking = true;
const encodedOutputTarget = this.runWebGLProgram(program, [tempDenseInputHandle], dtype, customValues, preventEagerUnpacking);
const outputTexData = this.texData.get(encodedOutputTarget.dataId);
texData.texShape = outputTexData.texShape;
texData.isPacked = outputTexData.isPacked;
texData.usage = outputTexData.usage;
if (!env().get("ENGINE_COMPILE_ONLY")) {
texData.texture = outputTexData.texture;
texData.values = null;
this.texData.delete(encodedOutputTarget.dataId);
} else {
this.disposeData(encodedOutputTarget.dataId);
}
this.disposeIntermediateTensorInfo(tempDenseInputHandle);
if (shouldTimeProgram) {
this.uploadWaitMs += util_exports.now() - start;
}
} else {
const newTexture = this.acquireTexture(texShape, usage, dtype, isPacked);
texData.texture = newTexture;
}
}
convertAndCacheOnCPU(dataId, float32Values) {
const texData = this.texData.get(dataId);
const { dtype } = texData;
this.releaseGPUData(dataId);
if (float32Values != null) {
texData.values = float32ToTypedArray(float32Values, dtype);
}
return texData.values;
}
acquireTexture(texShape, texType, dtype, isPacked) {
this.numBytesInGPU += this.computeBytes(texShape, dtype);
if (!this.warnedAboutMemory && this.numBytesInGPU > this.numMBBeforeWarning * 1024 * 1024) {
const mb = (this.numBytesInGPU / 1024 / 1024).toFixed(2);
this.warnedAboutMemory = true;
console.warn(`High memory usage in GPU: ${mb} MB, most likely due to a memory leak`);
}
return this.textureManager.acquireTexture(texShape, texType, isPacked);
}
computeBytes(shape, dtype) {
return shape[0] * shape[1] * util_exports.bytesPerElement(dtype);
}
checkCompileCompletion() {
for (const [, binary] of Object.entries(this.binaryCache)) {
this.checkCompletion_(binary);
}
}
async checkCompileCompletionAsync() {
const ps = [];
if (this.gpgpu.parallelCompilationExtension) {
for (const [, binary] of Object.entries(this.binaryCache)) {
ps.push(this.checkCompletionAsync_(binary));
}
return Promise.all(ps);
} else {
for (const [, binary] of Object.entries(this.binaryCache)) {
const p2 = new Promise((resolve) => {
try {
this.checkCompletion_(binary);
resolve(true);
} catch (error) {
throw error;
}
});
ps.push(p2);
}
return Promise.all(ps);
}
}
async checkCompletionAsync_(binary) {
if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.parallelCompilationExtension.COMPLETION_STATUS_KHR)) {
return this.checkCompletion_(binary);
} else {
await nextFrame();
return this.checkCompletionAsync_(binary);
}
}
checkCompletion_(binary) {
if (this.gpgpu.gl.getProgramParameter(binary.webGLProgram, this.gpgpu.gl.LINK_STATUS) === false) {
console.log(this.gpgpu.gl.getProgramInfoLog(binary.webGLProgram));
if (this.gpgpu.gl.getShaderParameter(binary.fragmentShader, this.gpgpu.gl.COMPILE_STATUS) === false) {
logShaderSourceAndInfoLog(binary.source, this.gpgpu.gl.getShaderInfoLog(binary.fragmentShader));
throw new Error("Failed to compile fragment shader.");
}
throw new Error("Failed to link vertex and fragment shaders.");
}
return true;
}
getUniformLocations() {
for (const [, binary] of Object.entries(this.binaryCache)) {
const { uniformLocations, customUniformLocations, infLoc, nanLoc, inShapesLocations, inTexShapesLocations, outShapeLocation, outShapeStridesLocation, outTexShapeLocation } = getUniformLocations(this.gpgpu, binary.program, binary.webGLProgram);
binary.uniformLocations = uniformLocations;
binary.customUniformLocations = customUniformLocations;
binary.infLoc = infLoc;
binary.nanLoc = nanLoc;
binary.inShapesLocations = inShapesLocations;
binary.inTexShapesLocations = inTexShapesLocations;
binary.outShapeLocation = outShapeLocation;
binary.outShapeStridesLocation = outShapeStridesLocation;
binary.outTexShapeLocation = outTexShapeLocation;
}
}
};
MathBackendWebGL.nextDataId = 0;
function float32ToTypedArray(a, dtype) {
if (dtype === "float32" || dtype === "complex64") {
return a;
} else if (dtype === "int32" || dtype === "bool") {
const result = dtype === "int32" ? new Int32Array(a.length) : new Uint8Array(a.length);
for (let i = 0; i < result.length; ++i) {
result[i] = Math.round(a[i]);
}
return result;
} else {
throw new Error(`Unknown dtype ${dtype}`);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/base.js
if (device_util_exports.isBrowser()) {
registerBackend("webgl", () => new MathBackendWebGL(), 2);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_gpu.js
init_define_BUILD_VERSION();
var CHECK_NAN_SNIPPET2 = `
if (isnan(a)) return a;
if (isnan(b)) return b;
`;
var BinaryOpProgram = class {
constructor(op2, aShape, bShape) {
this.variableNames = ["A", "B"];
this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
this.userCode = `
float binaryOperation(float a, float b) {
${op2}
}
void main() {
float a = getAAtOutCoords();
float b = getBAtOutCoords();
setOutput(binaryOperation(a, b));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_packed_gpu.js
init_define_BUILD_VERSION();
var CHECK_NAN_SNIPPET3 = `
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;
var BinaryOpPackedProgram = class {
constructor(op2, aShape, bShape, checkOutOfBounds = false) {
this.variableNames = ["A", "B"];
this.supportsBroadcasting = true;
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
const rank = this.outputShape.length;
this.enableShapeUniforms = useShapeUniforms(rank);
let checkOutOfBoundsString = "";
if (checkOutOfBounds) {
if (rank === 0 || util_exports.sizeFromShape(this.outputShape) === 1) {
checkOutOfBoundsString = `
result.y = 0.;
result.z = 0.;
result.w = 0.;
`;
} else {
const dtype = getCoordsDataType(rank);
checkOutOfBoundsString = `
${dtype} coords = getOutputCoords();
`;
if (rank === 1) {
if (this.enableShapeUniforms) {
checkOutOfBoundsString += `
result.y = (coords + 1) >= outShape ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;
} else {
checkOutOfBoundsString += `
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
result.z = 0.;
result.w = 0.;
`;
}
} else {
const channels = getChannels("coords", rank);
if (this.enableShapeUniforms) {
checkOutOfBoundsString += `
bool nextRowOutOfBounds =
(${channels[rank - 2]} + 1) >= outShape[${rank} - 2];
bool nextColOutOfBounds =
(${channels[rank - 1]} + 1) >= outShape[${rank} - 1];
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`;
} else {
checkOutOfBoundsString += `
bool nextRowOutOfBounds =
(${channels[rank - 2]} + 1) >= ${this.outputShape[rank - 2]};
bool nextColOutOfBounds =
(${channels[rank - 1]} + 1) >= ${this.outputShape[rank - 1]};
result.y = nextColOutOfBounds ? 0. : result.y;
result.z = nextRowOutOfBounds ? 0. : result.z;
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
`;
}
}
}
}
this.userCode = `
vec4 binaryOperation(vec4 a, vec4 b) {
${op2}
}
void main() {
vec4 a = getAAtOutCoords();
vec4 b = getBAtOutCoords();
vec4 result = binaryOperation(a, b);
${checkOutOfBoundsString}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Identity.js
init_define_BUILD_VERSION();
function identity2(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
backend2.incRef(x.dataId);
return { dataId: x.dataId, shape: x.shape, dtype: x.dtype };
}
var identityConfig2 = {
kernelName: Identity,
backendName: "webgl",
kernelFunc: identity2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Complex.js
function complex3(args) {
const { inputs, backend: backend2 } = args;
const { real: real4, imag: imag4 } = inputs;
const complexInfo = backend2.makeTensorInfo(real4.shape, "complex64");
const complex4 = backend2.texData.get(complexInfo.dataId);
const realTensorInfo = identity2({ inputs: { x: real4 }, backend: backend2 });
const imagTensorInfo = identity2({ inputs: { x: imag4 }, backend: backend2 });
complex4.complexTensorInfos = { real: realTensorInfo, imag: imagTensorInfo };
return complexInfo;
}
var complexConfig2 = {
kernelName: Complex,
backendName: "webgl",
kernelFunc: complex3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LeakyRelu.js
init_define_BUILD_VERSION();
var LEAKYRELU = `return (a < 0.) ? b * a : a;`;
var LEAKYRELU_PACKED = `
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function leakyRelu3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { alpha } = attrs;
const $alpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(alpha, "float32"));
const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(LEAKYRELU_PACKED, x.shape, $alpha.shape) : new BinaryOpProgram(LEAKYRELU, x.shape, $alpha.shape);
const result = backend2.runWebGLProgram(program, [x, $alpha], "float32");
backend2.disposeIntermediateTensorInfo($alpha);
return result;
}
var leakyReluConfig2 = {
kernelName: LeakyRelu,
backendName: "webgl",
kernelFunc: leakyRelu3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prelu.js
init_define_BUILD_VERSION();
var PRELU = `return (a < 0.) ? b * a : a;`;
var PRELU_PACKED = `
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
`;
function prelu3(args) {
const { inputs, backend: backend2 } = args;
const { x, alpha } = inputs;
const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(PRELU_PACKED, x.shape, alpha.shape) : new BinaryOpProgram(PRELU, x.shape, alpha.shape);
return backend2.runWebGLProgram(program, [x, alpha], "float32");
}
var preluConfig2 = {
kernelName: Prelu,
backendName: "webgl",
kernelFunc: prelu3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/kernel_funcs_utils.js
var CHECK_NAN_SNIPPET_UNARY = `if (isnan(x)) return x;`;
var CHECK_NAN_SNIPPET_BINARY = `
if (isnan(a)) return a;
if (isnan(b)) return b;
`;
var CHECK_NAN_SNIPPET_BINARY_PACKED = `
result.r = isNaN.r > 0. ? NAN : result.r;
result.g = isNaN.g > 0. ? NAN : result.g;
result.b = isNaN.b > 0. ? NAN : result.b;
result.a = isNaN.a > 0. ? NAN : result.a;
`;
function unaryKernelFunc2({ opSnippet, packedOpSnippet, cpuKernelImpl, dtype }) {
return ({ inputs, backend: backend2 }) => {
const { x } = inputs;
const webglBackend = backend2;
const $dtype = dtype || x.dtype;
if (webglBackend.shouldExecuteOnCPU([x]) && cpuKernelImpl != null) {
const xData = webglBackend.texData.get(x.dataId);
const outValues = cpuKernelImpl(xData.values, $dtype);
return webglBackend.makeTensorInfo(x.shape, $dtype, outValues);
}
const shouldUsePackedProgram = env().getBool("WEBGL_PACK_UNARY_OPERATIONS") && packedOpSnippet != null;
let program;
if (shouldUsePackedProgram) {
program = new UnaryOpPackedProgram(x.shape, packedOpSnippet);
} else {
program = new UnaryOpProgram(x.shape, opSnippet);
}
return webglBackend.runWebGLProgram(program, [x], $dtype);
};
}
function binaryKernelFunc2({ opSnippet, packedOpSnippet, checkOutOfBounds = false, supportsComplex = false, cpuKernelImpl, dtype }) {
return ({ inputs, backend: backend2 }) => {
const { a, b } = inputs;
const webglBackend = backend2;
if (supportsComplex && a.dtype === "complex64") {
const aData = webglBackend.texData.get(a.dataId);
const bData = webglBackend.texData.get(b.dataId);
const [real4, imag4] = [
[aData.complexTensorInfos.real, bData.complexTensorInfos.real],
[aData.complexTensorInfos.imag, bData.complexTensorInfos.imag]
].map((complexParts) => {
const [aPart, bPart] = complexParts;
const aHandle = {
dataId: aPart.dataId,
dtype: aPart.dtype,
shape: a.shape
};
const bHandle = {
dataId: bPart.dataId,
dtype: bPart.dtype,
shape: b.shape
};
const program2 = new BinaryOpProgram(opSnippet, a.shape, b.shape);
return webglBackend.runWebGLProgram(program2, [aHandle, bHandle], upcastType(aPart.dtype, bPart.dtype));
});
const complexOutput = complex3({ inputs: { real: real4, imag: imag4 }, backend: webglBackend });
webglBackend.disposeIntermediateTensorInfo(real4);
webglBackend.disposeIntermediateTensorInfo(imag4);
return complexOutput;
}
const $dtype = dtype || upcastType(a.dtype, b.dtype);
if ((a.dtype === "string" || b.dtype === "string" || webglBackend.shouldExecuteOnCPU([a, b])) && cpuKernelImpl != null) {
const aVals = webglBackend.texData.get(a.dataId).values;
const bVals = webglBackend.texData.get(b.dataId).values;
const decodedAVals = a.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(aVals) : aVals;
const decodedBVals = a.dtype === "string" ? backend_util_exports.fromUint8ToStringArray(bVals) : bVals;
const [outValues, outShape] = cpuKernelImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype);
const out = webglBackend.makeTensorInfo(outShape, $dtype);
const outData = webglBackend.texData.get(out.dataId);
outData.values = outValues;
return out;
}
const shouldUsePackedProgram = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") && packedOpSnippet != null;
let program;
if (shouldUsePackedProgram) {
program = new BinaryOpPackedProgram(packedOpSnippet, a.shape, b.shape, checkOutOfBounds);
} else {
program = new BinaryOpProgram(opSnippet, a.shape, b.shape);
}
return webglBackend.runWebGLProgram(program, [a, b], $dtype);
};
}
function mapActivationToShaderProgram(activation, packed = false) {
if (activation === "linear") {
if (packed) {
return LINEAR2;
}
return LINEAR;
} else if (activation === "relu") {
if (packed) {
return RELU2;
}
return RELU;
} else if (activation === "elu") {
if (packed) {
return ELU3;
}
return ELU2;
} else if (activation === "relu6") {
if (packed) {
return RELU62;
}
return RELU6;
} else if (activation === "prelu") {
if (packed) {
return PRELU_PACKED;
}
return PRELU;
} else if (activation === "leakyrelu") {
if (packed) {
return LEAKYRELU_PACKED;
}
return LEAKYRELU;
} else if (activation === "sigmoid") {
if (packed) {
return SIGMOID2;
}
return SIGMOID;
}
throw new Error(`Activation ${activation} has not been implemented for the WebGL backend.`);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/mulmat_packed_gpu.js
init_define_BUILD_VERSION();
var MatMulPackedProgram = class {
constructor(aShape, bShape, outputShape, transposeA = false, transposeB = false, addBias = false, activation = null, hasPreluActivation = false, hasLeakyreluActivation = false) {
this.variableNames = ["matrixA", "matrixB"];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
const sharedDim = transposeA ? aShape[1] : aShape[2];
const sharedDimensionPacked = Math.ceil(sharedDim / 2);
const aSample = transposeA ? "i * 2, rc.y" : "rc.y, i * 2";
const bSample = transposeB ? "rc.z, i * 2" : "i * 2, rc.z";
const aSwizzle = transposeA ? ["a.xxyy", "a.zzww"] : ["a.xxzz", "a.yyww"];
const bSwizzle = transposeB ? ["b.xzxz", "b.ywyw"] : ["b.xyxy", "b.zwzw"];
let activationSnippet = "", applyActivationSnippet = "";
if (activation) {
if (hasPreluActivation) {
activationSnippet = `vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${activation}
}`;
} else if (hasLeakyreluActivation) {
activationSnippet = `vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${activation}
}`;
} else {
activationSnippet = `vec4 activation(vec4 x) {
${activation}
}`;
}
applyActivationSnippet = `result = activation(result);`;
}
const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
if (addBias) {
this.variableNames.push("bias");
}
if (hasPreluActivation) {
this.variableNames.push("preluActivationWeights");
}
if (hasLeakyreluActivation) {
this.variableNames.push("leakyreluAlpha");
}
let batchASnippet = "rc.x";
let batchBSnippet = "rc.x";
if (aShape[0] < bShape[0]) {
batchASnippet = `int(min(float(rc.x), ${aShape[0] - 1}.))`;
} else if (bShape[0] < aShape[0]) {
batchBSnippet = `int(min(float(rc.x), ${bShape[0] - 1}.))`;
}
this.userCode = `
${activationSnippet}
// Don't use uniform for sharedDimensionPacked for performance.
const float sharedDimension = ${sharedDimensionPacked}.0;
vec4 dot2x2ARowBCol(ivec3 rc) {
vec4 result = vec4(0);
for (int i = 0; i < ${sharedDimensionPacked}; i++) {
int batchA = ${batchASnippet};
int batchB = ${batchBSnippet};
vec4 a = getMatrixA(batchA, ${aSample});
vec4 b = getMatrixB(batchB, ${bSample});
// These swizzled products need to be separately added.
// See: https://github.com/tensorflow/tfjs/issues/1735
result += (${aSwizzle[0]} * ${bSwizzle[0]});
result += (${aSwizzle[1]} * ${bSwizzle[1]});
}
return result;
}
void main() {
ivec3 rc = getOutputCoords();
vec4 result = dot2x2ARowBCol(rc);
${addBiasSnippet}
${applyActivationSnippet}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/binaryop_complex_gpu.js
init_define_BUILD_VERSION();
var COMPLEX_MULTIPLY = {
REAL: "return areal * breal - aimag * bimag;",
IMAG: "return areal * bimag + aimag * breal;"
};
var BinaryOpComplexProgram = class {
constructor(op2, aShape, bShape) {
this.variableNames = ["AReal", "AImag", "BReal", "BImag"];
this.outputShape = backend_util_exports.assertAndGetBroadcastShape(aShape, bShape);
this.userCode = `
float binaryOpComplex(
float areal, float aimag, float breal, float bimag) {
${op2}
}
void main() {
float areal = getARealAtOutCoords();
float aimag = getAImagAtOutCoords();
float breal = getBRealAtOutCoords();
float bimag = getBImagAtOutCoords();
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multiply.js
var MUL = "return a * b;";
function multiply2(args) {
const { inputs, backend: backend2 } = args;
const { a, b } = inputs;
const dtype = backend_util_exports.upcastType(a.dtype, b.dtype);
if (a.dtype === "complex64") {
const aData = backend2.texData.get(a.dataId);
const bData = backend2.texData.get(b.dataId);
const realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
const imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
const inputs2 = [
{
dataId: aData.complexTensorInfos.real.dataId,
dtype: aData.complexTensorInfos.real.dtype,
shape: a.shape
},
{
dataId: aData.complexTensorInfos.imag.dataId,
dtype: aData.complexTensorInfos.imag.dtype,
shape: a.shape
},
{
dataId: bData.complexTensorInfos.real.dataId,
dtype: bData.complexTensorInfos.real.dtype,
shape: b.shape
},
{
dataId: bData.complexTensorInfos.imag.dataId,
dtype: bData.complexTensorInfos.imag.dtype,
shape: b.shape
}
];
const realPart = backend2.runWebGLProgram(realProgram, inputs2, "float32");
const imagPart = backend2.runWebGLProgram(imagProgram, inputs2, "float32");
const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(realPart);
backend2.disposeIntermediateTensorInfo(imagPart);
return complexOutput;
}
if (backend2.shouldExecuteOnCPU([a, b])) {
const aData = backend2.texData.get(a.dataId);
const bData = backend2.texData.get(b.dataId);
const [outValues, outShape] = multiplyImplCPU(a.shape, b.shape, aData.values, bData.values, dtype);
const out = backend2.makeTensorInfo(outShape, dtype);
const outData = backend2.texData.get(out.dataId);
outData.values = outValues;
return out;
}
let program;
if (env().getBool("WEBGL_PACK_BINARY_OPERATIONS")) {
program = new BinaryOpPackedProgram(MUL, a.shape, b.shape);
} else {
program = new BinaryOpProgram(MUL, a.shape, b.shape);
}
return backend2.runWebGLProgram(program, [a, b], dtype);
}
var multiplyConfig2 = {
kernelName: Multiply,
backendName: "webgl",
kernelFunc: multiply2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reshape.js
init_define_BUILD_VERSION();
function packedReshape(input2, afterShape, backend2) {
const input3DShape = [
getBatchDim(input2.shape),
...getRowsCols(input2.shape)
];
const input3D = {
dtype: input2.dtype,
shape: input3DShape,
dataId: input2.dataId
};
const afterShapeAs3D = [
getBatchDim(afterShape),
...getRowsCols(afterShape)
];
const program = new ReshapePackedProgram(afterShapeAs3D, input3DShape);
const preventEagerUnpackingOfOutput = true;
const customValues = [input3DShape];
const output = backend2.runWebGLProgram(program, [input3D], input2.dtype, customValues, preventEagerUnpackingOfOutput);
return { dataId: output.dataId, shape: afterShape, dtype: output.dtype };
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reshape.js
function reshape3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { shape } = attrs;
const webglBackend = backend2;
const xSize = util_exports.sizeFromShape(x.shape);
const $shape = util_exports.inferFromImplicitShape(shape, xSize);
const $xSize = util_exports.sizeFromShape($shape);
util_exports.assert(xSize === $xSize, () => `The new shape (${$shape}) has ${$xSize} elements and the old shape (${x.shape}) has ${xSize} elements. The new shape and old shape must have the same number of elements.`);
const xTexData = webglBackend.texData.get(x.dataId);
if (xTexData.isPacked && !isReshapeFree(x.shape, $shape) && !(xTexData.texture !== null && isReshapeFree(xTexData.shape, $shape))) {
return packedReshape(x, $shape, webglBackend);
}
webglBackend.incRef(x.dataId);
return { dataId: x.dataId, shape: $shape, dtype: x.dtype };
}
var reshapeConfig2 = {
kernelName: Reshape,
backendName: "webgl",
kernelFunc: reshape3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/mean_gpu.js
init_define_BUILD_VERSION();
var MeanProgram = class {
constructor(reduceInfo, divisor) {
this.variableNames = ["x"];
const { windowSize, batchSize, inSize, outSize } = reduceInfo;
this.outputShape = [batchSize, outSize];
const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
const windowSizeVec4Remainder = windowSize % 4;
let updateSnippet = `sumValue += dot(values, ones);`;
if (divisor != null) {
const denominator = 1 / divisor;
updateSnippet = `sumValue += dot(values * ${util_exports.isInt(denominator) ? denominator.toPrecision(2) : denominator}, ones);`;
}
let checkOutOfBounds = "";
if (inSize % windowSize > 0) {
checkOutOfBounds = `
if (inIdx < 0 || inIdx >= ${inSize}) {
return 0.0;
}
`;
}
this.userCode = `
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${checkOutOfBounds}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${windowSize};
float sumValue = 0.0;
for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${updateSnippet}
}
int inIdx = inOffset + ${windowSizeNearestVec4};
if (${windowSizeVec4Remainder === 1}) {
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
${updateSnippet}
} else if (${windowSizeVec4Remainder === 2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1), 0.0, 0.0);
${updateSnippet}
} else if (${windowSizeVec4Remainder === 3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2), 0.0);
${updateSnippet}
}
setOutput(sumValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/reduce_gpu.js
init_define_BUILD_VERSION();
var ReduceProgram = class {
constructor(reduceInfo, reduceType) {
this.variableNames = ["x"];
const { windowSize, batchSize, inSize, outSize } = reduceInfo;
this.outputShape = [batchSize, outSize];
let initializationValue = "0.0";
let compareOp = ``;
if (reduceType === "prod") {
initializationValue = "1.0";
} else if (reduceType === "min") {
initializationValue = "1.0 / 1e-20";
compareOp = `min`;
} else if (reduceType === "max") {
initializationValue = "-1.0 / 1e-20";
compareOp = `max`;
}
let returnValue = `${reduceType}(${reduceType}(${reduceType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
if (reduceType === "sum") {
returnValue = `sumValue`;
} else if (reduceType === "prod") {
returnValue = `prodValue`;
} else if (reduceType === "all") {
returnValue = `allValue`;
} else if (reduceType === "any") {
returnValue = `anyValue`;
}
const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
const windowSizeVec4Remainder = windowSize % 4;
let updateSnippet = `
if (${reduceType === "sum"}) {
sumValue += dot(values, ones);
} else if (${reduceType === "prod"}) {
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
prodValue *= tmp[0] * tmp[1];
} else {
minMaxValue = ${compareOp}(values, minMaxValue);
if (${reduceType === "min"} || ${reduceType === "max"}) {
minMaxValue = ${compareOp}(values, minMaxValue);
bvec4 isNaN = isnan(values);
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
minMaxValue = vec4(NAN);
}
}
}
`;
let vecType = `vec4`;
if (reduceType === "all") {
initializationValue = "1.0";
updateSnippet = `
bool reducedAllValue = all(values);
float floatedReducedAllValue = float(reducedAllValue);
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
`;
vecType = `bvec4`;
} else if (reduceType === "any") {
initializationValue = "0.0";
updateSnippet = `
bool reducedAnyValue = any(values);
float floatedReducedAnyValue = float(reducedAnyValue);
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
`;
vecType = `bvec4`;
}
let checkOutOfBounds = "";
if (inSize % windowSize > 0) {
checkOutOfBounds = `
if (inIdx < 0 || inIdx >= ${inSize}) {
return initializationValue;
}
`;
}
this.userCode = `
const float initializationValue = ${initializationValue};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float getValue(int batch, int inIdx) {
${checkOutOfBounds}
return getX(batch, inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${windowSize};
vec4 minMaxValue = vec4(${initializationValue});
float prodValue = 1.0;
float sumValue = 0.0;
float allValue = 1.0;
float anyValue = 0.0;
for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
int inIdx = inOffset + i;
${vecType} values = ${vecType}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
${updateSnippet}
}
int inIdx = inOffset + ${windowSizeNearestVec4};
if (${windowSizeVec4Remainder === 1}) {
${vecType} values = ${vecType}(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
${updateSnippet}
} else if (${windowSizeVec4Remainder === 2}) {
${vecType} values = ${vecType}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
${updateSnippet}
} else if (${windowSizeVec4Remainder === 3}) {
${vecType} values = ${vecType}(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
${updateSnippet}
}
setOutput(${returnValue});
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/reduce.js
function getReductionStages(inShape) {
const stages = [];
while (stages.length === 0 || stages[stages.length - 1].outSize !== 1) {
const outSize = stages.length ? stages[stages.length - 1].outSize : inShape[1];
const windowSize = backend_util_exports.computeOptimalWindowSize(outSize);
stages.push({
inSize: outSize,
windowSize,
outSize: Math.ceil(outSize / windowSize)
});
}
return stages;
}
function reduce(x, dtype, reductionType, backend2) {
const reductionStages = getReductionStages(x.shape);
let result = x;
for (let i = 0; i < reductionStages.length; i++) {
const { inSize, windowSize, outSize } = reductionStages[i];
let program;
let previousResult;
if (reductionType === "mean") {
program = i === 0 ? new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, inSize) : new MeanProgram({ windowSize, inSize, batchSize: x.shape[0], outSize });
} else {
program = new ReduceProgram({ windowSize, inSize, batchSize: x.shape[0], outSize }, reductionType);
}
previousResult = result;
result = backend2.runWebGLProgram(program, [result], dtype);
if (previousResult.dataId !== x.dataId) {
backend2.disposeIntermediateTensorInfo(previousResult);
}
}
return result;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_gpu.js
init_define_BUILD_VERSION();
var TransposeProgram = class {
constructor(aShape, newDim) {
this.variableNames = ["A"];
const outputShape = new Array(aShape.length);
for (let i = 0; i < outputShape.length; i++) {
outputShape[i] = aShape[newDim[i]];
}
this.outputShape = outputShape;
this.rank = outputShape.length;
const dtype = getCoordsDataType(this.rank);
const switched = getSwitchedCoords(newDim);
this.userCode = `
void main() {
${dtype} resRC = getOutputCoords();
setOutput(getA(${switched}));
}
`;
}
};
function getSwitchedCoords(newDim) {
const rank = newDim.length;
if (rank > 6) {
throw Error(`Transpose for rank ${rank} is not yet supported`);
}
const originalOrder = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u", "resRC.v"];
const switchedCoords = new Array(rank);
for (let i = 0; i < newDim.length; i++) {
switchedCoords[newDim[i]] = originalOrder[i];
}
return switchedCoords.join();
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/transpose_packed_gpu.js
init_define_BUILD_VERSION();
var TransposePackedProgram = class {
constructor(aShape, newDim) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
const outputShape = new Array(aShape.length);
for (let i = 0; i < outputShape.length; i++) {
outputShape[i] = aShape[newDim[i]];
}
this.outputShape = outputShape;
this.rank = outputShape.length;
if (this.rank > 6) {
throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);
}
const dtype = getCoordsDataType(this.rank);
const outputOrder = getVecChannels("rc", this.rank);
const switchedOrder = new Array(this.rank);
for (let i = 0; i < newDim.length; i++) {
switchedOrder[newDim[i]] = outputOrder[i];
}
const innerDims = `vec2(${switchedOrder.slice(-2).join()})`;
const nextColumn = `++${outputOrder[this.rank - 1]} < ${outputShape[this.rank - 1]}`;
const getc = `getChannel(getA(${switchedOrder.join()}), ${innerDims})`;
this.userCode = `
void main() {
${dtype} rc = getOutputCoords();
vec4 result = vec4(0.);
result[0] = ${getc};
if(${nextColumn}) {
result[1] = ${getc};
}
--${outputOrder[this.rank - 1]};
if(++${outputOrder[this.rank - 2]} < ${outputShape[this.rank - 2]}) {
result[2] = ${getc};
if(${nextColumn}) {
result[3] = ${getc};
}
}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose_impl.js
function transposeImpl2(x, perm, backend2) {
const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new TransposePackedProgram(x.shape, perm) : new TransposeProgram(x.shape, perm);
return backend2.runWebGLProgram(program, [x], x.dtype);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum_impl.js
function sumImpl(x, axis, keepDims, backend2) {
const reductionIndices = axis;
const xRank = x.shape.length;
const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
const sumInputIsTransposed = permutedAxes != null;
let sumInput = x;
if (sumInputIsTransposed) {
sumInput = transposeImpl2(x, permutedAxes, backend2);
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
}
backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
const [sumOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(sumInput.shape, axes);
let outShape = sumOutShape;
if (keepDims) {
outShape = backend_util_exports.expandShapeToKeepDim(sumOutShape, origAxes);
}
const inSize = util_exports.sizeFromShape(reduceShape);
const xSize = util_exports.sizeFromShape(x.shape);
const batchSize = xSize / inSize;
const reshapedInput = reshape3({ inputs: { x: sumInput }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
const outType = sumOutType(x.dtype);
const reduced = reduce(reshapedInput, outType, "sum", backend2);
const out = reshape3({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(reshapedInput);
backend2.disposeIntermediateTensorInfo(reduced);
if (sumInputIsTransposed) {
backend2.disposeIntermediateTensorInfo(sumInput);
}
return out;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sum.js
function sum4(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
return sumImpl(x, axis, keepDims, backend2);
}
var sumConfig2 = {
kernelName: Sum,
backendName: "webgl",
kernelFunc: sum4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transpose.js
init_define_BUILD_VERSION();
function transpose3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { perm } = attrs;
const webglBackend = backend2;
const xRank = x.shape.length;
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = x.shape[perm[i]];
}
let out;
if (webglBackend.shouldExecuteOnCPU([x])) {
const xTexData = webglBackend.texData.get(x.dataId);
const values = xTexData.values;
const outValues = transposeImplCPU(values, x.shape, x.dtype, perm, newShape);
out = webglBackend.makeTensorInfo(newShape, x.dtype);
const outData = webglBackend.texData.get(out.dataId);
outData.values = outValues;
} else {
out = transposeImpl2(x, perm, webglBackend);
}
return out;
}
var transposeConfig2 = {
kernelName: Transpose,
backendName: "webgl",
kernelFunc: transpose3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul_impl.js
var MATMUL_SHARED_DIM_THRESHOLD = 1e3;
function batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
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 outShapeOuterDims = broadcast_util_exports.assertAndGetBroadcastShape(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 = reshape3({ inputs: { x: a }, backend: backend2, attrs: { shape: a3dShape } });
const b3d = reshape3({ inputs: { x: b }, backend: backend2, attrs: { shape: b3dShape } });
const intermediates = [a3d, b3d];
const batchDim = Math.max(batchDimA, batchDimB);
const sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2];
const hasBias = bias != null;
const hasPreluActivationWeights = preluActivationWeights != null;
const hasLeakyreluAlpha = activation === "leakyrelu";
const fusedActivation = activation != null ? mapActivationToShaderProgram(activation, true) : null;
const containsFusedOps = hasBias || hasPreluActivationWeights || hasLeakyreluAlpha || fusedActivation != null;
let out;
if ((outerShapeA === 1 || outerShapeB === 1) && sharedDim > MATMUL_SHARED_DIM_THRESHOLD && containsFusedOps === false) {
let aVec = a3d;
let bVec = b3d;
if (transposeA) {
aVec = transpose3({ inputs: { x: a3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });
intermediates.push(aVec);
}
if (transposeB) {
bVec = transpose3({ inputs: { x: b3d }, backend: backend2, attrs: { perm: [0, 2, 1] } });
intermediates.push(bVec);
}
const shouldReshapeA = outerShapeB !== 1;
const shouldReshapeB = outerShapeB === 1;
let aVec3d = aVec;
if (shouldReshapeA) {
aVec3d = reshape3({
inputs: { x: aVec },
backend: backend2,
attrs: { shape: [batchDim, sharedDim, 1] }
});
intermediates.push(aVec3d);
}
const axis = outerShapeB === 1 ? 2 : 1;
let bVec3d = bVec;
if (shouldReshapeB) {
bVec3d = reshape3({
inputs: { x: bVec },
backend: backend2,
attrs: { shape: [batchDim, 1, sharedDim] }
});
intermediates.push(bVec3d);
}
const product = multiply2({ inputs: { a: aVec3d, b: bVec3d }, backend: backend2 });
out = sum4({ inputs: { x: product }, backend: backend2, attrs: { axis, keepDims: true } });
intermediates.push(product);
} else {
const dtype = upcastType(a.dtype, b.dtype);
const program = new MatMulPackedProgram(a3dShape, b3dShape, [batchDim, outerShapeA, outerShapeB], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
const inputs = [a3d, b3d];
if (bias != null) {
inputs.push(bias);
}
if (hasPreluActivationWeights) {
inputs.push(preluActivationWeights);
}
if (hasLeakyreluAlpha) {
const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
inputs.push($leakyreluAlpha);
intermediates.push($leakyreluAlpha);
}
out = backend2.runWebGLProgram(program, inputs, dtype);
}
const outReshaped = reshape3({ inputs: { x: out }, backend: backend2, attrs: { shape: outShape } });
intermediates.push(out);
for (const i of intermediates) {
backend2.disposeIntermediateTensorInfo(i);
}
return outReshaped;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/_FusedMatMul.js
function _fusedMatMul2(args) {
const { inputs, backend: backend2, attrs } = args;
const { a, b, bias, preluActivationWeights } = inputs;
const { transposeA, transposeB, activation, leakyreluAlpha } = attrs;
return batchMatMulImpl({
a,
b,
transposeA,
transposeB,
backend: backend2,
bias,
preluActivationWeights,
leakyreluAlpha,
activation
});
}
var _fusedMatMulConfig2 = {
kernelName: _FusedMatMul,
backendName: "webgl",
kernelFunc: _fusedMatMul2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Abs.js
init_define_BUILD_VERSION();
var ABS2 = `return abs(x);`;
function abs3(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
if (backend2.shouldExecuteOnCPU([x]) && x.dtype !== "complex64") {
const xData = backend2.texData.get(x.dataId);
const outValues = simpleAbsImplCPU(xData.values);
return backend2.makeTensorInfo(x.shape, x.dtype, outValues);
}
let program;
if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
program = new UnaryOpPackedProgram(x.shape, ABS2);
} else {
program = new UnaryOpProgram(x.shape, ABS2);
}
return backend2.runWebGLProgram(program, [x], x.dtype);
}
var absConfig2 = {
kernelName: Abs,
backendName: "webgl",
kernelFunc: abs3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acos.js
init_define_BUILD_VERSION();
var ACOS = CHECK_NAN_SNIPPET + `
if (abs(x) > 1.) {
return NAN;
}
return acos(x);
`;
var acos3 = unaryKernelFunc2({ opSnippet: ACOS });
var acosConfig2 = {
kernelName: Acos,
backendName: "webgl",
kernelFunc: acos3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Acosh.js
init_define_BUILD_VERSION();
var ACOSH = CHECK_NAN_SNIPPET + `
if (x < 1.0) return NAN;
return log(x + sqrt(x * x - 1.0));`;
var acosh3 = unaryKernelFunc2({ opSnippet: ACOSH });
var acoshConfig2 = {
kernelName: Acosh,
backendName: "webgl",
kernelFunc: acosh3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Add.js
init_define_BUILD_VERSION();
var ADD = "return a + b;";
var addKernelFunc = binaryKernelFunc2({
opSnippet: ADD,
packedOpSnippet: ADD,
supportsComplex: true,
cpuKernelImpl: addImplCPU
});
var addConfig2 = {
kernelName: Add,
backendName: "webgl",
kernelFunc: addKernelFunc
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_gpu.js
init_define_BUILD_VERSION();
var AddNProgram = class {
constructor(outputShape, shapes) {
this.outputShape = [];
this.outputShape = outputShape;
this.variableNames = shapes.map((_, i) => `T${i}`);
const snippets = [];
this.variableNames.forEach((variable2) => {
snippets.push(`float v${variable2} = get${variable2}AtOutCoords();`);
});
const operation = this.variableNames.map((variable2) => {
return `v${variable2}`;
}).join(" + ");
this.userCode = `
void main() {
${snippets.join("\n ")}
float result = ${operation};
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/addn_packed_gpu.js
init_define_BUILD_VERSION();
var AddNPackedProgram = class {
constructor(outputShape, shapes) {
this.outputShape = [];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = outputShape;
this.variableNames = shapes.map((_, i) => `T${i}`);
const snippets = [];
this.variableNames.forEach((variable2) => {
snippets.push(`vec4 v${variable2} = get${variable2}AtOutCoords();`);
});
const operation = this.variableNames.map((variable2) => {
return `v${variable2}`;
}).join(" + ");
this.userCode = `
void main() {
${snippets.join("\n ")}
vec4 result = ${operation};
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AddN.js
function addN2(args) {
const { inputs, backend: backend2 } = args;
const tensors = inputs;
if (tensors.length === 1) {
return identity2({ inputs: { x: tensors[0] }, backend: backend2 });
}
if (tensors.length > env().get("WEBGL_MAX_TEXTURES_IN_SHADER")) {
const midIndex = Math.floor(tensors.length / 2);
const leftSide = addN2({ inputs: tensors.slice(0, midIndex), backend: backend2 });
const rightSide = addN2({ inputs: tensors.slice(midIndex), backend: backend2 });
return addN2({ inputs: [leftSide, rightSide], backend: backend2 });
}
const dtype = tensors.map((t) => t.dtype).reduce((d1, d2) => upcastType(d1, d2));
const shapes = tensors.map((t) => t.shape);
const usePackedOp = env().getBool("WEBGL_PACK");
const program = usePackedOp ? new AddNPackedProgram(tensors[0].shape, shapes) : new AddNProgram(tensors[0].shape, shapes);
return backend2.runWebGLProgram(program, tensors, dtype);
}
var addNConfig2 = {
kernelName: AddN,
backendName: "webgl",
kernelFunc: addN2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/All.js
init_define_BUILD_VERSION();
function all3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
const xRank = x.shape.length;
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
let permutedX = x;
if (permutedAxes != null) {
permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
}
backend_util_exports.assertAxesAreInnerMostDims("all", axes, xRank);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
const inSize = util_exports.sizeFromShape(reduceShape);
const a2D = reshape3({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
const reduced = reduce(a2D, a2D.dtype, "all", backend2);
let res;
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
} else {
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
}
backend2.disposeIntermediateTensorInfo(a2D);
backend2.disposeIntermediateTensorInfo(reduced);
if (permutedAxes != null) {
backend2.disposeIntermediateTensorInfo(permutedX);
}
return res;
}
var allConfig2 = {
kernelName: All,
backendName: "webgl",
kernelFunc: all3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Any.js
init_define_BUILD_VERSION();
function any3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
const xRank = x.shape.length;
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
let permutedX = x;
if (permutedAxes != null) {
permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
}
backend_util_exports.assertAxesAreInnerMostDims("any", axes, xRank);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
const inSize = util_exports.sizeFromShape(reduceShape);
const a2D = reshape3({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
const reduced = reduce(a2D, a2D.dtype, "any", backend2);
let res;
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
} else {
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
}
backend2.disposeIntermediateTensorInfo(a2D);
backend2.disposeIntermediateTensorInfo(reduced);
if (permutedAxes != null) {
backend2.disposeIntermediateTensorInfo(permutedX);
}
return res;
}
var anyConfig2 = {
kernelName: Any,
backendName: "webgl",
kernelFunc: any3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_gpu.js
init_define_BUILD_VERSION();
var ArgMinMaxProgram = class {
constructor(reduceInfo, op2, firstPass) {
this.variableNames = ["A"];
const { windowSize, batchSize, outSize } = reduceInfo;
if (!firstPass) {
this.variableNames.push("bestIndicesA");
}
this.outputShape = [batchSize, outSize];
const compOp = op2 === "max" ? ">" : "<";
const indexSnippet = firstPass ? "inOffset + i;" : "round(getBestIndicesA(batch, inOffset + i));";
this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = outIdx * ${windowSize};
int bestIndex = inOffset;
float bestValue = getA(batch, bestIndex);
for (int i = 0; i < ${windowSize}; i++) {
int inIdx = ${indexSnippet};
float candidate = getA(batch, inIdx);
if (candidate ${compOp} bestValue) {
bestValue = candidate;
bestIndex = inIdx;
}
}
setOutput(float(bestIndex));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/argminmax_packed_gpu.js
init_define_BUILD_VERSION();
var ArgMinMaxPackedProgram = class {
constructor(shape, windowSize, op2, firstPass) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
util_exports.assert(shape.length > 2, () => `Packed arg${op2.charAt(0).toUpperCase() + op2.slice(1)} supports only inputs with rank above 2.`);
const inSize = shape[shape.length - 1];
const outSize = Math.ceil(inSize / windowSize);
this.outputShape = shape.slice(0, -1);
if (outSize > 1) {
this.outputShape.push(outSize);
}
if (!firstPass) {
this.variableNames.push("bestIndicesA");
}
const outShape = this.outputShape;
const rank = outShape.length;
const dtype = getCoordsDataType(rank);
const coords2 = getChannels("coords", rank);
let sourceLocSetup;
let sourceRank;
if (outSize === 1) {
sourceRank = rank + 1;
const sourceLocDType = getCoordsDataType(sourceRank);
sourceLocSetup = `
${sourceLocDType} sourceLocR = ${sourceLocDType}(${coords2.join()}, 0);
++${coords2[rank - 1]};
${sourceLocDType} sourceLocG = ${sourceLocDType}(${coords2.join()}, 0);
++${coords2[rank - 2]};
${sourceLocDType} sourceLocA = ${sourceLocDType}(${coords2.join()}, 0);
--${coords2[rank - 1]};
${sourceLocDType} sourceLocB = ${sourceLocDType}(${coords2.join()}, 0);
--${coords2[rank - 2]};`;
} else {
sourceRank = rank;
sourceLocSetup = `
${dtype} sourceLocR = coords;
++${coords2[rank - 1]};
${dtype} sourceLocG = coords;
++${coords2[rank - 2]};
${dtype} sourceLocA = coords;
--${coords2[rank - 1]};
${dtype} sourceLocB = coords;
--${coords2[rank - 2]};`;
}
const channels = ["x", "y", "z", "w", "u", "v"].slice(0, sourceRank);
const inChannel = "." + channels[sourceRank - 1];
const intChannels = channels.map((x) => "int " + x);
const srcRCoords = getChannels("sourceLocR", sourceRank - 1).concat("inIdx.r");
const srcGCoords = getChannels("sourceLocG", sourceRank - 1).concat("inIdx.g");
const srcBCoords = getChannels("sourceLocB", sourceRank - 1).concat("inIdx.b");
const srcACoords = getChannels("sourceLocA", sourceRank - 1).concat("inIdx.a");
const compOp = op2 === "max" ? "greaterThan" : "lessThan";
const fetchCandidateIdx = firstPass ? "" : `
inIdx = round(vec4(getBestIndicesAChannel(${srcRCoords.join()}),
getBestIndicesAChannel(${srcGCoords.join()}),
getBestIndicesAChannel(${srcBCoords.join()}),
getBestIndicesAChannel(${srcACoords.join()})));`;
const fetchValue = `vec4(
getAChannel(${srcRCoords.join()}),
hasNextCol ? getAChannel(${srcGCoords.join()}) : 0.,
hasNextRow ? getAChannel(${srcBCoords.join()}) : 0.,
hasNextRow && hasNextCol ? getAChannel(${srcACoords.join()}) : 0.)`;
const getBestIndicesAChannelSnippet = firstPass ? "" : `
float getBestIndicesAChannel(${intChannels.join()}) {
return getChannel(getBestIndicesA(${channels.join()}),
vec2(${channels.slice(-2).join()}));
}`;
this.userCode = `
float getAChannel(${intChannels.join()}) {
return getChannel(getA(${channels.join()}),
vec2(${channels.slice(-2).join()}));
}
${getBestIndicesAChannelSnippet}
void main() {
${dtype} coords = getOutputCoords();
bool hasNextCol = ${coords2[rank - 1]} < ${outShape[rank - 1] - 1};
bool hasNextRow = ${coords2[rank - 2]} < ${outShape[rank - 2] - 1};
${sourceLocSetup}
ivec4 srcIdx = ivec4(sourceLocR${inChannel}, sourceLocG${inChannel},
sourceLocB${inChannel}, sourceLocA${inChannel}) * ${windowSize};
ivec4 inIdx = srcIdx;
vec4 bestIndex = vec4(inIdx);
vec4 bestValue = ${fetchValue};
for (int i = 0; i < ${windowSize}; i++) {
inIdx = srcIdx;
${fetchCandidateIdx}
vec4 candidate = ${fetchValue};
bvec4 nan = isnan(candidate);
bvec4 replace = bvec4(
vec4(${compOp}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
replace.y ? candidate.y : bestValue.y,
replace.z ? candidate.z : bestValue.z,
replace.w ? candidate.w : bestValue.w);
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
srcIdx++;
}
setOutput(bestIndex);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/arg_min_max.js
function argReduce(backend2, x, reduceType, bestIndicesA = null) {
let batchSize = x.shape[0];
let inSize = x.shape[1];
if (bestIndicesA != null) {
batchSize = bestIndicesA.shape[0];
inSize = bestIndicesA.shape[1];
}
const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
const reduceInfo = { windowSize, inSize, batchSize, outSize: Math.ceil(inSize / windowSize) };
const program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
const inputs = [x];
if (bestIndicesA != null) {
inputs.push(bestIndicesA);
}
const output = backend2.runWebGLProgram(program, inputs, "int32");
if (output.shape[1] === 1) {
return output;
}
const result = argReduce(backend2, x, reduceType, output);
backend2.disposeIntermediateTensorInfo(output);
return result;
}
function argReducePacked(backend2, x, reduceType, bestIndicesA = null) {
const inShape = bestIndicesA != null ? bestIndicesA.shape : x.shape;
const inSize = inShape[inShape.length - 1];
const windowSize = backend_util_exports.computeOptimalWindowSize(inSize);
const program = new ArgMinMaxPackedProgram(inShape, windowSize, reduceType, bestIndicesA == null);
const inputs = bestIndicesA == null ? [x] : [x, bestIndicesA];
const output = backend2.runWebGLProgram(program, inputs, "int32");
if (output.shape.length === x.shape.length) {
const result = argReducePacked(backend2, x, reduceType, output);
backend2.disposeIntermediateTensorInfo(output);
return result;
}
return output;
}
function argMinMaxReduce(backend2, x, axis, reduceType) {
const axes = [axis];
backend_util_exports.assertAxesAreInnerMostDims("arg" + reduceType.charAt(0).toUpperCase() + reduceType.slice(1), axes, x.shape.length);
if (!env().getBool("WEBGL_PACK_REDUCE") || x.shape.length <= 2) {
const intermediateTensorInfos = [];
const xtexData = backend2.texData.get(x.dataId);
const xIsPacked = xtexData !== null && xtexData.isPacked;
let xUnPacked = x;
if (xIsPacked) {
xUnPacked = backend2.unpackTensor(x);
intermediateTensorInfos.push(xUnPacked);
}
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(xUnPacked.shape, axes);
const inSize = util_exports.sizeFromShape(reduceShape);
const a2D = reshape3({ inputs: { x: xUnPacked }, backend: backend2, attrs: { shape: [-1, inSize] } });
intermediateTensorInfos.push(a2D);
const reduced = argReduce(backend2, a2D, reduceType);
intermediateTensorInfos.push(reduced);
const reshaped = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return reshaped;
}
return argReducePacked(backend2, x, reduceType);
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMax.js
function argMax3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis } = attrs;
let axes = util_exports.parseAxisParam(axis, x.shape);
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
const intermediateTensorInfos = [];
if (permutedAxes != null) {
$x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
intermediateTensorInfos.push($x);
axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
}
backend_util_exports.assertAxesAreInnerMostDims("argMax", [axes[0]], $x.shape.length);
const out = argMinMaxReduce(backend2, $x, axes[0], "max");
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return out;
}
var argMaxConfig2 = {
kernelName: ArgMax,
backendName: "webgl",
kernelFunc: argMax3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ArgMin.js
init_define_BUILD_VERSION();
function argMin3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis } = attrs;
let axes = util_exports.parseAxisParam(axis, x.shape);
const permutedAxes = backend_util_exports.getAxesPermutation(axes, x.shape.length);
let $x = x;
const intermediateTensorInfos = [];
if (permutedAxes != null) {
$x = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
intermediateTensorInfos.push($x);
axes = backend_util_exports.getInnerMostAxes(axes.length, $x.shape.length);
}
backend_util_exports.assertAxesAreInnerMostDims("argMin", [axes[0]], $x.shape.length);
const out = argMinMaxReduce(backend2, $x, axes[0], "min");
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return out;
}
var argMinConfig2 = {
kernelName: ArgMin,
backendName: "webgl",
kernelFunc: argMin3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asin.js
init_define_BUILD_VERSION();
var ASIN = CHECK_NAN_SNIPPET + `
if (abs(x) > 1.) {
return NAN;
}
return asin(x);
`;
var asin3 = unaryKernelFunc2({ opSnippet: ASIN });
var asinConfig2 = {
kernelName: Asin,
backendName: "webgl",
kernelFunc: asin3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Asinh.js
init_define_BUILD_VERSION();
var ASINH = CHECK_NAN_SNIPPET + `return log(x + sqrt(x * x + 1.0));`;
var asinh3 = unaryKernelFunc2({ opSnippet: ASINH });
var asinhConfig2 = {
kernelName: Asinh,
backendName: "webgl",
kernelFunc: asinh3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan.js
init_define_BUILD_VERSION();
var ATAN = CHECK_NAN_SNIPPET + `
return atan(x);
`;
var atan4 = unaryKernelFunc2({ opSnippet: ATAN });
var atanConfig2 = {
kernelName: Atan,
backendName: "webgl",
kernelFunc: atan4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atan2.js
init_define_BUILD_VERSION();
var ATAN2 = CHECK_NAN_SNIPPET_BINARY + `
return atan(a, b);
`;
var ATAN2_PACKED = `
vec4 result = atan(a, b);
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
` + CHECK_NAN_SNIPPET_BINARY_PACKED + `
return result;
`;
var atan23 = binaryKernelFunc2({ opSnippet: ATAN2, packedOpSnippet: ATAN2_PACKED });
var atan2Config2 = {
kernelName: Atan2,
backendName: "webgl",
kernelFunc: atan23
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Atanh.js
init_define_BUILD_VERSION();
var ATANH = CHECK_NAN_SNIPPET + `
if ((x < -1.0) || (x > 1.0)) return NAN;
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;
var atanh3 = unaryKernelFunc2({ opSnippet: ATANH });
var atanhConfig2 = {
kernelName: Atanh,
backendName: "webgl",
kernelFunc: atanh3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/pool_gpu.js
init_define_BUILD_VERSION();
var Pool2DProgram = class {
constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
this.variableNames = ["x"];
if (poolType === "avg" && computePositions) {
throw new Error("Cannot compute positions for average pool.");
}
const filterWidth = convInfo.filterWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
this.outputShape = convInfo.outShape;
const isAvgPool = poolType === "avg";
const batchFlattenPositionStr = `((batch * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
const flattenPositionStr = `(xR * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + d`;
let initializationValue = "0.0";
if (!isAvgPool) {
initializationValue = "-1.0 / 1e-20";
}
if (computePositions) {
const compareOp2 = ">=";
this.userCode = `
const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
const ivec2 pads = ivec2(${padTop}, ${padLeft});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
float avgValue = 0.0;
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int wC = 0; wC < ${effectiveFilterWidth};
wC += ${dilationWidth}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
float value = getX(batch, xR, xC, d);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${compareOp2} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${flattenPositions ? includeBatchInIndex ? batchFlattenPositionStr : flattenPositionStr : `wR * ${effectiveFilterWidth} + wC`};
}
}
}
setOutput(float(minMaxPosition));
}
`;
return;
}
const compareOp = "max";
let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
if (poolType === "avg") {
returnValue = `avgValue / count`;
}
const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
const filterWidthVec4Remainder = filterWidth % 4;
const updateSnippet = `
if (${isAvgPool}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${compareOp}(values, minMaxValue);
}
`;
this.userCode = `
const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
const ivec2 pads = ivec2(${padTop}, ${padLeft});
const float initializationValue = ${initializationValue};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xR, int xC, int d) {
if (xC < 0 || xC >= ${convInfo.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xR, xC, d);
}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d = coords[3];
ivec2 xRCCorner = coords.yz * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// max/min x(?, ?, d) to get y(yR, yC, d).
// ? = to be determined
vec4 minMaxValue = vec4(${initializationValue});
float avgValue = 0.0;
count = 0.0;
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
int xC = xCCorner + wC * ${dilationWidth};
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${dilationWidth}, d),
getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
getValue(batch, xR, xC + 3 * ${dilationWidth}, d)
);
${updateSnippet}
}
int xC = xCCorner + ${filterWidthNearestVec4};
if (${filterWidthVec4Remainder === 1}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
initializationValue,
initializationValue,
initializationValue
);
${updateSnippet}
} else if (${filterWidthVec4Remainder === 2}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${dilationWidth}, d),
initializationValue,
initializationValue
);
${updateSnippet}
} else if (${filterWidthVec4Remainder === 3}) {
vec4 values = vec4(
getValue(batch, xR, xC, d),
getValue(batch, xR, xC + ${dilationWidth}, d),
getValue(batch, xR, xC + 2 * ${dilationWidth}, d),
initializationValue
);
${updateSnippet}
}
}
setOutput(${returnValue});
}
`;
}
};
var Pool3DProgram = class {
constructor(convInfo, poolType, computePositions, flattenPositions = false, includeBatchInIndex = false) {
this.variableNames = ["x"];
if (poolType === "avg" && computePositions) {
throw new Error("Cannot compute positions for average pool.");
}
const filterWidth = convInfo.filterWidth;
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = convInfo.padInfo.front;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
this.outputShape = convInfo.outShape;
const isAvgPool = poolType === "avg";
let initializationValue = "0.0";
if (!isAvgPool) {
initializationValue = "-1.0 / 1e-20";
}
if (computePositions) {
const compareOp2 = ">=";
this.userCode = `
const ivec3 strides =
ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
// ? = to be determined
float minMaxValue = 0.0;
float minMaxValueFound = 0.0;
int minMaxPosition = 0;
for (int wD = 0; wD < ${effectiveFilterDepth};
wD += ${dilationDepth}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${convInfo.inDepth}) {
continue;
}
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int wC = 0; wC < ${effectiveFilterWidth};
wC += ${dilationWidth}) {
int xC = xCCorner + wC;
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
float value = getX(batch, xD, xR, xC, ch);
// If a min / max value has already been found, use it. If not,
// use the current value.
float currMinMaxValue = mix(
value, minMaxValue, minMaxValueFound);
if (value ${compareOp2} currMinMaxValue) {
minMaxValue = value;
minMaxValueFound = 1.0;
minMaxPosition = ${flattenPositions ? includeBatchInIndex ? `(((batch * ${convInfo.inDepth} + xD) * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `((xD * ${convInfo.inHeight} + xR) * ${convInfo.inWidth} + xC) * ${convInfo.inChannels} + ch` : `wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
wR * ${effectiveFilterWidth} + wC`};
}
}
}
}
setOutput(float(minMaxPosition));
}
`;
return;
}
const compareOp = "max";
let returnValue = `${poolType}(${poolType}(${poolType}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;
if (poolType === "avg") {
returnValue = `avgValue / count`;
}
const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
const filterWidthVec4Remainder = filterWidth % 4;
const updateSnippet = `
if (${isAvgPool}) {
avgValue += dot(values, ones);
} else {
minMaxValue = ${compareOp}(values, minMaxValue);
}
`;
this.userCode = `
const ivec3 strides =
ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
const float initializationValue = ${initializationValue};
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
float count = 0.0;
float getValue(int batch, int xD, int xR, int xC, int ch) {
if (xC < 0 || xC >= ${convInfo.inWidth}) {
return initializationValue;
}
count += 1.0;
return getX(batch, xD, xR, xC, ch);
}
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xDCorner = xCorner.x;
int xRCorner = xCorner.y;
int xCCorner = xCorner.z;
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
// ? = to be determined
vec4 minMaxValue = vec4(${initializationValue});
float avgValue = 0.0;
count = 0.0;
for (int wD = 0; wD < ${effectiveFilterDepth};
wD += ${dilationDepth}) {
int xD = xDCorner + wD;
if (xD < 0 || xD >= ${convInfo.inDepth}) {
continue;
}
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
int xR = xRCorner + wR;
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {
int xC = xCCorner + wC * ${dilationWidth};
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
getValue(batch, xD, xR, xC + 3 * ${dilationWidth}, ch)
);
${updateSnippet}
}
int xC = xCCorner + ${filterWidthNearestVec4};
if (${filterWidthVec4Remainder === 1}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
initializationValue,
initializationValue,
initializationValue
);
${updateSnippet}
} else if (${filterWidthVec4Remainder === 2}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
initializationValue,
initializationValue
);
${updateSnippet}
} else if (${filterWidthVec4Remainder === 3}) {
vec4 values = vec4(
getValue(batch, xD, xR, xC, ch),
getValue(batch, xD, xR, xC + ${dilationWidth}, ch),
getValue(batch, xD, xR, xC + 2 * ${dilationWidth}, ch),
initializationValue
);
${updateSnippet}
}
}
setOutput(${returnValue});
}
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool.js
function avgPool3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
assertNotComplex2(x, "avgPool");
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const dilations = 1;
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in avgPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode);
if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
return identity2({ inputs: { x }, backend: backend2 });
}
const avgPoolProgram = new Pool2DProgram(convInfo, "avg", false);
return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
}
var avgPoolConfig2 = {
kernelName: AvgPool,
backendName: "webgl",
kernelFunc: avgPool3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3D.js
init_define_BUILD_VERSION();
function avgPool3D2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { filterSize, strides, pad: pad2, dimRoundingMode, dataFormat } = attrs;
const dilations = [1, 1, 1];
const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode, dataFormat);
const avgPoolProgram = new Pool3DProgram(convInfo, "avg", false);
return backend2.runWebGLProgram(avgPoolProgram, [x], "float32");
}
var avgPool3DConfig2 = {
kernelName: AvgPool3D,
backendName: "webgl",
kernelFunc: avgPool3D2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/avg_pool_backprop_gpu.js
init_define_BUILD_VERSION();
var AvgPool2DBackpropProgram = class {
constructor(convInfo) {
this.variableNames = ["dy"];
this.outputShape = convInfo.inShape;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const avgMultiplier = 1 / (filterHeight * filterWidth);
this.userCode = `
const ivec2 pads = ivec2(${padTop}, ${padLeft});
const float avgMultiplier = float(${avgMultiplier});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${effectiveFilterWidth};
wC+= ${dilationWidth}) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
dotProd += dyValue * avgMultiplier;
}
}
setOutput(dotProd);
}
`;
}
};
var AvgPool3DBackpropProgram = class {
constructor(convInfo) {
this.variableNames = ["dy"];
this.outputShape = convInfo.inShape;
const filterDepth = convInfo.filterDepth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth);
this.userCode = `
const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
const float avgMultiplier = float(${avgMultiplier});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${effectiveFilterDepth};
wD += ${dilationDepth}) {
float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${effectiveFilterWidth};
wC += ${dilationWidth}) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
dotProd += dyValue * avgMultiplier;
}
}
}
setOutput(dotProd);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPool3DGrad.js
function avgPool3DGrad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2 } = inputs;
const x = input2;
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const dilations = [1, 1, 1];
const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode);
const avgPoolBackpropProgram = new AvgPool3DBackpropProgram(convInfo);
return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
}
var avgPool3DGradConfig3 = {
kernelName: AvgPool3DGrad,
backendName: "webgl",
kernelFunc: avgPool3DGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/AvgPoolGrad.js
init_define_BUILD_VERSION();
function avgPoolGrad3(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2 } = inputs;
const x = input2;
assertNotComplex2([dy, input2], "avgPoolGrad");
const { filterSize, strides, pad: pad2 } = attrs;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad2);
const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
return backend2.runWebGLProgram(avgPoolBackpropProgram, [dy], x.dtype);
}
var avgPoolGradConfig3 = {
kernelName: AvgPoolGrad,
backendName: "webgl",
kernelFunc: avgPoolGrad3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchMatMul.js
init_define_BUILD_VERSION();
function batchMatMul2(args) {
const { inputs, backend: backend2, attrs } = args;
const { a, b } = inputs;
const { transposeA, transposeB } = attrs;
return batchMatMulImpl({ a, b, transposeA, transposeB, backend: backend2 });
}
var batchMatMulConfig2 = {
kernelName: BatchMatMul,
backendName: "webgl",
kernelFunc: batchMatMul2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_gpu.js
init_define_BUILD_VERSION();
var BatchNormProgram = class {
constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
this.outputShape = [];
this.variableNames = ["x", "mean", "variance"];
backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
let offsetSnippet = "0.0";
if (offsetShape != null) {
backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
this.variableNames.push("offset");
offsetSnippet = "getOffsetAtOutCoords()";
}
let scaleSnippet = "1.0";
if (scaleShape != null) {
backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
this.variableNames.push("scale");
scaleSnippet = "getScaleAtOutCoords()";
}
this.outputShape = xShape;
this.userCode = `
void main() {
float x = getXAtOutCoords();
float mean = getMeanAtOutCoords();
float variance = getVarianceAtOutCoords();
float offset = ${offsetSnippet};
float scale = ${scaleSnippet};
float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/batchnorm_packed_gpu.js
init_define_BUILD_VERSION();
var BatchNormPackedProgram = class {
constructor(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
this.packedInputs = true;
this.packedOutput = true;
this.variableNames = ["x", "mean", "variance"];
backend_util_exports.assertAndGetBroadcastShape(xShape, meanShape);
backend_util_exports.assertAndGetBroadcastShape(xShape, varianceShape);
let offsetSnippet = "vec4(0.0)";
if (offsetShape != null) {
backend_util_exports.assertAndGetBroadcastShape(xShape, offsetShape);
this.variableNames.push("offset");
offsetSnippet = "getOffsetAtOutCoords()";
}
let scaleSnippet = "vec4(1.0)";
if (scaleShape != null) {
backend_util_exports.assertAndGetBroadcastShape(xShape, scaleShape);
this.variableNames.push("scale");
scaleSnippet = "getScaleAtOutCoords()";
}
this.outputShape = xShape;
this.userCode = `
void main() {
vec4 offset = ${offsetSnippet};
vec4 scale = ${scaleSnippet};
vec4 x = getXAtOutCoords();
vec4 mean = getMeanAtOutCoords();
vec4 variance = getVarianceAtOutCoords();
vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));
setOutput((x - mean) * inv + offset);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchNorm.js
var batchNorm3 = ({ inputs, backend: backend2, attrs }) => {
const { x, mean: mean3, variance, offset, scale: scale2 } = inputs;
util_exports.assert(mean3.shape.length === variance.shape.length, () => "Batch normalization gradient requires mean and variance to have equal ranks.");
util_exports.assert(offset == null || mean3.shape.length === offset.shape.length, () => "Batch normalization gradient requires mean and offset to have equal ranks.");
util_exports.assert(scale2 == null || mean3.shape.length === scale2.shape.length, () => "Batch normalization gradient requires mean and scale to have equal ranks.");
let { varianceEpsilon } = attrs;
if (varianceEpsilon == null) {
varianceEpsilon = 1e-3;
}
const finalInputs = [x, mean3, variance];
let offsetShape = null;
if (offset != null) {
offsetShape = offset.shape;
finalInputs.push(offset);
}
let scaleShape = null;
if (scale2 != null) {
scaleShape = scale2.shape;
finalInputs.push(scale2);
}
const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new BatchNormPackedProgram(x.shape, mean3.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon) : new BatchNormProgram(x.shape, mean3.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
const output = backend2.runWebGLProgram(program, finalInputs, finalInputs[0].dtype);
return output;
};
var batchNormConfig2 = {
kernelName: FusedBatchNorm,
backendName: "webgl",
kernelFunc: batchNorm3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_gpu.js
init_define_BUILD_VERSION();
var SliceProgram = class {
constructor(destSize) {
this.variableNames = ["source"];
this.outputShape = destSize;
this.rank = destSize.length;
const dtype = getCoordsDataType(this.rank);
this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
const sourceCoords = getCoords(this.rank);
let body;
const coordSum = destSize.map((_, i) => {
return `sourceLoc.${coords[i]} = start[${i}] + coords.${coords[i]};`;
});
body = `
${dtype} sourceLoc;
${dtype} coords = getOutputCoords();
${coordSum.join("\n")}
`;
this.userCode = `
void main() {
${body}
setOutput(getSource(${sourceCoords}));
}
`;
}
};
var coords = ["x", "y", "z", "w", "u", "v"];
function getCoords(rank) {
if (rank === 1) {
return "sourceLoc";
} else if (rank <= 6) {
return coords.slice(0, rank).map((x) => "sourceLoc." + x).join(",");
} else {
throw Error(`Slicing for rank ${rank} is not yet supported`);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/slice_packed_gpu.js
init_define_BUILD_VERSION();
var SlicePackedProgram = class {
constructor(destSize) {
this.variableNames = ["source"];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = destSize;
this.rank = destSize.length;
this.customUniforms = [{ name: "start", arrayIndex: this.rank, type: "int" }];
const dtype = getCoordsDataType(this.rank);
const coords2 = getChannels("coords", this.rank);
const sourceLoc = getChannels("sourceLoc", this.rank);
const innerDims = this.rank === 1 ? "sourceLoc" : `vec2(${sourceLoc.slice(-2).join()})`;
const getChannel = `getChannel(getSource(${sourceLoc.join()}), ${innerDims})`;
const upperRow = `
result.x = ${getChannel};
if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) {
++${sourceLoc[this.rank - 1]};
result.y = ${getChannel};
--${sourceLoc[this.rank - 1]};
}
`;
const lowerRow = this.rank === 1 ? "" : `
--${coords2[this.rank - 1]};
if (++${coords2[this.rank - 2]} < ${destSize[this.rank - 2]}) {
++${sourceLoc[this.rank - 2]};
result.z = ${getChannel};
if (++${coords2[this.rank - 1]} < ${destSize[this.rank - 1]}) {
++${sourceLoc[this.rank - 1]};
result.w = ${getChannel};
}
}
`;
const sourceLocSetup = this.rank <= 4 ? `sourceLoc = coords +
${dtype}(${destSize.map((_, i) => `start[${i}]`).join()});` : destSize.map((_, i) => `${sourceLoc[i]} = ${coords2[i]} + start[${i}];`).join("\n");
this.userCode = `
void main() {
${dtype} coords = getOutputCoords();
${dtype} sourceLoc;
${sourceLocSetup}
vec4 result = vec4(0.);
${upperRow}
${lowerRow}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Slice.js
function shallowSlice(x, begin, size, backend2) {
const xTexData = backend2.texData.get(x.dataId);
const t = backend2.makeTensorInfo(size, x.dtype);
const newTexData = backend2.texData.get(t.dataId);
Object.assign(newTexData, xTexData);
newTexData.refCount = 1;
newTexData.shape = size;
newTexData.dtype = x.dtype;
let flatOffset = slice_util_exports.computeFlatOffset(begin, util_exports.computeStrides(x.shape));
if (xTexData.slice) {
flatOffset += xTexData.slice.flatOffset;
}
newTexData.slice = {
flatOffset,
origDataId: xTexData.slice && xTexData.slice.origDataId || x.dataId
};
const refCount = backend2.dataRefCount.get(newTexData.slice.origDataId) || 1;
backend2.dataRefCount.set(newTexData.slice.origDataId, refCount + 1);
return t;
}
function slice3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { begin, size } = attrs;
const [$begin, $size] = slice_util_exports.parseSliceParams(x, begin, size);
slice_util_exports.assertParamsValid(x, $begin, $size);
if (util_exports.sizeFromShape($size) === 0) {
return backend2.makeTensorInfo($size, x.dtype, []);
}
if (backend2.shouldExecuteOnCPU([x]) || x.dtype === "string") {
const xTexData = backend2.texData.get(x.dataId);
const outValues = sliceImplCPU(xTexData.values, $begin, $size, x.shape, x.dtype);
return backend2.makeTensorInfo($size, x.dtype, outValues);
}
const { isPacked } = backend2.texData.get(x.dataId);
const isContinous = slice_util_exports.isSliceContinous(x.shape, $begin, $size);
if (isPacked || !isContinous) {
const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new SlicePackedProgram($size) : new SliceProgram($size);
const customValues = [$begin];
return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
}
backend2.uploadToGPU(x.dataId);
return shallowSlice(x, $begin, $size, backend2);
}
var sliceConfig2 = {
kernelName: Slice,
backendName: "webgl",
kernelFunc: slice3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BatchToSpaceND.js
var batchToSpaceND3 = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { blockShape, crops } = attrs;
util_exports.assert(x.shape.length <= 4, () => "batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");
const prod4 = blockShape.reduce((a, b) => a * b);
const reshaped = backend_util_exports.getReshaped(x.shape, blockShape, prod4);
const permuted = backend_util_exports.getPermuted(reshaped.length, blockShape.length);
const reshapedPermuted = backend_util_exports.getReshapedPermuted(x.shape, blockShape, prod4);
const sliceBeginCoords = backend_util_exports.getSliceBeginCoords(crops, blockShape.length);
const sliceSize = backend_util_exports.getSliceSize(reshapedPermuted, crops, blockShape.length);
const toDispose = [];
const reshapedIntermediate = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: reshaped } });
const transposedIntermediate = transpose3({ inputs: { x: reshapedIntermediate }, backend: backend2, attrs: { perm: permuted } });
const reshapedIntermediate2 = reshape3({
inputs: { x: transposedIntermediate },
backend: backend2,
attrs: { shape: reshapedPermuted }
});
const sliced = slice3({
inputs: { x: reshapedIntermediate2 },
backend: backend2,
attrs: { begin: sliceBeginCoords, size: sliceSize }
});
toDispose.push(reshapedIntermediate);
toDispose.push(transposedIntermediate);
toDispose.push(reshapedIntermediate2);
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return sliced;
};
var batchToSpaceNDConfig2 = {
kernelName: BatchToSpaceND,
backendName: "webgl",
kernelFunc: batchToSpaceND3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Bincount.js
init_define_BUILD_VERSION();
function bincount3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, weights } = inputs;
const { size } = attrs;
const xVals = backend2.readSync(x.dataId);
const weightsVals = backend2.readSync(weights.dataId);
const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
return backend2.makeTensorInfo([size], weights.dtype, outVals);
}
var bincountConfig2 = {
kernelName: Bincount,
backendName: "webgl",
kernelFunc: bincount3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/BroadcastArgs.js
init_define_BUILD_VERSION();
function broadcastArgs2(args) {
const { inputs, backend: backend2 } = args;
const { s0, s1 } = inputs;
const s0Vals = backend2.readSync(s0.dataId);
const s1Vals = backend2.readSync(s1.dataId);
const broadcastShape = backend_util_exports.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals));
return backend2.makeTensorInfo([broadcastShape.length], "int32", Int32Array.from(broadcastShape));
}
var broadcastArgsConfig2 = {
kernelName: BroadcastArgs,
backendName: "webgl",
kernelFunc: broadcastArgs2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NotEqual.js
init_define_BUILD_VERSION();
var NOT_EQUAL = `return float(a != b);`;
var notEqual3 = binaryKernelFunc2({ opSnippet: NOT_EQUAL, cpuKernelImpl: notEqualImplCPU, dtype: "bool" });
var notEqualConfig2 = {
kernelName: NotEqual,
backendName: "webgl",
kernelFunc: notEqual3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Real.js
init_define_BUILD_VERSION();
function real3(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
const inputData = backend2.texData.get(input2.dataId);
return identity2({ inputs: { x: inputData.complexTensorInfos.real }, backend: backend2 });
}
var realConfig2 = {
kernelName: Real,
backendName: "webgl",
kernelFunc: real3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernel_utils/int.js
init_define_BUILD_VERSION();
var TO_INT = `return float(int(x));`;
function int(input2, backend2) {
const program = new UnaryOpProgram(input2.shape, TO_INT);
const output = backend2.runWebGLProgram(program, [input2], "int32");
return { dataId: output.dataId, shape: output.shape, dtype: output.dtype };
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cast.js
function cast4(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { dtype } = attrs;
if (dtype === "complex64") {
if (x.dtype === "complex64") {
return identity2({ inputs: { x }, backend: backend2 });
}
const zerosTensor = zeros(x.shape);
const floatX = cast4({ inputs: { x }, backend: backend2, attrs: { dtype: "float32" } });
const result = complex3({ inputs: { real: floatX, imag: zerosTensor }, backend: backend2 });
zerosTensor.dispose();
backend2.disposeIntermediateTensorInfo(floatX);
return result;
}
if (x.dtype === "complex64") {
const realPart = real3({ inputs: { input: x }, backend: backend2 });
const result = cast4({ inputs: { x: realPart }, backend: backend2, attrs: { dtype } });
backend2.disposeIntermediateTensorInfo(realPart);
return result;
}
if (!util_exports.hasEncodingLoss(x.dtype, dtype)) {
const result = identity2({ inputs: { x }, backend: backend2 });
return { dataId: result.dataId, shape: result.shape, dtype };
}
if (dtype === "int32") {
return int(x, backend2);
}
if (dtype === "bool") {
const zerosTensorInfo = backend2.makeTensorInfo([], "bool", util_exports.getTypedArrayFromDType("bool", 1));
const binaryInputs = { a: x, b: zerosTensorInfo };
const result = notEqual3({ inputs: binaryInputs, backend: backend2 });
backend2.disposeIntermediateTensorInfo(zerosTensorInfo);
return result;
}
throw new Error(`Error in Cast: failed to cast ${x.dtype} to ${dtype}`);
}
var castConfig2 = {
kernelName: Cast,
backendName: "webgl",
kernelFunc: cast4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Ceil.js
init_define_BUILD_VERSION();
var CEIL = `return ceil(x);`;
var ceil3 = unaryKernelFunc2({ opSnippet: CEIL, packedOpSnippet: CEIL, cpuKernelImpl: ceilImplCPU });
var ceilConfig2 = {
kernelName: Ceil,
backendName: "webgl",
kernelFunc: ceil3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_gpu.js
init_define_BUILD_VERSION();
var ClipProgram = class {
constructor(aShape) {
this.variableNames = ["A"];
this.customUniforms = [
{ name: "minVal", type: "float" },
{ name: "maxVal", type: "float" }
];
this.outputShape = aShape;
this.userCode = `
void main() {
float value = getAAtOutCoords();
if (isnan(value)) {
setOutput(value);
return;
}
setOutput(clamp(value, minVal, maxVal));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/clip_packed_gpu.js
init_define_BUILD_VERSION();
var ClipPackedProgram = class {
constructor(aShape) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.customUniforms = [
{ name: "minVal", type: "float" },
{ name: "maxVal", type: "float" }
];
this.outputShape = aShape;
this.userCode = `
void main() {
vec4 value = getAAtOutCoords();
if (any(isnan(value))) {
setOutput(value);
return;
}
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ClipByValue.js
function clipByValue3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { clipValueMin, clipValueMax } = attrs;
let program;
if (env().getBool("WEBGL_PACK_CLIP")) {
program = new ClipPackedProgram(x.shape);
} else {
program = new ClipProgram(x.shape);
}
const customValues = [[clipValueMin], [clipValueMax]];
return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
}
var clipByValueConfig2 = {
kernelName: ClipByValue,
backendName: "webgl",
kernelFunc: clipByValue3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/complex_abs_gpu.js
init_define_BUILD_VERSION();
var ComplexAbsProgram = class {
constructor(shape) {
this.variableNames = ["real", "imag"];
this.outputShape = shape;
this.userCode = `
void main() {
float re = abs(getRealAtOutCoords());
float im = abs(getImagAtOutCoords());
float mx = max(re, im);
// sadly the length function in glsl is not underflow-safe
// (at least not on Intel GPUs). So the safe solution is
// to ensure underflow-safety in all cases.
setOutput(
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ComplexAbs.js
function makeComplexComponentTensorInfo(complexTensor, complexPart) {
return {
dataId: complexPart.dataId,
dtype: complexPart.dtype,
shape: complexTensor.shape
};
}
function complexAbs2(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
const xData = backend2.texData.get(x.dataId);
const program = new ComplexAbsProgram(x.shape);
const programInputs = [
makeComplexComponentTensorInfo(x, xData.complexTensorInfos.real),
makeComplexComponentTensorInfo(x, xData.complexTensorInfos.imag)
];
return backend2.runWebGLProgram(program, programInputs, programInputs[0].dtype);
}
var complexAbsConfig2 = {
kernelName: ComplexAbs,
backendName: "webgl",
kernelFunc: complexAbs2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_gpu.js
init_define_BUILD_VERSION();
var ConcatProgram = class {
constructor(shapes) {
this.outputShape = [];
this.outputShape = backend_util_exports.computeOutShape(shapes, 1);
this.variableNames = shapes.map((_, i) => `T${i}`);
const offsets = new Array(shapes.length - 1);
offsets[0] = shapes[0][1];
for (let i = 1; i < offsets.length; i++) {
offsets[i] = offsets[i - 1] + shapes[i][1];
}
const snippets = [`if (yC < ${offsets[0]}) setOutput(getT0(yR, yC));`];
for (let i = 1; i < offsets.length; i++) {
const shift = offsets[i - 1];
snippets.push(`else if (yC < ${offsets[i]}) setOutput(getT${i}(yR, yC-${shift}));`);
}
const lastIndex = offsets.length;
const lastShift = offsets[offsets.length - 1];
snippets.push(`else setOutput(getT${lastIndex}(yR, yC-${lastShift}));`);
this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int yR = coords.x;
int yC = coords.y;
${snippets.join("\n ")}
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/concat_packed_gpu.js
init_define_BUILD_VERSION();
var ConcatPackedProgram = class {
constructor(shapes, axis) {
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = [];
this.outputShape = backend_util_exports.computeOutShape(shapes, axis);
const shape = this.outputShape;
const rank = shape.length;
const dtype = getCoordsDataType(rank);
const coords2 = getChannels("coords", rank);
const channels = ["x", "y", "z", "w", "u", "v"].slice(0, rank);
this.variableNames = shapes.map((_, i) => `T${i}`);
const offsets = new Array(shapes.length - 1);
offsets[0] = shapes[0][axis];
for (let i = 1; i < offsets.length; i++) {
offsets[i] = offsets[i - 1] + shapes[i][axis];
}
const channel = channels[axis];
const lastChannels = channels.slice(-2);
const allChannels = channels.join();
let getValueSnippet = `if (${channel} < ${offsets[0]}) {
return getChannel(
getT0(${allChannels}), vec2(${lastChannels.join()}));
}`;
for (let i = 1; i < offsets.length; i++) {
const shift2 = offsets[i - 1];
getValueSnippet += `
if (${channel} < ${offsets[i]} && ${channel} >= ${offsets[i - 1]}) {
return getChannel(
getT${i}(${shiftedChannels(channels, channel, shift2)}),
vec2(${shiftedChannels(lastChannels, channel, shift2)}));
}`;
}
const lastIndex = offsets.length;
const shift = offsets[offsets.length - 1];
getValueSnippet += `
return getChannel(
getT${lastIndex}(${shiftedChannels(channels, channel, shift)}),
vec2(${shiftedChannels(lastChannels, channel, shift)}));`;
this.userCode = `
float getValue(${channels.map((x) => "int " + x)}) {
${getValueSnippet}
}
void main() {
${dtype} coords = getOutputCoords();
vec4 result = vec4(getValue(${coords2}), 0., 0., 0.);
${coords2[rank - 1]} = ${coords2[rank - 1]} + 1;
if (${coords2[rank - 1]} < ${shape[rank - 1]}) {
result.g = getValue(${coords2});
}
${coords2[rank - 2]} = ${coords2[rank - 2]} + 1;
if (${coords2[rank - 2]} < ${shape[rank - 2]}) {
result.a = getValue(${coords2});
}
${coords2[rank - 1]} = ${coords2[rank - 1]} - 1;
if (${coords2[rank - 2]} < ${shape[rank - 2]} &&
${coords2[rank - 1]} < ${shape[rank - 1]}) {
result.b = getValue(${coords2});
}
setOutput(result);
}
`;
}
};
function shiftedChannels(channels, channel, shift) {
const channelIdx = channels.indexOf(channel);
const res = channels.map((c, idx) => {
if (idx === channelIdx) {
return `${c} - ${shift}`;
} else {
return c;
}
});
return res.join();
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Imag.js
init_define_BUILD_VERSION();
function imag3(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
const inputData = backend2.texData.get(input2.dataId);
return identity2({ inputs: { x: inputData.complexTensorInfos.imag }, backend: backend2 });
}
var imagConfig2 = {
kernelName: Imag,
backendName: "webgl",
kernelFunc: imag3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat_impl.js
function concatImpl2(inputs, axis, backend2) {
const dtype = inputs[0].dtype;
if (dtype === "complex64") {
const reals = inputs.map((t) => real3({ inputs: { input: t }, backend: backend2 }));
const imags = inputs.map((t) => imag3({ inputs: { input: t }, backend: backend2 }));
const realConcated = concatImpl2(reals, axis, backend2);
const imagConcated = concatImpl2(imags, axis, backend2);
const result2 = complex3({ inputs: { real: realConcated, imag: imagConcated }, backend: backend2 });
reals.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
imags.forEach((i) => backend2.disposeIntermediateTensorInfo(i));
backend2.disposeIntermediateTensorInfo(realConcated);
backend2.disposeIntermediateTensorInfo(imagConcated);
return result2;
}
let runOnCpu = backend2.shouldExecuteOnCPU(inputs);
if (dtype === "string") {
runOnCpu = true;
}
if (runOnCpu) {
const tensors2D2 = inputs.map((t) => {
const innerSize = util_exports.sizeFromShape(t.shape.slice(axis));
const shape = [-1, innerSize];
return reshape3({ inputs: { x: t }, backend: backend2, attrs: { shape } });
});
const inputsValShapes = tensors2D2.map((t) => {
return { vals: backend2.readSync(t.dataId), shape: t.shape };
});
const outShape2 = backend_util_exports.computeOutShape(tensors2D2.map((t) => t.shape), 1);
const simplyConcat = tensors2D2[0].shape[0] === 1;
const outVals = concatImplCPU(inputsValShapes, outShape2, dtype, simplyConcat);
const finalOutShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
const outInfo = backend2.makeTensorInfo(finalOutShape, dtype, outVals);
tensors2D2.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return outInfo;
}
const maxTexturesInShader = env().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER");
if (inputs.length > maxTexturesInShader) {
const reducedInputs = [];
for (let i = 0; i < inputs.length; i += maxTexturesInShader) {
const subArray = inputs.slice(i, i + maxTexturesInShader);
reducedInputs.push(concatImpl2(subArray, axis, backend2));
}
const result2 = concatImpl2(reducedInputs, axis, backend2);
for (const i of reducedInputs) {
backend2.disposeIntermediateTensorInfo(i);
}
return result2;
}
if (env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") && inputs[0].shape.length > 1) {
const program2 = new ConcatPackedProgram(inputs.map((t) => t.shape), axis);
return backend2.runWebGLProgram(program2, inputs, dtype);
}
const { tensors2D, outShape } = computeTensors2D(inputs, axis, backend2);
const program = new ConcatProgram(tensors2D.map((t) => t.shape));
const result = backend2.runWebGLProgram(program, tensors2D, dtype);
tensors2D.forEach((r) => backend2.disposeIntermediateTensorInfo(r));
const reshapedResult = reshape3({ inputs: { x: result }, attrs: { shape: outShape }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(result);
return reshapedResult;
}
function computeTensors2D(inputs, axis, backend2) {
const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), axis);
const tensors2D = inputs.map((x) => reshape3({
inputs: { x },
attrs: { shape: [-1, util_exports.sizeFromShape(x.shape.slice(axis))] },
backend: backend2
}));
return { tensors2D, outShape };
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Concat.js
function concat3(args) {
const { inputs, backend: backend2, attrs } = args;
const { axis } = attrs;
const $axis = util_exports.parseAxisParam(axis, inputs[0].shape)[0];
const outShape = backend_util_exports.computeOutShape(inputs.map((t) => t.shape), $axis);
if (util_exports.sizeFromShape(outShape) === 0) {
return backend2.makeTensorInfo(outShape, inputs[0].dtype, []);
}
const $inputs = inputs.filter((t) => util_exports.sizeFromShape(t.shape) > 0);
if ($inputs.length === 1) {
return identity2({ inputs: { x: $inputs[0] }, backend: backend2 });
}
const shapes = $inputs.map((t) => t.shape);
backend_util_exports.assertParamsConsistent(shapes, $axis);
return concatImpl2($inputs, $axis, backend2);
}
var concatConfig2 = {
kernelName: Concat,
backendName: "webgl",
kernelFunc: concat3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu.js
init_define_BUILD_VERSION();
var Conv2DProgram = class {
constructor(convInfo, addBias = false, activation = null, hasPreluActivationWeights = false, hasLeakyreluAlpha = false) {
this.variableNames = ["x", "W"];
this.outputShape = convInfo.outShape;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
const inputDepthVec4Remainder = convInfo.inChannels % 4;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
const rowDim = isChannelsLast ? 1 : 2;
const colDim = isChannelsLast ? 2 : 3;
const channelDim = isChannelsLast ? 3 : 1;
let activationSnippet = "", applyActivationSnippet = "";
if (activation) {
if (hasPreluActivationWeights) {
activationSnippet = `float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${activation}
}`;
} else if (hasLeakyreluAlpha) {
activationSnippet = `float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${activation}
}`;
} else {
activationSnippet = `
float activation(float x) {
${activation}
}
`;
}
applyActivationSnippet = `result = activation(result);`;
}
const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
if (addBias) {
this.variableNames.push("bias");
}
if (hasPreluActivationWeights) {
this.variableNames.push("preluActivationWeights");
}
if (hasLeakyreluAlpha) {
this.variableNames.push("leakyreluAlpha");
}
this.userCode = `
${activationSnippet}
const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
const ivec2 pads = ivec2(${padTop}, ${padLeft});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d2 = coords[${channelDim}];
ivec2 xRCCorner =
ivec2(coords[${rowDim}], coords[${colDim}]) * strides - pads;
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${filterHeight}; wR++) {
int xR = xRCorner + wR * ${dilationHeight};
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int wC = 0; wC < ${filterWidth}; wC++) {
int xC = xCCorner + wC * ${dilationWidth};
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
vec4 wValues = vec4(
getW(wR, wC, d1, d2),
getW(wR, wC, d1 + 1, d2),
getW(wR, wC, d1 + 2, d2),
getW(wR, wC, d1 + 3, d2)
);
if (${isChannelsLast}) {
vec4 xValues = vec4(
getX(batch, xR, xC, d1),
getX(batch, xR, xC, d1 + 1),
getX(batch, xR, xC, d1 + 2),
getX(batch, xR, xC, d1 + 3)
);
dotProd += dot(xValues, wValues);
} else {
vec4 xValues = vec4(
getX(batch, d1, xR, xC),
getX(batch, d1 + 1, xR, xC),
getX(batch, d1 + 2, xR, xC),
getX(batch, d1 + 3, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
if (${inputDepthVec4Remainder === 1}) {
if (${isChannelsLast}) {
dotProd +=
getX(batch, xR, xC, ${inputDepthNearestVec4}) *
getW(wR, wC, ${inputDepthNearestVec4}, d2);
} else {
dotProd +=
getX(batch, ${inputDepthNearestVec4}, xR, xC) *
getW(wR, wC, ${inputDepthNearestVec4}, d2);
}
} else if (${inputDepthVec4Remainder === 2}) {
vec2 wValues = vec2(
getW(wR, wC, ${inputDepthNearestVec4}, d2),
getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)
);
if (${isChannelsLast}) {
vec2 xValues = vec2(
getX(batch, xR, xC, ${inputDepthNearestVec4}),
getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)
);
dotProd += dot(xValues, wValues);
} else {
vec2 xValues = vec2(
getX(batch, ${inputDepthNearestVec4}, xR, xC),
getX(batch, ${inputDepthNearestVec4} + 1, xR, xC)
);
dotProd += dot(xValues, wValues);
}
} else if (${inputDepthVec4Remainder === 3}) {
vec3 wValues = vec3(
getW(wR, wC, ${inputDepthNearestVec4}, d2),
getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),
getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)
);
if (${isChannelsLast}) {
vec3 xValues = vec3(
getX(batch, xR, xC, ${inputDepthNearestVec4}),
getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),
getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)
);
dotProd += dot(xValues, wValues);
} else {
vec3 xValues = vec3(
getX(batch, ${inputDepthNearestVec4}, xR, xC),
getX(batch, ${inputDepthNearestVec4} + 1, xR, xC),
getX(batch, ${inputDepthNearestVec4} + 2, xR, xC)
);
dotProd += dot(xValues, wValues);
}
}
}
}
float result = dotProd;
${addBiasSnippet}
${applyActivationSnippet}
setOutput(result);
}
`;
}
};
var Conv3DProgram = class {
constructor(convInfo) {
this.variableNames = ["x", "W"];
this.outputShape = convInfo.outShape;
const padFront = convInfo.padInfo.front;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const filterDepth = convInfo.filterDepth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
const inputDepthVec4Remainder = convInfo.inChannels % 4;
this.userCode = `
const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${strideWidth});
const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d2 = coords.u;
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
int xFCorner = xFRCCorner.x;
int xRCorner = xFRCCorner.y;
int xCCorner = xFRCCorner.z;
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
// y(yF, yR, yC, d2). ? = to be determined. : = across all
// values in that axis.
float dotProd = 0.0;
for (int wF = 0; wF < ${filterDepth}; wF++) {
int xF = xFCorner + wF * ${dilationDepth};
if (xF < 0 || xF >= ${convInfo.inDepth}) {
continue;
}
for (int wR = 0; wR < ${filterHeight}; wR++) {
int xR = xRCorner + wR * ${dilationHeight};
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int wC = 0; wC < ${filterWidth}; wC++) {
int xC = xCCorner + wC * ${dilationWidth};
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {
vec4 xValues = vec4(
getX(batch, xF, xR, xC, d1),
getX(batch, xF, xR, xC, d1 + 1),
getX(batch, xF, xR, xC, d1 + 2),
getX(batch, xF, xR, xC, d1 + 3)
);
vec4 wValues = vec4(
getW(wF, wR, wC, d1, d2),
getW(wF, wR, wC, d1 + 1, d2),
getW(wF, wR, wC, d1 + 2, d2),
getW(wF, wR, wC, d1 + 3, d2)
);
dotProd += dot(xValues, wValues);
}
if (${inputDepthVec4Remainder === 1}) {
dotProd +=
getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *
getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);
} else if (${inputDepthVec4Remainder === 2}) {
vec2 xValues = vec2(
getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)
);
vec2 wValues = vec2(
getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)
);
dotProd += dot(xValues, wValues);
} else if (${inputDepthVec4Remainder === 3}) {
vec3 xValues = vec3(
getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),
getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),
getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)
);
vec3 wValues = vec3(
getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),
getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),
getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)
);
dotProd += dot(xValues, wValues);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/im2col_packed_gpu.js
init_define_BUILD_VERSION();
var Im2ColPackedProgram = class {
constructor(outputShape, convInfo) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.customUniforms = [
{ name: "inputShape", type: "ivec4" },
{ name: "pad", type: "ivec2" },
{ name: "stride", type: "ivec2" },
{ name: "dilation", type: "ivec2" },
{ name: "inChannels", type: "int" },
{ name: "itemsPerBlockRow", type: "int" },
{ name: "outWidth", type: "int" }
];
this.outputShape = outputShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
const { dataFormat } = convInfo;
const glsl = getGlslDifferences();
const isChannelsLast = dataFormat === "channelsLast";
const rowDim = isChannelsLast ? 1 : 2;
const colDim = isChannelsLast ? 2 : 3;
const boundsCheckingSnippet = this.enableShapeUniforms ? "if(blockIndex < outShape[2] && pos < outShape[1]) {" : `if(blockIndex < ${outputShape[2]} && pos < ${outputShape[1]}) {`;
let unrolled = ``;
for (let row = 0; row <= 1; row++) {
for (let col = 0; col <= 1; col++) {
unrolled += `
blockIndex = rc.z + ${col};
pos = rc.y + ${row};
${boundsCheckingSnippet}
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
if(d0 < inputShape[${rowDim}] && d0 >= 0) {
// Use custom imod instead mod. On Intel GPU, mod may generate
// unexpected value.
// https://github.com/tensorflow/tfjs/issues/5447
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
inChannels);
if(d1 < inputShape[${colDim}] && d1 >= 0) {
ch = imod(pos, inChannels);
if (${isChannelsLast}) {
innerDims = vec2(d1, ch);
result[${row * 2 + col}] = getChannel(
getA(rc.x, d0, int(innerDims.x),
int(innerDims.y)), innerDims);
} else {
innerDims = vec2(d0, d1);
result[${row * 2 + col}] = getChannel(
getA(rc.x, ch, int(innerDims.x),
int(innerDims.y)), innerDims);
}
}
}
}
`;
}
}
this.userCode = `
void main() {
ivec3 rc = getOutputCoords();
vec4 result = vec4(0);
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
vec2 innerDims;
${unrolled}
${glsl.output} = result;
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D_impl.js
function getShapeForBatchMatMul(shape, isChannelsLast) {
const length = shape.length;
if (length >= 3) {
return isChannelsLast ? [
...shape.slice(0, -3),
shape[length - 3] * shape[length - 2],
shape[length - 1]
] : [
...shape.slice(0, -3),
shape[length - 3],
shape[length - 2] * shape[length - 1]
];
} else if (!isChannelsLast && length === 1 && shape[0] > 1) {
return [shape[0], 1];
} else {
return null;
}
}
function conv2dByMatMul({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
const xShape = x.shape;
const xTexData = backend2.texData.get(x.dataId);
const sharedMatMulDim = convInfo.inChannels;
const outerShapeX = xShape[0] * xShape[1] * xShape[2];
const outerShapeFilter = convInfo.outChannels;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
const transposeA = false;
const transposeB = false;
let out;
const intermediates = [];
if (preluActivationWeights != null) {
const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
if (targetShape != null) {
preluActivationWeights = reshape3({
inputs: { x: preluActivationWeights },
backend: backend2,
attrs: { shape: targetShape }
});
intermediates.push(preluActivationWeights);
}
}
if (bias != null) {
const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
if (targetShape != null) {
bias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });
intermediates.push(bias);
}
}
const batchMatMulWillBeUnpacked = (outerShapeX === 1 || outerShapeFilter === 1) && sharedMatMulDim > MATMUL_SHARED_DIM_THRESHOLD;
const canOptimize = !batchMatMulWillBeUnpacked && xTexData.isPacked && isChannelsLast && xTexData.texture != null && xShape[2] % 2 !== 0 && util_exports.arraysEqual(xTexData.shape.slice(-3), xShape.slice(-3));
if (canOptimize) {
const targetShape = xShape[0] * xShape[1] * (xShape[2] + 1);
const xReshaped = {
dataId: x.dataId,
shape: [1, targetShape, convInfo.inChannels],
dtype: x.dtype
};
const originalXTexDataShape = xTexData.shape;
xTexData.shape = xTexData.shape.slice();
xTexData.shape[xTexData.shape.length - 2]++;
util_exports.assert(isReshapeFree(xTexData.shape, xReshaped.shape), () => `packed reshape ${xTexData.shape} to ${xReshaped.shape} isn't free`);
const filterReshaped = reshape3({
inputs: { x: filter },
backend: backend2,
attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
});
intermediates.push(filterReshaped);
const pointwiseConv = batchMatMulImpl({
a: xReshaped,
b: filterReshaped,
backend: backend2,
transposeA,
transposeB,
bias,
activation,
preluActivationWeights,
leakyreluAlpha
});
const pointwiseConvTexData = backend2.texData.get(pointwiseConv.dataId);
util_exports.assert(pointwiseConvTexData.isPacked, () => "batchMatMul result is expected to be packed");
xTexData.shape = originalXTexDataShape;
pointwiseConvTexData.shape = convInfo.outShape;
out = identity2({ inputs: { x: pointwiseConv }, backend: backend2 });
out.shape = convInfo.outShape;
intermediates.push(pointwiseConv);
} else {
const numCols = convInfo.outHeight * convInfo.outWidth;
const xReshaped = reshape3({
inputs: { x },
backend: backend2,
attrs: {
shape: isChannelsLast ? [convInfo.batchSize, numCols, convInfo.inChannels] : [convInfo.batchSize, convInfo.inChannels, numCols]
}
});
const filterReshaped = reshape3({
inputs: { x: filter },
backend: backend2,
attrs: { shape: [1, convInfo.inChannels, convInfo.outChannels] }
});
const result = batchMatMulImpl({
a: isChannelsLast ? xReshaped : filterReshaped,
b: isChannelsLast ? filterReshaped : xReshaped,
transposeA: !isChannelsLast,
transposeB,
backend: backend2,
bias,
activation,
preluActivationWeights,
leakyreluAlpha
});
out = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: convInfo.outShape } });
intermediates.push(xReshaped);
intermediates.push(filterReshaped);
intermediates.push(result);
}
for (const i of intermediates) {
backend2.disposeIntermediateTensorInfo(i);
}
return out;
}
function conv2dWithIm2Row({ x, filter, convInfo, backend: backend2, bias = null, preluActivationWeights = null, leakyreluAlpha = 0, activation = null }) {
const { filterWidth, filterHeight, inChannels, outWidth, outHeight, dataFormat } = convInfo;
const isChannelsLast = dataFormat === "channelsLast";
const sharedDim = filterWidth * filterHeight * inChannels;
const numCols = outHeight * outWidth;
const x2ColShape = [convInfo.batchSize, sharedDim, numCols];
const transposeA = true;
const transposeB = false;
const intermediates = [];
if (preluActivationWeights != null) {
const targetShape = getShapeForBatchMatMul(preluActivationWeights.shape, isChannelsLast);
if (targetShape != null) {
preluActivationWeights = reshape3({
inputs: { x: preluActivationWeights },
backend: backend2,
attrs: { shape: targetShape }
});
intermediates.push(preluActivationWeights);
}
}
if (bias != null) {
const targetShape = getShapeForBatchMatMul(bias.shape, isChannelsLast);
if (targetShape != null) {
bias = reshape3({ inputs: { x: bias }, backend: backend2, attrs: { shape: targetShape } });
intermediates.push(bias);
}
}
const w2Row = reshape3({
inputs: { x: filter },
backend: backend2,
attrs: { shape: [1, sharedDim, util_exports.sizeFromShape(filter.shape) / sharedDim] }
});
intermediates.push(w2Row);
const im2ColProgram = new Im2ColPackedProgram(x2ColShape, convInfo);
const customValues = [
x.shape,
[convInfo.padInfo.top, convInfo.padInfo.left],
[convInfo.strideHeight, convInfo.strideWidth],
[convInfo.dilationHeight, convInfo.dilationWidth],
[convInfo.inChannels],
[convInfo.filterWidth * convInfo.inChannels],
[convInfo.outWidth]
];
const im2Col = backend2.runWebGLProgram(im2ColProgram, [x], "float32", customValues);
const im2ColReshaped = reshape3({ inputs: { x: im2Col }, backend: backend2, attrs: { shape: x2ColShape } });
intermediates.push(im2Col);
intermediates.push(im2ColReshaped);
const hasBias = bias != null;
const hasPreluActivationWeights = preluActivationWeights != null;
const hasLeakyreluAlpha = activation === "leakyrelu";
const fusedActivation = activation ? mapActivationToShaderProgram(activation, true) : null;
const matmulProgram = new MatMulPackedProgram(isChannelsLast ? im2ColReshaped.shape : w2Row.shape, isChannelsLast ? w2Row.shape : im2ColReshaped.shape, isChannelsLast ? [convInfo.batchSize, numCols, convInfo.outChannels] : [convInfo.batchSize, convInfo.outChannels, numCols], transposeA, transposeB, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
const inputs = isChannelsLast ? [im2ColReshaped, w2Row] : [w2Row, im2ColReshaped];
if (bias) {
inputs.push(bias);
}
if (hasPreluActivationWeights) {
inputs.push(preluActivationWeights);
}
if (hasLeakyreluAlpha) {
const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
inputs.push($leakyreluAlpha);
intermediates.push($leakyreluAlpha);
}
const product = backend2.runWebGLProgram(matmulProgram, inputs, "float32");
const out = reshape3({ inputs: { x: product }, backend: backend2, attrs: { shape: convInfo.outShape } });
intermediates.push(product);
for (const i of intermediates) {
backend2.disposeIntermediateTensorInfo(i);
}
return out;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2D.js
function conv2d3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dataFormat, dilations, dimRoundingMode } = attrs;
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad2, dimRoundingMode, false, $dataFormat);
let out;
if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
out = conv2dByMatMul({ x, filter, convInfo, backend: backend2 });
} else if (env().getBool("WEBGL_CONV_IM2COL")) {
out = conv2dWithIm2Row({ x, filter, convInfo, backend: backend2 });
} else {
const program = new Conv2DProgram(convInfo);
out = backend2.runWebGLProgram(program, [x, filter], "float32");
}
const outReshaped = reshape3({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
backend2.disposeIntermediateTensorInfo(out);
return outReshaped;
}
var conv2DConfig2 = {
kernelName: Conv2D,
backendName: "webgl",
kernelFunc: conv2d3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu.js
init_define_BUILD_VERSION();
var Conv2DDerFilterProgram = class {
constructor(convInfo) {
this.variableNames = ["x", "dy"];
this.outputShape = convInfo.filterShape;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int d2 = coords.w;
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int b = 0; b < ${convInfo.batchSize}; b++) {
for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
int xR = wR + yR * ${strideHeight} - ${padTop};
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
int xC = wC + yC * ${strideWidth} - ${padLeft};
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
if (${isChannelsLast}) {
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
} else {
float dyValue = getDy(b, d2, yR, yC);
float xValue = getX(b, d1, xR, xC);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Conv2DDerInputProgram = class {
constructor(convInfo) {
this.variableNames = ["dy", "W"];
this.outputShape = convInfo.inShape;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const isChannelsLast = convInfo.dataFormat === "channelsLast";
const padTop = filterHeight - 1 - convInfo.padInfo.top;
const padLeft = filterWidth - 1 - convInfo.padInfo.left;
const rowDim = isChannelsLast ? 1 : 2;
const colDim = isChannelsLast ? 2 : 3;
const channelDim = isChannelsLast ? 3 : 1;
this.userCode = `
const ivec2 pads = ivec2(${padTop}, ${padLeft});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[${channelDim}];
ivec2 dyCorner = ivec2(coords[${rowDim}], coords[${colDim}]) - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${filterHeight}; wR++) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${filterHeight} - 1 - wR;
for (int wC = 0; wC < ${filterWidth}; wC++) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${filterWidth} - 1 - wC;
for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
if (${isChannelsLast}) {
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
} else {
float xValue = getDy(batch, d2, idyR, idyC);
float wValue = getW(wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Conv3DDerFilterProgram = class {
constructor(convInfo) {
this.variableNames = ["x", "dy"];
this.outputShape = convInfo.filterShape;
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padFront = convInfo.padInfo.front;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
this.userCode = `
void main() {
ivec5 coords = getOutputCoords();
int wF = coords.x;
int wR = coords.y;
int wC = coords.z;
int d1 = coords.w;
int d2 = coords.u;
float dotProd = 0.0;
for (int b = 0; b < ${convInfo.batchSize}; b++) {
for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {
int xF = wF + yF * ${strideDepth} - ${padFront};
if (xF < 0 || xF >= ${convInfo.inDepth}) {
continue;
}
for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
int xR = wR + yR * ${strideHeight} - ${padTop};
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
int xC = wC + yC * ${strideWidth} - ${padLeft};
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
float dyValue = getDy(b, yF, yR, yC, d2);
float xValue = getX(b, xF, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
}
setOutput(dotProd);
}
`;
}
};
var Conv3DDerInputProgram = class {
constructor(convInfo) {
this.variableNames = ["dy", "W"];
this.outputShape = convInfo.inShape;
const filterDepth = convInfo.filterDepth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padFront = filterDepth - 1 - convInfo.padInfo.front;
const padTop = filterHeight - 1 - convInfo.padInfo.top;
const padLeft = filterWidth - 1 - convInfo.padInfo.left;
this.userCode = `
const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyFCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
float dotProd = 0.0;
for (int wF = 0; wF < ${filterDepth}; wF++) {
float dyF = float(dyFCorner + wF) / ${strideDepth}.0;
if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {
continue;
}
int idyF = int(dyF);
int wFPerm = ${filterDepth} - 1 - wF;
for (int wR = 0; wR < ${filterHeight}; wR++) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${filterHeight} - 1 - wR;
for (int wC = 0; wC < ${filterWidth}; wC++) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${filterWidth} - 1 - wC;
for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {
float xValue = getDy(batch, idyF, idyR, idyC, d2);
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
dotProd += xValue * wValue;
}
}
}
}
setOutput(dotProd);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropFilter.js
function conv2DBackpropFilter3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, dy } = inputs;
const { strides, pad: pad2, dataFormat, dimRoundingMode, filterShape } = attrs;
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, 1, pad2, dimRoundingMode, false, $dataFormat);
const program = new Conv2DDerFilterProgram(convInfo);
return backend2.runWebGLProgram(program, [x, dy], "float32");
}
var conv2DBackpropFilterConfig2 = {
kernelName: Conv2DBackpropFilter,
backendName: "webgl",
kernelFunc: conv2DBackpropFilter3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv2DBackpropInput.js
init_define_BUILD_VERSION();
function conv2DBackpropInput3(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, filter } = inputs;
const { inputShape, strides, pad: pad2, dataFormat, dimRoundingMode } = attrs;
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, 1, pad2, dimRoundingMode, false, $dataFormat);
const program = new Conv2DDerInputProgram(convInfo);
return backend2.runWebGLProgram(program, [dy, filter], "float32");
}
var conv2DBackpropInputConfig2 = {
kernelName: Conv2DBackpropInput,
backendName: "webgl",
kernelFunc: conv2DBackpropInput3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3D.js
init_define_BUILD_VERSION();
function conv3D2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dilations } = attrs;
const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad2);
const program = new Conv3DProgram(convInfo);
return backend2.runWebGLProgram(program, [x, filter], "float32");
}
var conv3DConfig2 = {
kernelName: Conv3D,
backendName: "webgl",
kernelFunc: conv3D2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropFilterV2.js
init_define_BUILD_VERSION();
function conv3DBackpropFilterV22(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, dy } = inputs;
const { strides, pad: pad2, filterShape } = attrs;
const convInfo = backend_util_exports.computeConv3DInfo(x.shape, filterShape, strides, 1, pad2);
const program = new Conv3DDerFilterProgram(convInfo);
return backend2.runWebGLProgram(program, [x, dy], "float32");
}
var conv3DBackpropFilterV2Config2 = {
kernelName: Conv3DBackpropFilterV2,
backendName: "webgl",
kernelFunc: conv3DBackpropFilterV22
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Conv3DBackpropInputV2.js
init_define_BUILD_VERSION();
function conv3DBackpropInput2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, filter } = inputs;
const { pad: pad2, strides, inputShape } = attrs;
const convInfo = backend_util_exports.computeConv3DInfo(inputShape, filter.shape, strides, 1, pad2);
const program = new Conv3DDerInputProgram(convInfo);
return backend2.runWebGLProgram(program, [dy, filter], "float32");
}
var conv3DBackpropInputConfig = {
kernelName: Conv3DBackpropInputV2,
backendName: "webgl",
kernelFunc: conv3DBackpropInput2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cos.js
init_define_BUILD_VERSION();
var COS = CHECK_NAN_SNIPPET_UNARY + `
return cos(x);
`;
var cos3 = unaryKernelFunc2({ opSnippet: COS });
var cosConfig2 = {
kernelName: Cos,
backendName: "webgl",
kernelFunc: cos3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cosh.js
init_define_BUILD_VERSION();
var COSH = `
float e2x = exp(-x);
return (e2x + 1.0 / e2x) / 2.0;
`;
var cosh3 = unaryKernelFunc2({ opSnippet: COSH });
var coshConfig2 = {
kernelName: Cosh,
backendName: "webgl",
kernelFunc: cosh3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/crop_and_resize_gpu.js
init_define_BUILD_VERSION();
var CropAndResizeProgram = class {
constructor(imageShape, boxShape, cropSize, method, extrapolationValue) {
this.variableNames = ["Image", "Boxes", "BoxInd"];
this.outputShape = [];
const [batch, imageHeight, imageWidth, depth] = imageShape;
const [numBoxes] = boxShape;
const [cropHeight, cropWidth] = cropSize;
this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
const methodId = method === "bilinear" ? 1 : 0;
const [inputHeightFloat, inputWidthFloat] = [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];
const [heightRatio, heightScale, inY] = cropHeight > 1 ? [
`${(imageHeight - 1) / (cropHeight - 1)}`,
"(y2-y1) * height_ratio",
`y1*${inputHeightFloat} + float(y)*(height_scale)`
] : [
"0.0",
"0.0",
`0.5 * (y1+y2) * ${inputHeightFloat}`
];
const [widthRatio, widthScale, inX] = cropWidth > 1 ? [
`${(imageWidth - 1) / (cropWidth - 1)}`,
"(x2-x1) * width_ratio",
`x1*${inputWidthFloat} + float(x)*(width_scale)`
] : [
"0.0",
"0.0",
`0.5 * (x1+x2) * ${inputWidthFloat}`
];
this.userCode = `
const float height_ratio = float(${heightRatio});
const float width_ratio = float(${widthRatio});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int y = coords[1];
int x = coords[2];
int d = coords[3];
// get box vals
float y1 = getBoxes(b,0);
float x1 = getBoxes(b,1);
float y2 = getBoxes(b,2);
float x2 = getBoxes(b,3);
// get image in batch index
int bInd = round(getBoxInd(b));
if(bInd < 0 || bInd >= ${batch}) {
return;
}
float height_scale = ${heightScale};
float width_scale = ${widthScale};
float in_y = ${inY};
if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {
setOutput(float(${extrapolationValue}));
return;
}
float in_x = ${inX};
if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {
setOutput(float(${extrapolationValue}));
return;
}
vec2 sourceFracIndexCR = vec2(in_x,in_y);
if(${methodId} == 1) {
// Compute the four integer indices.
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
float top = topLeft + (topRight - topLeft) * fracCR.x;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
float newValue = top + (bottom - top) * fracCR.y;
setOutput(newValue);
} else {
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestCR = ivec2(floor(
sourceFracIndexCR + vec2(0.5,0.5)));
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
setOutput(newValue);
}
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/CropAndResize.js
var cropAndResize3 = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { image: image3, boxes, boxInd } = inputs;
const { cropSize, method, extrapolationValue } = attrs;
const program = new CropAndResizeProgram(image3.shape, boxes.shape, cropSize, method, extrapolationValue);
return backend2.runWebGLProgram(program, [image3, boxes, boxInd], "float32");
};
var cropAndResizeConfig2 = {
kernelName: CropAndResize,
backendName: "webgl",
kernelFunc: cropAndResize3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/cum_gpu.js
init_define_BUILD_VERSION();
var CumOpType;
(function(CumOpType2) {
CumOpType2["Prod"] = "*";
CumOpType2["Sum"] = "+";
})(CumOpType || (CumOpType = {}));
var CumProgram = class {
constructor(op2, outputShape, exclusive, reverse4) {
this.op = op2;
this.outputShape = outputShape;
this.variableNames = ["x"];
this.customUniforms = [{ name: "index", type: "float" }];
const rank = this.outputShape.length;
const initVal = this.op === CumOpType.Prod ? "1.0" : "0.0";
const val = exclusive ? initVal : `getX(${getCoords2(rank, "coords", this.op)})`;
const length = this.outputShape[this.outputShape.length - 1];
let condition = "";
let idxString = "";
if (exclusive) {
condition = reverse4 ? `end != ${length - 1}` : "end != 0";
idxString = reverse4 ? "end + 1" : "end - 1";
} else {
condition = reverse4 ? `end + pow2 < ${length}` : "end >= pow2";
idxString = reverse4 ? "end + pow2" : "end - pow2";
}
this.userCode = `
void main() {
${getCoordsDataType(rank)} coords = getOutputCoords();
int end = ${getFinalCoord(rank, "coords", this.op)};
float val = ${val};
int pow2 = int(pow(2.0, index));
if (${condition}) {
int idx = ${idxString};
${getFinalCoord(rank, "coords", this.op)} = idx;
val ${this.op}= getX(${getCoords2(rank, "coords", this.op)});
}
setOutput(val);
}
`;
}
};
function getCoords2(rank, name, op2) {
if (rank === 1) {
return `${name}`;
} else if (rank === 2) {
return `${name}.x, ${name}.y`;
} else if (rank === 3) {
return `${name}.x, ${name}.y, ${name}.z`;
} else if (rank === 4) {
return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;
} else {
throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
}
}
function getFinalCoord(rank, name, op2) {
if (rank === 1) {
return `${name}`;
} else if (rank === 2) {
return `${name}.y`;
} else if (rank === 3) {
return `${name}.z`;
} else if (rank === 4) {
return `${name}.w`;
} else {
throw new Error(`Cumulative ${op2} for rank ${rank} is not yet supported`);
}
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cum_impl.js
init_define_BUILD_VERSION();
function cumImpl(op2, x, backend2, axis, exclusive, reverse4) {
const xRank = x.shape.length;
const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
let permutedX = x;
if (permutation != null) {
permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
}
const permutedAxis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
if (permutedAxis !== xRank - 1) {
throw new Error(`WebGL cumprod shader expects an inner-most axis=${x.shape.length - 1} but got axis=${axis}`);
}
const size = permutedX.shape[permutedAxis];
let result = identity2({ inputs: { x: permutedX }, backend: backend2 });
for (let i = 0; i <= Math.ceil(Math.log2(size)) - 1; i++) {
const program = new CumProgram(op2, permutedX.shape, false, reverse4);
const customValues = [[i]];
const prevResult = result;
result = backend2.runWebGLProgram(program, [result], result.dtype, customValues);
backend2.disposeIntermediateTensorInfo(prevResult);
}
if (exclusive) {
const program = new CumProgram(op2, permutedX.shape, exclusive, reverse4);
const prevResult = result;
result = backend2.runWebGLProgram(program, [result], result.dtype);
backend2.disposeIntermediateTensorInfo(prevResult);
}
if (permutation != null) {
const reversePermutation = backend_util_exports.getUndoAxesPermutation(permutation);
const reverseTransposedResult = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm: reversePermutation } });
backend2.disposeIntermediateTensorInfo(result);
backend2.disposeIntermediateTensorInfo(permutedX);
return reverseTransposedResult;
}
return result;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumprod.js
function cumprod3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, exclusive, reverse: reverse4 } = attrs;
return cumImpl(CumOpType.Prod, x, backend2, axis, exclusive, reverse4);
}
var cumprodConfig2 = {
kernelName: Cumprod,
backendName: "webgl",
kernelFunc: cumprod3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Cumsum.js
init_define_BUILD_VERSION();
function cumsum3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, exclusive, reverse: reverse4 } = attrs;
return cumImpl(CumOpType.Sum, x, backend2, axis, exclusive, reverse4);
}
var cumsumConfig2 = {
kernelName: Cumsum,
backendName: "webgl",
kernelFunc: cumsum3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DenseBincount.js
init_define_BUILD_VERSION();
function denseBincount2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, weights } = inputs;
const { size, binaryOutput } = attrs;
if (x.shape.length === 1) {
const xVals = backend2.readSync(x.dataId);
const weightsVals = backend2.readSync(weights.dataId);
const outVals = bincountImplCPU(xVals, weightsVals, weights.dtype, weights.shape, size);
return backend2.makeTensorInfo([size], weights.dtype, outVals);
} else if (x.shape.length === 2) {
const xBuf = backend2.bufferSync(x);
const weightsBuf = backend2.bufferSync(weights);
const outBuf = bincountReduceImplCPU(xBuf, weightsBuf, size, binaryOutput);
return backend2.makeTensorInfo(outBuf.shape, weights.dtype, outBuf.values);
}
throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${x.shape.length}.`);
}
var denseBincountConfig2 = {
kernelName: DenseBincount,
backendName: "webgl",
kernelFunc: denseBincount2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/depth_to_space_gpu.js
init_define_BUILD_VERSION();
var DepthToSpaceProgram = class {
constructor(outputShape, blockSize, dataFormat) {
this.variableNames = ["x"];
this.outputShape = [];
this.outputShape = outputShape;
this.blockSize = blockSize;
this.dataFormat = dataFormat;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int h = ${this.getHeightCoordString()};
int w = ${this.getWidthCoordString()};
int d = ${this.getDepthCoordString()};
int in_h = h / ${blockSize};
int offset_h = imod(h, ${blockSize});
int in_w = w / ${blockSize};
int offset_w = imod(w, ${blockSize});
int offset_d = (offset_h * ${blockSize} + offset_w) *
${this.getOutputDepthSize()};
int in_d = d + offset_d;
float result = ${this.getInputSamplingString()};
setOutput(result);
}
`;
}
getHeightCoordString() {
if (this.dataFormat === "NHWC") {
return `coords[1]`;
} else {
return `coords[2]`;
}
}
getWidthCoordString() {
if (this.dataFormat === "NHWC") {
return `coords[2]`;
} else {
return `coords[3]`;
}
}
getDepthCoordString() {
if (this.dataFormat === "NHWC") {
return `coords[3]`;
} else {
return `coords[1]`;
}
}
getOutputDepthSize() {
if (this.dataFormat === "NHWC") {
return this.outputShape[3];
} else {
return this.outputShape[1];
}
}
getInputSamplingString() {
if (this.dataFormat === "NHWC") {
return `getX(b, in_h, in_w, in_d)`;
} else {
return `getX(b, in_d, in_h, in_w)`;
}
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthToSpace.js
function depthToSpace3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { blockSize, dataFormat } = attrs;
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 program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
return backend2.runWebGLProgram(program, [x], x.dtype);
}
var depthToSpaceConfig2 = {
kernelName: DepthToSpace,
backendName: "webgl",
kernelFunc: depthToSpace3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_gpu_depthwise.js
init_define_BUILD_VERSION();
var DepthwiseConv2DProgram = class {
constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
this.variableNames = ["x", "W"];
this.customUniforms = [
{ name: "pads", type: "ivec2" },
{ name: "strides", type: "ivec2" },
{ name: "dilations", type: "ivec2" },
{ name: "inDims", type: "ivec2" }
];
this.outputShape = convInfo.outShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const channelMul = convInfo.outChannels / convInfo.inChannels;
let activationSnippet = "", applyActivationSnippet = "";
if (activation) {
if (hasPreluActivation) {
activationSnippet = `float activation(float a) {
float b = getPreluActivationWeightsAtOutCoords();
${activation}
}`;
} else if (hasLeakyReluAlpha) {
activationSnippet = `float activation(float a) {
float b = getLeakyreluAlphaAtOutCoords();
${activation}
}`;
} else {
activationSnippet = `
float activation(float x) {
${activation}
}
`;
}
applyActivationSnippet = `result = activation(result);`;
}
const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
if (addBias) {
this.variableNames.push("bias");
}
if (hasPreluActivation) {
this.variableNames.push("preluActivationWeights");
}
if (hasLeakyReluAlpha) {
this.variableNames.push("leakyreluAlpha");
}
this.userCode = `
${activationSnippet}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${channelMul};
int q = d2 - d1 * ${channelMul};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
for (int wR = 0; wR < ${filterHeight}; wR++) {
int xR = xRCorner + wR * dilations[0];
if (xR < 0 || xR >= inDims[0]) {
continue;
}
for (int wC = 0; wC < ${filterWidth}; wC++) {
int xC = xCCorner + wC * dilations[1];
if (xC < 0 || xC >= inDims[1]) {
continue;
}
float xVal = getX(batch, xR, xC, d1);
float wVal = getW(wR, wC, d1, q);
dotProd += xVal * wVal;
}
}
float result = dotProd;
${addBiasSnippet}
${applyActivationSnippet}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_packed_gpu_depthwise.js
init_define_BUILD_VERSION();
var DepthwiseConvPacked2DProgram = class {
constructor(convInfo, addBias = false, activation = null, hasPreluActivation = false, hasLeakyReluAlpha = false) {
this.variableNames = ["x", "W"];
this.packedInputs = true;
this.packedOutput = true;
this.customUniforms = [
{ name: "pads", type: "ivec2" },
{ name: "strides", type: "ivec2" },
{ name: "dilations", type: "ivec2" },
{ name: "inDims", type: "ivec2" }
];
this.outputShape = convInfo.outShape;
this.enableShapeUniforms = useShapeUniforms(this.outputShape.length);
const channelMul = convInfo.outChannels / convInfo.inChannels;
const padLeft = convInfo.padInfo.left;
const strideWidth = convInfo.strideWidth;
const dilationWidth = convInfo.dilationWidth;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const texelsAcross = filterWidth;
let mainLoop = `
int xR; int xC; int xCOffset;
vec4 wTexel; vec4 previous; vec4 final;`;
for (let c = 0; c < filterWidth; c++) {
mainLoop += `
vec4 xTexelC${c * 2};
int xTexelC${c * 2}Ready;
vec4 xTexelC${c * 2 + 1};
int xTexelC${c * 2 + 1}Ready;
vec4 xC${c};`;
}
mainLoop += `
for (int r = 0; r < ${filterHeight}; r++) {
`;
for (let c = 0; c < filterWidth; c++) {
mainLoop += `
xTexelC${c * 2} = vec4(0.0);
xTexelC${c * 2}Ready = 0;
xTexelC${c * 2 + 1} = vec4(0.0);
xTexelC${c * 2 + 1}Ready = 0;
xC${c} = vec4(0.0);`;
}
mainLoop += `
xR = xRCorner + r * dilations[0];
if (xR >=0 && xR < inDims[0]) {
`;
for (let texelC = 0; texelC < (texelsAcross + 1) / 2; texelC++) {
const colIndex = texelC * 2;
mainLoop += `
xC = xCCorner + ${colIndex * dilationWidth};
`;
if (strideWidth === 1) {
if (colIndex < filterWidth) {
if (padLeft % 2 === 1) {
mainLoop += `
xCOffset = xC + 1;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${colIndex}.zw = vec2(0.0);
}
xTexelC${colIndex}Ready = 1;
}
`;
if (dilationWidth === 1 && colIndex > 0) {
mainLoop += `
xC${colIndex} = vec4(xTexelC${colIndex - 2}.zw, xTexelC${colIndex}.xy);
`;
} else {
mainLoop += `
xCOffset = xC + 1 - 2;
if (xCOffset >= 0 && xCOffset < inDims[1]) {
previous = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
previous.zw = vec2(0.0);
}
xC${colIndex} = vec4(previous.zw, xTexelC${colIndex}.xy);
} else {
xC${colIndex} = vec4(0.0, 0.0, xTexelC${colIndex}.xy);
}
`;
}
} else {
mainLoop += `
if (xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
xTexelC${colIndex} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${colIndex}.zw = vec2(0.0);
}
xTexelC${colIndex}Ready = 1;
}
xC${colIndex} = xTexelC${colIndex};
`;
}
if (colIndex + 1 < filterWidth) {
const nextTexelOffset = padLeft % 2 === 0 ? util_exports.nearestLargerEven(dilationWidth) : dilationWidth;
if (dilationWidth % 2 === 0 && padLeft % 2 === 1 || dilationWidth % 2 !== 0 && padLeft % 2 !== 1) {
mainLoop += `
xCOffset = xC + imod(pads[1], 2) + ${nextTexelOffset};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${colIndex + 1}.zw = vec2(0.0);
}
xTexelC${colIndex + 1}Ready = 1;
}
`;
if (dilationWidth > 1) {
mainLoop += `
xCOffset -= 2;
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
xTexelC${colIndex}Ready = 1;
}
`;
}
mainLoop += `
xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.xy);
`;
} else {
if (nextTexelOffset === 1) {
mainLoop += `
xC${colIndex + 1} = xTexelC${colIndex};
`;
} else {
mainLoop += `
xCOffset = xC + ${nextTexelOffset};
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${colIndex + 1}.zw = vec2(0.0);
}
xTexelC${colIndex + 1}Ready = 1;
}
xC${colIndex + 1} = xTexelC${colIndex + 1};
`;
}
}
}
}
} else {
if (colIndex < filterWidth) {
if (padLeft % 2 === 1) {
mainLoop += `
xCOffset = xC + 1 - strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex}Ready == 0) {
xTexelC${colIndex} = getX(batch, xR, xCOffset, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xCOffset + 1 >= inDims[1]) {
xTexelC${colIndex}.zw = vec2(0.0);
}
xTexelC${colIndex}Ready = 1;
}
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
xTexelC${colIndex + 1} = getX(batch, xR, xC + 1, d1);
// Need to manually clear unused channels in case
// we're reading from recycled texture.
if (xC + 2 >= inDims[1]) {
xTexelC${colIndex + 1}.zw = vec2(0.0);
}
xTexelC${colIndex + 1}Ready = 1;
}
xC${colIndex} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
`;
if (colIndex + 1 < filterWidth) {
mainLoop += `
final = vec4(0.0);
xCOffset = xC + 1 + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1]) {
final = getX(batch, xR, xCOffset, d1);
}
xC${colIndex + 1} = vec4(xTexelC${colIndex + 1}.xy, final.xy);
`;
}
} else {
mainLoop += `
if(xC >= 0 && xC < inDims[1] && xTexelC${colIndex}Ready == 0) {
xTexelC${colIndex} = getX(batch, xR, xC, d1);
if (xC + 1 >= inDims[1]) {
xTexelC${colIndex}.zw = vec2(0.0);
}
xTexelC${colIndex}Ready = 1;
}
xCOffset = xC + strides[1];
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${colIndex + 1}Ready == 0) {
xTexelC${colIndex + 1} = getX(batch, xR, xCOffset, d1);
if (xCOffset + 1 >= inDims[1]) {
xTexelC${colIndex + 1}.zw = vec2(0.);
}
xTexelC${colIndex + 1}Ready = 1;
}
xC${colIndex} = vec4(
xTexelC${colIndex}.xy, xTexelC${colIndex + 1}.xy);
`;
if (colIndex + 1 < filterWidth) {
mainLoop += `
xC${colIndex + 1} = vec4(xTexelC${colIndex}.zw, xTexelC${colIndex + 1}.zw);
`;
}
}
}
}
if (colIndex < filterWidth) {
mainLoop += `
wTexel = getW(r, ${colIndex}, d1, q);
dotProd += xC${colIndex} * vec4(wTexel.xz, wTexel.xz);
`;
if (colIndex + 1 < filterWidth) {
mainLoop += `
wTexel = getW(r, ${colIndex + 1}, d1, q);
dotProd += xC${colIndex + 1} * vec4(wTexel.xz, wTexel.xz);
`;
}
}
}
mainLoop += `
}
`;
mainLoop += `
}
`;
let activationSnippet = "", applyActivationSnippet = "";
if (activation) {
if (hasPreluActivation) {
activationSnippet = `vec4 activation(vec4 a) {
vec4 b = getPreluActivationWeightsAtOutCoords();
${activation}
}`;
} else if (hasLeakyReluAlpha) {
activationSnippet = `vec4 activation(vec4 a) {
vec4 b = getLeakyreluAlphaAtOutCoords();
${activation}
}`;
} else {
activationSnippet = `vec4 activation(vec4 x) {
${activation}
}`;
}
applyActivationSnippet = `result = activation(result);`;
}
const addBiasSnippet = addBias ? "result += getBiasAtOutCoords();" : "";
if (addBias) {
this.variableNames.push("bias");
}
if (hasPreluActivation) {
this.variableNames.push("preluActivationWeights");
}
if (hasLeakyReluAlpha) {
this.variableNames.push("leakyreluAlpha");
}
this.userCode = `
${activationSnippet}
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
ivec2 xRCCorner = coords.yz * strides - pads;
int d2 = coords.w;
int d1 = d2 / ${channelMul};
int q = d2 - d1 * ${channelMul};
int xRCorner = xRCCorner.x;
int xCCorner = xRCCorner.y;
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
vec4 dotProd = vec4(0.000000000000001);
${mainLoop}
vec4 result = dotProd - vec4(0.000000000000001);
${addBiasSnippet}
${applyActivationSnippet}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNative.js
function depthwiseConv2dNative2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dilations, dimRoundingMode } = attrs;
let $dilations = dilations;
if ($dilations == null) {
$dilations = [1, 1];
}
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad2, dimRoundingMode, true);
let program;
if (env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1) {
program = new DepthwiseConvPacked2DProgram(convInfo);
} else {
program = new DepthwiseConv2DProgram(convInfo);
}
const customValues = [
[convInfo.padInfo.top, convInfo.padInfo.left],
[convInfo.strideHeight, convInfo.strideWidth],
[convInfo.dilationHeight, convInfo.dilationWidth],
[convInfo.inHeight, convInfo.inWidth]
];
return backend2.runWebGLProgram(program, [x, filter], "float32", customValues);
}
var depthwiseConv2dNativeConfig2 = {
kernelName: DepthwiseConv2dNative,
backendName: "webgl",
kernelFunc: depthwiseConv2dNative2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/conv_backprop_gpu_depthwise.js
init_define_BUILD_VERSION();
var DepthwiseConv2DDerFilterProgram = class {
constructor(convInfo) {
this.variableNames = ["x", "dy"];
this.outputShape = convInfo.filterShape;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padTop = convInfo.padInfo.top;
const padLeft = convInfo.padInfo.left;
const channelMul = convInfo.outChannels / convInfo.inChannels;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int wR = coords.x;
int wC = coords.y;
int d1 = coords.z;
int dm = coords.w;
int d2 = d1 * ${channelMul} + dm;
float dotProd = 0.0;
// TO DO: Vec4 over the batch size
for (int b = 0; b < ${convInfo.batchSize}; b++) {
for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {
int xR = wR + yR * ${strideHeight} - ${padTop};
if (xR < 0 || xR >= ${convInfo.inHeight}) {
continue;
}
for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {
int xC = wC + yC * ${strideWidth} - ${padLeft};
if (xC < 0 || xC >= ${convInfo.inWidth}) {
continue;
}
float dyValue = getDy(b, yR, yC, d2);
float xValue = getX(b, xR, xC, d1);
dotProd += (xValue * dyValue);
}
}
}
setOutput(dotProd);
}
`;
}
};
var DepthwiseConv2DDerInputProgram = class {
constructor(convInfo) {
this.variableNames = ["dy", "W"];
this.outputShape = convInfo.inShape;
const filterHeight = convInfo.filterHeight;
const filterWidth = convInfo.filterWidth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const padTop = filterHeight - 1 - convInfo.padInfo.top;
const padLeft = filterWidth - 1 - convInfo.padInfo.left;
const channelMul = convInfo.outChannels / convInfo.inChannels;
this.userCode = `
const ivec2 pads = ivec2(${padTop}, ${padLeft});
void main() {
ivec4 coords = getOutputCoords();
int batch = coords[0];
int d1 = coords[3];
ivec2 dyCorner = coords.yz - pads;
int dyRCorner = dyCorner.x;
int dyCCorner = dyCorner.y;
float dotProd = 0.0;
for (int wR = 0; wR < ${filterHeight}; wR++) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
int wRPerm = ${filterHeight} - 1 - wR;
for (int wC = 0; wC < ${filterWidth}; wC++) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
int wCPerm = ${filterWidth} - 1 - wC;
// TO DO: Vec4 over the channelMul
for (int dm = 0; dm < ${channelMul}; dm++) {
int d2 = d1 * ${channelMul} + dm;
float xValue = getDy(batch, idyR, idyC, d2);
float wValue = getW(wRPerm, wCPerm, d1, dm);
dotProd += xValue * wValue;
}
}
}
setOutput(dotProd);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropFilter.js
function depthwiseConv2dNativeBackpropFilter3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, dy } = inputs;
const { strides, dilations, pad: pad2, dimRoundingMode, filterShape } = attrs;
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad2, dimRoundingMode, true);
const program = new DepthwiseConv2DDerFilterProgram(convInfo);
return backend2.runWebGLProgram(program, [x, dy], "float32");
}
var depthwiseConv2dNativeBackpropFilterConfig2 = {
kernelName: DepthwiseConv2dNativeBackpropFilter,
backendName: "webgl",
kernelFunc: depthwiseConv2dNativeBackpropFilter3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/DepthwiseConv2dNativeBackpropInput.js
init_define_BUILD_VERSION();
function depthwiseConv2dNativeBackpropInput3(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, filter } = inputs;
const { strides, dilations, pad: pad2, dimRoundingMode, inputShape } = attrs;
const convInfo = backend_util_exports.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad2, dimRoundingMode, true);
const program = new DepthwiseConv2DDerInputProgram(convInfo);
return backend2.runWebGLProgram(program, [dy, filter], "float32");
}
var depthwiseConv2dNativeBackpropInputConfig2 = {
kernelName: DepthwiseConv2dNativeBackpropInput,
backendName: "webgl",
kernelFunc: depthwiseConv2dNativeBackpropInput3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/diag_gpu.js
init_define_BUILD_VERSION();
var DiagProgram = class {
constructor(size) {
this.variableNames = ["X"];
this.outputShape = [size, size];
this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
setOutput(val);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Diag.js
function diag2(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
const outShape = [...x.shape, ...x.shape];
const xSize = util_exports.sizeFromShape(x.shape);
const flat = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: [xSize] } });
const program = new DiagProgram(xSize);
const res = backend2.runWebGLProgram(program, [flat], flat.dtype);
const out = reshape3({ inputs: { x: res }, backend: backend2, attrs: { shape: outShape } });
backend2.disposeIntermediateTensorInfo(flat);
backend2.disposeIntermediateTensorInfo(res);
return out;
}
var diagConfig2 = {
kernelName: Diag,
backendName: "webgl",
kernelFunc: diag2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/dilation_gpu.js
init_define_BUILD_VERSION();
var Dilation2DProgram = class {
constructor(convInfo) {
this.variableNames = ["x", "W"];
this.outputShape = convInfo.outShape;
const { inHeight, inWidth, padInfo, strideHeight, strideWidth, filterHeight, filterWidth, dilationHeight, dilationWidth } = convInfo;
const { top: padTop, left: padLeft } = padInfo;
this.userCode = `
const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});
const ivec2 pads = ivec2(${padTop}, ${padLeft});
const float neg_infinity = -3.4e38;
void main() {
ivec4 coords = getOutputCoords();
int batch = coords.x;
int d1 = coords.w;
ivec2 outTopLeftCorner =
coords.yz * strides - pads;
int hBeg = outTopLeftCorner.x;
int wBeg = outTopLeftCorner.y;
float curVal = neg_infinity;
for (int h = 0; h < ${filterHeight}; h++) {
int hIn = hBeg + h * ${dilationHeight};
if (hIn >= 0 && hIn < ${inHeight}) {
for (int w = 0; w < ${filterWidth}; w++) {
int wIn = wBeg + w * ${dilationWidth};
if (wIn >= 0 && wIn < ${inWidth}) {
float xVal = getX(batch, hIn, wIn, d1);
float wVal = getW(h, w, d1);
float val = xVal + wVal;
if (val > curVal) {
curVal = val;
}
}
}
}
}
float result = curVal;
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Dilation2D.js
function dilation2D(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter } = inputs;
const { strides, pad: pad2, dilations } = attrs;
const convInfo = backend_util_exports.computeDilation2DInfo(x.shape, filter.shape, strides, pad2, "NHWC", dilations);
let out;
const program = new Dilation2DProgram(convInfo);
out = backend2.runWebGLProgram(program, [x, filter], "float32");
const outReshaped = reshape3({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
backend2.disposeIntermediateTensorInfo(out);
return outReshaped;
}
var dilation2DConfig2 = {
kernelName: Dilation2D,
backendName: "webgl",
kernelFunc: dilation2D
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Einsum.js
init_define_BUILD_VERSION();
function einsum2(args) {
const { inputs, backend: backend2, attrs } = args;
const { equation } = attrs;
const tensors = inputs;
const { allDims, summedDims, idDims } = backend_util_exports.decodeEinsumEquation(equation, tensors.length);
backend_util_exports.checkEinsumDimSizes(allDims.length, idDims, tensors);
const { path, steps } = backend_util_exports.getEinsumComputePath(summedDims, idDims);
const nSteps = steps.length;
let out = null;
let numDimsRemaining = allDims.length;
const tensorsToDispose = [];
for (let i = 0; i < nSteps; ++i) {
for (const idTerm of steps[i]) {
const { permutationIndices: perm, expandDims: dimsToExpand } = backend_util_exports.getEinsumPermutation(numDimsRemaining, idDims[idTerm]);
let x;
if (backend_util_exports.isIdentityPermutation(perm)) {
x = tensors[idTerm];
} else {
x = transpose3({ inputs: { x: tensors[idTerm] }, backend: backend2, attrs: { perm } });
tensorsToDispose.push(x);
}
const targetShape = x.shape.slice();
for (let k = 0; k < dimsToExpand.length; ++k) {
targetShape.splice(dimsToExpand[k], 0, 1);
}
if (!util_exports.arraysEqual(x.shape, targetShape)) {
x = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: targetShape } });
tensorsToDispose.push(x);
}
if (out === null) {
out = x;
} else {
out = multiply2({ inputs: { a: x, b: out }, backend: backend2 });
tensorsToDispose.push(out);
}
}
if (i < nSteps - 1) {
if (path[i] >= 0) {
out = sum4({
inputs: { x: out },
backend: backend2,
attrs: {
axis: path[i] - (allDims.length - numDimsRemaining),
keepDims: false
}
});
tensorsToDispose.push(out);
}
numDimsRemaining--;
}
}
for (const tensorInfo of tensorsToDispose) {
if (tensorInfo === out) {
continue;
}
backend2.disposeIntermediateTensorInfo(tensorInfo);
}
return out;
}
var einsumConfig2 = {
kernelName: Einsum,
backendName: "webgl",
kernelFunc: einsum2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Elu.js
init_define_BUILD_VERSION();
var ELU4 = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;
var ELU_PACKED = `
vec4 result;
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
return result;
`;
var elu4 = unaryKernelFunc2({ opSnippet: ELU4, packedOpSnippet: ELU_PACKED });
var eluConfig2 = {
kernelName: Elu,
backendName: "webgl",
kernelFunc: elu4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/EluGrad.js
init_define_BUILD_VERSION();
var ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;
var ELU_DER_PACKED = `
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
`;
var eluGrad2 = (args) => {
const { inputs, backend: backend2 } = args;
const { dy, y } = inputs;
const program = env().getBool("WEBGL_PACK_BINARY_OPERATIONS") ? new BinaryOpPackedProgram(ELU_DER_PACKED, dy.shape, y.shape) : new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
return backend2.runWebGLProgram(program, [dy, y], dy.dtype);
};
var eluGradConfig3 = {
kernelName: EluGrad,
backendName: "webgl",
kernelFunc: eluGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Equal.js
init_define_BUILD_VERSION();
var PACKED_EQUAL = `
return vec4(equal(a, b));
`;
var EQUAL = `return float(a == b);`;
var equal3 = binaryKernelFunc2({
opSnippet: EQUAL,
packedOpSnippet: PACKED_EQUAL,
dtype: "bool",
cpuKernelImpl: equalImplCPU
});
var equalConfig2 = {
kernelName: Equal,
backendName: "webgl",
kernelFunc: equal3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Erf.js
init_define_BUILD_VERSION();
var ERF = `
// Error function is calculated approximately with elementary function.
// See "Handbook of Mathematical Functions with Formulas,
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
float p = ${backend_util_exports.ERF_P};
float a1 = ${backend_util_exports.ERF_A1};
float a2 = ${backend_util_exports.ERF_A2};
float a3 = ${backend_util_exports.ERF_A3};
float a4 = ${backend_util_exports.ERF_A4};
float a5 = ${backend_util_exports.ERF_A5};
float sign = sign(x);
x = abs(x);
float t = 1.0 / (1.0 + p * x);
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
`;
var erf3 = unaryKernelFunc2({ opSnippet: ERF });
var erfConfig2 = {
kernelName: Erf,
backendName: "webgl",
kernelFunc: erf3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Exp.js
init_define_BUILD_VERSION();
var EXP = CHECK_NAN_SNIPPET_UNARY + `
return exp(x);
`;
var EXP_PACKED = `
vec4 result = exp(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var exp3 = unaryKernelFunc2({
opSnippet: EXP,
packedOpSnippet: EXP_PACKED,
cpuKernelImpl: expImplCPU,
dtype: "float32"
});
var expConfig2 = {
kernelName: Exp,
backendName: "webgl",
kernelFunc: exp3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ExpandDims.js
init_define_BUILD_VERSION();
function expandDims4(args) {
const { inputs, attrs, backend: backend2 } = args;
const { dim } = attrs;
const { input: input2 } = inputs;
const inputRank = input2.shape.length;
const newShape = input2.shape.slice();
let $dim = dim;
if (dim < 0) {
util_exports.assert(-(inputRank + 1) <= dim, () => `Axis must be in the interval [${-(inputRank + 1)}, ${inputRank}]`);
$dim = inputRank + dim + 1;
}
newShape.splice($dim, 0, 1);
return reshape3({ inputs: { x: input2 }, backend: backend2, attrs: { shape: newShape } });
}
var expandDimsConfig2 = {
kernelName: ExpandDims,
backendName: "webgl",
kernelFunc: expandDims4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Expm1.js
init_define_BUILD_VERSION();
var EXPM1 = `return exp(x) - 1.0;`;
var expm13 = unaryKernelFunc2({ opSnippet: EXPM1, packedOpSnippet: EXPM1, cpuKernelImpl: expm1ImplCPU });
var expm1Config2 = {
kernelName: Expm1,
backendName: "webgl",
kernelFunc: expm13
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/fft_gpu.js
init_define_BUILD_VERSION();
var FFTProgram = class {
constructor(component, inputShape, inverse) {
this.variableNames = ["real", "imag"];
const innerDim = inputShape[1];
this.outputShape = inputShape;
const exponentMultiplierSnippet = inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;
const resultDenominator = inverse ? `${innerDim}.0` : "1.0";
let opString;
if (component === "real") {
opString = "return real * expR - imag * expI;";
} else if (component === "imag") {
opString = "return real * expI + imag * expR;";
} else {
throw new Error(`FFT component must be either "real" or "imag", got ${component}.`);
}
this.userCode = `
const float exponentMultiplier = ${exponentMultiplierSnippet};
float unaryOpComplex(float real, float expR, float imag, float expI) {
${opString}
}
float mulMatDFT(int batch, int index) {
float indexRatio = float(index) / float(${innerDim});
float exponentMultiplierTimesIndexRatio =
exponentMultiplier * indexRatio;
float result = 0.0;
for (int i = 0; i < ${innerDim}; i++) {
// x = (-2|2 * PI / N) * index * i;
float x = exponentMultiplierTimesIndexRatio * float(i);
float expR = cos(x);
float expI = sin(x);
float real = getReal(batch, i);
float imag = getImag(batch, i);
result +=
unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};
}
return result;
}
void main() {
ivec2 coords = getOutputCoords();
setOutput(mulMatDFT(coords[0], coords[1]));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT_impl.js
function fftImpl2(x, inverse, backend2) {
const xData = backend2.texData.get(x.dataId);
const inputSize = util_exports.sizeFromShape(x.shape);
const innerDimensionSize = x.shape[x.shape.length - 1];
const batch = inputSize / innerDimensionSize;
const input2D = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: [batch, innerDimensionSize] } });
const xShape = input2D.shape;
const realProgram = new FFTProgram("real", xShape, inverse);
const imagProgram = new FFTProgram("imag", xShape, inverse);
const inputs = [
{
dataId: xData.complexTensorInfos.real.dataId,
dtype: xData.complexTensorInfos.real.dtype,
shape: xShape
},
{
dataId: xData.complexTensorInfos.imag.dataId,
dtype: xData.complexTensorInfos.imag.dtype,
shape: xShape
}
];
const realPart = backend2.runWebGLProgram(realProgram, inputs, "float32");
const imagPart = backend2.runWebGLProgram(imagProgram, inputs, "float32");
const complexOutput = complex3({ inputs: { real: realPart, imag: imagPart }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(realPart);
backend2.disposeIntermediateTensorInfo(imagPart);
const complexOutputReshaped = reshape3({ inputs: { x: complexOutput }, backend: backend2, attrs: { shape: x.shape } });
backend2.disposeIntermediateTensorInfo(input2D);
backend2.disposeIntermediateTensorInfo(complexOutput);
return complexOutputReshaped;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FFT.js
function fft3(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
return fftImpl2(input2, false, backend2);
}
var fftConfig2 = {
kernelName: FFT,
backendName: "webgl",
kernelFunc: fft3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/fill_gpu.js
init_define_BUILD_VERSION();
var FillProgram = class {
constructor(shape, value) {
this.outputShape = [];
this.customUniforms = [{ name: "value", type: "float" }];
this.variableNames = ["x"];
this.outputShape = shape;
this.userCode = `
void main() {
// Input can be obtained from uniform value.
setOutput(value);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Fill.js
function fill3(args) {
const { backend: backend2, attrs } = args;
const { shape, value } = attrs;
let { dtype } = attrs;
dtype = dtype || util_exports.inferDtype(value);
if (dtype === "string") {
const values = util_exports.getArrayFromDType(dtype, util_exports.sizeFromShape(shape));
values.fill(value);
return backend2.makeTensorInfo(shape, dtype, values);
} else {
const program = new FillProgram(shape, value);
const customValues = [[value]];
return backend2.runWebGLProgram(program, [], dtype, customValues);
}
}
var fillConfig2 = {
kernelName: Fill,
backendName: "webgl",
kernelFunc: fill3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/flip_left_right_gpu.js
init_define_BUILD_VERSION();
var FlipLeftRightProgram = class {
constructor(imageShape) {
this.variableNames = ["Image"];
this.outputShape = [];
const imageWidth = imageShape[2];
this.outputShape = imageShape;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int coordX = ${imageWidth} - x - 1;
float outputValue;
if(coordX >= 0 && coordX < ${imageWidth}) {
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
} else {
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
}
setOutput(outputValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FlipLeftRight.js
var flipLeftRightConfig2 = {
kernelName: FlipLeftRight,
backendName: "webgl",
kernelFunc: ({ inputs, backend: backend2 }) => {
const { image: image3 } = inputs;
const webglBackend = backend2;
const program = new FlipLeftRightProgram(image3.shape);
const output = webglBackend.runWebGLProgram(program, [image3], image3.dtype);
return output;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Floor.js
init_define_BUILD_VERSION();
var FLOOR = `return floor(x);`;
var floor3 = unaryKernelFunc2({ opSnippet: FLOOR, packedOpSnippet: FLOOR, cpuKernelImpl: floorImplCPU });
var floorConfig2 = {
kernelName: Floor,
backendName: "webgl",
kernelFunc: floor3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FloorDiv.js
init_define_BUILD_VERSION();
var INT_DIV = `
float s = sign(a) * sign(b);
int ia = round(a);
int ib = round(b);
if (ib != 0) {
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
return float(idiv(ia, ib, s));
} else {
return NAN;
}
`;
var INT_DIV_PACKED = `
ivec4 ia = round(a);
ivec4 ib = round(b);
bvec4 cond = notEqual(ib, ivec4(0));
ivec4 result = ivec4(0);
vec4 s = sign(a) * sign(b);
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
if (cond[0]) {
result[0] = idiv(ia[0], ib[0], s[0]);
}
if (cond[1]) {
result[1] = idiv(ia[1], ib[1], s[1]);
}
if (cond[2]) {
result[2] = idiv(ia[2], ib[2], s[2]);
}
if (cond[3]) {
result[3] = idiv(ia[3], ib[3], s[3]);
}
return vec4(result);
`;
var floorDiv3 = binaryKernelFunc2({ opSnippet: INT_DIV, packedOpSnippet: INT_DIV_PACKED, dtype: "int32" });
var floorDivConfig2 = {
kernelName: FloorDiv,
backendName: "webgl",
kernelFunc: floorDiv3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_gpu.js
init_define_BUILD_VERSION();
var FromPixelsProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
const glsl = getGlslDifferences();
const [height, width] = outputShape;
this.outputShape = outputShape;
this.userCode = `
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);
vec4 values = ${glsl.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
setOutput(floor(value * 255.0 + 0.5));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels_utils/from_pixels_packed_gpu.js
init_define_BUILD_VERSION();
var FromPixelsPackedProgram = class {
constructor(outputShape) {
this.variableNames = ["A"];
this.packedInputs = false;
this.packedOutput = true;
const glsl = getGlslDifferences();
const [height, width] = outputShape;
this.outputShape = outputShape;
this.userCode = `
void main() {
ivec3 coords = getOutputCoords();
int texR = coords[0];
int texC = coords[1];
int depth = coords[2];
vec4 result = vec4(0.);
for(int row=0; row<=1; row++) {
for(int col=0; col<=1; col++) {
texC = coords[1] + row;
depth = coords[2] + col;
vec2 uv = (vec2(texC, texR) + halfCR) /
vec2(${width}.0, ${height}.0);
vec4 values = ${glsl.texture2D}(A, uv);
float value;
if (depth == 0) {
value = values.r;
} else if (depth == 1) {
value = values.g;
} else if (depth == 2) {
value = values.b;
} else if (depth == 3) {
value = values.a;
}
result[row * 2 + col] = floor(value * 255.0 + 0.5);
}
}
${glsl.output} = result;
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FromPixels.js
var fromPixelsConfig = {
kernelName: FromPixels,
backendName: "webgl",
kernelFunc: fromPixels2
};
var fromPixels2DContext2;
function fromPixels2(args) {
const { inputs, backend: backend2, attrs } = args;
let { pixels } = inputs;
const { numChannels } = attrs;
const isVideo = typeof HTMLVideoElement !== "undefined" && pixels instanceof HTMLVideoElement;
const isImage = typeof HTMLImageElement !== "undefined" && pixels instanceof HTMLImageElement;
const [width, height] = isVideo ? [
pixels.videoWidth,
pixels.videoHeight
] : [pixels.width, pixels.height];
const texShape = [height, width];
const outShape = [height, width, numChannels];
if (isImage || isVideo) {
if (fromPixels2DContext2 == null) {
fromPixels2DContext2 = document.createElement("canvas").getContext("2d");
}
fromPixels2DContext2.canvas.width = width;
fromPixels2DContext2.canvas.height = height;
fromPixels2DContext2.drawImage(pixels, 0, 0, width, height);
pixels = fromPixels2DContext2.canvas;
}
const tempPixelHandle = backend2.makeTensorInfo(texShape, "int32");
backend2.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
backend2.gpgpu.uploadPixelDataToTexture(backend2.getTexture(tempPixelHandle.dataId), pixels);
const program = env().getBool("WEBGL_PACK") ? new FromPixelsPackedProgram(outShape) : new FromPixelsProgram(outShape);
const res = backend2.runWebGLProgram(program, [tempPixelHandle], "int32");
backend2.disposeData(tempPixelHandle.dataId);
return res;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedConv2D.js
init_define_BUILD_VERSION();
function fusedConv2d(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter, bias, preluActivationWeights } = inputs;
const { strides, pad: pad2, dataFormat, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
const $dataFormat = backend_util_exports.convertConv2DDataFormat(dataFormat);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad2, dimRoundingMode, false, $dataFormat);
let out;
const intermediates = [];
if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 && convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 && convInfo.strideHeight === 1 && convInfo.strideWidth === 1 && (convInfo.padInfo.type === "SAME" || convInfo.padInfo.type === "VALID")) {
out = conv2dByMatMul({
x,
filter,
convInfo,
backend: backend2,
bias,
activation,
preluActivationWeights,
leakyreluAlpha
});
} else if (env().getBool("WEBGL_CONV_IM2COL")) {
out = conv2dWithIm2Row({
x,
filter,
convInfo,
backend: backend2,
bias,
activation,
preluActivationWeights,
leakyreluAlpha
});
} else {
const hasBias = bias != null;
const hasPreluActivationWeights = preluActivationWeights != null;
const hasLeakyreluAlpha = activation === "leakyrelu";
const fusedActivation = activation ? mapActivationToShaderProgram(activation, false) : null;
const program = new Conv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
const inputs2 = [x, filter];
const alignInputWithDataFormat = (input2, dataFormat2) => {
if (dataFormat2 === "NCHW" && input2.shape.length === 1 && input2.shape[0] !== 1) {
const alignedInput = reshape3({
inputs: { x: input2 },
backend: backend2,
attrs: { shape: [input2.shape[0], 1, 1] }
});
intermediates.push(alignedInput);
return alignedInput;
}
return input2;
};
if (hasBias) {
inputs2.push(alignInputWithDataFormat(bias, dataFormat));
}
if (hasPreluActivationWeights) {
inputs2.push(alignInputWithDataFormat(preluActivationWeights, dataFormat));
}
if (hasLeakyreluAlpha) {
const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
inputs2.push($leakyreluAlpha);
intermediates.push($leakyreluAlpha);
}
out = backend2.runWebGLProgram(program, inputs2, "float32");
}
const outReshaped = reshape3({ inputs: { x: out }, backend: backend2, attrs: { shape: convInfo.outShape } });
intermediates.push(out);
intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return outReshaped;
}
var fusedConv2DConfig2 = {
kernelName: FusedConv2D,
backendName: "webgl",
kernelFunc: fusedConv2d
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/FusedDepthwiseConv2D.js
init_define_BUILD_VERSION();
function fusedDepthwiseConv2D2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, filter, bias, preluActivationWeights } = inputs;
const { strides, pad: pad2, dilations, dimRoundingMode, activation, leakyreluAlpha } = attrs;
const intermediates = [];
let $dilations = dilations;
if ($dilations == null) {
$dilations = [1, 1];
}
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, $dilations), () => `Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${strides} and dilations '${$dilations}'`);
const convInfo = backend_util_exports.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad2, dimRoundingMode, true);
const shouldPackDepthwiseConv = env().getBool("WEBGL_PACK_DEPTHWISECONV") && convInfo.strideWidth <= 2 && convInfo.outChannels / convInfo.inChannels === 1;
const fusedActivation = activation ? mapActivationToShaderProgram(activation, shouldPackDepthwiseConv) : null;
const programInputs = [x, filter];
const hasBias = bias != null;
const hasPreluActivationWeights = preluActivationWeights != null;
const hasLeakyreluAlpha = activation === "leakyrelu";
if (hasBias) {
programInputs.push(bias);
}
if (hasPreluActivationWeights) {
programInputs.push(preluActivationWeights);
}
if (hasLeakyreluAlpha) {
const $leakyreluAlpha = backend2.makeTensorInfo([], "float32", util_exports.createScalarValue(leakyreluAlpha, "float32"));
programInputs.push($leakyreluAlpha);
intermediates.push($leakyreluAlpha);
}
let program;
if (shouldPackDepthwiseConv) {
program = new DepthwiseConvPacked2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
} else {
program = new DepthwiseConv2DProgram(convInfo, hasBias, fusedActivation, hasPreluActivationWeights, hasLeakyreluAlpha);
}
const customValues = [
[convInfo.padInfo.top, convInfo.padInfo.left],
[convInfo.strideHeight, convInfo.strideWidth],
[convInfo.dilationHeight, convInfo.dilationWidth],
[convInfo.inHeight, convInfo.inWidth]
];
const result = backend2.runWebGLProgram(program, programInputs, "float32", customValues);
intermediates.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
}
var fusedDepthwiseConv2DConfig2 = {
kernelName: FusedDepthwiseConv2D,
backendName: "webgl",
kernelFunc: fusedDepthwiseConv2D2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_nd_gpu.js
init_define_BUILD_VERSION();
var GatherNDProgram = class {
constructor(sliceDim, strides, shape, paramsShape) {
this.sliceDim = sliceDim;
this.strides = strides;
this.paramsShape = paramsShape;
this.variableNames = ["x", "indices"];
this.outputShape = shape;
const stridesType = getCoordsDataType(strides.length);
const dtype = getCoordsDataType(shape.length);
const strideString = this.sliceDim > 1 ? "strides[j]" : "strides";
const paramsShapeType = getCoordsDataType(paramsShape.length);
const paramsShapeString = paramsShape.length > 1 ? "paramsShape[j]" : "paramsShape";
this.userCode = `
${stridesType} strides = ${stridesType}(${this.strides});
${paramsShapeType} paramsShape = ${paramsShapeType}(${this.paramsShape});
void main() {
${dtype} coords = getOutputCoords();
int flattenIndex = 0;
bool out_of_bounds = false;
for (int j = 0; j < ${this.sliceDim}; j++) {
int index = round(getIndices(coords[0], j));
out_of_bounds = out_of_bounds || index < 0;
out_of_bounds = out_of_bounds || index >= ${paramsShapeString};
flattenIndex += index * ${strideString};
}
setOutput(out_of_bounds ? 0.0 : getX(flattenIndex, coords[1]));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherNd.js
function gatherNd2(args) {
const { inputs, backend: backend2 } = args;
const { params, indices } = inputs;
const indicesShape = indices.shape;
const sliceRank = indicesShape[indicesShape.length - 1];
const paramsSize = util_exports.sizeFromShape(params.shape);
const [resultShape, numSlices, sliceSize, strides] = backend_util_exports.prepareAndValidate(params, indices);
const flattenIndices = reshape3({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numSlices, sliceRank] } });
const flattenX = reshape3({
inputs: { x: params },
backend: backend2,
attrs: { shape: [util_exports.sizeFromShape(params.shape) / sliceSize, sliceSize] }
});
if (backend2.shouldExecuteOnCPU([params, indices]) || params.dtype === "string") {
const indicesData = backend2.readSync(indices.dataId);
const paramsBuf = backend2.bufferSync(params);
const outValue = gatherNdImplCPU(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize);
return backend2.makeTensorInfo(resultShape, params.dtype, outValue.values);
}
const program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize], params.shape);
const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices], flattenX.dtype);
const reshaped = reshape3({ inputs: { x: res }, backend: backend2, attrs: { shape: resultShape } });
backend2.disposeIntermediateTensorInfo(flattenIndices);
backend2.disposeIntermediateTensorInfo(flattenX);
backend2.disposeIntermediateTensorInfo(res);
return reshaped;
}
var gatherNdConfig2 = {
kernelName: GatherNd,
backendName: "webgl",
kernelFunc: gatherNd2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/gather_gpu.js
init_define_BUILD_VERSION();
var GatherProgram = class {
constructor(aShape, outputShape) {
this.variableNames = ["A", "indices"];
this.outputShape = outputShape;
this.rank = outputShape.length;
const dtype = getCoordsDataType(this.rank);
const sourceCoords = getSourceCoords2(aShape, 2);
this.userCode = `
void main() {
${dtype} resRC = getOutputCoords();
int index = int(getIndices(resRC.x, resRC.z));
float inBounds = (index >= 0) && (index < ${aShape[2]}) ? 1.0 : 0.0;
setOutput(inBounds * getA(${sourceCoords}));
}
`;
}
};
function getSourceCoords2(aShape, axis) {
const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
const sourceCoords = [];
for (let i = 0; i < aShape.length; i++) {
if (i === 2) {
sourceCoords.push("index");
} else {
sourceCoords.push(`${currentCoords[i]}`);
}
}
return sourceCoords.join();
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GatherV2.js
function gatherV22(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, indices } = inputs;
const { axis, batchDims } = attrs;
const parsedAxis = util_exports.parseAxisParam(axis, x.shape)[0];
if (env().get("DEBUG")) {
const indicesVals = backend2.readSync(indices.dataId);
const axisDim = x.shape[parsedAxis];
for (let i = 0; i < indicesVals.length; ++i) {
const index = indicesVals[i];
util_exports.assert(index <= axisDim - 1 && index >= 0, () => `GatherV2: the index value ${index} is not in [0, ${axisDim - 1}]`);
}
}
const shapeInfo = backend_util_exports.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, batchDims);
const indicesSize = util_exports.sizeFromShape(indices.shape);
const toDispose = [];
const flattenX = reshape3({
inputs: { x },
backend: backend2,
attrs: {
shape: [
shapeInfo.batchSize,
shapeInfo.outerSize,
shapeInfo.dimSize,
shapeInfo.sliceSize
]
}
});
const flattenIndex = reshape3({
inputs: { x: indices },
backend: backend2,
attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] }
});
toDispose.push(flattenX);
toDispose.push(flattenIndex);
const flattenOutputShape = [
shapeInfo.batchSize,
shapeInfo.outerSize,
indicesSize / shapeInfo.batchSize,
shapeInfo.sliceSize
];
if (backend2.shouldExecuteOnCPU([x, indices]) || x.dtype === "string") {
const indicesBuf = backend2.bufferSync(flattenIndex);
const xBuf = backend2.bufferSync(flattenX);
const outBuf = gatherV2ImplCPU(xBuf, indicesBuf, flattenOutputShape);
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return backend2.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values);
}
const program = new GatherProgram(flattenX.shape, flattenOutputShape);
const res = backend2.runWebGLProgram(program, [flattenX, flattenIndex], flattenX.dtype);
toDispose.push(res);
const reshaped = reshape3({ inputs: { x: res }, backend: backend2, attrs: { shape: shapeInfo.outputShape } });
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return reshaped;
}
var gatherV2Config2 = {
kernelName: GatherV2,
backendName: "webgl",
kernelFunc: gatherV22
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Greater.js
init_define_BUILD_VERSION();
var GREATER = `return float(a > b);`;
var GREATER_PACKED = `
return vec4(greaterThan(a, b));
`;
var greater3 = binaryKernelFunc2({
opSnippet: GREATER,
packedOpSnippet: GREATER_PACKED,
cpuKernelImpl: greaterImplCPU,
dtype: "bool"
});
var greaterConfig2 = {
kernelName: Greater,
backendName: "webgl",
kernelFunc: greater3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/GreaterEqual.js
init_define_BUILD_VERSION();
var GREATER_EQUAL = `return float(a >= b);`;
var GREATER_EQUAL_PACKED = `
return vec4(greaterThanEqual(a, b));
`;
var greaterEqual3 = binaryKernelFunc2({
opSnippet: GREATER_EQUAL,
packedOpSnippet: GREATER_EQUAL_PACKED,
dtype: "bool",
cpuKernelImpl: greaterEqualImplCPU
});
var greaterEqualConfig2 = {
kernelName: GreaterEqual,
backendName: "webgl",
kernelFunc: greaterEqual3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IFFT.js
init_define_BUILD_VERSION();
function ifft3(args) {
const { inputs, backend: backend2 } = args;
const { input: input2 } = inputs;
return fftImpl2(input2, true, backend2);
}
var ifftConfig2 = {
kernelName: IFFT,
backendName: "webgl",
kernelFunc: ifft3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsFinite.js
init_define_BUILD_VERSION();
var IS_FINITE = `return float(!isnan(x) && !isinf(x));`;
var isFinite4 = unaryKernelFunc2({ opSnippet: IS_FINITE, dtype: "bool" });
var isFiniteConfig2 = {
kernelName: IsFinite,
backendName: "webgl",
kernelFunc: isFinite4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsInf.js
init_define_BUILD_VERSION();
var IS_INF = `return float(isinf(x));`;
var isInf3 = unaryKernelFunc2({ opSnippet: IS_INF, dtype: "bool" });
var isInfConfig2 = {
kernelName: IsInf,
backendName: "webgl",
kernelFunc: isInf3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/IsNaN.js
init_define_BUILD_VERSION();
var IS_NAN = `return float(isnan(x));`;
var isNaN4 = unaryKernelFunc2({ opSnippet: IS_NAN, dtype: "bool" });
var isNaNConfig2 = {
kernelName: IsNan,
backendName: "webgl",
kernelFunc: isNaN4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Less.js
init_define_BUILD_VERSION();
var LESS = `return float(a < b);`;
var LESS_PACKED = `
return vec4(lessThan(a, b));
`;
var less3 = binaryKernelFunc2({
opSnippet: LESS,
packedOpSnippet: LESS_PACKED,
cpuKernelImpl: lessImplCPU,
dtype: "bool"
});
var lessConfig2 = {
kernelName: Less,
backendName: "webgl",
kernelFunc: less3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LessEqual.js
init_define_BUILD_VERSION();
var LESS_EQUAL = `return float(a <= b);`;
var LESS_EQUAL_PACKED = `
return vec4(lessThanEqual(a, b));
`;
var lessEqual3 = binaryKernelFunc2({
opSnippet: LESS_EQUAL,
packedOpSnippet: LESS_EQUAL_PACKED,
cpuKernelImpl: lessEqualImplCPU,
dtype: "bool"
});
var lessEqualConfig2 = {
kernelName: LessEqual,
backendName: "webgl",
kernelFunc: lessEqual3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LinSpace.js
init_define_BUILD_VERSION();
function linSpace2(args) {
const { backend: backend2, attrs } = args;
const { start, stop, num } = attrs;
const outVals = linSpaceImplCPU(start, stop, num);
return backend2.makeTensorInfo([outVals.length], "float32", outVals);
}
var linSpaceConfig2 = {
kernelName: LinSpace,
backendName: "webgl",
kernelFunc: linSpace2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log.js
init_define_BUILD_VERSION();
var LOG = CHECK_NAN_SNIPPET_UNARY + `
return x < 0.0 ? 0./0. : log(x);
`;
var LOG_PACKED = `
vec4 result = log(x);
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : (x.r < 0.0 ? 0./0. : result.r);
result.g = isNaN.g ? x.g : (x.g < 0.0 ? 0./0. : result.g);
result.b = isNaN.b ? x.b : (x.b < 0.0 ? 0./0. : result.b);
result.a = isNaN.a ? x.a : (x.a < 0.0 ? 0./0. : result.a);
return result;
`;
var log4 = unaryKernelFunc2({ opSnippet: LOG, packedOpSnippet: LOG_PACKED, cpuKernelImpl: logImplCPU });
var logConfig2 = {
kernelName: Log,
backendName: "webgl",
kernelFunc: log4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Log1p.js
init_define_BUILD_VERSION();
var LOG1P = CHECK_NAN_SNIPPET_UNARY + `
return log(1.0 + x);
`;
var log1p3 = unaryKernelFunc2({ opSnippet: LOG1P });
var log1pConfig2 = {
kernelName: Log1p,
backendName: "webgl",
kernelFunc: log1p3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalAnd.js
init_define_BUILD_VERSION();
var LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;
var LOGICAL_AND_PACKED = `
return vec4(
vec4(greaterThanEqual(a, vec4(1.0))) *
vec4(greaterThanEqual(b, vec4(1.0))));
`;
var logicalAnd3 = binaryKernelFunc2({
opSnippet: LOGICAL_AND,
packedOpSnippet: LOGICAL_AND_PACKED,
dtype: "bool"
});
var logicalAndConfig2 = {
kernelName: LogicalAnd,
backendName: "webgl",
kernelFunc: logicalAnd3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalNot.js
init_define_BUILD_VERSION();
var LOGICAL_NOT = `return float(!(x >= 1.0));`;
var logicalNot3 = unaryKernelFunc2({ opSnippet: LOGICAL_NOT });
var logicalNotConfig2 = {
kernelName: LogicalNot,
backendName: "webgl",
kernelFunc: logicalNot3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LogicalOr.js
init_define_BUILD_VERSION();
var LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;
var LOGICAL_OR_PACKED = `
return min(
vec4(greaterThanEqual(a, vec4(1.0))) +
vec4(greaterThanEqual(b, vec4(1.0))),
vec4(1.0));
`;
var logicalOr3 = binaryKernelFunc2({ opSnippet: LOGICAL_OR, packedOpSnippet: LOGICAL_OR_PACKED, dtype: "bool" });
var logicalOrConfig2 = {
kernelName: LogicalOr,
backendName: "webgl",
kernelFunc: logicalOr3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_gpu.js
init_define_BUILD_VERSION();
var LRNProgram = class {
constructor(xShape, radius, bias, alpha, beta) {
this.variableNames = ["x"];
this.outputShape = [];
const rad = radius;
const maxD = xShape[3] - 1;
this.outputShape = xShape;
let powOperator;
const basis = `float(${bias}) + float(${alpha}) * sum`;
if (beta === 0.5) {
powOperator = `inversesqrt(${basis})`;
} else if (beta === 1) {
powOperator = `1.0/(${basis})`;
} else {
powOperator = `exp(log(${basis}) * float(-${beta}));`;
}
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
int d = coords[3];
float x = getX(b, r, c, d);
float sum = 0.0;
for (int j = -${rad}; j <= ${rad}; j++) {
int idx = d + j;
if (idx >= 0 && idx <= ${maxD}) {
float z = getX(b, r, c, idx);
sum += z * z;
}
}
float val = x * ${powOperator};
setOutput(val);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_packed_gpu.js
init_define_BUILD_VERSION();
var LRNPackedProgram = class {
constructor(xShape, radius, bias, alpha, beta) {
this.variableNames = ["x"];
this.outputShape = [];
this.packedInputs = true;
this.packedOutput = true;
const rad = radius;
const maxD = xShape[3] - 1;
this.outputShape = xShape;
let powOperator;
const basis = `float(${bias}) + float(${alpha}) * sum`;
if (beta === 0.5) {
powOperator = `inversesqrt(${basis})`;
} else if (beta === 1) {
powOperator = `1.0/(${basis})`;
} else {
powOperator = `exp(log(${basis}) * float(-${beta}));`;
}
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords.x;
int r = coords.y;
int c = coords.z;
int d = coords.w;
bool hasNextCol = d < ${this.outputShape[3]};
bool hasNextRow = c < ${this.outputShape[2]};
vec4 sum = vec4(0.);
vec4 xFragAtOutputCoords = getX(b, r, c, d);
vec4 xAtOutputCoords = vec4(
getChannel(xFragAtOutputCoords, vec2(c, d)),
hasNextCol ?
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
hasNextRow ?
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
(hasNextRow && hasNextCol) ?
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
);
int firstChannel = d - ${rad};
vec2 cache = vec2(0.);
if(firstChannel >= 0){
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
if(hasNextRow){
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
}
}
ivec2 depth = ivec2(d, d + 1);
for (int j = - ${rad}; j <= ${rad}; j++) {
ivec2 idx = depth + j;
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${maxD}));
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
if(depthInRange || depthPlusOneInRange){
vec4 z = vec4(0.);
vec4 xFragAtCurrentDepth;
z.xz = cache.xy;
if(depthPlusOneInRange && hasNextCol){
xFragAtCurrentDepth = idx.y != d ?
getX(b, r, c, idx.y) : xFragAtOutputCoords;
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
if(hasNextRow){
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
}
}
cache.xy = z.yw;
sum += z * z;
}
}
vec4 result = xAtOutputCoords * ${powOperator};
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRN.js
var lrn = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { depthRadius, bias, alpha, beta } = attrs;
const program = env().getBool("WEBGL_PACK_NORMALIZATION") ? new LRNPackedProgram(x.shape, depthRadius, bias, alpha, beta) : new LRNProgram(x.shape, depthRadius, bias, alpha, beta);
return backend2.runWebGLProgram(program, [x], x.dtype);
};
var LRNConfig2 = {
kernelName: LRN,
backendName: "webgl",
kernelFunc: lrn
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/lrn_grad_gpu.js
init_define_BUILD_VERSION();
var LRNGradProgram = class {
constructor(inputShape, depthRadius, bias, alpha, beta) {
this.variableNames = ["inputImage", "outputImage", "dy"];
this.outputShape = [];
this.outputShape = inputShape;
this.depth = inputShape[3];
this.depthRadius = depthRadius;
this.bias = bias;
this.alpha = alpha;
this.beta = beta;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int r = coords[1];
int c = coords[2];
float result = 0.0;
for (int d = 0; d < ${this.depth}; ++d) {
int depthBegin = int(max(0.0, float(d - ${depthRadius})));
int depthEnd = int(min(float(${this.depth}),
float(d + ${depthRadius} + 1)));
const int MIN_DEPTH_BEGIN = 0;
const int MAX_DEPTH_END = ${this.depth};
float norm = 0.0;
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd) {
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
}
else {
break;
}
}
norm = float(${alpha}) * norm + float(${bias});
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
if (k < depthBegin){
continue;
}
else if (k >= depthBegin && k < depthEnd){
float dyi = -2.0 * float(${alpha})
* float(${beta})
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
/ norm;
if (k == d) {
dyi += pow(norm, -1.0 * ${beta});
}
if (k == coords[3]) {
dyi *= getDy(b, r, c, d);
result += dyi;
}
}
else {
break;
}
}
}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/LRNGrad.js
var lrnGrad = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { x, y, dy } = inputs;
const { depthRadius, bias, alpha, beta } = attrs;
const program = new LRNGradProgram(x.shape, depthRadius, bias, alpha, beta);
return backend2.runWebGLProgram(program, [x, y, dy], x.dtype);
};
var LRNGradConfig2 = {
kernelName: LRNGrad,
backendName: "webgl",
kernelFunc: lrnGrad
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max_impl.js
init_define_BUILD_VERSION();
function maxImpl2(x, reduceShape, outShape, backend2) {
const inSize = util_exports.sizeFromShape(reduceShape);
const xSize = util_exports.sizeFromShape(x.shape);
const batchSize = xSize / inSize;
const reshapedInput = reshape3({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
const reduced = reduce(reshapedInput, x.dtype, "max", backend2);
const reshapedOutput = reshape3({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(reshapedInput);
backend2.disposeIntermediateTensorInfo(reduced);
return reshapedOutput;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Max.js
function max4(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { reductionIndices, keepDims } = attrs;
const xRank = x.shape.length;
const origAxes = util_exports.parseAxisParam(reductionIndices, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
const maxInputIsTransposed = permutedAxes != null;
const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
let maxInput = x;
if (maxInputIsTransposed) {
if (shouldExecuteOnCPU) {
const xTexData = backend2.texData.get(maxInput.dataId);
const values = xTexData.values;
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = x.shape[permutedAxes[i]];
}
const maxInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
maxInput = backend2.makeTensorInfo(newShape, x.dtype);
const maxInputData = backend2.texData.get(maxInput.dataId);
maxInputData.values = maxInputValues;
} else {
maxInput = transposeImpl2(x, permutedAxes, backend2);
}
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
}
backend_util_exports.assertAxesAreInnerMostDims("max", axes, xRank);
const [maxOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(maxInput.shape, axes);
let outShape = maxOutShape;
if (keepDims) {
outShape = backend_util_exports.expandShapeToKeepDim(maxOutShape, origAxes);
}
let out;
if (shouldExecuteOnCPU) {
const xTexData = backend2.texData.get(maxInput.dataId);
const values = xTexData.values;
const outValues = maxImplCPU(values, util_exports.sizeFromShape(reduceShape), outShape, x.dtype);
out = backend2.makeTensorInfo(outShape, x.dtype);
const outData = backend2.texData.get(out.dataId);
outData.values = outValues;
} else {
out = maxImpl2(maxInput, reduceShape, outShape, backend2);
}
if (maxInputIsTransposed) {
backend2.disposeIntermediateTensorInfo(maxInput);
}
return out;
}
var maxConfig2 = {
kernelName: Max,
backendName: "webgl",
kernelFunc: max4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Maximum.js
init_define_BUILD_VERSION();
var MAXIMUM = CHECK_NAN_SNIPPET2 + `
return max(a, b);
`;
var MAXIMUM_PACKED = `
vec4 result = vec4(max(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
` + CHECK_NAN_SNIPPET3 + `
return result;
`;
var maximum3 = binaryKernelFunc2({
opSnippet: MAXIMUM,
packedOpSnippet: MAXIMUM_PACKED,
cpuKernelImpl: maximumImplCPU
});
var maximumConfig2 = {
kernelName: Maximum,
backendName: "webgl",
kernelFunc: maximum3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool.js
init_define_BUILD_VERSION();
function maxPool3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
assertNotComplex2(x, "maxPool");
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const dilations = 1;
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode);
if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && util_exports.arraysEqual(convInfo.inShape, convInfo.outShape)) {
return identity2({ inputs: { x }, backend: backend2 });
}
const maxPoolProgram = new Pool2DProgram(convInfo, "max", false);
return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
}
var maxPoolConfig2 = {
kernelName: MaxPool,
backendName: "webgl",
kernelFunc: maxPool3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3D.js
init_define_BUILD_VERSION();
function maxPool3d2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { filterSize, strides, pad: pad2, dataFormat, dimRoundingMode } = attrs;
const dilations = [1, 1, 1];
const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode, dataFormat);
const maxPoolProgram = new Pool3DProgram(convInfo, "max", false);
return backend2.runWebGLProgram(maxPoolProgram, [x], x.dtype);
}
var maxPool3DConfig2 = {
kernelName: MaxPool3D,
backendName: "webgl",
kernelFunc: maxPool3d2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/max_pool_backprop_gpu.js
init_define_BUILD_VERSION();
var MaxPool2DBackpropProgram = class {
constructor(convInfo) {
this.variableNames = ["dy", "maxPos"];
this.outputShape = convInfo.inShape;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationHeight = convInfo.dilationHeight;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
this.userCode = `
const ivec2 pads = ivec2(${padTop}, ${padLeft});
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 dyRCCorner = coords.yz - pads;
int dyRCorner = dyRCCorner.x;
int dyCCorner = dyRCCorner.y;
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(b, idyR, idyC, d);
int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue = wR * ${effectiveFilterWidth} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
setOutput(dotProd);
}
`;
}
};
var MaxPool3DBackpropProgram = class {
constructor(convInfo) {
this.variableNames = ["dy", "maxPos"];
this.outputShape = convInfo.inShape;
const strideDepth = convInfo.strideDepth;
const strideHeight = convInfo.strideHeight;
const strideWidth = convInfo.strideWidth;
const dilationDepth = convInfo.dilationDepth;
const dilationHeight = convInfo.dilationHeight;
const dilationWidth = convInfo.dilationWidth;
const effectiveFilterDepth = convInfo.effectiveFilterDepth;
const effectiveFilterHeight = convInfo.effectiveFilterHeight;
const effectiveFilterWidth = convInfo.effectiveFilterWidth;
const padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front;
const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
const lastIndex = effectiveFilterDepth * effectiveFilterHeight * effectiveFilterWidth - 1;
this.userCode = `
const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});
void main() {
ivec5 coords = getOutputCoords();
int batch = coords.x;
int ch = coords.u;
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
int dyDCorner = dyCorner.x;
int dyRCorner = dyCorner.y;
int dyCCorner = dyCorner.z;
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
// dx(xD, xR, xC, ch).
// ? = to be determined. : = across all values in that axis.
float dotProd = 0.0;
for (int wD = 0; wD < ${effectiveFilterDepth};
wD += ${dilationDepth}) {
float dyD = float(dyDCorner + wD) / ${strideDepth}.0;
if (dyD < 0.0 || dyD >= ${convInfo.outDepth}.0 || fract(dyD) > 0.0) {
continue;
}
int idyD = int(dyD);
for (int wR = 0; wR < ${effectiveFilterHeight};
wR += ${dilationHeight}) {
float dyR = float(dyRCorner + wR) / ${strideHeight}.0;
if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||
fract(dyR) > 0.0) {
continue;
}
int idyR = int(dyR);
for (int wC = 0; wC < ${effectiveFilterWidth};
wC += ${dilationWidth}) {
float dyC = float(dyCCorner + wC) / ${strideWidth}.0;
if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||
fract(dyC) > 0.0) {
continue;
}
int idyC = int(dyC);
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
int maxPosValue = ${lastIndex} -
int(getMaxPos(batch, idyD, idyR, idyC, ch));
// Get the current value, check it against the value from the
// position matrix.
int curPosValue =
wD * ${effectiveFilterHeight} * ${effectiveFilterWidth} +
wR * ${effectiveFilterWidth} + wC;
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
dotProd += dyValue * mask;
}
}
}
setOutput(dotProd);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPool3DGrad.js
function maxPool3DGrad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2 } = inputs;
const x = input2;
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const dilations = [1, 1, 1];
const convInfo = backend_util_exports.computePool3DInfo(x.shape, filterSize, strides, dilations, pad2, dimRoundingMode);
const maxPool3dPositionsProgram = new Pool3DProgram(convInfo, "max", true);
const maxPool3dPositions2 = backend2.runWebGLProgram(maxPool3dPositionsProgram, [x], x.dtype);
const maxPoolBackpropProgram = new MaxPool3DBackpropProgram(convInfo);
const result = backend2.runWebGLProgram(maxPoolBackpropProgram, [dy, maxPool3dPositions2], x.dtype);
backend2.disposeIntermediateTensorInfo(maxPool3dPositions2);
return result;
}
var maxPool3DGradConfig3 = {
kernelName: MaxPool3DGrad,
backendName: "webgl",
kernelFunc: maxPool3DGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolGrad.js
init_define_BUILD_VERSION();
function maxPoolGrad3(args) {
const { inputs, backend: backend2, attrs } = args;
const { dy, input: input2, output } = inputs;
const x = input2;
assertNotComplex2([input2, output], "maxPoolGrad");
const { filterSize, strides, pad: pad2, dimRoundingMode } = attrs;
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, 1, pad2, dimRoundingMode);
const getPositions = true;
const maxPoolPositionsProgram = new Pool2DProgram(convInfo, "max", getPositions);
const maxPoolPositions2 = backend2.runWebGLProgram(maxPoolPositionsProgram, [x], x.dtype);
const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
const result = backend2.runWebGLProgram(maxPoolBackPropProgram, [dy, maxPoolPositions2], x.dtype);
backend2.disposeIntermediateTensorInfo(maxPoolPositions2);
return result;
}
var maxPoolGradConfig3 = {
kernelName: MaxPoolGrad,
backendName: "webgl",
kernelFunc: maxPoolGrad3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax_impl.js
init_define_BUILD_VERSION();
function maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, backend2) {
let program = new Pool2DProgram(convInfo, "max", false);
const poolOutput = backend2.runWebGLProgram(program, [x], "float32");
program = new Pool2DProgram(convInfo, "max", true, true, includeBatchInIndex);
const indexOutput = backend2.runWebGLProgram(program, [x], "float32");
return [poolOutput, indexOutput];
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MaxPoolWithArgmax.js
var maxPoolWithArgmaxConfig2 = {
kernelName: MaxPoolWithArgmax,
backendName: "webgl",
kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
const { x } = inputs;
const { filterSize, strides, pad: pad2, includeBatchInIndex } = attrs;
const webglBackend = backend2;
util_exports.assert(x.shape.length === 4, () => `Error in maxPool: input must be rank 4 but got rank ${x.shape.length}.`);
const dilations = [1, 1];
util_exports.assert(backend_util_exports.eitherStridesOrDilationsAreOne(strides, dilations), () => `Error in maxPool: Either strides or dilations must be 1. Got strides ${strides} and dilations '${dilations}'`);
const convInfo = backend_util_exports.computePool2DInfo(x.shape, filterSize, strides, dilations, pad2);
const [result, indexes] = maxPoolWithArgmaxImpl2(x, includeBatchInIndex, convInfo, webglBackend);
return [result, indexes];
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean_impl.js
init_define_BUILD_VERSION();
function meanImpl(x, reduceShape, outShape, backend2) {
const inSize = util_exports.sizeFromShape(reduceShape);
const xSize = util_exports.sizeFromShape(x.shape);
const batchSize = xSize / inSize;
const reshapedInput = reshape3({ inputs: { x }, attrs: { shape: [batchSize, inSize] }, backend: backend2 });
const reduced = reduce(reshapedInput, "float32", "mean", backend2);
const reshapedOutput = reshape3({ inputs: { x: reduced }, attrs: { shape: outShape }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(reshapedInput);
backend2.disposeIntermediateTensorInfo(reduced);
return reshapedOutput;
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mean.js
var meanConfig2 = {
kernelName: Mean,
backendName: "webgl",
kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
const { x } = inputs;
const { keepDims, axis } = attrs;
const webglBackend = backend2;
const xRank = x.shape.length;
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
const meanInputIsTransposed = permutedAxes != null;
const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
const intermediates = [];
let meanInput = x;
if (meanInputIsTransposed) {
if (shouldExecuteOnCPU) {
const xTexData = webglBackend.texData.get(meanInput.dataId);
const values = xTexData.values;
const newShape = new Array(xRank);
for (let i = 0; i < newShape.length; i++) {
newShape[i] = x.shape[permutedAxes[i]];
}
const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
const meanInputData = webglBackend.texData.get(meanInput.dataId);
meanInputData.values = meanInputValues;
} else {
meanInput = transposeImpl2(x, permutedAxes, webglBackend);
}
intermediates.push(meanInput);
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
}
backend_util_exports.assertAxesAreInnerMostDims("sum", axes, xRank);
const [meanOutShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(meanInput.shape, axes);
let outShape = meanOutShape;
if (keepDims) {
outShape = backend_util_exports.expandShapeToKeepDim(meanOutShape, origAxes);
}
const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
for (const i of intermediates) {
webglBackend.disposeIntermediateTensorInfo(i);
}
return out;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Min.js
init_define_BUILD_VERSION();
function min4(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
const xRank = x.shape.length;
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
let permutedX = x;
if (permutedAxes != null) {
permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, x.shape.length);
}
backend_util_exports.assertAxesAreInnerMostDims("min", axes, xRank);
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
const inSize = util_exports.sizeFromShape(reduceShape);
const a2D = reshape3({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
const reduced = reduce(a2D, a2D.dtype, "min", backend2);
let res;
if (keepDims) {
const newShape = backend_util_exports.expandShapeToKeepDim(outShape, origAxes);
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: newShape } });
} else {
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
}
backend2.disposeIntermediateTensorInfo(a2D);
backend2.disposeIntermediateTensorInfo(reduced);
if (permutedAxes != null) {
backend2.disposeIntermediateTensorInfo(permutedX);
}
return res;
}
var minConfig2 = {
kernelName: Min,
backendName: "webgl",
kernelFunc: min4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Minimum.js
init_define_BUILD_VERSION();
var MINIMUM = CHECK_NAN_SNIPPET2 + `
return min(a, b);
`;
var MINIMUM_PACKED = `
vec4 result = vec4(min(a, b));
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
` + CHECK_NAN_SNIPPET3 + `
return result;
`;
var minimum3 = binaryKernelFunc2({
opSnippet: MINIMUM,
packedOpSnippet: MINIMUM_PACKED,
cpuKernelImpl: minimumImplCPU
});
var minimumConfig2 = {
kernelName: Minimum,
backendName: "webgl",
kernelFunc: minimum3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_gpu.js
init_define_BUILD_VERSION();
var MirrorPadProgram = class {
constructor(xShape, paddings, mode) {
this.variableNames = ["x"];
this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
const rank = xShape.length;
const dtype = getCoordsDataType(rank);
const start = paddings.map((p2) => p2[0]).join(",");
const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
const offset = mode === "reflect" ? 0 : 1;
if (rank === 1) {
this.userCode = `
int start = ${start};
int end = ${end};
void main() {
int outC = getOutputCoords();
if (outC < start) {
outC = start * 2 - outC - ${offset};
} else if(outC >= end) {
outC = (end - 1) * 2 - outC + ${offset};
}
setOutput(getX(outC - start));
}
`;
return;
}
this.userCode = `
${dtype} start = ${dtype}(${start});
${dtype} end = ${dtype}(${end});
void main() {
${dtype} outC = getOutputCoords();
for (int i = 0; i < ${rank}; i++) {
if (outC[i] < start[i]) {
outC[i] = start[i] * 2 - outC[i] - ${offset};
} else if(outC[i] >= end[i]) {
outC[i] = (end[i] - 1) * 2 - outC[i] + ${offset};
}
}
${dtype} coords = outC - start;
setOutput(getX(${unpackedCoords}));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/mirror_pad_packed_gpu.js
init_define_BUILD_VERSION();
var MirrorPadPackedProgram = class {
constructor(xShape, paddings, mode) {
this.variableNames = ["x"];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
const rank = xShape.length;
const dtype = getCoordsDataType(rank);
const start = paddings.map((p2) => p2[0]).join(",");
const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
const coords2 = getChannels("rc", rank);
const source = getChannels("source", rank);
const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`;
const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
const offset = mode === "reflect" ? 0 : 1;
let mainLoop = "";
if (rank === 1) {
const padSetup = `
${dtype} source = rc;
if (source < start) {
source = start * 2 - source - ${offset};
} else if (source >= end) {
source = (end - 1) * 2 - source + ${offset};
}
source -= start;
`;
mainLoop = `
${dtype} rc = outputLoc;
${padSetup}
result[0] = getChannel(getX(${source.join()}), ${innerDims});
${coords2[rank - 1]} += 1;
if(${cLimit}) {
${padSetup}
result[1] = getChannel(getX(${source.join()}), ${innerDims});
}
`;
} else {
const padSetup = `
${dtype} source = rc;
${dtype} lt = ${dtype}(lessThan(source, start));
${dtype} gte = ${dtype}(greaterThanEqual(source, end));
${dtype} orig = 1 - (lt + gte);
source = orig * source +
lt * (start * 2 - source - ${offset}) +
gte * ((end - 1) * 2 - source + ${offset});
source -= start;
`;
mainLoop = `
${dtype} rc = outputLoc;
${padSetup}
result[0] = getChannel(getX(${source.join()}), ${innerDims});
${coords2[rank - 1]} += 1;
if(${cLimit}) {
${padSetup}
result[1] = getChannel(getX(${source.join()}), ${innerDims});
}
rc = outputLoc;
${coords2[rank - 2]} += 1;
if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {
${padSetup}
result[2] = getChannel(getX(${source.join()}), ${innerDims});
${coords2[rank - 1]} += 1;
if(${cLimit}) {
${padSetup}
result[3] = getChannel(getX(${source.join()}), ${innerDims});
}
}
`;
}
this.userCode = `
const ${dtype} start = ${dtype}(${start});
const ${dtype} end = ${dtype}(${end});
void main() {
${dtype} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${mainLoop}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/MirrorPad.js
var mirrorPadKernelFunc = ({ inputs, backend: backend2, attrs }) => {
const { x } = inputs;
const { paddings, mode } = attrs;
const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new MirrorPadPackedProgram(x.shape, paddings, mode) : new MirrorPadProgram(x.shape, paddings, mode);
const output = backend2.runWebGLProgram(program, [x], x.dtype);
return output;
};
var mirrorPadConfig2 = {
kernelName: MirrorPad,
backendName: "webgl",
kernelFunc: mirrorPadKernelFunc
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Mod.js
init_define_BUILD_VERSION();
var MOD = `if (b == 0.0) return NAN;
return mod(a, b);`;
var MOD_PACKED = `
vec4 result = mod(a, b);
vec4 isNaN = vec4(equal(b, vec4(0.0)));
` + CHECK_NAN_SNIPPET3 + `
return result;
`;
var mod3 = binaryKernelFunc2({
opSnippet: MOD,
packedOpSnippet: MOD_PACKED
});
var modConfig2 = {
kernelName: Mod,
backendName: "webgl",
kernelFunc: mod3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/multinomial_gpu.js
init_define_BUILD_VERSION();
var MultinomialProgram = class {
constructor(batchSize, numOutcomes, numSamples) {
this.variableNames = ["probs"];
this.customUniforms = [{ name: "seed", type: "float" }];
this.outputShape = [batchSize, numSamples];
this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
float r = random(seed);
float cdf = 0.0;
for (int i = 0; i < ${numOutcomes - 1}; i++) {
cdf += getProbs(batch, i);
if (r < cdf) {
setOutput(float(i));
return;
}
}
// If no other event happened, last event happened.
setOutput(float(${numOutcomes - 1}));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RealDiv.js
init_define_BUILD_VERSION();
var DIV = `
if (a == b) {
return 1.0;
};
return a / b;`;
var DIV_PACKED = `
// vec4 one = vec4(equal(a, b));
// return one + (vec4(1.0) - one) * a / b;
vec4 result = a / b;
if(a.x == b.x) {
result.x = 1.;
}
if(a.y == b.y) {
result.y = 1.;
}
if(a.z == b.z) {
result.z = 1.;
}
if(a.w == b.w) {
result.w = 1.;
}
return result;
`;
var realDiv = binaryKernelFunc2({ opSnippet: DIV, packedOpSnippet: DIV_PACKED, checkOutOfBounds: true });
var realDivConfig2 = {
kernelName: RealDiv,
backendName: "webgl",
kernelFunc: realDiv
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sub.js
init_define_BUILD_VERSION();
var SUB = "return a - b;";
var sub3 = binaryKernelFunc2({
opSnippet: SUB,
packedOpSnippet: SUB,
supportsComplex: true,
cpuKernelImpl: subImplCPU
});
var subConfig2 = {
kernelName: Sub,
backendName: "webgl",
kernelFunc: sub3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softmax.js
function softmax3(args) {
const { inputs, backend: backend2, attrs } = args;
const { logits } = inputs;
const { dim } = attrs;
const axes = util_exports.parseAxisParam([dim], logits.shape);
const maxLogit = max4({
inputs: { x: logits },
backend: backend2,
attrs: { reductionIndices: axes, keepDims: false }
});
const expandedShape = backend_util_exports.expandShapeToKeepDim(maxLogit.shape, axes);
const maxLogitsReshaped = reshape3({ inputs: { x: maxLogit }, backend: backend2, attrs: { shape: expandedShape } });
const a = sub3({ inputs: { a: logits, b: maxLogitsReshaped }, backend: backend2 });
const b = exp3({ inputs: { x: a }, backend: backend2 });
const sumExp = sum4({ inputs: { x: b }, backend: backend2, attrs: { axis: axes, keepDims: false } });
const sumExpReshaped = reshape3({ inputs: { x: sumExp }, backend: backend2, attrs: { shape: expandedShape } });
const res = realDiv({ inputs: { a: b, b: sumExpReshaped }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(maxLogit);
backend2.disposeIntermediateTensorInfo(maxLogitsReshaped);
backend2.disposeIntermediateTensorInfo(a);
backend2.disposeIntermediateTensorInfo(b);
backend2.disposeIntermediateTensorInfo(sumExp);
backend2.disposeIntermediateTensorInfo(sumExpReshaped);
return res;
}
var softmaxConfig2 = {
kernelName: Softmax,
backendName: "webgl",
kernelFunc: softmax3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Multinomial.js
function multinomial2(args) {
const { inputs, backend: backend2, attrs } = args;
const { logits } = inputs;
const { numSamples, seed, normalized } = attrs;
const probs = normalized ? logits : softmax3({ inputs: { logits }, backend: backend2, attrs: { dim: logits.shape.length - 1 } });
const batchSize = probs.shape[0];
const numOutcomes = probs.shape[1];
const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
const customValues = [[seed]];
const res = backend2.runWebGLProgram(program, [probs], "int32", customValues);
if (!normalized) {
backend2.disposeIntermediateTensorInfo(probs);
}
return res;
}
var multinomialConfig2 = {
kernelName: Multinomial,
backendName: "webgl",
kernelFunc: multinomial2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Neg.js
init_define_BUILD_VERSION();
var NEG = CHECK_NAN_SNIPPET + `
return -x;
`;
var NEG_PACKED = `
vec4 result = -x;
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
function neg3(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
if (backend2.shouldExecuteOnCPU([x])) {
const xData = backend2.texData.get(x.dataId);
const [outValues, newShape] = negImplCPU(xData.values, x.shape, x.dtype);
return backend2.makeTensorInfo(newShape, x.dtype, outValues);
}
let program;
if (env().getBool("WEBGL_PACK_UNARY_OPERATIONS")) {
program = new UnaryOpPackedProgram(x.shape, NEG_PACKED);
} else {
program = new UnaryOpProgram(x.shape, NEG);
}
return backend2.runWebGLProgram(program, [x], x.dtype);
}
var negConfig2 = {
kernelName: Neg,
backendName: "webgl",
kernelFunc: neg3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV3.js
init_define_BUILD_VERSION();
var nonMaxSuppressionV3Impl3 = kernel_impls_exports.nonMaxSuppressionV3Impl;
function nonMaxSuppressionV32(args) {
backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
const { inputs, backend: backend2, attrs } = args;
const { boxes, scores } = inputs;
const { maxOutputSize, iouThreshold, scoreThreshold } = attrs;
const boxesVals = backend2.readSync(boxes.dataId);
const scoresVals = backend2.readSync(scores.dataId);
const { selectedIndices } = nonMaxSuppressionV3Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
return backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices));
}
var nonMaxSuppressionV3Config2 = {
kernelName: NonMaxSuppressionV3,
backendName: "webgl",
kernelFunc: nonMaxSuppressionV32
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV4.js
init_define_BUILD_VERSION();
var nonMaxSuppressionV4Impl3 = kernel_impls_exports.nonMaxSuppressionV4Impl;
function nonMaxSuppressionV42(args) {
backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
const { inputs, backend: backend2, attrs } = args;
const { boxes, scores } = inputs;
const { maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize } = attrs;
const boxesVals = backend2.readSync(boxes.dataId);
const scoresVals = backend2.readSync(scores.dataId);
const { selectedIndices, validOutputs } = nonMaxSuppressionV4Impl3(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize);
return [
backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
backend2.makeTensorInfo([], "int32", new Int32Array([validOutputs]))
];
}
var nonMaxSuppressionV4Config2 = {
kernelName: NonMaxSuppressionV4,
backendName: "webgl",
kernelFunc: nonMaxSuppressionV42
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/NonMaxSuppressionV5.js
init_define_BUILD_VERSION();
var nonMaxSuppressionV5Impl3 = kernel_impls_exports.nonMaxSuppressionV5Impl;
function nonMaxSuppressionV52(args) {
backend_util_exports.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");
const { inputs, backend: backend2, attrs } = args;
const { boxes, scores } = inputs;
const { maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma } = attrs;
const boxesVals = backend2.readSync(boxes.dataId);
const scoresVals = backend2.readSync(scores.dataId);
const maxOutputSizeVal = maxOutputSize;
const iouThresholdVal = iouThreshold;
const scoreThresholdVal = scoreThreshold;
const softNmsSigmaVal = softNmsSigma;
const { selectedIndices, selectedScores } = nonMaxSuppressionV5Impl3(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal);
return [
backend2.makeTensorInfo([selectedIndices.length], "int32", new Int32Array(selectedIndices)),
backend2.makeTensorInfo([selectedScores.length], "float32", new Float32Array(selectedScores))
];
}
var nonMaxSuppressionV5Config2 = {
kernelName: NonMaxSuppressionV5,
backendName: "webgl",
kernelFunc: nonMaxSuppressionV52
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/onehot_gpu.js
init_define_BUILD_VERSION();
var OneHotProgram = class {
constructor(numIndices, depth, onValue, offValue) {
this.variableNames = ["indices"];
this.outputShape = [numIndices, depth];
this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int index = round(getIndices(coords.x));
setOutput(mix(float(${offValue}), float(${onValue}),
float(index == coords.y)));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OneHot.js
var oneHot3 = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { indices } = inputs;
const { depth, onValue, offValue } = attrs;
const indicesSize = util_exports.sizeFromShape(indices.shape);
const program = new OneHotProgram(indicesSize, depth, onValue, offValue);
const reshaped = reshape3({ inputs: { x: indices }, backend: backend2, attrs: { shape: [indicesSize] } });
const result = backend2.runWebGLProgram(program, [reshaped], indices.dtype);
backend2.disposeIntermediateTensorInfo(reshaped);
const outShape = [...indices.shape, depth];
const out = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: outShape } });
backend2.disposeIntermediateTensorInfo(result);
return out;
};
var oneHotConfig2 = {
kernelName: OneHot,
backendName: "webgl",
kernelFunc: oneHot3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ZerosLike.js
init_define_BUILD_VERSION();
function zerosLike3(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
if (x.dtype === "complex64") {
const realPart = real3({ inputs: { input: x }, backend: backend2 });
const r = zerosLike3({ inputs: { x: realPart }, backend: backend2 });
const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(realPart);
backend2.disposeIntermediateTensorInfo(r);
backend2.disposeIntermediateTensorInfo(imagPart);
backend2.disposeIntermediateTensorInfo(i);
return result;
} else {
return fill3({
attrs: {
shape: x.shape,
dtype: x.dtype,
value: x.dtype === "string" ? "" : 0
},
backend: backend2
});
}
}
var zerosLikeConfig2 = {
kernelName: ZerosLike,
backendName: "webgl",
kernelFunc: zerosLike3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/OnesLike.js
function onesLike3(args) {
const { inputs, backend: backend2 } = args;
const { x } = inputs;
if (x.dtype === "string") {
throw new Error("onesLike is not supported under string dtype");
} else if (x.dtype === "complex64") {
const realPart = real3({ inputs: { input: x }, backend: backend2 });
const r = onesLike3({ inputs: { x: realPart }, backend: backend2 });
const imagPart = imag3({ inputs: { input: x }, backend: backend2 });
const i = zerosLike3({ inputs: { x: imagPart }, backend: backend2 });
const result = complex3({ inputs: { real: r, imag: i }, backend: backend2 });
backend2.disposeIntermediateTensorInfo(realPart);
backend2.disposeIntermediateTensorInfo(r);
backend2.disposeIntermediateTensorInfo(imagPart);
backend2.disposeIntermediateTensorInfo(i);
return result;
} else {
return fill3({ attrs: { shape: x.shape, dtype: x.dtype, value: 1 }, backend: backend2 });
}
}
var onesLikeConfig2 = {
kernelName: OnesLike,
backendName: "webgl",
kernelFunc: onesLike3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pack.js
init_define_BUILD_VERSION();
function pack2(args) {
const { inputs, backend: backend2, attrs } = args;
const { axis } = attrs;
if (inputs.length === 1) {
return expandDims4({ inputs: { input: inputs[0] }, backend: backend2, attrs: { dim: axis } });
}
const shape = inputs[0].shape;
const dtype = inputs[0].dtype;
inputs.forEach((t) => {
util_exports.assertShapesMatch(shape, t.shape, "All tensors passed to stack must have matching shapes");
util_exports.assert(dtype === t.dtype, () => "All tensors passed to stack must have matching dtypes");
});
const intermediateTensorInfos = [];
const expandedTensors = inputs.map((t) => {
const expandedT = expandDims4({ inputs: { input: t }, backend: backend2, attrs: { dim: axis } });
intermediateTensorInfos.push(expandedT);
return expandedT;
});
const result = concat3({ inputs: expandedTensors, backend: backend2, attrs: { axis } });
intermediateTensorInfos.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
}
var packConfig2 = {
kernelName: Pack,
backendName: "webgl",
kernelFunc: pack2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_gpu.js
init_define_BUILD_VERSION();
var PadProgram = class {
constructor(xShape, paddings, constantValue) {
this.variableNames = ["x"];
this.customUniforms = [{ name: "value", type: "float" }];
this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
const rank = xShape.length;
const type = getCoordsDataType(rank);
const start = paddings.map((p2) => p2[0]).join(",");
const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
const unpackedCoords = ["coords[0]", "coords[1]", "coords[2]", "coords[3]"].slice(0, rank);
if (rank === 1) {
this.userCode = `
int start = ${start};
int end = ${end};
void main() {
int outC = getOutputCoords();
if (outC < start || outC >= end) {
setOutput(value);
} else {
setOutput(getX(outC - start));
}
}
`;
return;
}
this.userCode = `
${type} start = ${type}(${start});
${type} end = ${type}(${end});
void main() {
${type} outC = getOutputCoords();
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
setOutput(value);
} else {
${type} coords = outC - start;
setOutput(getX(${unpackedCoords}));
}
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/pad_packed_gpu.js
init_define_BUILD_VERSION();
var PadPackedProgram = class {
constructor(xShape, paddings, constantValue) {
this.variableNames = ["x"];
this.packedInputs = true;
this.packedOutput = true;
this.customUniforms = [{ name: "value", type: "float" }];
this.outputShape = paddings.map((p2, i) => p2[0] + xShape[i] + p2[1]);
const rank = xShape.length;
const dtype = getCoordsDataType(rank);
const start = paddings.map((p2) => p2[0]).join(",");
const end = paddings.map((p2, i) => p2[0] + xShape[i]).join(",");
const coords2 = getChannels("rc", rank);
const source = getChannels("source", rank);
const cLimit = `${coords2[rank - 1]} < ${this.outputShape[rank - 1]}`;
const innerDims = rank === 1 ? "source" : `vec2(${source.slice(-2).join()})`;
const componentSetup = [
`${dtype} rc = outputLoc;`,
`${coords2[rank - 1]} += 1;
if(${cLimit}) {
`,
rank === 1 ? "" : `}
rc = outputLoc;
${coords2[rank - 2]} += 1;
if(${coords2[rank - 2]} < ${this.outputShape[rank - 2]}) {`,
rank === 1 ? "" : ` ${coords2[rank - 1]} += 1;
if(${cLimit}) {`
];
const paddingArea = rank === 1 ? "rc < start || rc >= end" : "any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))";
let mainLoop = "";
for (let i = 0, j = rank === 1 ? 2 : 4; i < j; i++) {
mainLoop += `
${componentSetup[i]}
if (${paddingArea}) {
result[${i}] = float(value);
} else {
${dtype} source = rc - start;
result[${i}] = getChannel(getX(${source.join()}), ${innerDims});
}
`;
}
mainLoop += rank === 1 ? `} ` : `}}`;
this.userCode = `
const ${dtype} start = ${dtype}(${start});
const ${dtype} end = ${dtype}(${end});
void main() {
${dtype} outputLoc = getOutputCoords();
vec4 result = vec4(0.);
${mainLoop}
setOutput(result);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/PadV2.js
var padV22 = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { paddings, constantValue } = attrs;
if (util_exports.sizeFromShape(x.shape) === 0) {
const outputShape = paddings.map((p2, i) => p2[0] + x.shape[i] + p2[1]);
return fill3({
backend: backend2,
attrs: { shape: outputShape, value: constantValue, dtype: x.dtype }
});
}
const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new PadPackedProgram(x.shape, paddings, constantValue) : new PadProgram(x.shape, paddings, constantValue);
const customValues = [[constantValue]];
return backend2.runWebGLProgram(program, [x], x.dtype, customValues);
};
var padV2Config2 = {
kernelName: PadV2,
backendName: "webgl",
kernelFunc: padV22
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Pow.js
init_define_BUILD_VERSION();
var POW = `
if(a < 0.0 && floor(b) < b){
return NAN;
}
if (b == 0.0) {
return 1.0;
}
return (round(mod(b, 2.0)) != 1) ?
pow(abs(a), b) : sign(a) * pow(abs(a), b);
`;
var POW_PACKED = `
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
vec4 result = multiplier * pow(abs(a), b);
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
bvec4 isExpZero = equal(b, vec4(0.0));
result.r = isExpZero.r ? 1.0 : result.r;
result.g = isExpZero.g ? 1.0 : result.g;
result.b = isExpZero.b ? 1.0 : result.b;
result.a = isExpZero.a ? 1.0 : result.a;
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
` + CHECK_NAN_SNIPPET3 + `
return result;
`;
var pow3 = binaryKernelFunc2({ opSnippet: POW, packedOpSnippet: POW_PACKED });
var powConfig2 = {
kernelName: Pow,
backendName: "webgl",
kernelFunc: pow3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Prod.js
init_define_BUILD_VERSION();
function prod3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { axis, keepDims } = attrs;
const xRank = x.shape.length;
const toDispose = [];
const origAxes = util_exports.parseAxisParam(axis, x.shape);
let axes = origAxes;
const permutedAxes = backend_util_exports.getAxesPermutation(axes, xRank);
let permutedX = x;
if (permutedAxes != null) {
permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutedAxes } });
axes = backend_util_exports.getInnerMostAxes(axes.length, xRank);
toDispose.push(permutedX);
}
backend_util_exports.assertAxesAreInnerMostDims("prod", axes, xRank);
let res;
if (backend2.shouldExecuteOnCPU([permutedX])) {
const xVals = backend2.texData.get(permutedX.dataId).values;
const { outVals, outShape, outDtype } = prodImplCPU(permutedX.shape, permutedX.dtype, xVals, axes);
res = backend2.makeTensorInfo(outShape, outDtype, outVals);
} else {
const [outShape, reduceShape] = backend_util_exports.computeOutAndReduceShapes(permutedX.shape, axes);
const inSize = util_exports.sizeFromShape(reduceShape);
const a2D = reshape3({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
const outputDType = sumOutType(x.dtype);
const reduced = reduce(a2D, outputDType, "prod", backend2);
res = reshape3({ inputs: { x: reduced }, backend: backend2, attrs: { shape: outShape } });
toDispose.push(a2D);
toDispose.push(reduced);
}
if (keepDims) {
toDispose.push(res);
const newShape = backend_util_exports.expandShapeToKeepDim(res.shape, origAxes);
res = reshape3({ inputs: { x: res }, backend: backend2, attrs: { shape: newShape } });
}
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return res;
}
var prodConfig2 = {
kernelName: Prod,
backendName: "webgl",
kernelFunc: prod3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Range.js
init_define_BUILD_VERSION();
var range4 = (args) => {
const { backend: backend2, attrs } = args;
const { start, stop, step: step4, dtype } = attrs;
const values = rangeImplCPU(start, stop, step4, dtype);
return backend2.makeTensorInfo([values.length], dtype, values);
};
var rangeConfig2 = {
kernelName: Range,
backendName: "webgl",
kernelFunc: range4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reciprocal.js
init_define_BUILD_VERSION();
var RECIPROCAL = `return 1.0 / x;`;
var reciprocal3 = unaryKernelFunc2({ opSnippet: RECIPROCAL });
var reciprocalConfig2 = {
kernelName: Reciprocal,
backendName: "webgl",
kernelFunc: reciprocal3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu.js
init_define_BUILD_VERSION();
var RELU3 = CHECK_NAN_SNIPPET + `
return (x < 0.0) ? 0.0 : x;
`;
var RELU_PACKED = `
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var relu3 = unaryKernelFunc2({ opSnippet: RELU3, packedOpSnippet: RELU_PACKED });
var reluConfig2 = {
kernelName: Relu,
backendName: "webgl",
kernelFunc: relu3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Relu6.js
init_define_BUILD_VERSION();
var RELU63 = CHECK_NAN_SNIPPET + `
return (x < 0.0) ? 0.0 : min(6.0, x);
`;
var RELU6_PACKED = `
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var relu63 = unaryKernelFunc2({ opSnippet: RELU63, packedOpSnippet: RELU6_PACKED });
var relu6Config2 = {
kernelName: Relu6,
backendName: "webgl",
kernelFunc: relu63
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_gpu.js
init_define_BUILD_VERSION();
var ResizeBilinearProgram = class {
constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
this.variableNames = ["A"];
this.outputShape = [];
const [batch, oldHeight, oldWidth, depth] = inputShape;
this.outputShape = [batch, newHeight, newWidth, depth];
const effectiveInSize = [
alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
];
const effectiveOutSize = [
alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
];
let sourceFracIndexRC;
if (halfPixelCenters) {
sourceFracIndexRC = `(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)`;
} else {
sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
}
this.userCode = `
const vec2 effectiveInputOverOutputRatioRC = vec2(
${effectiveInSize[0] / effectiveOutSize[0]},
${effectiveInSize[1] / effectiveOutSize[1]});
const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${sourceFracIndexRC};
// Compute the four integer indices.
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
ivec2 sourceCeilRC = ivec2(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
float top = topLeft + (topRight - topLeft) * fracRC.y;
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
float newValue = top + (bottom - top) * fracRC.x;
setOutput(newValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_packed_gpu.js
init_define_BUILD_VERSION();
var ResizeBilinearPackedProgram = class {
constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = [];
const [batch, oldHeight, oldWidth, depth] = inputShape;
this.outputShape = [batch, newHeight, newWidth, depth];
const effectiveInSize = [
alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
];
const effectiveOutSize = [
alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
];
let sourceFracIndexRC;
if (halfPixelCenters) {
sourceFracIndexRC = `(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)`;
} else {
sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
}
this.userCode = `
const vec3 effectiveInputOverOutputRatioRC = vec3(
${effectiveInSize[0] / effectiveOutSize[0]},
${effectiveInSize[1] / effectiveOutSize[1]},
${effectiveInSize[1] / effectiveOutSize[1]});
const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
${oldWidth}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${sourceFracIndexRC};
// Compute the four integer indices.
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
ivec3 sourceCeilRC = ivec3(
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${depth - 1};
bool hasNextRow = coords.z < ${newWidth - 1};
// In parallel, construct four corners for all four components in
// packed 2x2 cell.
vec4 topLeft = vec4(
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 bottomLeft = vec4(
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
vec4 topRight = vec4(
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec4 bottomRight = vec4(
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
vec4 newValue = mix(top, bottom, fracRC.x);
setOutput(newValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinear.js
function resizeBilinear3(args) {
const { inputs, backend: backend2, attrs } = args;
const { images } = inputs;
const { alignCorners, halfPixelCenters, size } = attrs;
const [newHeight, newWidth] = size;
const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeBilinearPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeBilinearProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
return backend2.runWebGLProgram(program, [images], "float32");
}
var resizeBilinearConfig2 = {
kernelName: ResizeBilinear,
backendName: "webgl",
kernelFunc: resizeBilinear3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_bilinear_backprop_gpu.js
init_define_BUILD_VERSION();
var ResizeBilinearBackpropProgram = class {
constructor(dyShape, inputShape, alignCorners) {
this.variableNames = ["dy"];
this.outputShape = [];
this.outputShape = inputShape;
const [, xHeight, xWidth] = inputShape;
const [, yHeight, yWidth] = dyShape;
const effectiveXSize = [
alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
];
const effectiveYSize = [
alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
];
const heightScale = effectiveXSize[0] / effectiveYSize[0];
const widthScale = effectiveXSize[1] / effectiveYSize[1];
const invHeightScale = 1 / heightScale;
const invWidthScale = 1 / widthScale;
const winHeight = Math.ceil(invHeightScale) * 2 + 2;
const winWidth = Math.ceil(invWidthScale) * 2 + 2;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${heightScale});
const float widthScale = float(${widthScale});
const float invHeightScale = float(${invHeightScale});
const float invWidthScale = float(${invWidthScale});
const int winHeight = int(${winHeight});
const int winWidth = int(${winWidth});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(startRLerp - float(winHeight / 2));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(startCLerp - float(winWidth / 2));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${yHeight}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${yWidth}) {
continue;
}
float dxR = float(dyR) * heightScale;
int topDxRIndex = int(floor(dxR));
int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));
float dxRLerp = dxR - float(topDxRIndex);
float inverseDxRLerp = 1.0 - dxRLerp;
float dxC = float(dyC) * widthScale;
int leftDxCIndex = int(floor(dxC));
int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));
float dxCLerp = dxC - float(leftDxCIndex);
float inverseDxCLerp = 1.0 - dxCLerp;
if (r == topDxRIndex && c == leftDxCIndex) {
// topLeft
accumulator +=
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
}
if (r == topDxRIndex && c == rightDxCIndex) {
// topRight
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
}
if (r == bottomDxRIndex && c == leftDxCIndex) {
// bottomLeft
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
}
if (r == bottomDxRIndex && c == rightDxCIndex) {
// bottomRight
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
}
}
}
// End loop over dy
setOutput(accumulator);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeBilinearGrad.js
function resizeBilinearGrad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { images, dy } = inputs;
const { alignCorners } = attrs;
const program = new ResizeBilinearBackpropProgram(dy.shape, images.shape, alignCorners);
return backend2.runWebGLProgram(program, [dy], dy.dtype);
}
var resizeBilinearGradConfig3 = {
kernelName: ResizeBilinearGrad,
backendName: "webgl",
kernelFunc: resizeBilinearGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_gpu.js
init_define_BUILD_VERSION();
var ResizeNearestNeighborProgram = class {
constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
this.variableNames = ["A"];
this.outputShape = [];
const [batch, oldHeight, oldWidth, depth] = inputShape;
this.outputShape = [batch, newHeight, newWidth, depth];
const effectiveInSize = [
alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
];
const effectiveOutSize = [
alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
];
const roundBase = alignCorners ? "0.5" : "0.0";
let sourceFracIndexRC;
if (halfPixelCenters) {
sourceFracIndexRC = `max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))`;
} else {
sourceFracIndexRC = `vec2(yRC) * effectiveInputOverOutputRatioRC`;
}
this.userCode = `
const vec2 effectiveInputOverOutputRatioRC = vec2(
${effectiveInSize[0] / effectiveOutSize[0]},
${effectiveInSize[1] / effectiveOutSize[1]});
const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
ivec2 yRC = coords.yz;
// Fractional source index.
vec2 sourceFracIndexRC = ${sourceFracIndexRC};
// Compute the coordinators of nearest neighbor point.
ivec2 sourceNearestRC = ivec2(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
setOutput(newValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_packed_gpu.js
init_define_BUILD_VERSION();
var ResizeNearestNeighborPackedProgram = class {
constructor(inputShape, newHeight, newWidth, alignCorners, halfPixelCenters) {
this.variableNames = ["A"];
this.packedInputs = true;
this.packedOutput = true;
this.outputShape = [];
const [batch, oldHeight, oldWidth, depth] = inputShape;
this.outputShape = [batch, newHeight, newWidth, depth];
const effectiveInSize = [
alignCorners && newHeight > 1 ? oldHeight - 1 : oldHeight,
alignCorners && newWidth > 1 ? oldWidth - 1 : oldWidth
];
const effectiveOutSize = [
alignCorners && newHeight > 1 ? newHeight - 1 : newHeight,
alignCorners && newWidth > 1 ? newWidth - 1 : newWidth
];
const roundBase = alignCorners ? "0.5" : "0.0";
let sourceFracIndexRC;
if (halfPixelCenters) {
sourceFracIndexRC = `max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))`;
} else {
sourceFracIndexRC = `vec3(yRC) * effectiveInputOverOutputRatioRC`;
}
this.userCode = `
const vec3 effectiveInputOverOutputRatioRC = vec3(
${effectiveInSize[0] / effectiveOutSize[0]},
${effectiveInSize[1] / effectiveOutSize[1]},
${effectiveInSize[1] / effectiveOutSize[1]});
const vec3 inputShapeRC = vec3(${oldHeight}.0, ${oldWidth}.0,
${oldWidth}.0);
float getAValue(int b, int r, int c, int d) {
return getChannel(getA(b, r, c, d), vec2(c, d));
}
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
// Calculate values for next column in yRC.z.
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
// Fractional source index.
vec3 sourceFracIndexRC = ${sourceFracIndexRC};
// Compute the coordinators of nearest neighbor point.
ivec3 sourceNearestRC = ivec3(
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));
// Should we calculate next column and row elements in 2x2 packed cell.
bool hasNextCol = d < ${depth - 1};
bool hasNextRow = coords.z < ${newWidth - 1};
vec4 newValue = vec4(
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
: 0.0,
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
: 0.0,
(hasNextRow && hasNextCol) ?
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
setOutput(newValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighbor.js
function resizeNearestNeighbor3(args) {
const { inputs, backend: backend2, attrs } = args;
const { images } = inputs;
const { alignCorners, halfPixelCenters, size } = attrs;
const [newHeight, newWidth] = size;
const program = env().getBool("WEBGL_PACK_IMAGE_OPERATIONS") ? new ResizeNearestNeighborPackedProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters) : new ResizeNearestNeighborProgram(images.shape, newHeight, newWidth, alignCorners, halfPixelCenters);
return backend2.runWebGLProgram(program, [images], images.dtype);
}
var resizeNearestNeighborConfig2 = {
kernelName: ResizeNearestNeighbor,
backendName: "webgl",
kernelFunc: resizeNearestNeighbor3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/resize_nearest_neighbor_backprop_gpu.js
init_define_BUILD_VERSION();
var ResizeNearestNeigborBackpropProgram = class {
constructor(dyShape, inputShape, alignCorners) {
this.variableNames = ["dy"];
this.outputShape = [];
this.outputShape = inputShape;
const [, xHeight, xWidth] = inputShape;
const [, yHeight, yWidth] = dyShape;
const effectiveXSize = [
alignCorners && yHeight > 1 ? xHeight - 1 : xHeight,
alignCorners && yWidth > 1 ? xWidth - 1 : xWidth
];
const effectiveYSize = [
alignCorners && yHeight > 1 ? yHeight - 1 : yHeight,
alignCorners && yWidth > 1 ? yWidth - 1 : yWidth
];
const heightScale = effectiveXSize[0] / effectiveYSize[0];
const widthScale = effectiveXSize[1] / effectiveYSize[1];
const invHeightScale = 1 / heightScale;
const invWidthScale = 1 / widthScale;
const winHeight = Math.ceil(invHeightScale) * 2 + 2;
const winWidth = Math.ceil(invWidthScale) * 2 + 2;
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int b = coords[0];
int d = coords[3];
int r = coords[1];
int c = coords[2];
float accumulator = 0.0;
const float heightScale = float(${heightScale});
const float widthScale = float(${widthScale});
const float invHeightScale = float(${invHeightScale});
const float invWidthScale = float(${invWidthScale});
const int winHeight = int(${winHeight});
const int winWidth = int(${winWidth});
// Compute bounds for where in dy we will look
float startRLerp = floor(float(r) * invHeightScale);
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
float startCLerp = floor(float(c) * invWidthScale);
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
// Loop over dy
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
int dyR = dyROffset + startDyR;
// Guard against the window exceeding the bounds of dy
if (dyR < 0 || dyR >= ${yHeight}) {
continue;
}
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
int dyC = dyCOffset + startDyC;
// Guard against the window exceeding the bounds of dy
if (dyC < 0 || dyC >= ${yWidth}) {
continue;
}
float sourceFracRow =
float(${effectiveXSize[0]}) *
(float(dyR) / float(${effectiveYSize[0]}));
float sourceFracCol =
float(${effectiveXSize[1]}) *
(float(dyC) / float(${effectiveYSize[1]}));
int sourceNearestRow = int(min(
float(int(${xHeight}) - 1),
${alignCorners} ? float(round(sourceFracRow)) :
float(floor(sourceFracRow))));
int sourceNearestCol = int(min(
float(int(${xWidth}) - 1),
${alignCorners} ? float(round(sourceFracCol)) :
float(floor(sourceFracCol))));
if (r == sourceNearestRow && c == sourceNearestCol) {
accumulator += getDy(b, dyR, dyC, d);
}
}
}
// End loop over dy
setOutput(accumulator);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ResizeNearestNeighborGrad.js
function resizeNearestNeighborGrad2(args) {
const { inputs, backend: backend2, attrs } = args;
const { images, dy } = inputs;
const { alignCorners } = attrs;
const program = new ResizeNearestNeigborBackpropProgram(dy.shape, images.shape, alignCorners);
return backend2.runWebGLProgram(program, [dy], dy.dtype);
}
var resizeNearestNeighborGradConfig3 = {
kernelName: ResizeNearestNeighborGrad,
backendName: "webgl",
kernelFunc: resizeNearestNeighborGrad2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_gpu.js
init_define_BUILD_VERSION();
var ReverseProgram = class {
constructor(xShape, axis) {
this.variableNames = ["x"];
const rank = xShape.length;
if (rank > 4) {
throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
}
this.outputShape = xShape;
if (rank === 1) {
this.userCode = `
void main() {
int coord = getOutputCoords();
setOutput(getX(${xShape[0]} - coord - 1));
}
`;
return;
}
const getInCoord = (i) => {
if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
return `${xShape[i]} - coords[${i}] - 1`;
}
return `coords[${i}]`;
};
const inCoords = xShape.map((_, i) => getInCoord(i)).join(",");
const type = getCoordsDataType(rank);
this.userCode = `
void main() {
${type} coords = getOutputCoords();
setOutput(getX(${inCoords}));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/reverse_packed_gpu.js
init_define_BUILD_VERSION();
var ReversePackedProgram = class {
constructor(xShape, axis) {
this.variableNames = ["x"];
this.packedInputs = true;
this.packedOutput = true;
const rank = xShape.length;
if (rank > 4) {
throw new Error(`WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);
}
this.outputShape = xShape;
const channels = getChannels("rc", rank);
const nextColumn = `${channels[rank - 1]} + 1 < ${this.outputShape[rank - 1]}`;
const nextRow = `${channels[rank - 2]} + 1 < ${this.outputShape[rank - 2]}`;
const type = getCoordsDataType(rank);
if (rank === 1) {
this.userCode = `
void main(){
int rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = getChannel(getX(${xShape[0]} - rc - 1),
${xShape[0]} - rc - 1);
if(${nextColumn}){
result.g = getChannel(getX(${xShape[0]} - (rc + 1) - 1),
${xShape[0]} - (rc + 1) - 1);
}
setOutput(result);
}
`;
} else {
this.userCode = `
void main() {
${type} rc = getOutputCoords();
vec4 result = vec4(0.);
result.r = ${getR(channels.slice())};
if(${nextColumn}){
result.g = ${getG(channels.slice())};
}
if(${nextRow}) {
result.b = ${getB(channels.slice())};
if(${nextColumn}) {
result.a = ${getA(channels.slice())};
}
}
setOutput(result);
}
`;
}
function getR(channels2) {
return getChannel(channels2);
}
function getG(channels2) {
channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
return getChannel(channels2);
}
function getB(channels2) {
channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
return getChannel(channels2);
}
function getA(channels2) {
channels2[rank - 1] = "(" + channels2[rank - 1] + ` + 1)`;
channels2[rank - 2] = "(" + channels2[rank - 2] + ` + 1)`;
return getChannel(channels2);
}
function getChannel(channels2) {
const inCoordsArray = xShape.map((_, i) => getInCoord(i, channels2));
const inCoords = inCoordsArray.join(",");
const innerDims = inCoordsArray.slice(-2).join(",");
return `getChannel(getX(${inCoords}), vec2(${innerDims}))`;
}
function getInCoord(i, channels1) {
if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
return `${xShape[i]} - ${channels1[i]} - 1`;
} else {
return `${channels1[i]}`;
}
}
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Reverse.js
function reverse3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { dims } = attrs;
const xRank = x.shape.length;
const $dims = util_exports.parseAxisParam(dims, x.shape);
if (xRank === 0) {
return identity2({ inputs: { x }, backend: backend2 });
}
const program = env().getBool("WEBGL_PACK_ARRAY_OPERATIONS") ? new ReversePackedProgram(x.shape, $dims) : new ReverseProgram(x.shape, $dims);
return backend2.runWebGLProgram(program, [x], x.dtype);
}
var reverseConfig2 = {
kernelName: Reverse,
backendName: "webgl",
kernelFunc: reverse3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/rotate_gpu.js
init_define_BUILD_VERSION();
var RotateProgram = class {
constructor(imageShape, fillValue) {
this.variableNames = ["Image"];
this.outputShape = [];
this.customUniforms = [{ name: "params", type: "vec4" }];
const imageHeight = imageShape[1];
const imageWidth = imageShape[2];
this.outputShape = imageShape;
let fillSnippet = "";
if (typeof fillValue === "number") {
fillSnippet = `float outputValue = ${fillValue.toFixed(2)};`;
} else {
fillSnippet = `
vec3 fill = vec3(${fillValue.join(",")});
float outputValue = fill[coords[3]];`;
}
this.userCode = `
void main() {
ivec4 coords = getOutputCoords();
int x = coords[2];
int y = coords[1];
float coordXFloat = (float(x) - params[0]) * params[3] -
(float(y) - params[1]) * params[2];
float coordYFloat = (float(x) - params[0]) * params[2] +
(float(y) - params[1]) * params[3];
int coordX = int(round(coordXFloat + params[0]));
int coordY = int(round(coordYFloat + params[1]));
${fillSnippet}
if(coordX >= 0 && coordX < ${imageWidth} && coordY >= 0 && coordY < ${imageHeight}) {
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
}
setOutput(outputValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/RotateWithOffset.js
var rotateWithOffsetConfig2 = {
kernelName: RotateWithOffset,
backendName: "webgl",
kernelFunc: ({ inputs, attrs, backend: backend2 }) => {
const { image: image3 } = inputs;
const { radians, fillValue, center } = attrs;
const webglBackend = backend2;
const program = new RotateProgram(image3.shape, fillValue);
const [centerX, centerY] = backend_util_exports.getImageCenter(center, image3.shape[1], image3.shape[2]);
const customValues = [[centerX, centerY, Math.sin(radians), Math.cos(radians)]];
const output = webglBackend.runWebGLProgram(program, [image3], image3.dtype, customValues);
return output;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Round.js
init_define_BUILD_VERSION();
var ROUND = `
// OpenGL ES does not support round function.
// The algorithm is based on banker's rounding.
float base = floor(x);
if ((x - base) < 0.5) {
return floor(x);
} else if ((x - base) > 0.5) {
return ceil(x);
} else {
if (mod(base, 2.0) == 0.0) {
return base;
} else {
return base + 1.0;
}
}
`;
var round4 = unaryKernelFunc2({ opSnippet: ROUND });
var roundConfig2 = {
kernelName: Round,
backendName: "webgl",
kernelFunc: round4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Rsqrt.js
init_define_BUILD_VERSION();
var RSQRT = `return inversesqrt(x);`;
var rsqrt3 = unaryKernelFunc2({ opSnippet: RSQRT, cpuKernelImpl: rsqrtImplCPU });
var rsqrtConfig2 = {
kernelName: Rsqrt,
backendName: "webgl",
kernelFunc: rsqrt3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/scatter_gpu.js
init_define_BUILD_VERSION();
var ScatterProgram = class {
constructor(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex = true) {
this.variableNames = ["updates", "indices", "defaultValue"];
this.outputShape = shape;
const stridesType = getCoordsDataType(strides.length);
const dtype = getCoordsDataType(shape.length);
let indicesString = "";
if (indicesRank === 1) {
indicesString = "i";
} else if (indicesRank === 2) {
indicesString = "i, j";
}
const indicesSnippet = `getIndices(${indicesString})`;
let updatesString = "";
if (updatesRank === 1) {
updatesString = "i";
} else if (updatesRank === 2) {
updatesString = "i, coords[1]";
}
const updatesSnippet = `getUpdates(${updatesString})`;
const strideString = sliceDim > 1 ? "strides[j]" : "strides";
this.userCode = `
${stridesType} strides = ${stridesType}(${strides});
void main() {
${dtype} coords = getOutputCoords();
float sum = 0.0;
bool found = false;
for (int i = 0; i < ${updateSize}; i++) {
int flattenedIndex = 0;
for (int j = 0; j < ${sliceDim}; j++) {
int index = round(${indicesSnippet});
flattenedIndex += index * ${strideString};
}
if (flattenedIndex == coords[0]) {
sum += ${updatesSnippet};
found = true;
}
}
setOutput(mix(getDefaultValue(), sum, float(found)));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/ScatterNd.js
function scatterNd2(args) {
const { inputs, backend: backend2, attrs } = args;
const { indices, updates } = inputs;
const { shape } = attrs;
const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(updates, indices, shape);
const flattenShape = [outputSize / sliceSize, sliceSize];
if (outputSize === 0) {
return backend2.makeTensorInfo(shape, indices.dtype);
}
const flattenIndices = reshape3({ inputs: { x: indices }, backend: backend2, attrs: { shape: [numUpdates, sliceRank] } });
const flattenX = reshape3({ inputs: { x: updates }, backend: backend2, attrs: { shape: [numUpdates, sliceSize] } });
const defaultValue = backend2.makeTensorInfo([], "float32", new Float32Array([0]));
const program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.shape.length, flattenX.shape.length, strides, flattenShape);
const res = backend2.runWebGLProgram(program, [flattenX, flattenIndices, defaultValue], flattenX.dtype);
const reshaped = reshape3({ inputs: { x: res }, backend: backend2, attrs: { shape } });
backend2.disposeIntermediateTensorInfo(flattenIndices);
backend2.disposeIntermediateTensorInfo(flattenX);
backend2.disposeIntermediateTensorInfo(res);
backend2.disposeIntermediateTensorInfo(defaultValue);
return reshaped;
}
var scatterNdConfig2 = {
kernelName: ScatterNd,
backendName: "webgl",
kernelFunc: scatterNd2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/search_sorted_gpu.js
init_define_BUILD_VERSION();
var SearchSortedProgram = class {
constructor(batchSize, numInputs, numValues, side) {
this.variableNames = ["sortedSequence", "values"];
this.customUniforms = [{ name: "numInputs", type: "int" }];
this.outputShape = [batchSize, numValues];
const webGL2LoopHead = "while (left < right) {";
const webGL1LoopHead = `for (int i = 0; i < ${Math.ceil(Math.log2(numInputs + 1))}; ++i) { if (left >= right) break;`;
const loopHead = env().getNumber("WEBGL_VERSION") === 2 ? webGL2LoopHead : webGL1LoopHead;
const boundComparator = side === "left" ? "<" : "<=";
this.userCode = `
int findBound(int batch, float value) {
int left = 0;
int right = numInputs;
int mid;
${loopHead}
mid = (left + right) / 2;
if (getSortedSequence(batch, mid) ${boundComparator} value) {
left = mid + 1;
} else {
right = mid;
}
}
return right;
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int valueIndex = coords[1];
float value = getValues(batch, valueIndex);
setOutput(float(findBound(batch, value)));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SearchSorted.js
function searchSorted2(args) {
const { inputs, backend: backend2, attrs } = args;
const { sortedSequence, values } = inputs;
const { side } = attrs;
const program = new SearchSortedProgram(sortedSequence.shape[0], sortedSequence.shape[1], values.shape[1], side);
const customValues = [[sortedSequence.shape[1]]];
return backend2.runWebGLProgram(program, [sortedSequence, values], "int32", customValues);
}
var searchSortedConfig2 = {
kernelName: SearchSorted,
backendName: "webgl",
kernelFunc: searchSorted2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/select_gpu.js
init_define_BUILD_VERSION();
var SelectProgram = class {
constructor(cRank, shape, rank) {
this.variableNames = ["c", "a", "b"];
this.outputShape = shape;
let cCoords;
let abCoords;
if (rank > 4) {
throw Error(`Where for rank ${rank} is not yet supported`);
}
if (rank === 1) {
abCoords = `resRC`;
cCoords = `resRC`;
} else {
const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w"];
const cCoordVars = [];
const abCoordVars = [];
for (let i = 0; i < shape.length; i++) {
abCoordVars.push(`${currentCoords[i]}`);
if (i < cRank) {
cCoordVars.push(`${currentCoords[i]}`);
}
}
cCoords = cCoordVars.join();
abCoords = abCoordVars.join();
}
const dtype = getCoordsDataType(rank);
this.userCode = `
void main() {
${dtype} resRC = getOutputCoords();
float cVal = getC(${cCoords});
if (cVal >= 1.0) {
setOutput(getA(${abCoords}));
} else {
setOutput(getB(${abCoords}));
}
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Select.js
function select3(args) {
const { inputs, backend: backend2 } = args;
const { condition, t, e } = inputs;
const program = new SelectProgram(condition.shape.length, t.shape, t.shape.length);
return backend2.runWebGLProgram(program, [condition, t, e], upcastType(t.dtype, e.dtype));
}
var selectConfig2 = {
kernelName: Select,
backendName: "webgl",
kernelFunc: select3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Selu.js
init_define_BUILD_VERSION();
var SELU = `
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
// see: https://arxiv.org/abs/1706.02515
float scaleAlpha = ${backend_util_exports.SELU_SCALEALPHA};
float scale = ${backend_util_exports.SELU_SCALE};
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
`;
var selu3 = unaryKernelFunc2({ opSnippet: SELU });
var seluConfig2 = {
kernelName: Selu,
backendName: "webgl",
kernelFunc: selu3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sigmoid.js
init_define_BUILD_VERSION();
var SIGMOID3 = CHECK_NAN_SNIPPET_UNARY + `
return 1.0 / (1.0 + exp(-1.0 * x));
`;
var SIGMOID_PACKED = `
vec4 result = 1.0 / (1.0 + exp(-1.0 * x));
bvec4 isNaN = isnan(x);
result.r = isNaN.r ? x.r : result.r;
result.g = isNaN.g ? x.g : result.g;
result.b = isNaN.b ? x.b : result.b;
result.a = isNaN.a ? x.a : result.a;
return result;
`;
var sigmoid3 = unaryKernelFunc2({
opSnippet: SIGMOID3,
packedOpSnippet: SIGMOID_PACKED,
cpuKernelImpl: sigmoidImplCPU
});
var sigmoidConfig2 = {
kernelName: Sigmoid,
backendName: "webgl",
kernelFunc: sigmoid3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sign.js
init_define_BUILD_VERSION();
var SIGN = `
if (isnan(x)) { return 0.0; }
return sign(x);
`;
var sign3 = unaryKernelFunc2({ opSnippet: SIGN });
var signConfig2 = {
kernelName: Sign,
backendName: "webgl",
kernelFunc: sign3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sin.js
init_define_BUILD_VERSION();
var SIN = CHECK_NAN_SNIPPET_UNARY + `
return sin(x);
`;
var sin3 = unaryKernelFunc2({ opSnippet: SIN });
var sinConfig2 = {
kernelName: Sin,
backendName: "webgl",
kernelFunc: sin3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sinh.js
init_define_BUILD_VERSION();
var SINH = `
float e2x = exp(x);
return (e2x - 1.0 / e2x) / 2.0;
`;
var sinh3 = unaryKernelFunc2({ opSnippet: SINH });
var sinhConfig2 = {
kernelName: Sinh,
backendName: "webgl",
kernelFunc: sinh3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Softplus.js
init_define_BUILD_VERSION();
var SOFTPLUS = `
float epsilon = 1.1920928955078125e-7;
float threshold = log(epsilon) + 2.0;
bool too_large = x > -threshold;
bool too_small = x < threshold;
float result;
float exp_x = exp(x);
if (too_large){
result = x;
}
else if (too_small){
result = exp_x;
}
else{
result = log(exp_x + 1.0);
}
return result;
`;
var softplus3 = unaryKernelFunc2({ opSnippet: SOFTPLUS });
var softplusConfig2 = {
kernelName: Softplus,
backendName: "webgl",
kernelFunc: softplus3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SpaceToBatchND.js
init_define_BUILD_VERSION();
var spaceToBatchND3 = (args) => {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { blockShape, paddings } = attrs;
util_exports.assert(x.shape.length <= 4, () => "spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");
const prod4 = blockShape.reduce((a, b) => a * b);
const completePaddings = [[0, 0]];
completePaddings.push(...paddings);
for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {
completePaddings.push([0, 0]);
}
const toDispose = [];
const paddedX = padV22({
inputs: { x },
backend: backend2,
attrs: { paddings: completePaddings, constantValue: 0 }
});
const reshapedPaddedShape = backend_util_exports.getReshaped(paddedX.shape, blockShape, prod4, false);
const permutedReshapedPaddedPermutation = backend_util_exports.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
const flattenShape = backend_util_exports.getReshapedPermuted(paddedX.shape, blockShape, prod4, false);
const reshapedPaddedX = reshape3({ inputs: { x: paddedX }, backend: backend2, attrs: { shape: reshapedPaddedShape } });
const paddedXT = transpose3({
inputs: { x: reshapedPaddedX },
backend: backend2,
attrs: { perm: permutedReshapedPaddedPermutation }
});
const result = reshape3({ inputs: { x: paddedXT }, backend: backend2, attrs: { shape: flattenShape } });
toDispose.push(paddedX);
toDispose.push(reshapedPaddedX);
toDispose.push(paddedXT);
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
};
var spaceToBatchNDConfig2 = {
kernelName: SpaceToBatchND,
backendName: "webgl",
kernelFunc: spaceToBatchND3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseFillEmptyRows.js
init_define_BUILD_VERSION();
function sparseFillEmptyRows2(args) {
const { inputs, backend: backend2 } = args;
const { indices, values, denseShape, defaultValue } = inputs;
if (denseShape.shape.length !== 1) {
throw new Error(`Dense shape must be a vector, saw:
${denseShape.shape}`);
}
if (indices.shape.length !== 2) {
throw new Error(`Indices must be a matrix, saw:
${indices.shape}`);
}
if (values.shape.length !== 1) {
throw new Error(`Values must be a vector, saw:
${values.shape}`);
}
if (defaultValue.shape.length !== 0) {
throw new Error(`Default value must be a scalar, saw:
${defaultValue.shape}`);
}
const $indices = backend2.readSync(indices.dataId);
const $values = backend2.readSync(values.dataId);
const $denseShape = backend2.readSync(denseShape.dataId);
const $defaultValue = backend2.readSync(defaultValue.dataId)[0];
const [outputIndices, outputIndicesShape, outputValues, emptyRowIndicator, reverseIndexMap] = sparseFillEmptyRowsImplCPU($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue);
return [
backend2.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices),
backend2.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues),
backend2.makeTensorInfo([emptyRowIndicator.length], "bool", new Uint8Array(emptyRowIndicator.map((value) => Number(value)))),
backend2.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap))
];
}
var sparseFillEmptyRowsConfig2 = {
kernelName: SparseFillEmptyRows,
backendName: "webgl",
kernelFunc: sparseFillEmptyRows2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseReshape.js
init_define_BUILD_VERSION();
function sparseReshape2(args) {
const { inputs, backend: backend2 } = args;
const { inputIndices, inputShape, newShape } = inputs;
if (inputIndices.shape.length !== 2) {
throw new Error(`Input indices should be a matrix but received shape ${inputIndices.shape}`);
}
if (inputShape.shape.length !== 1) {
throw new Error(`Input shape should be a vector but received shape ${inputShape.shape}`);
}
if (newShape.shape.length !== 1) {
throw new Error(`Target shape should be a vector but received shape ${newShape.shape}`);
}
const $inputShape = Array.from(backend2.readSync(inputShape.dataId));
const $inputIndices = backend2.readSync(inputIndices.dataId);
const targetShape = Array.from(backend2.readSync(newShape.dataId));
const [newIndices, indicesShape, outputShape] = sparseReshapeImplCPU($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape);
return [
backend2.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices),
backend2.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape))
];
}
var sparseReshapeConfig2 = {
kernelName: SparseReshape,
backendName: "webgl",
kernelFunc: sparseReshape2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentMean.js
init_define_BUILD_VERSION();
function sparseSegmentMean2(args) {
const { inputs, backend: backend2 } = args;
const { data, indices, segmentIds } = inputs;
if (data.shape.length < 1) {
throw new Error(`Data should be at least 1 dimensional but received scalar`);
}
if (indices.shape.length !== 1) {
throw new Error(`Indices should be a vector but received shape
${indices.shape}`);
}
if (segmentIds.shape.length !== 1) {
throw new Error(`Segment ids should be a vector but received shape
${segmentIds.shape}`);
}
const $data = backend2.readSync(data.dataId);
const $indices = backend2.readSync(indices.dataId);
const $segmentIds = backend2.readSync(segmentIds.dataId);
const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds, true);
return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
var sparseSegmentMeanConfig2 = {
kernelName: SparseSegmentMean,
backendName: "webgl",
kernelFunc: sparseSegmentMean2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseSegmentSum.js
init_define_BUILD_VERSION();
function sparseSegmentSum2(args) {
const { inputs, backend: backend2 } = args;
const { data, indices, segmentIds } = inputs;
if (data.shape.length < 1) {
throw new Error(`Data should be at least 1 dimensional but received scalar`);
}
if (indices.shape.length !== 1) {
throw new Error(`Indices should be a vector but received shape
${indices.shape}`);
}
if (segmentIds.shape.length !== 1) {
throw new Error(`Segment ids should be a vector but received shape
${segmentIds.shape}`);
}
const $data = backend2.readSync(data.dataId);
const $indices = backend2.readSync(indices.dataId);
const $segmentIds = backend2.readSync(segmentIds.dataId);
const [outputData, outputDataShape] = sparseSegmentReductionImplCPU($data, data.shape, data.dtype, $indices, $segmentIds);
return backend2.makeTensorInfo(outputDataShape, data.dtype, outputData);
}
var sparseSegmentSumConfig2 = {
kernelName: SparseSegmentSum,
backendName: "webgl",
kernelFunc: sparseSegmentSum2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SparseToDense.js
init_define_BUILD_VERSION();
function sparseToDense2(args) {
const { inputs, backend: backend2, attrs } = args;
const { sparseIndices, sparseValues, defaultValue } = inputs;
const { outputShape } = attrs;
const { sliceRank, numUpdates, sliceSize, strides, outputSize } = backend_util_exports.calculateShapes(sparseValues, sparseIndices, outputShape);
const sumDupeIndices = false;
if (sparseValues.dtype === "string") {
const indicesBuf = backend2.bufferSync(sparseIndices);
const updatesBuf = backend2.bufferSync(sparseValues);
const $defaultValue = util_exports.decodeString(backend2.readSync(defaultValue.dataId)[0]);
const outBuf = scatterImplCPU(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices);
return backend2.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values);
}
const program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.shape.length, sparseValues.shape.length, strides, [outputSize, 1], sumDupeIndices);
const res = backend2.runWebGLProgram(program, [sparseValues, sparseIndices, defaultValue], sparseValues.dtype);
const reshaped = reshape3({ inputs: { x: res }, backend: backend2, attrs: { shape: outputShape } });
backend2.disposeIntermediateTensorInfo(res);
return reshaped;
}
var sparseToDenseConfig2 = {
kernelName: SparseToDense,
backendName: "webgl",
kernelFunc: sparseToDense2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SplitV.js
init_define_BUILD_VERSION();
function splitV2(args) {
const { inputs, backend: backend2, attrs } = 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, $axis);
const xRank = x.shape.length;
const begin = new Array(xRank).fill(0);
const size = x.shape.slice();
return splitSizes.map((s) => {
const sliceSize = [...size];
sliceSize[$axis] = s;
const sliceT = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size: sliceSize } });
begin[$axis] += s;
return sliceT;
});
}
var splitVConfig2 = {
kernelName: SplitV,
backendName: "webgl",
kernelFunc: splitV2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Sqrt.js
init_define_BUILD_VERSION();
var SQRT = `return sqrt(x);`;
var sqrt3 = unaryKernelFunc2({ opSnippet: SQRT, packedOpSnippet: SQRT, cpuKernelImpl: sqrtImplCPU });
var sqrtConfig2 = {
kernelName: Sqrt,
backendName: "webgl",
kernelFunc: sqrt3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Square.js
init_define_BUILD_VERSION();
var SQUARE = `return x * x;`;
var square3 = unaryKernelFunc2({ opSnippet: SQUARE });
var squareConfig2 = {
kernelName: Square,
backendName: "webgl",
kernelFunc: square3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/SquaredDifference.js
init_define_BUILD_VERSION();
var SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
var squaredDifference3 = binaryKernelFunc2({ opSnippet: SQUARED_DIFFERENCE, packedOpSnippet: SQUARED_DIFFERENCE });
var squaredDifferenceConfig2 = {
kernelName: SquaredDifference,
backendName: "webgl",
kernelFunc: squaredDifference3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Step.js
init_define_BUILD_VERSION();
function step3({ inputs, attrs, backend: backend2 }) {
const { x } = inputs;
const opSnippet = CHECK_NAN_SNIPPET + `
return x > 0.0 ? 1.0 : float(${attrs.alpha});
`;
const program = new UnaryOpProgram(x.shape, opSnippet);
return backend2.runWebGLProgram(program, [x], x.dtype);
}
var stepConfig2 = {
kernelName: Step,
backendName: "webgl",
kernelFunc: step3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/strided_slice_gpu.js
init_define_BUILD_VERSION();
var StridedSliceProgram = class {
constructor(begin, strides, size) {
this.variableNames = ["x"];
this.outputShape = size;
const rank = size.length;
const inputDtype = getCoordsDataType(size.length);
const dtype = getCoordsDataType(size.length);
let newCoords = "";
if (rank === 1) {
newCoords = "coords * strides + begin";
} else {
let outputAxis = 0;
newCoords = size.map((_, i) => {
outputAxis++;
return size.length === 1 ? `coords * strides[${i}] + begin[${i}]` : `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;
}).join(",");
}
this.userCode = `
${inputDtype} begin = ${inputDtype}(${begin});
${inputDtype} strides = ${inputDtype}(${strides});
void main() {
${dtype} coords = getOutputCoords();
setOutput(getX(${newCoords}));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StridedSlice.js
function stridedSlice3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask } = attrs;
const { finalShapeSparse, finalShape, isIdentity, sliceDim0, isSimpleSlice, begin: $begin, end: $end, strides: $strides } = slice_util_exports.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
let result;
if (isIdentity) {
result = reshape3({ inputs: { x }, backend: backend2, attrs: { shape: finalShape } });
} else if (sliceDim0 || isSimpleSlice) {
util_exports.assert(x.shape.length >= 1, () => `Input must have rank at least 1, got: ${x.shape.length}`);
const size = slice_util_exports.computeOutShape($begin, $end, $strides);
const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
result = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
backend2.disposeIntermediateTensorInfo(sliced);
} else {
const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
if (shouldExecuteOnCPU) {
const values = backend2.readSync(x.dataId);
const xBuf = buffer(x.shape, x.dtype, values);
const resultValues = stridedSliceImplCPU(finalShapeSparse, xBuf, $strides, $begin);
result = backend2.makeTensorInfo(finalShape, x.dtype, resultValues.values);
} else {
const program = new StridedSliceProgram($begin, $strides, finalShapeSparse);
result = backend2.runWebGLProgram(program, [x], x.dtype);
}
}
const resultReshaped = reshape3({ inputs: { x: result }, backend: backend2, attrs: { shape: finalShape } });
backend2.disposeIntermediateTensorInfo(result);
return resultReshaped;
}
var stridedSliceConfig2 = {
kernelName: StridedSlice,
backendName: "webgl",
kernelFunc: stridedSlice3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringNGrams.js
init_define_BUILD_VERSION();
function stringNGrams2(args) {
const { inputs, backend: backend2, attrs } = args;
const { separator, nGramWidths, leftPad, rightPad: rightPad2, padWidth, preserveShortSequences } = attrs;
const { data, dataSplits } = inputs;
const $data = backend2.readSync(data.dataId);
const $dataSplits = backend2.readSync(dataSplits.dataId);
const [nGrams, nGramsSplits] = stringNGramsImplCPU($data, $dataSplits, separator, nGramWidths, leftPad, rightPad2, padWidth, preserveShortSequences);
return [
backend2.makeTensorInfo([nGrams.length], "string", nGrams),
backend2.makeTensorInfo(dataSplits.shape, "int32", nGramsSplits)
];
}
var stringNGramsConfig2 = {
kernelName: StringNGrams,
backendName: "webgl",
kernelFunc: stringNGrams2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringSplit.js
init_define_BUILD_VERSION();
function stringSplit2(args) {
const { inputs, backend: backend2, attrs } = args;
const { skipEmpty } = attrs;
const { input: input2, delimiter } = inputs;
if (input2.dtype !== "string") {
throw new Error("Input must be of datatype string");
}
if (input2.shape.length !== 1) {
throw new Error(`Input must be a vector, got shape: ${input2.shape}`);
}
if (delimiter.shape.length !== 0) {
throw new Error(`Delimiter must be a scalar, got shape: ${delimiter.shape}`);
}
const $input = backend2.readSync(input2.dataId);
const $delimiter = backend2.readSync(delimiter.dataId)[0];
const [indices, values, shape] = stringSplitImplCPU($input, $delimiter, skipEmpty);
const outputSize = values.length;
return [
backend2.makeTensorInfo([outputSize, 2], "int32", indices),
backend2.makeTensorInfo([outputSize], "string", values),
backend2.makeTensorInfo([2], "int32", new Int32Array(shape))
];
}
var stringSplitConfig2 = {
kernelName: StringSplit,
backendName: "webgl",
kernelFunc: stringSplit2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/StringToHashBucketFast.js
init_define_BUILD_VERSION();
function stringToHashBucketFast2(args) {
const { inputs, backend: backend2, attrs } = args;
const { numBuckets } = attrs;
const { input: input2 } = inputs;
if (input2.dtype !== "string") {
throw new Error("Input must be of datatype string");
}
if (numBuckets <= 0) {
throw new Error(`Number of buckets must be at least 1`);
}
const $input = backend2.readSync(input2.dataId);
const output = stringToHashBucketFastImplCPU($input, numBuckets);
return backend2.makeTensorInfo(input2.shape, "int32", output);
}
var stringToHashBucketFastConfig2 = {
kernelName: StringToHashBucketFast,
backendName: "webgl",
kernelFunc: stringToHashBucketFast2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tan.js
init_define_BUILD_VERSION();
var TAN = `return tan(x);`;
var tan3 = unaryKernelFunc2({ opSnippet: TAN });
var tanConfig2 = {
kernelName: Tan,
backendName: "webgl",
kernelFunc: tan3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tanh.js
init_define_BUILD_VERSION();
var TANH = `
float e2x = exp(-2.0 * abs(x));
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
`;
var tanh4 = unaryKernelFunc2({ opSnippet: TANH });
var tanhConfig2 = {
kernelName: Tanh,
backendName: "webgl",
kernelFunc: tanh4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/tile_gpu.js
init_define_BUILD_VERSION();
var TileProgram = class {
constructor(aShape, reps) {
this.variableNames = ["A"];
const outputShape = new Array(aShape.length);
for (let i = 0; i < outputShape.length; i++) {
outputShape[i] = aShape[i] * reps[i];
}
this.outputShape = outputShape;
this.rank = outputShape.length;
const dtype = getCoordsDataType(this.rank);
const sourceCoords = getSourceCoords3(aShape);
this.userCode = `
void main() {
${dtype} resRC = getOutputCoords();
setOutput(getA(${sourceCoords}));
}
`;
}
};
function getSourceCoords3(aShape) {
const rank = aShape.length;
if (rank > 5) {
throw Error(`Tile for rank ${rank} is not yet supported`);
}
if (rank === 1) {
return `imod(resRC, ${aShape[0]})`;
}
const currentCoords = ["resRC.x", "resRC.y", "resRC.z", "resRC.w", "resRC.u"];
const sourceCoords = [];
for (let i = 0; i < aShape.length; i++) {
sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);
}
return sourceCoords.join();
}
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Tile.js
function tile4(params) {
const { inputs, backend: backend2, attrs } = params;
const { x } = inputs;
const { reps } = attrs;
if (x.dtype === "string" || x.shape.length > 5) {
const data = backend2.readSync(x.dataId);
const value = x.dtype === "string" ? data.map((d) => util_exports.decodeString(d)) : data;
const buf = buffer(x.shape, x.dtype, value);
const outBuf = tileImplCPU(buf, reps);
return backend2.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values);
}
const program = new TileProgram(x.shape, reps);
const output = backend2.runWebGLProgram(program, [x], x.dtype);
return output;
}
var tileConfig2 = {
kernelName: Tile,
backendName: "webgl",
kernelFunc: tile4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/top_k_gpu.js
init_define_BUILD_VERSION();
var SwapProgram = class {
constructor(shape) {
this.variableNames = ["x", "indices"];
this.customUniforms = [
{ name: "n", type: "int" },
{ name: "firstPass", type: "int" },
{ name: "negativeInf", type: "float" },
{ name: "dir", type: "int" },
{ name: "inc", type: "int" }
];
this.outputShape = shape;
this.userCode = `
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// We compare elements pair-wise within a group of size 2 * inc.
// The comparing rule for each group alternates between ascending
// and descending. Within each group, we compare each pair at
// positions i and i+inc. To decide whether an element at position i
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
// inc, it is in the first half of the group, we denote it as x0,
// otherwise we denote it as x1.
// For example, as shown in the Bitonic top K paper referenced above,
// Figure5(a) shows that element[1] is in the
// second half of the group when group size is 2, but it is in the
// first half of the group when group size is 4.
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
int i = isFirstInPair ? elemIdx : elemIdx - inc;
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
// Denotes which direction indices are in (ascending or descending).
bool reverse = imod(elemIdx, 2 * dir) >= dir;
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
if (reverse == isGreater) { // Elements in opposite order of direction
int iTemp = i0;
i0 = i1;
i1 = iTemp;
}
if (isFirstInPair) {
setOutput(float(i0));
} else {
setOutput(float(i1));
}
}
`;
}
};
var MergeProgram = class {
constructor(shape) {
this.variableNames = ["x", "indices"];
this.customUniforms = [
{ name: "n", type: "int" },
{ name: "firstPass", type: "int" },
{ name: "k", type: "int" }
];
this.outputShape = shape;
this.userCode = `
void main() {
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
ivec2 coords = getOutputCoords();
int batch = coords[0];
int elemIdx = coords[1];
// The output size is half of the previous size.
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
// we only need to output the indices at positions |, the indices at
// positions _ can be thrown away, see Figure5(b) After Phase 2
// (Merge phase) in the Bitonic Top K paper referenced above.
// For example, the paper shows we only need to output the orange bars.
// The output sequence should look like this | | | | | | | |.
// Because the sequence is halved, to map the output index back
// to the previous sequence to find the corresponding value,
// we need to double the index. When we double the index,
// we basically interpolate a position, so 2i looks like
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
// of each 2k positions by - elemIdx % k. E.g. for output at
// index 4,5,6,7, we want to get the corresponding element at
// original index 8,9,10,11, for output at index 8,9,10,11,
// we want to get the corresponding element at original index
// 16,17,18,19, so on and so forth.
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
float x0 = getX(batch, i0);
float x1 = i1 < n ? getX(batch, i1) : x0;
setOutput(x0 >= x1 ? float(i0) : float(i1));
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/TopK.js
function disposeIntermediateTensorInfoOrNull(backend2, tensorInfo) {
if (tensorInfo !== null) {
backend2.disposeIntermediateTensorInfo(tensorInfo);
}
}
function roundUpToPow2(num) {
let pow22 = 1;
while (pow22 < num) {
pow22 *= 2;
}
return pow22;
}
function topK2(args) {
const { inputs, backend: backend2, attrs } = args;
const { x } = inputs;
const { k, sorted } = attrs;
const TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD = env().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD");
const TOPK_K_CPU_HANDOFF_THRESHOLD = env().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD");
const xShape = x.shape;
const lastDim = xShape[xShape.length - 1];
if (backend2.shouldExecuteOnCPU([x]) || lastDim < TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD || k > TOPK_K_CPU_HANDOFF_THRESHOLD) {
const xVals = backend2.readSync(x.dataId);
const [allTopKVals, allTopKIndices] = topKImplCPU(xVals, xShape, x.dtype, k, sorted);
return [
backend2.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values),
backend2.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values)
];
}
if (k === 0) {
xShape[xShape.length - 1] = 0;
return [
backend2.makeTensorInfo(xShape, x.dtype, []),
backend2.makeTensorInfo(xShape, "int32", [])
];
}
if (lastDim === 1) {
return [
x,
fill3({ attrs: { shape: xShape, dtype: "int32", value: 0 }, backend: backend2 })
];
}
const xtexData = backend2.texData.get(x.dataId);
const xIsPacked = xtexData !== null && xtexData.isPacked;
const xUnPacked = xIsPacked ? backend2.unpackTensor(x) : x;
const xSize = util_exports.sizeFromShape(xShape);
const batch = xSize / lastDim;
const x2D = reshape3({ inputs: { x: xUnPacked }, attrs: { shape: [batch, lastDim] }, backend: backend2 });
if (xIsPacked) {
disposeIntermediateTensorInfoOrNull(backend2, xUnPacked);
}
const kPow2 = roundUpToPow2(k);
const lastDimPow2 = roundUpToPow2(lastDim);
let indices = null;
const getInputs = () => indices === null ? [x2D, x2D] : [x2D, indices];
const runSwap = (dir, inc, shape) => {
const inputs2 = getInputs();
const program = new SwapProgram(shape);
const fistPass = indices === null ? 1 : 0;
const customValues = [[lastDim], [fistPass], [Number.NEGATIVE_INFINITY], [dir], [inc]];
const prevIndices2 = indices;
indices = backend2.runWebGLProgram(program, inputs2, "int32", customValues);
disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);
};
for (let len = 1; len < kPow2; len *= 2) {
const dir = len * 2;
for (let inc = len; inc >= 1; inc /= 2) {
runSwap(dir, inc, [batch, lastDimPow2]);
}
}
for (let indicesSize = lastDimPow2; indicesSize > kPow2; indicesSize /= 2) {
const inputs2 = getInputs();
const mergeProgram = new MergeProgram([batch, indicesSize / 2]);
const firstPass = indices === null ? 1 : 0;
const customValues = [[lastDim], [firstPass], [kPow2]];
const prevIndices2 = indices;
indices = backend2.runWebGLProgram(mergeProgram, inputs2, "int32", customValues);
disposeIntermediateTensorInfoOrNull(backend2, prevIndices2);
const len = kPow2 / 2;
const dir = len * 2;
for (let inc = len; inc >= 1; inc /= 2) {
runSwap(dir, inc, indices.shape);
}
}
let prevIndices = indices;
indices = slice3({ inputs: { x: indices }, backend: backend2, attrs: { begin: 0, size: [batch, k] } });
disposeIntermediateTensorInfoOrNull(backend2, prevIndices);
let values = gatherV22({ inputs: { x: x2D, indices }, backend: backend2, attrs: { axis: 1, batchDims: 1 } });
disposeIntermediateTensorInfoOrNull(backend2, x2D);
const newShape = xShape.slice(0, -1);
newShape.push(k);
prevIndices = indices;
indices = reshape3({ inputs: { x: indices }, attrs: { shape: newShape }, backend: backend2 });
disposeIntermediateTensorInfoOrNull(backend2, prevIndices);
const prevValues = values;
values = reshape3({ inputs: { x: values }, attrs: { shape: newShape }, backend: backend2 });
disposeIntermediateTensorInfoOrNull(backend2, prevValues);
return [values, indices];
}
var topKConfig2 = {
kernelName: TopK,
backendName: "webgl",
kernelFunc: topK2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/transform_gpu.js
init_define_BUILD_VERSION();
var TransformProgram = class {
constructor(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape) {
this.variableNames = ["Image", "Transforms"];
this.outputShape = outShape;
const interpolationModeId = interpolation === "nearest" ? 1 : 2;
let fillModeId;
switch (fillMode) {
case "constant":
fillModeId = 1;
break;
case "reflect":
fillModeId = 2;
break;
case "wrap":
fillModeId = 3;
break;
case "nearest":
fillModeId = 4;
break;
default:
fillModeId = 1;
break;
}
this.userCode = `
float mapCoord(float outCoord, float len) {
float inCoord = outCoord;
if(${fillModeId} == 2) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
if (inCoord < sz2) {
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
inCoord;
}
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz2 = 2.0 * len;
inCoord -= sz2 * float(int(float(inCoord / sz2)));
if (inCoord >= len) {
inCoord = sz2 - inCoord - 1.0;
}
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${fillModeId} == 3) {
if (inCoord < 0.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
}
} else if (inCoord > len - 1.0) {
if (len <= 1.0) {
inCoord = 0.0;
} else {
float sz = len - 1.0;
inCoord -= len * float(int(float(inCoord / sz)));
}
}
return clamp(inCoord, 0.0, len - 1.0);
} else if (${fillModeId} == 4) {
return clamp(outCoord, 0.0, len - 1.0);
} else {
return outCoord;
}
}
float readWithFillValue(int batch, int coordY, int coordX,
int channel) {
float outputValue;
if (0 <= coordY && coordY < ${imageHeight} && 0 <= coordX && coordX < ${imageWidth}) {
outputValue = getImage(batch, coordY, coordX, channel);
} else {
outputValue = float(${fillValue});
}
return outputValue;
}
void main() {
ivec4 coords = getOutputCoords();
float outputValue;
int batch = coords[0];
int x = coords[2];
int y = coords[1];
int channel = coords[3];
float xf = float(x);
float yf = float(y);
float a1 = getTransforms(batch, 0);
float a2 = getTransforms(batch, 1);
float a3 = getTransforms(batch, 2);
float b1 = getTransforms(batch, 3);
float b2 = getTransforms(batch, 4);
float b3 = getTransforms(batch, 5);
float c1 = getTransforms(batch, 6);
float c2 = getTransforms(batch, 7);
float projection = c1 * xf + c2 * yf + 1.0;
if (projection == 0.0) {
outputValue = float(${fillValue});
} else {
float inX = (a1 * xf + a2 * yf + a3) / projection;
float inY = (b1 * xf + b2 * yf + b3) / projection;
float mapX = mapCoord(inX, float(${imageWidth}));
float mapY = mapCoord(inY, float(${imageHeight}));
if (${interpolationModeId} == 1) {
int coordY = int(round(mapY));
int coordX = int(round(mapX));
outputValue = readWithFillValue(batch, coordY, coordX,
channel);
} else {
float yFloor = floor(mapY);
float xFloor = floor(mapX);
float yCeil = yFloor + 1.0;
float xCeil = xFloor + 1.0;
float valueYFloor = (xCeil - mapX) *
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
float valueYCeil = (xCeil - mapX) *
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
(mapX - xFloor) *
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
outputValue = (yCeil - mapY) * valueYFloor +
(mapY - yFloor) * valueYCeil;
}
}
setOutput(outputValue);
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Transform.js
function transform3(args) {
const { inputs, backend: backend2, attrs } = args;
const { image: image3, transforms } = inputs;
const { interpolation, fillMode, fillValue, outputShape } = attrs;
const [batch, imageHeight, imageWidth, numChannels] = image3.shape;
const [outHeight, outWidth] = outputShape != null ? outputShape : [imageHeight, imageWidth];
const outShape = [
batch,
outHeight,
outWidth,
numChannels
];
const program = new TransformProgram(imageHeight, imageWidth, interpolation, fillMode, fillValue, outShape);
return backend2.runWebGLProgram(program, [image3, transforms], "float32");
}
var transformConfig2 = {
kernelName: Transform,
backendName: "webgl",
kernelFunc: transform3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unique.js
init_define_BUILD_VERSION();
function unique4(args) {
const { inputs, attrs, backend: backend2 } = args;
const { axis } = attrs;
const { x } = inputs;
assertNotComplex2(x, "unique");
console.warn("WARNING: ", "UI might be locked temporarily as data is being downloaded");
const values = backend2.readSync(x.dataId);
const { outputValues, outputShape, indices } = uniqueImplCPU(values, axis, x.shape, x.dtype);
return [
backend2.makeTensorInfo(outputShape, x.dtype, outputValues),
backend2.makeTensorInfo([indices.length], "int32", indices)
];
}
var uniqueConfig2 = {
kernelName: Unique,
backendName: "webgl",
kernelFunc: unique4
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/Unpack.js
init_define_BUILD_VERSION();
function unpack2(args) {
const { inputs, backend: backend2, attrs } = args;
const { value } = inputs;
let { axis } = attrs;
if (axis < 0) {
axis += value.shape.length;
}
const x = value;
const xRank = x.shape.length;
const num = value.shape[axis];
const outShape = new Array(xRank - 1);
let outIndex = 0;
for (let i = 0; i < xRank; i++) {
if (i !== axis) {
outShape[outIndex++] = x.shape[i];
}
}
const toDispose = [];
const begin = new Array(xRank).fill(0);
const size = x.shape.slice();
size[axis] = 1;
const res = new Array(num);
for (let i = 0; i < res.length; i++) {
begin[axis] = i;
const sliced = slice3({ inputs: { x }, backend: backend2, attrs: { begin, size } });
const reshaped = reshape3({ inputs: { x: sliced }, backend: backend2, attrs: { shape: outShape } });
res[i] = reshaped;
toDispose.push(sliced);
}
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return res;
}
var unpackConfig2 = {
kernelName: Unpack,
backendName: "webgl",
kernelFunc: unpack2
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js
init_define_BUILD_VERSION();
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/segment_gpu.js
init_define_BUILD_VERSION();
var SegmentOpProgram = class {
constructor(segOpInfo, segOpType) {
this.variableNames = ["x", "segmentIds"];
const windowSize = segOpInfo.windowSize;
const batchSize = segOpInfo.batchSize;
const inSize = segOpInfo.inSize;
const numSegments = segOpInfo.numSegments;
const outSize = numSegments * Math.ceil(inSize / windowSize);
this.outputShape = [batchSize, outSize];
const initializationValue = "0.0";
const returnValue = `sumValue`;
const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
const windowSizeVec4Remainder = windowSize % 4;
const updateSnippet = `
sumValue += dot(values, segFilter);
`;
let checkValueOutOfBounds = "";
if (inSize % windowSize > 0) {
checkValueOutOfBounds = `
if (inIdx < 0 || inIdx >= ${inSize}) {
return initializationValue;
}
`;
}
let checkSegmentIdOutOfBounds = "";
if (inSize % windowSize > 0) {
checkSegmentIdOutOfBounds = `
if (inIdx < 0 || inIdx >= ${inSize}) {
return -1.0;
}
`;
}
this.userCode = `
const float initializationValue = ${initializationValue};
float getValue(int batch, int inIdx) {
${checkValueOutOfBounds}
return getX(batch, inIdx);
}
float getSegmentIdAtIndex(int inIdx) {
${checkSegmentIdOutOfBounds}
return getSegmentIds(inIdx);
}
void main() {
ivec2 coords = getOutputCoords();
int batch = coords[0];
int outIdx = coords[1];
int inOffset = int(floor(float(outIdx) / float(
${numSegments})) * float(${windowSize}));
int currentSeg = int(mod(float(outIdx), float(${numSegments})));
float sumValue = 0.0;
for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {
int inIdx = inOffset + i;
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
getValue(batch, inIdx + 3)
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
);
${updateSnippet}
}
int inIdx = inOffset + ${windowSizeNearestVec4};
if (${windowSizeVec4Remainder === 1}) {
vec4 values = vec4(
getValue(batch, inIdx),
initializationValue,
initializationValue,
initializationValue
);
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
0,
0,
0
);
${updateSnippet}
} else if (${windowSizeVec4Remainder === 2}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
initializationValue,
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
0,
0
);
${updateSnippet}
} else if (${windowSizeVec4Remainder === 3}) {
vec4 values = vec4(
getValue(batch, inIdx),
getValue(batch, inIdx + 1),
getValue(batch, inIdx + 2),
initializationValue
);
vec4 segFilter = vec4(
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
0
);
${updateSnippet}
}
setOutput(${returnValue});
}
`;
}
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/kernels/UnsortedSegmentSum.js
function unsortedSegmentSum3(args) {
const { inputs, backend: backend2, attrs } = args;
const { x, segmentIds } = inputs;
const { numSegments } = attrs;
const xRank = x.shape.length;
const toDispose = [];
let axis = 0;
const permutation = backend_util_exports.getAxesPermutation([axis], xRank);
let permutedX = x;
if (permutation != null) {
permutedX = transpose3({ inputs: { x }, backend: backend2, attrs: { perm: permutation } });
toDispose.push(permutedX);
axis = backend_util_exports.getInnerMostAxes(1, xRank)[0];
}
const outShape = backend_util_exports.segment_util.computeOutShape(permutedX.shape, axis, numSegments);
const inSize = util_exports.sizeFromShape([permutedX.shape[axis]]);
const a2D = reshape3({ inputs: { x: permutedX }, backend: backend2, attrs: { shape: [-1, inSize] } });
toDispose.push(a2D);
const outputDType = sumOutType(x.dtype);
const segOpCompute = (x2, segOpType, segmentIds2, dtype, numSegments2) => {
const batchSize = x2.shape[0];
const inSize2 = x2.shape[1];
const windowSize = backend_util_exports.segment_util.segOpComputeOptimalWindowSize(inSize2, numSegments2);
const segOpInfo = { windowSize, inSize: inSize2, batchSize, numSegments: numSegments2 };
const program = new SegmentOpProgram(segOpInfo, segOpType);
const output = backend2.compileAndRun(program, [x2, segmentIds2], dtype);
toDispose.push(output);
if (output.shape[1] === numSegments2) {
return output;
}
const rangeInfo = range4({
backend: backend2,
attrs: { start: 0, stop: numSegments2, step: 1, dtype: "float32" }
});
const tileInfo = tile4({
inputs: { x: rangeInfo },
backend: backend2,
attrs: { reps: [inSize2 / windowSize] }
});
toDispose.push(rangeInfo);
toDispose.push(tileInfo);
const result2 = segOpCompute(output, segOpType, tileInfo, dtype, numSegments2);
return result2;
};
const segOpResult = segOpCompute(a2D, "unsortedSegmentSum", segmentIds, outputDType, numSegments);
const reshaped = reshape3({ inputs: { x: segOpResult }, backend: backend2, attrs: { shape: outShape } });
let result = reshaped;
if (permutation != null) {
toDispose.push(reshaped);
const perm = backend_util_exports.getUndoAxesPermutation(permutation);
result = transpose3({ inputs: { x: result }, backend: backend2, attrs: { perm } });
}
toDispose.forEach((t) => backend2.disposeIntermediateTensorInfo(t));
return result;
}
var unsortedSegmentSumConfig2 = {
kernelName: UnsortedSegmentSum,
backendName: "webgl",
kernelFunc: unsortedSegmentSum3
};
// node_modules/.pnpm/@[email protected]_hek32lflchivueqv5i4vgonghu/node_modules/@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js
var kernelConfigs2 = [
_fusedMatMulConfig2,
absConfig2,
acosConfig2,
acoshConfig2,
addConfig2,
addNConfig2,
allConfig2,
anyConfig2,
argMaxConfig2,
argMinConfig2,
asinConfig2,
asinhConfig2,
atanConfig2,
atan2Config2,
atanhConfig2,
avgPoolConfig2,
avgPool3DConfig2,
avgPool3DGradConfig3,
avgPoolGradConfig3,
batchMatMulConfig2,
batchNormConfig2,
batchToSpaceNDConfig2,
bincountConfig2,
broadcastArgsConfig2,
castConfig2,
ceilConfig2,
clipByValueConfig2,
complexConfig2,
complexAbsConfig2,
concatConfig2,
conv2DConfig2,
conv2DBackpropFilterConfig2,
conv2DBackpropInputConfig2,
conv3DConfig2,
conv3DBackpropFilterV2Config2,
conv3DBackpropInputConfig,
cosConfig2,
coshConfig2,
cropAndResizeConfig2,
cumprodConfig2,
cumsumConfig2,
denseBincountConfig2,
depthToSpaceConfig2,
depthwiseConv2dNativeConfig2,
depthwiseConv2dNativeBackpropFilterConfig2,
depthwiseConv2dNativeBackpropInputConfig2,
diagConfig2,
dilation2DConfig2,
einsumConfig2,
eluConfig2,
eluGradConfig3,
equalConfig2,
erfConfig2,
expConfig2,
expandDimsConfig2,
expm1Config2,
fftConfig2,
fillConfig2,
flipLeftRightConfig2,
floorConfig2,
floorDivConfig2,
fromPixelsConfig,
fusedConv2DConfig2,
fusedDepthwiseConv2DConfig2,
gatherNdConfig2,
gatherV2Config2,
greaterConfig2,
greaterEqualConfig2,
identityConfig2,
ifftConfig2,
imagConfig2,
isFiniteConfig2,
isInfConfig2,
isNaNConfig2,
leakyReluConfig2,
lessConfig2,
lessEqualConfig2,
linSpaceConfig2,
logConfig2,
log1pConfig2,
logicalAndConfig2,
logicalNotConfig2,
logicalOrConfig2,
LRNConfig2,
LRNGradConfig2,
maxConfig2,
maximumConfig2,
maxPoolConfig2,
maxPool3DConfig2,
maxPool3DGradConfig3,
maxPoolGradConfig3,
maxPoolWithArgmaxConfig2,
meanConfig2,
minConfig2,
minimumConfig2,
mirrorPadConfig2,
modConfig2,
multinomialConfig2,
multiplyConfig2,
negConfig2,
nonMaxSuppressionV3Config2,
nonMaxSuppressionV4Config2,
nonMaxSuppressionV5Config2,
notEqualConfig2,
oneHotConfig2,
onesLikeConfig2,
packConfig2,
padV2Config2,
powConfig2,
preluConfig2,
prodConfig2,
rangeConfig2,
realConfig2,
realDivConfig2,
reciprocalConfig2,
reluConfig2,
relu6Config2,
reshapeConfig2,
resizeBilinearConfig2,
resizeBilinearGradConfig3,
resizeNearestNeighborConfig2,
resizeNearestNeighborGradConfig3,
reverseConfig2,
rotateWithOffsetConfig2,
roundConfig2,
rsqrtConfig2,
scatterNdConfig2,
searchSortedConfig2,
selectConfig2,
seluConfig2,
sigmoidConfig2,
signConfig2,
sinConfig2,
sinhConfig2,
sliceConfig2,
softmaxConfig2,
softplusConfig2,
spaceToBatchNDConfig2,
sparseFillEmptyRowsConfig2,
sparseReshapeConfig2,
sparseSegmentMeanConfig2,
sparseSegmentSumConfig2,
sparseToDenseConfig2,
splitVConfig2,
sqrtConfig2,
squareConfig2,
squaredDifferenceConfig2,
stepConfig2,
stridedSliceConfig2,
stringNGramsConfig2,
stringSplitConfig2,
stringToHashBucketFastConfig2,
subConfig2,
sumConfig2,
tanConfig2,
tanhConfig2,
tileConfig2,
topKConfig2,
transformConfig2,
transposeConfig2,
uniqueConfig2,
unpackConfig2,
unsortedSegmentSumConfig2,
zerosLikeConfig2
];
for (const kernelConfig of kernelConfigs2) {
registerKernel(kernelConfig);
}
// node_modules/.pnpm/@[email protected][email protected]/node_modules/@tensorflow/tfjs/dist/version.js
init_define_BUILD_VERSION();
// src/main.js
var charset = [
"",
"0",
"2",
"4",
"8",
"A",
"D",
"G",
"H",
"J",
"K",
"M",
"N",
"P",
"Q",
"R",
"S",
"T",
"V",
"W",
"X",
"Y"
];
var model2;
setBackend("cpu");
function toggle(obj, v) {
if (v)
obj.style.display = "";
else
obj.style.display = "none";
}
function base64ToArray(base64) {
const binaryString = window.atob(base64);
const len = binaryString.length;
const bytes = new Uint8Array(len);
for (let i = 0; i < len; i++) {
bytes[i] = binaryString.charCodeAt(i);
}
return bytes.buffer;
}
var iohander = {
load: function() {
return new Promise((resolve, reject) => {
resolve({
modelTopology: window.modelJSON.modelTopology,
weightSpecs: window.modelJSON.weightsManifest[0].weights,
weightData: base64ToArray(window.weights64),
format: window.modelJSON.format,
generatedBy: window.modelJSON.generatedBy,
convertedBy: window.modelJSON.convertedBy
});
});
}
};
async function load() {
const uploadJSONInput = document.getElementById("upload-json");
const uploadWeightsInput = document.getElementById("upload-weights-1");
model2 = await loadLayersModel(iohander);
return model2;
}
function black(x) {
return x < 64;
}
function calculateDisorder(imgdata) {
const a = imgdata.data;
const w = imgdata.width;
const h = imgdata.height;
const pic = [];
const visited = [];
for (let c = 0; c < w * h; c++) {
if (visited[c])
continue;
if (!black(a[c * 4]))
continue;
let blackCount = 0;
const items = [];
const toVisit = [c];
while (toVisit.length > 0) {
const cc = toVisit[toVisit.length - 1];
toVisit.splice(toVisit.length - 1, 1);
if (visited[cc])
continue;
visited[cc] = 1;
if (black(a[cc * 4])) {
items.push(cc);
blackCount++;
toVisit.push(cc + 1);
toVisit.push(cc - 1);
toVisit.push(cc + w);
toVisit.push(cc - w);
}
}
if (blackCount >= 24) {
items.forEach(function(x) {
pic[x] = 1;
});
}
}
let res = 0;
let total = 0;
for (let c = 0; c < w * h - w; c++) {
if (pic[c] !== pic[c + w])
res += 1;
if (pic[c])
total += 1;
}
return res / (total === 0 ? 1 : total);
}
function imageFromCanvas(img, bg, off) {
const h = img.height;
const w = img.width;
const th = 80;
const ph = 0;
const pw = 16;
const scale2 = th / h;
const canvas = document.createElement("canvas");
canvas.height = w * scale2 + pw * 2;
canvas.width = th;
const ctx = canvas.getContext("2d");
ctx.fillStyle = "rgb(238,238,238)";
ctx.fillRect(0, 0, canvas.width, canvas.height);
ctx.translate(canvas.width / 2, canvas.height / 2);
ctx.scale(-scale2, scale2);
ctx.rotate(90 * Math.PI / 180);
const draw = function(off2) {
if (bg) {
const border = 4;
ctx.drawImage(
bg,
-off2 + border,
0,
w - border * 2,
h,
-w / 2 + border,
-h / 2,
w - border * 2,
h
);
}
ctx.drawImage(img, -w / 2, -h / 2, w, h);
};
if (bg && off == null) {
let bestDisorder = 999;
let bestImagedata = null;
let bestOff = -1;
for (let off2 = 0; off2 >= -50; off2--) {
draw(off2);
const imgdata = ctx.getImageData(0, 0, canvas.width, canvas.height);
const disorder = calculateDisorder(imgdata);
if (disorder < bestDisorder) {
bestDisorder = disorder;
bestImagedata = imgdata;
bestOff = off2;
}
}
setTimeout(function() {
const bg2 = document.getElementById("t-bg");
const slider = document.getElementById("t-slider");
if (!bg2 || !slider)
return;
slider.value = -bestOff * 2;
bg2.style.backgroundPositionX = bestOff + "px";
}, 1);
return bestImagedata;
} else {
draw(off);
return ctx.getImageData(0, 0, canvas.width, canvas.height);
}
}
async function predict(img, bg, off) {
if (!model2) {
model2 = await load();
}
image = imageFromCanvas(img, bg, off);
tensor = browser_exports.fromPixels(image, 1).mul(-1 / 238).add(1);
prediction = await model2.predict(tensor.expandDims(0)).data();
return createSequence(prediction);
}
function createSequence(prediction2) {
const csl2 = charset.length;
sequence = [];
for (let pos = 0; pos < prediction2.length; pos += csl2) {
const preds = prediction2.slice(pos, pos + csl2);
const max5 = Math.max(...preds);
const seqElem = {};
for (let i = 0; i < csl2; i++) {
const p2 = preds[i] / max5;
const c = charset[i + 1];
if (p2 >= 0.05) {
seqElem[c || ""] = p2;
}
}
sequence.push(seqElem);
}
return sequence;
}
function postprocess(sequence2, overrides2) {
csl = charset.length;
possibilities = [{ sequence: [] }];
sequence2.forEach(function(e, i) {
let additions;
if (overrides2 && overrides2[i] !== void 0) {
additions = [{ sym: overrides2[i], off: i, conf: 1 }];
} else {
additions = Object.keys(e).map(function(sym) {
return { sym, off: i, conf: e[sym] };
});
}
if (additions.length === 1 && additions[0].sym === "")
return;
oldpos = possibilities;
possibilities = [];
oldpos.forEach(function(possibility) {
additions.forEach(function(a) {
const seq = [...possibility.sequence];
if (a.sym !== "")
seq.push([a.sym, a.off, a.conf]);
const obj = {
sequence: seq
};
possibilities.push(obj);
});
});
});
const res = {};
possibilities.forEach(function(p2) {
let line = "";
let lastSym;
let lastOff = -1;
let count2 = 0;
let prob = 0;
p2.sequence.forEach(function(e) {
const sym = e[0];
const off = e[1];
const conf = e[2];
if (sym === lastSym && lastOff + 2 >= off) {
return;
}
line += sym;
lastSym = sym;
lastOff = off;
prob += conf;
count2++;
});
if (count2 > 0)
prob /= count2;
if (prob > res[line] || !res[line]) {
res[line] = prob;
}
});
let keys = Object.keys(res).sort(function(a, b) {
return res[a] < res[b];
});
const keysFitting = keys.filter(function(x) {
return x.length === 5 || x.length === 6;
});
if (keysFitting.length > 0)
keys = keysFitting;
return keys.map(function(x) {
return { seq: x, prob: res[x] };
});
}
async function imageFromUri(uri) {
if (uri.startsWith('url("')) {
uri = uri.substr(5, uri.length - 7);
}
if (!uri.startsWith("data:")) {
return null;
}
const img = new Image();
await new Promise((r) => img.onload = r, img.src = uri);
return img;
}
async function predictUri(uri, uribg, bgoff) {
const img = await imageFromUri(uri);
const bg = uribg ? await imageFromUri(uribg) : null;
const off = bgoff ? parseInt(bgoff) : null;
return await predict(img, bg, off);
}
var solveButton = document.createElement("input");
solveButton.id = "t-auto-solve";
solveButton.value = "Solve";
solveButton.type = "button";
solveButton.style.fontSize = "11px";
solveButton.style.padding = "0 2px";
solveButton.style.margin = "0px 0px 0px 6px";
solveButton.style.height = "18px";
solveButton.onclick = async function() {
solve(true);
};
var altsDiv = document.createElement("div");
altsDiv.id = "t-auto-options";
altsDiv.style.margin = "0";
altsDiv.style.padding = "0";
var storedPalceholder;
var overrides = {};
function placeAfter(elem, sibling) {
if (elem.parentElement !== sibling.parentElement) {
setTimeout(function() {
sibling.parentElement.insertBefore(elem, sibling.nextElementSibling);
}, 1);
}
}
var previousText = null;
async function solve(force) {
const resp = document.getElementById("t-resp");
if (!resp)
return;
const bg = document.getElementById("t-bg");
if (!bg)
return;
const fg = document.getElementById("t-fg");
if (!fg)
return;
const help = document.getElementById("t-help");
if (!help)
return;
placeAfter(solveButton, resp);
placeAfter(altsDiv, help);
setTimeout(function() {
toggle(solveButton, bg.style.backgroundImage);
}, 1);
const text = fg.style.backgroundImage;
if (!text) {
altsDiv.innerHTML = "";
return;
}
if (text === previousText && !force)
return;
previousText = text;
altsDiv.innerHTML = "";
if (!storedPalceholder)
storedPalceholder = resp.placeholder;
resp.placeholder = "solving captcha...";
overrides = {};
const sequence2 = await predictUri(
text,
bg.style.backgroundImage,
force ? bg.style.backgroundPositionX : null
);
const opts = postprocess(sequence2);
resp.placeholder = storedPalceholder;
showOpts(opts);
}
function showOpts(opts) {
const resp = document.getElementById("t-resp");
if (!resp)
return;
altsDiv.innerHTML = "";
if (opts.length === 0) {
resp.value = "";
return;
}
resp.value = opts[0].seq;
if (opts.length === 1 || true) {
}
}
var observer = new MutationObserver(async function(mutationsList, observer2) {
solve(false);
});
observer.observe(document.body, {
attributes: true,
childList: true,
subtree: true
});
})();
/**
* @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.
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
*
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
*
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @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.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (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.
* =============================================================================
*/
/**
* @license
* Copyright 2022 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.
* =============================================================================
*/
/**
* @license
* Copyright 2022 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.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/**
* @license
* Copyright 2022 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the 'License');
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an 'AS IS' BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
/** @license See the LICENSE file. */