From 6c81abf8b182ce677b74cf2c570281ffea7f63f8 Mon Sep 17 00:00:00 2001 From: Fuwn Date: Mon, 1 Dec 2025 18:16:09 -0800 Subject: Add files via upload --- css/bootstrap.min.css | 7 + images/after.jpg | Bin 0 -> 369911 bytes images/before.jpg | Bin 0 -> 353605 bytes index.html | 348 + js/analysis.js | 673 + js/bootstrap.bundle.min.js | 7 + js/face-landmarks-detection.js | 1497 ++ js/index.js | 409 + js/tf-backend-cpu.js | 9526 +++++++++++++ js/tf-converter.js | 29751 +++++++++++++++++++++++++++++++++++++++ js/tf-core.js | 27144 +++++++++++++++++++++++++++++++++++ 11 files changed, 69362 insertions(+) create mode 100644 css/bootstrap.min.css create mode 100644 images/after.jpg create mode 100644 images/before.jpg create mode 100644 index.html create mode 100644 js/analysis.js create mode 100644 js/bootstrap.bundle.min.js create mode 100644 js/face-landmarks-detection.js create mode 100644 js/index.js create mode 100644 js/tf-backend-cpu.js create mode 100644 js/tf-converter.js create mode 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right:1.5rem!important;padding-left:1.5rem!important}.px-xxl-5{padding-right:3rem!important;padding-left:3rem!important}.py-xxl-0{padding-top:0!important;padding-bottom:0!important}.py-xxl-1{padding-top:.25rem!important;padding-bottom:.25rem!important}.py-xxl-2{padding-top:.5rem!important;padding-bottom:.5rem!important}.py-xxl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xxl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xxl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xxl-0{padding-top:0!important}.pt-xxl-1{padding-top:.25rem!important}.pt-xxl-2{padding-top:.5rem!important}.pt-xxl-3{padding-top:1rem!important}.pt-xxl-4{padding-top:1.5rem!important}.pt-xxl-5{padding-top:3rem!important}.pe-xxl-0{padding-right:0!important}.pe-xxl-1{padding-right:.25rem!important}.pe-xxl-2{padding-right:.5rem!important}.pe-xxl-3{padding-right:1rem!important}.pe-xxl-4{padding-right:1.5rem!important}.pe-xxl-5{padding-right:3rem!important}.pb-xxl-0{padding-bottom:0!important}.pb-xxl-1{padding-bottom:.25rem!important}.pb-xxl-2{padding-bottom:.5rem!important}.pb-xxl-3{padding-bottom:1rem!important}.pb-xxl-4{padding-bottom:1.5rem!important}.pb-xxl-5{padding-bottom:3rem!important}.ps-xxl-0{padding-left:0!important}.ps-xxl-1{padding-left:.25rem!important}.ps-xxl-2{padding-left:.5rem!important}.ps-xxl-3{padding-left:1rem!important}.ps-xxl-4{padding-left:1.5rem!important}.ps-xxl-5{padding-left:3rem!important}.text-xxl-start{text-align:left!important}.text-xxl-end{text-align:right!important}.text-xxl-center{text-align:center!important}}@media (min-width:1200px){.fs-1{font-size:2.5rem!important}.fs-2{font-size:2rem!important}.fs-3{font-size:1.75rem!important}.fs-4{font-size:1.5rem!important}}@media print{.d-print-inline{display:inline!important}.d-print-inline-block{display:inline-block!important}.d-print-block{display:block!important}.d-print-grid{display:grid!important}.d-print-table{display:table!important}.d-print-table-row{display:table-row!important}.d-print-table-cell{display:table-cell!important}.d-print-flex{display:flex!important}.d-print-inline-flex{display:inline-flex!important}.d-print-none{display:none!important}} +/*# sourceMappingURL=bootstrap.min.css.map */ \ No newline at end of file diff --git a/images/after.jpg b/images/after.jpg new file mode 100644 index 0000000..d72ed34 Binary files /dev/null and b/images/after.jpg differ diff --git a/images/before.jpg b/images/before.jpg new file mode 100644 index 0000000..78932f8 Binary files /dev/null and b/images/before.jpg differ diff --git a/index.html b/index.html new file mode 100644 index 0000000..379dd92 --- /dev/null +++ b/index.html @@ -0,0 +1,348 @@ + + + Incel Solutions + + + + + + + +
+ +
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+ + FeatureRatingMeasurementIdeal
+
+ +
+ 		Total
Midface ratio
Facial width to height ratio
Chin to philtrum ratio
Canthal tilt
Mouth to nose ratio
Bigonial width
Lip ratio
Eye separation ratio
Eye to mouth angle
Lower third height
Palpebral fissure length
Eye color + + +
+
+ + + + + +
+
+

$INCEL Technology

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+ Introducing the latest advancement in incel technology: a full facial structure analysis completed in just a few seconds from a single picture. It's completely private and runs securely on your browser; no data is sent to our servers. +

+

+ This project has been funded by INCEL COIN, contract address GACL2mEL9BR4baJBbYWfNKa5mxBYXRdU4ZthedKcVkx1 and the Vers and Lukas Podcast +

+

There is no meme. I love you.

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If Vers or Lukas or og dev want me to take this down, just let me know. I just missed the site :)

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+ + + + \ No newline at end of file diff --git a/js/analysis.js b/js/analysis.js new file mode 100644 index 0000000..fd78855 --- /dev/null +++ b/js/analysis.js @@ -0,0 +1,673 @@ +function analyseCriteria(face) { + let points = { + leftIris: face.annotations.rightEyeIris[0], + rightIris: face.annotations.leftEyeIris[0], + leftLateralCanthus: face.annotations.rightEyeLower1[0], + leftMedialCanthus: face.annotations.rightEyeLower1[7], + rightLateralCanthus: face.annotations.leftEyeLower1[0], + rightMedialCanthus: face.annotations.leftEyeLower1[7], + leftEyeUpper: face.annotations.rightEyeUpper0[4], + leftEyeLower: face.annotations.rightEyeLower0[4], + rightEyeUpper: face.annotations.leftEyeUpper0[4], + rightEyeLower: face.annotations.leftEyeLower0[4], + leftEyebrow: face.annotations.rightEyebrowUpper[6], + rightEyebrow: face.annotations.leftEyebrowUpper[6], + leftZygo: face.annotations.silhouette[28], + rightZygo: face.annotations.silhouette[8], + noseBottom: face.annotations.noseBottom[0], + leftNoseCorner: face.annotations.noseRightCorner[0], + rightNoseCorner: face.annotations.noseLeftCorner[0], + leftCupidBow: face.annotations.lipsUpperOuter[4], + lipSeparation: face.annotations.lipsUpperInner[5], + rightCupidBow: face.annotations.lipsUpperOuter[6], + leftLipCorner: face.annotations.lipsUpperOuter[0], + rightLipCorner: face.annotations.lipsUpperOuter[10], + lowerLip: face.annotations.lipsLowerOuter[4], + upperLip: face.annotations.lipsUpperOuter[5], + leftGonial: face.annotations.silhouette[24], + rightGonial: face.annotations.silhouette[12], + chinLeft: face.annotations.silhouette[19], + chinTip: face.annotations.silhouette[18], + chinRight: face.annotations.silhouette[17], + }; + return [points, { + midfaceRatio: new MidfaceRatio(face, points), + facialWidthToHeightRatio: new FacialWidthToHeightRatio(face, points), + chinToPhiltrumRatio: new ChinToPhiltrumRatio(face, points), + canthalTilt: new CanthalTilt(face, points), + mouthToNoseRatio: new MouthToNoseRatio(face, points), + bigonialWidth: new BigonialWidth(face, points), + lipRatio: new LipRatio(face, points), + eyeSeparationRatio: new EyeSeparationRatio(face, points), + eyeToMouthAngle: new EyeToMouthAngle(face, points), + lowerThirdHeight: new LowerThirdHeight(face, points), + palpebralFissureLength: new PalpebralFissureLength(face, points), + eyeColor: new EyeColor(face, points), + }]; +} + +async function setupDatabase() { + return await fetch("database.json") + .then(res => res.text()) + .then(text => { + console.log("✅ database.json loaded"); + return JSON.parse(text); + }) + .catch(err => { + console.error("❌ Failed to load or parse database.json:", err); + return { entries: {} }; + }); + +} + +class Criteria { + constructor(face, points) { + this.face = face; + this.points = points; + + for(let i in points) { + if (points.hasOwnProperty(i)) { + Object.defineProperty(this, i, { get: () => this.points[i] }); + } + } + + return this; + } + + createPoint(name, value) { + this.points[name] = value; + Object.defineProperty(this, name, { get: () => this.points[name] }); + } + + calculate() { + /* abstract */ + } + + render() { + /* abstract */ + } + + ideal() { + /* abstract */ + } + + assess() { + /* abstract */ + } + + draw(ctx) { + /* abstract */ + } + + necessaryPoints() { + /* abstract */ + } +} + +class MidfaceRatio extends Criteria { + constructor(face, points) { + super(face, points); + + let bottomLine = Fn.fromTwoPoints(this.leftCupidBow, this.rightCupidBow); + let leftLine = bottomLine.perpendicular(this.leftIris); + let rightLine = bottomLine.perpendicular(this.rightIris); + this.createPoint("bottomLeftMidface", bottomLine.intersect(leftLine)); + this.createPoint("bottomRightMidface", bottomLine.intersect(rightLine)); + } + + calculate() { + this.ratio = ((distance(this.leftIris, this.rightIris) / distance(this.leftIris, this.bottomLeftMidface)) + + (distance(this.leftIris, this.rightIris) / distance(this.rightIris, this.bottomRightMidface))) / 2; + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `${database.entries.midfaceRatio.idealLower} to ${database.entries.midfaceRatio.idealUpper}`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.midfaceRatio; + return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "red", [this.leftIris, this.rightIris, this.bottomRightMidface, this.bottomLeftMidface]); + } + + necessaryPoints() { + return ["leftIris", "rightIris", "bottomLeftMidface", "bottomRightMidface"]; + } +} + +class FacialWidthToHeightRatio extends Criteria { + constructor(face, points) { + super(face, points); + + let topLine = Fn.fromTwoPoints(this.leftEyeUpper, this.rightEyeUpper); + let bottomLine = Fn.fromTwoPoints(this.leftCupidBow, this.rightCupidBow); + let leftLine = topLine.perpendicular(this.leftZygo); + let rightLine = topLine.perpendicular(this.rightZygo); + this.createPoint("topLeft", leftLine.intersect(topLine)); + this.createPoint("topRight", rightLine.intersect(topLine)); + this.createPoint("bottomLeft", leftLine.intersect(bottomLine)); + this.createPoint("bottomRight", rightLine.intersect(bottomLine)); + } + + calculate() { + this.ratio = (((distance(this.topLeft, this.topRight) / distance(this.topLeft, this.bottomLeft)) + + (distance(this.bottomLeft, this.bottomRight) / distance(this.topRight, this.bottomRight))) / 2); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `more than ${database.entries.facialWidthToHeightRatio.idealLower}`; + } + + assess() { + let { idealLower, deviation, deviatingLow } = database.entries.facialWidthToHeightRatio; + return assess(this.ratio, idealLower, undefined, deviation, deviatingLow, undefined); + } + + draw(ctx) { + draw(ctx, "lightblue", [this.topLeft, this.topRight, this.bottomRight, this.bottomLeft]) + } + + necessaryPoints() { + return ["topLeft", "topRight", "bottomLeft", "bottomRight"]; + } +} + +class ChinToPhiltrumRatio extends Criteria { + calculate() { + this.ratio = distance(this.chinTip, this.lowerLip) / distance(this.upperLip, this.noseBottom); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `${database.entries.chinToPhiltrumRatio.idealLower} to ${database.entries.chinToPhiltrumRatio.idealUpper}`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.chinToPhiltrumRatio; + return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "blue", [this.chinTip, this.lowerLip]); + draw(ctx, "blue", [this.upperLip, this.noseBottom]); + } + + necessaryPoints() { + return ["chinTip", "lowerLip", "upperLip", "noseBottom"]; + } +} + +class CanthalTilt extends Criteria { + calculate() { + let line = [this.rightZygo[0] - this.leftZygo[0], this.rightZygo[1] - this.leftZygo[1]]; + let lineFn = Fn.fromTwoPoints(this.rightZygo, this.leftZygo); + let left = [this.leftLateralCanthus[0] - this.leftMedialCanthus[0], this.leftLateralCanthus[1] - this.leftMedialCanthus[1]]; + let right = [this.rightLateralCanthus[0] - this.rightMedialCanthus[0], this.rightLateralCanthus[1] - this.rightMedialCanthus[1]]; + let pointOnLeftLine = lineFn.getY(this.leftMedialCanthus[0]) + left[1]; + let pointOnRightLine = lineFn.getY(this.rightMedialCanthus[0]) + right[1]; + this.leftCanthalTilt = Math.acos( + Math.abs((-1) * (line[0] * left[0] + line[1] * left[1])) + / + (Math.sqrt(line[0] ** 2 + line[1] ** 2) * Math.sqrt(left[0] ** 2 + left[1] ** 2)) + ) * (180 / Math.PI) * (lineFn.getY(this.leftLateralCanthus[0]) - pointOnLeftLine > 0 ? 1 : -1); + this.rightCanthalTilt = Math.acos( + Math.abs(line[0] * right[0] + line[1] * right[1]) + / + (Math.sqrt(line[0] ** 2 + line[1] ** 2) * Math.sqrt(right[0] ** 2 + right[1] ** 2)) + ) * (180 / Math.PI) * (lineFn.getY(this.rightLateralCanthus[0]) - pointOnRightLine > 0 ? 1 : -1); + } + + render() { + return `left ${round(this.rightCanthalTilt, 0)}°, right ${round(this.leftCanthalTilt, 0)}°`; + } + + ideal() { + return `more than ${database.entries.canthalTilt.idealLower}`; + } + + assess() { + let { idealLower, deviation, deviatingLow } = database.entries.canthalTilt; + return assess((this.leftCanthalTilt + this.rightCanthalTilt) / 2, idealLower, undefined, deviation, deviatingLow, undefined); + } + + draw(ctx) { + draw(ctx, "pink", [this.leftLateralCanthus, this.leftMedialCanthus]); + draw(ctx, "pink", [this.rightLateralCanthus, this.rightMedialCanthus]); + } + + necessaryPoints() { + return ["leftLateralCanthus", "leftMedialCanthus", "rightLateralCanthus", "rightMedialCanthus"]; + } +} + +class MouthToNoseRatio extends Criteria { + calculate() { + this.ratio = distance(this.leftLipCorner, this.rightLipCorner) / distance(this.leftNoseCorner, this.rightNoseCorner); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `${database.entries.mouthToNoseRatio.idealLower} to ${database.entries.mouthToNoseRatio.idealUpper}`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.mouthToNoseRatio; + return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "purple", [this.leftLipCorner, this.rightLipCorner]); + draw(ctx, "purple", [this.leftNoseCorner, this.rightNoseCorner]); + } + + necessaryPoints() { + return ["leftLipCorner", "rightLipCorner", "leftNoseCorner", "rightNoseCorner"]; + } +} + +class BigonialWidth extends Criteria { + calculate() { + this.ratio = distance(this.leftZygo, this.rightZygo) / distance(this.leftGonial, this.rightGonial); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `${database.entries.bigonialWidth.idealLower} to ${database.entries.bigonialWidth.idealUpper}`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.bigonialWidth; + return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "gold", [this.leftGonial, this.rightGonial]); + draw(ctx, "gold", [this.leftZygo, this.rightZygo]); + } + + necessaryPoints() { + return ["leftZygo", "rightZygo", "leftGonial", "rightGonial"]; + } +} + +class LipRatio extends Criteria { + constructor(face, points) { + super(face, points); + + let topLip = Fn.fromTwoPoints(this.leftCupidBow, this.rightCupidBow); + let lowerLip = topLip.parallel(this.lowerLip); + this.createPoint("upperLipEnd", topLip.intersect(topLip.perpendicular(this.lipSeparation))); + this.createPoint("lowerLipEnd", lowerLip.intersect(lowerLip.perpendicular(this.lipSeparation))); + } + + calculate() { + this.ratio = distance(this.lowerLipEnd, this.lipSeparation) / distance(this.upperLipEnd, this.lipSeparation); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `${database.entries.lipRatio.idealLower} to ${database.entries.lipRatio.idealUpper}`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.lipRatio; + return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "lightgreen", [this.upperLipEnd, this.lipSeparation]); + draw(ctx, "lightgreen", [this.lipSeparation, this.lowerLipEnd]); + } + + necessaryPoints() { + return ["upperLipEnd", "lowerLipEnd", "lipSeparation"]; + } +} + +class EyeSeparationRatio extends Criteria { + calculate() { + this.ratio = distance(this.leftIris, this.rightIris) / distance(this.leftZygo, this.rightZygo); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `${database.entries.eyeSeparationRatio.idealLower} to ${database.entries.eyeSeparationRatio.idealUpper}`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.eyeSeparationRatio; + return assess(this.ratio, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "orange", [this.leftIris, this.rightIris]); + draw(ctx, "orange", [this.leftZygo, this.rightZygo]); + } + + necessaryPoints() { + return ["leftIris", "rightIris", "leftZygo", "rightZygo"]; + } +} + +class EyeToMouthAngle extends Criteria { + calculate() { + let a = [this.leftIris[0] - this.lipSeparation[0], this.leftIris[1] - this.lipSeparation[1]]; + let b = [this.rightIris[0] - this.lipSeparation[0], this.rightIris[1] - this.lipSeparation[1]]; + this.angle = Math.acos( + (a[0] * b[0] + a[1] * b[1]) + / + (Math.sqrt(a[0] ** 2 + a[1] ** 2) * Math.sqrt(b[0] ** 2 + b[1] ** 2)) + ) * (180 / Math.PI); + } + + render() { + return `${round(this.angle, 0)}°`; + } + + ideal() { + return `${database.entries.eyeToMouthAngle.idealLower}° to ${database.entries.eyeToMouthAngle.idealUpper}°`; + } + + assess() { + let { idealLower, idealUpper, deviation, deviatingLow, deviatingHigh } = database.entries.eyeToMouthAngle; + return assess(this.angle, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh); + } + + draw(ctx) { + draw(ctx, "brown", [this.leftIris, this.lipSeparation, this.rightIris, this.lipSeparation, this.leftIris]); + } + + necessaryPoints() { + return ["leftIris", "lipSeparation", "rightIris"]; + } +} + +class LowerThirdHeight extends Criteria { + calculate() { + let middlePoint = [this.leftNoseCorner[0] + (1/2) * (this.rightNoseCorner[0] - this.leftNoseCorner[0]), this.leftNoseCorner[1] + (1/2) * (this.rightNoseCorner[1] - this.leftNoseCorner[1])]; + let middleLine = Fn.fromTwoPoints(this.leftNoseCorner, this.rightNoseCorner).perpendicular(middlePoint); + let topPoint = middleLine.intersect(Fn.fromTwoPoints(this.leftEyebrow, this.rightEyebrow)); + let bottomPoint = middleLine.intersect(Fn.fromTwoPoints(this.chinLeft, this.chinRight)); + this.ratio = distance(bottomPoint, middlePoint) / distance(middlePoint, topPoint); + } + + render() { + return `${round(this.ratio, 2)}`; + } + + ideal() { + return `more than ${database.entries.lowerThirdHeight.idealLower}`; + } + + assess() { + let { idealLower, deviation, deviatingLow } = database.entries.lowerThirdHeight; + return assess(this.ratio, idealLower, undefined, deviation, deviatingLow, undefined); + } + + draw(ctx) { + draw(ctx, "grey", [this.leftEyebrow, this.rightEyebrow, this.rightNoseCorner, this.leftNoseCorner]); + draw(ctx, "grey", [this.leftNoseCorner, this.rightNoseCorner, this.chinRight, this.chinLeft]); + } + + necessaryPoints() { + return ["leftNoseCorner", "rightNoseCorner", "leftEyebrow", "rightEyebrow", "chinLeft", "chinRight"]; + } +} + +class PalpebralFissureLength extends Criteria { + calculate() { + this.leftPFL = distance(this.leftLateralCanthus, this.leftMedialCanthus) / distance(this.leftEyeUpper, this.leftEyeLower); + this.rightPFL = distance(this.rightLateralCanthus, this.rightMedialCanthus) / distance(this.rightEyeUpper, this.rightEyeLower); + } + + render() { + return `left ${round(this.rightPFL, 2)}, right ${round(this.leftPFL, 2)}`; + } + + ideal() { + return `more than ${database.entries.palpebralFissureLength.idealLower}`; + } + + assess() { + let { idealLower, deviation, deviatingLow } = database.entries.palpebralFissureLength; + return assess((this.leftPFL + this.rightPFL) / 2, idealLower, undefined, deviation, deviatingLow, undefined); + } + + draw(ctx) { + draw(ctx, "aquamarine", [this.leftLateralCanthus, this.leftMedialCanthus]); + draw(ctx, "aquamarine", [this.leftEyeUpper, this.leftEyeLower]); + draw(ctx, "aquamarine", [this.rightLateralCanthus, this.rightMedialCanthus]); + draw(ctx, "aquamarine", [this.rightEyeUpper, this.rightEyeLower]); + } + + necessaryPoints() { + return ["leftLateralCanthus", "leftMedialCanthus", "leftEyeUpper", "leftEyeLower", "rightLateralCanthus", "rightMedialCanthus", "rightEyeUpper", "rightEyeLower"]; + } +} + +class EyeColor extends Criteria { + calculate() { + this.leftIrisCoordinates = this.face.annotations.rightEyeIris; + this.leftIrisWidth = this.leftIrisCoordinates[1][0] - this.leftIrisCoordinates[3][0]; + this.leftIrisHeight = this.leftIrisCoordinates[4][1] - this.leftIrisCoordinates[2][1]; + this.rightIrisCoordinates = this.face.annotations.leftEyeIris; + this.rightIrisWidth = this.rightIrisCoordinates[3][0] - this.rightIrisCoordinates[1][0]; + this.rightIrisHeight = this.rightIrisCoordinates[4][1] - this.rightIrisCoordinates[2][1]; + } + + render() { + return ``; + } + + ideal() { + return ""; + } + + assess() { + return ""; + } + + detect(image, [ctx0, ctx1]) { + ctx0.canvas.width = this.leftIrisWidth; + ctx0.canvas.height = this.leftIrisHeight; + ctx0.drawImage(image, this.leftIrisCoordinates[3][0], this.leftIrisCoordinates[2][1], this.leftIrisWidth, this.leftIrisHeight, 0, 0, this.leftIrisWidth, this.leftIrisHeight); + ctx1.canvas.width = this.rightIrisWidth; + ctx1.canvas.height = this.rightIrisHeight; + ctx1.drawImage(image, this.rightIrisCoordinates[1][0], this.rightIrisCoordinates[2][1], this.rightIrisWidth, this.rightIrisHeight, 0, 0, this.rightIrisWidth, this.rightIrisHeight); + } + + necessaryPoints() { + return []; + } +} + +function distance([ax, ay], [bx, by]) { + return Math.sqrt((ax - bx) ** 2 + (ay - by) ** 2); +} + +class Fn { + constructor(a, b) { + // y = ax + b + this.a = a; + this.b = b; + + this.slope = this.a; + this.yintersect = this.b; + } + + static fromTwoPoints([ax, ay], [bx, by]) { + // y = (ay / by) / (ax - bx) * (x - ax) + ay + return Fn.fromOffset((ay - by) / (ax - bx), ax, ay); + } + + static fromOffset(a, b, c) { + // y = a * (x - b) + c + return new Fn(a, c - (a * b)); + } + + getY(x) { + return this.a * x + this.b; + } + + perpendicular([x, y]) { + return Fn.fromOffset((-1) * (1 / this.a), x, y); + } + + parallel([x, y]) { + return Fn.fromOffset(this.a, x, y); + } + + intersect(fn) { + let x = (fn.b - this.b) / (this.a - fn.a); + return [x, this.getY(x)]; + } + + draw(ctx, color) { + let points = []; + for(let i = 0; i < ctx.canvas.width; i += 1) { + points.push([i, this.getY(i)]); + } + draw(ctx, color || "red", points); + } +} + +function round(n, digits) { + digits = 10 ** (isNaN(digits) ? 2 : digits); + return Math.round(n * digits) / digits; +} + +function assess(value, idealLower, idealUpper, deviation, deviatingLow, deviatingHigh) { + function renderMultiplier(multiplier) { + if (multiplier === 0) { + return "slightly too"; + } else if (multiplier === 1) { + return "noticeably"; + } else if (multiplier === 2) { + return "significantly too"; + } else if (multiplier === 3) { + return "horribly"; + } else { + return "extremely"; + } + } + + function calculate(value, idealLower, idealUpper, deviation) { + + if (idealUpper !== undefined && idealLower !== undefined) { + if (idealUpper >= value && idealLower <= value) { + return { + type: "perfect", + }; + } + } else if (((idealUpper && !idealLower) && value <= idealUpper) || ((!idealUpper && idealLower) && value >= idealLower)) { + return { + type: "perfect", + }; + } + + if (value < idealLower) { + let multiplier = 0; + while ((value += deviation) < idealLower) { + multiplier++; + } + return { + type: "low", + multiplier: Math.min(multiplier, 4), + text: renderMultiplier(multiplier), + }; + } + + if (value > idealUpper) { + let multiplier = 0; + while ((value -= deviation) > idealUpper) { + multiplier++; + } + return { + type: "high", + multiplier: Math.min(multiplier, 4), + text: renderMultiplier(multiplier), + }; + } + + } + + let { type, multiplier, text } = calculate(value, idealLower, idealUpper, deviation); + + if (type === "perfect") { + return `perfect`; + } else if (type === "low") { + return `${text} ${deviatingLow}`; + } else if (type === "high") { + return `${text} ${deviatingHigh}`; + } +} + +function draw(ctx, color, points) { + ctx.strokeStyle = color; + ctx.fillStyle = color; + + let current = points[0]; + + var fontBase = canvas.width * 0.6; // selected default width for canvas + var fontSize = 20; // default size for font + + + var ratio = fontSize / fontBase; // calc ratio + var size = canvas.width * ratio; // get font size based on current width + if(canvas.width > 600) { + ctx.font= size + 'px sans-serif'; + } else { + ctx.font= '18px sans-serif'; + + } var textWidth=ctx.measureText("text").width; + ctx.globalAlpha=.50; + ctx.fillStyle='white' + var text = "www.incel.solutions (powered by $INCEL COIN)" + cw = canvas.width; + ch = canvas.height; + var textWidth=ctx.measureText(text).width; + ctx.fillStyle='gray' + ctx.fillText(text,cw-textWidth-10,ch-20); + ctx.fillStyle='white' + ctx.fillText(text,cw-textWidth-10+2,ch-20+2); + + for (let i of points.concat([points[0]])) { + let [x, y] = i; + + ctx.beginPath(); + ctx.moveTo(current[0], current[1]); + ctx.lineTo(x, y); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(x, y, ctx.arcRadius, 0, 2 * Math.PI); + ctx.fill(); + + current = i; + } +} diff --git a/js/bootstrap.bundle.min.js b/js/bootstrap.bundle.min.js new file mode 100644 index 0000000..cc0a255 --- /dev/null +++ b/js/bootstrap.bundle.min.js @@ -0,0 +1,7 @@ +/*! + * Bootstrap v5.1.3 (https://getbootstrap.com/) + * Copyright 2011-2021 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */ +!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t="transitionend",e=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return 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this._config.reference&&(e=this._config.reference);const i=this._getPopperConfig(),n=i.modifiers.find((t=>"applyStyles"===t.name&&!1===t.enabled));this._popper=qe(e,this._menu,i),n&&U.setDataAttribute(this._menu,"popper","static")}_isShown(t=this._element){return t.classList.contains(Je)}_getMenuElement(){return V.next(this._element,ei)[0]}_getPlacement(){const t=this._element.parentNode;if(t.classList.contains("dropend"))return ri;if(t.classList.contains("dropstart"))return ai;const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ni:ii:e?oi:si}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return"static"===this._config.display&&(t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,..."function"==typeof this._config.popperConfig?this._config.popperConfig(t):this._config.popperConfig}}_selectMenuItem({key:t,target:e}){const i=V.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter(l);i.length&&v(i,e,t===Ye,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(t&&(2===t.button||"keyup"===t.type&&"Tab"!==t.key))return;const e=V.find(ti);for(let i=0,n=e.length;ie+t)),this._setElementAttributes(di,"paddingRight",(e=>e+t)),this._setElementAttributes(ui,"marginRight",(e=>e-t))}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t)[e];t.style[e]=`${i(Number.parseFloat(s))}px`}))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,"paddingRight"),this._resetElementAttributes(di,"paddingRight"),this._resetElementAttributes(ui,"marginRight")}_saveInitialAttribute(t,e){const i=t.style[e];i&&U.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=U.getDataAttribute(t,e);void 0===i?t.style.removeProperty(e):(U.removeDataAttribute(t,e),t.style[e]=i)}))}_applyManipulationCallback(t,e){o(t)?e(t):V.find(t,this._element).forEach(e)}isOverflowing(){return this.getWidth()>0}}const pi={className:"modal-backdrop",isVisible:!0,isAnimated:!1,rootElement:"body",clickCallback:null},mi={className:"string",isVisible:"boolean",isAnimated:"boolean",rootElement:"(element|string)",clickCallback:"(function|null)"},gi="show",_i="mousedown.bs.backdrop";class bi{constructor(t){this._config=this._getConfig(t),this._isAppended=!1,this._element=null}show(t){this._config.isVisible?(this._append(),this._config.isAnimated&&u(this._getElement()),this._getElement().classList.add(gi),this._emulateAnimation((()=>{_(t)}))):_(t)}hide(t){this._config.isVisible?(this._getElement().classList.remove(gi),this._emulateAnimation((()=>{this.dispose(),_(t)}))):_(t)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_getConfig(t){return(t={...pi,..."object"==typeof t?t:{}}).rootElement=r(t.rootElement),a("backdrop",t,mi),t}_append(){this._isAppended||(this._config.rootElement.append(this._getElement()),j.on(this._getElement(),_i,(()=>{_(this._config.clickCallback)})),this._isAppended=!0)}dispose(){this._isAppended&&(j.off(this._element,_i),this._element.remove(),this._isAppended=!1)}_emulateAnimation(t){b(t,this._getElement(),this._config.isAnimated)}}const vi={trapElement:null,autofocus:!0},yi={trapElement:"element",autofocus:"boolean"},wi=".bs.focustrap",Ei="backward";class Ai{constructor(t){this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}activate(){const{trapElement:t,autofocus:e}=this._config;this._isActive||(e&&t.focus(),j.off(document,wi),j.on(document,"focusin.bs.focustrap",(t=>this._handleFocusin(t))),j.on(document,"keydown.tab.bs.focustrap",(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,j.off(document,wi))}_handleFocusin(t){const{target:e}=t,{trapElement:i}=this._config;if(e===document||e===i||i.contains(e))return;const n=V.focusableChildren(i);0===n.length?i.focus():this._lastTabNavDirection===Ei?n[n.length-1].focus():n[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?Ei:"forward")}_getConfig(t){return t={...vi,..."object"==typeof t?t:{}},a("focustrap",t,yi),t}}const Ti="modal",Oi="Escape",Ci={backdrop:!0,keyboard:!0,focus:!0},ki={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean"},Li="hidden.bs.modal",xi="show.bs.modal",Di="resize.bs.modal",Si="click.dismiss.bs.modal",Ni="keydown.dismiss.bs.modal",Ii="mousedown.dismiss.bs.modal",Pi="modal-open",ji="show",Mi="modal-static";class Hi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._dialog=V.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._ignoreBackdropClick=!1,this._isTransitioning=!1,this._scrollBar=new fi}static get Default(){return Ci}static get NAME(){return Ti}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||j.trigger(this._element,xi,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isAnimated()&&(this._isTransitioning=!0),this._scrollBar.hide(),document.body.classList.add(Pi),this._adjustDialog(),this._setEscapeEvent(),this._setResizeEvent(),j.on(this._dialog,Ii,(()=>{j.one(this._element,"mouseup.dismiss.bs.modal",(t=>{t.target===this._element&&(this._ignoreBackdropClick=!0)}))})),this._showBackdrop((()=>this._showElement(t))))}hide(){if(!this._isShown||this._isTransitioning)return;if(j.trigger(this._element,"hide.bs.modal").defaultPrevented)return;this._isShown=!1;const t=this._isAnimated();t&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),this._focustrap.deactivate(),this._element.classList.remove(ji),j.off(this._element,Si),j.off(this._dialog,Ii),this._queueCallback((()=>this._hideModal()),this._element,t)}dispose(){[window,this._dialog].forEach((t=>j.off(t,".bs.modal"))),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new bi({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_getConfig(t){return t={...Ci,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Ti,t,ki),t}_showElement(t){const e=this._isAnimated(),i=V.findOne(".modal-body",this._dialog);this._element.parentNode&&this._element.parentNode.nodeType===Node.ELEMENT_NODE||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0,i&&(i.scrollTop=0),e&&u(this._element),this._element.classList.add(ji),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,j.trigger(this._element,"shown.bs.modal",{relatedTarget:t})}),this._dialog,e)}_setEscapeEvent(){this._isShown?j.on(this._element,Ni,(t=>{this._config.keyboard&&t.key===Oi?(t.preventDefault(),this.hide()):this._config.keyboard||t.key!==Oi||this._triggerBackdropTransition()})):j.off(this._element,Ni)}_setResizeEvent(){this._isShown?j.on(window,Di,(()=>this._adjustDialog())):j.off(window,Di)}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(Pi),this._resetAdjustments(),this._scrollBar.reset(),j.trigger(this._element,Li)}))}_showBackdrop(t){j.on(this._element,Si,(t=>{this._ignoreBackdropClick?this._ignoreBackdropClick=!1:t.target===t.currentTarget&&(!0===this._config.backdrop?this.hide():"static"===this._config.backdrop&&this._triggerBackdropTransition())})),this._backdrop.show(t)}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(j.trigger(this._element,"hidePrevented.bs.modal").defaultPrevented)return;const{classList:t,scrollHeight:e,style:i}=this._element,n=e>document.documentElement.clientHeight;!n&&"hidden"===i.overflowY||t.contains(Mi)||(n||(i.overflowY="hidden"),t.add(Mi),this._queueCallback((()=>{t.remove(Mi),n||this._queueCallback((()=>{i.overflowY=""}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;(!i&&t&&!m()||i&&!t&&m())&&(this._element.style.paddingLeft=`${e}px`),(i&&!t&&!m()||!i&&t&&m())&&(this._element.style.paddingRight=`${e}px`)}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=Hi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}j.on(document,"click.bs.modal.data-api",'[data-bs-toggle="modal"]',(function(t){const e=n(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),j.one(e,xi,(t=>{t.defaultPrevented||j.one(e,Li,(()=>{l(this)&&this.focus()}))}));const i=V.findOne(".modal.show");i&&Hi.getInstance(i).hide(),Hi.getOrCreateInstance(e).toggle(this)})),R(Hi),g(Hi);const Bi="offcanvas",Ri={backdrop:!0,keyboard:!0,scroll:!1},Wi={backdrop:"boolean",keyboard:"boolean",scroll:"boolean"},$i="show",zi=".offcanvas.show",qi="hidden.bs.offcanvas";class Fi extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get NAME(){return Bi}static get Default(){return Ri}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||j.trigger(this._element,"show.bs.offcanvas",{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._element.style.visibility="visible",this._backdrop.show(),this._config.scroll||(new fi).hide(),this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add($i),this._queueCallback((()=>{this._config.scroll||this._focustrap.activate(),j.trigger(this._element,"shown.bs.offcanvas",{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(j.trigger(this._element,"hide.bs.offcanvas").defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.remove($i),this._backdrop.hide(),this._queueCallback((()=>{this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._element.style.visibility="hidden",this._config.scroll||(new fi).reset(),j.trigger(this._element,qi)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_getConfig(t){return t={...Ri,...U.getDataAttributes(this._element),..."object"==typeof t?t:{}},a(Bi,t,Wi),t}_initializeBackDrop(){return new bi({className:"offcanvas-backdrop",isVisible:this._config.backdrop,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:()=>this.hide()})}_initializeFocusTrap(){return new Ai({trapElement:this._element})}_addEventListeners(){j.on(this._element,"keydown.dismiss.bs.offcanvas",(t=>{this._config.keyboard&&"Escape"===t.key&&this.hide()}))}static jQueryInterface(t){return this.each((function(){const e=Fi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}j.on(document,"click.bs.offcanvas.data-api",'[data-bs-toggle="offcanvas"]',(function(t){const e=n(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),c(this))return;j.one(e,qi,(()=>{l(this)&&this.focus()}));const i=V.findOne(zi);i&&i!==e&&Fi.getInstance(i).hide(),Fi.getOrCreateInstance(e).toggle(this)})),j.on(window,"load.bs.offcanvas.data-api",(()=>V.find(zi).forEach((t=>Fi.getOrCreateInstance(t).show())))),R(Fi),g(Fi);const Ui=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Vi=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^#&/:?]*(?:[#/?]|$))/i,Ki=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[\d+/a-z]+=*$/i,Xi=(t,e)=>{const i=t.nodeName.toLowerCase();if(e.includes(i))return!Ui.has(i)||Boolean(Vi.test(t.nodeValue)||Ki.test(t.nodeValue));const n=e.filter((t=>t instanceof RegExp));for(let t=0,e=n.length;t{Xi(t,r)||i.removeAttribute(t.nodeName)}))}return n.body.innerHTML}const Qi="tooltip",Gi=new Set(["sanitize","allowList","sanitizeFn"]),Zi={animation:"boolean",template:"string",title:"(string|element|function)",trigger:"string",delay:"(number|object)",html:"boolean",selector:"(string|boolean)",placement:"(string|function)",offset:"(array|string|function)",container:"(string|element|boolean)",fallbackPlacements:"array",boundary:"(string|element)",customClass:"(string|function)",sanitize:"boolean",sanitizeFn:"(null|function)",allowList:"object",popperConfig:"(null|object|function)"},Ji={AUTO:"auto",TOP:"top",RIGHT:m()?"left":"right",BOTTOM:"bottom",LEFT:m()?"right":"left"},tn={animation:!0,template:'',trigger:"hover focus",title:"",delay:0,html:!1,selector:!1,placement:"top",offset:[0,0],container:!1,fallbackPlacements:["top","right","bottom","left"],boundary:"clippingParents",customClass:"",sanitize:!0,sanitizeFn:null,allowList:{"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},popperConfig:null},en={HIDE:"hide.bs.tooltip",HIDDEN:"hidden.bs.tooltip",SHOW:"show.bs.tooltip",SHOWN:"shown.bs.tooltip",INSERTED:"inserted.bs.tooltip",CLICK:"click.bs.tooltip",FOCUSIN:"focusin.bs.tooltip",FOCUSOUT:"focusout.bs.tooltip",MOUSEENTER:"mouseenter.bs.tooltip",MOUSELEAVE:"mouseleave.bs.tooltip"},nn="fade",sn="show",on="show",rn="out",an=".tooltip-inner",ln=".modal",cn="hide.bs.modal",hn="hover",dn="focus";class un extends B{constructor(t,e){if(void 0===Fe)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t),this._isEnabled=!0,this._timeout=0,this._hoverState="",this._activeTrigger={},this._popper=null,this._config=this._getConfig(e),this.tip=null,this._setListeners()}static get Default(){return tn}static get NAME(){return Qi}static get Event(){return en}static get DefaultType(){return Zi}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(t){if(this._isEnabled)if(t){const e=this._initializeOnDelegatedTarget(t);e._activeTrigger.click=!e._activeTrigger.click,e._isWithActiveTrigger()?e._enter(null,e):e._leave(null,e)}else{if(this.getTipElement().classList.contains(sn))return void this._leave(null,this);this._enter(null,this)}}dispose(){clearTimeout(this._timeout),j.off(this._element.closest(ln),cn,this._hideModalHandler),this.tip&&this.tip.remove(),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this.isWithContent()||!this._isEnabled)return;const t=j.trigger(this._element,this.constructor.Event.SHOW),e=h(this._element),i=null===e?this._element.ownerDocument.documentElement.contains(this._element):e.contains(this._element);if(t.defaultPrevented||!i)return;"tooltip"===this.constructor.NAME&&this.tip&&this.getTitle()!==this.tip.querySelector(an).innerHTML&&(this._disposePopper(),this.tip.remove(),this.tip=null);const n=this.getTipElement(),s=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME);n.setAttribute("id",s),this._element.setAttribute("aria-describedby",s),this._config.animation&&n.classList.add(nn);const o="function"==typeof this._config.placement?this._config.placement.call(this,n,this._element):this._config.placement,r=this._getAttachment(o);this._addAttachmentClass(r);const{container:a}=this._config;H.set(n,this.constructor.DATA_KEY,this),this._element.ownerDocument.documentElement.contains(this.tip)||(a.append(n),j.trigger(this._element,this.constructor.Event.INSERTED)),this._popper?this._popper.update():this._popper=qe(this._element,n,this._getPopperConfig(r)),n.classList.add(sn);const l=this._resolvePossibleFunction(this._config.customClass);l&&n.classList.add(...l.split(" ")),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>{j.on(t,"mouseover",d)}));const c=this.tip.classList.contains(nn);this._queueCallback((()=>{const t=this._hoverState;this._hoverState=null,j.trigger(this._element,this.constructor.Event.SHOWN),t===rn&&this._leave(null,this)}),this.tip,c)}hide(){if(!this._popper)return;const t=this.getTipElement();if(j.trigger(this._element,this.constructor.Event.HIDE).defaultPrevented)return;t.classList.remove(sn),"ontouchstart"in document.documentElement&&[].concat(...document.body.children).forEach((t=>j.off(t,"mouseover",d))),this._activeTrigger.click=!1,this._activeTrigger.focus=!1,this._activeTrigger.hover=!1;const e=this.tip.classList.contains(nn);this._queueCallback((()=>{this._isWithActiveTrigger()||(this._hoverState!==on&&t.remove(),this._cleanTipClass(),this._element.removeAttribute("aria-describedby"),j.trigger(this._element,this.constructor.Event.HIDDEN),this._disposePopper())}),this.tip,e),this._hoverState=""}update(){null!==this._popper&&this._popper.update()}isWithContent(){return Boolean(this.getTitle())}getTipElement(){if(this.tip)return this.tip;const t=document.createElement("div");t.innerHTML=this._config.template;const e=t.children[0];return this.setContent(e),e.classList.remove(nn,sn),this.tip=e,this.tip}setContent(t){this._sanitizeAndSetContent(t,this.getTitle(),an)}_sanitizeAndSetContent(t,e,i){const n=V.findOne(i,t);e||!n?this.setElementContent(n,e):n.remove()}setElementContent(t,e){if(null!==t)return o(e)?(e=r(e),void(this._config.html?e.parentNode!==t&&(t.innerHTML="",t.append(e)):t.textContent=e.textContent)):void(this._config.html?(this._config.sanitize&&(e=Yi(e,this._config.allowList,this._config.sanitizeFn)),t.innerHTML=e):t.textContent=e)}getTitle(){const t=this._element.getAttribute("data-bs-original-title")||this._config.title;return this._resolvePossibleFunction(t)}updateAttachment(t){return"right"===t?"end":"left"===t?"start":t}_initializeOnDelegatedTarget(t,e){return e||this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return"function"==typeof t?t.call(this._element):t}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"onChange",enabled:!0,phase:"afterWrite",fn:t=>this._handlePopperPlacementChange(t)}],onFirstUpdate:t=>{t.options.placement!==t.placement&&this._handlePopperPlacementChange(t)}};return{...e,..."function"==typeof this._config.popperConfig?this._config.popperConfig(e):this._config.popperConfig}}_addAttachmentClass(t){this.getTipElement().classList.add(`${this._getBasicClassPrefix()}-${this.updateAttachment(t)}`)}_getAttachment(t){return Ji[t.toUpperCase()]}_setListeners(){this._config.trigger.split(" ").forEach((t=>{if("click"===t)j.on(this._element,this.constructor.Event.CLICK,this._config.selector,(t=>this.toggle(t)));else if("manual"!==t){const e=t===hn?this.constructor.Event.MOUSEENTER:this.constructor.Event.FOCUSIN,i=t===hn?this.constructor.Event.MOUSELEAVE:this.constructor.Event.FOCUSOUT;j.on(this._element,e,this._config.selector,(t=>this._enter(t))),j.on(this._element,i,this._config.selector,(t=>this._leave(t)))}})),this._hideModalHandler=()=>{this._element&&this.hide()},j.on(this._element.closest(ln),cn,this._hideModalHandler),this._config.selector?this._config={...this._config,trigger:"manual",selector:""}:this._fixTitle()}_fixTitle(){const t=this._element.getAttribute("title"),e=typeof this._element.getAttribute("data-bs-original-title");(t||"string"!==e)&&(this._element.setAttribute("data-bs-original-title",t||""),!t||this._element.getAttribute("aria-label")||this._element.textContent||this._element.setAttribute("aria-label",t),this._element.setAttribute("title",""))}_enter(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusin"===t.type?dn:hn]=!0),e.getTipElement().classList.contains(sn)||e._hoverState===on?e._hoverState=on:(clearTimeout(e._timeout),e._hoverState=on,e._config.delay&&e._config.delay.show?e._timeout=setTimeout((()=>{e._hoverState===on&&e.show()}),e._config.delay.show):e.show())}_leave(t,e){e=this._initializeOnDelegatedTarget(t,e),t&&(e._activeTrigger["focusout"===t.type?dn:hn]=e._element.contains(t.relatedTarget)),e._isWithActiveTrigger()||(clearTimeout(e._timeout),e._hoverState=rn,e._config.delay&&e._config.delay.hide?e._timeout=setTimeout((()=>{e._hoverState===rn&&e.hide()}),e._config.delay.hide):e.hide())}_isWithActiveTrigger(){for(const t in this._activeTrigger)if(this._activeTrigger[t])return!0;return!1}_getConfig(t){const e=U.getDataAttributes(this._element);return Object.keys(e).forEach((t=>{Gi.has(t)&&delete e[t]})),(t={...this.constructor.Default,...e,..."object"==typeof t&&t?t:{}}).container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),a(Qi,t,this.constructor.DefaultType),t.sanitize&&(t.template=Yi(t.template,t.allowList,t.sanitizeFn)),t}_getDelegateConfig(){const t={};for(const e in this._config)this.constructor.Default[e]!==this._config[e]&&(t[e]=this._config[e]);return t}_cleanTipClass(){const t=this.getTipElement(),e=new 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Dn="toast",Sn="hide",Nn="show",In="showing",Pn={animation:"boolean",autohide:"boolean",delay:"number"},jn={animation:!0,autohide:!0,delay:5e3};class Mn extends B{constructor(t,e){super(t),this._config=this._getConfig(e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get DefaultType(){return Pn}static get Default(){return jn}static get NAME(){return 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t&&t?t:{}},a(Dn,t,this.constructor.DefaultType),t}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){j.on(this._element,"mouseover.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"mouseout.bs.toast",(t=>this._onInteraction(t,!1))),j.on(this._element,"focusin.bs.toast",(t=>this._onInteraction(t,!0))),j.on(this._element,"focusout.bs.toast",(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=Mn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(Mn),g(Mn),{Alert:W,Button:z,Carousel:st,Collapse:pt,Dropdown:hi,Modal:Hi,Offcanvas:Fi,Popover:gn,ScrollSpy:An,Tab:xn,Toast:Mn,Tooltip:un}})); +//# sourceMappingURL=bootstrap.bundle.min.js.map \ No newline at end of file diff --git a/js/face-landmarks-detection.js b/js/face-landmarks-detection.js new file mode 100644 index 0000000..cb2f93d --- /dev/null +++ b/js/face-landmarks-detection.js @@ -0,0 +1,1497 @@ +/** + * @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. + * ============================================================================= + */ +(function (global, factory) { + typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('@tensorflow/tfjs-converter')) : + typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', '@tensorflow/tfjs-converter'], factory) : + (global = global || self, factory(global.faceLandmarksDetection = {}, global.tf, global.tf)); +}(this, (function (exports, tf, tfconv) { 'use strict'; + + /*! ***************************************************************************** + Copyright (c) Microsoft Corporation. + + Permission to use, copy, modify, and/or distribute this software for any + purpose with or without fee is hereby granted. + + THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH + REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, + INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM + LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR + OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + ***************************************************************************** */ + + var __assign = function() { + __assign = Object.assign || function __assign(t) { + for (var s, i = 1, n = arguments.length; i < n; i++) { + s = arguments[i]; + for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p]; + } + return t; + }; + return __assign.apply(this, arguments); + }; + + function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } + function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); + } + + function __generator(thisArg, body) { + var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g; + return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g; + function verb(n) { return function (v) { return step([n, v]); }; } + function step(op) { + if (f) throw new TypeError("Generator is already executing."); + while (_) try { + if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t; + if (y = 0, t) op = [op[0] & 2, t.value]; + switch (op[0]) { + case 0: case 1: t = op; break; + case 4: _.label++; return { value: op[1], done: false }; + case 5: _.label++; y = op[1]; op = [0]; continue; + case 7: op = _.ops.pop(); _.trys.pop(); continue; + default: + if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; } + if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; } + if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; } + if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; } + if (t[2]) _.ops.pop(); + _.trys.pop(); continue; + } + op = body.call(thisArg, _); + } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; } + if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true }; + } + } + + /** + * @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. + * ============================================================================= + */ + function __awaiter$1(e,t,n,r){return new(n||(n=Promise))(function(o,i){function a(e){try{c(r.next(e));}catch(e){i(e);}}function s(e){try{c(r.throw(e));}catch(e){i(e);}}function c(e){var t;e.done?o(e.value):(t=e.value,t instanceof n?t:new n(function(e){e(t);})).then(a,s);}c((r=r.apply(e,t||[])).next());})}function __generator$1(e,t){var n,r,o,i,a={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return i={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(i[Symbol.iterator]=function(){return this}),i;function s(i){return function(s){return function(i){if(n)throw new TypeError("Generator is already executing.");for(;a;)try{if(n=1,r&&(o=2&i[0]?r.return:i[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,i[1])).done)return o;switch(r=0,o&&(i=[2&i[0],o.value]),i[0]){case 0:case 1:o=i;break;case 4:return a.label++,{value:i[1],done:!1};case 5:a.label++,r=i[1],i=[0];continue;case 7:i=a.ops.pop(),a.trys.pop();continue;default:if(!(o=(o=a.trys).length>0&&o[o.length-1])&&(6===i[0]||2===i[0])){a=0;continue}if(3===i[0]&&(!o||i[1]>o[0]&&i[1]= LANDMARKS_COUNT; + var indexOfMouth = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[0], indexOfForehead = MESH_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[1]; + if (boxLandmarksFromMeshModel === false) { + indexOfMouth = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[0], indexOfForehead = BLAZEFACE_KEYPOINTS_LINE_OF_SYMMETRY_INDICES[1]; + } + angle = computeRotation(box.landmarks[indexOfMouth], box.landmarks[indexOfForehead]); + var faceCenter = getBoxCenter({ startPoint: box.startPoint, endPoint: box.endPoint }); + var faceCenterNormalized = [faceCenter[0] / input.shape[2], faceCenter[1] / input.shape[1]]; + var rotatedImage = input; + var rotationMatrix = IDENTITY_MATRIX; + if (angle !== 0) { + rotatedImage = + tf.image.rotateWithOffset(input, angle, 0, faceCenterNormalized); + rotationMatrix = buildRotationMatrix(-angle, faceCenter); + } + var boxCPU = { startPoint: box.startPoint, endPoint: box.endPoint }; + var face = tf.div(cutBoxFromImageAndResize(boxCPU, rotatedImage, [ + _this.meshHeight, _this.meshWidth + ]), 255); + // The first returned tensor represents facial contours, which are + // included in the coordinates. + var _a = _this.meshDetector.predict(face), flag = _a[1], coords = _a[2]; + var coordsReshaped = tf.reshape(coords, [-1, 3]); + var rawCoords = coordsReshaped.arraySync(); + if (predictIrises) { + var _b = _this.getEyeBox(rawCoords, face, LEFT_EYE_BOUNDS[0], LEFT_EYE_BOUNDS[1], true), leftEyeBox = _b.box, leftEyeBoxSize = _b.boxSize, leftEyeCrop = _b.crop; + var _c = _this.getEyeBox(rawCoords, face, RIGHT_EYE_BOUNDS[0], RIGHT_EYE_BOUNDS[1]), rightEyeBox = _c.box, rightEyeBoxSize = _c.boxSize, rightEyeCrop = _c.crop; + var eyePredictions = (_this.irisModel.predict(tf.concat([leftEyeCrop, rightEyeCrop]))); + var eyePredictionsData = eyePredictions.dataSync(); + var leftEyeData = eyePredictionsData.slice(0, IRIS_NUM_COORDINATES * 3); + var _d = _this.getEyeCoords(leftEyeData, leftEyeBox, leftEyeBoxSize, true), leftEyeRawCoords = _d.rawCoords, leftIrisRawCoords = _d.iris; + var rightEyeData = eyePredictionsData.slice(IRIS_NUM_COORDINATES * 3); + var _e = _this.getEyeCoords(rightEyeData, rightEyeBox, rightEyeBoxSize), rightEyeRawCoords = _e.rawCoords, rightIrisRawCoords = _e.iris; + var leftToRightEyeDepthDifference = _this.getLeftToRightEyeDepthDifference(rawCoords); + if (Math.abs(leftToRightEyeDepthDifference) < + 30) { // User is looking straight ahead. + replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left'); + replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right'); + } + else if (leftToRightEyeDepthDifference < 1) { // User is looking + // towards the + // right. + // If the user is looking to the left or to the right, the iris + // coordinates tend to diverge too much from the mesh coordinates + // for them to be merged. So we only update a single contour line + // above and below the eye. + replaceRawCoordinates(rawCoords, leftEyeRawCoords, 'left', ['EyeUpper0', 'EyeLower0']); + } + else { // User is looking towards the left. + replaceRawCoordinates(rawCoords, rightEyeRawCoords, 'right', ['EyeUpper0', 'EyeLower0']); + } + var adjustedLeftIrisCoords = _this.getAdjustedIrisCoords(rawCoords, leftIrisRawCoords, 'left'); + var adjustedRightIrisCoords = _this.getAdjustedIrisCoords(rawCoords, rightIrisRawCoords, 'right'); + rawCoords = rawCoords.concat(adjustedLeftIrisCoords) + .concat(adjustedRightIrisCoords); + } + var transformedCoordsData = _this.transformRawCoords(rawCoords, box, angle, rotationMatrix); + var transformedCoords = tf.tensor2d(transformedCoordsData); + var landmarksBox = enlargeBox(_this.calculateLandmarksBoundingBox(transformedCoordsData)); + var squarifiedLandmarksBox = squarifyBox(landmarksBox); + _this.regionsOfInterest[i] = __assign({}, squarifiedLandmarksBox, { landmarks: transformedCoords.arraySync() }); + var prediction = { + coords: tf.tensor2d(rawCoords, [rawCoords.length, 3]), + scaledCoords: transformedCoords, + box: landmarksBox, + flag: tf.squeeze(flag) + }; + return prediction; + }); + })]; + } + }); + }); + }; + // Updates regions of interest if the intersection over union between + // the incoming and previous regions falls below a threshold. + Pipeline.prototype.updateRegionsOfInterest = function (boxes) { + for (var i = 0; i < boxes.length; i++) { + var box = boxes[i]; + var previousBox = this.regionsOfInterest[i]; + var iou = 0; + if (previousBox && previousBox.startPoint) { + var _a = box.startPoint, boxStartX = _a[0], boxStartY = _a[1]; + var _b = box.endPoint, boxEndX = _b[0], boxEndY = _b[1]; + var _c = previousBox.startPoint, previousBoxStartX = _c[0], previousBoxStartY = _c[1]; + var _d = previousBox.endPoint, previousBoxEndX = _d[0], previousBoxEndY = _d[1]; + var xStartMax = Math.max(boxStartX, previousBoxStartX); + var yStartMax = Math.max(boxStartY, previousBoxStartY); + var xEndMin = Math.min(boxEndX, previousBoxEndX); + var yEndMin = Math.min(boxEndY, previousBoxEndY); + var intersection = (xEndMin - xStartMax) * (yEndMin - yStartMax); + var boxArea = (boxEndX - boxStartX) * (boxEndY - boxStartY); + var previousBoxArea = (previousBoxEndX - previousBoxStartX) * + (previousBoxEndY - boxStartY); + iou = intersection / (boxArea + previousBoxArea - intersection); + } + if (iou < UPDATE_REGION_OF_INTEREST_IOU_THRESHOLD) { + this.regionsOfInterest[i] = box; + } + } + this.regionsOfInterest = this.regionsOfInterest.slice(0, boxes.length); + }; + Pipeline.prototype.clearRegionOfInterest = function (index) { + if (this.regionsOfInterest[index] != null) { + this.regionsOfInterest = this.regionsOfInterest.slice(0, index).concat(this.regionsOfInterest.slice(index + 1)); + } + }; + Pipeline.prototype.shouldUpdateRegionsOfInterest = function () { + var roisCount = this.regionsOfInterest.length; + var noROIs = roisCount === 0; + if (this.maxFaces === 1 || noROIs) { + return noROIs; + } + return roisCount !== this.maxFaces && + this.runsWithoutFaceDetector >= this.maxContinuousChecks; + }; + Pipeline.prototype.calculateLandmarksBoundingBox = function (landmarks) { + var xs = landmarks.map(function (d) { return d[0]; }); + var ys = landmarks.map(function (d) { return d[1]; }); + var startPoint = [Math.min.apply(Math, xs), Math.min.apply(Math, ys)]; + var endPoint = [Math.max.apply(Math, xs), Math.max.apply(Math, ys)]; + return { startPoint: startPoint, endPoint: endPoint }; + }; + return Pipeline; + }()); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * https://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var UV_COORDS = [ + [0.499976992607117, 0.652534008026123], + [0.500025987625122, 0.547487020492554], + [0.499974012374878, 0.602371990680695], + [0.482113003730774, 0.471979022026062], + [0.500150978565216, 0.527155995368958], + [0.499909996986389, 0.498252987861633], + [0.499523013830185, 0.40106201171875], + 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All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (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. + * ============================================================================= + */ + var FACEMESH_GRAPHMODEL_PATH = 'https://tfhub.dev/mediapipe/tfjs-model/facemesh/1/default/1'; + var IRIS_GRAPHMODEL_PATH = 'https://tfhub.dev/mediapipe/tfjs-model/iris/1/default/2'; + var MESH_MODEL_INPUT_WIDTH = 192; + var MESH_MODEL_INPUT_HEIGHT = 192; + var PREDICTION_VALUES = 'MediaPipePredictionValues'; + var PREDICTION_TENSORS = 'MediaPipePredictionTensors'; + /** + * Load the model. + * + * @param options - a configuration object with the following properties: + * - `maxContinuousChecks` How many frames to go without running the bounding + * box detector. Only relevant if maxFaces > 1. Defaults to 5. + * - `detectionConfidence` Threshold for discarding a prediction. Defaults to + * 0.9. + * - `maxFaces` The maximum number of faces detected in the input. Should be + * set to the minimum number for performance. Defaults to 10. + * - `iouThreshold` A float representing the threshold for deciding whether + * boxes overlap too much in non-maximum suppression. Must be between [0, 1]. + * Defaults to 0.3. + * - `scoreThreshold` A threshold for deciding when to remove boxes based + * on score in non-maximum suppression. Defaults to 0.75. + * - `shouldLoadIrisModel` Whether to also load the iris detection model. + * Defaults to true. + * - `modelUrl` Optional param for specifying a custom facemesh model url or + * a `tf.io.IOHandler` object. + * - `detectorModelUrl` Optional param for specifying a custom blazeface model + * url or a `tf.io.IOHandler` object. + * - `irisModelUrl` Optional param for specifying a custom iris model url or + * a `tf.io.IOHandler` object. + */ + function load$1(config) { + return __awaiter(this, void 0, void 0, function () { + var _a, maxContinuousChecks, _b, detectionConfidence, _c, maxFaces, _d, iouThreshold, _e, scoreThreshold, _f, shouldLoadIrisModel, modelUrl, detectorModelUrl, irisModelUrl, models, faceMesh; + return __generator(this, function (_g) { + switch (_g.label) { + case 0: + _a = config.maxContinuousChecks, maxContinuousChecks = _a === void 0 ? 5 : _a, _b = config.detectionConfidence, detectionConfidence = _b === void 0 ? 0.9 : _b, _c = config.maxFaces, maxFaces = _c === void 0 ? 10 : _c, _d = config.iouThreshold, iouThreshold = _d === void 0 ? 0.3 : _d, _e = config.scoreThreshold, scoreThreshold = _e === void 0 ? 0.75 : _e, _f = config.shouldLoadIrisModel, shouldLoadIrisModel = _f === void 0 ? true : _f, modelUrl = config.modelUrl, detectorModelUrl = config.detectorModelUrl, irisModelUrl = config.irisModelUrl; + if (!shouldLoadIrisModel) return [3 /*break*/, 2]; + return [4 /*yield*/, Promise.all([ + loadDetectorModel(detectorModelUrl, maxFaces, iouThreshold, scoreThreshold), + loadMeshModel(modelUrl), + loadIrisModel(irisModelUrl) + ])]; + case 1: + models = _g.sent(); + return [3 /*break*/, 4]; + case 2: return [4 /*yield*/, Promise.all([ + loadDetectorModel(detectorModelUrl, maxFaces, iouThreshold, scoreThreshold), + loadMeshModel(modelUrl) + ])]; + case 3: + models = _g.sent(); + _g.label = 4; + case 4: + faceMesh = new FaceMesh(models[0], models[1], maxContinuousChecks, detectionConfidence, maxFaces, shouldLoadIrisModel ? models[2] : null); + return [2 /*return*/, faceMesh]; + } + }); + }); + } + function loadDetectorModel(modelUrl, maxFaces, iouThreshold, scoreThreshold) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + return [2 /*return*/, load({ modelUrl: modelUrl, maxFaces: maxFaces, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold })]; + }); + }); + } + function loadMeshModel(modelUrl) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + if (modelUrl != null) { + return [2 /*return*/, tfconv.loadGraphModel(modelUrl)]; + } + return [2 /*return*/, tfconv.loadGraphModel(FACEMESH_GRAPHMODEL_PATH, { fromTFHub: true })]; + }); + }); + } + function loadIrisModel(modelUrl) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + if (modelUrl != null) { + return [2 /*return*/, tfconv.loadGraphModel(modelUrl)]; + } + return [2 /*return*/, tfconv.loadGraphModel(IRIS_GRAPHMODEL_PATH, { fromTFHub: true })]; + }); + }); + } + function getInputTensorDimensions$1(input) { + return input instanceof tf.Tensor ? [input.shape[0], input.shape[1]] : + [input.height, input.width]; + } + function flipFaceHorizontal$1(face, imageWidth) { + if (face.mesh instanceof tf.Tensor) { + var _a = tf.tidy(function () { + var subtractBasis = tf.tensor1d([imageWidth - 1, 0, 0]); + var multiplyBasis = tf.tensor1d([1, -1, 1]); + return tf.tidy(function () { + return [ + tf.concat([ + tf.sub(imageWidth - 1, tf.slice(face.boundingBox.topLeft, 0, 1)), + tf.slice(face.boundingBox.topLeft, 1, 1) + ]), + tf.concat([ + tf.sub(imageWidth - 1, tf.slice(face.boundingBox.bottomRight, 0, 1)), + tf.slice(face.boundingBox.bottomRight, 1, 1) + ]), + tf.mul(tf.sub(subtractBasis, face.mesh), multiplyBasis), + tf.mul(tf.sub(subtractBasis, face.scaledMesh), multiplyBasis) + ]; + }); + }), topLeft = _a[0], bottomRight = _a[1], mesh = _a[2], scaledMesh = _a[3]; + return Object.assign({}, face, { boundingBox: { topLeft: topLeft, bottomRight: bottomRight }, mesh: mesh, scaledMesh: scaledMesh }); + } + return Object.assign({}, face, { + boundingBox: { + topLeft: [ + imageWidth - 1 - face.boundingBox.topLeft[0], + face.boundingBox.topLeft[1] + ], + bottomRight: [ + imageWidth - 1 - face.boundingBox.bottomRight[0], + face.boundingBox.bottomRight[1] + ] + }, + mesh: (face.mesh).map(function (coord) { + var flippedCoord = coord.slice(0); + flippedCoord[0] = imageWidth - 1 - coord[0]; + return flippedCoord; + }), + scaledMesh: face.scaledMesh.map(function (coord) { + var flippedCoord = coord.slice(0); + flippedCoord[0] = imageWidth - 1 - coord[0]; + return flippedCoord; + }) + }); + } + var FaceMesh = /** @class */ (function () { + function FaceMesh(blazeFace, blazeMeshModel, maxContinuousChecks, detectionConfidence, maxFaces, irisModel) { + this.kind = 'MediaPipeFaceMesh'; + this.pipeline = new Pipeline(blazeFace, blazeMeshModel, MESH_MODEL_INPUT_WIDTH, MESH_MODEL_INPUT_HEIGHT, maxContinuousChecks, maxFaces, irisModel); + this.detectionConfidence = detectionConfidence; + } + FaceMesh.getAnnotations = function () { + return MESH_ANNOTATIONS; + }; + /** + * Returns an array of UV coordinates for the 468 facial keypoint vertices in + * mesh_map.jpg. Can be used to map textures to the facial mesh. + */ + FaceMesh.getUVCoords = function () { + return UV_COORDS; + }; + /** + * Returns an array of faces in an image. + * + * @param input The image to classify. Can be a tensor, DOM element image, + * video, or canvas. + * @param returnTensors (defaults to `false`) Whether to return tensors as + * opposed to values. + * @param flipHorizontal Whether to flip/mirror the facial keypoints + * horizontally. Should be true for videos that are flipped by default (e.g. + * webcams). + * @param predictIrises + * + * @return An array of AnnotatedPrediction objects. + */ + FaceMesh.prototype.estimateFaces = function (config) { + return __awaiter(this, void 0, void 0, function () { + var _a, returnTensors, _b, flipHorizontal, _c, predictIrises, input, _d, width, image, predictions, savedWebglPackDepthwiseConvFlag; + var _this = this; + return __generator(this, function (_e) { + switch (_e.label) { + case 0: + _a = config.returnTensors, returnTensors = _a === void 0 ? false : _a, _b = config.flipHorizontal, flipHorizontal = _b === void 0 ? false : _b, _c = config.predictIrises, predictIrises = _c === void 0 ? true : _c; + input = config.input; + if (predictIrises && this.pipeline.irisModel == null) { + throw new Error('The iris model was not loaded as part of facemesh. ' + + 'Please initialize the model with ' + + 'facemesh.load({shouldLoadIrisModel: true}).'); + } + _d = getInputTensorDimensions$1(input), width = _d[1]; + image = tf.tidy(function () { + if (!(input instanceof tf.Tensor)) { + input = tf.browser.fromPixels(input); + } + return tf.expandDims(tf.cast(input, 'float32'), 0); + }); + if (!(tf.getBackend() === 'webgl')) return [3 /*break*/, 2]; + savedWebglPackDepthwiseConvFlag = tf.env().get('WEBGL_PACK_DEPTHWISECONV'); + tf.env().set('WEBGL_PACK_DEPTHWISECONV', true); + return [4 /*yield*/, this.pipeline.predict(image, predictIrises)]; + case 1: + predictions = _e.sent(); + tf.env().set('WEBGL_PACK_DEPTHWISECONV', savedWebglPackDepthwiseConvFlag); + return [3 /*break*/, 4]; + case 2: return [4 /*yield*/, this.pipeline.predict(image, predictIrises)]; + case 3: + predictions = _e.sent(); + _e.label = 4; + case 4: + image.dispose(); + if (predictions != null && predictions.length > 0) { + return [2 /*return*/, Promise.all(predictions.map(function (prediction, i) { return __awaiter(_this, void 0, void 0, function () { + var coords, scaledCoords, box, flag, tensorsToRead, tensorValues, flagValue, annotatedPrediction_1, _a, coordsArr, coordsArrScaled, annotatedPrediction, annotations, key; + var _this = this; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + coords = prediction.coords, scaledCoords = prediction.scaledCoords, box = prediction.box, flag = prediction.flag; + tensorsToRead = [flag]; + if (!returnTensors) { + tensorsToRead = tensorsToRead.concat([coords, scaledCoords]); + } + return [4 /*yield*/, Promise.all(tensorsToRead.map(function (d) { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) { + return [2 /*return*/, d.array()]; + }); }); }))]; + case 1: + tensorValues = _b.sent(); + flagValue = tensorValues[0]; + flag.dispose(); + if (flagValue < this.detectionConfidence) { + this.pipeline.clearRegionOfInterest(i); + } + if (returnTensors) { + annotatedPrediction_1 = { + kind: PREDICTION_TENSORS, + faceInViewConfidence: flagValue, + mesh: coords, + scaledMesh: scaledCoords, + boundingBox: { + topLeft: tf.tensor1d(box.startPoint), + bottomRight: tf.tensor1d(box.endPoint) + } + }; + if (flipHorizontal) { + return [2 /*return*/, flipFaceHorizontal$1(annotatedPrediction_1, width)]; + } + return [2 /*return*/, annotatedPrediction_1]; + } + _a = tensorValues.slice(1), coordsArr = _a[0], coordsArrScaled = _a[1]; + scaledCoords.dispose(); + coords.dispose(); + annotatedPrediction = { + kind: PREDICTION_VALUES, + faceInViewConfidence: flagValue, + boundingBox: { topLeft: box.startPoint, bottomRight: box.endPoint }, + mesh: coordsArr, + scaledMesh: coordsArrScaled + }; + if (flipHorizontal) { + annotatedPrediction = + flipFaceHorizontal$1(annotatedPrediction, width); + } + annotations = {}; + for (key in MESH_ANNOTATIONS) { + if (predictIrises || key.includes('Iris') === false) { + annotations[key] = MESH_ANNOTATIONS[key].map(function (index) { return annotatedPrediction.scaledMesh[index]; }); + } + } + annotatedPrediction['annotations'] = annotations; + return [2 /*return*/, annotatedPrediction]; + } + }); + }); }))]; + } + return [2 /*return*/, []]; + } + }); + }); + }; + return FaceMesh; + }()); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * https://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + (function (SupportedPackages) { + SupportedPackages["mediapipeFacemesh"] = "mediapipe-facemesh"; + })(exports.SupportedPackages || (exports.SupportedPackages = {})); + /** + * Load face-landmarks-detection. + * + * @param pkg - The name of the package to load, e.g. 'mediapipe-facemesh'. + * @param config - a configuration object with the following properties: + * - `maxContinuousChecks` How many frames to go without running the bounding + * box detector. Only relevant if maxFaces > 1. Defaults to 5. + * - `detectionConfidence` Threshold for discarding a prediction. Defaults to + * 0.9. + * - `maxFaces` The maximum number of faces detected in the input. Should be + * set to the minimum number for performance. Defaults to 10. + * - `iouThreshold` A float representing the threshold for deciding whether + * boxes overlap too much in non-maximum suppression. Must be between [0, 1]. + * Defaults to 0.3. + * - `scoreThreshold` A threshold for deciding when to remove boxes based + * on score in non-maximum suppression. Defaults to 0.75. + * - `shouldLoadIrisModel` Whether to also load the iris detection model. + * Defaults to true. + */ + function load$2(pkg, config) { + if (pkg === void 0) { pkg = exports.SupportedPackages.mediapipeFacemesh; } + if (config === void 0) { config = {}; } + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + if (pkg === exports.SupportedPackages.mediapipeFacemesh) { + return [2 /*return*/, load$1(config)]; + } + else { + throw new Error(pkg + " is not a valid package name."); + } + }); + }); + } + + exports.load = load$2; + + Object.defineProperty(exports, '__esModule', { value: true }); + +}))); diff --git a/js/index.js b/js/index.js new file mode 100644 index 0000000..0bc87ea --- /dev/null +++ b/js/index.js @@ -0,0 +1,409 @@ +console.log("🧪 JS is updated: 2024-05-20"); + +let setStatus = function () {}; + +// Summary calculator +function summarizeResults() { + console.log("▶ summarizeResults() invoked"); + const ratingCells = document.querySelectorAll('td[id^="assessment-"]'); + + const counts = { + perfect: 0, + deviation0: 0, + deviation1: 0, + deviation2: 0, + deviation3: 0, + deviation4: 0, + }; + + const weights = { + perfect: 2, + deviation0: 1, + deviation1: 0, + deviation2: -1, + deviation3: -2, + deviation4: -2, + }; + + ratingCells.forEach(cell => { + const html = cell.innerHTML; + if (html.includes("perfect")) counts.perfect++; + else if (html.includes("deviation-0")) counts.deviation0++; + else if (html.includes("deviation-1")) counts.deviation1++; + else if (html.includes("deviation-2")) counts.deviation2++; + else if (html.includes("deviation-3")) counts.deviation3++; + else if (html.includes("deviation-4")) counts.deviation4++; + }); + + const totalScore = + counts.perfect * weights.perfect + + counts.deviation0 * weights.deviation0 + + counts.deviation1 * weights.deviation1 + + counts.deviation2 * weights.deviation2 + + counts.deviation3 * weights.deviation3 + + counts.deviation4 * weights.deviation4; + + const maxScore = 22; + const normalized = Math.max(0, Math.min(8, Math.round((totalScore / maxScore) * 9))); + const psl = `PSL${normalized}`; + + const labels = { + 0: "Subhuman", + 1: "Sub5", + 2: "Low-tier Normie", + 3: "Normie", + 4: "Upper Normie", + 5: "Chadlite", + 6: "Chad", + 7: "Gigachad", + 8: "Terachad" + }; + const label = labels[normalized] || "Unknown"; + +const hierarchyLabels = { + 8: { + titles: ["Era Defining Man", "Immortal", "God Among Men"], + description: "Men destined to define eras and change history, who will be remembered for millennia to come", + color: "#A0D8EF" + }, + 7: { + titles: ["King", "Explorer", "Philosopher"], + description: "Philosophers, Kings, Explorers, and Inventors", + color: "#4682B4" + }, + 6: { + titles: ["Influential Artist", "Writer", "General"], + description: "Influential Artists, Writers, and Generals", + color: "#5F9EA0" + }, + 5: { + titles: ["Diplomat", "Official", "Officer"], + description: "Officers, Officials, Diplomats", + color: "#8FBC8F" + }, + 4: { + titles: ["Middle Management", "Soldier", "Craftsman"], + description: "Middle Management, Soldiers, Craftsmen", + color: "#8B5A2B" + }, + 3: { + titles: ["Manual Laborerer", "Peasant", "Street Merchant"], + description: "Manual Labourers, Peasants, Street Merchants", + color: "#A52A2A" + }, + 2: { + titles: ["Street Sweeper", "Drain Cleaner", "Waste Collector"], + description: "Street Cleaners", + color: "#B22222" + }, + 1: { + titles: ["Sanitation Worker", "Toilet Scrubber", "Gutterman"], + description: "Sanitation Workers", + color: "#800000" + }, + 0: { + titles: ["Untouchable", "Subhuman", "Bottom of the Barrel"], + description: "Untouchables", + color: "#2F2F2F" + } +}; + + + const badgeClass = normalized <= 3 ? "danger" : normalized <= 6 ? "warning" : "success"; + const gradingToggle = document.getElementById("grading-toggle"); + const usePSL = gradingToggle ? gradingToggle.checked : true; + + const scoreCell = document.getElementById("total-score"); + const breakdownCell = document.getElementById("total-breakdown"); + const resultCell = document.getElementById("total-psl"); + + if (scoreCell) scoreCell.innerHTML = `${totalScore}`; + if (breakdownCell) breakdownCell.innerHTML = ` + Perfect: ${counts.perfect}, + Slight: ${counts.deviation0}, + Noticeable: ${counts.deviation1}, + Significant: ${counts.deviation2}, + Horrible: ${counts.deviation3}, + Extreme: ${counts.deviation4} + `; + + if (resultCell) { + if (usePSL) { + resultCell.innerHTML = `${psl}
${label}`; + } else { + const h = hierarchyLabels[normalized] || hierarchyLabels[1]; +const title = Array.isArray(h.titles) + ? h.titles[Math.floor(Math.random() * h.titles.length)] + : h.titles; + +const lightColors = ["#A0D8EF", "#8FBC8F", "#A52A2A"]; +const isLight = lightColors.includes(h.color); +const textColor = isLight ? "black" : "white"; + +resultCell.innerHTML = ` + + ${title} +
+ ${h.description} +`; + + } + } +} + +async function main() { + const _model = await faceLandmarksDetection.load(faceLandmarksDetection.SupportedPackages.mediapipeFacemesh, { + maxFaces: 1 + }); + window.database = await setupDatabase(); + const imageInputFile = document.getElementById("image-file"); + const imageInputUrl = document.getElementById("image-url"); + const introductionElement = document.getElementById("introduction"); + const analyzingElement = document.getElementById("analyzing"); + const canvas = document.getElementById("canvas"); + const ctx = canvas.getContext("2d"); + + let data = void 0; + +const gradingToggle = document.getElementById("grading-toggle"); +if (gradingToggle) { + gradingToggle.addEventListener("change", summarizeResults); +} + + + imageInputFile.addEventListener("change", async () => { + if (imageInputFile.files[0]) { + introductionElement.style.display = "none"; + analyzingElement.classList.remove("d-none"); + data && clearData(); + + setStatus("Reading image"); + + imageInputUrl.value = ""; + + let url = URL.createObjectURL(imageInputFile.files[0]); + + await onChange(url); + } + }); + + imageInputUrl.addEventListener("change", async () => { + if (imageInputUrl.value) { + introductionElement.style.display = "none"; + analyzingElement.classList.remove("d-none"); + data && clearData(); + + setStatus("Downloading image"); + + imageInputFile.value = ""; + + let file = await (await fetch(imageInputUrl.value)).blob(); + let url = URL.createObjectURL(file); + + await onChange(url); + } + }); + + async function onChange(url) { + setStatus("Analyzing"); + + let analysis = await analyze(canvas, ctx, url); + + data = analysis.criteria = { + midfaceRatio: {...createBindings(analysis.criteria.midfaceRatio, "midface-ratio")}, + facialWidthToHeightRatio: {...createBindings(analysis.criteria.facialWidthToHeightRatio, "facial-width-to-height-ratio")}, + chinToPhiltrumRatio: {...createBindings(analysis.criteria.chinToPhiltrumRatio, "chin-to-philtrum-ratio")}, + canthalTilt: {...createBindings(analysis.criteria.canthalTilt, "canthal-tilt")}, + mouthToNoseRatio: {...createBindings(analysis.criteria.mouthToNoseRatio, "mouth-to-nose-ratio")}, + bigonialWidth: {...createBindings(analysis.criteria.bigonialWidth, "bigonial-width")}, + lipRatio: {...createBindings(analysis.criteria.lipRatio, "lip-ratio")}, + eyeSeparationRatio: {...createBindings(analysis.criteria.eyeSeparationRatio, "eye-separation-ratio")}, + eyeToMouthAngle: {...createBindings(analysis.criteria.eyeToMouthAngle, "eye-to-mouth-angle")}, + lowerThirdHeight: {...createBindings(analysis.criteria.lowerThirdHeight, "lower-third-height")}, + palpebralFissureLength: {...createBindings(analysis.criteria.palpebralFissureLength, "palpebral-fissure-length")}, + eyeColor: {...createBindings(analysis.criteria.eyeColor, "eye-color")}, + }; + + function createBindings(metric, id) { + return { + analysis: metric, + render: document.getElementById(`value-${id}`), + toggle: document.getElementById(`toggle-${id}`), + ideal: document.getElementById(`ideal-${id}`), + assessment: document.getElementById(`assessment-${id}`), + }; + } + + let calculate = () => { + for (let i of Object.values(analysis.criteria)) { + i.analysis.calculate(); + i.render.innerHTML = i.analysis.render(); + i.ideal.innerHTML = i.analysis.ideal(); + i.assessment.innerHTML = i.analysis.assess(); + } + + analysis.criteria.eyeColor.analysis.detect( + analysis.image, + Array.from(analysis.criteria.eyeColor.render.children).map(i => i.getContext("2d")) + ); + + summarizeResults(); // 👈 Score update + } + + let render = () => { + analysis.resetToImage(); + for (let i of Object.values(analysis.criteria)) { + if (i.toggle.checked) { + i.analysis.draw(ctx); + } + } + } + + for (let i of Object.values(analysis.criteria)) { + i.toggle.onchange = () => render(); + } + + let moving = false; + + canvas.onmousedown = ({ offsetX: x, offsetY: y }) => { + let necessaryPoints = Object.values(analysis.criteria).filter(i => i.toggle.checked).map(i => i.analysis.necessaryPoints()).flat(); + + for (let i in analysis.points) { + if (analysis.points.hasOwnProperty(i) && necessaryPoints.includes(i)) { + if (Math.sqrt( + (analysis.points[i][0] - x) ** 2 + + (analysis.points[i][1] - y) ** 2 + ) <= analysis.arcRadius) { + moving = i; + return; + } + } + } + } + + canvas.ontouchstart = (e) => { + let bcr = e.target.getBoundingClientRect(); + let x = e.targetTouches[0].clientX - bcr.x; + let y = e.targetTouches[0].clientY - bcr.y; + canvas.onmousedown({ offsetX: x, offsetY: y }); + } + + canvas.onmouseup = () => { + moving = false; + } + + canvas.ontouchend = canvas.ontouchcancel = (e) => { + canvas.onmouseup(); + } + + canvas.onmousemove = ({ offsetX: x, offsetY: y }) => { + if (moving) { + analysis.points[moving] = [x, y]; + calculate(); + render(); + } else { + let necessaryPoints = Object.values(analysis.criteria).filter(i => i.toggle.checked).map(i => i.analysis.necessaryPoints()).flat(); + + for (let i in analysis.points) { + if (analysis.points.hasOwnProperty(i) && necessaryPoints.includes(i)) { + if (Math.sqrt( + (analysis.points[i][0] - x) ** 2 + + (analysis.points[i][1] - y) ** 2 + ) <= analysis.arcRadius) { + render(); + ctx.beginPath(); + ctx.strokeStyle = "gray"; + let oldLineWidth = ctx.lineWidth; + ctx.lineWidth = 0.5; + ctx.arc(analysis.points[i][0], analysis.points[i][1], ctx.arcRadius + 1.5, 0, 2 * Math.PI); + ctx.stroke(); + ctx.lineWidth = oldLineWidth; + return; + } + } + } + render(); + } + } + + canvas.ontouchmove = (e) => { + let bcr = e.target.getBoundingClientRect(); + let x = e.targetTouches[0].clientX - bcr.x; + let y = e.targetTouches[0].clientY - bcr.y; + canvas.onmousemove({ offsetX: x, offsetY: y }); + } + + analyzingElement.classList.add("d-none"); + + calculate(); + render(); + } + + function clearData() { + canvas.width = 0; + canvas.height = 0; + for (let i of Object.values(data)) { + i.render.innerHTML = ""; + i.ideal.innerHTML = ""; + i.assessment.innerHTML = ""; + } + } + + setStatus = (text) => document.getElementById("analyzing-status").innerHTML = text; + + document.querySelector("#loading").style.display = "none"; + document.querySelector(".container").classList.remove("d-none"); +} + +async function analyze(canvas, ctx, url) { + setStatus("Loading image"); + + let image = await loadImage(url); + + canvas.width = image.width; + canvas.height = image.height; + resetToImage(ctx, image); + ctx.lineWidth = Math.sqrt((image.width * image.height) / 100000); + ctx.arcRadius = Math.sqrt((image.width * image.height) / 100000); + + setStatus("Analyzing"); + + const model = await faceLandmarksDetection.load(faceLandmarksDetection.SupportedPackages.mediapipeFacemesh, { + maxFaces: 1 + }); + let face = await findLandmarks(model, image); + let [points, criteria] = analyseCriteria(face); + + return { + image, + resetToImage: () => resetToImage(ctx, image), + points, + criteria, + arcRadius: ctx.arcRadius, + }; +} + +function loadImage(url) { + return new Promise((resolve) => { + const image = new Image(); + image.src = url; + image.addEventListener("load", () => resolve(image)); + }); +} + +function resetToImage(ctx, image) { + ctx.drawImage(image, 0, 0); +} + +async function findLandmarks(model, image) { + const predictions = await model.estimateFaces({ input: image }); + if (predictions.length > 0) { + return predictions[0]; + } else { + throw new Error("No face detected"); + } +} + +(async function () { + await main(); +})(); diff --git a/js/tf-backend-cpu.js b/js/tf-backend-cpu.js new file mode 100644 index 0000000..539d2c5 --- /dev/null +++ b/js/tf-backend-cpu.js @@ -0,0 +1,9526 @@ +/** + * @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. + * ============================================================================= + */ +(function (global, factory) { + typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core'), require('seedrandom')) : + typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core', 'seedrandom'], factory) : + (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.tf, global.seedrandom)); +}(this, (function (exports, tfjsCore, seedrandom) { 'use strict'; + + function _interopNamespace(e) { + if (e && e.__esModule) return e; + var n = Object.create(null); + if (e) { + Object.keys(e).forEach(function (k) { + if (k !== 'default') { + var d = Object.getOwnPropertyDescriptor(e, k); + Object.defineProperty(n, k, d.get ? d : { + enumerable: true, + get: function () { + return e[k]; + } + }); + } + }); + } + n['default'] = e; + return n; + } + + var seedrandom__namespace = /*#__PURE__*/_interopNamespace(seedrandom); + + /*! ***************************************************************************** + Copyright (c) Microsoft Corporation. + + Permission to use, copy, modify, and/or distribute this software for any + purpose with or without fee is hereby granted. + + THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH + REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, + INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM + LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR + OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + ***************************************************************************** */ + /* global Reflect, Promise */ + var extendStatics = function (d, b) { + extendStatics = Object.setPrototypeOf || + ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) || + function (d, b) { for (var p in b) + if (b.hasOwnProperty(p)) + d[p] = b[p]; }; + return extendStatics(d, b); + }; + function __extends(d, b) { + extendStatics(d, b); + function __() { this.constructor = d; } + d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __()); + } + function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { + step(generator.next(value)); + } + catch (e) { + reject(e); + } } + function rejected(value) { try { + step(generator["throw"](value)); + } + catch (e) { + reject(e); + } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); + } + function __generator(thisArg, body) { + var _ = { label: 0, sent: function () { if (t[0] & 1) + throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g; + return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function () { return this; }), g; + function verb(n) { return function (v) { return step([n, v]); }; } + function step(op) { + if (f) + throw new TypeError("Generator is already executing."); + while (_) + try { + if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) + return t; + if (y = 0, t) + op = [op[0] & 2, t.value]; + switch (op[0]) { + case 0: + case 1: + t = op; + break; + case 4: + _.label++; + return { value: op[1], done: false }; + case 5: + _.label++; + y = op[1]; + op = [0]; + continue; + case 7: + op = _.ops.pop(); + _.trys.pop(); + continue; + default: + if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { + _ = 0; + continue; + } + if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { + _.label = op[1]; + break; + } + if (op[0] === 6 && _.label < t[1]) { + _.label = t[1]; + t = op; + break; + } + if (t && _.label < t[2]) { + _.label = t[2]; + _.ops.push(op); + break; + } + if (t[2]) + _.ops.pop(); + _.trys.pop(); + continue; + } + op = body.call(thisArg, _); + } + catch (e) { + op = [6, e]; + y = 0; + } + finally { + f = t = 0; + } + if (op[0] & 5) + throw op[1]; + return { value: op[0] ? op[1] : void 0, done: true }; + } + } + function __values(o) { + var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0; + if (m) + return m.call(o); + if (o && typeof o.length === "number") + return { + next: function () { + if (o && i >= o.length) + o = void 0; + return { value: o && o[i++], done: !o }; + } + }; + throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined."); + } + function __read(o, n) { + var m = typeof Symbol === "function" && o[Symbol.iterator]; + if (!m) + return o; + var i = m.call(o), r, ar = [], e; + try { + while ((n === void 0 || n-- > 0) && !(r = i.next()).done) + ar.push(r.value); + } + catch (error) { + e = { error: error }; + } + finally { + try { + if (r && !r.done && (m = i["return"])) + m.call(i); + } + finally { + if (e) + throw e.error; + } + } + return ar; + } + function __spread() { + for (var ar = [], i = 0; i < arguments.length; i++) + ar = ar.concat(__read(arguments[i])); + return ar; + } + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function assertNotComplex(tensor, opName) { + if (!Array.isArray(tensor)) { + tensor = [tensor]; + } + tensor.forEach(function (t) { + if (t != null) { + tfjsCore.util.assert(t.dtype !== 'complex64', function () { return opName + " does not support complex64 tensors in the CPU backend."; }); + } + }); + } + + var whereImpl = tfjsCore.kernel_impls.whereImpl; + var MathBackendCPU = /** @class */ (function (_super) { + __extends(MathBackendCPU, _super); + function MathBackendCPU() { + var _this = _super.call(this) || this; + _this.blockSize = 48; + _this.firstUse = true; + _this.data = new tfjsCore.DataStorage(_this, tfjsCore.engine()); + return _this; + } + MathBackendCPU.prototype.nextDataId = function () { + return MathBackendCPU.nextDataId++; + }; + MathBackendCPU.prototype.write = function (values, shape, dtype) { + if (this.firstUse) { + this.firstUse = false; + if (tfjsCore.env().get('IS_NODE')) { + tfjsCore.backend_util.warn('\n============================\n' + + 'Hi there 👋. Looks like you are running TensorFlow.js in ' + + 'Node.js. To speed things up dramatically, install our node ' + + 'backend, which binds to TensorFlow C++, by running ' + + 'npm i @tensorflow/tfjs-node, ' + + 'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' + + 'Then call require(\'@tensorflow/tfjs-node\'); (-gpu ' + + 'suffix for CUDA) at the start of your program. ' + + 'Visit https://github.com/tensorflow/tfjs-node for more details.' + + '\n============================'); + } + } + var dataId = { id: this.nextDataId() }; + this.data.set(dataId, { values: values, dtype: dtype, refCount: 1 }); + return dataId; + }; + /** + * Create a data bucket in cpu backend. + * @param shape Shape of the `TensorInfo`. + * @param dtype DType of the `TensorInfo`. + * @param values The value of the `TensorInfo` stored as a flattened array. + */ + MathBackendCPU.prototype.makeTensorInfo = function (shape, dtype, values) { + var outId; + if (dtype === 'string' && values != null && values.length > 0 && + tfjsCore.util.isString(values[0])) { + var encodedValues = values.map(function (d) { return tfjsCore.util.encodeString(d); }); + outId = this.write(encodedValues, shape, dtype); + } + else { + outId = this.write(values, shape, dtype); + } + return { dataId: outId, shape: shape, dtype: dtype }; + }; + /** Return refCount of a `TensorData`. */ + MathBackendCPU.prototype.refCount = function (dataId) { + if (this.data.has(dataId)) { + var tensorData = this.data.get(dataId); + return tensorData.refCount; + } + return 0; + }; + /** Increase refCount of a `TensorData`. */ + MathBackendCPU.prototype.incRef = function (dataId) { + var tensorData = this.data.get(dataId); + tensorData.refCount++; + }; + /** Decrease refCount of a `TensorData`. */ + MathBackendCPU.prototype.decRef = function (dataId) { + if (this.data.has(dataId)) { + var tensorData = this.data.get(dataId); + tensorData.refCount--; + } + }; + MathBackendCPU.prototype.move = function (dataId, values, shape, dtype, refCount) { + this.data.set(dataId, { values: values, dtype: dtype, refCount: refCount }); + }; + MathBackendCPU.prototype.numDataIds = function () { + return this.data.numDataIds(); + }; + MathBackendCPU.prototype.read = function (dataId) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_b) { + return [2 /*return*/, this.readSync(dataId)]; + }); + }); + }; + MathBackendCPU.prototype.readSync = function (dataId) { + var _b = this.data.get(dataId), dtype = _b.dtype, complexTensorInfos = _b.complexTensorInfos; + if (dtype === 'complex64') { + var realValues = this.readSync(complexTensorInfos.real.dataId); + var imagValues = this.readSync(complexTensorInfos.imag.dataId); + return tfjsCore.backend_util.mergeRealAndImagArrays(realValues, imagValues); + } + return this.data.get(dataId).values; + }; + MathBackendCPU.prototype.bufferSync = function (t) { + var data = this.readSync(t.dataId); + var decodedData = data; + if (t.dtype === 'string') { + try { + // Decode the bytes into string. + decodedData = data.map(function (d) { return tfjsCore.util.decodeString(d); }); + } + catch (_a) { + throw new Error('Failed to decode encoded string bytes into utf-8'); + } + } + return tfjsCore.buffer(t.shape, t.dtype, decodedData); + }; + MathBackendCPU.prototype.makeOutput = function (values, shape, dtype) { + var dataId = this.write(values, shape, dtype); + return tfjsCore.engine().makeTensorFromDataId(dataId, shape, dtype, this); + }; + /** + * Dispose the memory if the dataId has 0 refCount. Return true if the memory + * is released or memory is not managed in this backend, false if memory is + * not cleared. + * @param dataId + * @oaram force Optional, remove the data regardless of refCount + */ + MathBackendCPU.prototype.disposeData = function (dataId, force) { + if (force === void 0) { force = false; } + if (this.data.has(dataId)) { + this.data.get(dataId).refCount--; + if (!force && this.data.get(dataId).refCount > 0) { + return false; + } + var complexTensorInfos = this.data.get(dataId).complexTensorInfos; + if (complexTensorInfos != null) { + this.disposeData(complexTensorInfos.real.dataId, true); + this.disposeData(complexTensorInfos.imag.dataId, true); + } + this.data.delete(dataId); + } + return true; + }; + MathBackendCPU.prototype.disposeIntermediateTensorInfo = function (tensorInfo) { + this.disposeData(tensorInfo.dataId); + }; + MathBackendCPU.prototype.time = function (f) { + return __awaiter(this, void 0, void 0, function () { + var start, kernelMs; + return __generator(this, function (_b) { + start = tfjsCore.util.now(); + f(); + kernelMs = tfjsCore.util.now() - start; + return [2 /*return*/, { kernelMs: kernelMs }]; + }); + }); + }; + MathBackendCPU.prototype.memory = function () { + return { + // Unreliable due to automatic gc. The numbers above are cumulative. + unreliable: true, + reasons: ['The reported memory is an upper bound. Due to automatic garbage ' + + 'collection, the true allocated memory may be less.'] + }; + }; + MathBackendCPU.prototype.where = function (condition) { + assertNotComplex([condition], 'where'); + var condVals = this.readSync(condition.dataId); + return whereImpl(condition.shape, condVals); + }; + MathBackendCPU.prototype.dispose = function () { }; + MathBackendCPU.prototype.floatPrecision = function () { + return 32; + }; + /** Returns the smallest representable number. */ + MathBackendCPU.prototype.epsilon = function () { + return _super.prototype.epsilon.call(this); + }; + return MathBackendCPU; + }(tfjsCore.KernelBackend)); + MathBackendCPU.nextDataId = 0; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function simpleAbsImpl(vals) { + var resultValues = new Float32Array(vals.length); + for (var i = 0; i < vals.length; ++i) { + resultValues[i] = Math.abs(vals[i]); + } + return resultValues; + } + var abs = function (args) { + var x = args.inputs.x; + var cpuBackend = args.backend; + assertNotComplex(x, 'abs'); + var resultValues = new Float32Array(tfjsCore.util.sizeFromShape(x.shape)); + var values = cpuBackend.data.get(x.dataId).values; + resultValues = simpleAbsImpl(values); + return cpuBackend.makeOutput(resultValues, x.shape, x.dtype); + }; + var absConfig = { + kernelName: tfjsCore.Abs, + backendName: 'cpu', + kernelFunc: abs, + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Template that creates implementation for binary ops. Supports broadcast. + */ + function createSimpleBinaryKernelImpl(op) { + return function (aShape, bShape, aVals, bVals, dtype) { + var newShape = tfjsCore.backend_util.assertAndGetBroadcastShape(aShape, bShape); + var resultRank = newShape.length; + var resultStrides = tfjsCore.util.computeStrides(newShape); + var resultSize = tfjsCore.util.sizeFromShape(newShape); + var result = tfjsCore.util.getTypedArrayFromDType(dtype, resultSize); + var aRank = aShape.length; + var bRank = bShape.length; + var aStrides = tfjsCore.util.computeStrides(aShape); + var bStrides = tfjsCore.util.computeStrides(bShape); + var aBroadcastDims = tfjsCore.backend_util.getBroadcastDims(aShape, newShape); + var bBroadcastDims = tfjsCore.backend_util.getBroadcastDims(bShape, newShape); + if (aBroadcastDims.length + bBroadcastDims.length === 0) { + for (var i = 0; i < result.length; ++i) { + result[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]); + } + } + else { + var _loop_1 = function (i) { + var loc = tfjsCore.util.indexToLoc(i, resultRank, resultStrides); + var aLoc = loc.slice(-aRank); + aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; }); + var aIndex = tfjsCore.util.locToIndex(aLoc, aRank, aStrides); + var bLoc = loc.slice(-bRank); + bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; }); + var bIndex = tfjsCore.util.locToIndex(bLoc, bRank, bStrides); + result[i] = op(aVals[aIndex], bVals[bIndex]); + }; + for (var i = 0; i < result.length; ++i) { + _loop_1(i); + } + } + return [result, newShape]; + }; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function complex(args) { + var inputs = args.inputs, backend = args.backend; + var real = inputs.real, imag = inputs.imag; + var realVals = backend.data.get(real.dataId).values; + var imagVals = backend.data.get(imag.dataId).values; + var complexInfo = backend.makeTensorInfo(real.shape, 'complex64'); + var complex = backend.data.get(complexInfo.dataId); + // The complex tensor owns the underlying real and imag tensorInfos, only the + // complex tensor tracks refCount, when complexData is disposed the + // underlying tensorData will be disposed. + complex.complexTensorInfos = { + real: backend.makeTensorInfo(real.shape, 'float32', realVals), + imag: backend.makeTensorInfo(imag.shape, 'float32', imagVals) + }; + return complexInfo; + } + var complexConfig = { + kernelName: tfjsCore.Complex, + backendName: 'cpu', + kernelFunc: complex + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Generates a tensorInfo with all zeros value. + * @param backend cpu backend. + * @param shape Shape for the zeros tensor. + * @param dtype Optional. If set, the result has this dtype. + */ + function zeros(backend, shape, dtype) { + if (dtype === void 0) { dtype = 'float32'; } + if (dtype === 'complex64') { + var real = zeros(backend, shape, 'float32'); + var imag = zeros(backend, shape, 'float32'); + return complex({ inputs: { real: real, imag: imag }, backend: backend }); + } + var values = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(shape), dtype); + return backend.makeTensorInfo(shape, dtype, values); + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function identity(args) { + var inputs = args.inputs, backend = args.backend; + var x = inputs.x; + backend.incRef(x.dataId); + return { dataId: x.dataId, shape: x.shape, dtype: x.dtype }; + } + var identityConfig = { + kernelName: tfjsCore.Identity, + backendName: 'cpu', + kernelFunc: identity + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function real(args) { + var inputs = args.inputs, backend = args.backend; + var input = inputs.input; + var real = backend.data.get(input.dataId).complexTensorInfos.real; + var realVal = backend.data.get(real.dataId).values; + // When complex tensor is disposed, its underlying parts will be disposed too. + // Make new tensor out of the real value of the complex. This makes sure the + // value is still accessible even if complex tensor is disposed. + return backend.makeTensorInfo(real.shape, real.dtype, realVal); + } + var realConfig = { + kernelName: tfjsCore.Real, + backendName: 'cpu', + kernelFunc: real + }; + + function cast(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var dtype = attrs.dtype; + // Casting to complex64. + if (dtype === 'complex64') { + if (x.dtype === 'complex64') { + return identity({ inputs: { x: x }, backend: backend }); + } + var zerosTensorInfo = zeros(backend, x.shape, x.dtype); + var floatX = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'float32' } }); + var result = complex({ inputs: { real: floatX, imag: zerosTensorInfo }, backend: backend }); + backend.disposeIntermediateTensorInfo(zerosTensorInfo); + backend.disposeIntermediateTensorInfo(floatX); + return result; + } + // Casting from complex64 + if (x.dtype === 'complex64') { + var realPart = real({ inputs: { input: x }, backend: backend }); + var result = cast({ inputs: { x: realPart }, backend: backend, attrs: { dtype: dtype } }); + backend.disposeIntermediateTensorInfo(realPart); + return result; + } + if (!tfjsCore.util.hasEncodingLoss(x.dtype, dtype)) { + // We don't change the underlying data, since we cast to higher + // precision. + var result = identity({ inputs: { x: x }, backend: backend }); + return { dataId: result.dataId, shape: result.shape, dtype: dtype }; + } + if (dtype === 'int32') { + var values = backend.data.get(x.dataId).values; + var resultValues = Int32Array.from(values); + return backend.makeTensorInfo(x.shape, 'int32', resultValues); + } + if (dtype === 'bool') { + // This is essentially the result of notEqual(x, 0). We avoid using + // kernel notEqual to avoid circular dependency, i.e. binary_utils -> + // cast -> notEqual -> binary_utils. + var xVals = backend.data.get(x.dataId).values; + var zero = tfjsCore.util.toTypedArray([0], x.dtype); + var _a = __read(createSimpleBinaryKernelImpl(function (a, b) { return (a !== b) ? 1 : 0; })(x.shape, [], xVals, zero, 'bool'), 2), resultData = _a[0], resultShape = _a[1]; + return backend.makeTensorInfo(resultShape, 'bool', resultData); + } + throw new Error("Error in Cast: failed to cast " + x.dtype + " to " + dtype); + } + var castConfig = { + kernelName: tfjsCore.Cast, + backendName: 'cpu', + kernelFunc: cast + }; + + /** + * Template that creates a `KernelFunc` for binary ops. + * @param name Kernel name. + * @param binaryKernelImpl A `SimpleBinaryKernelImpl` for the kernel. + * @param binaryKernelComplexImpl Optional. If exists, represents a + * `ComplexBinaryKernelImpl` for the kernel, will be used when input dtype + * is `complex64`. + * @param dtype Optional. If set, the result has this dtype. Otherwise, the + * result has the same dtype as the first input. This is mainly used in + * comparison kernels, such as Equal, Less, Greater, etc. + */ + function binaryKernelFunc(name, simpleImpl, complexImpl, dtype) { + if (complexImpl == null) { + return function (_a) { + var inputs = _a.inputs, backend = _a.backend; + var a = inputs.a, b = inputs.b; + var cpuBackend = backend; + assertNotComplex([a, b], name); + var aVals = cpuBackend.data.get(a.dataId).values; + var bVals = cpuBackend.data.get(b.dataId).values; + var decodedAVals = a.dtype === 'string' ? + // tslint:disable-next-line: no-any + tfjsCore.backend_util.fromUint8ToStringArray(aVals) : + aVals; + var decodedBVals = a.dtype === 'string' ? + // tslint:disable-next-line: no-any + tfjsCore.backend_util.fromUint8ToStringArray(bVals) : + bVals; + var $dtype = dtype || a.dtype; + var _b = __read(simpleImpl(a.shape, b.shape, decodedAVals, decodedBVals, $dtype), 2), resultData = _b[0], resultShape = _b[1]; + return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData); + }; + } + return function (_a) { + var inputs = _a.inputs, backend = _a.backend; + var a = inputs.a, b = inputs.b; + var cpuBackend = backend; + if (a.dtype === 'complex64' || b.dtype === 'complex64') { + var $aComplex = cast({ inputs: { x: a }, backend: cpuBackend, attrs: { dtype: 'complex64' } }); + var $aComplexVals = cpuBackend.data.get($aComplex.dataId); + var aReal = $aComplexVals.complexTensorInfos.real; + var aImag = $aComplexVals.complexTensorInfos.imag; + var aRealVals = cpuBackend.data.get(aReal.dataId).values; + var aImagVals = cpuBackend.data.get(aImag.dataId).values; + var $bComplex = cast({ inputs: { x: b }, backend: cpuBackend, attrs: { dtype: 'complex64' } }); + var $bComplexVals = cpuBackend.data.get($bComplex.dataId); + var bReal = $bComplexVals.complexTensorInfos.real; + var bImag = $bComplexVals.complexTensorInfos.imag; + var bRealVals = cpuBackend.data.get(bReal.dataId).values; + var bImagVals = cpuBackend.data.get(bImag.dataId).values; + var _b = __read(complexImpl(a.shape, b.shape, aRealVals, aImagVals, bRealVals, bImagVals), 3), resultRealData = _b[0], resultImagData = _b[1], resultShape = _b[2]; + var resultReal = cpuBackend.makeTensorInfo(resultShape, 'float32', resultRealData); + var resultImag = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImagData); + var result = complex({ inputs: { real: resultReal, imag: resultImag }, backend: cpuBackend }); + cpuBackend.disposeIntermediateTensorInfo($aComplex); + cpuBackend.disposeIntermediateTensorInfo($bComplex); + cpuBackend.disposeIntermediateTensorInfo(resultReal); + cpuBackend.disposeIntermediateTensorInfo(resultImag); + return result; + } + else { + var aVals = cpuBackend.data.get(a.dataId).values; + var bVals = cpuBackend.data.get(b.dataId).values; + var $dtype = dtype || a.dtype; + var _c = __read(simpleImpl(a.shape, b.shape, aVals, bVals, $dtype), 2), resultData = _c[0], resultShape = _c[1]; + return cpuBackend.makeTensorInfo(resultShape, $dtype, resultData); + } + }; + } + /** + * Template that creates the complex type implementation for binary ops. + * Supports broadcast. + */ + function createComplexBinaryKernelImpl(op) { + return function (aShape, bShape, aRealVals, aImagVals, bRealVals, bImagVals) { + var resultShape = tfjsCore.backend_util.assertAndGetBroadcastShape(aShape, bShape); + var resultSize = tfjsCore.util.sizeFromShape(resultShape); + var resultRank = resultShape.length; + var resultStrides = tfjsCore.util.computeStrides(resultShape); + var resultRealVals = tfjsCore.util.getTypedArrayFromDType('float32', resultSize); + var resultImagVals = tfjsCore.util.getTypedArrayFromDType('float32', resultSize); + var aBroadcastDims = tfjsCore.backend_util.getBroadcastDims(aShape, resultShape); + var bBroadcastDims = tfjsCore.backend_util.getBroadcastDims(bShape, resultShape); + var aVals = tfjsCore.backend_util.mergeRealAndImagArrays(aRealVals, aImagVals); + var bVals = tfjsCore.backend_util.mergeRealAndImagArrays(bRealVals, bImagVals); + var aRank = aShape.length; + var aStrides = tfjsCore.util.computeStrides(aShape); + var bRank = bShape.length; + var bStrides = tfjsCore.util.computeStrides(bShape); + if (aBroadcastDims.length + bBroadcastDims.length === 0) { + for (var i = 0; i < resultRealVals.length; i++) { + var aIdx = i % aVals.length; + var bIdx = i % bVals.length; + var result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]); + resultRealVals[i] = result.real; + resultImagVals[i] = result.imag; + } + } + else { + var _loop_1 = function (i) { + var loc = tfjsCore.util.indexToLoc(i, resultRank, resultStrides); + var aLoc = loc.slice(-aRank); + aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; }); + var aIndex = tfjsCore.util.locToIndex(aLoc, aRank, aStrides); + var bLoc = loc.slice(-bRank); + bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; }); + var bIndex = tfjsCore.util.locToIndex(bLoc, bRank, bStrides); + var opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]); + resultRealVals[i] = opResult.real; + resultImagVals[i] = opResult.imag; + }; + for (var i = 0; i < resultRealVals.length; i++) { + _loop_1(i); + } + } + return [resultRealVals, resultImagVals, resultShape]; + }; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var addImpl = createSimpleBinaryKernelImpl((function (a, b) { return a + b; })); + var addComplexImpl = createComplexBinaryKernelImpl((function (aReal, aImag, bReal, bImag) { + return { real: aReal + bReal, imag: aImag + bImag }; + })); + var add = binaryKernelFunc(tfjsCore.Add, addImpl, addComplexImpl); + var addConfig = { + kernelName: tfjsCore.Add, + backendName: 'cpu', + kernelFunc: add + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function bincountImpl(xVals, weightsVals, weightsDtype, weightsShape, size) { + var weightsSize = tfjsCore.util.sizeFromShape(weightsShape); + var outVals = tfjsCore.util.makeZerosTypedArray(size, weightsDtype); + for (var i = 0; i < xVals.length; i++) { + var 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) { + if (binaryOutput === void 0) { binaryOutput = false; } + var numRows = xBuf.shape[0]; + var numCols = xBuf.shape[1]; + var outBuf = tfjsCore.buffer([numRows, size], weightsBuf.dtype); + for (var i = 0; i < numRows; i++) { + for (var j = 0; j < numCols; j++) { + var 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; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Template that creates implementation for unary op. + */ + function createSimpleUnaryImpl(op) { + return function (values, dtype, attrs) { + var newValues = tfjsCore.util.getTypedArrayFromDType(dtype, values.length); + for (var i = 0; i < values.length; ++i) { + newValues[i] = op(values[i], attrs); + } + return newValues; + }; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Template that creates a `KernelFunc` for unary ops. + * @param name Kernel name. + * @param op A `SimpleUnaryOperation` for the kernel. + * @param dtype Optional. If set, the result has this dtype. Otherwise, the + * result has the same dtype as the input. This is mainly used in certain + * kernels that return bool type, such as isFinite, isInf, etc. + */ + function unaryKernelFunc(name, op, dtype) { + return function (_a) { + var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend; + var x = inputs.x; + assertNotComplex(x, name); + if (x.dtype === 'string' || dtype === 'string') { + throw new Error('unaryKernelFunc does not support string input/output'); + } + var cpuBackend = backend; + var values = cpuBackend.data.get(x.dataId).values; + var xSize = tfjsCore.util.sizeFromShape(x.shape); + var $dtype = dtype || x.dtype; + var newValues = tfjsCore.util.getArrayFromDType($dtype, xSize); + for (var i = 0; i < xSize; ++i) { + newValues[i] = op(values[i], attrs); + } + return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues); + }; + } + /** + * Template that creates a `KernelFunc` for unary ops from the given + * `SimpleUnaryImpl`.. + * @param name Kernel name. + * @param unaryImpl A `SimpleUnaryImpl` that implements the op. + * @param dtype Optional. If set, the result has this dtype. Otherwise, the + * result has the same dtype as the input. This is mainly used in certain + * kernels that return bool type, such as isFinite, isInf, etc. + */ + function unaryKernelFuncFromImpl(name, unaryImpl, dtype) { + return function (_a) { + var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend; + var x = inputs.x; + assertNotComplex(x, name); + if (x.dtype === 'string' || dtype === 'string') { + throw new Error('unaryKernelFunc does not support string input/output'); + } + var cpuBackend = backend; + var values = cpuBackend.data.get(x.dataId).values; + var $dtype = dtype || x.dtype; + var newValues = unaryImpl(values, $dtype, attrs); + return cpuBackend.makeTensorInfo(x.shape, $dtype, newValues); + }; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var ceilImpl = createSimpleUnaryImpl(function (xi) { return Math.ceil(xi); }); + var ceil = unaryKernelFuncFromImpl(tfjsCore.Ceil, ceilImpl); + var ceilConfig = { + kernelName: tfjsCore.Ceil, + backendName: 'cpu', + kernelFunc: ceil, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function concatImpl(inputs, outShape, dtype, simplyConcat) { + var outVals = tfjsCore.util.getArrayFromDType(dtype, tfjsCore.util.sizeFromShape(outShape)); + if (simplyConcat && dtype !== 'string') { + // Use built-in TypedArray.set() method for speed. + var offset_1 = 0; + inputs.forEach(function (input) { + var size = tfjsCore.util.sizeFromShape(input.shape); + outVals.set(input.vals, offset_1); + offset_1 += size; + }); + } + else { + var colOffset_1 = 0; + inputs.forEach(function (input) { + var decodedData = dtype === 'string' ? + tfjsCore.backend_util.fromUint8ToStringArray(input.vals) : + input.vals; + var tIdx = 0; + for (var row = 0; row < input.shape[0]; ++row) { + var resIdx = row * outShape[1] + colOffset_1; + for (var col = 0; col < input.shape[1]; ++col) { + outVals[resIdx + col] = decodedData[tIdx++]; + } + } + colOffset_1 += input.shape[1]; + }); + } + return outVals; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var equalImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a === b) ? 1 : 0; }); + var equal = binaryKernelFunc(tfjsCore.Equal, equalImpl, null /* complexImpl */, 'bool'); + var equalConfig = { + kernelName: tfjsCore.Equal, + backendName: 'cpu', + kernelFunc: equal + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var expImpl = createSimpleUnaryImpl(function (xi) { return Math.exp(xi); }); + var exp = unaryKernelFuncFromImpl(tfjsCore.Exp, expImpl, 'float32'); + var expConfig = { + kernelName: tfjsCore.Exp, + backendName: 'cpu', + kernelFunc: exp, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var expm1Impl = createSimpleUnaryImpl(function (xi) { return Math.expm1(xi); }); + var expm1 = unaryKernelFuncFromImpl(tfjsCore.Expm1, expm1Impl); + var expm1Config = { + kernelName: tfjsCore.Expm1, + backendName: 'cpu', + kernelFunc: expm1, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var floorImpl = createSimpleUnaryImpl(function (xi) { return Math.floor(xi); }); + var floor = unaryKernelFuncFromImpl(tfjsCore.Floor, floorImpl); + var floorConfig = { + kernelName: tfjsCore.Floor, + backendName: 'cpu', + kernelFunc: floor, + }; + + function gatherNdImpl(indicesData, paramsBuf, dtype, numSlices, sliceRank, sliceSize, strides, paramsShape, paramsSize) { + var outBuf = tfjsCore.buffer([numSlices, sliceSize], dtype); + for (var i = 0; i < numSlices; i++) { + var index = []; + var flattenIndex = 0; + for (var j = 0; j < sliceRank; j++) { + var 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 (var k = 0; k < sliceSize; k++) { + outBuf.values[i * sliceSize + k] = paramsBuf.get.apply(paramsBuf, __spread(paramsBuf.indexToLoc(flattenIndex * sliceSize + k))); + } + } + return outBuf; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function gatherV2Impl(xBuf, indicesBuf, flattenOutputShape) { + var outBuf = tfjsCore.buffer(flattenOutputShape, xBuf.dtype); + for (var i = 0; i < outBuf.size; ++i) { + var newLoc = outBuf.indexToLoc(i); + var originalLoc = newLoc.slice(); + var batchIdx = originalLoc[0]; + var indicesIdx = originalLoc[2]; + var indicesIndex = indicesBuf.locToIndex([batchIdx, indicesIdx]); + originalLoc[2] = indicesBuf.values[indicesIndex]; + var originalIndex = xBuf.locToIndex(originalLoc); + outBuf.values[i] = xBuf.values[originalIndex]; + } + return outBuf; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var greaterImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a > b) ? 1 : 0; }); + var greater = binaryKernelFunc(tfjsCore.Greater, greaterImpl, null /* complexImpl */, 'bool'); + var greaterConfig = { + kernelName: tfjsCore.Greater, + backendName: 'cpu', + kernelFunc: greater + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var greaterEqualImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a >= b) ? 1 : 0; }); + var greaterEqual = binaryKernelFunc(tfjsCore.GreaterEqual, greaterEqualImpl, null /* complexImpl */, 'bool'); + var greaterEqualConfig = { + kernelName: tfjsCore.GreaterEqual, + backendName: 'cpu', + kernelFunc: greaterEqual + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var lessImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a < b) ? 1 : 0; }); + var less = binaryKernelFunc(tfjsCore.Less, lessImpl, null /* complexImpl */, 'bool'); + var lessConfig = { + kernelName: tfjsCore.Less, + backendName: 'cpu', + kernelFunc: less + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var lessEqualImpl = createSimpleBinaryKernelImpl(function (a, b) { return (a <= b) ? 1 : 0; }); + var lessEqual = binaryKernelFunc(tfjsCore.LessEqual, lessEqualImpl, null /* complexImpl */, 'bool'); + var lessEqualConfig = { + kernelName: tfjsCore.LessEqual, + backendName: 'cpu', + kernelFunc: lessEqual + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function linSpaceImpl(start, stop, num) { + var step = (stop - start) / (num - 1); + var values = tfjsCore.util.makeZerosTypedArray(num, 'float32'); + values[0] = start; + for (var i = 1; i < values.length; i++) { + values[i] = values[i - 1] + step; + } + return values; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var logImpl = createSimpleUnaryImpl(function (xi) { return Math.log(xi); }); + var log = unaryKernelFuncFromImpl(tfjsCore.Log, logImpl); + var logConfig = { + kernelName: tfjsCore.Log, + backendName: 'cpu', + kernelFunc: log, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function maxImpl(aVals, reduceSize, outShape, dtype) { + var vals = tfjsCore.util.getTypedArrayFromDType(dtype, tfjsCore.util.sizeFromShape(outShape)); + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var max = aVals[offset]; + for (var j = 0; j < reduceSize; ++j) { + var value = aVals[offset + j]; + if (Number.isNaN(value) || + value > max) { // comparison with NaN always return false + max = value; + } + } + vals[i] = max; + } + return vals; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var maximumImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return Math.max(aValue, bValue); })); + var maximum = binaryKernelFunc(tfjsCore.Maximum, maximumImpl); + var maximumConfig = { + kernelName: tfjsCore.Maximum, + backendName: 'cpu', + kernelFunc: maximum + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var minimumImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return Math.min(aValue, bValue); })); + var minimum = binaryKernelFunc(tfjsCore.Minimum, minimumImpl); + var minimumConfig = { + kernelName: tfjsCore.Minimum, + backendName: 'cpu', + kernelFunc: minimum + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var multiplyImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return aValue * bValue; })); + var multiplyComplexImpl = createComplexBinaryKernelImpl((function (aReal, aImag, bReal, bImag) { + return { + real: aReal * bReal - aImag * bImag, + imag: aReal * bImag + aImag * bReal + }; + })); + var multiply = binaryKernelFunc(tfjsCore.Multiply, multiplyImpl, multiplyComplexImpl); + var multiplyConfig = { + kernelName: tfjsCore.Multiply, + backendName: 'cpu', + kernelFunc: multiply + }; + + function negImpl(xVals, xShape, xDtype) { + var minusOne = tfjsCore.util.createScalarValue(-1, xDtype); + return multiplyImpl([], xShape, minusOne, xVals, xDtype); + } + function neg(args) { + var inputs = args.inputs, backend = args.backend; + var x = inputs.x; + assertNotComplex(x, 'neg'); + var xVals = backend.data.get(x.dataId).values; + var _a = __read(negImpl(xVals, x.shape, x.dtype), 2), res = _a[0], newShape = _a[1]; + return backend.makeTensorInfo(newShape, x.dtype, res); + } + var negConfig = { + kernelName: tfjsCore.Neg, + backendName: 'cpu', + kernelFunc: neg + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var notEqualImpl = createSimpleBinaryKernelImpl((function (a, b) { return (a !== b) ? 1 : 0; })); + var notEqual = binaryKernelFunc(tfjsCore.NotEqual, notEqualImpl, null /* complexOp */, 'bool'); + var notEqualConfig = { + kernelName: tfjsCore.NotEqual, + backendName: 'cpu', + kernelFunc: notEqual + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function transposeImpl(xVals, xShape, dtype, perm, newShape) { + var xRank = xShape.length; + var xSize = tfjsCore.util.sizeFromShape(xShape); + var xStrides = tfjsCore.util.computeStrides(xShape); + var newStrides = tfjsCore.util.computeStrides(newShape); + var result = tfjsCore.util.getTypedArrayFromDType(dtype, tfjsCore.util.sizeFromShape(newShape)); + for (var i = 0; i < xSize; ++i) { + var loc = tfjsCore.util.indexToLoc(i, xRank, xStrides); + // Permute location. + var newLoc = new Array(loc.length); + for (var i_1 = 0; i_1 < newLoc.length; i_1++) { + newLoc[i_1] = loc[perm[i_1]]; + } + var newIndex = tfjsCore.util.locToIndex(newLoc, xRank, newStrides); + result[newIndex] = xVals[i]; + } + return result; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function transpose(args) { + var inputs = args.inputs, attrs = args.attrs, backend = args.backend; + var x = inputs.x; + var perm = attrs.perm; + assertNotComplex(x, 'transpose'); + var xRank = x.shape.length; + var newShape = new Array(xRank); + for (var i = 0; i < newShape.length; i++) { + newShape[i] = x.shape[perm[i]]; + } + var values = backend.data.get(x.dataId).values; + var result = transposeImpl(values, x.shape, x.dtype, perm, newShape); + var dataId = backend.write(result, newShape, x.dtype); + return { dataId: dataId, shape: newShape, dtype: x.dtype }; + } + var transposeConfig = { + kernelName: tfjsCore.Transpose, + backendName: 'cpu', + kernelFunc: transpose + }; + + function prodImpl(xShape, xDtype, xVals, reductionAxes) { + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes(xShape, reductionAxes), 2), outShape = _a[0], reduceShape = _a[1]; + var outDtype = tfjsCore.upcastType(xDtype, 'int32'); + var outVals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), outDtype); + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + for (var i = 0; i < outVals.length; ++i) { + var offset = i * reduceSize; + var prod_1 = 1; + for (var j = 0; j < reduceSize; ++j) { + prod_1 *= xVals[offset + j]; + } + outVals[i] = prod_1; + } + return { outVals: outVals, outShape: outShape, outDtype: outDtype }; + } + function prod(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, keepDims = attrs.keepDims; + assertNotComplex(x, 'prod'); + var xRank = x.shape.length; + var axes = tfjsCore.util.parseAxisParam(axis, x.shape); + var permutation = tfjsCore.backend_util.getAxesPermutation(axes, xRank); + var reductionAxes = axes; + var permutedX = x; + var intermediateTensorInfos = []; + if (permutation != null) { + permutedX = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutation } }); + intermediateTensorInfos.push(permutedX); + reductionAxes = tfjsCore.backend_util.getInnerMostAxes(reductionAxes.length, xRank); + } + var xVals = backend.data.get(permutedX.dataId).values; + var _a = prodImpl(permutedX.shape, permutedX.dtype, xVals, reductionAxes), outVals = _a.outVals, outShape = _a.outShape, outDtype = _a.outDtype; + var resultShape = outShape; + if (keepDims) { + resultShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, axes); + } + intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return backend.makeTensorInfo(resultShape, outDtype, outVals); + } + var prodConfig = { + kernelName: tfjsCore.Prod, + backendName: 'cpu', + kernelFunc: prod + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function rangeImpl(start, stop, step, dtype) { + var sameStartStop = start === stop; + var increasingRangeNegativeStep = start < stop && step < 0; + var decreasingRangePositiveStep = stop < start && step > 1; + if (sameStartStop || increasingRangeNegativeStep || + decreasingRangePositiveStep) { + return tfjsCore.util.makeZerosTypedArray(0, dtype); + } + var numElements = Math.abs(Math.ceil((stop - start) / step)); + var values = tfjsCore.util.makeZerosTypedArray(numElements, dtype); + if (stop < start && step === 1) { + // Auto adjust the step's sign if it hasn't been set + // (or was set to 1) + step = -1; + } + values[0] = start; + for (var i = 1; i < values.length; i++) { + values[i] = values[i - 1] + step; + } + return values; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var rsqrtImpl = createSimpleUnaryImpl(function (xi) { return 1 / Math.sqrt(xi); }); + var rsqrt = unaryKernelFuncFromImpl(tfjsCore.Rsqrt, rsqrtImpl); + var rsqrtConfig = { + kernelName: tfjsCore.Rsqrt, + backendName: 'cpu', + kernelFunc: rsqrt, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var sigmoidImpl = createSimpleUnaryImpl(function (xi) { return 1 / (1 + Math.exp(-xi)); }); + var sigmoid = unaryKernelFunc(tfjsCore.Sigmoid, function (xi) { return 1 / (1 + Math.exp(-xi)); }); + var sigmoidConfig = { + kernelName: tfjsCore.Sigmoid, + backendName: 'cpu', + kernelFunc: sigmoid, + }; + + function sliceImpl(vals, begin, size, shape, dtype) { + var isContinous = tfjsCore.slice_util.isSliceContinous(shape, begin, size); + var length = tfjsCore.util.sizeFromShape(size); + var xStrides = tfjsCore.util.computeStrides(shape); + if (isContinous) { + var flatOffset = tfjsCore.slice_util.computeFlatOffset(begin, xStrides); + if (dtype === 'string') { + return vals.slice(flatOffset, flatOffset + length); + } + return vals.subarray(flatOffset, flatOffset + length); + } + var decodedData = dtype === 'string' ? + tfjsCore.backend_util.fromUint8ToStringArray(vals) : + vals; + var inBuf = tfjsCore.buffer(shape, dtype, decodedData); + var outBuf = tfjsCore.buffer(size, dtype); + for (var i = 0; i < outBuf.size; ++i) { + var outLoc = outBuf.indexToLoc(i); + var inLoc = outLoc.map(function (idx, j) { return idx + begin[j]; }); + outBuf.set.apply(outBuf, __spread([inBuf.get.apply(inBuf, __spread(inLoc))], outLoc)); + } + if (dtype === 'string') { + return tfjsCore.backend_util.fromStringArrayToUint8(outBuf.values); + } + return outBuf.values; + } + function slice(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var begin = attrs.begin, size = attrs.size; + assertNotComplex(x, 'slice'); + var _a = __read(tfjsCore.slice_util.parseSliceParams(x, begin, size), 2), $begin = _a[0], $size = _a[1]; + tfjsCore.slice_util.assertParamsValid(x, $begin, $size); + var vals = backend.data.get(x.dataId).values; + var outVals = sliceImpl(vals, $begin, $size, x.shape, x.dtype); + return backend.makeTensorInfo($size, x.dtype, outVals); + } + var sliceConfig = { + kernelName: tfjsCore.Slice, + backendName: 'cpu', + kernelFunc: slice + }; + + /** + * @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. + * ============================================================================= + */ + function sparseFillEmptyRowsImpl(indices, indicesShape, indicesDType, values, valuesDType, denseShape, defaultValue) { + var indicesCount = indicesShape[0]; + var denseRows = denseShape[0]; + var emptyRowIndicator = new Array(denseRows); + var reverseIndexMap = new Array(indicesCount); + var rank = indicesShape[1]; + if (denseRows === 0) { + if (indicesCount !== 0) { + throw new Error(tfjsCore.backend_util.getSparseFillEmptyRowsIndicesDenseShapeMismatch(indicesCount)); + } + var outputIndices = tfjsCore.util.getArrayFromDType(indicesDType, 0); + var outputValues = tfjsCore.util.getArrayFromDType(valuesDType, 0); + return [ + outputIndices, [0, rank], outputValues, emptyRowIndicator, reverseIndexMap + ]; + } + var rowsAreOrdered = true; + var lastIndicesRow = 0; + var csrOffset = new Array(denseRows).fill(0); + for (var i = 0; i < indicesCount; ++i) { + // indices is a 2d tensor with shape of [N, rank] + var row = indices[i * rank]; + if (row < 0) { + throw new Error(tfjsCore.backend_util.getSparseFillEmptyRowsNegativeIndexErrorMessage(i, row)); + } + if (row >= denseRows) { + throw new Error(tfjsCore.backend_util.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(i, row, denseRows)); + } + ++csrOffset[row]; + rowsAreOrdered = rowsAreOrdered && (row >= lastIndicesRow); + lastIndicesRow = row; + } + var allRowsFull = true; + for (var row = 0; row < denseRows; ++row) { + // csrOffset here describes the number of elements in this dense row + var rowEmpty = (csrOffset[row] === 0); + emptyRowIndicator[row] = rowEmpty; + allRowsFull = allRowsFull && !rowEmpty; + // In filled version, each row has at least one element. + csrOffset[row] = Math.max(csrOffset[row], 1); + // Update csrOffset to represent the number of elements up to and + // including denseRows + 1: + // csrOffset[0] == #{elements of row 0} + // csrOffset[1] == #{elements of row 1} + #{elements of row 0} + // .. + // csrOffset[i] == starting index for elements in row i + 1. + if (row > 0) { + csrOffset[row] += csrOffset[row - 1]; + } + } + if (allRowsFull && rowsAreOrdered) { + var outputIndices = indices; + var outputValues = values; + for (var i = 0; i < indicesCount; ++i) { + reverseIndexMap[i] = i; + } + return [ + outputIndices, [indicesCount, rank], outputValues, emptyRowIndicator, + reverseIndexMap + ]; + } + else { + var fullIndicesCount = csrOffset[denseRows - 1]; + var outputIndices = tfjsCore.util.getArrayFromDType(indicesDType, fullIndicesCount * rank); + var outputValues = tfjsCore.util.getArrayFromDType(valuesDType, fullIndicesCount); + var filledCount = new Array(denseRows).fill(0); + // Fill in values for rows that are not missing + for (var i = 0; i < indicesCount; ++i) { + // indices is a 2d tensor with shape of [N, rank] + var row = indices[i * rank]; + var offset = filledCount[row]; + var outputI = ((row === 0) ? 0 : csrOffset[row - 1]) + offset; + filledCount[row]++; // Increment the filled count for this row. + for (var j = 0; j < rank; ++j) { + // indices and outputIndices are 2d tensors with shape of [N, rank] + outputIndices[outputI * rank + j] = indices[i * rank + j]; + } + outputValues[outputI] = values[i]; + // We'll need this reverse index map to backprop correctly. + reverseIndexMap[i] = outputI; + } + // Fill in values for rows that are missing + for (var row = 0; row < denseRows; ++row) { + var rowCount = filledCount[row]; + if (rowCount === 0) { // We haven't filled this row + var startingIndex = (row === 0) ? 0 : csrOffset[row - 1]; + // Remaining index values were set to zero already. + // Just need to set the row index in the right location. + // outputIndices is a 2d tensor with shape of [N, rank] + outputIndices[startingIndex * rank + 0] = row; + for (var col = 1; col < rank; ++col) { + outputIndices[startingIndex * rank + col] = 0; + } + outputValues[startingIndex] = defaultValue; + } + } + return [ + outputIndices, [fullIndicesCount, rank], outputValues, emptyRowIndicator, + reverseIndexMap + ]; + } + } + + /** + * @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. + * ============================================================================= + */ + function sparseReshapeImpl(inputIndices, inputIndicesShape, inputDType, inputShape, targetShape) { + var denseSize = tfjsCore.util.sizeFromShape(inputShape); + var nnz = inputIndicesShape[0]; + var outputRank = targetShape.length; + // Compute the output shape. Determine product of specified dimensions, and + // find the index of the unspecified one. + var outputShape = []; + var product = 1; + var unknownIndex = -1; + for (var d = 0; d < outputRank; ++d) { + var size = targetShape[d]; + if (size === -1) { + if (unknownIndex !== -1) { + throw new Error(tfjsCore.backend_util + .getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(unknownIndex, d)); + } + unknownIndex = d; + outputShape.push(1); + } + else { + if (size < 0) { + throw new Error(tfjsCore.backend_util.getSparseReshapeNegativeOutputDimErrorMessage(d, size)); + } + product *= size; + outputShape.push(size); + } + } + if (unknownIndex !== -1) { + if (product <= 0) { + throw new Error(tfjsCore.backend_util.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage()); + } + var missing = Math.trunc(denseSize / product); + if (product * missing !== denseSize) { + throw new Error(tfjsCore.backend_util.getSparseReshapeInputOutputMultipleErrorMessage(inputShape, outputShape)); + } + outputShape[unknownIndex] = missing; + } + var outputSize = tfjsCore.util.sizeFromShape(outputShape); + if (outputSize !== denseSize) { + throw new Error(tfjsCore.backend_util.getSparseReshapeInputOutputMismatchErrorMessage(inputShape, outputShape)); + } + var inputRank = inputShape.length; + var inputStrides = []; + if (inputRank > 0) { + inputStrides[inputRank - 1] = 1; + for (var d = inputRank - 2; d >= 0; --d) { + inputStrides[d] = inputStrides[d + 1] * inputShape[d + 1]; + } + } + var outputStrides = []; + if (outputRank > 0) { + outputStrides[outputRank - 1] = 1; + for (var d = outputRank - 2; d >= 0; --d) { + outputStrides[d] = outputStrides[d + 1] * outputShape[d + 1]; + } + } + var newIndices = tfjsCore.util.getArrayFromDType(inputDType, nnz * outputRank); + for (var i = 0; i < nnz; ++i) { + var id = 0; + for (var j = 0; j < inputRank; ++j) { + // inputIndices is a 2d tensor with shape of [nnz, inputRank] + id += inputIndices[i * inputRank + j] * inputStrides[j]; + } + for (var j = 0; j < outputRank; ++j) { + // newIndices is a 2d tensor with shape of [nnz, outputRank] + newIndices[i * outputRank + j] = Math.trunc(id / outputStrides[j]); + id %= outputStrides[j]; + } + } + return [newIndices, [nnz, outputRank], outputShape]; + } + + /** + * @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. + * ============================================================================= + */ + function sparseSegmentReductionImpl(input, inputShape, inputDType, indices, segmentIds, isMean, defaultValue) { + if (isMean === void 0) { isMean = false; } + if (defaultValue === void 0) { defaultValue = 0; } + var numIndices = indices.length; + // Flatten the array to two dimensions + var inputFlat = [inputShape[0], input.length / inputShape[0]]; + var numCol = inputFlat[1]; + // Note that the current implementation assumes that segmentIds values are + // sorted. + var lastSegmentIdPlusOne = numIndices > 0 ? segmentIds[numIndices - 1] + 1 : 0; + var outputRows = lastSegmentIdPlusOne; + if (outputRows < 0) { + throw new Error(tfjsCore.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + var outputShape = inputShape.slice(); + outputShape[0] = outputRows; + var outputLength = outputShape.reduce(function (product, value) { return product * value; }, 1); + // Output array is initialized with the value 0 by default. + var output = tfjsCore.util.getArrayFromDType(inputDType, outputLength); + // Note that we do not initialize the output buffer with a default value, so + // we need to explicitly set missing indices to the default value. + if (numIndices === 0) { + if (outputRows > 0) { + output.fill(defaultValue); + } + return [output, outputShape]; + } + if (outputRows <= 0) { + throw new Error(tfjsCore.backend_util.getSparseSegmentReductionNegativeSegmentIdsErrorMessage()); + } + var start = 0, end = 1; + // Index from which the output is not initialized. + var uninitializedIndex = 0; + var outIndex = segmentIds[start]; + while (true) { + // We initialize nextIndex to 0 to avoid may be uninitialized warning + var nextIndex = 0; + if (end < numIndices) { + nextIndex = segmentIds[end]; + if (outIndex === nextIndex) { + ++end; + continue; + } + // We have a new segment here. Verify that the segment ids are growing. + if (outIndex >= nextIndex) { + throw new Error(tfjsCore.backend_util + .getSparseSegmentReductionNonIncreasingSegmentIdsErrorMessage()); + } + } + if (outIndex < 0 || outIndex >= outputRows) { + throw new Error(tfjsCore.backend_util.getSparseSegmentReductionSegmentIdOutOfRangeErrorMessage(outIndex, outputRows)); + } + // If there is a gap between two indices, we need to set that gap to the + // default value. + if (outIndex > uninitializedIndex) { + output.fill(defaultValue, uninitializedIndex * numCol, outIndex * numCol); + } + for (var i = start; i < end; ++i) { + var index = indices[i]; + if (index < 0 || index >= inputFlat[0]) { + throw new Error(tfjsCore.backend_util.getSparseSegmentReductionIndicesOutOfRangeErrorMessage(i, indices[i], inputFlat[0])); + } + for (var j = 0; j < numCol; j++) { + output[outIndex * numCol + j] += input[index * numCol + j]; + } + } + if (isMean) { + for (var j = 0; j < numCol; j++) { + output[outIndex * numCol + j] /= end - start; + } + } + start = end; + ++end; + uninitializedIndex = outIndex + 1; + outIndex = nextIndex; + if (end > numIndices) { + break; + } + } + // Fill the gap at the end with the default value. + if (uninitializedIndex < outputRows) { + output.fill(defaultValue, uninitializedIndex * numCol, outputRows * numCol); + } + return [output, outputShape]; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var sqrtImpl = createSimpleUnaryImpl(function (xi) { return Math.sqrt(xi); }); + var sqrt = unaryKernelFunc(tfjsCore.Sqrt, function (xi) { return Math.sqrt(xi); }); + var sqrtConfig = { + kernelName: tfjsCore.Sqrt, + backendName: 'cpu', + kernelFunc: sqrt, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var squaredDifferenceImpl = createSimpleBinaryKernelImpl((function (a, b) { + var diff = a - b; + return diff * diff; + })); + var squaredDifference = binaryKernelFunc(tfjsCore.SquaredDifference, squaredDifferenceImpl); + var squaredDifferenceConfig = { + kernelName: tfjsCore.SquaredDifference, + backendName: 'cpu', + kernelFunc: squaredDifference + }; + + function stridedSliceImpl(outShape, xBuf, strides, begin) { + var outBuf = tfjsCore.buffer(outShape, xBuf.dtype); + for (var i = 0; i < outBuf.size; i++) { + var loc = outBuf.indexToLoc(i); + var newLoc = new Array(loc.length); + for (var j = 0; j < newLoc.length; j++) { + newLoc[j] = loc[j] * strides[j] + begin[j]; + } + outBuf.set.apply(outBuf, __spread([xBuf.get.apply(xBuf, __spread(newLoc))], loc)); + } + return outBuf; + } + + /** + * @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. + * ============================================================================= + */ + /** + * The StringNGramsOp class creates ngrams from ragged string data. + * The constructor contains all attributes related to the operation such as + * padding widths and strings, and the compute function can be used to + * compute the ngrams for different ragged tensor inputs. + */ + var StringNGramsOp = /** @class */ (function () { + function StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) { + this.separator = tfjsCore.util.encodeString(separator); + this.nGramWidths = nGramWidths; + this.leftPad = tfjsCore.util.encodeString(leftPad); + this.rightPad = tfjsCore.util.encodeString(rightPad); + this.padWidth = padWidth; + this.preserveShort = preserveShortSequences; + } + StringNGramsOp.prototype.getPadWidth = function (nGramWidth) { + // Ngrams can be padded with either a fixed pad width or a dynamic pad + // width depending on the 'padWidth' arg, but in no case should the padding + // ever be wider than 'nGramWidth' - 1. + return Math.min(this.padWidth < 0 ? nGramWidth - 1 : this.padWidth, nGramWidth - 1); + }; + StringNGramsOp.prototype.getNumNGrams = function (length, nGramWidth) { + var padWidth = this.getPadWidth(nGramWidth); + return Math.max(0, ((length + 2 * padWidth) - nGramWidth) + 1); + }; + StringNGramsOp.prototype.createNGrams = function (data, splitIndex, output, outputStartIndex, numNGrams, nGramWidth) { + var _loop_1 = function (nGramIndex) { + var padWidth = this_1.getPadWidth(nGramWidth); + var leftPadding = Math.max(0, padWidth - nGramIndex); + var rightPadding = Math.max(0, padWidth - (numNGrams - (nGramIndex + 1))); + var numTokens = nGramWidth - (leftPadding + rightPadding); + var dataStartIndex = splitIndex + (leftPadding > 0 ? 0 : nGramIndex - padWidth); + // Calculate the total expected size of the nGram so we can reserve the + // correct amount of space in the string. + var nGramSize = 0; + // Size of the left padding. + nGramSize += leftPadding * this_1.leftPad.length; + // Size of the tokens. + for (var n = 0; n < numTokens; ++n) { + nGramSize += data[dataStartIndex + n].length; + } + // Size of the right padding. + nGramSize += rightPadding * this_1.rightPad.length; + // Size of the separators. + var numSeparators = leftPadding + rightPadding + numTokens - 1; + nGramSize += numSeparators * this_1.separator.length; + // Build the nGram. + output[outputStartIndex + nGramIndex] = new Uint8Array(nGramSize); + var nGram = output[outputStartIndex + nGramIndex]; + var nextNGramIndex = 0; + var appendToNGram = function (str) { return str.forEach(function (value) { return nGram[nextNGramIndex++] = value; }); }; + for (var n = 0; n < leftPadding; ++n) { + appendToNGram(this_1.leftPad); + appendToNGram(this_1.separator); + } + // Only output first numTokens - 1 pairs of data and separator + for (var n = 0; n < numTokens - 1; ++n) { + appendToNGram(data[dataStartIndex + n]); + appendToNGram(this_1.separator); + } + // Handle case when there are no tokens or no right padding as these + // can result in consecutive separators. + if (numTokens > 0) { + // If we have tokens, then output last and then pair each separator + // with the right padding that follows, to ensure nGram ends either with + // the token or with the right pad. + appendToNGram(data[dataStartIndex + numTokens - 1]); + for (var n = 0; n < rightPadding; ++n) { + appendToNGram(this_1.separator); + appendToNGram(this_1.rightPad); + } + } + else { + // If we don't have tokens, then the last item inserted into the nGram + // has been the separator from the left padding loop above. Hence, + // output right pad and separator and make sure to finish with a + // padding, not a separator. + for (var n = 0; n < rightPadding - 1; ++n) { + appendToNGram(this_1.rightPad); + appendToNGram(this_1.separator); + } + appendToNGram(this_1.rightPad); + } + }; + var this_1 = this; + for (var nGramIndex = 0; nGramIndex < numNGrams; ++nGramIndex) { + _loop_1(nGramIndex); + } + }; + // Data and splits together form the definition of the ragged tensor, + // where data is 1 dimensional and contains the values of the tensor + // and splits denotes the indices at which each row starts. + StringNGramsOp.prototype.compute = function (data, splits) { + var _this = this; + // Validate that the splits are valid indices into data, only if there are + // splits specified. + var inputDataSize = data.length; + var splitsSize = splits.length; + if (splitsSize > 0) { + var prevSplit = splits[0]; + if (prevSplit !== 0) { + throw new Error("First split value must be 0, got " + prevSplit); + } + for (var i = 1; i < splitsSize; ++i) { + var 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); + } + } + var numBatchItems = splitsSize - 1; + var nGramsSplits = tfjsCore.util.getArrayFromDType('int32', splitsSize); + // If there is no data or size, return an empty ragged tensor. + if (inputDataSize === 0 || splitsSize === 0) { + var empty = new Array(inputDataSize); + for (var i = 0; i <= numBatchItems; ++i) { + nGramsSplits[i] = 0; + } + return [empty, nGramsSplits]; + } + nGramsSplits[0] = 0; + var _loop_2 = function (i) { + var length = splits[i] - splits[i - 1]; + var numNGrams = 0; + this_2.nGramWidths.forEach(function (nGramWidth) { + numNGrams += _this.getNumNGrams(length, nGramWidth); + }); + if (this_2.preserveShort && length > 0 && numNGrams === 0) { + numNGrams = 1; + } + nGramsSplits[i] = nGramsSplits[i - 1] + numNGrams; + }; + var this_2 = this; + for (var i = 1; i <= numBatchItems; ++i) { + _loop_2(i); + } + var nGrams = new Array(nGramsSplits[numBatchItems]); + var _loop_3 = function (i) { + var splitIndex = splits[i]; + var outputStartIdx = nGramsSplits[i]; + this_3.nGramWidths.forEach(function (nGramWidth) { + var length = splits[i + 1] - splits[i]; + var numNGrams = _this.getNumNGrams(length, nGramWidth); + _this.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth); + outputStartIdx += numNGrams; + }); + // If we're preserving short sequences, check to see if no sequence was + // generated by comparing the current output start idx to the original + // one (nGramSplitsdata). If no ngrams were generated, then they will + // be equal (since we increment outputStartIdx by numNGrams every + // time we create a set of ngrams.) + if (this_3.preserveShort && outputStartIdx === nGramsSplits[i]) { + var dataLength = splits[i + 1] - splits[i]; + // One legitimate reason to not have any ngrams when this.preserveShort + // is true is if the sequence itself is empty. In that case, move on. + if (dataLength === 0) { + return "continue"; + } + // We don't have to worry about dynamic padding sizes here: if padding + // was dynamic, every sequence would have had sufficient padding to + // generate at least one nGram. + var nGramWidth = dataLength + 2 * this_3.padWidth; + var numNGrams = 1; + this_3.createNGrams(data, splitIndex, nGrams, outputStartIdx, numNGrams, nGramWidth); + } + }; + var this_3 = this; + for (var i = 0; i < numBatchItems; ++i) { + _loop_3(i); + } + return [nGrams, nGramsSplits]; + }; + return StringNGramsOp; + }()); + function stringNGramsImpl(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) { + return new StringNGramsOp(separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) + .compute(data, dataSplits); + } + + /** + * @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. + * ============================================================================= + */ + function split(str, delimiters, skipEmpty, result) { + if (!str.length) { + return; + } + // When the delimiter is empty, the input is split into individual characters. + if (delimiters.length === 0) { + for (var i = 0; i < str.length; ++i) { + result.push(str.subarray(i, i + 1)); + } + return; + } + // When there is one delimiter, the input is split only at that delimiter. + if (delimiters.length === 1) { + var delimiter = delimiters[0]; + var f = str.indexOf(delimiter); + while (f !== -1) { + var 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; + } + // When there are multiple delimiters, the input is split at every instance + // one of the delimiters appears. + var tokenStart = 0; + for (var i = 0; i < str.length + 1; i++) { + if ((i === str.length) || (delimiters.indexOf(str[i]) !== -1)) { + var token = str.subarray(tokenStart, i); + if (!skipEmpty || token.length !== 0) { + result.push(token); + } + tokenStart = i + 1; + } + } + } + function stringSplitImpl(input, delimiter, skipEmpty) { + var batchSize = input.length; + // Empty delimiter means split the input character by character. + var tokens = []; + var outputSize = 0; + var maxNumEntries = 0; + var numIndices = new Array(batchSize); + for (var i = 0; i < batchSize; ++i) { + var prevTokensLength = tokens.length; + split(input[i], delimiter, skipEmpty, tokens); + var nEntries = tokens.length - prevTokensLength; + numIndices[i] = nEntries; + outputSize += nEntries; + maxNumEntries = Math.max(maxNumEntries, nEntries); + } + var indices = tfjsCore.util.getArrayFromDType('int32', outputSize * 2); + var values = new Array(outputSize); + var shape = [batchSize, maxNumEntries]; + var c = 0; + for (var i = 0; i < batchSize; ++i) { + for (var j = 0; j < numIndices[i]; ++j) { + // indices is a 2d tensor with shape of [outputSize, 2] + indices[c * 2] = i; + indices[c * 2 + 1] = j; + values[c] = tokens[c]; + ++c; + } + } + return [indices, values, shape]; + } + + /** + * @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. + * ============================================================================= + */ + function stringToHashBucketFastImpl(input, numBuckets) { + var output = tfjsCore.util.getArrayFromDType('int32', input.length); + for (var i = 0; i < input.length; ++i) { + output[i] = + tfjsCore.util.fingerPrint64(input[i]).modulo(numBuckets).getLowBitsUnsigned(); + } + return output; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var subImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { return aValue - bValue; })); + var subComplexImpl = createComplexBinaryKernelImpl((function (aReal, aImag, bReal, bImag) { + return { real: aReal - bReal, imag: aImag - bImag }; + })); + var sub = binaryKernelFunc(tfjsCore.Sub, subImpl, subComplexImpl); + var subConfig = { + kernelName: tfjsCore.Sub, + backendName: 'cpu', + kernelFunc: sub + }; + + /** + * @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. + * ============================================================================= + */ + /** + * An implementation of the tile kernel shared between webgl and cpu for string + * tensors only. + */ + function tileImpl(xBuf, reps) { + var newShape = new Array(xBuf.rank); + for (var i = 0; i < newShape.length; i++) { + newShape[i] = xBuf.shape[i] * reps[i]; + } + var result = tfjsCore.buffer(newShape, xBuf.dtype); + for (var i = 0; i < result.values.length; ++i) { + var newLoc = result.indexToLoc(i); + var originalLoc = new Array(xBuf.rank); + for (var j = 0; j < originalLoc.length; j++) { + originalLoc[j] = newLoc[j] % xBuf.shape[j]; + } + var originalIndex = xBuf.locToIndex(originalLoc); + result.values[i] = xBuf.values[originalIndex]; + } + return result; + } + + var comparePair = function (a, b) { + var valueDiff = b.value - a.value; + return valueDiff === 0 ? a.index - b.index : valueDiff; + }; + /** + * Partitions array where all elements smaller than the (k+1) smallest element + * are found to the left of it, and all larger to the right of it. + * Based on the Floyd-Rivest Algorithm, ref: + * https://en.wikipedia.org/wiki/Floyd%E2%80%93Rivest_algorithm + * @param array: Array to partition + * @param left: Left index for the interval + * @param right: Right index for the interval + * @param k: Desired index value, where array[k] is the (k+1)th smallest element + * when left = 0 + */ + function select$1(array, k, left, right) { + if (left === void 0) { left = 0; } + if (right === void 0) { right = array.length - 1; } + while (right > left) { + // Use select recursively to sample a smaller set of size s + // the arbitrary constants 600 and 0.5 are used in the original + // version to minimize execution time. + if (right - left > 600) { + var n = right - left + 1; + var i_1 = k - left + 1; + var z = Math.log(n); + var s = 0.5 * Math.exp(2 * z / 3); + var sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * Math.sign(i_1 - n / 2); + var newLeft = Math.max(left, Math.floor(k - i_1 * s / n + sd)); + var newRight = Math.min(right, Math.floor(k + (n - i_1) * s / n + sd)); + select$1(array, k, newLeft, newRight); + } + // partition the elements between left and right around t + var t = array[k]; + var i = left; + var j = right; + tfjsCore.util.swap(array, left, k); + if (comparePair(array[right], t) > 0) { + tfjsCore.util.swap(array, left, right); + } + while (i < j) { + tfjsCore.util.swap(array, i, j); + i++; + j--; + while (comparePair(array[i], t) < 0) { + i = i + 1; + } + while (comparePair(array[j], t) > 0) { + j = j - 1; + } + } + if (comparePair(array[left], t) === 0) { + tfjsCore.util.swap(array, left, j); + } + else { + j = j + 1; + tfjsCore.util.swap(array, j, right); + } + // Adjust left and right towards the boundaries of the subset + // containing the (k - left + 1)th smallest element. + if (j <= k) { + left = j + 1; + } + if (k <= j) { + right = j - 1; + } + } + } + function topKImpl(x, xShape, xDtype, k, sorted) { + // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim. + var lastDim = xShape[xShape.length - 1]; + var _a = __read([x.length / lastDim, lastDim], 2), batch = _a[0], size = _a[1]; + var allTopKVals = tfjsCore.util.getTypedArrayFromDType(xDtype, batch * k); + var allTopKIndices = tfjsCore.util.getTypedArrayFromDType('int32', batch * k); + var _loop_1 = function (b) { + var offset = b * size; + var vals = x.subarray(offset, offset + size); + var valAndInd = new Array(vals.length); + vals.forEach(function (value, index) { return valAndInd[index] = { value: value, index: index }; }); + if (k < valAndInd.length) { + select$1(valAndInd, k); + valAndInd = valAndInd.slice(0, k); + } + if (sorted) { + valAndInd.sort(comparePair); + } + var outOffset = b * k; + var topKVals = allTopKVals.subarray(outOffset, outOffset + k); + var topKIndices = allTopKIndices.subarray(outOffset, outOffset + k); + for (var i = 0; i < k; i++) { + topKVals[i] = valAndInd[i].value; + topKIndices[i] = valAndInd[i].index; + } + }; + for (var b = 0; b < batch; b++) { + _loop_1(b); + } + // Reshape back to the original input shape, except that the last + // dimension is k. + var outputShape = xShape.slice(); + outputShape[outputShape.length - 1] = k; + return [ + tfjsCore.buffer(outputShape, xDtype, allTopKVals), + tfjsCore.buffer(outputShape, 'int32', allTopKIndices) + ]; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function uniqueImpl(values, axis, shape, dtype) { + // Normalize and validate axis. + var $axis = tfjsCore.util.parseAxisParam(axis, shape)[0]; + // Calculate the new shape that is suitable for extracting data along the + // given axis. + // + // The rank is 3. + // The size of the 1st dimension is the size of all the axes < the given axis. + // The size of the 2nd dimension is the same as the size of the given axis. + // The size of the 3rd dimension is the size of all the axes > the given axis. + // + // For example, for a 4D tensor with shape=[2, 3, 5, 4] and axis=2, the + // newShape would be: [2*3, 5, 4]. + // + // Note that this is not the final output shape. This will be the shape for an + // intermediate TensorBuffer (see inputBuffer below) to allow us to extract + // values along the given axis. To demonstrate how it works, consider the + // following example: + // + // Input: a 3D tensor, with shape [1, 2, 3] + // [ + // [ + // [1,2,3], + // [4,5,6] + // ] + // ] + // Axis: 2 (the last axis). + // Along axis 2, we expect to extract 3 tensors: [1,4], [2,5], [3,6]. + // + // For this example, newShape would be: [2, 3, 1], where 2 is calculated from + // 1*2. The re-shaped data would look like: + // + // [ + // [ + // [1], [2], [3] + // ], + // [ + // [4], [5], [6] + // ] + // ] + // + // Then, we can construct a 3-level nested loop by the following dimension + // order to extract the values along the axis (dimension1): + // i: dimension1 // 0,1,2 (newShape[1]) + // m: dimension0 // 0,1 (newShape[0]) + // n: dimension2 // 0 (newShape[2]) + // + // m, i, n + // --------- + // Iteration 0: data at [0, 0, 0] => "1" + // Iteration 1: data at [1, 0, 0] => "4" + // We got [1,4]. + // Iteration 2: data at [0, 1, 0] => "2" + // Iteration 3: data at [1, 1, 0] => "5" + // We got [2,5]. + // Iteration 4: data at [0, 2, 0] => "3" + // Iteration 5: data at [1, 2, 0] => "6" + // We got [3,6]. + var newShape = [1, shape[0], 1]; + for (var i = 0; i < $axis; i++) { + newShape[0] *= shape[i]; + } + newShape[1] = shape[$axis]; + for (var i = $axis + 1; i < shape.length; i++) { + newShape[2] *= shape[i]; + } + // A map from unique elements (their string representations) to their values + // in "indices" (below). + var uniqueElements = {}; + // The indices of each unique element in the original tensor along the given + // axis. It is 1D and has the same size as the given axis. + var indices = new Int32Array(shape[$axis]); + // Create a buffer so we can easily extract value at a given location. + var inputBuffer = new tfjsCore.TensorBuffer(newShape, dtype, values); + // The indices along the given axis that have unique elements. This is a + // de-duped version of "indices" above. + var uniqueIndices = []; + var is1DTensor = newShape[0] === 1 && newShape[2] === 1; + for (var i = 0; i < shape[$axis]; i++) { + // Extract values along the axis. + var element = void 0; + if (is1DTensor) { + // Fast path for 1D tensor input. + element = values[i].toString(); + } + else { + var axisValues = []; + for (var m = 0; m < newShape[0]; m++) { + for (var n = 0; n < newShape[2]; n++) { + axisValues.push(inputBuffer.get(m, i, n)); + } + } + element = axisValues.join(','); + } + // Dedup and update various indices. + if (uniqueElements[element] !== undefined) { + indices[i] = uniqueElements[element]; + } + else { + var uniqueIndex = Object.keys(uniqueElements).length; + uniqueElements[element] = uniqueIndex; + indices[i] = uniqueIndex; + uniqueIndices.push(i); + } + } + // Now we know where each of the unique elements are located along the axis + // (uniqueIndices). Extract them from input buffer and store them in the + // output buffer. + var outputTmpShape = newShape.slice(); + outputTmpShape[1] = Object.keys(uniqueElements).length; + var outputBuffer = new tfjsCore.TensorBuffer(outputTmpShape, dtype); + uniqueIndices.forEach(function (uniqueElementIndex, i) { + for (var m = 0; m < newShape[0]; m++) { + for (var n = 0; n < newShape[2]; n++) { + outputBuffer.set(inputBuffer.get(m, uniqueElementIndex, n), m, i, n); + } + } + }); + // The output shape can be calculated from the input shape with the size of + // the given axis replaced by the number of unique elements along that axis. + var outputShape = shape.slice(); + outputShape[$axis] = outputTmpShape[1]; + return { + outputValues: outputBuffer.values, + outputShape: outputShape, + indices: indices, + }; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + + var shared = { + __proto__: null, + simpleAbsImpl: simpleAbsImpl, + addImpl: addImpl, + bincountImpl: bincountImpl, + bincountReduceImpl: bincountReduceImpl, + ceilImpl: ceilImpl, + concatImpl: concatImpl, + equalImpl: equalImpl, + expImpl: expImpl, + expm1Impl: expm1Impl, + floorImpl: floorImpl, + gatherNdImpl: gatherNdImpl, + gatherV2Impl: gatherV2Impl, + greaterImpl: greaterImpl, + greaterEqualImpl: greaterEqualImpl, + lessImpl: lessImpl, + lessEqualImpl: lessEqualImpl, + linSpaceImpl: linSpaceImpl, + logImpl: logImpl, + maxImpl: maxImpl, + maximumImpl: maximumImpl, + minimumImpl: minimumImpl, + multiplyImpl: multiplyImpl, + negImpl: negImpl, + notEqualImpl: notEqualImpl, + prodImpl: prodImpl, + rangeImpl: rangeImpl, + rsqrtImpl: rsqrtImpl, + sigmoidImpl: sigmoidImpl, + 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 + }; + + /** @license See the LICENSE file. */ + // This code is auto-generated, do not modify this file! + var version = '3.12.0'; + + /** + * @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. + * ============================================================================= + */ + // Side effects for default initialization of MathBackendCPU + tfjsCore.registerBackend('cpu', function () { return new MathBackendCPU(); }, 1 /* priority */); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var elu = unaryKernelFunc(tfjsCore.Elu, function (xi) { return xi >= 0 ? xi : (Math.exp(xi) - 1); }); + var eluConfig = { + kernelName: tfjsCore.Elu, + backendName: 'cpu', + kernelFunc: elu, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function leakyRelu(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var alpha = attrs.alpha; + assertNotComplex([x], 'leakyRelu'); + var xSize = tfjsCore.util.sizeFromShape(x.shape); + var xVals = backend.data.get(x.dataId).values; + var outVals = tfjsCore.util.getTypedArrayFromDType('float32', xSize); + for (var i = 0; i < xVals.length; i++) { + outVals[i] = xVals[i] < 0 ? alpha * xVals[i] : xVals[i]; + } + return backend.makeTensorInfo(x.shape, 'float32', outVals); + } + var leakyReluConfig = { + kernelName: tfjsCore.LeakyRelu, + backendName: 'cpu', + kernelFunc: leakyRelu + }; + + var preluImpl = createSimpleBinaryKernelImpl(function (xValue, aValue) { return xValue < 0 ? aValue * xValue : xValue; }); + function prelu(args) { + var inputs = args.inputs, backend = args.backend; + var x = inputs.x, alpha = inputs.alpha; + assertNotComplex([x, alpha], 'prelu'); + var aVals = backend.data.get(x.dataId).values; + var bVals = backend.data.get(alpha.dataId).values; + var _a = __read(preluImpl(x.shape, alpha.shape, aVals, bVals, 'float32'), 2), resultData = _a[0], resultShape = _a[1]; + return backend.makeTensorInfo(resultShape, 'float32', resultData); + } + var preluConfig = { + kernelName: tfjsCore.Prelu, + backendName: 'cpu', + kernelFunc: prelu, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var relu = unaryKernelFunc(tfjsCore.Relu, function (xi) { return Math.max(0, xi); }); + var reluConfig = { + kernelName: tfjsCore.Relu, + backendName: 'cpu', + kernelFunc: relu, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var relu6 = unaryKernelFunc(tfjsCore.Relu6, function (xi) { return Math.min(Math.max(0, xi), 6); }); + var relu6Config = { + kernelName: tfjsCore.Relu6, + backendName: 'cpu', + kernelFunc: relu6, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function applyActivation(backend, x, activation, preluActivationWeights, leakyreluAlpha) { + if (activation === 'linear') { + return identity({ inputs: { x: x }, backend: backend }); + } + else if (activation === 'relu') { + return relu({ inputs: { x: x }, backend: backend }); + } + else if (activation === 'elu') { + return elu({ inputs: { x: x }, backend: backend }); + } + else if (activation === 'relu6') { + return relu6({ inputs: { x: x }, backend: backend }); + } + else if (activation === 'prelu') { + return prelu({ inputs: { x: x, alpha: preluActivationWeights }, backend: backend }); + } + else if (activation === 'leakyrelu') { + return leakyRelu({ inputs: { x: x }, backend: backend, attrs: { alpha: leakyreluAlpha } }); + } + else if (activation === 'sigmoid') { + return sigmoid({ inputs: { x: x }, backend: backend }); + } + throw new Error("Activation " + activation + " has not been implemented for the CPU backend."); + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function reshape(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var shape = attrs.shape; + var xSize = tfjsCore.util.sizeFromShape(x.shape); + var $shape = tfjsCore.util.inferFromImplicitShape(shape, xSize); + var $xSize = tfjsCore.util.sizeFromShape($shape); + tfjsCore.util.assert(xSize === $xSize, function () { return "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."; }); + backend.incRef(x.dataId); + var xData = backend.data.get(x.dataId); + if (xData.complexTensorInfos != null) { + var real = xData.complexTensorInfos.real; + var imag = xData.complexTensorInfos.imag; + real.shape = $shape; + imag.shape = $shape; + } + return { dataId: x.dataId, shape: $shape, dtype: x.dtype }; + } + var reshapeConfig = { + kernelName: tfjsCore.Reshape, + backendName: 'cpu', + kernelFunc: reshape + }; + + function batchMatMul(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var a = inputs.a, b = inputs.b; + var transposeA = attrs.transposeA, transposeB = attrs.transposeB; + assertNotComplex([a, b], 'matMul'); + var aRank = a.shape.length; + var bRank = b.shape.length; + var innerShapeA = transposeA ? a.shape[aRank - 2] : a.shape[aRank - 1]; + var innerShapeB = transposeB ? b.shape[bRank - 1] : b.shape[bRank - 2]; + var outerShapeA = transposeA ? a.shape[aRank - 1] : a.shape[aRank - 2]; + var outerShapeB = transposeB ? b.shape[bRank - 2] : b.shape[bRank - 1]; + var outerDimsA = a.shape.slice(0, -2); + var outerDimsB = b.shape.slice(0, -2); + var batchDimA = tfjsCore.util.sizeFromShape(outerDimsA); + var batchDimB = tfjsCore.util.sizeFromShape(outerDimsB); + var outShapeOuterDims = tfjsCore.broadcast_util.assertAndGetBroadcastShape(a.shape.slice(0, -2), b.shape.slice(0, -2)); + var outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + tfjsCore.util.assert(innerShapeA === innerShapeB, function () { return "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."); }); + var a3dShape = transposeA ? [batchDimA, innerShapeA, outerShapeA] : + [batchDimA, outerShapeA, innerShapeA]; + var b3dShape = transposeB ? [batchDimB, outerShapeB, innerShapeB] : + [batchDimB, innerShapeB, outerShapeB]; + // The rest of the implementation is designed to operate on rank-3 tensors + var a3d = reshape({ inputs: { x: a }, backend: backend, attrs: { shape: a3dShape } }); + var b3d = reshape({ inputs: { x: b }, backend: backend, attrs: { shape: b3dShape } }); + var sharedDim = transposeA ? a3d.shape[1] : a3d.shape[2]; + var leftDim = transposeA ? a3d.shape[2] : a3d.shape[1]; + var rightDim = transposeB ? b3d.shape[1] : b3d.shape[2]; + var batchDim = Math.max(batchDimA, batchDimB); + var a3dValues = backend.data.get(a3d.dataId).values; + var b3dValues = backend.data.get(b3d.dataId).values; + var a3dStrides = tfjsCore.util.computeStrides(a3d.shape); + var b3dStrides = tfjsCore.util.computeStrides(b3d.shape); + var _a = __read(transposeA ? + [a3dStrides[0], 1, a3dStrides[1]] : + [a3dStrides[0], a3dStrides[1], 1], 3), aBatch = _a[0], aOuterStep = _a[1], aInnerStep = _a[2]; + var _b = __read(transposeB ? + [1, b3dStrides[1], b3dStrides[0]] : + [b3dStrides[1], 1, b3dStrides[0]], 3), bInnerStep = _b[0], bOuterStep = _b[1], bBatch = _b[2]; + var size = leftDim * rightDim; + var result = tfjsCore.buffer([batchDim, leftDim, rightDim], a3d.dtype); + var resVals = result.values; + var blockSize = backend.blockSize; + for (var bi = 0; bi < batchDim; bi++) { + for (var i0 = 0; i0 < leftDim; i0 += blockSize) { + for (var j0 = 0; j0 < rightDim; j0 += blockSize) { + for (var k0 = 0; k0 < sharedDim; k0 += blockSize) { + // for when blockSize doesn't evenly divide the input + var iBlock = Math.min(i0 + blockSize, leftDim); + var jBlock = Math.min(j0 + blockSize, rightDim); + var kBlock = Math.min(k0 + blockSize, sharedDim); + for (var i = i0; i < iBlock; i++) { + for (var j = j0; j < jBlock; j++) { + var sum = 0.0; + for (var k = k0; k < kBlock; k++) { + var batchOffsetA = Math.min(bi, batchDimA - 1) * aBatch; + var batchOffsetB = Math.min(bi, batchDimB - 1) * bBatch; + var aVal = a3dValues[batchOffsetA + i * aOuterStep + k * aInnerStep]; + var bVal = b3dValues[k * bInnerStep + j * bOuterStep + batchOffsetB]; + sum += aVal * bVal; + } + resVals[bi * size + (i * rightDim + j)] += sum; + } + } + } + } + } + } + backend.disposeIntermediateTensorInfo(a3d); + backend.disposeIntermediateTensorInfo(b3d); + // set correct shape on output. + return backend.makeTensorInfo(outShape, result.dtype, result.values); + } + var batchMatMulConfig = { + kernelName: tfjsCore.BatchMatMul, + backendName: 'cpu', + kernelFunc: batchMatMul, + }; + + function _fusedMatMul(args) { + var e_1, _a; + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var a = inputs.a, b = inputs.b, bias = inputs.bias, preluActivationWeights = inputs.preluActivationWeights; + var transposeA = attrs.transposeA, transposeB = attrs.transposeB, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha; + var current; + var addRes; + var activationRes; + var intermediates = []; + var matMulRes = batchMatMul({ inputs: { a: a, b: b }, attrs: { transposeA: transposeA, transposeB: transposeB }, backend: backend }); + current = matMulRes; + if (bias) { + addRes = add({ inputs: { a: current, b: bias }, backend: backend }); + intermediates.push(current); + current = addRes; + } + if (activation) { + activationRes = applyActivation(backend, current, activation, preluActivationWeights, leakyreluAlpha); + intermediates.push(current); + current = activationRes; + } + try { + for (var intermediates_1 = __values(intermediates), intermediates_1_1 = intermediates_1.next(); !intermediates_1_1.done; intermediates_1_1 = intermediates_1.next()) { + var i = intermediates_1_1.value; + backend.disposeIntermediateTensorInfo(i); + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (intermediates_1_1 && !intermediates_1_1.done && (_a = intermediates_1.return)) _a.call(intermediates_1); + } + finally { if (e_1) throw e_1.error; } + } + return current; + } + var _fusedMatMulConfig = { + kernelName: tfjsCore._FusedMatMul, + backendName: 'cpu', + kernelFunc: _fusedMatMul, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var acos = unaryKernelFunc(tfjsCore.Acos, function (xi) { return Math.acos(xi); }); + var acosConfig = { + kernelName: tfjsCore.Acos, + backendName: 'cpu', + kernelFunc: acos, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var acosh = unaryKernelFunc(tfjsCore.Acosh, function (xi) { return Math.acosh(xi); }); + var acoshConfig = { + kernelName: tfjsCore.Acosh, + backendName: 'cpu', + kernelFunc: acosh, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function addN(args) { + var inputs = args.inputs, backend = args.backend; + var tensors = inputs; + assertNotComplex(inputs, 'addN'); + var vals = tensors.map(function (t) { return backend.data.get(t.dataId).values; }); + var outBuf = tfjsCore.buffer(tensors[0].shape, tensors[0].dtype); + var outVals = outBuf.values; + for (var i = 0; i < tensors.length; i++) { + var currVals = vals[i]; + for (var j = 0; j < outVals.length; j++) { + outVals[j] += currVals[j]; + } + } + return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); + } + var addNConfig = { + kernelName: tfjsCore.AddN, + backendName: 'cpu', + kernelFunc: addN + }; + + function all(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, keepDims = attrs.keepDims; + assertNotComplex(x, 'all'); + var origAxes = tfjsCore.util.parseAxisParam(axis, x.shape); + var axes = origAxes; + var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length); + var $x = x; + if (permutedAxes != null) { + $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } }); + axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, x.shape.length); + } + tfjsCore.backend_util.assertAxesAreInnerMostDims('all', axes, $x.shape.length); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1]; + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), $x.dtype); + var aVals = backend.data.get($x.dataId).values; + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var all_1 = aVals[offset]; + for (var j = 0; j < reduceSize; ++j) { + var value = aVals[offset + j]; + all_1 = all_1 && value; + } + vals[i] = all_1; + } + if (permutedAxes != null) { + backend.disposeIntermediateTensorInfo($x); + } + var result = backend.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, origAxes); + var reshapedResult = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: expandedShape } }); + backend.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; + } + var allConfig = { + kernelName: tfjsCore.All, + backendName: 'cpu', + kernelFunc: all + }; + + function any(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, keepDims = attrs.keepDims; + assertNotComplex(x, 'any'); + var origAxes = tfjsCore.util.parseAxisParam(axis, x.shape); + var axes = origAxes; + var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length); + var $x = x; + if (permutedAxes != null) { + $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } }); + axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, x.shape.length); + } + tfjsCore.backend_util.assertAxesAreInnerMostDims('any', axes, $x.shape.length); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1]; + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), $x.dtype); + var aVals = backend.data.get($x.dataId).values; + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var anyVal = aVals[offset]; + for (var j = 0; j < reduceSize; ++j) { + var value = aVals[offset + j]; + anyVal = anyVal || value; + } + vals[i] = anyVal; + } + if (permutedAxes != null) { + backend.disposeIntermediateTensorInfo($x); + } + var result = backend.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, origAxes); + var reshapedResult = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: expandedShape } }); + backend.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; + } + var anyConfig = { + kernelName: tfjsCore.Any, + backendName: 'cpu', + kernelFunc: any + }; + + function argMax(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis; + assertNotComplex(x, 'argMax'); + var axes = tfjsCore.util.parseAxisParam(axis, x.shape); + var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length); + var $x = x; + var intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, $x.shape.length); + } + axes = [axes[0]]; + tfjsCore.backend_util.assertAxesAreInnerMostDims('argMax', axes, $x.shape.length); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1]; + var outSize = tfjsCore.util.sizeFromShape(outShape); + var vals = tfjsCore.util.makeZerosTypedArray(outSize, 'int32'); + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var aVals = backend.data.get($x.dataId).values; + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var max = aVals[offset]; + var maxIndex = 0; + for (var j = 0; j < reduceSize; ++j) { + var value = aVals[offset + j]; + if (value > max) { + max = value; + maxIndex = j; + } + } + vals[i] = maxIndex; + } + intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return backend.makeTensorInfo(outShape, 'int32', vals); + } + var argMaxConfig = { + kernelName: tfjsCore.ArgMax, + backendName: 'cpu', + kernelFunc: argMax + }; + + function argMin(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis; + assertNotComplex(x, 'argMin'); + var axes = tfjsCore.util.parseAxisParam(axis, x.shape); + var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length); + var $x = x; + var intermediateTensorInfos = []; + if (permutedAxes != null) { + $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } }); + intermediateTensorInfos.push($x); + axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, $x.shape.length); + } + axes = [axes[0]]; + tfjsCore.backend_util.assertAxesAreInnerMostDims('argMin', axes, $x.shape.length); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1]; + var outSize = tfjsCore.util.sizeFromShape(outShape); + var vals = tfjsCore.util.makeZerosTypedArray(outSize, 'int32'); + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var aVals = backend.data.get($x.dataId).values; + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var min = aVals[offset]; + var minIndex = 0; + for (var j = 0; j < reduceSize; ++j) { + var value = aVals[offset + j]; + if (value < min) { + min = value; + minIndex = j; + } + } + vals[i] = minIndex; + } + intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return backend.makeTensorInfo(outShape, 'int32', vals); + } + var argMinConfig = { + kernelName: tfjsCore.ArgMin, + backendName: 'cpu', + kernelFunc: argMin + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var asin = unaryKernelFunc(tfjsCore.Asin, function (xi) { return Math.asin(xi); }); + var asinConfig = { + kernelName: tfjsCore.Asin, + backendName: 'cpu', + kernelFunc: asin, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var asinh = unaryKernelFunc(tfjsCore.Asinh, function (xi) { return Math.asinh(xi); }); + var asinhConfig = { + kernelName: tfjsCore.Asinh, + backendName: 'cpu', + kernelFunc: asinh, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var atan = unaryKernelFunc(tfjsCore.Atan, function (xi) { return Math.atan(xi); }); + var atanConfig = { + kernelName: tfjsCore.Atan, + backendName: 'cpu', + kernelFunc: atan, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var atan2Impl = createSimpleBinaryKernelImpl(function (aValue, bValue) { return Math.atan2(aValue, bValue); }); + var atan2 = binaryKernelFunc(tfjsCore.Atan2, atan2Impl); + var atan2Config = { + kernelName: tfjsCore.Atan2, + backendName: 'cpu', + kernelFunc: atan2, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var atanh = unaryKernelFunc(tfjsCore.Atanh, function (xi) { return Math.atanh(xi); }); + var atanhConfig = { + kernelName: tfjsCore.Atanh, + backendName: 'cpu', + kernelFunc: atanh, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function pool(xValues, xShape, dtype, strides, convInfo, poolType) { + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padTop = convInfo.padInfo.top; + var padLeft = convInfo.padInfo.left; + var initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY : + Number.POSITIVE_INFINITY); + var output = tfjsCore.buffer(convInfo.outShape, dtype); + var outputVals = output.values; + var outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3]; + var outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3]; + var outputColStrides = convInfo.outShape[3]; + for (var b = 0; b < convInfo.batchSize; ++b) { + var outputBatchOffset = b * outputBatchStrides; + var inputBatchOffset = b * strides[0]; + for (var d = 0; d < convInfo.inChannels; ++d) { + for (var yR = 0; yR < convInfo.outHeight; ++yR) { + var xRCorner = yR * strideHeight - padTop; + var xRMin = Math.max(0, xRCorner); + var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner); + var outputRowOffset = outputBatchOffset + yR * outputRowStrides; + for (var yC = 0; yC < convInfo.outWidth; ++yC) { + var xCCorner = yC * strideWidth - padLeft; + var xCMin = Math.max(0, xCCorner); + var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner); + var minMaxValue = initialValue; + var avgValue = 0; + var count = 0; + for (var xR = xRMin; xR < xRMax; xR += dilationHeight) { + var xROffset = inputBatchOffset + xR * strides[1]; + for (var xC = xCMin; xC < xCMax; xC += dilationWidth) { + var xCOffset = xROffset + xC * strides[2]; + var pixel = xValues[xCOffset + d]; + if ((poolType === 'max' && pixel > minMaxValue)) { + minMaxValue = pixel; + } + else if (poolType === 'avg') { + avgValue += pixel; + count++; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + var outputOffset = outputRowOffset + yC * outputColStrides + d; + outputVals[outputOffset] = + poolType === 'avg' ? avgValue / count : minMaxValue; + } + } + } + } + return output; + } + function maxPoolPositions(xValues, xShape, dtype, convInfo, flattenPositions, includeBatchInIndex) { + if (flattenPositions === void 0) { flattenPositions = false; } + if (includeBatchInIndex === void 0) { includeBatchInIndex = false; } + var maxPositions = tfjsCore.buffer(convInfo.outShape, 'int32'); + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padTop = convInfo.padInfo.top; + var padLeft = convInfo.padInfo.left; + var xBuf = tfjsCore.buffer(xShape, dtype, xValues); + for (var b = 0; b < convInfo.batchSize; ++b) { + for (var d = 0; d < convInfo.inChannels; ++d) { + for (var yR = 0; yR < convInfo.outHeight; ++yR) { + var xRCorner = yR * strideHeight - padTop; + var xRMin = xRCorner; + while (xRMin < 0) { + xRMin += dilationHeight; + } + // const xRMin = Math.max(0, xRCorner); + var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner); + for (var yC = 0; yC < convInfo.outWidth; ++yC) { + var xCCorner = yC * strideWidth - padLeft; + var xCMin = xCCorner; + while (xCMin < 0) { + xCMin += dilationWidth; + } + var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner); + var maxValue = Number.NEGATIVE_INFINITY; + var maxPosition = -1; + for (var xR = xRMin; xR < xRMax; xR += dilationHeight) { + var wR = xR - xRCorner; + for (var xC = xCMin; xC < xCMax; xC += dilationWidth) { + var wC = xC - xCCorner; + var 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 pool3d(xValues, xShape, dtype, strides, convInfo, poolType) { + var strideDepth = convInfo.strideDepth; + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var dilationDepth = convInfo.dilationDepth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterDepth = convInfo.effectiveFilterDepth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padFront = convInfo.padInfo.front; + var padTop = convInfo.padInfo.top; + var padLeft = convInfo.padInfo.left; + var initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY : + Number.POSITIVE_INFINITY); + var output = tfjsCore.buffer(convInfo.outShape, dtype); + var outputVals = output.values; + var outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * + convInfo.outShape[3] * convInfo.outShape[4]; + var outputDepthStrides = convInfo.outShape[2] * convInfo.outShape[3] * convInfo.outShape[4]; + var outputRowStrides = convInfo.outShape[3] * convInfo.outShape[4]; + var outputColStrides = convInfo.outShape[4]; + for (var batch = 0; batch < convInfo.batchSize; ++batch) { + var outputBatchOffset = batch * outputBatchStrides; + var inputBatchOffset = batch * strides[0]; + for (var channel = 0; channel < convInfo.inChannels; ++channel) { + for (var yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) { + var xDepthCorner = yDepth * strideDepth - padFront; + var xDepthMin = xDepthCorner; + while (xDepthMin < 0) { + xDepthMin += dilationDepth; + } + var xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner); + var outputDepthOffset = outputBatchOffset + yDepth * outputDepthStrides; + for (var yRow = 0; yRow < convInfo.outHeight; ++yRow) { + var xRowCorner = yRow * strideHeight - padTop; + var xRowMin = xRowCorner; + while (xRowMin < 0) { + xRowMin += dilationHeight; + } + var xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner); + var outputRowOffset = outputDepthOffset + yRow * outputRowStrides; + for (var yCol = 0; yCol < convInfo.outWidth; ++yCol) { + var xColCorner = yCol * strideWidth - padLeft; + var xColMin = xColCorner; + while (xColMin < 0) { + xColMin += dilationWidth; + } + var xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner); + // Shader code begins + var outputColOffset = outputRowOffset + yCol * outputColStrides; + var minMaxValue = initialValue; + var avgValue = 0; + var count = 0; + for (var xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) { + var xDepthOffset = inputBatchOffset + xDepth * strides[1]; + for (var xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) { + var xRowOffset = xDepthOffset + xRow * strides[2]; + for (var xCol = xColMin; xCol < xColMax; xCol += dilationWidth) { + var xColOffset = xRowOffset + xCol * strides[3]; + var pixel = xValues[xColOffset + channel]; + if ((poolType === 'max' && pixel > minMaxValue)) { + minMaxValue = pixel; + } + else if (poolType === 'avg') { + avgValue += pixel; + count++; + } + if (isNaN(minMaxValue)) { + break; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + if (isNaN(minMaxValue)) { + break; + } + } + var outputOffset = outputColOffset + channel; + outputVals[outputOffset] = + poolType === 'avg' ? avgValue / count : minMaxValue; + } + } + } + } + } + return output; + } + function maxPool3dPositions(xBuf, convInfo) { + var maxPositions = tfjsCore.buffer(convInfo.outShape, 'int32'); + var strideDepth = convInfo.strideDepth; + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var dilationDepth = convInfo.dilationDepth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterDepth = convInfo.effectiveFilterDepth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padFront = convInfo.padInfo.front; + var padTop = convInfo.padInfo.top; + var padLeft = convInfo.padInfo.left; + for (var batch = 0; batch < convInfo.batchSize; ++batch) { + for (var channel = 0; channel < convInfo.inChannels; ++channel) { + for (var yDepth = 0; yDepth < convInfo.outDepth; ++yDepth) { + var xDepthCorner = yDepth * strideDepth - padFront; + var xDepthMin = xDepthCorner; + while (xDepthMin < 0) { + xDepthMin += dilationDepth; + } + var xDepthMax = Math.min(convInfo.inDepth, effectiveFilterDepth + xDepthCorner); + for (var yRow = 0; yRow < convInfo.outHeight; ++yRow) { + var xRowCorner = yRow * strideHeight - padTop; + var xRowMin = xRowCorner; + while (xRowMin < 0) { + xRowMin += dilationHeight; + } + var xRowMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRowCorner); + for (var yCol = 0; yCol < convInfo.outWidth; ++yCol) { + var xColCorner = yCol * strideWidth - padLeft; + var xColMin = xColCorner; + while (xColMin < 0) { + xColMin += dilationWidth; + } + var xColMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xColCorner); + // Shader code begins + var maxValue = Number.NEGATIVE_INFINITY; + var maxPosition = -1; + for (var xDepth = xDepthMin; xDepth < xDepthMax; xDepth += dilationDepth) { + var wDepth = xDepth - xDepthCorner; + for (var xRow = xRowMin; xRow < xRowMax; xRow += dilationHeight) { + var wRow = xRow - xRowCorner; + for (var xCol = xColMin; xCol < xColMax; xCol += dilationWidth) { + var wCol = xCol - xColCorner; + var 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; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function avgPool(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + assertNotComplex(x, 'avgPool'); + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode; + var dilations = 1; + tfjsCore.util.assert(tfjsCore.backend_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in avgPool: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + var convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode); + var res; + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && + tfjsCore.util.arraysEqual(convInfo.inShape, convInfo.outShape)) { + res = identity({ inputs: { x: x }, backend: backend }); + } + else { + var xValues = backend.data.get(x.dataId).values; + var strides_1 = tfjsCore.util.computeStrides(x.shape); + var buffer = pool(xValues, x.shape, x.dtype, strides_1, convInfo, 'avg'); + res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values); + } + return res; + } + var avgPoolConfig = { + kernelName: tfjsCore.AvgPool, + backendName: 'cpu', + kernelFunc: avgPool + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function avgPool3D(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, dataFormat = attrs.dataFormat; + assertNotComplex(x, 'avgPool3d'); + var convInfo = tfjsCore.backend_util.computePool3DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode, dataFormat); + var xValues = backend.data.get(x.dataId).values; + var outBuf = pool3d(xValues, x.shape, x.dtype, tfjsCore.util.computeStrides(x.shape), convInfo, 'avg'); + return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values); + } + var avgPool3DConfig = { + kernelName: tfjsCore.AvgPool3D, + backendName: 'cpu', + kernelFunc: avgPool3D + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function avgPool3DGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, input = inputs.input; + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode; + assertNotComplex([dy, input], 'avgPool3DGrad'); + var convInfo = tfjsCore.backend_util.computePool3DInfo(input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode); + var strideDepth = convInfo.strideDepth; + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var filterDepth = convInfo.filterDepth; + var filterHeight = convInfo.filterHeight; + var filterWidth = convInfo.filterWidth; + var dilationDepth = convInfo.dilationDepth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterDepth = convInfo.effectiveFilterDepth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + var dx = tfjsCore.buffer(input.shape, 'float32'); + var avgMultiplier = 1 / (filterDepth * filterHeight * filterWidth); + var dyBuf = backend.bufferSync(dy); + for (var batch = 0; batch < convInfo.batchSize; ++batch) { + for (var channel = 0; channel < convInfo.inChannels; ++channel) { + for (var dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) { + for (var dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) { + for (var dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) { + // Shader code begins. + var dyDepthCorner = dxDepth - padFront; + var dyRowCorner = dxRow - padTop; + var dyColCorner = dxCol - padLeft; + var dotProd = 0; + for (var wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) { + var dyDepth = (dyDepthCorner + wDepth) / strideDepth; + if (dyDepth < 0 || dyDepth >= convInfo.outDepth || + Math.floor(dyDepth) !== dyDepth) { + continue; + } + for (var wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) { + var dyRow = (dyRowCorner + wRow) / strideHeight; + if (dyRow < 0 || dyRow >= convInfo.outHeight || + Math.floor(dyRow) !== dyRow) { + continue; + } + for (var wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) { + var dyCol = (dyColCorner + wCol) / strideWidth; + if (dyCol < 0 || dyCol >= convInfo.outWidth || + Math.floor(dyCol) !== dyCol) { + continue; + } + var pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel); + dotProd += pixel; + } + } + } + dx.set(dotProd * avgMultiplier, batch, dxDepth, dxRow, dxCol, channel); + } + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var avgPool3DGradConfig = { + kernelName: tfjsCore.AvgPool3DGrad, + backendName: 'cpu', + kernelFunc: avgPool3DGrad + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function avgPoolGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, input = inputs.input; + var x = input; + assertNotComplex([dy, input], 'avgPoolGrad'); + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad; + var convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad); + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var filterHeight = convInfo.filterHeight; + var filterWidth = convInfo.filterWidth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + var dx = tfjsCore.buffer(x.shape, 'float32'); + var avgMultiplier = 1 / (filterHeight * filterWidth); + var dyData = backend.data.get(dy.dataId).values; + var dyBuf = tfjsCore.buffer(dy.shape, 'float32', dyData); + for (var b = 0; b < convInfo.batchSize; ++b) { + for (var d = 0; d < convInfo.inChannels; ++d) { + for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) { + for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) { + // Shader code begins. + var dyRCorner = dxR - padTop; + var dyCCorner = dxC - padLeft; + var dotProd = 0; + for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) { + var dyR = (dyRCorner + wR) / strideHeight; + if (dyR < 0 || dyR >= convInfo.outHeight || + Math.floor(dyR) !== dyR) { + continue; + } + for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) { + var dyC = (dyCCorner + wC) / strideWidth; + if (dyC < 0 || dyC >= convInfo.outWidth || + Math.floor(dyC) !== dyC) { + continue; + } + var pixel = dyBuf.get(b, dyR, dyC, d); + dotProd += pixel; + } + } + dx.set(dotProd * avgMultiplier, b, dxR, dxC, d); + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var avgPoolGradConfig = { + kernelName: tfjsCore.AvgPoolGrad, + backendName: 'cpu', + kernelFunc: avgPoolGrad + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function batchNorm(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, scale = inputs.scale, offset = inputs.offset, mean = inputs.mean, variance = inputs.variance; + tfjsCore.util.assert(mean.shape.length === variance.shape.length, function () { return 'Batch normalization gradient requires mean and variance to have ' + + 'equal ranks.'; }); + tfjsCore.util.assert(offset == null || mean.shape.length === offset.shape.length, function () { return 'Batch normalization gradient requires mean and offset to have ' + + 'equal ranks.'; }); + tfjsCore.util.assert(scale == null || mean.shape.length === scale.shape.length, function () { return 'Batch normalization gradient requires mean and scale to have ' + + 'equal ranks.'; }); + assertNotComplex([x, mean, variance, scale, offset], 'batchNorm'); + var varianceEpsilon = attrs.varianceEpsilon; + if (varianceEpsilon == null) { + varianceEpsilon = 0.001; + } + var xVals = backend.data.get(x.dataId).values; + var mVals = backend.data.get(mean.dataId).values; + var varVals = backend.data.get(variance.dataId).values; + var sVals = scale ? backend.data.get(scale.dataId).values : + new Float32Array([1]); + var offVals = offset ? + backend.data.get(offset.dataId).values : + new Float32Array([0]); + var outVals = new Float32Array(xVals.length); + var offValsLength = offVals.length; + var sValsLength = sVals.length; + var varValsLength = varVals.length; + var mValsLength = mVals.length; + var offi = 0; + var mi = 0; + var si = 0; + var vi = 0; + for (var 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 backend.makeTensorInfo(x.shape, x.dtype, outVals); + } + var batchNormConfig = { + kernelName: tfjsCore.FusedBatchNorm, + backendName: 'cpu', + kernelFunc: batchNorm, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function batchToSpaceND(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var blockShape = attrs.blockShape, crops = attrs.crops; + assertNotComplex([x], 'batchToSpaceND'); + var prod = blockShape.reduce(function (a, b) { return a * b; }); + var reshaped = tfjsCore.backend_util.getReshaped(x.shape, blockShape, prod); + var permuted = tfjsCore.backend_util.getPermuted(reshaped.length, blockShape.length); + var reshapedPermuted = tfjsCore.backend_util.getReshapedPermuted(x.shape, blockShape, prod); + var sliceBeginCoords = tfjsCore.backend_util.getSliceBeginCoords(crops, blockShape.length); + var sliceSize = tfjsCore.backend_util.getSliceSize(reshapedPermuted, crops, blockShape.length); + var xReshaped = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: reshaped } }); + var xTransposed = transpose({ inputs: { x: xReshaped }, backend: backend, attrs: { perm: permuted } }); + var xTransposedReshaped = reshape({ inputs: { x: xTransposed }, backend: backend, attrs: { shape: reshapedPermuted } }); + var result = slice({ + inputs: { x: xTransposedReshaped }, + backend: backend, + attrs: { begin: sliceBeginCoords, size: sliceSize } + }); + backend.disposeIntermediateTensorInfo(xReshaped); + backend.disposeIntermediateTensorInfo(xTransposed); + backend.disposeIntermediateTensorInfo(xTransposedReshaped); + return result; + } + var batchToSpaceNDConfig = { + kernelName: tfjsCore.BatchToSpaceND, + backendName: 'cpu', + kernelFunc: batchToSpaceND + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function bincount(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, weights = inputs.weights; + var size = attrs.size; + var xVals = backend.data.get(x.dataId).values; + var weightsVals = backend.data.get(weights.dataId).values; + var outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend.makeTensorInfo([size], weights.dtype, outVals); + } + var bincountConfig = { + kernelName: tfjsCore.Bincount, + backendName: 'cpu', + kernelFunc: bincount + }; + + /** + * @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. + * ============================================================================= + */ + function broadcastArgs(args) { + var inputs = args.inputs, backend = args.backend; + var s0 = inputs.s0, s1 = inputs.s1; + var s0Vals = backend.data.get(s0.dataId).values; + var s1Vals = backend.data.get(s1.dataId).values; + var broadcastShape = tfjsCore.backend_util.assertAndGetBroadcastShape(Array.from(s0Vals), Array.from(s1Vals)); + return backend.makeTensorInfo([broadcastShape.length], 'int32', Int32Array.from(broadcastShape)); + } + var broadcastArgsConfig = { + kernelName: tfjsCore.BroadcastArgs, + backendName: 'cpu', + kernelFunc: broadcastArgs + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var clip = unaryKernelFunc(tfjsCore.ClipByValue, function (xi, attrs) { + var clipAttrs = attrs; + if (xi > clipAttrs.clipValueMax) { + return clipAttrs.clipValueMax; + } + return xi < clipAttrs.clipValueMin ? clipAttrs.clipValueMin : xi; + }); + var clipConfig = { + kernelName: tfjsCore.ClipByValue, + backendName: 'cpu', + kernelFunc: clip, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var complexAbs = function (args) { + var x = args.inputs.x; + var cpuBackend = args.backend; + var resultValues = new Float32Array(tfjsCore.util.sizeFromShape(x.shape)); + var complexVals = cpuBackend.data.get(x.dataId); + var real = complexVals.complexTensorInfos.real; + var imag = complexVals.complexTensorInfos.imag; + var realVals = cpuBackend.data.get(real.dataId).values; + var imagVals = cpuBackend.data.get(imag.dataId).values; + for (var i = 0; i < realVals.length; i++) { + var real_1 = realVals[i]; + var imag_1 = imagVals[i]; + resultValues[i] = Math.hypot(real_1, imag_1); + } + return cpuBackend.makeOutput(resultValues, x.shape, 'float32'); + }; + var complexAbsConfig = { + kernelName: tfjsCore.ComplexAbs, + backendName: 'cpu', + kernelFunc: complexAbs, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function imag(args) { + var inputs = args.inputs, backend = args.backend; + var input = inputs.input; + var imag = backend.data.get(input.dataId).complexTensorInfos.imag; + var imagVal = backend.data.get(imag.dataId).values; + // When complex tensor is disposed, its underlying parts will be disposed too. + // Make new tensor out of the imag value of the complex. This makes sure the + // value is still accessible even if complex tensor is disposed. + return backend.makeTensorInfo(imag.shape, imag.dtype, imagVal); + } + var imagConfig = { + kernelName: tfjsCore.Imag, + backendName: 'cpu', + kernelFunc: imag + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function concat(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var axis = attrs.axis; + var $axis = tfjsCore.util.parseAxisParam(axis, inputs[0].shape)[0]; + var outShape = tfjsCore.backend_util.computeOutShape(inputs.map(function (t) { return t.shape; }), $axis); + if (tfjsCore.util.sizeFromShape(outShape) === 0) { + return backend.makeTensorInfo(outShape, inputs[0].dtype, []); + } + // Keep only non-empty tensors (ignore tensors with 0 in their shape). + var $inputs = inputs.filter(function (t) { return tfjsCore.util.sizeFromShape(t.shape) > 0; }); + if ($inputs.length === 1) { + return identity({ inputs: { x: $inputs[0] }, backend: backend }); + } + var shapes = $inputs.map(function (t) { return t.shape; }); + tfjsCore.backend_util.assertParamsConsistent(shapes, $axis); + if ($inputs[0].dtype === 'complex64') { + var reals = $inputs.map(function (t) { return real({ inputs: { input: t }, backend: backend }); }); + var imags = $inputs.map(function (t) { return imag({ inputs: { input: t }, backend: backend }); }); + var realConcated = concat({ inputs: reals, backend: backend, attrs: { axis: $axis } }); + var imagConcated = concat({ inputs: imags, backend: backend, attrs: { axis: $axis } }); + var result = complex({ inputs: { real: realConcated, imag: imagConcated }, backend: backend }); + reals.forEach(function (r) { return backend.disposeIntermediateTensorInfo(r); }); + imags.forEach(function (i) { return backend.disposeIntermediateTensorInfo(i); }); + backend.disposeIntermediateTensorInfo(realConcated); + backend.disposeIntermediateTensorInfo(imagConcated); + return result; + } + // Any concat of n-dimensional tensors across any axis can be reduced to + // a concatenation of two-dimensional tensors across the axis 1 by first + // partitioning the axes of the original tensors into those less than the + // axis to be concatenated and the rest. Then reshape the tensors + // into a two-dimensional tensor by collapsing these two sets of axes and + // concatenate the resulting matrices across the axis 1, finally reshaping + // the result to have the proper shape. + var inputs2D = $inputs.map(function (t) { + var innerSize = tfjsCore.util.sizeFromShape(t.shape.slice($axis)); + var shape = [-1, innerSize]; + return reshape({ inputs: { x: t }, backend: backend, attrs: { shape: shape } }); + }); + var inputsValShapes = inputs2D.map(function (t) { + return { vals: backend.data.get(t.dataId).values, shape: t.shape }; + }); + // Concats 2d tensors along axis=1. + outShape = + tfjsCore.backend_util.computeOutShape(inputs2D.map(function (t) { return t.shape; }), 1 /* axis */); + var simplyConcat = inputs2D[0].shape[0] === 1; + var outVals = concatImpl(inputsValShapes, outShape, inputs[0].dtype, simplyConcat); + var finalOutShape = tfjsCore.backend_util.computeOutShape($inputs.map(function (t) { return t.shape; }), $axis); + var outInfo = backend.makeTensorInfo(finalOutShape, inputs[0].dtype, outVals); + inputs2D.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return outInfo; + } + var concatConfig = { + kernelName: tfjsCore.Concat, + backendName: 'cpu', + kernelFunc: concat + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function conv2D(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, filter = inputs.filter; + var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode; + assertNotComplex([x, filter], 'conv2d'); + var $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat); + var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, dilations, pad, dimRoundingMode, false /* depthwise */, $dataFormat); + var filterHeight = convInfo.filterHeight; + var filterWidth = convInfo.filterWidth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var padLeft = convInfo.padInfo.left; + var padTop = convInfo.padInfo.top; + var isChannelsLast = convInfo.dataFormat === 'channelsLast'; + var y = new tfjsCore.TensorBuffer(convInfo.outShape, x.dtype); + var xStrides = tfjsCore.util.computeStrides(x.shape); + var filterStrides = tfjsCore.util.computeStrides(filter.shape); + var xBatchStride = xStrides[0]; + var xRowStride = isChannelsLast ? xStrides[1] : xStrides[2]; + var xColStride = isChannelsLast ? xStrides[2] : 1; + var xChannelStride = isChannelsLast ? 1 : xStrides[1]; + var yBatchStride = y.strides[0]; + var yRowStride = isChannelsLast ? y.strides[1] : y.strides[2]; + var yColStride = isChannelsLast ? y.strides[2] : 1; + var yChannelStride = isChannelsLast ? 1 : y.strides[1]; + var xVals = backend.data.get(x.dataId).values; + var wVals = backend.data.get(filter.dataId).values; + var yVals = y.values; + for (var b = 0; b < convInfo.batchSize; ++b) { + var xOffset1 = b * xBatchStride; + var yOffset1 = b * yBatchStride; + for (var yR = 0; yR < convInfo.outHeight; ++yR) { + var yOffset2 = yOffset1 + yR * yRowStride; + var xRCorner = yR * convInfo.strideHeight - padTop; + for (var wR = 0; wR < filterHeight; ++wR) { + var xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + var wOffset1 = wR * filterStrides[0]; + var xOffset2 = xOffset1 + xR * xRowStride; + for (var yC = 0; yC < convInfo.outWidth; ++yC) { + var yOffset3 = yOffset2 + yC * yColStride; + var xCCorner = yC * convInfo.strideWidth - padLeft; + for (var wC = 0; wC < filterWidth; ++wC) { + var xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + var wOffset2 = wOffset1 + wC * filterStrides[1]; + var xOffset3 = xOffset2 + xC * xColStride; + var wOffset3 = wOffset2; + for (var d1 = 0; d1 < convInfo.inChannels; ++d1) { + var xVal = xVals[xOffset3 + d1 * xChannelStride]; + for (var d2 = 0; d2 < convInfo.outChannels; ++d2) { + yVals[yOffset3 + d2 * yChannelStride] += + xVal * wVals[wOffset3 + d2]; + } + wOffset3 += convInfo.outChannels; + } + } + } + } + } + } + return backend.makeTensorInfo(y.shape, y.dtype, yVals); + } + var conv2DConfig = { + kernelName: tfjsCore.Conv2D, + backendName: 'cpu', + kernelFunc: conv2D + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function conv2DBackpropFilter(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, dy = inputs.dy; + var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dimRoundingMode = attrs.dimRoundingMode, filterShape = attrs.filterShape; + assertNotComplex([x, dy], 'conv2dBackpropFilter'); + var $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat); + var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad, dimRoundingMode, false /* depthwise */, $dataFormat); + var strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth; + var isChannelsLast = convInfo.dataFormat === 'channelsLast'; + var dW = new tfjsCore.TensorBuffer(convInfo.filterShape, 'float32'); + var leftPad = convInfo.padInfo.left; + var topPad = convInfo.padInfo.top; + var xVals = backend.data.get(x.dataId).values; + var dyVals = backend.data.get(dy.dataId).values; + var xBuf = new tfjsCore.TensorBuffer(x.shape, x.dtype, xVals); + var dyBuf = new tfjsCore.TensorBuffer(dy.shape, dy.dtype, dyVals); + for (var wR = 0; wR < filterHeight; ++wR) { + var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + for (var wC = 0; wC < filterWidth; ++wC) { + var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + for (var d1 = 0; d1 < convInfo.inChannels; ++d1) { + for (var d2 = 0; d2 < convInfo.outChannels; ++d2) { + var dotProd = 0; + for (var b = 0; b < convInfo.batchSize; ++b) { + for (var yR = yRMin; yR < yRMax; ++yR) { + var xR = wR + yR * strideHeight - topPad; + for (var yC = yCMin; yC < yCMax; ++yC) { + var 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 backend.makeTensorInfo(dW.shape, dW.dtype, dW.values); + } + var conv2DBackpropFilterConfig = { + kernelName: tfjsCore.Conv2DBackpropFilter, + backendName: 'cpu', + kernelFunc: conv2DBackpropFilter + }; + + function conv2DBackpropInput(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, filter = inputs.filter; + var inputShape = attrs.inputShape, strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dimRoundingMode = attrs.dimRoundingMode; + assertNotComplex([dy, filter], 'conv2dBackpropInput'); + var filterStrides = tfjsCore.util.computeStrides(filter.shape); + var dyStrides = tfjsCore.util.computeStrides(dy.shape); + var $dataFormat = tfjsCore.backend_util.convertConv2DDataFormat(dataFormat); + var convInfo = tfjsCore.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad, dimRoundingMode, false, $dataFormat); + var dx = new tfjsCore.TensorBuffer(convInfo.inShape, 'float32'); + var dxValues = dx.values; + var dyValues = backend.data.get(dy.dataId).values; + var fltValues = backend.data.get(filter.dataId).values; + var _a = __read(filterStrides, 3), fltS0 = _a[0], fltS1 = _a[1], fltS2 = _a[2]; + var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth; + $dataFormat = convInfo.dataFormat; + var topPad = filterHeight - 1 - convInfo.padInfo.top; + var leftPad = filterWidth - 1 - convInfo.padInfo.left; + var isChannelsLast = $dataFormat === 'channelsLast'; + var xBatchStride = dx.strides[0]; + var xRowStride = isChannelsLast ? dx.strides[1] : dx.strides[2]; + var xColStride = isChannelsLast ? dx.strides[2] : 1; + var xChannelStride = isChannelsLast ? 1 : dx.strides[1]; + var yBatchStride = dyStrides[0]; + var yRowStride = isChannelsLast ? dyStrides[1] : dyStrides[2]; + var yColStride = isChannelsLast ? dyStrides[2] : 1; + var yChannelStride = isChannelsLast ? 1 : dyStrides[1]; + for (var b = 0; b < batchSize; ++b) { + for (var d1 = 0; d1 < inChannels; ++d1) { + for (var xR = 0; xR < inHeight; ++xR) { + var xRCorner = xR - topPad; + var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (var xC = 0; xC < inWidth; ++xC) { + var xCCorner = xC - leftPad; + var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + var dotProd = 0; + for (var yR = xRMin; yR < yRMax; ++yR) { + var wR = yR * strideHeight - xRCorner; + for (var yC = xCMin; yC < yCMax; ++yC) { + var wC = yC * strideWidth - xCCorner; + var dyOffset = yBatchStride * b + yRowStride * yR + yColStride * yC; + var fltOffset = fltS0 * (filterHeight - 1 - wR) + + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1; + for (var d2 = 0; d2 < outChannels; ++d2) { + var pixel = dyValues[dyOffset + yChannelStride * d2]; + var weight = fltValues[fltOffset + d2]; + dotProd += pixel * weight; + } + } + } + var dxOffset = xBatchStride * b + xRowStride * xR + + xColStride * xC + xChannelStride * d1; + dxValues[dxOffset] = dotProd; + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var conv2DBackpropInputConfig = { + kernelName: tfjsCore.Conv2DBackpropInput, + backendName: 'cpu', + kernelFunc: conv2DBackpropInput + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function conv3D(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, filter = inputs.filter; + var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations; + assertNotComplex([x, filter], 'conv3d'); + var convInfo = tfjsCore.backend_util.computeConv3DInfo(x.shape, filter.shape, strides, dilations, pad); + var filterDepth = convInfo.filterDepth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, dilationDepth = convInfo.dilationDepth, dilationHeight = convInfo.dilationHeight, dilationWidth = convInfo.dilationWidth, padInfo = convInfo.padInfo; + var padFront = padInfo.front; + var padLeft = padInfo.left; + var padTop = padInfo.top; + var y = new tfjsCore.TensorBuffer(convInfo.outShape, x.dtype); + var xVals = backend.data.get(x.dataId).values; + var wVals = backend.data.get(filter.dataId).values; + var yVals = y.values; + var xStrides = tfjsCore.util.computeStrides(x.shape); + var filterStrides = tfjsCore.util.computeStrides(filter.shape); + for (var b = 0; b < convInfo.batchSize; ++b) { + var xOffset1 = b * xStrides[0]; + var yOffset1 = b * y.strides[0]; + for (var yF = 0; yF < convInfo.outDepth; ++yF) { + var yOffset2 = yOffset1 + yF * y.strides[1]; + var xFCorner = yF * convInfo.strideDepth - padFront; + for (var wF = 0; wF < filterDepth; ++wF) { + var xF = xFCorner + wF * dilationDepth; + if (xF < 0 || xF >= convInfo.inDepth) { + continue; + } + var wOffset1 = wF * filterStrides[0]; + var xOffset2 = xOffset1 + xF * xStrides[1]; + for (var yR = 0; yR < convInfo.outHeight; ++yR) { + var yOffset3 = yOffset2 + yR * y.strides[2]; + var xRCorner = yR * convInfo.strideHeight - padTop; + for (var wR = 0; wR < filterHeight; ++wR) { + var xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + var wOffset2 = wOffset1 + wR * filterStrides[1]; + var xOffset3 = xOffset2 + xR * xStrides[2]; + for (var yC = 0; yC < convInfo.outWidth; ++yC) { + var yOffset4 = yOffset3 + yC * convInfo.outChannels; + var xCCorner = yC * convInfo.strideWidth - padLeft; + for (var wC = 0; wC < filterWidth; ++wC) { + var xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + var wOffset3 = wOffset2 + wC * filterStrides[2]; + var xOffset4 = xOffset3 + xC * convInfo.inChannels; + var wOffset4 = wOffset3; + for (var d1 = 0; d1 < convInfo.inChannels; ++d1) { + var xVal = xVals[xOffset4 + d1]; + for (var d2 = 0; d2 < convInfo.outChannels; ++d2) { + yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2]; + } + wOffset4 += convInfo.outChannels; + } + } + } + } + } + } + } + } + return backend.makeTensorInfo(y.shape, y.dtype, y.values); + } + var conv3DConfig = { + kernelName: tfjsCore.Conv3D, + backendName: 'cpu', + kernelFunc: conv3D + }; + + function conv3DBackpropFilterV2(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, dy = inputs.dy; + var strides = attrs.strides, pad = attrs.pad, filterShape = attrs.filterShape; + assertNotComplex([x, dy], 'conv3dBackpropFilterV2'); + var xStrides = tfjsCore.util.computeStrides(x.shape); + var dyStrides = tfjsCore.util.computeStrides(dy.shape); + var convInfo = tfjsCore.backend_util.computeConv3DInfo(x.shape, filterShape, strides, 1 /* dilations */, pad); + var strideDepth = convInfo.strideDepth; + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var filterDepth = convInfo.filterDepth; + var filterHeight = convInfo.filterHeight; + var filterWidth = convInfo.filterWidth; + var dw = new tfjsCore.TensorBuffer(convInfo.filterShape, 'float32'); + var dwValues = dw.values; + var _a = __read(dw.strides, 4), dwS0 = _a[0], dwS1 = _a[1], dwS2 = _a[2], dwS3 = _a[3]; + var dyValues = backend.data.get(dy.dataId).values; + var _b = __read(dyStrides, 4), dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3]; + var xValues = backend.data.get(x.dataId).values; + var _c = __read(xStrides, 4), xS0 = _c[0], xS1 = _c[1], xS2 = _c[2], xS3 = _c[3]; + var frontPad = convInfo.padInfo.front; + var leftPad = convInfo.padInfo.left; + var topPad = convInfo.padInfo.top; + for (var wF = 0; wF < filterDepth; ++wF) { + var yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth)); + var yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth); + var wOffset1 = wF * dwS0; + for (var wR = 0; wR < filterHeight; ++wR) { + var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + var wOffset2 = wR * dwS1 + wOffset1; + for (var wC = 0; wC < filterWidth; ++wC) { + var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + var wOffset3 = wC * dwS2 + wOffset2; + for (var d1 = 0; d1 < convInfo.inChannels; ++d1) { + var wOffset4 = d1 * dwS3 + wOffset3; + for (var d2 = 0; d2 < convInfo.outChannels; ++d2) { + var dotProd = 0; + for (var b = 0; b < convInfo.batchSize; ++b) { + var xOffset1 = b * xS0; + var yOffset1 = b * dyS0; + for (var yF = yFMin; yF < yFMax; ++yF) { + var xF = wF + yF * strideDepth - frontPad; + var xOffset2 = xF * xS1 + xOffset1; + var yOffset2 = yF * dyS1 + yOffset1; + for (var yR = yRMin; yR < yRMax; ++yR) { + var xR = wR + yR * strideHeight - topPad; + var xOffset3 = xR * xS2 + xOffset2; + var yOffset3 = yR * dyS2 + yOffset2; + for (var yC = yCMin; yC < yCMax; ++yC) { + var xC = wC + yC * strideWidth - leftPad; + var xOffset4 = xC * xS3 + xOffset3; + var yOffset4 = yC * dyS3 + yOffset3; + dotProd += xValues[xOffset4 + d1] * dyValues[yOffset4 + d2]; + } + } + } + } + dwValues[wOffset4 + d2] = dotProd; + } + } + } + } + } + return backend.makeTensorInfo(dw.shape, dw.dtype, dw.values); + } + var conv3DBackpropFilterV2Config = { + kernelName: tfjsCore.Conv3DBackpropFilterV2, + backendName: 'cpu', + kernelFunc: conv3DBackpropFilterV2 + }; + + function conv3DBackpropInputV2(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, filter = inputs.filter; + var pad = attrs.pad, strides = attrs.strides, inputShape = attrs.inputShape; + assertNotComplex([dy], 'conv3dBackpropInputV2'); + var dyStrides = tfjsCore.util.computeStrides(dy.shape); + var filterStrides = tfjsCore.util.computeStrides(filter.shape); + var convInfo = tfjsCore.backend_util.computeConv3DInfo(inputShape, filter.shape, strides, 1 /* dilations */, pad); + var dx = new tfjsCore.TensorBuffer(convInfo.inShape, 'float32'); + var dxValues = dx.values; + var _a = __read(dx.strides, 4), dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2], dxS3 = _a[3]; + var dyValues = backend.data.get(dy.dataId).values; + var _b = __read(dyStrides, 4), dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3]; + var fltValues = backend.data.get(filter.dataId).values; + var _c = __read(filterStrides, 4), fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2], fltS3 = _c[3]; + var batchSize = convInfo.batchSize, filterDepth = convInfo.filterDepth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inDepth = convInfo.inDepth, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outDepth = convInfo.outDepth, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideDepth = convInfo.strideDepth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth; + var frontPad = filterDepth - 1 - convInfo.padInfo.front; + var topPad = filterHeight - 1 - convInfo.padInfo.top; + var leftPad = filterWidth - 1 - convInfo.padInfo.left; + for (var b = 0; b < batchSize; ++b) { + for (var d1 = 0; d1 < inChannels; ++d1) { + // Frames of depth + for (var xF = 0; xF < inDepth; ++xF) { + var xFCorner = xF - frontPad; + var xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth)); + var yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth); + // Rows as per standard 2d matrix notation + for (var xR = 0; xR < inHeight; ++xR) { + var xRCorner = xR - topPad; + var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + // Columns as per standard 2d matrix notation + for (var xC = 0; xC < inWidth; ++xC) { + var xCCorner = xC - leftPad; + var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + var dotProd = 0; + for (var yF = xFMin; yF < yFMax; ++yF) { + var wF = yF * strideDepth - xFCorner; + for (var yR = xRMin; yR < yRMax; ++yR) { + var wR = yR * strideHeight - xRCorner; + for (var yC = xCMin; yC < yCMax; ++yC) { + var wC = yC * strideWidth - xCCorner; + var dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC; + var fltOffset = fltS0 * (filterDepth - 1 - wF) + + fltS1 * (filterHeight - 1 - wR) + + fltS2 * (filterWidth - 1 - wC) + fltS3 * d1; + for (var d2 = 0; d2 < outChannels; ++d2) { + var pixel = dyValues[dyOffset + d2]; + var weight = fltValues[fltOffset + d2]; + dotProd += pixel * weight; + } + } + } + } + dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] = + dotProd; + } + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var conv3DBackpropInputV2Config = { + kernelName: tfjsCore.Conv3DBackpropInputV2, + backendName: 'cpu', + kernelFunc: conv3DBackpropInputV2 + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var cos = unaryKernelFunc(tfjsCore.Cos, function (xi) { return Math.cos(xi); }); + var cosConfig = { + kernelName: tfjsCore.Cos, + backendName: 'cpu', + kernelFunc: cos, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var cosh = unaryKernelFunc(tfjsCore.Cosh, function (xi) { return Math.cosh(xi); }); + var coshConfig = { + kernelName: tfjsCore.Cosh, + backendName: 'cpu', + kernelFunc: cosh, + }; + + function cropAndResize(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var image = inputs.image, boxes = inputs.boxes, boxInd = inputs.boxInd; + var cropSize = attrs.cropSize, method = attrs.method, extrapolationValue = attrs.extrapolationValue; + var _a = __read(image.shape, 4), batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3]; + var numBoxes = boxes.shape[0]; + var _b = __read(cropSize, 2), cropHeight = _b[0], cropWidth = _b[1]; + var output = tfjsCore.buffer([numBoxes, cropHeight, cropWidth, numChannels], 'float32'); + var boxVals = backend.data.get(boxes.dataId).values; + var boxIndVals = backend.data.get(boxInd.dataId).values; + var imageVals = backend.data.get(image.dataId).values; + var inStride = tfjsCore.util.computeStrides(image.shape); // to calculate flat indexes into image + var outStride = tfjsCore.util.computeStrides(output.shape); // to calculate flat indexes into output + // Reference implementation + // tslint:disable-next-line:max-line-length + // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc + for (var b = 0; b < numBoxes; b++) { + var startInd = b * 4; + var y1 = boxVals[startInd]; + var x1 = boxVals[startInd + 1]; + var y2 = boxVals[startInd + 2]; + var x2 = boxVals[startInd + 3]; + var bInd = boxIndVals[b]; + if (bInd >= batch) { + continue; + } + var heightScale = (cropHeight > 1) ? (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) : 0; + var widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0; + for (var y = 0; y < cropHeight; y++) { + var yInd = (cropHeight > 1) ? + y1 * (imageHeight - 1) + y * (heightScale) : + 0.5 * (y1 + y2) * (imageHeight - 1); + if (yInd < 0 || yInd > imageHeight - 1) { + for (var x = 0; x < cropWidth; x++) { + for (var c = 0; c < numChannels; c++) { + var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + } + continue; + } + if (method === 'bilinear') { + var topInd = Math.floor(yInd); + var bottomInd = Math.ceil(yInd); + var yLerp = yInd - topInd; + for (var x = 0; x < cropWidth; x++) { + var xInd = (cropWidth > 1) ? + x1 * (imageWidth - 1) + x * widthScale : + 0.5 * (x1 + x2) * (imageWidth - 1); + if (xInd < 0 || xInd > imageWidth - 1) { + for (var c = 0; c < numChannels; c++) { + var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + continue; + } + var leftInd = Math.floor(xInd); + var rightInd = Math.ceil(xInd); + var xLerp = xInd - leftInd; + for (var c = 0; c < numChannels; c++) { + var ind = c + leftInd * inStride[2] + topInd * inStride[1] + + bInd * inStride[0]; + var topLeft = imageVals[ind]; + ind = c + rightInd * inStride[2] + topInd * inStride[1] + + bInd * inStride[0]; + var topRight = imageVals[ind]; + ind = c + leftInd * inStride[2] + bottomInd * inStride[1] + + bInd * inStride[0]; + var bottomLeft = imageVals[ind]; + ind = c + rightInd * inStride[2] + bottomInd * inStride[1] + + bInd * inStride[0]; + var bottomRight = imageVals[ind]; + var top = topLeft + (topRight - topLeft) * xLerp; + var bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp; + ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = top + ((bottom - top) * yLerp); + } + } + } + else { // method == "nearest" + for (var x = 0; x < cropWidth; ++x) { + var xInd = (cropWidth > 1) ? + x1 * (imageWidth - 1) + x * widthScale : + 0.5 * (x1 + x2) * (imageWidth - 1); + if (xInd < 0 || xInd > imageWidth - 1) { + for (var c = 0; c < numChannels; c++) { + var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[ind] = extrapolationValue; + } + continue; + } + var closestX = Math.round(xInd); + var closestY = Math.round(yInd); + for (var c = 0; c < numChannels; c++) { + var inInd = c + closestX * inStride[2] + closestY * inStride[1] + + bInd * inStride[0]; + var outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0]; + output.values[outInd] = imageVals[inInd]; + } + } + } + } + } + return backend.makeTensorInfo(output.shape, output.dtype, output.values); + } + var cropAndResizeConfig = { + kernelName: tfjsCore.CropAndResize, + backendName: 'cpu', + kernelFunc: cropAndResize + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function cumsum(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, exclusive = attrs.exclusive, reverse = attrs.reverse; + assertNotComplex(x, 'cumsum'); + var permutation = tfjsCore.backend_util.getAxesPermutation([axis], x.shape.length); + var $x = x; + if (permutation != null) { + $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutation } }); + } + var permutedAxis = tfjsCore.backend_util.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)); + } + var resultDtype = tfjsCore.upcastType($x.dtype, 'int32'); + var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape($x.shape), resultDtype); + var aVals = backend.data.get($x.dataId).values; + var finalDim = $x.shape[$x.shape.length - 1]; + var indexAdjuster = reverse ? + function (i, j) { return i + finalDim - j - 1; } : + function (i, j) { return i + j; }; + for (var i = 0; i < aVals.length; i += finalDim) { + for (var j = 0; j < finalDim; j++) { + var idx = indexAdjuster(i, j); + if (j === 0) { + vals[idx] = exclusive ? 0 : aVals[idx]; + } + else { + var prevIdx = indexAdjuster(i, j - 1); + vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] : + aVals[idx] + vals[prevIdx]; + } + } + } + var result = backend.makeTensorInfo($x.shape, resultDtype, vals); + if (permutation != null) { + var reversePermutation = tfjsCore.backend_util.getUndoAxesPermutation(permutation); + var reverseTransposedResult = transpose({ inputs: { x: result }, backend: backend, attrs: { perm: reversePermutation } }); + backend.disposeIntermediateTensorInfo(result); + backend.disposeIntermediateTensorInfo($x); + return reverseTransposedResult; + } + return result; + } + var cumsumConfig = { + kernelName: tfjsCore.Cumsum, + backendName: 'cpu', + kernelFunc: cumsum + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function denseBincount(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, weights = inputs.weights; + var size = attrs.size, binaryOutput = attrs.binaryOutput; + if (x.shape.length === 1) { + var xVals = backend.data.get(x.dataId).values; + var weightsVals = backend.data.get(weights.dataId).values; + var outVals = bincountImpl(xVals, weightsVals, weights.dtype, weights.shape, size); + return backend.makeTensorInfo([size], weights.dtype, outVals); + } + else if (x.shape.length === 2) { + var xBuf = backend.bufferSync(x); + var weightsBuf = backend.bufferSync(weights); + var outBuf = bincountReduceImpl(xBuf, weightsBuf, size, binaryOutput); + return backend.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: tfjsCore.DenseBincount, + backendName: 'cpu', + kernelFunc: denseBincount + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function depthToSpace(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var blockSize = attrs.blockSize, dataFormat = attrs.dataFormat; + tfjsCore.util.assert(dataFormat === 'NHWC', function () { return "Only NHWC dataFormat supported on CPU for depthToSpace. Got " + dataFormat; }); + var batchSize = x.shape[0]; + var inputHeight = x.shape[1]; + var inputWidth = x.shape[2]; + var inputDepth = x.shape[3]; + var outputHeight = inputHeight * blockSize; + var outputWidth = inputWidth * blockSize; + var outputDepth = inputDepth / (blockSize * blockSize); + var xValues = backend.data.get(x.dataId).values; + var result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth); + var outputIdx = 0; + for (var b = 0; b < batchSize; ++b) { + for (var h = 0; h < outputHeight; ++h) { + var inH = Math.floor(h / blockSize); + var offsetH = (h % blockSize); + for (var w = 0; w < outputWidth; ++w) { + var inW = Math.floor(w / blockSize); + var offsetW = (w % blockSize); + var offsetD = (offsetH * blockSize + offsetW) * outputDepth; + for (var d = 0; d < outputDepth; ++d) { + var inD = d + offsetD; + var inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b)); + result[outputIdx++] = xValues[inputIdx]; + } + } + } + } + return backend.makeTensorInfo([batchSize, outputHeight, outputWidth, outputDepth], x.dtype, result); + } + var depthToSpaceConfig = { + kernelName: tfjsCore.DepthToSpace, + backendName: 'cpu', + kernelFunc: depthToSpace + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function depthwiseConv2dNative(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, filter = inputs.filter; + var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode; + assertNotComplex([x, filter], 'depthwiseConv2DNative'); + var xStrides = tfjsCore.util.computeStrides(x.shape); + var filterStrides = tfjsCore.util.computeStrides(filter.shape); + var $dilations = dilations; + if ($dilations == null) { + $dilations = [1, 1]; + } + tfjsCore.util.assert(tfjsCore.backend_util.eitherStridesOrDilationsAreOne(strides, $dilations), function () { return 'Error in depthwiseConv2d: Either strides or dilations must be ' + + ("1. Got strides " + strides + " and dilations '" + $dilations + "'"); }); + var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filter.shape, strides, $dilations, pad, dimRoundingMode, true /* depthwise */); + var filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, dilationHeight = convInfo.dilationHeight, dilationWidth = convInfo.dilationWidth, padInfo = convInfo.padInfo; + var padLeft = padInfo.left; + var padTop = padInfo.top; + var chMul = convInfo.outChannels / convInfo.inChannels; + var y = new tfjsCore.TensorBuffer(convInfo.outShape, x.dtype); + var xVals = backend.data.get(x.dataId).values; + var wVals = backend.data.get(filter.dataId).values; + var yVals = y.values; + for (var b = 0; b < convInfo.batchSize; ++b) { + var xOffset1 = b * xStrides[0]; + var yOffset1 = b * y.strides[0]; + for (var yR = 0; yR < convInfo.outHeight; ++yR) { + var yOffset2 = yOffset1 + yR * y.strides[1]; + var xRCorner = yR * convInfo.strideHeight - padTop; + for (var wR = 0; wR < filterHeight; ++wR) { + var xR = xRCorner + wR * dilationHeight; + if (xR < 0 || xR >= convInfo.inHeight) { + continue; + } + var wOffset1 = wR * filterStrides[0]; + var xOffset2 = xOffset1 + xR * xStrides[1]; + for (var yC = 0; yC < convInfo.outWidth; ++yC) { + var yOffset3 = yOffset2 + yC * y.strides[2]; + var xCCorner = yC * convInfo.strideWidth - padLeft; + for (var wC = 0; wC < filterWidth; ++wC) { + var xC = xCCorner + wC * dilationWidth; + if (xC < 0 || xC >= convInfo.inWidth) { + continue; + } + var wOffset2 = wOffset1 + wC * filterStrides[1]; + var xOffset3 = xOffset2 + xC * convInfo.inChannels; + var yOffset4 = yOffset3; + var wOffset3 = wOffset2; + for (var d1 = 0; d1 < convInfo.inChannels; ++d1) { + var xVal = xVals[xOffset3 + d1]; + for (var q = 0; q < chMul; ++q) { + yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q]; + } + yOffset4 += chMul; + wOffset3 += chMul; + } + } + } + } + } + } + return backend.makeTensorInfo(y.shape, y.dtype, y.values); + } + var depthwiseConv2dNativeConfig = { + kernelName: tfjsCore.DepthwiseConv2dNative, + backendName: 'cpu', + kernelFunc: depthwiseConv2dNative + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function depthwiseConv2dNativeBackpropFilter(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, dy = inputs.dy; + var strides = attrs.strides, dilations = attrs.dilations, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, filterShape = attrs.filterShape; + assertNotComplex([x, dy], 'depthwiseConv2dNativeBackpropFilter'); + var convInfo = tfjsCore.backend_util.computeConv2DInfo(x.shape, filterShape, strides, dilations, pad, dimRoundingMode, true /* depthwise */); + var strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth; + var dW = new tfjsCore.TensorBuffer(convInfo.filterShape, 'float32'); + var leftPad = convInfo.padInfo.left; + var topPad = convInfo.padInfo.top; + var chMul = convInfo.outChannels / convInfo.inChannels; + var xVals = backend.data.get(x.dataId).values; + var xBuf = new tfjsCore.TensorBuffer(x.shape, x.dtype, xVals); + var dyVals = backend.data.get(dy.dataId).values; + var dyBuf = new tfjsCore.TensorBuffer(dy.shape, dy.dtype, dyVals); + for (var wR = 0; wR < filterHeight; ++wR) { + var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight)); + var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight); + for (var wC = 0; wC < filterWidth; ++wC) { + var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth)); + var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth); + for (var d2 = 0; d2 < convInfo.outChannels; ++d2) { + var d1 = Math.trunc(d2 / chMul); + var dm = d2 % chMul; + var dotProd = 0; + for (var b = 0; b < convInfo.batchSize; ++b) { + for (var yR = yRMin; yR < yRMax; ++yR) { + var xR = wR + yR * strideHeight - topPad; + for (var yC = yCMin; yC < yCMax; ++yC) { + var 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 backend.makeTensorInfo(dW.shape, dW.dtype, dW.values); + } + var depthwiseConv2dNativeBackpropFilterConfig = { + kernelName: tfjsCore.DepthwiseConv2dNativeBackpropFilter, + backendName: 'cpu', + kernelFunc: depthwiseConv2dNativeBackpropFilter + }; + + function depthwiseConv2dNativeBackpropInput(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, filter = inputs.filter; + var strides = attrs.strides, dilations = attrs.dilations, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, inputShape = attrs.inputShape; + assertNotComplex([dy, filter], 'depthwiseConv2DNativeBackpropInput'); + var dyStrides = tfjsCore.util.computeStrides(dy.shape); + var filterStrides = tfjsCore.util.computeStrides(filter.shape); + var convInfo = tfjsCore.backend_util.computeConv2DInfo(inputShape, filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */); + var dx = new tfjsCore.TensorBuffer(convInfo.inShape, 'float32'); + var dxValues = dx.values; + var _a = __read(dx.strides, 3), dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2]; + var dyValues = backend.data.get(dy.dataId).values; + var _b = __read(dyStrides, 3), dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2]; + var fltValues = backend.data.get(filter.dataId).values; + var _c = __read(filterStrides, 3), fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2]; + var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth; + var topPad = filterHeight - 1 - convInfo.padInfo.top; + var leftPad = filterWidth - 1 - convInfo.padInfo.left; + var chMul = outChannels / inChannels; + for (var b = 0; b < batchSize; ++b) { + for (var d1 = 0; d1 < inChannels; ++d1) { + for (var xR = 0; xR < inHeight; ++xR) { + var xRCorner = xR - topPad; + var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight)); + var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight); + for (var xC = 0; xC < inWidth; ++xC) { + var xCCorner = xC - leftPad; + var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth)); + var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth); + var dotProd = 0; + for (var yR = xRMin; yR < yRMax; ++yR) { + var wR = yR * strideHeight - xRCorner; + for (var yC = xCMin; yC < yCMax; ++yC) { + var wC = yC * strideWidth - xCCorner; + var dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC; + var fltOffset = fltS0 * (filterHeight - 1 - wR) + + fltS1 * (filterWidth - 1 - wC) + fltS2 * d1; + for (var dm = 0; dm < chMul; ++dm) { + var d2 = d1 * chMul + dm; + var pixel = dyValues[dyOffset + d2]; + var weight = fltValues[fltOffset + dm]; + dotProd += pixel * weight; + } + } + } + dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd; + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var depthwiseConv2dNativeBackpropInputConfig = { + kernelName: tfjsCore.DepthwiseConv2dNativeBackpropInput, + backendName: 'cpu', + kernelFunc: depthwiseConv2dNativeBackpropInput + }; + + function diag(args) { + var inputs = args.inputs, backend = args.backend; + var x = inputs.x; + var xSize = tfjsCore.util.sizeFromShape(x.shape); + var xVals = backend.data.get(x.dataId).values; + var outBuf = tfjsCore.buffer([xSize, xSize], x.dtype); + var vals = outBuf.values; + for (var i = 0; i < xVals.length; i++) { + vals[i * xSize + i] = xVals[i]; + } + var outShape = __spread(x.shape, x.shape); + return backend.makeTensorInfo(outShape, outBuf.dtype, outBuf.values); + } + var diagConfig = { + kernelName: tfjsCore.Diag, + backendName: 'cpu', + kernelFunc: diag + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var dilation2dConfig = { + kernelName: tfjsCore.Dilation2D, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs; + var x = inputs.x, filter = inputs.filter; + var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations; + var cpuBackend = backend; + var xVals = cpuBackend.data.get(x.dataId).values; + var xRank = x.shape.length; + var filterVals = cpuBackend.data.get(filter.dataId).values; + var filterRank = filter.shape.length; + var _b = tfjsCore.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations), batchSize = _b.batchSize, inHeight = _b.inHeight, inWidth = _b.inWidth, inChannels = _b.inChannels, outHeight = _b.outHeight, outWidth = _b.outWidth, padInfo = _b.padInfo, strideHeight = _b.strideHeight, strideWidth = _b.strideWidth, filterHeight = _b.filterHeight, filterWidth = _b.filterWidth, dilationHeight = _b.dilationHeight, dilationWidth = _b.dilationWidth, outShape = _b.outShape; + var outSize = tfjsCore.util.sizeFromShape(outShape); + var outRank = outShape.length; + var outputVals = tfjsCore.util.getArrayFromDType(x.dtype, outSize); + // Upsampling the input by fill in `dilation size - 1` values between each + // input value. + // This implementation follows the TF c++ implementation: + // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc + for (var b = 0; b < batchSize; ++b) { + for (var hOut = 0; hOut < outHeight; ++hOut) { + var hBeg = hOut * strideHeight - padInfo.top; + for (var wOut = 0; wOut < outWidth; ++wOut) { + var wBeg = wOut * strideWidth - padInfo.left; + for (var d = 0; d < inChannels; ++d) { + var curVal = Number.MIN_SAFE_INTEGER; + for (var h = 0; h < filterHeight; ++h) { + var hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (var w = 0; w < filterWidth; ++w) { + var wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + var xIndex = tfjsCore.util.locToIndex([b, hIn, wIn, d], xRank, tfjsCore.util.computeStrides(x.shape)); + var filterIndex = tfjsCore.util.locToIndex([h, w, d], filterRank, tfjsCore.util.computeStrides(filter.shape)); + var val = xVals[xIndex] + filterVals[filterIndex]; + if (val > curVal) { + curVal = val; + } + } + } + } + } + var outputIndex = tfjsCore.util.locToIndex([b, hOut, wOut, d], outRank, tfjsCore.util.computeStrides(outShape)); + outputVals[outputIndex] = curVal; + } + } + } + } + var dataId = cpuBackend.write(tfjsCore.util.toTypedArray(outputVals, x.dtype), outShape, x.dtype); + return { dataId: dataId, shape: outShape, dtype: x.dtype }; + } + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var dilation2dBackpropFilterConfig = { + kernelName: tfjsCore.Dilation2DBackpropFilter, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs; + var x = inputs.x, filter = inputs.filter, dy = inputs.dy; + var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations; + var cpuBackend = backend; + var $x = tfjsCore.util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); + var $filter = tfjsCore.util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); + var _b = tfjsCore.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations), batchSize = _b.batchSize, inHeight = _b.inHeight, inWidth = _b.inWidth, inChannels = _b.inChannels, outHeight = _b.outHeight, outWidth = _b.outWidth, padInfo = _b.padInfo, strideHeight = _b.strideHeight, strideWidth = _b.strideWidth, filterHeight = _b.filterHeight, filterWidth = _b.filterWidth, dilationHeight = _b.dilationHeight, dilationWidth = _b.dilationWidth, outShape = _b.outShape; + tfjsCore.util.assert(dy.rank === outShape.length, function () { return "Error in " + tfjsCore.Dilation2DBackpropFilter + ", dy " + + ("must have the same rank as output " + outShape.length + ", but got ") + + ("" + dy.rank); }); + var $dy = tfjsCore.util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); + // The computed filter gradients has the same dimensions as the filter: + // [filterHeight, filterWidth, depth] + var gradients = tfjsCore.util.makeZerosNestedTypedArray(filter.shape, filter.dtype); + // In the case of multiple argmax branches, we only back-propagate along the + // last branch, i.e., the one with largest value of `h * filter_cols + w`, + // similarly to the max-pooling backward routines. + // This implementation follows the TF c++ implementation: + // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc + for (var b = 0; b < batchSize; ++b) { + for (var hOut = 0; hOut < outHeight; ++hOut) { + var hBeg = hOut * strideHeight - padInfo.top; + for (var wOut = 0; wOut < outWidth; ++wOut) { + var wBeg = wOut * strideWidth - padInfo.left; + for (var d = 0; d < inChannels; ++d) { + var curVal = Number.MIN_SAFE_INTEGER; + var hMax = 0; + var wMax = 0; + for (var h = 0; h < filterHeight; ++h) { + var hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (var w = 0; w < filterWidth; ++w) { + var wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + var 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]; + } + } + } + } + var dataId = cpuBackend.write(tfjsCore.util.toTypedArray(gradients, x.dtype), filter.shape, filter.dtype); + return { dataId: dataId, shape: filter.shape, dtype: filter.dtype }; + } + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var dilation2dBackpropInputConfig = { + kernelName: tfjsCore.Dilation2DBackpropInput, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs; + var x = inputs.x, filter = inputs.filter, dy = inputs.dy; + var strides = attrs.strides, pad = attrs.pad, dilations = attrs.dilations; + var cpuBackend = backend; + var $x = tfjsCore.util.toNestedArray(x.shape, cpuBackend.data.get(x.dataId).values); + var $filter = tfjsCore.util.toNestedArray(filter.shape, cpuBackend.data.get(filter.dataId).values); + var _b = tfjsCore.backend_util.computeDilation2DInfo(x.shape, filter.shape, strides, pad, 'NHWC' /* dataFormat */, dilations), batchSize = _b.batchSize, inHeight = _b.inHeight, inWidth = _b.inWidth, inChannels = _b.inChannels, outHeight = _b.outHeight, outWidth = _b.outWidth, padInfo = _b.padInfo, strideHeight = _b.strideHeight, strideWidth = _b.strideWidth, filterHeight = _b.filterHeight, filterWidth = _b.filterWidth, dilationHeight = _b.dilationHeight, dilationWidth = _b.dilationWidth, outShape = _b.outShape; + tfjsCore.util.assert(dy.rank === outShape.length, function () { return "Error in " + tfjsCore.Dilation2DBackpropInput + ", dy " + + ("must have the same rank as output " + outShape.length + ", but got ") + + ("" + dy.rank); }); + var $dy = tfjsCore.util.toNestedArray(outShape, cpuBackend.data.get(dy.dataId).values); + // The computed gradients has the same dimensions as the input: + // [batch, inputHeight, inputCols, inChannel] + var gradients = tfjsCore.util.makeZerosNestedTypedArray(x.shape, x.dtype); + // In the case of multiple argmax branches, we only back-propagate along the + // last branch, i.e., the one with largest value of `h * filter_cols + w`, + // similarly to the max-pooling backward routines. + // This implementation follows the TF c++ implementation: + // https://github.com/tensorflow/tensorflow/blob/d9a3a849edc198e90172bc58eb293de457f9d986/tensorflow/core/kernels/dilation_ops.cc + for (var b = 0; b < batchSize; ++b) { + for (var hOut = 0; hOut < outHeight; ++hOut) { + var hBeg = hOut * strideHeight - padInfo.top; + for (var wOut = 0; wOut < outWidth; ++wOut) { + var wBeg = wOut * strideWidth - padInfo.left; + for (var d = 0; d < inChannels; ++d) { + var curVal = Number.MIN_SAFE_INTEGER; + var hInMax = (hBeg < 0) ? 0 : hBeg; + var wInMax = (wBeg < 0) ? 0 : wBeg; + for (var h = 0; h < filterHeight; ++h) { + var hIn = hBeg + h * dilationHeight; + if (hIn >= 0 && hIn < inHeight) { + for (var w = 0; w < filterWidth; ++w) { + var wIn = wBeg + w * dilationWidth; + if (wIn >= 0 && wIn < inWidth) { + var 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]; + } + } + } + } + var dataId = cpuBackend.write(tfjsCore.util.toTypedArray(gradients, x.dtype), x.shape, x.dtype); + return { dataId: dataId, shape: x.shape, dtype: x.dtype }; + } + }; + + function sum(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, keepDims = attrs.keepDims; + assertNotComplex(x, 'sum'); + var $x; + if (x.dtype === 'bool') { + $x = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'int32' } }); + } + else { + $x = identity({ inputs: { x: x }, backend: backend }); + } + var xRank = $x.shape.length; + var axes = tfjsCore.util.parseAxisParam(axis, $x.shape); + var permutation = tfjsCore.backend_util.getAxesPermutation(axes, xRank); + var reductionAxes = axes; + var permutedX = $x; + if (permutation != null) { + permutedX = + transpose({ inputs: { x: $x }, backend: backend, attrs: { perm: permutation } }); + reductionAxes = tfjsCore.backend_util.getInnerMostAxes(reductionAxes.length, xRank); + } + tfjsCore.backend_util.assertAxesAreInnerMostDims('sum', reductionAxes, permutedX.shape.length); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes(permutedX.shape, reductionAxes), 2), outShape = _a[0], reduceShape = _a[1]; + var resultDtype = tfjsCore.backend_util.upcastType(permutedX.dtype, 'int32'); + var result = zeros(backend, outShape, resultDtype); + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var vals = backend.data.get(result.dataId).values; + var aVals = backend.data.get(permutedX.dataId).values; + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var sum_1 = 0; + for (var j = 0; j < reduceSize; ++j) { + sum_1 += aVals[offset + j]; + } + vals[i] = sum_1; + } + if (keepDims) { + var newShape = tfjsCore.backend_util.expandShapeToKeepDim(result.shape, axes); + var oldResult = result; + result = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: newShape } }); + backend.disposeIntermediateTensorInfo(oldResult); + } + backend.disposeIntermediateTensorInfo($x); + if (permutation != null) { + backend.disposeIntermediateTensorInfo(permutedX); + } + return result; + } + var sumConfig = { + kernelName: tfjsCore.Sum, + backendName: 'cpu', + kernelFunc: sum + }; + + function einsum(args) { + var e_1, _a, e_2, _b; + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var equation = attrs.equation; + var tensors = inputs; + var _c = tfjsCore.backend_util.decodeEinsumEquation(equation, tensors.length), allDims = _c.allDims, summedDims = _c.summedDims, idDims = _c.idDims; + tfjsCore.backend_util.checkEinsumDimSizes(allDims.length, idDims, tensors); + var _d = tfjsCore.backend_util.getEinsumComputePath(summedDims, idDims), path = _d.path, steps = _d.steps; + var nSteps = steps.length; + var out = null; + var numDimsRemaining = allDims.length; + var tensorsToDispose = []; + for (var i = 0; i < nSteps; ++i) { + try { + for (var _e = (e_1 = void 0, __values(steps[i])), _f = _e.next(); !_f.done; _f = _e.next()) { + var idTerm = _f.value; + var _g = tfjsCore.backend_util.getEinsumPermutation(numDimsRemaining, idDims[idTerm]), perm = _g.permutationIndices, dimsToExpand = _g.expandDims; + var x = void 0; + if (tfjsCore.backend_util.isIdentityPermutation(perm)) { + x = tensors[idTerm]; + } + else { + x = transpose({ inputs: { x: tensors[idTerm] }, backend: backend, attrs: { perm: perm } }); + tensorsToDispose.push(x); + } + var targetShape = x.shape.slice(); + for (var k = 0; k < dimsToExpand.length; ++k) { + targetShape.splice(dimsToExpand[k], 0, 1); + } + if (!tfjsCore.util.arraysEqual(x.shape, targetShape)) { + x = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: targetShape } }); + tensorsToDispose.push(x); + } + if (out === null) { + out = x; + } + else { + // tslint:disable-next-line: no-unnecessary-type-assertion + out = multiply({ inputs: { a: x, b: out }, backend: backend }); + tensorsToDispose.push(out); + } + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (_f && !_f.done && (_a = _e.return)) _a.call(_e); + } + finally { if (e_1) throw e_1.error; } + } + if (i < nSteps - 1) { + if (path[i] >= 0) { + out = sum({ + inputs: { x: out }, + backend: backend, + attrs: { + axis: path[i] - (allDims.length - numDimsRemaining), + keepDims: false + } + }); + tensorsToDispose.push(out); + } + numDimsRemaining--; + } + } + try { + // Clean up intermediate tensors. + for (var tensorsToDispose_1 = __values(tensorsToDispose), tensorsToDispose_1_1 = tensorsToDispose_1.next(); !tensorsToDispose_1_1.done; tensorsToDispose_1_1 = tensorsToDispose_1.next()) { + var tensorInfo = tensorsToDispose_1_1.value; + if (tensorInfo === out) { + continue; + } + backend.disposeIntermediateTensorInfo(tensorInfo); + } + } + catch (e_2_1) { e_2 = { error: e_2_1 }; } + finally { + try { + if (tensorsToDispose_1_1 && !tensorsToDispose_1_1.done && (_b = tensorsToDispose_1.return)) _b.call(tensorsToDispose_1); + } + finally { if (e_2) throw e_2.error; } + } + return out; + } + var einsumConfig = { + kernelName: tfjsCore.Einsum, + backendName: 'cpu', + kernelFunc: einsum + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function eluGrad(args) { + var inputs = args.inputs, backend = args.backend; + var dy = inputs.dy, y = inputs.y; + assertNotComplex([dy, y], 'eluGrad'); + var resultValues = new Float32Array(tfjsCore.util.sizeFromShape(y.shape)); + var values = backend.data.get(y.dataId).values; + var dyValues = backend.data.get(dy.dataId).values; + for (var i = 0; i < values.length; ++i) { + var v = values[i]; + if (v >= 1) { + resultValues[i] = dyValues[i]; + } + else { + resultValues[i] = dyValues[i] * (v + 1); + } + } + return backend.makeTensorInfo(y.shape, 'float32', resultValues); + } + var eluGradConfig = { + kernelName: tfjsCore.EluGrad, + backendName: 'cpu', + kernelFunc: eluGrad + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var p = tfjsCore.backend_util.ERF_P; + var a1 = tfjsCore.backend_util.ERF_A1; + var a2 = tfjsCore.backend_util.ERF_A2; + var a3 = tfjsCore.backend_util.ERF_A3; + var a4 = tfjsCore.backend_util.ERF_A4; + var a5 = tfjsCore.backend_util.ERF_A5; + var erf = unaryKernelFunc(tfjsCore.Erf, function (xi) { + var sign = Math.sign(xi); + var v = Math.abs(xi); + var t = 1.0 / (1.0 + p * v); + return sign * + (1.0 - + (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * + Math.exp(-v * v)); + }); + var erfConfig = { + kernelName: tfjsCore.Erf, + backendName: 'cpu', + kernelFunc: erf, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function expandDims(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var input = inputs.input; + var dim = attrs.dim; + var inputRank = input.shape.length; + var newShape = input.shape.slice(); + var $dim = dim; + if (dim < 0) { + // Negative value is counted from the tail of rank. + tfjsCore.util.assert(-(inputRank + 1) <= dim, function () { return "Axis must be in the interval [" + -(inputRank + 1) + ", " + inputRank + "]"; }); + $dim = inputRank + dim + 1; + } + newShape.splice($dim, 0, 1); + return reshape({ inputs: { x: input }, backend: backend, attrs: { shape: newShape } }); + } + var expandDimsConfig = { + kernelName: tfjsCore.ExpandDims, + backendName: 'cpu', + kernelFunc: expandDims + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var realDivImpl = createSimpleBinaryKernelImpl(function (a, b) { return a / b; }); + var div = binaryKernelFunc(tfjsCore.RealDiv, realDivImpl); + var realDivConfig = { + kernelName: tfjsCore.RealDiv, + backendName: 'cpu', + kernelFunc: div + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Calculate FFT of inner most elements of batch tensor. + */ + function fftBatch(input, inverse, cpuBackend) { + var inputShape = input.shape; + var batch = inputShape[0]; + var innerDim = inputShape[1]; + var inputVals = cpuBackend.data.get(input.dataId); + var real2D = inputVals.complexTensorInfos.real; + var imag2D = inputVals.complexTensorInfos.imag; + // Collects real and imaginary values separately. + var resultShape = [batch, innerDim]; + var resultSize = tfjsCore.util.sizeFromShape(resultShape); + var resultReal = tfjsCore.util.getTypedArrayFromDType('float32', resultSize); + var resultImag = tfjsCore.util.getTypedArrayFromDType('float32', resultSize); + for (var b = 0; b < batch; b++) { + // TODO: Support slice ops for complex type. + var r = slice({ + inputs: { x: real2D }, + backend: cpuBackend, + attrs: { begin: [b, 0], size: [1, innerDim] } + }); + var i = slice({ + inputs: { x: imag2D }, + backend: cpuBackend, + attrs: { begin: [b, 0], size: [1, innerDim] } + }); + var input_1 = complex({ inputs: { real: r, imag: i }, backend: cpuBackend }); + // Run FFT by batch element. + var _a = fftImpl(input_1, inverse, cpuBackend), real_1 = _a.real, imag_1 = _a.imag; + var res = tfjsCore.backend_util.mergeRealAndImagArrays(real_1, imag_1); + for (var d = 0; d < innerDim; d++) { + var c = tfjsCore.backend_util.getComplexWithIndex(res, d); + resultReal[b * innerDim + d] = c.real; + resultImag[b * innerDim + d] = c.imag; + } + cpuBackend.disposeIntermediateTensorInfo(r); + cpuBackend.disposeIntermediateTensorInfo(i); + cpuBackend.disposeIntermediateTensorInfo(input_1); + } + var $realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultReal); + var $imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', resultImag); + var result = complex({ inputs: { real: $realInfo, imag: $imagInfo }, backend: cpuBackend }); + cpuBackend.disposeIntermediateTensorInfo($realInfo); + cpuBackend.disposeIntermediateTensorInfo($imagInfo); + return result; + } + function fftImpl(input, inverse, cpuBackend) { + var inputSize = tfjsCore.util.sizeFromShape(input.shape); + var inputVals = cpuBackend.data.get(input.dataId); + var realVals = cpuBackend.data.get(inputVals.complexTensorInfos.real.dataId).values; + var imagVals = cpuBackend.data.get(inputVals.complexTensorInfos.imag.dataId).values; + if (isExponentOf2(inputSize)) { + var result = fftRadix2(realVals, imagVals, inputSize, inverse, cpuBackend); + var resultShape = [input.shape[0], input.shape[1]]; + if (inverse) { + var realInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.real); + var imagInfo = cpuBackend.makeTensorInfo(resultShape, 'float32', result.imag); + var sizeInfo = cpuBackend.makeTensorInfo([], 'float32', tfjsCore.util.createScalarValue(inputSize, 'float32')); + var sizeInfoCopy = identity({ inputs: { x: sizeInfo }, backend: cpuBackend }); + var divRealInfo = realDivConfig.kernelFunc({ inputs: { a: realInfo, b: sizeInfo }, backend: cpuBackend }); + var divImagInfo = realDivConfig.kernelFunc({ inputs: { a: imagInfo, b: sizeInfoCopy }, backend: cpuBackend }); + var divRealVals = cpuBackend.data.get(divRealInfo.dataId).values; + var 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 { + var data = tfjsCore.backend_util.mergeRealAndImagArrays(realVals, imagVals); + var rawOutput = fourierTransformByMatmul(data, inputSize, inverse); + return tfjsCore.backend_util.splitRealAndImagArrays(rawOutput); + } + } + function isExponentOf2(size) { + return (size & size - 1) === 0; + } + // FFT using Cooley-Tukey algorithm on radix 2 dimensional input. + function fftRadix2(realVals, imagVals, size, inverse, cpuBackend) { + if (size === 1) { + return { real: realVals, imag: imagVals }; + } + var data = tfjsCore.backend_util.mergeRealAndImagArrays(realVals, imagVals); + var half = size / 2; + var evenComplex = tfjsCore.backend_util.complexWithEvenIndex(data); + var evenRealVals = evenComplex.real; + var evenImagVals = evenComplex.imag; + var evenShape = [evenRealVals.length]; + var evenRealInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenRealVals); + var evenImagInfo = cpuBackend.makeTensorInfo(evenShape, 'float32', evenImagVals); + var evenTensorInfo = complex({ inputs: { real: evenRealInfo, imag: evenImagInfo }, backend: cpuBackend }); + var oddComplex = tfjsCore.backend_util.complexWithOddIndex(data); + var oddRealVals = oddComplex.real; + var oddImagVals = oddComplex.imag; + var oddShape = [oddRealVals.length]; + var oddRealInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddRealVals); + var oddImagInfo = cpuBackend.makeTensorInfo(oddShape, 'float32', oddImagVals); + var oddTensorInfo = complex({ inputs: { real: oddRealInfo, imag: oddImagInfo }, backend: cpuBackend }); + // Recursive call for half part of original input. + var $evenComplex = fftRadix2(evenRealVals, evenImagVals, half, inverse, cpuBackend); + var $evenRealVals = $evenComplex.real; + var $evenImagVals = $evenComplex.imag; + var $evenShape = [$evenRealVals.length]; + var $evenRealInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenRealVals); + var $evenImagInfo = cpuBackend.makeTensorInfo($evenShape, 'float32', $evenImagVals); + var $evenTensorInfo = complex({ + inputs: { real: $evenRealInfo, imag: $evenImagInfo }, + backend: cpuBackend + }); + var $oddComplex = fftRadix2(oddRealVals, oddImagVals, half, inverse, cpuBackend); + var $oddRealVals = $oddComplex.real; + var $oddImagVals = $oddComplex.imag; + var $oddShape = [$oddRealVals.length]; + var $oddRealInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddRealVals); + var $oddImagInfo = cpuBackend.makeTensorInfo($oddShape, 'float32', $oddImagVals); + var $oddTensorInfo = complex({ inputs: { real: $oddRealInfo, imag: $oddImagInfo }, backend: cpuBackend }); + var e = tfjsCore.backend_util.exponents(size, inverse); + var eShape = [e.real.length]; + var eRealInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.real); + var eImagInfo = cpuBackend.makeTensorInfo(eShape, 'float32', e.imag); + var complexInfo = complex({ inputs: { real: eRealInfo, imag: eImagInfo }, backend: cpuBackend }); + var exponentInfo = multiply({ inputs: { a: complexInfo, b: $oddTensorInfo }, backend: cpuBackend }); + var addPart = add({ + inputs: { a: $evenTensorInfo, b: exponentInfo }, + backend: cpuBackend + }); + var subPart = sub({ + inputs: { a: $evenTensorInfo, b: exponentInfo }, + backend: cpuBackend + }); + var addPartReal = real({ inputs: { input: addPart }, backend: cpuBackend }); + var subPartReal = real({ inputs: { input: subPart }, backend: cpuBackend }); + var addPartImag = imag({ inputs: { input: addPart }, backend: cpuBackend }); + var subPartImag = imag({ inputs: { input: subPart }, backend: cpuBackend }); + var $real = concat({ + inputs: [addPartReal, subPartReal], + backend: cpuBackend, + attrs: { axis: 0 } + }); + var $imag = concat({ + inputs: [addPartImag, subPartImag], + backend: cpuBackend, + attrs: { axis: 0 } + }); + var $realVals = cpuBackend.data.get($real.dataId).values; + var $imagVals = cpuBackend.data.get($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 }; + } + // Calculate fourier transform by multplying sinusoid matrix. + function fourierTransformByMatmul(data, size, inverse) { + var ret = new Float32Array(size * 2); + // TODO: Use matmul instead once it supports complex64 type. + for (var r = 0; r < size; r++) { + var real_2 = 0.0; + var imag_2 = 0.0; + for (var c = 0; c < size; c++) { + var e = tfjsCore.backend_util.exponent(r * c, size, inverse); + var term = tfjsCore.backend_util.getComplexWithIndex(data, c); + real_2 += term.real * e.real - term.imag * e.imag; + imag_2 += term.real * e.imag + term.imag * e.real; + } + if (inverse) { + real_2 /= size; + imag_2 /= size; + } + tfjsCore.backend_util.assignToTypedArray(ret, real_2, imag_2, r); + } + return ret; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function fft(args) { + var inputs = args.inputs, backend = args.backend; + var input = inputs.input; + var inputSize = tfjsCore.util.sizeFromShape(input.shape); + // Collapse all outer dimensions to a single batch dimension. + var innerDimensionSize = input.shape[input.shape.length - 1]; + var batch = inputSize / innerDimensionSize; + var input2D = reshape({ + inputs: { x: input }, + backend: backend, + attrs: { shape: [batch, innerDimensionSize] } + }); + var result = fftBatch(input2D, false, backend); + var resultReshaped = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: input.shape } }); + backend.disposeIntermediateTensorInfo(input2D); + backend.disposeIntermediateTensorInfo(result); + return resultReshaped; + } + var fftConfig = { + kernelName: tfjsCore.FFT, + backendName: 'cpu', + kernelFunc: fft + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function fill(args) { + var backend = args.backend, attrs = args.attrs; + var shape = attrs.shape, value = attrs.value, dtype = attrs.dtype; + var $dtype = dtype || tfjsCore.util.inferDtype(value); + var values = tfjsCore.util.getArrayFromDType($dtype, tfjsCore.util.sizeFromShape(shape)); + fillValues(values, value, $dtype); + return backend.makeTensorInfo(shape, $dtype, values); + } + var fillConfig = { + kernelName: tfjsCore.Fill, + backendName: 'cpu', + kernelFunc: fill + }; + function fillValues(values, value, dtype) { + if (dtype === 'string') { + values.fill(value); + } + else { + values.fill(value); + } + } + + var flipLeftRightConfig = { + kernelName: tfjsCore.FlipLeftRight, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs; _a.attrs; var backend = _a.backend; + var image = inputs.image; + var cpuBackend = backend; + var output = tfjsCore.util.getTypedArrayFromDType(image.dtype, tfjsCore.util.sizeFromShape(image.shape)); + var _b = __read(image.shape, 4), batch = _b[0], imageHeight = _b[1], imageWidth = _b[2], numChannels = _b[3]; + var imageVals = cpuBackend.data.get(image.dataId).values; + for (var batchIdx = 0; batchIdx < batch; batchIdx++) { + var batchOffset = batchIdx * imageWidth * imageHeight * numChannels; + for (var row = 0; row < imageHeight; row++) { + var rowOffset = row * (imageWidth * numChannels); + for (var col = 0; col < imageWidth; col++) { + var colOffset = col * numChannels; + for (var channel = 0; channel < numChannels; channel++) { + var coordX = Math.round(imageWidth - col - 1); + var outIdx = batchOffset + rowOffset + colOffset + channel; + var outputValue = imageVals[outIdx]; + // If the coordinate position falls within the image boundaries... + if (coordX >= 0 && coordX < imageWidth) { + // set the output to the image value at the coordinate position. + var rotatedColOffset = coordX * numChannels; + var imageIdx = batchOffset + rowOffset + rotatedColOffset + channel; + outputValue = imageVals[imageIdx]; + } + output[outIdx] = outputValue; + } + } + } + } + var dataId = cpuBackend.write(output, image.shape, image.dtype); + return { dataId: dataId, shape: image.shape, dtype: image.dtype }; + } + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var floorDivImpl = createSimpleBinaryKernelImpl(function (a, b) { return Math.floor(a / b); }); + var floorDiv = binaryKernelFunc(tfjsCore.FloorDiv, floorDivImpl, null /* complexImpl */, 'int32'); + var floorDivConfig = { + kernelName: tfjsCore.FloorDiv, + backendName: 'cpu', + kernelFunc: floorDiv + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function fusedConv2D(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, filter = inputs.filter, bias = inputs.bias, preluActivationWeights = inputs.preluActivationWeights; + var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha; + var result = conv2D({ + inputs: { x: x, filter: filter }, + backend: backend, + attrs: { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode } + }); + if (bias) { + var resultOld = result; + result = add({ inputs: { a: result, b: bias }, backend: backend }); + backend.disposeIntermediateTensorInfo(resultOld); + } + if (activation) { + var resultOld = result; + result = applyActivation(backend, result, activation, preluActivationWeights, leakyreluAlpha); + backend.disposeIntermediateTensorInfo(resultOld); + } + return result; + } + var fusedConv2DConfig = { + kernelName: tfjsCore.FusedConv2D, + backendName: 'cpu', + kernelFunc: fusedConv2D + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function fusedDepthwiseConv2D(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, filter = inputs.filter, bias = inputs.bias, preluActivationWeights = inputs.preluActivationWeights; + var strides = attrs.strides, pad = attrs.pad, dataFormat = attrs.dataFormat, dilations = attrs.dilations, dimRoundingMode = attrs.dimRoundingMode, activation = attrs.activation, leakyreluAlpha = attrs.leakyreluAlpha; + var result = depthwiseConv2dNative({ + inputs: { x: x, filter: filter }, + backend: backend, + attrs: { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode } + }); + if (bias) { + var oldResult = result; + result = add({ inputs: { a: result, b: bias }, backend: backend }); + backend.disposeIntermediateTensorInfo(oldResult); + } + if (activation) { + var oldResult = result; + result = applyActivation(backend, result, activation, preluActivationWeights, leakyreluAlpha); + backend.disposeIntermediateTensorInfo(oldResult); + } + return result; + } + var fusedDepthwiseConv2DConfig = { + kernelName: tfjsCore.FusedDepthwiseConv2D, + backendName: 'cpu', + kernelFunc: fusedDepthwiseConv2D + }; + + function gatherNd(args) { + var inputs = args.inputs, backend = args.backend; + var params = inputs.params, indices = inputs.indices; + var paramsSize = tfjsCore.util.sizeFromShape(params.shape); + var indicesShape = indices.shape; + var sliceRank = indicesShape[indicesShape.length - 1]; + var _a = __read(tfjsCore.backend_util.prepareAndValidate(params, indices), 4), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3]; + if (numSlices === 0) { + return backend.makeTensorInfo(resultShape, params.dtype, []); + } + var indicesData = backend.data.get(indices.dataId).values; + var paramsBuf = backend.bufferSync(params); + var outBuf = gatherNdImpl(indicesData, paramsBuf, params.dtype, numSlices, sliceRank, sliceSize, strides, params.shape, paramsSize); + return backend.makeTensorInfo(resultShape, params.dtype, outBuf.values); + } + var gatherNdConfig = { + kernelName: tfjsCore.GatherNd, + backendName: 'cpu', + kernelFunc: gatherNd + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function gatherV2(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, indices = inputs.indices; + var axis = attrs.axis, batchDims = attrs.batchDims; + assertNotComplex([x, indices], 'gatherV2'); + // Throw error when any index is out of bound. + var parsedAxis = tfjsCore.util.parseAxisParam(axis, x.shape)[0]; + var indicesVals = backend.data.get(indices.dataId).values; + var axisDim = x.shape[parsedAxis]; + var _loop_1 = function (i) { + var index = indicesVals[i]; + tfjsCore.util.assert(index <= axisDim - 1 && index >= 0, function () { return "GatherV2: the index value " + index + " is not in [0, " + (axisDim - 1) + "]"; }); + }; + for (var i = 0; i < indicesVals.length; ++i) { + _loop_1(i); + } + var $batchDims = batchDims; + if (batchDims == null) { + $batchDims = 0; + } + var indicesSize = tfjsCore.util.sizeFromShape(indices.shape); + var shapeInfo = tfjsCore.backend_util.segment_util.collectGatherOpShapeInfo(x, indices, parsedAxis, $batchDims); + var flattenX = reshape({ + inputs: { x: x }, + backend: backend, + attrs: { + shape: [ + shapeInfo.batchSize, shapeInfo.outerSize, shapeInfo.dimSize, + shapeInfo.sliceSize + ] + } + }); + var flattenIndex = reshape({ + inputs: { x: indices }, + backend: backend, + attrs: { shape: [shapeInfo.batchSize, indicesSize / shapeInfo.batchSize] } + }); + var flattenOutputShape = [ + shapeInfo.batchSize, shapeInfo.outerSize, indicesSize / shapeInfo.batchSize, + shapeInfo.sliceSize + ]; + var indicesBuf = backend.bufferSync(flattenIndex); + var xBuf = backend.bufferSync(flattenX); + var outBuf = gatherV2Impl(xBuf, indicesBuf, flattenOutputShape); + backend.disposeIntermediateTensorInfo(flattenX); + backend.disposeIntermediateTensorInfo(flattenIndex); + return backend.makeTensorInfo(shapeInfo.outputShape, outBuf.dtype, outBuf.values); + } + var gatherV2Config = { + kernelName: tfjsCore.GatherV2, + backendName: 'cpu', + kernelFunc: gatherV2 + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function ifft(args) { + var inputs = args.inputs, backend = args.backend; + var input = inputs.input; + var inputSize = tfjsCore.util.sizeFromShape(input.shape); + // Collapse all outer dimensions to a single batch dimension. + var innerDimensionSize = input.shape[input.shape.length - 1]; + var batch = inputSize / innerDimensionSize; + var input2D = reshape({ + inputs: { x: input }, + backend: backend, + attrs: { shape: [batch, innerDimensionSize] } + }); + var result = fftBatch(input2D, true, backend); + var resultReshaped = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: input.shape } }); + backend.disposeIntermediateTensorInfo(input2D); + backend.disposeIntermediateTensorInfo(result); + return resultReshaped; + } + var ifftConfig = { + kernelName: tfjsCore.IFFT, + backendName: 'cpu', + kernelFunc: ifft + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var isFinite = unaryKernelFunc(tfjsCore.IsFinite, function (xi) { return Number.isFinite(xi) ? 1 : 0; }, 'bool'); + var isFiniteConfig = { + kernelName: tfjsCore.IsFinite, + backendName: 'cpu', + kernelFunc: isFinite, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var isInf = unaryKernelFunc(tfjsCore.IsInf, function (xi) { return Math.abs(xi) === Infinity ? 1 : 0; }, 'bool'); + var isInfConfig = { + kernelName: tfjsCore.IsInf, + backendName: 'cpu', + kernelFunc: isInf, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var isNaN$1 = unaryKernelFunc(tfjsCore.IsNan, function (xi) { return Number.isNaN(xi) ? 1 : 0; }, 'bool'); + var isNaNConfig = { + kernelName: tfjsCore.IsNan, + backendName: 'cpu', + kernelFunc: isNaN$1, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function linSpace(args) { + var backend = args.backend, attrs = args.attrs; + var start = attrs.start, stop = attrs.stop, num = attrs.num; + var outVals = linSpaceImpl(start, stop, num); + return backend.makeTensorInfo([outVals.length], 'float32', outVals); + } + var linSpaceConfig = { + kernelName: tfjsCore.LinSpace, + backendName: 'cpu', + kernelFunc: linSpace + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var log1p = unaryKernelFunc(tfjsCore.Log1p, function (xi) { return Math.log1p(xi); }); + var log1pConfig = { + kernelName: tfjsCore.Log1p, + backendName: 'cpu', + kernelFunc: log1p, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var logicalAndImpl = createSimpleBinaryKernelImpl(function (a, b) { return a && b; }); + var logicalAnd = binaryKernelFunc(tfjsCore.LogicalAnd, logicalAndImpl, null /* complexImpl */, 'bool'); + var logicalAndConfig = { + kernelName: tfjsCore.LogicalAnd, + backendName: 'cpu', + kernelFunc: logicalAnd + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var logicalNot = unaryKernelFunc(tfjsCore.LogicalNot, function (xi) { return xi ? 0 : 1; }, 'bool'); + var logicalNotConfig = { + kernelName: tfjsCore.LogicalNot, + backendName: 'cpu', + kernelFunc: logicalNot, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var logicalOrImpl = createSimpleBinaryKernelImpl(function (a, b) { return a || b; }); + var logicalOr = binaryKernelFunc(tfjsCore.LogicalOr, logicalOrImpl, null /* complexImpl */, 'bool'); + var logicalOrConfig = { + kernelName: tfjsCore.LogicalOr, + backendName: 'cpu', + kernelFunc: logicalOr + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function lRN(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var depthRadius = attrs.depthRadius, bias = attrs.bias, alpha = attrs.alpha, beta = attrs.beta; + assertNotComplex(x, 'LRN'); + var channels = x.shape[3]; + var maxD = channels - 1; + var xValues = backend.data.get(x.dataId).values; + var size = tfjsCore.util.sizeFromShape(x.shape); + var result = new Float32Array(size); + function sumAcrossChannels(offset) { + var currentChannel = offset % channels; + var beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius); + var endSumOffset = offset - currentChannel + Math.min(currentChannel + depthRadius, maxD); + var sum = 0.0; + for (; beginSumOffset <= endSumOffset; beginSumOffset++) { + var z = xValues[beginSumOffset]; + sum += z * z; + } + return sum; + } + for (var offset = 0; offset < size; offset++) { + var sum = sumAcrossChannels(offset); + var val = xValues[offset] * Math.pow(bias + alpha * sum, -beta); + result[offset] = val; + } + return backend.makeTensorInfo(x.shape, x.dtype, result); + } + var lRNConfig = { + kernelName: tfjsCore.LRN, + backendName: 'cpu', + kernelFunc: lRN + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function lRNGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, y = inputs.y, dy = inputs.dy; + var depthRadius = attrs.depthRadius, bias = attrs.bias, alpha = attrs.alpha, beta = attrs.beta; + assertNotComplex(dy, 'LRNGrad'); + var dySize = tfjsCore.util.sizeFromShape(dy.shape); + var channels = dy.shape[3]; + var dyValues = backend.data.get(dy.dataId).values; + var xValues = backend.data.get(x.dataId).values; + var yValues = backend.data.get(y.dataId).values; + var result = new Float32Array(dySize); + var size = dySize; + for (var offset = 0; offset < size; offset++) { + var currentChannel = offset % channels; + var depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius); + var depthEnd = (offset - currentChannel) + + Math.min(channels, currentChannel + depthRadius + 1); + var norm = 0; + for (var k = depthBegin; k < depthEnd; k++) { + norm += Math.pow(xValues[k], 2); + } + norm = alpha * norm + bias; + for (var k = depthBegin; k < depthEnd; k++) { + var dyi = -2 * alpha * beta * xValues[k] * yValues[offset] / norm; + if (offset === k) { + dyi += Math.pow(norm, -beta); + } + dyi *= dyValues[offset]; + result[k] += dyi; + } + } + return backend.makeTensorInfo(dy.shape, x.dtype, result); + } + var lRNGradConfig = { + kernelName: tfjsCore.LRNGrad, + backendName: 'cpu', + kernelFunc: lRNGrad + }; + + function max(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var reductionIndices = attrs.reductionIndices, keepDims = attrs.keepDims; + var cpuBackend = backend; + var xShape = x.shape; + var xRank = xShape.length; + var origAxes = tfjsCore.util.parseAxisParam(reductionIndices, xShape); + var axes = origAxes; + var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, xRank); + var xVals = cpuBackend.data.get(x.dataId).values; + if (permutedAxes != null) { + var newShape = new Array(xRank); + for (var i = 0; i < newShape.length; i++) { + newShape[i] = xShape[permutedAxes[i]]; + } + xVals = transposeImpl(xVals, xShape, x.dtype, permutedAxes, newShape); + axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, xRank); + xShape = newShape; + } + assertNotComplex(x, 'max'); + tfjsCore.backend_util.assertAxesAreInnerMostDims('max', axes, xRank); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes(xShape, axes), 2), maxOutShape = _a[0], reduceShape = _a[1]; + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var result = maxImpl(xVals, reduceSize, maxOutShape, x.dtype); + var dataId = cpuBackend.write(result, maxOutShape, x.dtype); + var outShape = maxOutShape; + if (keepDims) { + // reshape + var newShape = tfjsCore.backend_util.expandShapeToKeepDim(maxOutShape, origAxes); + outShape = newShape; + } + return { dataId: dataId, shape: outShape, dtype: x.dtype }; + } + var maxConfig = { + kernelName: tfjsCore.Max, + backendName: 'cpu', + kernelFunc: max + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function maxPool(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + assertNotComplex(x, 'maxPool'); + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode; + var dilations = 1; + tfjsCore.util.assert(tfjsCore.backend_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + var convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, dilations, pad, dimRoundingMode); + var res; + if (convInfo.filterWidth === 1 && convInfo.filterHeight === 1 && + tfjsCore.util.arraysEqual(convInfo.inShape, convInfo.outShape)) { + res = identity({ inputs: { x: x }, backend: backend }); + } + else { + var xValues = backend.data.get(x.dataId).values; + var strides_1 = tfjsCore.util.computeStrides(x.shape); + var buffer = pool(xValues, x.shape, x.dtype, strides_1, convInfo, 'max'); + res = backend.makeTensorInfo(convInfo.outShape, x.dtype, buffer.values); + } + return res; + } + var maxPoolConfig = { + kernelName: tfjsCore.MaxPool, + backendName: 'cpu', + kernelFunc: maxPool + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function maxPool3D(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode, dataFormat = attrs.dataFormat; + assertNotComplex(x, 'maxPool3d'); + var convInfo = tfjsCore.backend_util.computePool3DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode, dataFormat); + var xValues = backend.data.get(x.dataId).values; + var outBuf = pool3d(xValues, x.shape, x.dtype, tfjsCore.util.computeStrides(x.shape), convInfo, 'max'); + return backend.makeTensorInfo(outBuf.shape, 'float32', outBuf.values); + } + var maxPool3DConfig = { + kernelName: tfjsCore.MaxPool3D, + backendName: 'cpu', + kernelFunc: maxPool3D + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function maxPool3DGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, input = inputs.input; + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode; + assertNotComplex([dy, input], 'maxPool3DGrad'); + var convInfo = tfjsCore.backend_util.computePool3DInfo(input.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode); + var inputBuf = backend.bufferSync(input); + var maxPosBuf = maxPool3dPositions(inputBuf, convInfo); + var strideDepth = convInfo.strideDepth; + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var dilationDepth = convInfo.dilationDepth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterDepth = convInfo.effectiveFilterDepth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padFront = effectiveFilterDepth - 1 - convInfo.padInfo.front; + var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + var dx = tfjsCore.buffer(input.shape, 'float32'); + var dyBuf = backend.bufferSync(dy); + for (var batch = 0; batch < convInfo.batchSize; ++batch) { + for (var channel = 0; channel < convInfo.inChannels; ++channel) { + for (var dxDepth = 0; dxDepth < convInfo.inDepth; ++dxDepth) { + for (var dxRow = 0; dxRow < convInfo.inHeight; ++dxRow) { + for (var dxCol = 0; dxCol < convInfo.inWidth; ++dxCol) { + // Shader code begins + var dyDepthCorner = dxDepth - padFront; + var dyRowCorner = dxRow - padTop; + var dyColCorner = dxCol - padLeft; + var dotProd = 0; + for (var wDepth = 0; wDepth < effectiveFilterDepth; wDepth += dilationDepth) { + var dyDepth = (dyDepthCorner + wDepth) / strideDepth; + if (dyDepth < 0 || dyDepth >= convInfo.outDepth || + Math.floor(dyDepth) !== dyDepth) { + continue; + } + for (var wRow = 0; wRow < effectiveFilterHeight; wRow += dilationHeight) { + var dyRow = (dyRowCorner + wRow) / strideHeight; + if (dyRow < 0 || dyRow >= convInfo.outHeight || + Math.floor(dyRow) !== dyRow) { + continue; + } + for (var wCol = 0; wCol < effectiveFilterWidth; wCol += dilationWidth) { + var dyCol = (dyColCorner + wCol) / strideWidth; + if (dyCol < 0 || dyCol >= convInfo.outWidth || + Math.floor(dyCol) !== dyCol) { + continue; + } + var maxPos = effectiveFilterDepth * effectiveFilterHeight * + effectiveFilterWidth - + 1 - + maxPosBuf.get(batch, dyDepth, dyRow, dyCol, channel); + var curPos = wDepth * effectiveFilterHeight * effectiveFilterWidth + + wRow * effectiveFilterWidth + wCol; + var mask = maxPos === curPos ? 1 : 0; + if (mask === 0) { + continue; + } + var pixel = dyBuf.get(batch, dyDepth, dyRow, dyCol, channel); + dotProd += pixel * mask; + } + } + } + dx.set(dotProd, batch, dxDepth, dxRow, dxCol, channel); + } + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var maxPool3DGradConfig = { + kernelName: tfjsCore.MaxPool3DGrad, + backendName: 'cpu', + kernelFunc: maxPool3DGrad + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function maxPoolGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var dy = inputs.dy, input = inputs.input, output = inputs.output; + var x = input; + assertNotComplex([input, output], 'maxPoolGrad'); + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, dimRoundingMode = attrs.dimRoundingMode; + var convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, 1 /* dilations */, pad, dimRoundingMode); + var xValues = backend.data.get(x.dataId).values; + var maxPosBuf = tfjsCore.buffer(convInfo.outShape, x.dtype, maxPoolPositions(xValues, x.shape, x.dtype, convInfo).values); + var strideHeight = convInfo.strideHeight; + var strideWidth = convInfo.strideWidth; + var dilationHeight = convInfo.dilationHeight; + var dilationWidth = convInfo.dilationWidth; + var effectiveFilterHeight = convInfo.effectiveFilterHeight; + var effectiveFilterWidth = convInfo.effectiveFilterWidth; + var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left; + var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top; + var dx = tfjsCore.buffer(x.shape, 'float32'); + var dyData = backend.data.get(dy.dataId).values; + var dyBuf = tfjsCore.buffer(dy.shape, 'float32', dyData); + for (var b = 0; b < convInfo.batchSize; ++b) { + for (var d = 0; d < convInfo.inChannels; ++d) { + for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) { + for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) { + // Shader code begins. + var dyRCorner = dxR - padTop; + var dyCCorner = dxC - padLeft; + var dotProd = 0; + for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) { + var dyR = (dyRCorner + wR) / strideHeight; + if (dyR < 0 || dyR >= convInfo.outHeight || + Math.floor(dyR) !== dyR) { + continue; + } + for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) { + var dyC = (dyCCorner + wC) / strideWidth; + if (dyC < 0 || dyC >= convInfo.outWidth || + Math.floor(dyC) !== dyC) { + continue; + } + var maxPos = effectiveFilterHeight * effectiveFilterWidth - 1 - + maxPosBuf.get(b, dyR, dyC, d); + var curPos = wR * effectiveFilterWidth + wC; + var mask = maxPos === curPos ? 1 : 0; + if (mask === 0) { + continue; + } + var pixel = dyBuf.get(b, dyR, dyC, d); + dotProd += pixel * mask; + } + } + dx.set(dotProd, b, dxR, dxC, d); + } + } + } + } + return backend.makeTensorInfo(dx.shape, dx.dtype, dx.values); + } + var maxPoolGradConfig = { + kernelName: tfjsCore.MaxPoolGrad, + backendName: 'cpu', + kernelFunc: maxPoolGrad + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function maxPoolWithArgmaxImpl(xValues, xShape, dtype, includeBatchInIndex, convInfo) { + var strides = tfjsCore.util.computeStrides(xShape); + var maxPools = pool(xValues, xShape, dtype, strides, convInfo, 'max'); + var maxPositions = maxPoolPositions(xValues, xShape, dtype, convInfo, true, includeBatchInIndex); + return [maxPools.values, maxPositions.values]; + } + + var maxPoolWithArgmaxConfig = { + kernelName: tfjsCore.MaxPoolWithArgmax, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend; + var x = inputs.x; + var filterSize = attrs.filterSize, strides = attrs.strides, pad = attrs.pad, includeBatchInIndex = attrs.includeBatchInIndex; + var cpuBackend = backend; + assertNotComplex(x, 'MaxPoolWithArgmax'); + var values = cpuBackend.data.get(x.dataId).values; + var convInfo = tfjsCore.backend_util.computePool2DInfo(x.shape, filterSize, strides, [1, 1], pad); + var _b = __read(maxPoolWithArgmaxImpl(values, x.shape, x.dtype, includeBatchInIndex, convInfo), 2), pooled = _b[0], indexes = _b[1]; + var pooledDataId = cpuBackend.write(pooled, convInfo.outShape, x.dtype); + var indexesDataId = cpuBackend.write(indexes, convInfo.outShape, x.dtype); + return [ + { dataId: pooledDataId, shape: convInfo.outShape, dtype: x.dtype }, + { dataId: indexesDataId, shape: convInfo.outShape, dtype: 'int32' } + ]; + } + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function mean(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, keepDims = attrs.keepDims; + var axes = tfjsCore.util.parseAxisParam(axis, x.shape); + var shapes = tfjsCore.backend_util.computeOutAndReduceShapes(x.shape, axes); + var reduceShape = shapes[1]; + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var toDispose = []; + var reduceSizeScalar = backend.makeTensorInfo([], 'float32', new Float32Array([reduceSize])); + toDispose.push(reduceSizeScalar); + var $x = cast({ inputs: { x: x }, backend: backend, attrs: { dtype: 'float32' } }); + toDispose.push($x); + var res = div({ inputs: { a: $x, b: reduceSizeScalar }, backend: backend }); + toDispose.push(res); + var result = sum({ inputs: { x: res }, backend: backend, attrs: { axis: axis, keepDims: keepDims } }); + toDispose.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return result; + } + var meanConfig = { + kernelName: tfjsCore.Mean, + backendName: 'cpu', + kernelFunc: mean + }; + + function min(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var axis = attrs.axis, keepDims = attrs.keepDims; + assertNotComplex(x, 'min'); + var origAxes = tfjsCore.util.parseAxisParam(axis, x.shape); + var axes = origAxes; + var permutedAxes = tfjsCore.backend_util.getAxesPermutation(axes, x.shape.length); + var $x = x; + if (permutedAxes != null) { + $x = transpose({ inputs: { x: x }, backend: backend, attrs: { perm: permutedAxes } }); + axes = tfjsCore.backend_util.getInnerMostAxes(axes.length, x.shape.length); + } + tfjsCore.backend_util.assertAxesAreInnerMostDims('min', axes, $x.shape.length); + var _a = __read(tfjsCore.backend_util.computeOutAndReduceShapes($x.shape, axes), 2), outShape = _a[0], reduceShape = _a[1]; + var reduceSize = tfjsCore.util.sizeFromShape(reduceShape); + var vals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(outShape), $x.dtype); + var aVals = backend.data.get($x.dataId).values; + for (var i = 0; i < vals.length; ++i) { + var offset = i * reduceSize; + var min_1 = aVals[offset]; + for (var j = 0; j < reduceSize; ++j) { + var value = aVals[offset + j]; + if (Number.isNaN(value) || + value < min_1) { // comparison with NaN always return false + min_1 = value; + } + } + vals[i] = min_1; + } + if (permutedAxes != null) { + backend.disposeIntermediateTensorInfo($x); + } + var result = backend.makeTensorInfo(outShape, $x.dtype, vals); + if (keepDims) { + var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(outShape, origAxes); + var reshapedResult = reshape({ inputs: { x: result }, backend: backend, attrs: { shape: expandedShape } }); + backend.disposeIntermediateTensorInfo(result); + return reshapedResult; + } + return result; + } + var minConfig = { + kernelName: tfjsCore.Min, + backendName: 'cpu', + kernelFunc: min + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function mirrorPad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var paddings = attrs.paddings, mode = attrs.mode; + assertNotComplex(x, 'mirrorPad'); + var outShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + x.shape[i] + p[1]; } /* afterPad */); + var start = paddings.map(function (p) { return p[0]; }); + var end = paddings.map(function (p, i) { return p[0] + x.shape[i]; }); + var offset = mode === 'reflect' ? 0 : 1; + var xVals = backend.data.get(x.dataId).values; + var xRank = x.shape.length; + var xStrides = tfjsCore.util.computeStrides(x.shape); + var resultSize = tfjsCore.util.sizeFromShape(outShape); + var resultRank = outShape.length; + var resultStrides = tfjsCore.util.computeStrides(outShape); + var resVals = tfjsCore.util.getTypedArrayFromDType(x.dtype, resultSize); + for (var i = 0; i < resultSize; i++) { + var coords = tfjsCore.util.indexToLoc(i, resultRank, resultStrides); + for (var i_1 = 0; i_1 < resultRank; i_1++) { + if (coords[i_1] < start[i_1]) { + coords[i_1] = start[i_1] * 2 - coords[i_1] - offset; + } + else if (coords[i_1] >= end[i_1]) { + coords[i_1] = (end[i_1] - 1) * 2 - coords[i_1] + offset; + } + } + coords = coords.map(function (c, i) { return c - start[i]; }); + var inIndex = tfjsCore.util.locToIndex(coords, xRank, xStrides); + resVals[i] = xVals[inIndex]; + } + var outId = backend.write(resVals, outShape, x.dtype); + return { dataId: outId, shape: outShape, dtype: x.dtype }; + } + var mirrorPadConfig = { + kernelName: tfjsCore.MirrorPad, + backendName: 'cpu', + kernelFunc: mirrorPad + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var modImpl = createSimpleBinaryKernelImpl((function (aValue, bValue) { + var rem = aValue % bValue; + if ((aValue < 0 && bValue < 0) || (aValue >= 0 && bValue >= 0)) { + return rem; + } + else { + return (rem + bValue) % bValue; + } + })); + var mod = binaryKernelFunc(tfjsCore.Mod, modImpl); + var modConfig = { + kernelName: tfjsCore.Mod, + backendName: 'cpu', + kernelFunc: mod + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function softmax(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var logits = inputs.logits; + var dim = attrs.dim; + var logitsRank = logits.shape.length; + var $dim = dim; + if ($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 " + $dim)); + } + var axes = tfjsCore.util.parseAxisParam([$dim], logits.shape); + var maxLogit = max({ + inputs: { x: logits }, + backend: backend, + attrs: { reductionIndices: axes, keepDims: false } + }); + var expandedShape = tfjsCore.backend_util.expandShapeToKeepDim(maxLogit.shape, axes); + var maxLogitReshaped = reshape({ inputs: { x: maxLogit }, backend: backend, attrs: { shape: expandedShape } }); + var a = sub({ inputs: { a: logits, b: maxLogitReshaped }, backend: backend }); + var b = exp({ inputs: { x: a }, backend: backend }); + var sumExp = sum({ inputs: { x: b }, backend: backend, attrs: { axis: axes, keepDims: false } }); + var sumReshaped = reshape({ inputs: { x: sumExp }, backend: backend, attrs: { shape: expandedShape } }); + var result = div({ inputs: { a: b, b: sumReshaped }, backend: backend }); + backend.disposeIntermediateTensorInfo(maxLogit); + backend.disposeIntermediateTensorInfo(maxLogitReshaped); + backend.disposeIntermediateTensorInfo(a); + backend.disposeIntermediateTensorInfo(b); + backend.disposeIntermediateTensorInfo(sumExp); + backend.disposeIntermediateTensorInfo(sumReshaped); + return result; + } + var softmaxConfig = { + kernelName: tfjsCore.Softmax, + backendName: 'cpu', + kernelFunc: softmax + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function multinomial(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var logits = inputs.logits; + var numSamples = attrs.numSamples, seed = attrs.seed, normalized = attrs.normalized; + assertNotComplex(logits, 'multinomial'); + var probabilities = normalized ? + logits : + softmax({ inputs: { logits: logits }, backend: backend, attrs: { dim: -1 } }); + var batchSize = probabilities.shape[0]; + var numEvents = probabilities.shape[1]; + var probVals = backend.data.get(probabilities.dataId).values; + var resShape = [batchSize, numSamples]; + var resVals = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(resShape), 'int32'); + for (var b = 0; b < batchSize; ++b) { + var offset = b * numEvents; + // The cdf won't include the last event. It will be implicit if no other + // event happened. + var cdf = new Float32Array(numEvents - 1); + cdf[0] = probVals[offset]; + for (var event = 1; event < cdf.length; ++event) { + cdf[event] = cdf[event - 1] + probVals[offset + event]; + } + var random = seedrandom__namespace.alea(seed.toString()); + var outOffset = b * numSamples; + for (var sampleId = 0; sampleId < numSamples; ++sampleId) { + var r = random(); + // Assume last event happened by default. + resVals[outOffset + sampleId] = cdf.length; + for (var event = 0; event < cdf.length; event++) { + if (r < cdf[event]) { + resVals[outOffset + sampleId] = event; + break; + } + } + } + } + if (!normalized) { + backend.disposeIntermediateTensorInfo(probabilities); + } + return backend.makeTensorInfo(resShape, 'int32', resVals); + } + var multinomialConfig = { + kernelName: tfjsCore.Multinomial, + backendName: 'cpu', + kernelFunc: multinomial + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var nonMaxSuppressionV3Impl = tfjsCore.kernel_impls.nonMaxSuppressionV3Impl; + function nonMaxSuppressionV3(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var boxes = inputs.boxes, scores = inputs.scores; + var maxOutputSize = attrs.maxOutputSize, iouThreshold = attrs.iouThreshold, scoreThreshold = attrs.scoreThreshold; + assertNotComplex(boxes, 'NonMaxSuppression'); + var boxesVals = backend.data.get(boxes.dataId).values; + var scoresVals = backend.data.get(scores.dataId).values; + var selectedIndices = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold).selectedIndices; + return backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices)); + } + var nonMaxSuppressionV3Config = { + kernelName: tfjsCore.NonMaxSuppressionV3, + backendName: 'cpu', + kernelFunc: nonMaxSuppressionV3 + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var nonMaxSuppressionV4Impl = tfjsCore.kernel_impls.nonMaxSuppressionV4Impl; + function nonMaxSuppressionV4(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var boxes = inputs.boxes, scores = inputs.scores; + var maxOutputSize = attrs.maxOutputSize, iouThreshold = attrs.iouThreshold, scoreThreshold = attrs.scoreThreshold, padToMaxOutputSize = attrs.padToMaxOutputSize; + assertNotComplex(boxes, 'NonMaxSuppressionPadded'); + var boxesVals = backend.data.get(boxes.dataId).values; + var scoresVals = backend.data.get(scores.dataId).values; + var _a = nonMaxSuppressionV4Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize), selectedIndices = _a.selectedIndices, validOutputs = _a.validOutputs; + return [ + backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices)), + backend.makeTensorInfo([], 'int32', new Int32Array([validOutputs])) + ]; + } + var nonMaxSuppressionV4Config = { + kernelName: tfjsCore.NonMaxSuppressionV4, + backendName: 'cpu', + kernelFunc: nonMaxSuppressionV4 + }; + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var nonMaxSuppressionV5Impl = tfjsCore.kernel_impls.nonMaxSuppressionV5Impl; + function nonMaxSuppressionV5(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var boxes = inputs.boxes, scores = inputs.scores; + var maxOutputSize = attrs.maxOutputSize, iouThreshold = attrs.iouThreshold, scoreThreshold = attrs.scoreThreshold, softNmsSigma = attrs.softNmsSigma; + assertNotComplex(boxes, 'NonMaxSuppressionWithScore'); + var boxesVals = backend.data.get(boxes.dataId).values; + var scoresVals = backend.data.get(scores.dataId).values; + var maxOutputSizeVal = maxOutputSize; + var iouThresholdVal = iouThreshold; + var scoreThresholdVal = scoreThreshold; + var softNmsSigmaVal = softNmsSigma; + var _a = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSizeVal, iouThresholdVal, scoreThresholdVal, softNmsSigmaVal), selectedIndices = _a.selectedIndices, selectedScores = _a.selectedScores; + return [ + backend.makeTensorInfo([selectedIndices.length], 'int32', new Int32Array(selectedIndices)), + backend.makeTensorInfo([selectedScores.length], 'float32', new Float32Array(selectedScores)) + ]; + } + var nonMaxSuppressionV5Config = { + kernelName: tfjsCore.NonMaxSuppressionV5, + backendName: 'cpu', + kernelFunc: nonMaxSuppressionV5 + }; + + function oneHot(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var indices = inputs.indices; + var depth = attrs.depth, onValue = attrs.onValue, offValue = attrs.offValue; + assertNotComplex(indices, 'oneHot'); + var indicesSize = tfjsCore.util.sizeFromShape(indices.shape); + var res = new Float32Array(indicesSize * depth); + res.fill(offValue); + var indicesVal = backend.data.get(indices.dataId).values; + for (var event = 0; event < indicesSize; ++event) { + if (indicesVal[event] >= 0 && indicesVal[event] < depth) { + res[event * depth + indicesVal[event]] = onValue; + } + } + return backend.makeTensorInfo(__spread(indices.shape, [depth]), 'int32', res); + } + var oneHotConfig = { + kernelName: tfjsCore.OneHot, + backendName: 'cpu', + kernelFunc: oneHot + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function zerosLike(args) { + var inputs = args.inputs, backend = args.backend; + var x = inputs.x; + if (x.dtype === 'string') { + throw new Error('zerosLike is not supported for string tensors'); + } + else if (x.dtype === 'complex64') { + var realPart = real({ inputs: { input: x }, backend: backend }); + var r = zerosLike({ inputs: { x: realPart }, backend: backend }); + var imagPart = imag({ inputs: { input: x }, backend: backend }); + var i = zerosLike({ inputs: { x: imagPart }, backend: backend }); + var result = complex({ inputs: { real: r, imag: i }, backend: backend }); + backend.disposeIntermediateTensorInfo(realPart); + backend.disposeIntermediateTensorInfo(r); + backend.disposeIntermediateTensorInfo(imagPart); + backend.disposeIntermediateTensorInfo(i); + return result; + } + else { + return fill({ backend: backend, attrs: { shape: x.shape, value: 0, dtype: x.dtype } }); + } + } + var zerosLikeConfig = { + kernelName: tfjsCore.ZerosLike, + backendName: 'cpu', + kernelFunc: zerosLike + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function onesLike(args) { + var inputs = args.inputs, backend = args.backend; + var x = inputs.x; + if (x.dtype === 'string') { + throw new Error('onesLike is not supported for string tensors'); + } + else if (x.dtype === 'complex64') { + var realPart = real({ inputs: { input: x }, backend: backend }); + var r = onesLike({ inputs: { x: realPart }, backend: backend }); + var imagPart = imag({ inputs: { input: x }, backend: backend }); + var i = zerosLike({ inputs: { x: imagPart }, backend: backend }); + var result = complex({ inputs: { real: r, imag: i }, backend: backend }); + backend.disposeIntermediateTensorInfo(realPart); + backend.disposeIntermediateTensorInfo(r); + backend.disposeIntermediateTensorInfo(imagPart); + backend.disposeIntermediateTensorInfo(i); + return result; + } + else { + return fill({ backend: backend, attrs: { shape: x.shape, value: 1, dtype: x.dtype } }); + } + } + var onesLikeConfig = { + kernelName: tfjsCore.OnesLike, + backendName: 'cpu', + kernelFunc: onesLike + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function pack(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var axis = attrs.axis; + if (inputs.length === 1) { + return expandDims({ inputs: { input: inputs[0] }, backend: backend, attrs: { dim: axis } }); + } + var shape = inputs[0].shape; + var dtype = inputs[0].dtype; + inputs.forEach(function (t) { + tfjsCore.util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes'); + tfjsCore.util.assert(dtype === t.dtype, function () { return 'All tensors passed to stack must have matching dtypes'; }); + }); + var intermediateTensorInfos = []; + var expandedTensors = inputs.map(function (t) { + var expandedT = expandDims({ inputs: { input: t }, backend: backend, attrs: { dim: axis } }); + intermediateTensorInfos.push(expandedT); + return expandedT; + }); + var result = concat({ inputs: expandedTensors, backend: backend, attrs: { axis: axis } }); + intermediateTensorInfos.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return result; + } + var packConfig = { + kernelName: tfjsCore.Pack, + backendName: 'cpu', + kernelFunc: pack + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function padV2(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var paddings = attrs.paddings, constantValue = attrs.constantValue; + assertNotComplex(x, 'pad'); + var outShape = paddings.map(function (p, i) { return p[0] /* beforePad */ + x.shape[i] + p[1]; } /* afterPad */); + var start = paddings.map(function (p) { return p[0]; }); + var xVals = backend.data.get(x.dataId).values; + var xSize = tfjsCore.util.sizeFromShape(x.shape); + var xRank = x.shape.length; + var xStrides = tfjsCore.util.computeStrides(x.shape); + var resultSize = tfjsCore.util.sizeFromShape(outShape); + var resultRank = outShape.length; + var resultStrides = tfjsCore.util.computeStrides(outShape); + var resVals = tfjsCore.util.getTypedArrayFromDType(x.dtype, resultSize); + if (constantValue !== 0) { + resVals.fill(constantValue); + } + for (var i = 0; i < xSize; i++) { + var coords = tfjsCore.util.indexToLoc(i, xRank, xStrides); + var outCoords = coords.map(function (c, i) { return c + start[i]; }); + var outIndex = tfjsCore.util.locToIndex(outCoords, resultRank, resultStrides); + resVals[outIndex] = xVals[i]; + } + var outId = backend.write(resVals, outShape, x.dtype); + return { dataId: outId, shape: outShape, dtype: x.dtype }; + } + var padV2Config = { + kernelName: tfjsCore.PadV2, + backendName: 'cpu', + kernelFunc: padV2 + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var powImpl = createSimpleBinaryKernelImpl(function (a, b) { return Math.pow(a, b); }); + var pow = binaryKernelFunc(tfjsCore.Pow, powImpl); + var powConfig = { + kernelName: tfjsCore.Pow, + backendName: 'cpu', + kernelFunc: pow + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function range(args) { + var backend = args.backend, attrs = args.attrs; + var start = attrs.start, stop = attrs.stop, dtype = attrs.dtype, step = attrs.step; + var values = rangeImpl(start, stop, step, dtype); + return backend.makeTensorInfo([values.length], dtype, values); + } + var rangeConfig = { + kernelName: tfjsCore.Range, + backendName: 'cpu', + kernelFunc: range + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var reciprocal = unaryKernelFunc(tfjsCore.Reciprocal, function (xi) { return 1 / xi; }); + var reciprocalConfig = { + kernelName: tfjsCore.Reciprocal, + backendName: 'cpu', + kernelFunc: reciprocal, + }; + + function resizeBilinear(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var images = inputs.images; + var alignCorners = attrs.alignCorners, halfPixelCenters = attrs.halfPixelCenters, size = attrs.size; + assertNotComplex(images, 'resizeBilinear'); + var imagesStrides = tfjsCore.util.computeStrides(images.shape); + var _a = __read(size, 2), newHeight = _a[0], newWidth = _a[1]; + var _b = __read(images.shape, 4), batch = _b[0], oldHeight = _b[1], oldWidth = _b[2], numChannels = _b[3]; + var xValues = backend.data.get(images.dataId).values; + var result = new Float32Array(tfjsCore.util.sizeFromShape([batch, newHeight, newWidth, numChannels])); + var effectiveInputSize = [ + (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight, + (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth + ]; + var effectiveOutputSize = [ + (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight, + (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth + ]; + var outputIdx = 0; + var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0]; + var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1]; + for (var b = 0; b < batch; b++) { + for (var r = 0; r < newHeight; r++) { + var sourceFracRow = void 0; + if (halfPixelCenters) { + sourceFracRow = effectiveRowSizeRatio * (r + 0.5) - 0.5; + } + else { + sourceFracRow = effectiveRowSizeRatio * r; + } + var sourceRowFloor = Math.max(0, Math.floor(sourceFracRow)); + var rowFrac = sourceFracRow - sourceRowFloor; + var sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow)); + var topRowOffset = b * imagesStrides[0] + sourceRowFloor * imagesStrides[1]; + var botRowOffset = b * imagesStrides[0] + sourceRowCeil * imagesStrides[1]; + for (var c = 0; c < newWidth; c++) { + var sourceFracCol = void 0; + if (halfPixelCenters) { + sourceFracCol = effectiveColSizeRatio * (c + 0.5) - 0.5; + } + else { + sourceFracCol = effectiveColSizeRatio * c; + } + var sourceColFloor = Math.max(0, Math.floor(sourceFracCol)); + var colFrac = sourceFracCol - sourceColFloor; + var sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol)); + var topLeftOffest = topRowOffset + sourceColFloor * imagesStrides[2]; + var botLeftOffset = botRowOffset + sourceColFloor * imagesStrides[2]; + var topRightOffset = topRowOffset + sourceColCeil * imagesStrides[2]; + var botRightOffest = botRowOffset + sourceColCeil * imagesStrides[2]; + for (var d = 0; d < numChannels; d++) { + // Begin shader. + // Compute the fractional index of the source. + var topLeft = xValues[topLeftOffest + d]; + var bottomLeft = xValues[botLeftOffset + d]; + var topRight = xValues[topRightOffset + d]; + var bottomRight = xValues[botRightOffest + d]; + var top = topLeft + (topRight - topLeft) * colFrac; + var bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac; + var newValue = top + (bottom - top) * rowFrac; + result[outputIdx++] = newValue; + } + } + } + } + return backend.makeTensorInfo([batch, newHeight, newWidth, numChannels], 'float32', result); + } + var resizeBilinearConfig = { + kernelName: tfjsCore.ResizeBilinear, + backendName: 'cpu', + kernelFunc: resizeBilinear + }; + + function resizeBilinearGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var images = inputs.images, dy = inputs.dy; + var alignCorners = attrs.alignCorners; + assertNotComplex([dy, images], 'resizeBilinearGrad'); + var imagesStrides = tfjsCore.util.computeStrides(images.shape); + var _a = __read(images.shape, 4), batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3]; + var _b = __read(dy.shape, 3), yHeight = _b[1], yWidth = _b[2]; + var output = new Float32Array(batch * xHeight * xWidth * depth); + // In the backwards pass, we want to find the pixels that were generated + // for each pixel in the input image the forward pass and add the + // corresponding coefficient from dy to the gradient (with some + // interpolation). + var effectiveXSize = [ + (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight, + (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth + ]; + var effectiveYSize = [ + (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight, + (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth + ]; + var heightScale = effectiveXSize[0] / effectiveYSize[0]; + var widthScale = effectiveXSize[1] / effectiveYSize[1]; + // Reference implementation + // tslint:disable-next-line:max-line-length + // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275 + var dyValues = backend.data.get(dy.dataId).values; + var offset = 0; + for (var b = 0; b < batch; b++) { + var bOffset = b * imagesStrides[0]; + for (var r = 0; r < yHeight; r++) { + var dxR = r * heightScale; + var topDxRIndex = Math.floor(dxR); + var bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1); + var topDxROffset = bOffset + topDxRIndex * imagesStrides[1]; + var bottomDxROffset = bOffset + bottomDxRIndex * imagesStrides[1]; + var dxRLerp = dxR - topDxRIndex; + var inverseDxRLerp = 1.0 - dxRLerp; + for (var c = 0; c < yWidth; c++) { + var dxC = c * widthScale; + var leftDxCIndex = Math.floor(dxC); + var rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1); + var dxCLerp = dxC - leftDxCIndex; + var inverseDxCLerp = 1.0 - dxCLerp; + var topLeftRCOffset = topDxROffset + leftDxCIndex * imagesStrides[2]; + var topRightRCOffset = topDxROffset + rightDxCIndex * imagesStrides[2]; + var bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * imagesStrides[2]; + var bottomRightRCOffset = bottomDxROffset + rightDxCIndex * imagesStrides[2]; + var inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp; + var inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp; + var dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp; + var dxRLerpTimesDxCLerp = dxRLerp * dxCLerp; + for (var d = 0; d < depth; d++) { + var dyVal = dyValues[offset++]; + output[topLeftRCOffset + d] += + dyVal * inverseDxRLerpTimesInverseDxCLerp; + output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp; + output[bottomLeftRCOffset + d] += dyVal * dxRLerpTimesInverseDxCLerp; + output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp; + } + } + } + } + return backend.makeTensorInfo([batch, xWidth, xHeight, depth], 'float32', output); + } + var resizeBilinearGradConfig = { + kernelName: tfjsCore.ResizeBilinearGrad, + backendName: 'cpu', + kernelFunc: resizeBilinearGrad + }; + + function resizeNearestNeighbor(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var images = inputs.images; + var alignCorners = attrs.alignCorners, halfPixelCenters = attrs.halfPixelCenters, size = attrs.size; + assertNotComplex(images, 'resizeNearestNeighbor'); + var imagesStrides = tfjsCore.util.computeStrides(images.shape); + var _a = __read(size, 2), newHeight = _a[0], newWidth = _a[1]; + var _b = __read(images.shape, 4), batch = _b[0], oldHeight = _b[1], oldWidth = _b[2], numChannels = _b[3]; + var xValues = backend.data.get(images.dataId).values; + var output = new Float32Array(batch * newHeight * newWidth * numChannels); + var effectiveInputSize = [ + (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight, + (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth + ]; + var effectiveOutputSize = [ + (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight, + (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth + ]; + var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0]; + var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1]; + var outputOffset = 0; + for (var b = 0; b < batch; b++) { + var batchOffset = b * imagesStrides[0]; + for (var r = 0; r < newHeight; r++) { + var sourceFracRow = halfPixelCenters ? + effectiveRowSizeRatio * (r + 0.5) : + effectiveRowSizeRatio * r; + var sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) : Math.floor(sourceFracRow)); + if (halfPixelCenters) { + sourceNearestRow = Math.max(0, sourceNearestRow); + } + var rowOffset = batchOffset + sourceNearestRow * imagesStrides[1]; + for (var c = 0; c < newWidth; c++) { + var sourceFracCol = halfPixelCenters ? + effectiveColSizeRatio * (c + 0.5) : + effectiveColSizeRatio * c; + var sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) : + Math.floor(sourceFracCol)); + if (halfPixelCenters) { + sourceNearestCol = Math.max(0, sourceNearestCol); + } + var colOffset = rowOffset + sourceNearestCol * imagesStrides[2]; + for (var d = 0; d < numChannels; d++) { + // Begin shader. + // Compute the fractional index of the source. + var newVal = xValues[colOffset + d]; + output[outputOffset++] = newVal; + } + } + } + } + return backend.makeTensorInfo([batch, newHeight, newWidth, numChannels], images.dtype, output); + } + var resizeNearestNeighborConfig = { + kernelName: tfjsCore.ResizeNearestNeighbor, + backendName: 'cpu', + kernelFunc: resizeNearestNeighbor + }; + + function resizeNearestNeighborGrad(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var images = inputs.images, dy = inputs.dy; + var alignCorners = attrs.alignCorners; + assertNotComplex([dy, images], 'resizeNearestNeighborGrad'); + var imagesStrides = tfjsCore.util.computeStrides(images.shape); + var dyStrides = tfjsCore.util.computeStrides(dy.shape); + var _a = __read(images.shape, 4), batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3]; + var _b = __read(dy.shape, 3), yHeight = _b[1], yWidth = _b[2]; + var output = new Float32Array(batch * xHeight * xWidth * depth); + var dyValues = backend.data.get(dy.dataId).values; + // In the backwards pass, we want to find the pixels that were generated + // for each pixel in the input image the forward pass + var effectiveXSize = [ + (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight, + (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth + ]; + var effectiveYSize = [ + (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight, + (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth + ]; + var heightScale = effectiveXSize[0] / effectiveYSize[0]; + var widthScale = effectiveXSize[1] / effectiveYSize[1]; + var invHeightScale = 1 / heightScale; + var invWidthScale = 1 / widthScale; + // This defines the size of the window of values around a particular + // index in dy that we want to search for contributions to dx. + var winHeight = (Math.ceil(invHeightScale) * 2) + 2; + var winWidth = (Math.ceil(invWidthScale) * 2) + 2; + // Loop over the output space. + for (var b = 0; b < batch; b++) { + var batchOffset = b * imagesStrides[0]; + for (var r = 0; r < xHeight; r++) { + var rowOffset = batchOffset + r * imagesStrides[1]; + // Compute bounds for where in dy we will look + var startRLerp = Math.floor(r * invHeightScale); + var startDyR = Math.floor(startRLerp - (winHeight / 2)); + for (var c = 0; c < xWidth; c++) { + var colOffset = rowOffset + c * imagesStrides[2]; + // Compute bounds for where in dy we will look + var startCLerp = Math.floor(c * invWidthScale); + var startDyC = Math.floor(startCLerp - (winWidth / 2)); + for (var d = 0; d < depth; d++) { + var accum = 0; + // loop over dy + for (var dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) { + var dyR = dyRIndex + startDyR; + // Guard against the window exceeding the bounds of dy + if (dyR < 0 || dyR >= yHeight) { + continue; + } + var dyROffset = batchOffset + dyR * dyStrides[1]; + var sourceFracRow = dyR * heightScale; + var sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) : + Math.floor(sourceFracRow)); + if (r !== sourceNearestRow) { + continue; + } + for (var dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) { + var dyC = dyCIndex + startDyC; + // Guard against the window exceeding the bounds of dy + if (dyC < 0 || dyC >= yWidth) { + continue; + } + var dyCOffset = dyROffset + dyC * dyStrides[2]; + var sourceFracCol = dyC * widthScale; + var sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) : + Math.floor(sourceFracCol)); + if (c === sourceNearestCol) { + accum += dyValues[dyCOffset + d]; + } + } + } + output[colOffset + d] = accum; + } + } + } + } + return backend.makeTensorInfo(images.shape, images.dtype, output); + } + var resizeNearestNeighborGradConfig = { + kernelName: tfjsCore.ResizeNearestNeighborGrad, + backendName: 'cpu', + kernelFunc: resizeNearestNeighborGrad + }; + + function reverse(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var dims = attrs.dims; + assertNotComplex(x, 'reverse'); + var xRank = x.shape.length; + var $dims = tfjsCore.util.parseAxisParam(dims, x.shape); + if (xRank === 0) { + return identity({ inputs: { x: x }, backend: backend }); + } + var outBuf = new tfjsCore.TensorBuffer(x.shape, x.dtype); + var xBuf = backend.bufferSync(x); + var _loop_1 = function (i) { + var outLoc = outBuf.indexToLoc(i); + var inLoc = outLoc.slice(); + $dims.forEach(function (d) { return inLoc[d] = x.shape[d] - 1 - inLoc[d]; }); + outBuf.set.apply(outBuf, __spread([xBuf.get.apply(xBuf, __spread(inLoc))], outLoc)); + }; + for (var i = 0; i < outBuf.size; i++) { + _loop_1(i); + } + return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); + } + var reverseConfig = { + kernelName: tfjsCore.Reverse, + backendName: 'cpu', + kernelFunc: reverse + }; + + var rotateWithOffsetConfig = { + kernelName: tfjsCore.RotateWithOffset, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs, attrs = _a.attrs, backend = _a.backend; + var image = inputs.image; + var radians = attrs.radians, fillValue = attrs.fillValue, center = attrs.center; + var cpuBackend = backend; + var output = tfjsCore.util.getTypedArrayFromDType(image.dtype, tfjsCore.util.sizeFromShape(image.shape)); + var _b = __read(image.shape, 4), batch = _b[0], imageHeight = _b[1], imageWidth = _b[2], numChannels = _b[3]; + var _c = __read(tfjsCore.backend_util.getImageCenter(center, imageHeight, imageWidth), 2), centerX = _c[0], centerY = _c[1]; + var fullOpacityValue = 255; + var sinFactor = Math.sin(radians); + var cosFactor = Math.cos(radians); + var imageVals = cpuBackend.data.get(image.dataId).values; + for (var batchIdx = 0; batchIdx < batch; batchIdx++) { + var batchOffset = batchIdx * imageWidth * imageHeight * numChannels; + for (var row = 0; row < imageHeight; row++) { + var rowOffset = row * (imageWidth * numChannels); + for (var col = 0; col < imageWidth; col++) { + var colOffset = col * numChannels; + for (var channel = 0; channel < numChannels; channel++) { + var coords = [batch, row, col, channel]; + var x = coords[2]; + var y = coords[1]; + // coordX/coordY are the result of rotating and translating x/y. + var coordX = (x - centerX) * cosFactor - (y - centerY) * sinFactor; + var coordY = (x - centerX) * sinFactor + (y - centerY) * cosFactor; + coordX = Math.round(coordX + centerX); + coordY = Math.round(coordY + centerY); + var outputValue = fillValue; + if (typeof fillValue !== 'number') { + if (channel === 3) { + outputValue = fullOpacityValue; + } + else { + outputValue = fillValue[channel]; + } + } + // If the coordinate position falls within the image boundaries... + if (coordX >= 0 && coordX < imageWidth && coordY >= 0 && + coordY < imageHeight) { + // set the output to the image value at the coordinate position. + var rotatedRowOffset = coordY * (imageWidth * numChannels); + var rotatedColOffset = coordX * numChannels; + var imageIdx = batchOffset + rotatedRowOffset + rotatedColOffset + channel; + outputValue = imageVals[imageIdx]; + } + var outIdx = batchOffset + rowOffset + colOffset + channel; + output[outIdx] = outputValue; + } + } + } + } + var dataId = cpuBackend.write(output, image.shape, image.dtype); + return { dataId: dataId, shape: image.shape, dtype: image.dtype }; + } + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var round = unaryKernelFunc(tfjsCore.Round, function (xi) { + // The algorithm is based on banker's rounding. + var 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 === 0.0) { + return base; + } + else { + return base + 1.0; + } + } + }); + var roundConfig = { + kernelName: tfjsCore.Round, + backendName: 'cpu', + kernelFunc: round, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function scatterImpl(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) { + var flattenShape = [outputSize / sliceSize, sliceSize]; + var indicesData = indices.values; + var updatesData = updates.values; + if (outputSize === 0) { + return tfjsCore.buffer(shape, updates.dtype); + } + var outBuf = tfjsCore.buffer(flattenShape, updates.dtype); + outBuf.values.fill(defaultValue); + for (var i = 0; i < numUpdates; i++) { + var index = []; + var flattenIndex = 0; + for (var j = 0; j < sliceRank; j++) { + var 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 (var 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; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function scatterNd(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var indices = inputs.indices, updates = inputs.updates; + var shape = attrs.shape; + var _a = tfjsCore.backend_util.calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize; + var sumDupeIndices = true; + var indicesBuf = backend.bufferSync(indices); + var updatesBuf = backend.bufferSync(updates); + var outBuf = scatterImpl(indicesBuf, updatesBuf, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, 0 /* defaultValue */, sumDupeIndices); + return backend.makeTensorInfo(shape, outBuf.dtype, outBuf.values); + } + var scatterNdConfig = { + kernelName: tfjsCore.ScatterNd, + backendName: 'cpu', + kernelFunc: scatterNd + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function select(args) { + var inputs = args.inputs, backend = args.backend; + var condition = inputs.condition, t = inputs.t, e = inputs.e; + assertNotComplex([condition, t, e], 'select'); + var conditionRank = condition.shape.length; + var values = backend.data.get(condition.dataId).values; + var tValues = backend.data.get(t.dataId).values; + var eValues = backend.data.get(e.dataId).values; + var resultDtype = tfjsCore.upcastType(t.dtype, e.dtype); + var newValues = tfjsCore.util.makeZerosTypedArray(tfjsCore.util.sizeFromShape(t.shape), resultDtype); + var index = 0; + var offset = conditionRank === 0 || conditionRank > 1 || t.shape.length === 1 ? + 1 : + tfjsCore.util.sizeFromShape(t.shape.slice(1)); + for (var i = 0; i < values.length; i++) { + for (var j = 0; j < offset; j++) { + if (values[i] === 1) { + newValues[index++] = tValues[i]; + } + else { + newValues[index++] = eValues[i]; + } + } + } + return backend.makeTensorInfo(t.shape, resultDtype, newValues); + } + var selectConfig = { + kernelName: tfjsCore.Select, + backendName: 'cpu', + kernelFunc: select + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var scaleAlpha = tfjsCore.backend_util.SELU_SCALEALPHA; + var scale = tfjsCore.backend_util.SELU_SCALE; + var selu = unaryKernelFunc(tfjsCore.Selu, function (xi) { + if (xi >= 0) { + return scale * xi; + } + else { + return scaleAlpha * (Math.exp(xi) - 1); + } + }); + var seluConfig = { + kernelName: tfjsCore.Selu, + backendName: 'cpu', + kernelFunc: selu, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var sign = unaryKernelFunc(tfjsCore.Sign, function (xi) { + if (xi < 0) { + return -1; + } + else if (xi > 0) { + return 1; + } + else { + return 0; + } + }); + var signConfig = { + kernelName: tfjsCore.Sign, + backendName: 'cpu', + kernelFunc: sign, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var sin = unaryKernelFunc(tfjsCore.Sin, function (xi) { return Math.sin(xi); }); + var sinConfig = { + kernelName: tfjsCore.Sin, + backendName: 'cpu', + kernelFunc: sin, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var sinh = unaryKernelFunc(tfjsCore.Sinh, function (xi) { return Math.sinh(xi); }); + var sinhConfig = { + kernelName: tfjsCore.Sinh, + backendName: 'cpu', + kernelFunc: sinh, + }; + + /** + * @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. + * ============================================================================= + */ + // mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX + // epsilon is the difference between 1.0 and the next representable float. + // For a single precision 32 bit float this should be 2^-23, see: + // https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm + var epsilon = 1.1920928955078125e-7; + var threshold = Math.log(epsilon) + 2.0; + var softplus = unaryKernelFunc(tfjsCore.Softplus, function (xi) { + // Value above which exp(x) may overflow, but softplus(x) == x + // is within machine epsilon. + var tooLarge = xi > -threshold; + // Value below which exp(x) may underflow, but softplus(x) == exp(x) + // is within machine epsilon. + var tooSmall = xi < threshold; + var expX = Math.exp(xi); + var result; + if (tooSmall) { + result = expX; + } + else if (tooLarge) { + result = xi; + } + else { + result = Math.log(1.0 + expX); + } + return result; + }); + var softplusConfig = { + kernelName: tfjsCore.Softplus, + backendName: 'cpu', + kernelFunc: softplus, + }; + + function spaceToBatchND(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var blockShape = attrs.blockShape, paddings = attrs.paddings; + assertNotComplex([x], 'spaceToBatchND'); + var prod = tfjsCore.util.sizeFromShape(blockShape); + var completePaddings = [[0, 0]]; + completePaddings.push.apply(completePaddings, __spread(paddings)); + for (var i = 1 + blockShape.length; i < x.shape.length; ++i) { + completePaddings.push([0, 0]); + } + var paddedX = padV2Config.kernelFunc({ + inputs: { x: x }, + backend: backend, + attrs: { paddings: completePaddings, constantValue: 0 } + }); + var reshapedPaddedShape = tfjsCore.backend_util.getReshaped(paddedX.shape, blockShape, prod, false); + var permutedReshapedPaddedPermutation = tfjsCore.backend_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false); + var flattenShape = tfjsCore.backend_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false); + var reshapeInputs = { x: paddedX }; + var reshapeAttrs = { shape: reshapedPaddedShape }; + var paddedXReshaped = reshape({ inputs: reshapeInputs, backend: backend, attrs: reshapeAttrs }); + var transposeInputs = { x: paddedXReshaped }; + var transposeAttrs = { perm: permutedReshapedPaddedPermutation }; + var paddedXT = transpose({ inputs: transposeInputs, backend: backend, attrs: transposeAttrs }); + var resultReshapeInputs = { x: paddedXT }; + var resultReshapeAttrs = { shape: flattenShape }; + var result = reshape({ inputs: resultReshapeInputs, backend: backend, attrs: resultReshapeAttrs }); + backend.disposeIntermediateTensorInfo(paddedX); + backend.disposeIntermediateTensorInfo(paddedXReshaped); + backend.disposeIntermediateTensorInfo(paddedXT); + return result; + } + var spaceToBatchNDConfig = { + kernelName: tfjsCore.SpaceToBatchND, + backendName: 'cpu', + kernelFunc: spaceToBatchND + }; + + function sparseFillEmptyRows(args) { + var inputs = args.inputs, backend = args.backend; + var indices = inputs.indices, values = inputs.values, denseShape = inputs.denseShape, defaultValue = inputs.defaultValue; + if (denseShape.shape.length !== 1) { + throw new Error("Dense shape must be a vector, saw:\n " + denseShape.shape); + } + if (indices.shape.length !== 2) { + throw new Error("Indices must be a matrix, saw:\n " + indices.shape); + } + if (values.shape.length !== 1) { + throw new Error("Values must be a vector, saw:\n " + values.shape); + } + if (defaultValue.shape.length !== 0) { + throw new Error("Default value must be a scalar, saw:\n " + defaultValue.shape); + } + var $indices = backend.data.get(indices.dataId).values; + var $values = backend.data.get(values.dataId).values; + var $denseShape = backend.data.get(denseShape.dataId).values; + var $defaultValue = backend.data.get(defaultValue.dataId).values[0]; + var _a = __read(sparseFillEmptyRowsImpl($indices, indices.shape, indices.dtype, $values, values.dtype, $denseShape, $defaultValue), 5), outputIndices = _a[0], outputIndicesShape = _a[1], outputValues = _a[2], emptyRowIndicator = _a[3], reverseIndexMap = _a[4]; + return [ + backend.makeTensorInfo(outputIndicesShape, indices.dtype, outputIndices), + backend.makeTensorInfo([outputIndicesShape[0]], values.dtype, outputValues), + backend.makeTensorInfo([emptyRowIndicator.length], 'bool', new Uint8Array(emptyRowIndicator.map(function (value) { return Number(value); }))), + backend.makeTensorInfo([reverseIndexMap.length], indices.dtype, new Int32Array(reverseIndexMap)), + ]; + } + var sparseFillEmptyRowsConfig = { + kernelName: tfjsCore.SparseFillEmptyRows, + backendName: 'cpu', + kernelFunc: sparseFillEmptyRows, + }; + + function sparseReshape(args) { + var inputs = args.inputs, backend = args.backend; + var inputIndices = inputs.inputIndices, inputShape = inputs.inputShape, newShape = inputs.newShape; + if (inputIndices.shape.length !== 2) { + throw new Error("Input indices should be a matrix but received shape\n " + inputIndices.shape); + } + if (inputShape.shape.length !== 1) { + throw new Error("Input shape should be a vector but received shape\n " + inputShape.shape); + } + if (newShape.shape.length !== 1) { + throw new Error("Target shape should be a vector but received shape " + newShape.shape); + } + var $inputShape = Array.from(backend.data.get(inputShape.dataId).values); + var $inputIndices = backend.data.get(inputIndices.dataId).values; + var targetShape = Array.from(backend.data.get(newShape.dataId).values); + var _a = __read(sparseReshapeImpl($inputIndices, inputIndices.shape, inputIndices.dtype, $inputShape, targetShape), 3), newIndices = _a[0], indicesShape = _a[1], outputShape = _a[2]; + return [ + backend.makeTensorInfo(indicesShape, inputIndices.dtype, newIndices), + backend.makeTensorInfo([outputShape.length], newShape.dtype, new Int32Array(outputShape)), + ]; + } + var sparseReshapeConfig = { + kernelName: tfjsCore.SparseReshape, + backendName: 'cpu', + kernelFunc: sparseReshape, + }; + + function sparseSegmentMean(args) { + var inputs = args.inputs, backend = args.backend; + var data = inputs.data, indices = inputs.indices, segmentIds = inputs.segmentIds; + 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\n " + indices.shape); + } + if (segmentIds.shape.length !== 1) { + throw new Error("Segment ids should be a vector but received shape\n " + segmentIds.shape); + } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error("segmentIds and indices should have same size."); + } + var $data = backend.data.get(data.dataId).values; + var $indices = backend.data.get(indices.dataId).values; + var $segmentIds = backend.data.get(segmentIds.dataId).values; + var _a = __read(sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds, true), 2), outputData = _a[0], outputDataShape = _a[1]; + return backend.makeTensorInfo(outputDataShape, data.dtype, outputData); + } + var sparseSegmentMeanConfig = { + kernelName: tfjsCore.SparseSegmentMean, + backendName: 'cpu', + kernelFunc: sparseSegmentMean, + }; + + function sparseSegmentSum(args) { + var inputs = args.inputs, backend = args.backend; + var data = inputs.data, indices = inputs.indices, segmentIds = inputs.segmentIds; + 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\n " + indices.shape); + } + if (segmentIds.shape.length !== 1) { + throw new Error("Segment ids should be a vector but received shape\n " + segmentIds.shape); + } + if (indices.shape[0] !== segmentIds.shape[0]) { + throw new Error("segmentIds and indices should have same size."); + } + var $data = backend.data.get(data.dataId).values; + var $indices = backend.data.get(indices.dataId).values; + var $segmentIds = backend.data.get(segmentIds.dataId).values; + var _a = __read(sparseSegmentReductionImpl($data, data.shape, data.dtype, $indices, $segmentIds), 2), outputData = _a[0], outputDataShape = _a[1]; + return backend.makeTensorInfo(outputDataShape, data.dtype, outputData); + } + var sparseSegmentSumConfig = { + kernelName: tfjsCore.SparseSegmentSum, + backendName: 'cpu', + kernelFunc: sparseSegmentSum, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function sparseToDense(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var sparseIndices = inputs.sparseIndices, sparseValues = inputs.sparseValues, defaultValue = inputs.defaultValue; + var outputShape = attrs.outputShape; + var _a = tfjsCore.backend_util.calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize; + var sumDupeIndices = false; + var indicesBuf = backend.bufferSync(sparseIndices); + var updatesBuf = backend.bufferSync(sparseValues); + var $defaultValue = backend.data.get(defaultValue.dataId).values[0]; + var outBuf = scatterImpl(indicesBuf, updatesBuf, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, $defaultValue, sumDupeIndices); + return backend.makeTensorInfo(outputShape, outBuf.dtype, outBuf.values); + } + var sparseToDenseConfig = { + kernelName: tfjsCore.SparseToDense, + backendName: 'cpu', + kernelFunc: sparseToDense + }; + + function splitV(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var numOrSizeSplits = attrs.numOrSizeSplits, axis = attrs.axis; + var $axis = tfjsCore.util.parseAxisParam(axis, x.shape)[0]; + var splitSizes = tfjsCore.backend_util.prepareSplitSize(x, numOrSizeSplits, $axis); + var begin = new Array(x.shape.length).fill(0); + var size = x.shape.slice(); + return splitSizes.map(function (s) { + var sliceSize = __spread(size); + sliceSize[$axis] = s; + var sliceT = slice({ inputs: { x: x }, backend: backend, attrs: { begin: begin, size: sliceSize } }); + begin[$axis] += s; + return sliceT; + }); + } + var splitVConfig = { + kernelName: tfjsCore.SplitV, + backendName: 'cpu', + kernelFunc: splitV + }; + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var squareConfig = { + kernelName: tfjsCore.Square, + backendName: 'cpu', + kernelFunc: function (_a) { + var inputs = _a.inputs, backend = _a.backend; + var x = inputs.x; + var cpuBackend = backend; + assertNotComplex(x, 'square'); + var values = cpuBackend.data.get(x.dataId).values; + var newValues = new Float32Array(values.length); + for (var i = 0; i < values.length; ++i) { + var value = values[i]; + newValues[i] = value * value; + } + var dataId = cpuBackend.write(newValues, x.shape, x.dtype); + return { dataId: dataId, shape: x.shape, dtype: x.dtype }; + } + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var step = unaryKernelFunc(tfjsCore.Step, function (xi, attrs) { + var stepAttrs = attrs; + if (isNaN(xi)) { + return NaN; + } + else { + return xi > 0 ? 1 : stepAttrs.alpha; + } + }); + var stepConfig = { + kernelName: tfjsCore.Step, + backendName: 'cpu', + kernelFunc: step, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function stridedSlice(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var begin = attrs.begin, end = attrs.end, strides = attrs.strides, beginMask = attrs.beginMask, endMask = attrs.endMask, ellipsisMask = attrs.ellipsisMask, newAxisMask = attrs.newAxisMask, shrinkAxisMask = attrs.shrinkAxisMask; + assertNotComplex(x, 'stridedSlice'); + var _a = tfjsCore.slice_util.sliceInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), finalShapeSparse = _a.finalShapeSparse, finalShape = _a.finalShape, isIdentity = _a.isIdentity, sliceDim0 = _a.sliceDim0, isSimpleSlice = _a.isSimpleSlice, $begin = _a.begin, $end = _a.end, $strides = _a.strides; + var result; + // ref: + // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/strided_slice_op.cc + if (isIdentity) { + // Optimization #1, slice is a no-op plus reshape + result = reshape({ inputs: { x: x }, backend: backend, attrs: { shape: finalShape } }); + } + else if (sliceDim0 || isSimpleSlice) { + // Optimization #2, slice is memory contiguous (only occurs in dim 0) + tfjsCore.util.assert(x.shape.length >= 1, function () { return "Input must have rank at least 1, got: " + x.shape.length; }); + var size = tfjsCore.slice_util.computeOutShape($begin, $end, $strides); + // To tolerate begin[0] > end[0] (a 0-output slice), we min(begin, end). + var sliced = slice({ inputs: { x: x }, backend: backend, attrs: { begin: $begin, size: size } }); + result = + reshape({ inputs: { x: sliced }, backend: backend, attrs: { shape: finalShape } }); + backend.disposeIntermediateTensorInfo(sliced); + } + else { + var xBuf = backend.bufferSync(x); + var outBuf = stridedSliceImpl(finalShapeSparse, xBuf, $strides, $begin); + result = backend.makeTensorInfo(finalShape, outBuf.dtype, outBuf.values); + } + return result; + } + var stridedSliceConfig = { + kernelName: tfjsCore.StridedSlice, + backendName: 'cpu', + kernelFunc: stridedSlice + }; + + function stringNGrams(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var separator = attrs.separator, nGramWidths = attrs.nGramWidths, leftPad = attrs.leftPad, rightPad = attrs.rightPad, padWidth = attrs.padWidth, preserveShortSequences = attrs.preserveShortSequences; + var data = inputs.data, dataSplits = inputs.dataSplits; + var $data = backend.data.get(data.dataId).values; + var $dataSplits = backend.data.get(dataSplits.dataId).values; + var _a = __read(stringNGramsImpl($data, $dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences), 2), nGrams = _a[0], nGramsSplits = _a[1]; + return [ + backend.makeTensorInfo([nGrams.length], 'string', nGrams), + backend.makeTensorInfo(dataSplits.shape, 'int32', nGramsSplits), + ]; + } + var stringNGramsConfig = { + kernelName: tfjsCore.StringNGrams, + backendName: 'cpu', + kernelFunc: stringNGrams, + }; + + function stringSplit(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var skipEmpty = attrs.skipEmpty; + var input = inputs.input, delimiter = inputs.delimiter; + if (input.dtype !== 'string') { + throw new Error('Input must be of datatype string'); + } + if (input.shape.length !== 1) { + throw new Error("Input must be a vector, got shape: " + input.shape); + } + if (delimiter.shape.length !== 0) { + throw new Error("Delimiter must be a scalar, got shape: " + delimiter.shape); + } + var $input = backend.data.get(input.dataId).values; + var $delimiter = backend.data.get(delimiter.dataId).values[0]; + var _a = __read(stringSplitImpl($input, $delimiter, skipEmpty), 3), indices = _a[0], values = _a[1], shape = _a[2]; + var outputSize = values.length; + return [ + backend.makeTensorInfo([outputSize, 2], 'int32', indices), + backend.makeTensorInfo([outputSize], 'string', values), + backend.makeTensorInfo([2], 'int32', new Int32Array(shape)) + ]; + } + var stringSplitConfig = { + kernelName: tfjsCore.StringSplit, + backendName: 'cpu', + kernelFunc: stringSplit, + }; + + /** + * @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. + * ============================================================================= + */ + function stringToHashBucketFast(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var numBuckets = attrs.numBuckets; + var input = inputs.input; + if (input.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"); + } + var $input = backend.data.get(input.dataId).values; + var output = stringToHashBucketFastImpl($input, numBuckets); + return backend.makeTensorInfo(input.shape, 'int32', output); + } + var stringToHashBucketFastConfig = { + kernelName: tfjsCore.StringToHashBucketFast, + backendName: 'cpu', + kernelFunc: stringToHashBucketFast, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var tan = unaryKernelFunc(tfjsCore.Tan, function (xi) { return Math.tan(xi); }); + var tanConfig = { + kernelName: tfjsCore.Tan, + backendName: 'cpu', + kernelFunc: tan, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var tanh = unaryKernelFunc(tfjsCore.Tanh, function (xi) { return Math.tanh(xi); }); + var tanhConfig = { + kernelName: tfjsCore.Tanh, + backendName: 'cpu', + kernelFunc: tanh, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function tile(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var reps = attrs.reps; + assertNotComplex(x, 'tile'); + var outBuf = tileImpl(backend.bufferSync(x), reps); + return backend.makeTensorInfo(outBuf.shape, outBuf.dtype, outBuf.values); + } + var tileConfig = { + kernelName: tfjsCore.Tile, + backendName: 'cpu', + kernelFunc: tile + }; + + function topK(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x; + var k = attrs.k, sorted = attrs.sorted; + assertNotComplex(x, 'topk'); + var xVals = backend.data.get(x.dataId).values; + var _a = __read(topKImpl(xVals, x.shape, x.dtype, k, sorted), 2), allTopKVals = _a[0], allTopKIndices = _a[1]; + return [ + backend.makeTensorInfo(allTopKVals.shape, allTopKVals.dtype, allTopKVals.values), + backend.makeTensorInfo(allTopKIndices.shape, allTopKIndices.dtype, allTopKIndices.values) + ]; + } + var topKConfig = { + kernelName: tfjsCore.TopK, + backendName: 'cpu', + kernelFunc: topK + }; + + function transform(args) { + var inputs = args.inputs, attrs = args.attrs, backend = args.backend; + var image = inputs.image, transforms = inputs.transforms; + var interpolation = attrs.interpolation, fillMode = attrs.fillMode, fillValue = attrs.fillValue, outputShape = attrs.outputShape; + var _a = __read(image.shape, 4), batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3]; + var _b = __read(outputShape != null ? outputShape : [imageHeight, imageWidth], 2), outHeight = _b[0], outWidth = _b[1]; + var outShape = [batch, outHeight, outWidth, numChannels]; + var strides = tfjsCore.util.computeStrides(image.shape); + var batchStride = strides[0]; + var rowStride = strides[1]; + var colStride = strides[2]; + var outVals = tfjsCore.util.getTypedArrayFromDType(image.dtype, tfjsCore.util.sizeFromShape(outShape)); + outVals.fill(fillValue); + var imageVals = backend.data.get(image.dataId).values; + var transformVals = backend.data.get(transforms.dataId).values; + // Ref TF implementation: + // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/image/image_ops.h + for (var b = 0; b < batch; ++b) { + var transform_1 = transforms.shape[0] === 1 ? + transformVals : + transformVals.subarray(b * 8, b * 8 + 8); + for (var outY = 0; outY < outHeight; ++outY) { + for (var outX = 0; outX < outWidth; ++outX) { + for (var channel = 0; channel < numChannels; ++channel) { + var val = void 0; + var projection = transform_1[6] * outX + transform_1[7] * outY + 1; + if (projection === 0) { + // Return the fill value for infinite coordinates, + // which are outside the input image + continue; + } + var inX = (transform_1[0] * outX + transform_1[1] * outY + transform_1[2]) / + projection; + var inY = (transform_1[3] * outX + transform_1[4] * outY + transform_1[5]) / + projection; + var x = mapCoord(inX, imageWidth, fillMode); + var 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)); + } + var ind = b * batchStride + outY * rowStride + outX * colStride + channel; + outVals[ind] = val; + } + } + } + return backend.makeTensorInfo(outShape, image.dtype, outVals); + } + var dataId = backend.write(outVals, outShape, image.dtype); + return { dataId: dataId, shape: image.shape, dtype: image.dtype }; + } + var transformConfig = { + kernelName: tfjsCore.Transform, + backendName: 'cpu', + kernelFunc: transform + }; + 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); + } + } + function mapCoordReflect(outCoord, len) { + // Reflect [abcd] to [dcba|abcd|dcba]. + var inCoord = outCoord; + if (inCoord < 0) { + if (len <= 1) { + inCoord = 0; + } + else { + var 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 { + var sz2 = 2 * len; + inCoord -= sz2 * Math.trunc(inCoord / sz2); + if (inCoord >= len) { + inCoord = sz2 - inCoord - 1; + } + } + } + // clamp is necessary because when outCoord = 3.5 and len = 4, + // inCoord = 3.5 and will be rounded to 4 in nearest interpolation. + return tfjsCore.util.clamp(0, inCoord, len - 1); + } + function mapCoordWrap(outCoord, len) { + // Wrap [abcd] to [abcd|abcd|abcd]. + var inCoord = outCoord; + if (inCoord < 0) { + if (len <= 1) { + inCoord = 0; + } + else { + var sz = len - 1; + inCoord += len * (Math.trunc(-inCoord / sz) + 1); + } + } + else if (inCoord > len - 1) { + if (len <= 1) { + inCoord = 0; + } + else { + var sz = len - 1; + inCoord -= len * Math.trunc(inCoord / sz); + } + } + // clamp is necessary because when outCoord = -0.5 and len = 4, + // inCoord = 3.5 and will be rounded to 4 in nearest interpolation. + return tfjsCore.util.clamp(0, inCoord, len - 1); + } + function mapCoordConstant(outCoord, len) { + return outCoord; + } + function mapCoordNearest(outCoord, len) { + return tfjsCore.util.clamp(0, outCoord, len - 1); + } + function readWithFillValue(imageVals, imageHeight, imageWidth, batchStride, rowStride, colStride, batch, y, x, channel, fillValue) { + var 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) { + var $y = Math.round(y); + var $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) { + var yFloor = Math.floor(y); + var xFloor = Math.floor(x); + var yCeil = yFloor + 1; + var xCeil = xFloor + 1; + // f(x, yFloor) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yFloor) + // + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yFloor) + var 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); + // f(x, yCeil) = (xCeil - x) / (xCeil - xFloor) * f(xFloor, yCeil) + // + (x - xFloor) / (xCeil - xFloor) * f(xCeil, yCeil) + var 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); + // f(x, y) = (yCeil - y) / (yCeil - yFloor) * f(x, yFloor) + // + (y - yFloor) / (yCeil - yFloor) * f(x, yCeil) + return (yCeil - y) * valueYFloor + (y - yFloor) * valueYCeil; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the License); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an AS IS BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function unique(args) { + var inputs = args.inputs, attrs = args.attrs, backend = args.backend; + var axis = attrs.axis; + var x = inputs.x; + assertNotComplex(x, 'unique'); + var values = backend.data.get(x.dataId).values; + var _a = uniqueImpl(values, axis, x.shape, x.dtype), outputValues = _a.outputValues, outputShape = _a.outputShape, indices = _a.indices; + return [ + backend.makeTensorInfo(outputShape, x.dtype, outputValues), + backend.makeTensorInfo([indices.length], 'int32', indices), + ]; + } + var uniqueConfig = { + kernelName: tfjsCore.Unique, + backendName: 'cpu', + kernelFunc: unique, + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function unpack(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var value = inputs.value; + var axis = attrs.axis; + if (axis < 0) { + axis += value.shape.length; + } + var valueRank = value.shape.length; + var num = value.shape[axis]; + var outShape = new Array(valueRank - 1); + var outIndex = 0; + for (var i = 0; i < valueRank; i++) { + if (i !== axis) { + outShape[outIndex++] = value.shape[i]; + } + } + var begin = new Array(valueRank).fill(0); + var size = value.shape.slice(); + size[axis] = 1; + var res = new Array(num); + for (var i = 0; i < res.length; i++) { + begin[axis] = i; + var tempRes = slice({ inputs: { x: value }, backend: backend, attrs: { begin: begin, size: size } }); + res[i] = reshape({ inputs: { x: tempRes }, backend: backend, attrs: { shape: outShape } }); + backend.disposeIntermediateTensorInfo(tempRes); + } + return res; + } + var unpackConfig = { + kernelName: tfjsCore.Unpack, + backendName: 'cpu', + kernelFunc: unpack + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function unsortedSegmentSum(args) { + var inputs = args.inputs, backend = args.backend, attrs = args.attrs; + var x = inputs.x, segmentIds = inputs.segmentIds; + var numSegments = attrs.numSegments; + assertNotComplex(x, 'unsortedSegmentSum'); + var xRank = x.shape.length; + var segmentIdsRank = segmentIds.shape.length; + var res = []; + var intermediates = []; + // Reshape the segment id's so that they can be broadcast with + // x. The new shape should be [segmentIds.shape, 1, ..., 1] + var numIters = xRank - segmentIdsRank; + var $segmentIds = segmentIds; + for (var i = 0; i < numIters; ++i) { + var expanded = expandDims({ inputs: { input: $segmentIds }, backend: backend, attrs: { dim: i + 1 } }); + $segmentIds = expanded; + intermediates.push(expanded); + } + for (var i = 0; i < numSegments; ++i) { + var scalarValue = tfjsCore.util.createScalarValue(i, 'int32'); + var segmentId = backend.makeTensorInfo([], 'int32', scalarValue); + var mask = equal({ inputs: { a: segmentId, b: $segmentIds }, backend: backend }); + var maskCasted = cast({ inputs: { x: mask }, backend: backend, attrs: { dtype: 'float32' } }); + var mul = multiply({ inputs: { a: maskCasted, b: x }, backend: backend }); + var sumTensorInfo = sum({ inputs: { x: mul }, backend: backend, attrs: { axis: 0, keepDims: false } }); + res.push(sumTensorInfo); + intermediates.push(segmentId); + intermediates.push(mask); + intermediates.push(maskCasted); + intermediates.push(mul); + intermediates.push(sumTensorInfo); + } + var result = pack({ inputs: res, backend: backend, attrs: { axis: 0 } }); + intermediates.forEach(function (t) { return backend.disposeIntermediateTensorInfo(t); }); + return result; + } + var unsortedSegmentSumConfig = { + kernelName: tfjsCore.UnsortedSegmentSum, + backendName: 'cpu', + kernelFunc: unsortedSegmentSum + }; + + var e_1, _a; + // List all kernel configs here + var kernelConfigs = [ + _fusedMatMulConfig, + absConfig, + acosConfig, + acoshConfig, + addConfig, + addNConfig, + allConfig, + anyConfig, + argMaxConfig, + argMinConfig, + asinConfig, + asinhConfig, + atanConfig, + atan2Config, + atanhConfig, + avgPoolConfig, + avgPool3DConfig, + avgPool3DGradConfig, + avgPoolGradConfig, + batchMatMulConfig, + batchNormConfig, + batchToSpaceNDConfig, + bincountConfig, + broadcastArgsConfig, + castConfig, + ceilConfig, + clipConfig, + complexConfig, + complexAbsConfig, + concatConfig, + conv2DBackpropFilterConfig, + conv2DBackpropInputConfig, + conv2DConfig, + conv3DBackpropFilterV2Config, + conv3DBackpropInputV2Config, + conv3DConfig, + cosConfig, + coshConfig, + cropAndResizeConfig, + cumsumConfig, + denseBincountConfig, + depthToSpaceConfig, + depthwiseConv2dNativeConfig, + depthwiseConv2dNativeBackpropFilterConfig, + depthwiseConv2dNativeBackpropInputConfig, + diagConfig, + dilation2dConfig, + dilation2dBackpropInputConfig, + dilation2dBackpropFilterConfig, + realDivConfig, + einsumConfig, + eluConfig, + eluGradConfig, + 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, + maximumConfig, + maxPoolConfig, + maxPool3DConfig, + maxPool3DGradConfig, + maxPoolGradConfig, + maxPoolWithArgmaxConfig, + maxConfig, + meanConfig, + minConfig, + minimumConfig, + mirrorPadConfig, + modConfig, + multinomialConfig, + multiplyConfig, + negConfig, + nonMaxSuppressionV3Config, + nonMaxSuppressionV4Config, + nonMaxSuppressionV5Config, + notEqualConfig, + oneHotConfig, + onesLikeConfig, + packConfig, + padV2Config, + powConfig, + preluConfig, + prodConfig, + rangeConfig, + realConfig, + reciprocalConfig, + reluConfig, + relu6Config, + reshapeConfig, + resizeBilinearConfig, + resizeBilinearGradConfig, + resizeNearestNeighborConfig, + resizeNearestNeighborGradConfig, + reverseConfig, + rotateWithOffsetConfig, + roundConfig, + rsqrtConfig, + scatterNdConfig, + 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, + transposeConfig, + transformConfig, + uniqueConfig, + unpackConfig, + unsortedSegmentSumConfig, + zerosLikeConfig + ]; + try { + for (var kernelConfigs_1 = __values(kernelConfigs), kernelConfigs_1_1 = kernelConfigs_1.next(); !kernelConfigs_1_1.done; kernelConfigs_1_1 = kernelConfigs_1.next()) { + var kernelConfig = kernelConfigs_1_1.value; + tfjsCore.registerKernel(kernelConfig); + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (kernelConfigs_1_1 && !kernelConfigs_1_1.done && (_a = kernelConfigs_1.return)) _a.call(kernelConfigs_1); + } + finally { if (e_1) throw e_1.error; } + } + + exports.MathBackendCPU = MathBackendCPU; + exports.shared = shared; + exports.version_cpu = version; + + Object.defineProperty(exports, '__esModule', { value: true }); + +}))); +//# sourceMappingURL=tf-backend-cpu.js.map diff --git a/js/tf-converter.js b/js/tf-converter.js new file mode 100644 index 0000000..8f0d976 --- /dev/null +++ b/js/tf-converter.js @@ -0,0 +1,29751 @@ +/** + * @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. + * ============================================================================= + */ +(function (global, factory) { + typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('@tensorflow/tfjs-core')) : + typeof define === 'function' && define.amd ? define(['exports', '@tensorflow/tfjs-core'], factory) : + (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.tf)); +}(this, (function (exports, tfc) { 'use strict'; + + function _interopNamespace(e) { + if (e && e.__esModule) return e; + var n = Object.create(null); + if (e) { + Object.keys(e).forEach(function (k) { + if (k !== 'default') { + var d = Object.getOwnPropertyDescriptor(e, k); + Object.defineProperty(n, k, d.get ? d : { + enumerable: true, + get: function () { + return e[k]; + } + }); + } + }); + } + n['default'] = e; + return n; + } + + var tfc__namespace = /*#__PURE__*/_interopNamespace(tfc); + + /** + * @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. + * ============================================================================= + */ + var ENV$1 = tfc.env(); + /** Whether to keep intermediate tensors. */ + ENV$1.registerFlag('KEEP_INTERMEDIATE_TENSORS', function () { return false; }, function (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.'); + } + }); + + /*! ***************************************************************************** + Copyright (c) Microsoft Corporation. + + Permission to use, copy, modify, and/or distribute this software for any + purpose with or without fee is hereby granted. + + THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH + REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, + INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM + LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR + OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + ***************************************************************************** */ + /* global Reflect, Promise */ + var extendStatics = function (d, b) { + extendStatics = Object.setPrototypeOf || + ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) || + function (d, b) { for (var p in b) + if (b.hasOwnProperty(p)) + d[p] = b[p]; }; + return extendStatics(d, b); + }; + function __extends(d, b) { + extendStatics(d, b); + function __() { this.constructor = d; } + d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __()); + } + function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { + step(generator.next(value)); + } + catch (e) { + reject(e); + } } + function rejected(value) { try { + step(generator["throw"](value)); + } + catch (e) { + reject(e); + } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); + } + function __generator(thisArg, body) { + var _ = { label: 0, sent: function () { if (t[0] & 1) + throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g; + return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function () { return this; }), g; + function verb(n) { return function (v) { return step([n, v]); }; } + function step(op) { + if (f) + throw new TypeError("Generator is already executing."); + while (_) + try { + if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) + return t; + if (y = 0, t) + op = [op[0] & 2, t.value]; + switch (op[0]) { + case 0: + case 1: + t = op; + break; + case 4: + _.label++; + return { value: op[1], done: false }; + case 5: + _.label++; + y = op[1]; + op = [0]; + continue; + case 7: + op = _.ops.pop(); + _.trys.pop(); + continue; + default: + if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { + _ = 0; + continue; + } + if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { + _.label = op[1]; + break; + } + if (op[0] === 6 && _.label < t[1]) { + _.label = t[1]; + t = op; + break; + } + if (t && _.label < t[2]) { + _.label = t[2]; + _.ops.push(op); + break; + } + if (t[2]) + _.ops.pop(); + _.trys.pop(); + continue; + } + op = body.call(thisArg, _); + } + catch (e) { + op = [6, e]; + y = 0; + } + finally { + f = t = 0; + } + if (op[0] & 5) + throw op[1]; + return { value: op[0] ? op[1] : void 0, done: true }; + } + } + function __values(o) { + var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0; + if (m) + return m.call(o); + if (o && typeof o.length === "number") + return { + next: function () { + if (o && i >= o.length) + o = void 0; + return { value: o && o[i++], done: !o }; + } + }; + throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined."); + } + function __read(o, n) { + var m = typeof Symbol === "function" && o[Symbol.iterator]; + if (!m) + return o; + var i = m.call(o), r, ar = [], e; + try { + while ((n === void 0 || n-- > 0) && !(r = i.next()).done) + ar.push(r.value); + } + catch (error) { + e = { error: error }; + } + finally { + try { + if (r && !r.done && (m = i["return"])) + m.call(i); + } + finally { + if (e) + throw e.error; + } + } + return ar; + } + function __spread() { + for (var ar = [], i = 0; i < arguments.length; i++) + ar = ar.concat(__read(arguments[i])); + return ar; + } + + /** + * @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. + * + * ============================================================================= + */ + /** DataType enum. */ + var DataType; + (function (DataType) { + // Not a legal value for DataType. Used to indicate a DataType field + // has not been set. + DataType[DataType["DT_INVALID"] = 0] = "DT_INVALID"; + // Data types that all computation devices are expected to be + // capable to support. + DataType[DataType["DT_FLOAT"] = 1] = "DT_FLOAT"; + DataType[DataType["DT_DOUBLE"] = 2] = "DT_DOUBLE"; + DataType[DataType["DT_INT32"] = 3] = "DT_INT32"; + DataType[DataType["DT_UINT8"] = 4] = "DT_UINT8"; + DataType[DataType["DT_INT16"] = 5] = "DT_INT16"; + DataType[DataType["DT_INT8"] = 6] = "DT_INT8"; + DataType[DataType["DT_STRING"] = 7] = "DT_STRING"; + DataType[DataType["DT_COMPLEX64"] = 8] = "DT_COMPLEX64"; + DataType[DataType["DT_INT64"] = 9] = "DT_INT64"; + DataType[DataType["DT_BOOL"] = 10] = "DT_BOOL"; + DataType[DataType["DT_QINT8"] = 11] = "DT_QINT8"; + DataType[DataType["DT_QUINT8"] = 12] = "DT_QUINT8"; + DataType[DataType["DT_QINT32"] = 13] = "DT_QINT32"; + DataType[DataType["DT_BFLOAT16"] = 14] = "DT_BFLOAT16"; + DataType[DataType["DT_QINT16"] = 15] = "DT_QINT16"; + DataType[DataType["DT_QUINT16"] = 16] = "DT_QUINT16"; + DataType[DataType["DT_UINT16"] = 17] = "DT_UINT16"; + DataType[DataType["DT_COMPLEX128"] = 18] = "DT_COMPLEX128"; + DataType[DataType["DT_HALF"] = 19] = "DT_HALF"; + DataType[DataType["DT_RESOURCE"] = 20] = "DT_RESOURCE"; + DataType[DataType["DT_VARIANT"] = 21] = "DT_VARIANT"; + DataType[DataType["DT_UINT32"] = 22] = "DT_UINT32"; + DataType[DataType["DT_UINT64"] = 23] = "DT_UINT64"; + // Do not use! These are only for parameters. Every enum above + // should have a corresponding value below (verified by types_test). + DataType[DataType["DT_FLOAT_REF"] = 101] = "DT_FLOAT_REF"; + DataType[DataType["DT_DOUBLE_REF"] = 102] = "DT_DOUBLE_REF"; + DataType[DataType["DT_INT32_REF"] = 103] = "DT_INT32_REF"; + DataType[DataType["DT_UINT8_REF"] = 104] = "DT_UINT8_REF"; + DataType[DataType["DT_INT16_REF"] = 105] = "DT_INT16_REF"; + DataType[DataType["DT_INT8_REF"] = 106] = "DT_INT8_REF"; + DataType[DataType["DT_STRING_REF"] = 107] = "DT_STRING_REF"; + DataType[DataType["DT_COMPLEX64_REF"] = 108] = "DT_COMPLEX64_REF"; + DataType[DataType["DT_INT64_REF"] = 109] = "DT_INT64_REF"; + DataType[DataType["DT_BOOL_REF"] = 110] = "DT_BOOL_REF"; + DataType[DataType["DT_QINT8_REF"] = 111] = "DT_QINT8_REF"; + DataType[DataType["DT_QUINT8_REF"] = 112] = "DT_QUINT8_REF"; + DataType[DataType["DT_QINT32_REF"] = 113] = "DT_QINT32_REF"; + DataType[DataType["DT_BFLOAT16_REF"] = 114] = "DT_BFLOAT16_REF"; + DataType[DataType["DT_QINT16_REF"] = 115] = "DT_QINT16_REF"; + DataType[DataType["DT_QUINT16_REF"] = 116] = "DT_QUINT16_REF"; + DataType[DataType["DT_UINT16_REF"] = 117] = "DT_UINT16_REF"; + DataType[DataType["DT_COMPLEX128_REF"] = 118] = "DT_COMPLEX128_REF"; + DataType[DataType["DT_HALF_REF"] = 119] = "DT_HALF_REF"; + DataType[DataType["DT_RESOURCE_REF"] = 120] = "DT_RESOURCE_REF"; + DataType[DataType["DT_VARIANT_REF"] = 121] = "DT_VARIANT_REF"; + DataType[DataType["DT_UINT32_REF"] = 122] = "DT_UINT32_REF"; + DataType[DataType["DT_UINT64_REF"] = 123] = "DT_UINT64_REF"; + })(DataType || (DataType = {})); + var SaverDef; + (function (SaverDef) { + (function (CheckpointFormatVersion) { + CheckpointFormatVersion[CheckpointFormatVersion["LEGACY"] = 0] = "LEGACY"; + CheckpointFormatVersion[CheckpointFormatVersion["V1"] = 1] = "V1"; + CheckpointFormatVersion[CheckpointFormatVersion["V2"] = 2] = "V2"; + })(SaverDef.CheckpointFormatVersion || (SaverDef.CheckpointFormatVersion = {})); + })(SaverDef || (SaverDef = {})); + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var CUSTOM_OPS = {}; + /** + * Register an Op for graph model executor. This allow you to register + * TensorFlow custom op or override existing op. + * + * Here is an example of registering a new MatMul Op. + * ```js + * const customMatmul = (node) => + * tf.matMul( + * node.inputs[0], node.inputs[1], + * node.attrs['transpose_a'], node.attrs['transpose_b']); + * + * tf.registerOp('MatMul', customMatmul); + * ``` + * The inputs and attrs of the node object is based on the TensorFlow op + * registry. + * + * @param name The Tensorflow Op name. + * @param opFunc An op function which is called with the current graph node + * during execution and needs to return a tensor or a list of tensors. The node + * has the following attributes: + * - attr: A map from attribute name to its value + * - inputs: A list of input tensors + * + * @doc {heading: 'Models', subheading: 'Op Registry'} + */ + function registerOp(name, opFunc) { + var opMapper = { + tfOpName: name, + category: 'custom', + inputs: [], + attrs: [], + customExecutor: opFunc + }; + CUSTOM_OPS[name] = opMapper; + } + /** + * Retrieve the OpMapper object for the registered op. + * + * @param name The Tensorflow Op name. + * + * @doc {heading: 'Models', subheading: 'Op Registry'} + */ + function getRegisteredOp(name) { + return CUSTOM_OPS[name]; + } + /** + * Deregister the Op for graph model executor. + * + * @param name The Tensorflow Op name. + * + * @doc {heading: 'Models', subheading: 'Op Registry'} + */ + function deregisterOp(name) { + delete CUSTOM_OPS[name]; + } + + function getParamValue(paramName, node, tensorMap, context, resourceManager) { + var inputParam = node.inputParams[paramName]; + if (inputParam && inputParam.inputIndexStart !== undefined) { + var start = inputParam.inputIndexStart; + var end = inputParam.inputIndexEnd === 0 ? + undefined : + (inputParam.inputIndexEnd === undefined ? start + 1 : + inputParam.inputIndexEnd); + if (inputParam.type === 'tensor') { + return getTensor(node.inputNames[inputParam.inputIndexStart], tensorMap, context, resourceManager); + } + if (inputParam.type === 'tensors') { + var inputs = node.inputNames.slice(start, end); + return inputs.map(function (name) { return getTensor(name, tensorMap, context, resourceManager); }); + } + var tensor = getTensor(node.inputNames.slice(start)[0], tensorMap, context, resourceManager); + var data = tensor.dataSync(); + return inputParam.type === 'number' ? + data[0] : + tfc.util.toNestedArray(tensor.shape, data); + } + var attrParam = node.attrParams[paramName]; + return attrParam && attrParam.value; + } + /** + * Retrieve the tensor from tensorsMap based on input name. + * @param name Node input name + * @param tensorsMap Tensors map keyed by the node + * @param context contains tensors and information for running the current node. + * @param resourceManager Optional. Contains global resources of the model. + */ + function getTensor(name, tensorsMap, context, resourceManager) { + var _a = __read(parseNodeName(name), 2), nodeName = _a[0], index = _a[1]; + if (resourceManager != null) { + var tensor = resourceManager.getHashTableHandleByName(nodeName); + if (tensor != null) { + return tensor; + } + } + var contextId = context.currentContextIds.find(function (contextId) { + return !!tensorsMap[getNodeNameWithContextId(nodeName, contextId)]; + }); + return contextId !== undefined ? + tensorsMap[getNodeNameWithContextId(nodeName, contextId)][index] : + undefined; + } + /** + * Retrieve the tensors based on input name for current context. + * @param name Node input name + * @param tensorsMap Tensors map keyed by the node + */ + function getTensorsForCurrentContenxt(name, tensorsMap, context) { + return tensorsMap[getNodeNameWithContextId(name, context.currentContextId)]; + } + /** + * Returns the node name, outputName and index from the Node input name. + * @param inputName The input name of the node, in format of + * node_name:output_index, i.e. MatMul:0, if the output_index is not set, it is + * default to 0. + * If the input name contains output name i.e. StringSplit:indices:0, it will + * return ['StringSplit', 0, 'indices']. + */ + function getNodeNameAndIndex(inputName, context) { + var _a = __read(parseNodeName(inputName), 3), nodeName = _a[0], index = _a[1], outputName = _a[2]; + return [ + getNodeNameWithContextId(nodeName, context && context.currentContextId), + index, outputName + ]; + } + function getNodeNameWithContextId(name, contextId) { + return !!contextId ? name + "-" + contextId : name; + } + function parseNodeName(name) { + var parts = name.split(':'); + if (parts.length === 1) { + return [name, 0, undefined]; + } + var nodeName = parts[0]; + var outputName = parts.length === 3 ? parts[1] : undefined; + var index = Number(parts[parts.length - 1]); + return [nodeName, index, outputName]; + } + function getPadding(node, tensorMap, context) { + var pad = getParamValue('pad', node, tensorMap, context); + if (pad === 'explicit') { + // This is 1d array, we need to convert it to 2d array + pad = getParamValue('explicitPaddings', node, tensorMap, context); + var explicitPadding = [[0, 0], [0, 0], [0, 0], [0, 0]]; + for (var i = 0; i < 4; i++) { + explicitPadding[i][0] = pad[i * 2]; + explicitPadding[i][1] = pad[i * 2 + 1]; + } + return explicitPadding; + } + return pad; + } + /** + * Reuse the tensor if it is marked as keep, otherwise clone the tensor to + * avoid disposal. This is important for TensorArray and TensorList ops, since + * internally they use a tensor as the id for TensorArray and TensorList, and + * to simplify lookup, they also use Tensor.id as the key to the internal map. + * These id tensors have been marked as kept in the backend, we need avoid clone + * them in order to create new Tensor.id. + * @param tensor + */ + function cloneTensor(tensor) { + return tensor.kept ? tensor : tfc.clone(tensor); + } + + /** + * @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. + * ============================================================================= + */ + var json$i = [ + { + 'tfOpName': 'Add', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'AddV2', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'AddN', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'end': 0, + 'name': 'tensors', + 'type': 'tensors' + } + ] + }, + { + 'tfOpName': 'BiasAdd', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Sub', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'RealDiv', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Div', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'DivNoNan', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'FloorDiv', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Mul', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Maximum', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Minimum', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Pow', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'SquaredDifference', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Mod', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'FloorMod', + 'category': 'arithmetic', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + } + ]; + + var arithmetic = { + __proto__: null, + json: json$i + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$h = [ + { + 'tfOpName': 'Abs', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Acos', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Asin', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Atan', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Atan2', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'y', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Ceil', + 'category': 'basic_math', + 'inputs': [ 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'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Real', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'Tout', + 'name': 'outputType', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Prelu', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'alpha', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Relu', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Relu6', + 'category': 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'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Sqrt', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Rsqrt', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Square', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Tan', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Tanh', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Sign', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Round', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Expm1', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Log1p', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Reciprocal', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Softplus', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Asinh', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Acosh', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Atanh', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Erf', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Prod', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axes', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool', + 'notSupported': true + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LeakyRelu', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'alpha', + 'name': 'alpha', + 'type': 'number', + 'defaultValue': 0.2 + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'IsNan', + 'category': 'basic_math', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + } + ]; + + var basicMath = { + __proto__: null, + json: json$h + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$g = [ + { + 'tfOpName': 'EmptyTensorList', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'elementShape', + 'type': 'shape' + }, + { + 'start': 1, + 'name': 'maxNumElements', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'LoopCond', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'pred', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'Switch', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'data', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'pred', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'Merge', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'end': 0, + 'name': 'tensors', + 'type': 'tensors' + } + ] + }, + { + 'tfOpName': 'Enter', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensor', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'frame_name', + 'name': 'frameName', + 'type': 'string' + }, + { + 'tfName': 'is_constant', + 'name': 'isConstant', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Exit', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensor', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'NextIteration', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensor', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'TensorArrayV3', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'size', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'dtype', + 'name': 'dtype', + 'type': 'dtype' + }, + { + 'tfName': 'element_shape', + 'name': 'elementShape', + 'type': 'shape' + }, + { + 'tfName': 'dynamic_size', + 'name': 'dynamicSize', + 'type': 'bool' + }, + { + 'tfName': 'clear_after_read', + 'name': 'clearAfterRead', + 'type': 'bool' + }, + { + 'tfName': 'identical_element_shapes', + 'name': 'identicalElementShapes', + 'type': 'bool' + }, + { + 'tfName': 'tensor_array_name', + 'name': 'name', + 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'start': 0, + 'name': 'tensorListId', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'index', + 'type': 'number' + }, + { + 'start': 2, + 'name': 'tensor', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListReserve', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'elementShape', + 'type': 'shape' + }, + { + 'start': 1, + 'name': 'numElements', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListFromTensor', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensor', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'elementShape', + 'type': 'shape' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListStack', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensorListId', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'elementShape', + 'type': 'shape' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + }, + { + 'tfName': 'num_elements', + 'name': 'numElements', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListSplit', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensor', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'elementShape', + 'type': 'shape' + }, + { + 'start': 2, + 'name': 'lengths', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListConcat', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensorListId', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'element_shape', + 'name': 'elementShape', + 'type': 'shape' + }, + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListPopBack', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensorListId', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'elementShape', + 'type': 'shape' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TensorListPushBack', + 'category': 'control', + 'inputs': [ + { + 'start': 0, + 'name': 'tensorListId', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'tensor', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'element_dtype', + 'name': 'elementDType', + 'type': 'dtype' + } + ] + } + ]; + + var control = { + __proto__: null, + json: json$g + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$f = [ + { + 'tfOpName': 'AvgPool', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + }, + { + 'tfName': 'ksize', + 'name': 'kernelSize', + 'type': 'number[]' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'MaxPool', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + }, + { + 'tfName': 'ksize', + 'name': 'kernelSize', + 'type': 'number[]' + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [], + 'notSupported': true + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'MaxPoolWithArgmax', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'ksize', + 'name': 'kernelSize', + 'type': 'number[]' + }, + { + 'tfName': 'include_batch_in_index', + 'name': 'includeBatchInIndex', + 'type': 'bool' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'AvgPool3D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + }, + { + 'tfName': 'ksize', + 'name': 'kernelSize', + 'type': 'number[]' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'MaxPool3D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + }, + { + 'tfName': 'ksize', + 'name': 'kernelSize', + 'type': 'number[]' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Conv1D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'stride', + 'name': 'stride', + 'type': 'number' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NWC' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'dilation', + 'name': 'dilation', + 'type': 'number', + 'defaultValue': 1 + } + ] + }, + { + 'tfOpName': 'Conv2D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'useCudnnOnGpu', + 'name': 'useCudnnOnGpu', + 'type': 'bool' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NHWC' + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [] + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': '_FusedConv2D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + }, + { + 'start': 2, + 'end': 0, + 'name': 'args', + 'type': 'tensors' + } + ], + 'attrs': [ + { + 'tfName': 'num_args', + 'name': 'numArgs', + 'type': 'number' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [] + }, + { + 'tfName': 'use_cudnn_on_gpu', + 'name': 'useCudnnOnGpu', + 'type': 'bool', + 'defaultValue': true + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NHWC' + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]', + 'defaultValue': [ + 1, + 1, + 1, + 1 + ] + }, + { + 'tfName': 'fused_ops', + 'name': 'fusedOps', + 'type': 'string[]', + 'defaultValue': [] + }, + { + 'tfName': 'epsilon', + 'name': 'epsilon', + 'type': 'number', + 'defaultValue': 0.0001 + }, + { + 'tfName': 'leakyrelu_alpha', + 'name': 'leakyreluAlpha', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'Conv2DBackpropInput', + 'category': 'convolution', + 'inputs': [ + { + 'start': 2, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + }, + { + 'start': 0, + 'name': 'outputShape', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [] + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'DepthwiseConv2d', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'input', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NHWC' + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [] + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'DepthwiseConv2dNative', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'input', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NHWC' + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [] + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'FusedDepthwiseConv2dNative', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + }, + { + 'start': 2, + 'end': 0, + 'name': 'args', + 'type': 'tensors' + } + ], + 'attrs': [ + { + 'tfName': 'num_args', + 'name': 'numArgs', + 'type': 'number' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NHWC' + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]', + 'defaultValue': [ + 1, + 1, + 1, + 1 + ] + }, + { + 'tfName': 'fused_ops', + 'name': 'fusedOps', + 'type': 'string[]', + 'defaultValue': [] + }, + { + 'tfName': 'explicit_paddings', + 'name': 'explicitPaddings', + 'type': 'number[]', + 'defaultValue': [] + } + ] + }, + { + 'tfOpName': 'Conv3D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'defaultValue': 'NHWC' + }, + { + 'tfName': 'dilations', + 'name': 'dilations', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'Dilation2D', + 'category': 'convolution', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'filter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'strides', + 'name': 'strides', + 'type': 'number[]' + }, + { + 'tfName': 'rates', + 'name': 'dilations', + 'type': 'number[]' + }, + { + 'tfName': 'padding', + 'name': 'pad', + 'type': 'string' + } + ] + } + ]; + + var convolution = { + __proto__: null, + json: json$f + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$e = [ + { + 'tfOpName': 'Fill', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'shape', + 'type': 'number[]' + }, + { + 'start': 1, + 'name': 'value', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'LinSpace', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'start', + 'type': 'number' + }, + { + 'start': 1, + 'name': 'stop', + 'type': 'number' + }, + { + 'start': 2, + 'name': 'num', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'OneHot', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'indices', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'depth', + 'type': 'number' + }, + { + 'start': 2, + 'name': 'onValue', + 'type': 'number', + 'defaultValue': 1 + }, + { + 'start': 3, + 'name': 'offValue', + 'type': 'number', + 'defaultValue': 0 + } + ], + 'attrs': [ + { + 'tfName': 'axis', + 'name': 'axis', + 'type': 'number', + 'notSupported': true + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Ones', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'shape', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'OnesLike', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'dtype', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'RandomUniform', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'shape', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'minval', + 'name': 'minval', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'maxval', + 'name': 'maxval', + 'type': 'number', + 'defaultValue': 1 + }, + { + 'tfName': 'dtype', + 'name': 'dtype', + 'type': 'dtype' + }, + { + 'tfName': 'seed', + 'name': 'seed', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'seed2', + 'name': 'seed2', + 'type': 'number', + 'defaultValue': 0, + 'notSupported': true + }, + { + 'tfName': 'T', + 'name': 'T', + 'type': 'number', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Range', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'start', + 'type': 'number' + }, + { + 'start': 1, + 'name': 'stop', + 'type': 'number' + }, + { + 'start': 2, + 'name': 'step', + 'type': 'number', + 'defaultValue': 0 + } + ], + 'attrs': [ + { + 'tfName': 'Tidx', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'TruncatedNormal', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'shape', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'means', + 'name': 'mean', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'stddev', + 'name': 'stdDev', + 'type': 'number', + 'defaultValue': 1 + }, + { + 'tfName': 'seed', + 'name': 'seed', + 'type': 'number' + }, + { + 'tfName': 'seed2', + 'name': 'seed2', + 'type': 'number', + 'defaultValue': 0, + 'notSupported': true + }, + { + 'tfName': 'dtype', + 'name': 'dtype', + 'type': 'dtype' + }, + { + 'tfName': 'T', + 'name': 'T', + 'type': 'number', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Zeros', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'shape', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'ZerosLike', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'Multinomial', + 'category': 'creation', + 'inputs': [ + { + 'start': 0, + 'name': 'logits', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'numSamples', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'seed', + 'name': 'seed', + 'type': 'number' + }, + { + 'tfName': 'seed2', + 'name': 'seed2', + 'type': 'number' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype' + }, + { + 'tfName': 'output_dtype', + 'name': 'output_dtype', + 'type': 'dtype' + } + ] + } + ]; + + var creation = { + __proto__: null, + json: json$e + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$d = [ + { + 'tfOpName': 'NonMaxSuppressionV2', + 'category': 'dynamic', + 'inputs': [ + { + 'start': 0, + 'name': 'boxes', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scores', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'maxOutputSize', + 'type': 'number' + }, + { + 'start': 3, + 'name': 'iouThreshold', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'NonMaxSuppressionV3', + 'category': 'dynamic', + 'inputs': [ + { + 'start': 0, + 'name': 'boxes', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scores', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'maxOutputSize', + 'type': 'number' + }, + { + 'start': 3, + 'name': 'iouThreshold', + 'type': 'number' + }, + { + 'start': 4, + 'name': 'scoreThreshold', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'NonMaxSuppressionV4', + 'category': 'dynamic', + 'inputs': [ + { + 'start': 0, + 'name': 'boxes', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scores', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'maxOutputSize', + 'type': 'number' + }, + { + 'start': 3, + 'name': 'iouThreshold', + 'type': 'number' + }, + { + 'start': 4, + 'name': 'scoreThreshold', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'T_threshold', + 'name': 'threshold', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'pad_to_max_output_size', + 'name': 'padToMaxOutputSize', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'NonMaxSuppressionV5', + 'category': 'dynamic', + 'inputs': [ + { + 'start': 0, + 'name': 'boxes', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scores', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'maxOutputSize', + 'type': 'number' + }, + { + 'start': 3, + 'name': 'iouThreshold', + 'type': 'number' + }, + { + 'start': 4, + 'name': 'scoreThreshold', + 'type': 'number' + }, + { + 'start': 5, + 'name': 'softNmsSigma', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'Where', + 'category': 'dynamic', + 'inputs': [ + { + 'start': 0, + 'name': 'condition', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'ListDiff', + 'category': 'dynamic', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'y', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + } + ]; + + var dynamic = { + __proto__: null, + json: json$d + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$c = [ + { + 'tfOpName': 'TopKV2', + 'category': 'evaluation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'k', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'sorted', + 'name': 'sorted', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Unique', + 'category': 'evaluation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'UniqueV2', + 'category': 'evaluation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number' + } + ] + } + ]; + + var evaluation = { + __proto__: null, + json: json$c + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$b = [ + { + 'tfOpName': 'PlaceholderWithDefault', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'default', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'shape', + 'name': 'shape', + 'type': 'shape' + }, + { + 'tfName': 'dtype', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'Placeholder', + 'category': 'graph', + 'attrs': [ + { + 'tfName': 'shape', + 'name': 'shape', + 'type': 'shape' + }, + { + 'tfName': 'dtype', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'Const', + 'category': 'graph' + }, + { + 'tfOpName': 'Identity', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'IdentityN', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'end': 0, + 'name': 'x', + 'type': 'tensors' + } + ] + }, + { + 'tfOpName': 'Snapshot', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'Rank', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'Size', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'Shape', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'ShapeN', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'end': 0, + 'name': 'x', + 'type': 'tensors' + } + ] + }, + { + 'tfOpName': 'Print', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'data', + 'type': 'tensors' + } + ], + 'attrs': [ + { + 'tfName': 'message', + 'name': 'message', + 'type': 'string' + }, + { + 'tfName': 'first_n', + 'name': 'firstN', + 'type': 'number', + 'notSupported': true + }, + { + 'tfName': 'summarize', + 'name': 'summarize', + 'type': 'number', + 'defaultValue': 3 + } + ] + }, + { + 'tfOpName': 'NoOp', + 'category': 'graph', + 'inputs': [] + }, + { + 'tfOpName': 'StopGradient', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'FakeQuantWithMinMaxVars', + 'category': 'graph', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'min', + 'name': 'min', + 'type': 'number' + }, + { + 'tfName': 'max', + 'name': 'max', + 'type': 'number' + } + ] + } + ]; + + var graph = { + __proto__: null, + json: json$b + }; + + /** + * @license + * Copyright 2021 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var json$a = [ + { + 'tfOpName': 'HashTable', + 'category': 'hash_table', + 'inputs': [], + 'attrs': [ + { + 'tfName': 'shared_name', + 'name': 'sharedName', + 'type': 'string' + }, + { + 'tfName': 'use_node_name_sharing', + 'name': 'useNodeNameSharing', + 'type': 'bool' + }, + { + 'tfName': 'key_dtype', + 'name': 'keyDType', + 'type': 'dtype' + }, + { + 'tfName': 'value_dtype', + 'name': 'valueDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'HashTableV2', + 'category': 'hash_table', + 'inputs': [], + 'attrs': [ + { + 'tfName': 'shared_name', + 'name': 'sharedName', + 'type': 'string' + }, + { + 'tfName': 'use_node_name_sharing', + 'name': 'useNodeNameSharing', + 'type': 'bool' + }, + { + 'tfName': 'key_dtype', + 'name': 'keyDType', + 'type': 'dtype' + }, + { + 'tfName': 'value_dtype', + 'name': 'valueDType', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'LookupTableImport', + 'category': 'hash_table', + 'inputs': [ + { + 'start': 0, + 'name': 'tableHandle', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'keys', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'values', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'Tin', + 'name': 'tIn', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'Tout', + 'name': 'tOut', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LookupTableImportV2', + 'category': 'hash_table', + 'inputs': [ + { + 'start': 0, + 'name': 'tableHandle', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'keys', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'values', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'Tin', + 'name': 'tIn', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'Tout', + 'name': 'tOut', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LookupTableFind', + 'category': 'hash_table', + 'inputs': [ + { + 'start': 0, + 'name': 'tableHandle', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'keys', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'defaultValue', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'Tin', + 'name': 'tIn', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'Tout', + 'name': 'tOut', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LookupTableFindV2', + 'category': 'hash_table', + 'inputs': [ + { + 'start': 0, + 'name': 'tableHandle', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'keys', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'defaultValue', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'Tin', + 'name': 'tIn', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'Tout', + 'name': 'tOut', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LookupTableSize', + 'category': 'hash_table', + 'inputs': [ + { + 'start': 0, + 'name': 'tableHandle', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'LookupTableSizeV2', + 'category': 'hash_table', + 'inputs': [ + { + 'start': 0, + 'name': 'tableHandle', + 'type': 'tensor' + } + ] + } + ]; + + var hashTable = { + __proto__: null, + json: json$a + }; + + /** + * @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. + * ============================================================================= + */ + var json$9 = [ + { + 'tfOpName': 'ResizeBilinear', + 'category': 'image', + 'inputs': [ + { + 'start': 0, + 'name': 'images', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'size', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'align_corners', + 'name': 'alignCorners', + 'type': 'bool' + }, + { + 'tfName': 'half_pixel_centers', + 'name': 'halfPixelCenters', + 'type': 'bool' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'ResizeNearestNeighbor', + 'category': 'image', + 'inputs': [ + { + 'start': 0, + 'name': 'images', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'size', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'align_corners', + 'name': 'alignCorners', + 'type': 'bool' + }, + { + 'tfName': 'half_pixel_centers', + 'name': 'halfPixelCenters', + 'type': 'bool' + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'CropAndResize', + 'category': 'image', + 'inputs': [ + { + 'start': 0, + 'name': 'image', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'boxes', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'boxInd', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'cropSize', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'method', + 'name': 'method', + 'type': 'string' + }, + { + 'tfName': 'extrapolation_value', + 'name': 'extrapolationValue', + 'type': 'number' + } + ] + } + ]; + + var image$1 = { + __proto__: null, + json: json$9 + }; + + /** + * @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. + * ============================================================================= + */ + var json$8 = [ + { + 'tfOpName': 'Equal', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'NotEqual', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Greater', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'GreaterEqual', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Less', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LessEqual', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LogicalAnd', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LogicalNot', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LogicalOr', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Select', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'condition', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'SelectV2', + 'category': 'logical', + 'inputs': [ + { + 'start': 0, + 'name': 'condition', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + } + ]; + + var logical = { + __proto__: null, + json: json$8 + }; + + /** + * @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. + * ============================================================================= + */ + var json$7 = [ + { + 'tfOpName': '_FusedMatMul', + 'category': 'matrices', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + }, + { + 'start': 2, + 'end': 0, + 'name': 'args', + 'type': 'tensors' + } + ], + 'attrs': [ + { + 'tfName': 'num_args', + 'name': 'numArgs', + 'type': 'number' + }, + { + 'tfName': 'fused_ops', + 'name': 'fusedOps', + 'type': 'string[]', + 'defaultValue': [] + }, + { + 'tfName': 'epsilon', + 'name': 'epsilon', + 'type': 'number', + 'defaultValue': 0.0001 + }, + { + 'tfName': 'transpose_a', + 'name': 'transposeA', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'transpose_b', + 'name': 'transposeB', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'MatMul', + 'category': 'matrices', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'transpose_a', + 'name': 'transposeA', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'transpose_b', + 'name': 'transposeB', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'BatchMatMul', + 'category': 'matrices', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'adj_x', + 'name': 'transposeA', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'adj_y', + 'name': 'transposeB', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'BatchMatMulV2', + 'category': 'matrices', + 'inputs': [ + { + 'start': 0, + 'name': 'a', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'b', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'adj_x', + 'name': 'transposeA', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'adj_y', + 'name': 'transposeB', + 'type': 'bool', + 'defaultValue': false + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Transpose', + 'category': 'matrices', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'perm', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Einsum', + 'category': 'matrices', + 'inputs': [ + { + 'start': 0, + 'end': 0, + 'name': 'tensors', + 'type': 'tensors' + } + ], + 'attrs': [ + { + 'tfName': 'equation', + 'name': 'equation', + 'type': 'string' + }, + { + 'tfName': 'N', + 'name': 'n', + 'type': 'number', + 'defaultValue': 2 + }, + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype' + } + ] + } + ]; + + var matrices = { + __proto__: null, + json: json$7 + }; + + /** + * @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. + * ============================================================================= + */ + var json$6 = [ + { + 'tfOpName': 'FusedBatchNorm', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scale', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'offset', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'mean', + 'type': 'tensor' + }, + { + 'start': 4, + 'name': 'variance', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'epsilon', + 'name': 'epsilon', + 'type': 'number', + 'defaultValue': 0.001 + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'FusedBatchNormV2', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scale', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'offset', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'mean', + 'type': 'tensor' + }, + { + 'start': 4, + 'name': 'variance', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'epsilon', + 'name': 'epsilon', + 'type': 'number', + 'defaultValue': 0.001 + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'FusedBatchNormV3', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'scale', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'offset', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'mean', + 'type': 'tensor' + }, + { + 'start': 4, + 'name': 'variance', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'epsilon', + 'name': 'epsilon', + 'type': 'number', + 'defaultValue': 0.001 + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'LRN', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'depth_radius', + 'name': 'radius', + 'type': 'number', + 'defaultValue': 5 + }, + { + 'tfName': 'bias', + 'name': 'bias', + 'type': 'number', + 'defaultValue': 1 + }, + { + 'tfName': 'alpha', + 'name': 'alpha', + 'type': 'number', + 'defaultValue': 1 + }, + { + 'tfName': 'beta', + 'name': 'beta', + 'type': 'number', + 'defaultValue': 0.5 + } + ] + }, + { + 'tfOpName': 'Softmax', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'LogSoftmax', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'SparseToDense', + 'category': 'normalization', + 'inputs': [ + { + 'start': 0, + 'name': 'sparseIndices', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'outputShape', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'sparseValues', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'defaultValue', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'validate_indices', + 'name': 'validateIndices', + 'type': 'bool', + 'defaultValue': true, + 'notSupported': true + } + ] + } + ]; + + var normalization = { + __proto__: null, + json: json$6 + }; + + /** + * @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. + * ============================================================================= + */ + var json$5 = [ + { + 'tfOpName': 'Bincount', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'size', + 'type': 'number' + }, + { + 'start': 2, + 'name': 'weights', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'DenseBincount', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'size', + 'type': 'number' + }, + { + 'start': 2, + 'name': 'weights', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'binary_output', + 'name': 'binaryOutput', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Max', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Mean', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Min', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Sum', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'All', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Any', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'ArgMax', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'ArgMin', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'Prod', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'keep_dims', + 'name': 'keepDims', + 'type': 'bool' + } + ] + }, + { + 'tfOpName': 'Cumsum', + 'category': 'reduction', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'exclusive', + 'name': 'exclusive', + 'type': 'bool' + }, + { + 'tfName': 'reverse', + 'name': 'reverse', + 'type': 'bool' + } + ] + } + ]; + + var reduction = { + __proto__: null, + json: json$5 + }; + + /** + * @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. + * ============================================================================= + */ + var json$4 = [ + { + 'tfOpName': 'ConcatV2', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'end': -1, + 'name': 'tensors', + 'type': 'tensors' + }, + { + 'start': -1, + 'name': 'axis', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'N', + 'name': 'n', + 'type': 'number', + 'defaultValue': 2 + } + ] + }, + { + 'tfOpName': 'Concat', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 1, + 'end': 0, + 'name': 'tensors', + 'type': 'tensors' + }, + { + 'start': 0, + 'name': 'axis', + 'type': 'number' + } + ], + 'attrs': [ + { + 'tfName': 'N', + 'name': 'n', + 'type': 'number', + 'defaultValue': 2 + } + ] + }, + { + 'tfOpName': 'GatherV2', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'indices', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'axis', + 'type': 'number', + 'defaultValue': 0 + } + ], + 'attrs': [ + { + 'tfName': 'batch_dims', + 'name': 'batchDims', + 'type': 'number', + 'defaultValue': 0 + } + ] + }, + { + 'tfOpName': 'Gather', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'indices', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'validate_indices', + 'name': 'validateIndices', + 'type': 'bool', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Reverse', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'dims', + 'type': 'bool[]' + } + ] + }, + { + 'tfOpName': 'ReverseV2', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'Slice', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'begin', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'size', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'StridedSlice', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'begin', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'end', + 'type': 'number[]' + }, + { + 'start': 3, + 'name': 'strides', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'begin_mask', + 'name': 'beginMask', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'end_mask', + 'name': 'endMask', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'new_axis_mask', + 'name': 'newAxisMask', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'ellipsis_mask', + 'name': 'ellipsisMask', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'shrink_axis_mask', + 'name': 'shrinkAxisMask', + 'type': 'number', + 'defaultValue': 0 + } + ] + }, + { + 'tfOpName': 'Pack', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'end': 0, + 'name': 'tensors', + 'type': 'tensors' + } + ], + 'attrs': [ + { + 'tfName': 'axis', + 'name': 'axis', + 'type': 'number', + 'defaultValue': 0 + } + ] + }, + { + 'tfOpName': 'Unpack', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'tensor', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'axis', + 'name': 'axis', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'tfName': 'num', + 'name': 'num', + 'type': 'number', + 'defaultValue': 0, + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'Tile', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'reps', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'Split', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'axis', + 'type': 'number', + 'defaultValue': 0 + }, + { + 'start': 1, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'num_split', + 'name': 'numOrSizeSplits', + 'type': 'number', + 'defaultValue': 1 + } + ] + }, + { + 'tfOpName': 'SplitV', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'numOrSizeSplits', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'axis', + 'type': 'number', + 'defaultValue': 0 + } + ] + }, + { + 'tfOpName': 'ScatterNd', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'indices', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'values', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'shape', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'GatherNd', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'indices', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'SparseToDense', + 'category': 'slice_join', + 'inputs': [ + { + 'start': 0, + 'name': 'sparseIndices', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'outputShape', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'sparseValues', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'defaultValue', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'validate_indices', + 'name': 'validateIndices', + 'type': 'bool', + 'defaultValue': false, + 'notSupported': true + } + ] + } + ]; + + var sliceJoin = { + __proto__: null, + json: json$4 + }; + + /** + * @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. + * ============================================================================= + */ + var json$3 = [ + { + 'tfOpName': 'SparseFillEmptyRows', + 'category': 'sparse', + 'inputs': [ + { + 'start': 0, + 'name': 'indices', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'values', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'denseShape', + 'type': 'tensor' + }, + { + 'start': 3, + 'name': 'defaultValue', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'SparseReshape', + 'category': 'sparse', + 'inputs': [ + { + 'start': 0, + 'name': 'inputIndices', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'inputShape', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'newShape', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'T', + 'name': 'dtype', + 'type': 'dtype', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'SparseSegmentMean', + 'category': 'sparse', + 'inputs': [ + { + 'start': 0, + 'name': 'data', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'indices', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'segmentIds', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'SparseSegmentSum', + 'category': 'sparse', + 'inputs': [ + { + 'start': 0, + 'name': 'data', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'indices', + 'type': 'tensor' + }, + { + 'start': 2, + 'name': 'segmentIds', + 'type': 'tensor' + } + ] + } + ]; + + var sparse$1 = { + __proto__: null, + json: json$3 + }; + + /** + * @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. + * ============================================================================= + */ + var json$2 = [ + { + 'tfOpName': 'FFT', + 'category': 'spectral', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'IFFT', + 'category': 'spectral', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ] + }, + { + 'tfOpName': 'RFFT', + 'category': 'spectral', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'fft_length', + 'type': 'number', + 'notSupported': true + } + ] + }, + { + 'tfOpName': 'IRFFT', + 'category': 'spectral', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'fft_length', + 'type': 'number', + 'notSupported': true + } + ] + } + ]; + + var spectral$1 = { + __proto__: null, + json: json$2 + }; + + /** + * @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. + * ============================================================================= + */ + var json$1 = [ + { + 'tfOpName': 'StringNGrams', + 'category': 'string', + 'inputs': [ + { + 'start': 0, + 'name': 'data', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'dataSplits', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'separator', + 'name': 'separator', + 'type': 'string' + }, + { + 'tfName': 'ngram_widths', + 'name': 'nGramWidths', + 'type': 'number[]' + }, + { + 'tfName': 'left_pad', + 'name': 'leftPad', + 'type': 'string' + }, + { + 'tfName': 'right_pad', + 'name': 'rightPad', + 'type': 'string' + }, + { + 'tfName': 'pad_width', + 'name': 'padWidth', + 'type': 'number' + }, + { + 'tfName': 'preserve_short_sequences', + 'name': 'preserveShortSequences', + 'type': 'bool' + } + ], + 'outputs': [ + 'ngrams', + 'ngrams_splits' + ] + }, + { + 'tfOpName': 'StringSplit', + 'category': 'string', + 'inputs': [ + { + 'start': 0, + 'name': 'input', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'delimiter', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'skip_empty', + 'name': 'skipEmpty', + 'type': 'bool' + } + ], + 'outputs': [ + 'indices', + 'values', + 'shape' + ] + }, + { + 'tfOpName': 'StringToHashBucketFast', + 'category': 'string', + 'inputs': [ + { + 'start': 0, + 'name': 'input', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'num_buckets', + 'name': 'numBuckets', + 'type': 'number' + } + ] + } + ]; + + var string$1 = { + __proto__: null, + json: json$1 + }; + + /** + * @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. + * ============================================================================= + */ + var json = [ + { + 'tfOpName': 'Cast', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'SrcT', + 'name': 'sdtype', + 'type': 'dtype', + 'notSupported': true + }, + { + 'tfName': 'DstT', + 'name': 'dtype', + 'type': 'dtype' + } + ] + }, + { + 'tfOpName': 'ExpandDims', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'axis', + 'type': 'number' + } + ] + }, + { + 'tfOpName': 'MirrorPad', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'padding', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'mode', + 'name': 'mode', + 'type': 'string' + } + ] + }, + { + 'tfOpName': 'Pad', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'padding', + 'type': 'number[]' + } + ], + 'attrs': [ + { + 'tfName': 'constant_value', + 'name': 'constantValue', + 'type': 'number', + 'defaultValue': 0 + } + ] + }, + { + 'tfOpName': 'PadV2', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'padding', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'constantValue', + 'type': 'number', + 'defaultValue': 0 + } + ] + }, + { + 'tfOpName': 'Reshape', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'shape', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'Squeeze', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'axis', + 'tfDeprecatedName': 'squeeze_dims', + 'name': 'axis', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'SpaceToBatchND', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'blockShape', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'paddings', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'BatchToSpaceND', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'blockShape', + 'type': 'number[]' + }, + { + 'start': 2, + 'name': 'crops', + 'type': 'number[]' + } + ] + }, + { + 'tfOpName': 'DepthToSpace', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + } + ], + 'attrs': [ + { + 'tfName': 'block_size', + 'name': 'blockSize', + 'type': 'number' + }, + { + 'tfName': 'data_format', + 'name': 'dataFormat', + 'type': 'string' + } + ] + }, + { + 'tfOpName': 'BroadcastTo', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 'x', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 'shape', + 'type': 'number[]' + } + ], + 'attrs': [] + }, + { + 'tfOpName': 'BroadcastArgs', + 'category': 'transformation', + 'inputs': [ + { + 'start': 0, + 'name': 's0', + 'type': 'tensor' + }, + { + 'start': 1, + 'name': 's1', + 'type': 'tensor' + } + ], + 'attrs': [] + } + ]; + + var transformation = { + __proto__: null, + json: json + }; + + var OperationMapper = /** @class */ (function () { + // Loads the op mapping from the JSON file. + function OperationMapper() { + var ops = [ + arithmetic, basicMath, control, convolution, creation, dynamic, + evaluation, graph, hashTable, image$1, logical, matrices, normalization, + reduction, sliceJoin, sparse$1, spectral$1, string$1, transformation + ]; + var mappersJson = [].concat.apply([], __spread(ops.map(function (op) { return op.json; }))); + this.opMappers = mappersJson.reduce(function (map, mapper) { + map[mapper.tfOpName] = mapper; + return map; + }, {}); + } + Object.defineProperty(OperationMapper, "Instance", { + // Singleton instance for the mapper + get: function () { + return this._instance || (this._instance = new this()); + }, + enumerable: true, + configurable: true + }); + // Converts the model inference graph from Tensorflow GraphDef to local + // representation for TensorFlow.js API + OperationMapper.prototype.transformGraph = function (graph, signature) { + var _this = this; + if (signature === void 0) { signature = {}; } + var tfNodes = graph.node; + var placeholders = []; + var weights = []; + var initNodes = []; + var nodes = tfNodes.reduce(function (map, node) { + map[node.name] = _this.mapNode(node); + if (node.op.startsWith('Placeholder')) { + placeholders.push(map[node.name]); + } + else if (node.op === 'Const') { + weights.push(map[node.name]); + } + else if (node.input == null || node.input.length === 0) { + initNodes.push(map[node.name]); + } + return map; + }, {}); + var inputs = []; + var outputs = []; + var inputNodeNameToKey = {}; + var outputNodeNameToKey = {}; + if (signature != null) { + inputNodeNameToKey = this.mapSignatureEntries(signature.inputs); + outputNodeNameToKey = this.mapSignatureEntries(signature.outputs); + } + var allNodes = Object.keys(nodes); + allNodes.forEach(function (key) { + var node = nodes[key]; + node.inputNames.forEach(function (name, index) { + var _a = __read(getNodeNameAndIndex(name), 3), nodeName = _a[0], outputName = _a[2]; + var inputNode = nodes[nodeName]; + if (inputNode.outputs != null) { + var outputIndex = inputNode.outputs.indexOf(outputName); + if (outputIndex !== -1) { + var inputName = nodeName + ":" + outputIndex; + // update the input name to use the mapped output index directly. + node.inputNames[index] = inputName; + } + } + node.inputs.push(inputNode); + inputNode.children.push(node); + }); + }); + // if signature has not outputs set, add any node that does not have + // outputs. + if (Object.keys(outputNodeNameToKey).length === 0) { + allNodes.forEach(function (key) { + var node = nodes[key]; + if (node.children.length === 0) { + outputs.push(node); + } + }); + } + else { + Object.keys(outputNodeNameToKey).forEach(function (name) { + var _a = __read(getNodeNameAndIndex(name), 1), nodeName = _a[0]; + var node = nodes[nodeName]; + if (node != null) { + node.signatureKey = outputNodeNameToKey[name]; + outputs.push(node); + } + }); + } + if (Object.keys(inputNodeNameToKey).length > 0) { + Object.keys(inputNodeNameToKey).forEach(function (name) { + var _a = __read(getNodeNameAndIndex(name), 1), nodeName = _a[0]; + var node = nodes[nodeName]; + if (node) { + node.signatureKey = inputNodeNameToKey[name]; + inputs.push(node); + } + }); + } + else { + inputs = placeholders; + } + var functions = {}; + if (graph.library != null && graph.library.function != null) { + functions = graph.library.function.reduce(function (functions, func) { + functions[func.signature.name] = _this.mapFunction(func); + return functions; + }, {}); + } + var result = { nodes: nodes, inputs: inputs, outputs: outputs, weights: weights, placeholders: placeholders, signature: signature, functions: functions }; + if (initNodes.length > 0) { + result.initNodes = initNodes; + } + return result; + }; + OperationMapper.prototype.mapSignatureEntries = function (entries) { + return Object.keys(entries || {}) + .reduce(function (prev, curr) { + prev[entries[curr].name] = curr; + return prev; + }, {}); + }; + OperationMapper.prototype.mapNode = function (node) { + // Unsupported ops will cause an error at run-time (not parse time), since + // they may not be used by the actual execution subgraph. + var mapper = getRegisteredOp(node.op) || this.opMappers[node.op] || {}; + if (node.attr == null) { + node.attr = {}; + } + var newNode = { + name: node.name, + op: node.op, + category: mapper.category, + inputNames: (node.input || + []).map(function (input) { return input.startsWith('^') ? input.substr(1) : input; }), + inputs: [], + children: [], + inputParams: {}, + attrParams: {}, + rawAttrs: node.attr, + outputs: mapper.outputs + }; + if (mapper.inputs != null) { + newNode.inputParams = + mapper.inputs.reduce(function (map, param) { + map[param.name] = { + type: param.type, + inputIndexStart: param.start, + inputIndexEnd: param.end + }; + return map; + }, {}); + } + if (mapper.attrs != null) { + newNode.attrParams = + mapper.attrs.reduce(function (map, param) { + var type = param.type; + var value = undefined; + switch (param.type) { + case 'string': + value = getStringParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getStringParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'string[]': + value = getStringArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getStringArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'number': + value = getNumberParam(node.attr, param.tfName, (param.defaultValue || 0)); + if (value === undefined && !!param.tfDeprecatedName) { + value = getNumberParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'number[]': + value = getNumericArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getNumericArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'bool': + value = getBoolParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getBoolParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'bool[]': + value = getBoolArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getBoolArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'shape': + value = getTensorShapeParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getTensorShapeParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'shape[]': + value = getTensorShapeArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getTensorShapeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'dtype': + value = getDtypeParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getDtypeParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'dtype[]': + value = getDtypeArrayParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getDtypeArrayParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'func': + value = getFuncParam(node.attr, param.tfName, param.defaultValue); + if (value === undefined && !!param.tfDeprecatedName) { + value = getFuncParam(node.attr, param.tfDeprecatedName, param.defaultValue); + } + break; + case 'tensor': + case 'tensors': + break; + default: + throw new Error("Unsupported param type: " + param.type + " for op: " + node.op); + } + map[param.name] = { value: value, type: type }; + return map; + }, {}); + } + return newNode; + }; + // map the TFunctionDef to TFJS graph object + OperationMapper.prototype.mapFunction = function (functionDef) { + var _this = this; + var tfNodes = functionDef.nodeDef; + var placeholders = []; + var weights = []; + var nodes = {}; + if (tfNodes != null) { + nodes = tfNodes.reduce(function (map, node) { + map[node.name] = _this.mapNode(node); + if (node.op === 'Const') { + weights.push(map[node.name]); + } + return map; + }, {}); + } + var inputs = []; + var outputs = []; + functionDef.signature.inputArg.forEach(function (arg) { + var _a = __read(getNodeNameAndIndex(arg.name), 1), nodeName = _a[0]; + var node = { + name: nodeName, + op: 'Placeholder', + inputs: [], + inputNames: [], + category: 'graph', + inputParams: {}, + attrParams: { dtype: { value: parseDtypeParam(arg.type), type: 'dtype' } }, + children: [] + }; + node.signatureKey = arg.name; + inputs.push(node); + nodes[nodeName] = node; + }); + var allNodes = Object.keys(nodes); + allNodes.forEach(function (key) { + var node = nodes[key]; + node.inputNames.forEach(function (name, index) { + var _a = __read(getNodeNameAndIndex(name), 3), nodeName = _a[0], outputName = _a[2]; + var inputNode = nodes[nodeName]; + if (inputNode.outputs != null) { + var outputIndex = inputNode.outputs.indexOf(outputName); + if (outputIndex !== -1) { + var inputName = nodeName + ":" + outputIndex; + // update the input name to use the mapped output index directly. + node.inputNames[index] = inputName; + } + } + node.inputs.push(inputNode); + inputNode.children.push(node); + }); + }); + var returnNodeMap = functionDef.ret; + functionDef.signature.outputArg.forEach(function (output) { + var _a = __read(getNodeNameAndIndex(returnNodeMap[output.name]), 2), nodeName = _a[0], index = _a[1]; + var node = nodes[nodeName]; + if (node != null) { + node.defaultOutput = index; + outputs.push(node); + } + }); + var signature = this.mapArgsToSignature(functionDef); + return { nodes: nodes, inputs: inputs, outputs: outputs, weights: weights, placeholders: placeholders, signature: signature }; + }; + OperationMapper.prototype.mapArgsToSignature = function (functionDef) { + var _this = this; + return { + methodName: functionDef.signature.name, + inputs: functionDef.signature.inputArg.reduce(function (map, arg) { + map[arg.name] = _this.mapArgToTensorInfo(arg); + return map; + }, {}), + outputs: functionDef.signature.outputArg.reduce(function (map, arg) { + map[arg.name] = _this.mapArgToTensorInfo(arg, functionDef.ret); + return map; + }, {}), + }; + }; + OperationMapper.prototype.mapArgToTensorInfo = function (arg, nameMap) { + var name = arg.name; + if (nameMap != null) { + name = nameMap[name]; + } + return { name: name, dtype: arg.type }; + }; + return OperationMapper; + }()); + function decodeBase64(text) { + var global = tfc.env().global; + if (typeof global.atob !== 'undefined') { + return global.atob(text); + } + else if (typeof Buffer !== 'undefined') { + return new Buffer(text, 'base64').toString(); + } + else { + throw new Error('Unable to decode base64 in this environment. ' + + 'Missing built-in atob() or Buffer()'); + } + } + function parseStringParam(s, keepCase) { + var value = Array.isArray(s) ? String.fromCharCode.apply(null, s) : decodeBase64(s); + return keepCase ? value : value.toLowerCase(); + } + function getStringParam(attrs, name, def, keepCase) { + if (keepCase === void 0) { keepCase = false; } + var param = attrs[name]; + if (param != null) { + return parseStringParam(param.s, keepCase); + } + return def; + } + function getBoolParam(attrs, name, def) { + var param = attrs[name]; + return param ? param.b : def; + } + function getNumberParam(attrs, name, def) { + var param = attrs[name] || {}; + var value = param['i'] != null ? param['i'] : (param['f'] != null ? param['f'] : def); + return (typeof value === 'number') ? value : parseInt(value, 10); + } + function parseDtypeParam(value) { + if (typeof (value) === 'string') { + // tslint:disable-next-line:no-any + value = DataType[value]; + } + switch (value) { + case DataType.DT_FLOAT: + case DataType.DT_HALF: + return 'float32'; + case DataType.DT_INT32: + case DataType.DT_INT64: + case DataType.DT_INT8: + case DataType.DT_UINT8: + return 'int32'; + case DataType.DT_BOOL: + return 'bool'; + case DataType.DT_DOUBLE: + return 'float32'; + case DataType.DT_STRING: + return 'string'; + default: + // Unknown dtype error will happen at runtime (instead of parse time), + // since these nodes might not be used by the actual subgraph execution. + return null; + } + } + function getFuncParam(attrs, name, def) { + var param = attrs[name]; + if (param && param.func) { + return param.func.name; + } + return def; + } + function getDtypeParam(attrs, name, def) { + var param = attrs[name]; + if (param && param.type) { + return parseDtypeParam(param.type); + } + return def; + } + function getDtypeArrayParam(attrs, name, def) { + var param = attrs[name]; + if (param && param.list && param.list.type) { + return param.list.type.map(function (v) { return parseDtypeParam(v); }); + } + return def; + } + function parseTensorShapeParam(shape) { + if (shape.unknownRank) { + return undefined; + } + if (shape.dim != null) { + return shape.dim.map(function (dim) { return (typeof dim.size === 'number') ? dim.size : parseInt(dim.size, 10); }); + } + return []; + } + function getTensorShapeParam(attrs, name, def) { + var param = attrs[name]; + if (param && param.shape) { + return parseTensorShapeParam(param.shape); + } + return def; + } + function getNumericArrayParam(attrs, name, def) { + var param = attrs[name]; + if (param) { + return ((param.list.f && param.list.f.length ? param.list.f : + param.list.i) || + []) + .map(function (v) { return (typeof v === 'number') ? v : parseInt(v, 10); }); + } + return def; + } + function getStringArrayParam(attrs, name, def, keepCase) { + if (keepCase === void 0) { keepCase = false; } + var param = attrs[name]; + if (param && param.list && param.list.s) { + return param.list.s.map(function (v) { + return parseStringParam(v, keepCase); + }); + } + return def; + } + function getTensorShapeArrayParam(attrs, name, def) { + var param = attrs[name]; + if (param && param.list && param.list.shape) { + return param.list.shape.map(function (v) { + return parseTensorShapeParam(v); + }); + } + return def; + } + function getBoolArrayParam(attrs, name, def) { + var param = attrs[name]; + if (param && param.list && param.list.b) { + return param.list.b; + } + return def; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Helper class for lookup inputs and params for nodes in the model graph. + */ + var NodeValueImpl = /** @class */ (function () { + function NodeValueImpl(node, tensorMap, context) { + var _this = this; + this.node = node; + this.tensorMap = tensorMap; + this.context = context; + this.inputs = []; + this.attrs = {}; + this.inputs = node.inputNames.map(function (name) { return _this.getInput(name); }); + if (node.rawAttrs != null) { + this.attrs = Object.keys(node.rawAttrs) + .reduce(function (attrs, key) { + attrs[key] = _this.getAttr(key); + return attrs; + }, {}); + } + } + /** + * Return the value of the attribute or input param. + * @param name String: name of attribute or input param. + */ + NodeValueImpl.prototype.getInput = function (name) { + return getTensor(name, this.tensorMap, this.context); + }; + /** + * Return the value of the attribute or input param. + * @param name String: name of attribute or input param. + */ + NodeValueImpl.prototype.getAttr = function (name, defaultValue) { + var value = this.node.rawAttrs[name]; + if (value.tensor != null) { + return getTensor(name, this.tensorMap, this.context); + } + if (value.i != null || value.f != null) { + return getNumberParam(this.node.rawAttrs, name, defaultValue); + } + if (value.s != null) { + return getStringParam(this.node.rawAttrs, name, defaultValue); + } + if (value.b != null) { + return getBoolParam(this.node.rawAttrs, name, defaultValue); + } + if (value.shape != null) { + return getTensorShapeParam(this.node.rawAttrs, name, defaultValue); + } + if (value.type != null) { + return getDtypeParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list != null) { + if (value.list.i != null || value.list.f != null) { + return getNumericArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.s != null) { + return getStringArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.shape != null) { + return getTensorShapeArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.b != null) { + return getBoolArrayParam(this.node.rawAttrs, name, defaultValue); + } + if (value.list.type != null) { + return getDtypeArrayParam(this.node.rawAttrs, name, defaultValue); + } + } + return defaultValue; + }; + return NodeValueImpl; + }()); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var EPSILON_FLOAT32 = 1e-7; + var EPSILON_FLOAT16 = 1e-4; + /** + * The interface that defines the kernels that should be implemented when + * adding a new backend. New backends don't need to implement every one of the + * methods, this can be done gradually (throw an error for unimplemented + * methods). + */ + var KernelBackend = /** @class */ (function () { + function KernelBackend() { + } + KernelBackend.prototype.refCount = function (dataId) { + return notYetImplemented('refCount'); + }; + KernelBackend.prototype.incRef = function (dataId) { + return notYetImplemented('incRef'); + }; + KernelBackend.prototype.timerAvailable = function () { + return true; + }; + KernelBackend.prototype.time = function (f) { + return notYetImplemented('time'); + }; + KernelBackend.prototype.read = function (dataId) { + return notYetImplemented('read'); + }; + KernelBackend.prototype.readSync = function (dataId) { + return notYetImplemented('readSync'); + }; + KernelBackend.prototype.numDataIds = function () { + return notYetImplemented('numDataIds'); + }; + KernelBackend.prototype.disposeData = function (dataId, force) { + return notYetImplemented('disposeData'); + }; + KernelBackend.prototype.write = function (values, shape, dtype) { + return notYetImplemented('write'); + }; + KernelBackend.prototype.move = function (dataId, values, shape, dtype, refCount) { + return notYetImplemented('move'); + }; + KernelBackend.prototype.memory = function () { + return notYetImplemented('memory'); + }; + /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ + KernelBackend.prototype.floatPrecision = function () { + return notYetImplemented('floatPrecision'); + }; + /** Returns the smallest representable number. */ + KernelBackend.prototype.epsilon = function () { + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; + }; + KernelBackend.prototype.dispose = function () { + return notYetImplemented('dispose'); + }; + return KernelBackend; + }()); + 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"); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Asserts that the expression is true. Otherwise throws an error with the + * provided message. + * + * ```js + * const x = 2; + * tf.util.assert(x === 2, 'x is not 2'); + * ``` + * + * @param expr The expression to assert (as a boolean). + * @param msg A function that returns the message to report when throwing an + * error. We use a function for performance reasons. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === 'string' ? msg : msg()); + } + } + function assertShapesMatch(shapeA, shapeB, errorMessagePrefix) { + if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; } + assert(arraysEqual(shapeA, shapeB), function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); }); + } + function assertNonNull(a) { + assert(a != null, function () { return "The input to the tensor constructor must be a non-null value."; }); + } + // NOTE: We explicitly type out what T extends instead of any so that + // util.flatten on a nested array of number doesn't try to infer T as a + // number[][], causing us to explicitly type util.flatten(). + /** + * Flattens an arbitrarily nested array. + * + * ```js + * const a = [[1, 2], [3, 4], [5, [6, [7]]]]; + * const flat = tf.util.flatten(a); + * console.log(flat); + * ``` + * + * @param arr The nested array to flatten. + * @param result The destination array which holds the elements. + * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults + * to false. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function flatten(arr, result, skipTypedArray) { + if (result === void 0) { result = []; } + if (skipTypedArray === void 0) { skipTypedArray = false; } + if (result == null) { + result = []; + } + if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) { + for (var i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } + else { + result.push(arr); + } + return result; + } + /** + * Returns the size (number of elements) of the tensor given its shape. + * + * ```js + * const shape = [3, 4, 2]; + * const size = tf.util.sizeFromShape(shape); + * console.log(size); + * ``` + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function sizeFromShape(shape) { + if (shape.length === 0) { + // Scalar. + return 1; + } + var size = shape[0]; + for (var i = 1; i < shape.length; i++) { + size *= shape[i]; + } + return size; + } + function arraysEqual(n1, n2) { + if (n1 === n2) { + return true; + } + if (n1 == null || n2 == null) { + return false; + } + if (n1.length !== n2.length) { + return false; + } + for (var i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { + return false; + } + } + return true; + } + function isInt(a) { + return a % 1 === 0; + } + function rightPad(a, size) { + if (size <= a.length) { + return a; + } + return a + ' '.repeat(size - a.length); + } + function parseAxisParam(axis, shape) { + var rank = shape.length; + // Normalize input + axis = axis == null ? shape.map(function (s, i) { return i; }) : [].concat(axis); + // Check for valid range + assert(axis.every(function (ax) { return ax >= -rank && ax < rank; }), function () { return "All values in axis param must be in range [-" + rank + ", " + rank + ") but " + + ("got axis " + axis); }); + // Check for only integers + assert(axis.every(function (ax) { return isInt(ax); }), function () { return "All values in axis param must be integers but " + + ("got axis " + axis); }); + // Handle negative axis. + return axis.map(function (a) { return a < 0 ? rank + a : a; }); + } + /** Reduces the shape by removing all dimensions of shape 1. */ + function squeezeShape(shape, axis) { + var newShape = []; + var keptDims = []; + var isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + var axes = (axis == null || isEmptyArray) ? + null : + parseAxisParam(axis, shape).sort(); + var j = 0; + for (var 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: newShape, keptDims: keptDims }; + } + function getTypedArrayFromDType(dtype, size) { + var 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) { + var 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 (var i = 0; i < vals.length; i++) { + var num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error("A tensor of type " + dtype + " being uploaded contains " + num + "."); + } + } + } + /** Returns true if the dtype is valid. */ + function isValidDtype(dtype) { + return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' || + dtype === 'int32' || dtype === 'string'; + } + 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); + } + } + /** + * Returns the approximate number of bytes allocated in the string array - 2 + * bytes per character. Computing the exact bytes for a native string in JS is + * not possible since it depends on the encoding of the html page that serves + * the website. + */ + function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + var bytes = 0; + arr.forEach(function (x) { return bytes += x.length; }); + return bytes; + } + /** Returns true if the value is a string. */ + 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 computeStrides(shape) { + var rank = shape.length; + if (rank < 2) { + return []; + } + // Last dimension has implicit stride of 1, thus having D-1 (instead of D) + // strides. + var strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (var i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + return strides; + } + function createNestedArray(offset, shape, a, isComplex) { + if (isComplex === void 0) { isComplex = false; } + var ret = new Array(); + if (shape.length === 1) { + var d = shape[0] * (isComplex ? 2 : 1); + for (var i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } + else { + var d = shape[0]; + var rest = shape.slice(1); + var len = rest.reduce(function (acc, c) { return acc * c; }) * (isComplex ? 2 : 1); + for (var i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); + } + } + return ret; + } + // Provide a nested array of TypedArray in given shape. + function toNestedArray(shape, a, isComplex) { + if (isComplex === void 0) { isComplex = false; } + if (shape.length === 0) { + // Scalar type should return a single number. + return a[0]; + } + var size = shape.reduce(function (acc, c) { return acc * c; }) * (isComplex ? 2 : 1); + if (size === 0) { + // A tensor with shape zero should be turned into empty list. + 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) { + var array = makeZerosTypedArray(size, dtype); + for (var i = 0; i < array.length; i++) { + array[i] = 1; + } + return array; + } + 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 assertNonNegativeIntegerDimensions(shape) { + shape.forEach(function (dimSize) { + assert(Number.isInteger(dimSize) && dimSize >= 0, function () { return "Tensor must have a shape comprised of positive integers but got " + + ("shape [" + shape + "]."); }); + }); + } + /** + * This method asserts whether an object is a Promise instance. + * @param object + */ + // tslint:disable-next-line: no-any + function isPromise(object) { + // We chose to not use 'obj instanceOf Promise' for two reasons: + // 1. It only reliably works for es6 Promise, not other Promise + // implementations. + // 2. It doesn't work with framework that uses zone.js. zone.js monkey patch + // the async calls, so it is possible the obj (patched) is comparing to a + // pre-patched Promise. + return object && object.then && typeof object.then === 'function'; + } + + // Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true. + var TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags'; + /** + * The environment contains evaluated flags as well as the registered platform. + * This is always used as a global singleton and can be retrieved with + * `tf.env()`. + * + * @doc {heading: 'Environment'} + */ + var Environment = /** @class */ (function () { + // tslint:disable-next-line: no-any + function Environment(global) { + this.global = global; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + // Jasmine spies on this in 'environment_test.ts' + this.getQueryParams = getQueryParams; + this.populateURLFlags(); + } + Environment.prototype.setPlatform = function (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 " + platform + ".")); + } + } + this.platformName = platformName; + this.platform = platform; + }; + Environment.prototype.registerFlag = function (flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = { evaluationFn: evaluationFn, setHook: setHook }; + // Override the flag value from the URL. This has to happen here because + // the environment is initialized before flags get registered. + if (this.urlFlags[flagName] != null) { + var 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); + } + }; + Environment.prototype.getAsync = function (flagName) { + return __awaiter(this, void 0, void 0, function () { + var _a, _b; + return __generator(this, function (_c) { + switch (_c.label) { + case 0: + if (flagName in this.flags) { + return [2 /*return*/, this.flags[flagName]]; + } + _a = this.flags; + _b = flagName; + return [4 /*yield*/, this.evaluateFlag(flagName)]; + case 1: + _a[_b] = _c.sent(); + return [2 /*return*/, this.flags[flagName]]; + } + }); + }); + }; + Environment.prototype.get = function (flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + var 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]; + }; + Environment.prototype.getNumber = function (flagName) { + return this.get(flagName); + }; + Environment.prototype.getBool = function (flagName) { + return this.get(flagName); + }; + Environment.prototype.getFlags = function () { + return this.flags; + }; + Object.defineProperty(Environment.prototype, "features", { + // For backwards compatibility. + get: function () { + return this.flags; + }, + enumerable: true, + configurable: true + }); + Environment.prototype.set = function (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); + } + }; + Environment.prototype.evaluateFlag = function (flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error("Cannot evaluate flag '" + flagName + "': no evaluation function found."); + } + return this.flagRegistry[flagName].evaluationFn(); + }; + Environment.prototype.setFlags = function (flags) { + this.flags = Object.assign({}, flags); + }; + Environment.prototype.reset = function () { + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); + }; + Environment.prototype.populateURLFlags = function () { + var _this = this; + if (typeof this.global === 'undefined' || + typeof this.global.location === 'undefined' || + typeof this.global.location.search === 'undefined') { + return; + } + var urlParams = this.getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + var keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(','); + keyValues.forEach(function (keyValue) { + var _a = __read(keyValue.split(':'), 2), key = _a[0], value = _a[1]; + _this.urlFlags[key] = parseValue(key, value); + }); + } + }; + return Environment; + }()); + function getQueryParams(queryString) { + var params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (s) { + var t = []; + for (var _i = 1; _i < arguments.length; _i++) { + t[_i - 1] = arguments[_i]; + } + 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 + "."); + } + /** + * Returns the current environment (a global singleton). + * + * The environment object contains the evaluated feature values as well as the + * active platform. + * + * @doc {heading: 'Environment'} + */ + function env() { + return ENV; + } + var ENV = null; + function setEnvironmentGlobal(environment) { + ENV = environment; + } + + /** + * @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. + * ============================================================================= + */ + // Note that the identifier globalNameSpace is scoped to this module, but will + // always resolve to the same global object regardless of how the module is + // resolved. + // tslint:disable-next-line:no-any + var globalNameSpace; + // tslint:disable-next-line:no-any + function getGlobalNamespace() { + if (globalNameSpace == null) { + // tslint:disable-next-line:no-any + var ns = void 0; + if (typeof (window) !== 'undefined') { + ns = window; + } + else if (typeof (global) !== 'undefined') { + ns = global; + } + else if (typeof (process) !== 'undefined') { + ns = process; + } + else if (typeof (self) !== 'undefined') { + ns = self; + } + else { + throw new Error('Could not find a global object'); + } + globalNameSpace = ns; + } + return globalNameSpace; + } + // tslint:disable-next-line:no-any + function getGlobalMap() { + var ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = new Map(); + } + return ns._tfGlobals; + } + /** + * Returns a globally accessible 'singleton' object. + * + * @param key the name of the object + * @param init a function to initialize to initialize this object + * the first time it is fetched. + */ + function getGlobal(key, init) { + var globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } + else { + var singleton = init(); + globalMap.set(key, singleton); + return globalMap.get(key); + } + } + + 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 AvgPool3D = 'AvgPool3D'; + var BatchMatMul = 'BatchMatMul'; + var BatchToSpaceND = 'BatchToSpaceND'; + var Bincount = 'Bincount'; + 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 Conv3DBackpropInputV2 = 'Conv3DBackpropInputV2'; + var Cos = 'Cos'; + var Cosh = 'Cosh'; + 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 RealDiv = 'RealDiv'; + var Einsum = 'Einsum'; + var Elu = 'Elu'; + 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 LRN = 'LRN'; + var Max = 'Max'; + var Maximum = 'Maximum'; + var MaxPool = 'MaxPool'; + var MaxPool3D = 'MaxPool3D'; + 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 ResizeBilinear = 'ResizeBilinear'; + var Relu6 = 'Relu6'; + var Reverse = 'Reverse'; + var Round = 'Round'; + var Rsqrt = 'Rsqrt'; + var ScatterNd = 'ScatterNd'; + 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 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'; + /** + * TensorFlow.js-only kernels + */ + var Step = 'Step'; + var RotateWithOffset = 'RotateWithOffset'; + var _FusedMatMul = '_FusedMatMul'; + var FusedConv2D = 'FusedConv2D'; + var FusedDepthwiseConv2D = 'FusedDepthwiseConv2D'; + + function warn() { + var msg = []; + for (var _i = 0; _i < arguments.length; _i++) { + msg[_i] = arguments[_i]; + } + if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) { + console.warn.apply(console, __spread(msg)); + } + } + + var kernelRegistry = getGlobal('kernelRegistry', function () { return new Map(); }); + var gradRegistry = getGlobal('gradRegistry', function () { return new Map(); }); + /** + * Returns the kernel function (code) associated with the provided names. + * + * @param kernelName The official name of the kernel. + * @param backendName The official name of the backend. + */ + function getKernel(kernelName, backendName) { + var key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); + } + /** + * Returns the registered gradient info associated with the provided kernel. + * @param kernelName The official TF kernel name. + */ + function getGradient(kernelName) { + return gradRegistry.get(kernelName); + } + function getKernelsForBackend(backendName) { + var it = kernelRegistry.entries(); + var result = []; + while (true) { + var _a = it.next(), done = _a.done, value = _a.value; + if (done) { + break; + } + var _b = __read(value, 2), key = _b[0], config = _b[1]; + var _c = __read(key.split('_'), 1), backend = _c[0]; + if (backend === backendName) { + result.push(config); + } + } + return result; + } + function makeKey(kernelName, backendName) { + return backendName + "_" + kernelName; + } + + var long = Long$1; + /** + * wasm optimizations, to do native i64 multiplication and divide + */ + var wasm = null; + try { + wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([ + 0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 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, 132, 127, 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, 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, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 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, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11 + ])), {}).exports; + } + catch (e) { + // no wasm support :( + } + /** + * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers. + * See the from* functions below for more convenient ways of constructing Longs. + * @exports Long + * @class A Long class for representing a 64 bit two's-complement integer value. + * @param {number} low The low (signed) 32 bits of the long + * @param {number} high The high (signed) 32 bits of the long + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @constructor + */ + function Long$1(low, high, unsigned) { + /** + * The low 32 bits as a signed value. + * @type {number} + */ + this.low = low | 0; + /** + * The high 32 bits as a signed value. + * @type {number} + */ + this.high = high | 0; + /** + * Whether unsigned or not. + * @type {boolean} + */ + this.unsigned = !!unsigned; + } + // The internal representation of a long is the two given signed, 32-bit values. + // We use 32-bit pieces because these are the size of integers on which + // Javascript performs bit-operations. For operations like addition and + // multiplication, we split each number into 16 bit pieces, which can easily be + // multiplied within Javascript's floating-point representation without overflow + // or change in sign. + // + // In the algorithms below, we frequently reduce the negative case to the + // positive case by negating the input(s) and then post-processing the result. + // Note that we must ALWAYS check specially whether those values are MIN_VALUE + // (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as + // a positive number, it overflows back into a negative). Not handling this + // case would often result in infinite recursion. + // + // Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from* + // methods on which they depend. + /** + * An indicator used to reliably determine if an object is a Long or not. + * @type {boolean} + * @const + * @private + */ + Long$1.prototype.__isLong__; + Object.defineProperty(Long$1.prototype, "__isLong__", { value: true }); + /** + * @function + * @param {*} obj Object + * @returns {boolean} + * @inner + */ + function isLong(obj) { + return (obj && obj["__isLong__"]) === true; + } + /** + * Tests if the specified object is a Long. + * @function + * @param {*} obj Object + * @returns {boolean} + */ + Long$1.isLong = isLong; + /** + * A cache of the Long representations of small integer values. + * @type {!Object} + * @inner + */ + var INT_CACHE = {}; + /** + * A cache of the Long representations of small unsigned integer values. + * @type {!Object} + * @inner + */ + var UINT_CACHE = {}; + /** + * @param {number} value + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + 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; + } + } + /** + * Returns a Long representing the given 32 bit integer value. + * @function + * @param {number} value The 32 bit integer in question + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} The corresponding Long value + */ + Long$1.fromInt = fromInt; + /** + * @param {number} value + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + 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); + } + /** + * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned. + * @function + * @param {number} value The number in question + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} The corresponding Long value + */ + Long$1.fromNumber = fromNumber; + /** + * @param {number} lowBits + * @param {number} highBits + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + function fromBits(lowBits, highBits, unsigned) { + return new Long$1(lowBits, highBits, unsigned); + } + /** + * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is + * assumed to use 32 bits. + * @function + * @param {number} lowBits The low 32 bits + * @param {number} highBits The high 32 bits + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} The corresponding Long value + */ + Long$1.fromBits = fromBits; + /** + * @function + * @param {number} base + * @param {number} exponent + * @returns {number} + * @inner + */ + var pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4) + /** + * @param {string} str + * @param {(boolean|number)=} unsigned + * @param {number=} radix + * @returns {!Long} + * @inner + */ + 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') { + // For goog.math.long compatibility + radix = unsigned, + unsigned = false; + } + else { + unsigned = !!unsigned; + } + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError('radix'); + var p; + if ((p = str.indexOf('-')) > 0) + throw Error('interior hyphen'); + else if (p === 0) { + return fromString(str.substring(1), unsigned, radix).neg(); + } + // Do several (8) digits each time through the loop, so as to + // minimize the calls to the very expensive emulated div. + 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; + } + /** + * Returns a Long representation of the given string, written using the specified radix. + * @function + * @param {string} str The textual representation of the Long + * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed + * @param {number=} radix The radix in which the text is written (2-36), defaults to 10 + * @returns {!Long} The corresponding Long value + */ + Long$1.fromString = fromString; + /** + * @function + * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + function fromValue(val, unsigned) { + if (typeof val === 'number') + return fromNumber(val, unsigned); + if (typeof val === 'string') + return fromString(val, unsigned); + // Throws for non-objects, converts non-instanceof Long: + return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned); + } + /** + * Converts the specified value to a Long using the appropriate from* function for its type. + * @function + * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} + */ + Long$1.fromValue = fromValue; + // NOTE: the compiler should inline these constant values below and then remove these variables, so there should be + // no runtime penalty for these. + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_16_DBL = 1 << 16; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_24_DBL = 1 << 24; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; + /** + * @type {!Long} + * @const + * @inner + */ + var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); + /** + * @type {!Long} + * @inner + */ + var ZERO = fromInt(0); + /** + * Signed zero. + * @type {!Long} + */ + Long$1.ZERO = ZERO; + /** + * @type {!Long} + * @inner + */ + var UZERO = fromInt(0, true); + /** + * Unsigned zero. + * @type {!Long} + */ + Long$1.UZERO = UZERO; + /** + * @type {!Long} + * @inner + */ + var ONE = fromInt(1); + /** + * Signed one. + * @type {!Long} + */ + Long$1.ONE = ONE; + /** + * @type {!Long} + * @inner + */ + var UONE = fromInt(1, true); + /** + * Unsigned one. + * @type {!Long} + */ + Long$1.UONE = UONE; + /** + * @type {!Long} + * @inner + */ + var NEG_ONE = fromInt(-1); + /** + * Signed negative one. + * @type {!Long} + */ + Long$1.NEG_ONE = NEG_ONE; + /** + * @type {!Long} + * @inner + */ + var MAX_VALUE = fromBits(0xFFFFFFFF | 0, 0x7FFFFFFF | 0, false); + /** + * Maximum signed value. + * @type {!Long} + */ + Long$1.MAX_VALUE = MAX_VALUE; + /** + * @type {!Long} + * @inner + */ + var MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF | 0, 0xFFFFFFFF | 0, true); + /** + * Maximum unsigned value. + * @type {!Long} + */ + Long$1.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; + /** + * @type {!Long} + * @inner + */ + var MIN_VALUE = fromBits(0, 0x80000000 | 0, false); + /** + * Minimum signed value. + * @type {!Long} + */ + Long$1.MIN_VALUE = MIN_VALUE; + /** + * @alias Long.prototype + * @inner + */ + var LongPrototype = Long$1.prototype; + /** + * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer. + * @returns {number} + */ + LongPrototype.toInt = function toInt() { + return this.unsigned ? this.low >>> 0 : this.low; + }; + /** + * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa). + * @returns {number} + */ + 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); + }; + /** + * Converts the Long to a string written in the specified radix. + * @param {number=} radix Radix (2-36), defaults to 10 + * @returns {string} + * @override + * @throws {RangeError} If `radix` is out of range + */ + LongPrototype.toString = function toString(radix) { + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError('radix'); + if (this.isZero()) + return '0'; + if (this.isNegative()) { // Unsigned Longs are never negative + if (this.eq(MIN_VALUE)) { + // We need to change the Long value before it can be negated, so we remove + // the bottom-most digit in this base and then recurse to do the rest. + var radixLong = fromNumber(radix), div = this.div(radixLong), rem1 = div.mul(radixLong).sub(this); + return div.toString(radix) + rem1.toInt().toString(radix); + } + else + return '-' + this.neg().toString(radix); + } + // Do several (6) digits each time through the loop, so as to + // minimize the calls to the very expensive emulated div. + 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; + } + } + }; + /** + * Gets the high 32 bits as a signed integer. + * @returns {number} Signed high bits + */ + LongPrototype.getHighBits = function getHighBits() { + return this.high; + }; + /** + * Gets the high 32 bits as an unsigned integer. + * @returns {number} Unsigned high bits + */ + LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { + return this.high >>> 0; + }; + /** + * Gets the low 32 bits as a signed integer. + * @returns {number} Signed low bits + */ + LongPrototype.getLowBits = function getLowBits() { + return this.low; + }; + /** + * Gets the low 32 bits as an unsigned integer. + * @returns {number} Unsigned low bits + */ + LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { + return this.low >>> 0; + }; + /** + * Gets the number of bits needed to represent the absolute value of this Long. + * @returns {number} + */ + LongPrototype.getNumBitsAbs = function getNumBitsAbs() { + if (this.isNegative()) // Unsigned Longs are never negative + 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; + }; + /** + * Tests if this Long's value equals zero. + * @returns {boolean} + */ + LongPrototype.isZero = function isZero() { + return this.high === 0 && this.low === 0; + }; + /** + * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}. + * @returns {boolean} + */ + LongPrototype.eqz = LongPrototype.isZero; + /** + * Tests if this Long's value is negative. + * @returns {boolean} + */ + LongPrototype.isNegative = function isNegative() { + return !this.unsigned && this.high < 0; + }; + /** + * Tests if this Long's value is positive. + * @returns {boolean} + */ + LongPrototype.isPositive = function isPositive() { + return this.unsigned || this.high >= 0; + }; + /** + * Tests if this Long's value is odd. + * @returns {boolean} + */ + LongPrototype.isOdd = function isOdd() { + return (this.low & 1) === 1; + }; + /** + * Tests if this Long's value is even. + * @returns {boolean} + */ + LongPrototype.isEven = function isEven() { + return (this.low & 1) === 0; + }; + /** + * Tests if this Long's value equals the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + 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; + }; + /** + * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.eq = LongPrototype.equals; + /** + * Tests if this Long's value differs from the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.notEquals = function notEquals(other) { + return !this.eq(/* validates */ other); + }; + /** + * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.neq = LongPrototype.notEquals; + /** + * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.ne = LongPrototype.notEquals; + /** + * Tests if this Long's value is less than the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lessThan = function lessThan(other) { + return this.comp(/* validates */ other) < 0; + }; + /** + * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lt = LongPrototype.lessThan; + /** + * Tests if this Long's value is less than or equal the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { + return this.comp(/* validates */ other) <= 0; + }; + /** + * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lte = LongPrototype.lessThanOrEqual; + /** + * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.le = LongPrototype.lessThanOrEqual; + /** + * Tests if this Long's value is greater than the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.greaterThan = function greaterThan(other) { + return this.comp(/* validates */ other) > 0; + }; + /** + * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.gt = LongPrototype.greaterThan; + /** + * Tests if this Long's value is greater than or equal the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { + return this.comp(/* validates */ other) >= 0; + }; + /** + * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.gte = LongPrototype.greaterThanOrEqual; + /** + * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.ge = LongPrototype.greaterThanOrEqual; + /** + * Compares this Long's value with the specified's. + * @param {!Long|number|string} other Other value + * @returns {number} 0 if they are the same, 1 if the this is greater and -1 + * if the given one is greater + */ + 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; + // At this point the sign bits are the same + if (!this.unsigned) + return this.sub(other).isNegative() ? -1 : 1; + // Both are positive if at least one is unsigned + return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1; + }; + /** + * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}. + * @function + * @param {!Long|number|string} other Other value + * @returns {number} 0 if they are the same, 1 if the this is greater and -1 + * if the given one is greater + */ + LongPrototype.comp = LongPrototype.compare; + /** + * Negates this Long's value. + * @returns {!Long} Negated Long + */ + LongPrototype.negate = function negate() { + if (!this.unsigned && this.eq(MIN_VALUE)) + return MIN_VALUE; + return this.not().add(ONE); + }; + /** + * Negates this Long's value. This is an alias of {@link Long#negate}. + * @function + * @returns {!Long} Negated Long + */ + LongPrototype.neg = LongPrototype.negate; + /** + * Returns the sum of this and the specified Long. + * @param {!Long|number|string} addend Addend + * @returns {!Long} Sum + */ + LongPrototype.add = function add(addend) { + if (!isLong(addend)) + addend = fromValue(addend); + // Divide each number into 4 chunks of 16 bits, and then sum the chunks. + var a48 = this.high >>> 16; + var a32 = this.high & 0xFFFF; + var a16 = this.low >>> 16; + var a00 = this.low & 0xFFFF; + var b48 = addend.high >>> 16; + var b32 = addend.high & 0xFFFF; + var b16 = addend.low >>> 16; + var b00 = addend.low & 0xFFFF; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 + b00; + c16 += c00 >>> 16; + c00 &= 0xFFFF; + c16 += a16 + b16; + c32 += c16 >>> 16; + c16 &= 0xFFFF; + c32 += a32 + b32; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c48 += a48 + b48; + c48 &= 0xFFFF; + return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned); + }; + /** + * Returns the difference of this and the specified Long. + * @param {!Long|number|string} subtrahend Subtrahend + * @returns {!Long} Difference + */ + LongPrototype.subtract = function subtract(subtrahend) { + if (!isLong(subtrahend)) + subtrahend = fromValue(subtrahend); + return this.add(subtrahend.neg()); + }; + /** + * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}. + * @function + * @param {!Long|number|string} subtrahend Subtrahend + * @returns {!Long} Difference + */ + LongPrototype.sub = LongPrototype.subtract; + /** + * Returns the product of this and the specified Long. + * @param {!Long|number|string} multiplier Multiplier + * @returns {!Long} Product + */ + LongPrototype.multiply = function multiply(multiplier) { + if (this.isZero()) + return ZERO; + if (!isLong(multiplier)) + multiplier = fromValue(multiplier); + // use wasm support if present + 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 both longs are small, use float multiplication + if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) + return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); + // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products. + // We can skip products that would overflow. + var a48 = this.high >>> 16; + var a32 = this.high & 0xFFFF; + var a16 = this.low >>> 16; + var a00 = this.low & 0xFFFF; + var b48 = multiplier.high >>> 16; + var b32 = multiplier.high & 0xFFFF; + var b16 = multiplier.low >>> 16; + var b00 = multiplier.low & 0xFFFF; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 * b00; + c16 += c00 >>> 16; + c00 &= 0xFFFF; + c16 += a16 * b00; + c32 += c16 >>> 16; + c16 &= 0xFFFF; + c16 += a00 * b16; + c32 += c16 >>> 16; + c16 &= 0xFFFF; + c32 += a32 * b00; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c32 += a16 * b16; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c32 += a00 * b32; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; + c48 &= 0xFFFF; + return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned); + }; + /** + * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}. + * @function + * @param {!Long|number|string} multiplier Multiplier + * @returns {!Long} Product + */ + LongPrototype.mul = LongPrototype.multiply; + /** + * Returns this Long divided by the specified. The result is signed if this Long is signed or + * unsigned if this Long is unsigned. + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Quotient + */ + LongPrototype.divide = function divide(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (divisor.isZero()) + throw Error('division by zero'); + // use wasm support if present + if (wasm) { + // guard against signed division overflow: the largest + // negative number / -1 would be 1 larger than the largest + // positive number, due to two's complement. + if (!this.unsigned && + this.high === -0x80000000 && + divisor.low === -1 && divisor.high === -1) { + // be consistent with non-wasm code path + 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) { + // This section is only relevant for signed longs and is derived from the + // closure library as a whole. + if (this.eq(MIN_VALUE)) { + if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) + return MIN_VALUE; // recall that -MIN_VALUE == MIN_VALUE + else if (divisor.eq(MIN_VALUE)) + return ONE; + else { + // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|. + 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 { + // The algorithm below has not been made for unsigned longs. It's therefore + // required to take special care of the MSB prior to running it. + if (!divisor.unsigned) + divisor = divisor.toUnsigned(); + if (divisor.gt(this)) + return UZERO; + if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true + return UONE; + res = UZERO; + } + // Repeat the following until the remainder is less than other: find a + // floating-point that approximates remainder / other *from below*, add this + // into the result, and subtract it from the remainder. It is critical that + // the approximate value is less than or equal to the real value so that the + // remainder never becomes negative. + rem = this; + while (rem.gte(divisor)) { + // Approximate the result of division. This may be a little greater or + // smaller than the actual value. + approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); + // We will tweak the approximate result by changing it in the 48-th digit or + // the smallest non-fractional digit, whichever is larger. + var log2 = Math.ceil(Math.log(approx) / Math.LN2), delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48), + // Decrease the approximation until it is smaller than the remainder. Note + // that if it is too large, the product overflows and is negative. + approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); + while (approxRem.isNegative() || approxRem.gt(rem)) { + approx -= delta; + approxRes = fromNumber(approx, this.unsigned); + approxRem = approxRes.mul(divisor); + } + // We know the answer can't be zero... and actually, zero would cause + // infinite recursion since we would make no progress. + if (approxRes.isZero()) + approxRes = ONE; + res = res.add(approxRes); + rem = rem.sub(approxRem); + } + return res; + }; + /** + * Returns this Long divided by the specified. This is an alias of {@link Long#divide}. + * @function + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Quotient + */ + LongPrototype.div = LongPrototype.divide; + /** + * Returns this Long modulo the specified. + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Remainder + */ + LongPrototype.modulo = function modulo(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + // use wasm support if present + 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)); + }; + /** + * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}. + * @function + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Remainder + */ + LongPrototype.mod = LongPrototype.modulo; + /** + * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}. + * @function + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Remainder + */ + LongPrototype.rem = LongPrototype.modulo; + /** + * Returns the bitwise NOT of this Long. + * @returns {!Long} + */ + LongPrototype.not = function not() { + return fromBits(~this.low, ~this.high, this.unsigned); + }; + /** + * Returns the bitwise AND of this Long and the specified. + * @param {!Long|number|string} other Other Long + * @returns {!Long} + */ + LongPrototype.and = function and(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low & other.low, this.high & other.high, this.unsigned); + }; + /** + * Returns the bitwise OR of this Long and the specified. + * @param {!Long|number|string} other Other Long + * @returns {!Long} + */ + LongPrototype.or = function or(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low | other.low, this.high | other.high, this.unsigned); + }; + /** + * Returns the bitwise XOR of this Long and the given one. + * @param {!Long|number|string} other Other Long + * @returns {!Long} + */ + LongPrototype.xor = function xor(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); + }; + /** + * Returns this Long with bits shifted to the left by the given amount. + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + 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); + }; + /** + * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shl = LongPrototype.shiftLeft; + /** + * Returns this Long with bits arithmetically shifted to the right by the given amount. + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + 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); + }; + /** + * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shr = LongPrototype.shiftRight; + /** + * Returns this Long with bits logically shifted to the right by the given amount. + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + 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); + } + }; + /** + * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shru = LongPrototype.shiftRightUnsigned; + /** + * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; + /** + * Converts this Long to signed. + * @returns {!Long} Signed long + */ + LongPrototype.toSigned = function toSigned() { + if (!this.unsigned) + return this; + return fromBits(this.low, this.high, false); + }; + /** + * Converts this Long to unsigned. + * @returns {!Long} Unsigned long + */ + LongPrototype.toUnsigned = function toUnsigned() { + if (this.unsigned) + return this; + return fromBits(this.low, this.high, true); + }; + /** + * Converts this Long to its byte representation. + * @param {boolean=} le Whether little or big endian, defaults to big endian + * @returns {!Array.} Byte representation + */ + LongPrototype.toBytes = function toBytes(le) { + return le ? this.toBytesLE() : this.toBytesBE(); + }; + /** + * Converts this Long to its little endian byte representation. + * @returns {!Array.} Little endian byte representation + */ + LongPrototype.toBytesLE = function toBytesLE() { + var hi = this.high, lo = this.low; + return [ + lo & 0xff, + lo >>> 8 & 0xff, + lo >>> 16 & 0xff, + lo >>> 24, + hi & 0xff, + hi >>> 8 & 0xff, + hi >>> 16 & 0xff, + hi >>> 24 + ]; + }; + /** + * Converts this Long to its big endian byte representation. + * @returns {!Array.} Big endian byte representation + */ + LongPrototype.toBytesBE = function toBytesBE() { + var hi = this.high, lo = this.low; + return [ + hi >>> 24, + hi >>> 16 & 0xff, + hi >>> 8 & 0xff, + hi & 0xff, + lo >>> 24, + lo >>> 16 & 0xff, + lo >>> 8 & 0xff, + lo & 0xff + ]; + }; + /** + * Creates a Long from its byte representation. + * @param {!Array.} bytes Byte representation + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @param {boolean=} le Whether little or big endian, defaults to big endian + * @returns {Long} The corresponding Long value + */ + Long$1.fromBytes = function fromBytes(bytes, unsigned, le) { + return le ? Long$1.fromBytesLE(bytes, unsigned) : Long$1.fromBytesBE(bytes, unsigned); + }; + /** + * Creates a Long from its little endian byte representation. + * @param {!Array.} bytes Little endian byte representation + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {Long} The corresponding Long value + */ + Long$1.fromBytesLE = function fromBytesLE(bytes, unsigned) { + return new Long$1(bytes[0] | + bytes[1] << 8 | + bytes[2] << 16 | + bytes[3] << 24, bytes[4] | + bytes[5] << 8 | + bytes[6] << 16 | + bytes[7] << 24, unsigned); + }; + /** + * Creates a Long from its big endian byte representation. + * @param {!Array.} bytes Big endian byte representation + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {Long} The corresponding Long value + */ + Long$1.fromBytesBE = function fromBytesBE(bytes, unsigned) { + return new Long$1(bytes[4] << 24 | + bytes[5] << 16 | + bytes[6] << 8 | + bytes[7], bytes[0] << 24 | + bytes[1] << 16 | + bytes[2] << 8 | + bytes[3], unsigned); + }; + + var LongExports = /*#__PURE__*/Object.assign(/*#__PURE__*/Object.create(null), long, { + 'default': long + }); + + // tslint:disable-next-line + var Long = + // tslint:disable-next-line + long || LongExports; + function hexToLong(hex) { + return Long.fromString(hex, true, 16); + } + // Some primes between 2^63 and 2^64 for various uses. + // Hex 0xc3a5c85c97cb3127 + hexToLong('c3a5c85c97cb3127'); + // Hex 0xb492b66fbe98f273 + hexToLong('b492b66fbe98f273'); + // Hex 0x9ae16a3b2f90404f + hexToLong('9ae16a3b2f90404f'); + + /** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function 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') { + var bool = new Uint8Array(a.length); + for (var 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); + } + } + /** + * Returns the current high-resolution time in milliseconds relative to an + * arbitrary time in the past. It works across different platforms (node.js, + * browsers). + * + * ```js + * console.log(tf.util.now()); + * ``` + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function now() { + return env().platform.now(); + } + /** + * Encodes the provided string into bytes using the provided encoding scheme. + * + * @param s The string to encode. + * @param encoding The encoding scheme. Defaults to utf-8. + * + * @doc {heading: 'Util'} + */ + function encodeString(s, encoding) { + if (encoding === void 0) { encoding = 'utf-8'; } + encoding = encoding || 'utf-8'; + return env().platform.encode(s, encoding); + } + /** + * Decodes the provided bytes into a string using the provided encoding scheme. + * @param bytes The bytes to decode. + * + * @param encoding The encoding scheme. Defaults to utf-8. + * + * @doc {heading: 'Util'} + */ + function decodeString(bytes, encoding) { + if (encoding === void 0) { encoding = 'utf-8'; } + encoding = encoding || 'utf-8'; + return env().platform.decode(bytes, encoding); + } + + var Profiler = /** @class */ (function () { + function Profiler(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + Profiler.prototype.profileKernel = function (kernelName, inputs, f) { + var e_1, _a; + var outputs; + var holdResultWrapperFn = function () { + outputs = f(); + }; + var timer; + var start = now(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } + else { + holdResultWrapperFn(); + try { + for (var outputs_1 = __values(outputs), outputs_1_1 = outputs_1.next(); !outputs_1_1.done; outputs_1_1 = outputs_1.next()) { + var output = outputs_1_1.value; + output.dataSync(); + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (outputs_1_1 && !outputs_1_1.done && (_a = outputs_1.return)) _a.call(outputs_1); + } + finally { if (e_1) throw e_1.error; } + } + timer = Promise.resolve({ kernelMs: now() - start }); + } + if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) { + var _loop_1 = function (i) { + var output = outputs[i]; + // Dangling promise here because we don't want to propagate up + // asynchronicity. + output.data().then(function (tensorVals) { + checkComputationForErrors(tensorVals, output.dtype, kernelName); + }); + }; + for (var i = 0; i < outputs.length; i++) { + _loop_1(i); + } + } + var kernelProfile = { + kernelName: kernelName, + outputs: outputs, + inputs: inputs, + timeMs: timer.then(function (timing) { return timing.kernelMs; }), + extraInfo: timer.then(function (timing) { return timing.getExtraProfileInfo != null ? + timing.getExtraProfileInfo() : + ''; }) + }; + return kernelProfile; + }; + Profiler.prototype.logKernelProfile = function (kernelProfile) { + var _this = this; + var kernelName = kernelProfile.kernelName, outputs = kernelProfile.outputs, timeMs = kernelProfile.timeMs, inputs = kernelProfile.inputs, extraInfo = kernelProfile.extraInfo; + outputs.forEach(function (result) { + Promise.all([result.data(), timeMs, extraInfo]).then(function (valueContainer) { + _this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); + }); + }); + }; + return Profiler; + }()); + function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== 'float32') { + // Only floating point computations will generate NaN values + return false; + } + for (var i = 0; i < vals.length; i++) { + var num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + // Throwing custom exception so behavior is testable. + console.warn("Found " + num + " in the result of '" + kernelName + "'"); + return true; + } + } + return false; + } + var Logger = /** @class */ (function () { + function Logger() { + } + Logger.prototype.logKernelProfile = function (name, result, vals, timeMs, inputs, extraInfo) { + var time = typeof timeMs === 'number' ? rightPad(timeMs + "ms", 9) : + timeMs['error']; + var paddedName = rightPad(name, 25); + var rank = result.rank; + var size = result.size; + var shape = rightPad(result.shape.toString(), 14); + var inputShapesDescription = ''; + for (var name_1 in inputs) { + var input = inputs[name_1]; + if (input != null) { + // The input might be a non-tensor (e.g HTMLImageElement), in which case + // we claim the output shape as input shape. + var inputShape = input.shape || result.shape; + var inputRank = inputShape.length; + inputShapesDescription += + name_1 + ": " + inputRank + "D " + (inputRank > 0 ? inputShape : '') + " "; + } + } + console.log("%c" + paddedName + "\t%c" + time + "\t%c" + rank + "D " + shape + "\t%c" + size + "\t%c" + inputShapesDescription + "\t%c" + extraInfo, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue'); + }; + return Logger; + }()); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes a list of TapeNodes that connect x to y, filtering everything else + * out and preserving the order of the original tape elements. + * + * @param tape The tape elements to filter. + * @param xs The input Tensors. + * @param y The output Tensor. + */ + function getFilteredNodesXToY(tape, xs, y) { + // Forward pass to compute all the nodes and Tensors that are transitively a + // function of x. + var tensorsFromX = {}; + var nodesFromX = {}; + for (var i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (var i = 0; i < tape.length; i++) { + var node = tape[i]; + var nodeInputs = node.inputs; + for (var inputName in nodeInputs) { + var input = nodeInputs[inputName]; + var anyInputFromX = false; + for (var j = 0; j < xs.length; j++) { + if (tensorsFromX[input.id]) { + node.outputs.forEach(function (output) { return tensorsFromX[output.id] = true; }); + anyInputFromX = true; + nodesFromX[node.id] = true; + break; + } + } + if (anyInputFromX) { + break; + } + } + } + // Backward pass to find all of the nodes and Tensors that lead to y. + var tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + var nodesToY = {}; + for (var i = tape.length - 1; i >= 0; i--) { + var node = tape[i]; + var nodeInputs = node.inputs; + // If any of the outputs lead to y, mark all of the inputs as leading to y. + for (var j = 0; j < node.outputs.length; j++) { + if (tensorsLeadToY[node.outputs[j].id]) { + for (var inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node.id] = true; + } + break; + } + } + } + // Return the paths that come from x and lead to y. + var filteredTape = []; + for (var i = 0; i < tape.length; i++) { + var node = tape[i]; + if (nodesFromX[node.id] && nodesToY[node.id]) { + // Prune the inputs from the node that aren't a function of x. + var prunedInputs = {}; + for (var inputName in node.inputs) { + var nodeInput = node.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + // Copy the node and overwrite inputsAndArgs to the pruned version. + var prunedNode = Object.assign({}, node); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; + } + /** + * Backpropagate gradients through the filtered TapeNodes. + * + * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map + * is mutated by this method. + * @param filteredTape The filtered TapeNodes to backprop through. + */ + function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) { + var _loop_1 = function (i) { + var node = filteredTape[i]; + var dys = []; + node.outputs.forEach(function (o) { + var gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } + else { + // This particular output is not in the back-propagation subgraph, so it + // does not affect the final output, thus we put null for its dy. + dys.push(null); + } + }); + if (node.gradient == null) { + throw new Error("Cannot compute gradient: gradient function not found " + + ("for " + node.kernelName + ".")); + } + // Backprop dy through this node and accumulate gradients over the inputs. + var inputGradients = node.gradient(dys); + var _loop_2 = function (inputName) { + if (!(inputName in inputGradients)) { + throw new Error("Cannot backprop through input " + inputName + ". " + + ("Available gradients found: " + Object.keys(inputGradients) + ".")); + } + // Call the gradient function. + var dx = tidy(function () { return 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 + "'")); + } + var 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 { + var curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add(curGradient, dx); + curGradient.dispose(); + } + }; + for (var inputName in node.inputs) { + _loop_2(inputName); + } + }; + // Walk the tape backward and keep a map of Tensor to its gradient. + for (var i = filteredTape.length - 1; i >= 0; i--) { + _loop_1(i); + } + } + + // Maximum number of values before we decide to show ellipsis. + var FORMAT_LIMIT_NUM_VALS = 20; + // Number of first and last values to show when displaying a, b,...,y, z. + var FORMAT_NUM_FIRST_LAST_VALS = 3; + // Number of significant digits to show. + var FORMAT_NUM_SIG_DIGITS = 7; + function tensorToString(vals, shape, dtype, verbose) { + var strides = computeStrides(shape); + var padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); + var rank = shape.length; + var valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); + var lines = ['Tensor']; + if (verbose) { + lines.push(" dtype: " + dtype); + lines.push(" rank: " + rank); + lines.push(" shape: [" + shape + "]"); + lines.push(" values:"); + } + lines.push(valsLines.map(function (l) { return ' ' + l; }).join('\n')); + return lines.join('\n'); + } + function computeMaxSizePerColumn(vals, shape, dtype, strides) { + var n = sizeFromShape(shape); + var numCols = strides[strides.length - 1]; + var padPerCol = new Array(numCols).fill(0); + var rank = shape.length; + var valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (var row = 0; row < n / numCols; row++) { + var offset = row * numCols; + for (var j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); + } + } + } + return padPerCol; + } + function valToString(val, pad, dtype) { + var 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, pad); + } + function boolNumToString(v) { + return v === 0 ? 'false' : 'true'; + } + function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast) { + if (isLast === void 0) { isLast = true; } + var storagePerElement = dtype === 'complex64' ? 2 : 1; + var size = shape[0]; + var rank = shape.length; + if (rank === 0) { + if (dtype === 'complex64') { + var 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) { + var firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + var firstVals = Array.from(vals.slice(0, firstValsSize)); + var 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(function (x, i) { return valToString(x, padPerCol[i], dtype); }) + .join(', ') + + ', ..., ' + + lastVals + .map(function (x, i) { return valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype); }) + .join(', ') + + ']' + ]; + } + var displayVals = dtype === 'complex64' ? createComplexTuples(vals) : + Array.from(vals); + return [ + '[' + + displayVals.map(function (x, i) { return valToString(x, padPerCol[i], dtype); }) + .join(', ') + + ']' + ]; + } + // The array is rank 2 or more. + var subshape = shape.slice(1); + var substrides = strides.slice(1); + var stride = strides[0] * storagePerElement; + var lines = []; + if (size > FORMAT_LIMIT_NUM_VALS) { + for (var i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + var start = i * stride; + var end = start + stride; + lines.push.apply(lines, __spread(subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */))); + } + lines.push('...'); + for (var i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { + var start = i * stride; + var end = start + stride; + lines.push.apply(lines, __spread(subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */))); + } + } + else { + for (var i = 0; i < size; i++) { + var start = i * stride; + var end = start + stride; + lines.push.apply(lines, __spread(subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */))); + } + } + var sep = rank === 2 ? ',' : ''; + lines[0] = '[' + lines[0] + sep; + for (var i = 1; i < lines.length - 1; i++) { + lines[i] = ' ' + lines[i] + sep; + } + var newLineSep = ',\n'; + for (var i = 2; i < rank; i++) { + newLineSep += '\n'; + } + lines[lines.length - 1] = + ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep); + return lines; + } + function createComplexTuples(vals) { + var complexTuples = []; + for (var i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; + } + + /** + * A mutable object, similar to `tf.Tensor`, that allows users to set values + * at locations before converting to an immutable `tf.Tensor`. + * + * See `tf.buffer` for creating a tensor buffer. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + var TensorBuffer = /** @class */ (function () { + function TensorBuffer(shape, dtype, values) { + var _this = this; + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + var n_1 = values.length; + assert(n_1 === this.size, function () { return "Length of values '" + n_1 + "' 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); + } + /** + * Sets a value in the buffer at a given location. + * + * @param value The value to set. + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + TensorBuffer.prototype.set = function (value) { + var _this = this; + var locs = []; + for (var _i = 1; _i < arguments.length; _i++) { + locs[_i - 1] = arguments[_i]; + } + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, function () { return "The number of provided coordinates (" + locs.length + ") must " + + ("match the rank (" + _this.rank + ")"); }); + var index = this.locToIndex(locs); + this.values[index] = value; + }; + /** + * Returns the value in the buffer at the provided location. + * + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + TensorBuffer.prototype.get = function () { + var e_1, _b; + var locs = []; + for (var _i = 0; _i < arguments.length; _i++) { + locs[_i] = arguments[_i]; + } + if (locs.length === 0) { + locs = [0]; + } + var i = 0; + try { + for (var locs_1 = __values(locs), locs_1_1 = locs_1.next(); !locs_1_1.done; locs_1_1 = locs_1.next()) { + var loc = locs_1_1.value; + if (loc < 0 || loc >= this.shape[i]) { + var msg = "Requested out of range element at " + locs + ". " + + (" Buffer shape=" + this.shape); + throw new Error(msg); + } + i++; + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (locs_1_1 && !locs_1_1.done && (_b = locs_1.return)) _b.call(locs_1); + } + finally { if (e_1) throw e_1.error; } + } + var index = locs[locs.length - 1]; + for (var i_1 = 0; i_1 < locs.length - 1; ++i_1) { + index += this.strides[i_1] * locs[i_1]; + } + return this.values[index]; + }; + TensorBuffer.prototype.locToIndex = function (locs) { + if (this.rank === 0) { + return 0; + } + else if (this.rank === 1) { + return locs[0]; + } + var index = locs[locs.length - 1]; + for (var i = 0; i < locs.length - 1; ++i) { + index += this.strides[i] * locs[i]; + } + return index; + }; + TensorBuffer.prototype.indexToLoc = function (index) { + if (this.rank === 0) { + return []; + } + else if (this.rank === 1) { + return [index]; + } + var locs = new Array(this.shape.length); + for (var 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; + }; + Object.defineProperty(TensorBuffer.prototype, "rank", { + get: function () { + return this.shape.length; + }, + enumerable: true, + configurable: true + }); + /** + * Creates an immutable `tf.Tensor` object from the buffer. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + TensorBuffer.prototype.toTensor = function () { + return trackerFn().makeTensor(this.values, this.shape, this.dtype); + }; + return TensorBuffer; + }()); + // For tracking tensor creation and disposal. + var trackerFn = null; + // Used by chaining methods to call into ops. + var opHandler = null; + /** + * An external consumer can register itself as the tensor tracker. This way + * the Tensor class can notify the tracker for every tensor created and + * disposed. + */ + function setTensorTracker(fn) { + trackerFn = fn; + } + /** + * A `tf.Tensor` object represents an immutable, multidimensional array of + * numbers that has a shape and a data type. + * + * For performance reasons, functions that create tensors do not necessarily + * perform a copy of the data passed to them (e.g. if the data is passed as a + * `Float32Array`), and changes to the data will change the tensor. This is not + * a feature and is not supported. To avoid this behavior, use the tensor before + * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`. + * + * See `tf.tensor` for details on how to create a `tf.Tensor`. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + var Tensor = /** @class */ (function () { + function Tensor(shape, dtype, dataId, id) { + /** Whether this tensor has been globally kept. */ + 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'); + } + Object.defineProperty(Tensor.prototype, "rank", { + get: function () { + return this.shape.length; + }, + enumerable: true, + configurable: true + }); + /** + * Returns a promise of `tf.TensorBuffer` that holds the underlying data. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.buffer = function () { + return __awaiter(this, void 0, void 0, function () { + var vals; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: return [4 /*yield*/, this.data()]; + case 1: + vals = _b.sent(); + return [2 /*return*/, opHandler.buffer(this.shape, this.dtype, vals)]; + } + }); + }); + }; + /** + * Returns a `tf.TensorBuffer` that holds the underlying data. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.bufferSync = function () { + return opHandler.buffer(this.shape, this.dtype, this.dataSync()); + }; + /** + * Returns the tensor data as a nested array. The transfer of data is done + * asynchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.array = function () { + return __awaiter(this, void 0, void 0, function () { + var vals; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: return [4 /*yield*/, this.data()]; + case 1: + vals = _b.sent(); + return [2 /*return*/, toNestedArray(this.shape, vals, this.dtype === 'complex64')]; + } + }); + }); + }; + /** + * Returns the tensor data as a nested array. The transfer of data is done + * synchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.arraySync = function () { + return toNestedArray(this.shape, this.dataSync(), this.dtype === 'complex64'); + }; + /** + * Asynchronously downloads the values from the `tf.Tensor`. Returns a + * promise of `TypedArray` that resolves when the computation has finished. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.data = function () { + return __awaiter(this, void 0, void 0, function () { + var data, bytes; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + this.throwIfDisposed(); + data = trackerFn().read(this.dataId); + if (!(this.dtype === 'string')) return [3 /*break*/, 2]; + return [4 /*yield*/, data]; + case 1: + bytes = _b.sent(); + try { + return [2 /*return*/, bytes.map(function (b) { return decodeString(b); })]; + } + catch (_a) { + throw new Error('Failed to decode the string bytes into utf-8. ' + + 'To get the original bytes, call tensor.bytes().'); + } + _b.label = 2; + case 2: return [2 /*return*/, data]; + } + }); + }); + }; + /** + * Synchronously downloads the values from the `tf.Tensor`. This blocks the + * UI thread until the values are ready, which can cause performance issues. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.dataSync = function () { + this.throwIfDisposed(); + var data = trackerFn().readSync(this.dataId); + if (this.dtype === 'string') { + try { + return data.map(function (b) { return 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; + }; + /** Returns the underlying bytes of the tensor's data. */ + Tensor.prototype.bytes = function () { + return __awaiter(this, void 0, void 0, function () { + var data; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + this.throwIfDisposed(); + return [4 /*yield*/, trackerFn().read(this.dataId)]; + case 1: + data = _b.sent(); + if (this.dtype === 'string') { + return [2 /*return*/, data]; + } + else { + return [2 /*return*/, new Uint8Array(data.buffer)]; + } + } + }); + }); + }; + /** + * Disposes `tf.Tensor` from memory. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.dispose = function () { + if (this.isDisposed) { + return; + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; + }; + Object.defineProperty(Tensor.prototype, "isDisposed", { + get: function () { + return this.isDisposedInternal; + }, + enumerable: true, + configurable: true + }); + Tensor.prototype.throwIfDisposed = function () { + if (this.isDisposed) { + throw new Error("Tensor is disposed."); + } + }; + /** + * Prints the `tf.Tensor`. See `tf.print` for details. + * + * @param verbose Whether to print verbose information about the tensor, + * including dtype and size. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.print = function (verbose) { + if (verbose === void 0) { verbose = false; } + return opHandler.print(this, verbose); + }; + /** + * Returns a copy of the tensor. See `tf.clone` for details. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.clone = function () { + this.throwIfDisposed(); + return opHandler.clone(this); + }; + /** + * Returns a human-readable description of the tensor. Useful for logging. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.toString = function (verbose) { + if (verbose === void 0) { verbose = false; } + var vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); + }; + Tensor.prototype.cast = function (dtype) { + this.throwIfDisposed(); + return opHandler.cast(this, dtype); + }; + Tensor.prototype.variable = function (trainable, name, dtype) { + if (trainable === void 0) { trainable = true; } + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); + }; + return Tensor; + }()); + Object.defineProperty(Tensor, Symbol.hasInstance, { + value: function (instance) { + // Implementation note: we should use properties of the object that will be + // defined before the constructor body has finished executing (methods). + // This is because when this code is transpiled by babel, babel will call + // classCallCheck before the constructor body is run. + // See https://github.com/tensorflow/tfjs/issues/3384 for backstory. + return !!instance && instance.data != null && instance.dataSync != null && + instance.throwIfDisposed != null; + } + }); + function getGlobalTensorClass() { + // Use getGlobal so that we can augment the Tensor class across package + // boundaries becase the node resolution alg may result in different modules + // being returned for this file depending on the path they are loaded from. + return getGlobal('Tensor', function () { + return Tensor; + }); + } + // Global side effect. Cache global reference to Tensor class + getGlobalTensorClass(); + /** + * A mutable `tf.Tensor`, useful for persisting state, e.g. for training. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + var Variable = /** @class */ (function (_super) { + __extends(Variable, _super); + function Variable(initialValue, trainable, name, tensorId) { + var _this = _super.call(this, initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId) || this; + _this.trainable = trainable; + _this.name = name; + return _this; + } + /** + * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have + * the same shape and dtype as the old `tf.Tensor`. + * + * @param newValue New tensor to be assigned to this variable. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Variable.prototype.assign = function (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 /* backend */); + }; + Variable.prototype.dispose = function () { + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; + }; + return Variable; + }(Tensor)); + Object.defineProperty(Variable, Symbol.hasInstance, { + value: function (instance) { + return instance instanceof Tensor && instance.assign != null && + instance.assign instanceof Function; + } + }); + + /** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var Rank; + (function (Rank) { + Rank["R0"] = "R0"; + Rank["R1"] = "R1"; + Rank["R2"] = "R2"; + Rank["R3"] = "R3"; + Rank["R4"] = "R4"; + Rank["R5"] = "R5"; + Rank["R6"] = "R6"; + })(Rank || (Rank = {})); + // Looks for upcasting types. Used, for example, in operations with mixed dtype + // inputs. + var UpcastInt32AndMap; + (function (UpcastInt32AndMap) { + UpcastInt32AndMap["float32"] = "float32"; + UpcastInt32AndMap["int32"] = "int32"; + UpcastInt32AndMap["bool"] = "int32"; + UpcastInt32AndMap["complex64"] = "complex64"; + })(UpcastInt32AndMap || (UpcastInt32AndMap = {})); + var UpcastBoolAndMap; + (function (UpcastBoolAndMap) { + UpcastBoolAndMap["float32"] = "float32"; + UpcastBoolAndMap["int32"] = "int32"; + UpcastBoolAndMap["bool"] = "bool"; + UpcastBoolAndMap["complex64"] = "complex64"; + })(UpcastBoolAndMap || (UpcastBoolAndMap = {})); + var UpcastFloat32AndMap; + (function (UpcastFloat32AndMap) { + UpcastFloat32AndMap["float32"] = "float32"; + UpcastFloat32AndMap["int32"] = "float32"; + UpcastFloat32AndMap["bool"] = "float32"; + UpcastFloat32AndMap["complex64"] = "complex64"; + })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); + var UpcastComplex64AndMap; + (function (UpcastComplex64AndMap) { + UpcastComplex64AndMap["float32"] = "complex64"; + UpcastComplex64AndMap["int32"] = "complex64"; + UpcastComplex64AndMap["bool"] = "complex64"; + UpcastComplex64AndMap["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]; + } + + /** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + var dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; + } + function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, function () { return "The dtypes of the first(" + a.dtype + ") and" + + (" second(" + b.dtype + ") input must match"); }); + } + /** + * Extracts any `Tensor`s found within the provided object. + * + * @param container an object that may be a `Tensor` or may directly contain + * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it + * is safe to pass any object here, except that `Promise`s are not + * supported. + * @returns An array of `Tensors` found within the passed object. If the + * argument is simply a `Tensor', a list containing that `Tensor` is + * returned. If the object is not a `Tensor` or does not + * contain `Tensors`, an empty list is returned. + */ + function getTensorsInContainer(result) { + var list = []; + var seen = 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; + } + // Iteration over keys works also for arrays. + var iterable = container; + for (var k in iterable) { + var val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } + } + } + // tslint:disable-next-line:no-any + function isIterable(obj) { + return Array.isArray(obj) || typeof obj === 'object'; + } + + function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; + } + var EngineState = /** @class */ (function () { + function EngineState() { + // Public since optimizers will use it. + this.registeredVariables = {}; + this.nextTapeNodeId = 0; + this.numBytes = 0; + this.numTensors = 0; + this.numStringTensors = 0; + this.numDataBuffers = 0; + // Number of nested tf.grad() statements when computing higher-order + // gradients. E.g. `1` for first-order gradients and `2` for second-order + // gradients. Used to track if the tape should be removed after a backprop. + this.gradientDepth = 0; + // Number of nested kernel calls. When kernel depth is greater than 1, we turn + // off the tape. + this.kernelDepth = 0; + this.scopeStack = []; + /** + * Keeps track of the number of data moves during a kernel execution. We + * maintain a stack since kernels can call other kernels, recursively. + */ + this.numDataMovesStack = []; + this.nextScopeId = 0; + this.tensorInfo = 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(function (k) { return k.name; }))); + } + }; + } + EngineState.prototype.dispose = function () { + for (var variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } + }; + return EngineState; + }()); + var Engine = /** @class */ (function () { + function Engine(ENV) { + this.ENV = ENV; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); + } + Engine.prototype.ready = function () { + return __awaiter(this, void 0, void 0, function () { + var sortedBackends, i, backendName, success; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + if (this.pendingBackendInit != null) { + return [2 /*return*/, this.pendingBackendInit.then(function () { })]; + } + if (this.backendInstance != null) { + return [2 /*return*/]; + } + sortedBackends = this.getSortedBackends(); + i = 0; + _a.label = 1; + case 1: + if (!(i < sortedBackends.length)) return [3 /*break*/, 5]; + backendName = sortedBackends[i]; + return [4 /*yield*/, this.initializeBackend(backendName).success]; + case 2: + success = _a.sent(); + if (!success) return [3 /*break*/, 4]; + return [4 /*yield*/, this.setBackend(backendName)]; + case 3: + _a.sent(); + return [2 /*return*/]; + case 4: + i++; + return [3 /*break*/, 1]; + case 5: throw new Error("Could not initialize any backends, all backend initializations " + + "failed."); + } + }); + }); + }; + Object.defineProperty(Engine.prototype, "backend", { + get: function () { + 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) { + var _a = this.initializeBackendsAndReturnBest(), name = _a.name, asyncInit = _a.asyncInit; + 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; + }, + enumerable: true, + configurable: true + }); + Engine.prototype.backendNames = function () { + return Object.keys(this.registryFactory); + }; + Engine.prototype.findBackend = function (backendName) { + if (!(backendName in this.registry)) { + // If the backend hasn't been initialized but we have a registry entry for + // it, initialize it and return it. + if (backendName in this.registryFactory) { + var asyncInit = this.initializeBackend(backendName).asyncInit; + if (asyncInit) { + // Backend is not ready yet. + return null; + } + } + else { + return null; + } + } + return this.registry[backendName]; + }; + Engine.prototype.findBackendFactory = function (backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + }; + Engine.prototype.registerBackend = function (backendName, factory, priority) { + if (priority === void 0) { priority = 1; } + if (backendName in this.registryFactory) { + warn(backendName + " backend was already registered. " + + "Reusing existing backend factory."); + return false; + } + this.registryFactory[backendName] = { factory: factory, priority: priority }; + return true; + }; + Engine.prototype.setBackend = function (backendName) { + return __awaiter(this, void 0, void 0, function () { + var _a, success, asyncInit, result, _b; + return __generator(this, function (_c) { + switch (_c.label) { + case 0: + if (this.registryFactory[backendName] == null) { + throw new Error("Backend name '" + backendName + "' not found in registry"); + } + this.backendName = backendName; + if (!(this.registry[backendName] == null)) return [3 /*break*/, 4]; + this.backendInstance = null; + _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit; + if (!asyncInit) return [3 /*break*/, 2]; + return [4 /*yield*/, success]; + case 1: + _b = _c.sent(); + return [3 /*break*/, 3]; + case 2: + _b = success; + _c.label = 3; + case 3: + result = _b; + if (!result) { + return [2 /*return*/, false]; + } + _c.label = 4; + case 4: + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + // Reset the profiler. + this.profiler = new Profiler(this.backendInstance); + return [2 /*return*/, true]; + } + }); + }); + }; + Engine.prototype.setupRegisteredKernels = function () { + var _this = this; + var kernels = getKernelsForBackend(this.backendName); + kernels.forEach(function (kernel) { + if (kernel.setupFunc != null) { + kernel.setupFunc(_this.backendInstance); + } + }); + }; + Engine.prototype.disposeRegisteredKernels = function (backendName) { + var _this = this; + var kernels = getKernelsForBackend(backendName); + kernels.forEach(function (kernel) { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(_this.registry[backendName]); + } + }); + }; + /** + * Initializes a backend by looking up the backend name in the factory + * registry and calling the factory method. Returns a boolean representing + * whether the initialization of the backend suceeded. Throws an error if + * there is no backend in the factory registry. + */ + Engine.prototype.initializeBackend = function (backendName) { + var _this = this; + var registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error("Cannot initialize backend " + backendName + ", no registration found."); + } + try { + var backend = registryFactoryEntry.factory(); + /* Test if the factory returns a promise. + Done in a more liberal way than + previous 'Promise.resolve(backend)===backend' + as we needed to account for custom Promise + implementations (e.g. Angular) */ + if (backend && !(backend instanceof KernelBackend) && + typeof backend.then === 'function') { + var promiseId_1 = ++this.pendingBackendInitId; + var success = backend + .then(function (backendInstance) { + // Outdated promise. Another backend was set in the meantime. + if (promiseId_1 < _this.pendingBackendInitId) { + return false; + } + _this.registry[backendName] = backendInstance; + _this.pendingBackendInit = null; + return true; + }) + .catch(function (err) { + // Outdated promise. Another backend was set in the meantime. + if (promiseId_1 < _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: success, asyncInit: true }; + } + else { + this.registry[backendName] = backend; + return { success: true, asyncInit: false }; + } + } + catch (err) { + warn("Initialization of backend " + backendName + " failed"); + warn(err.stack || err.message); + return { success: false, asyncInit: false }; + } + }; + Engine.prototype.removeBackend = function (backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(backendName + " backend not found in registry"); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + // There is a pending promise of the backend we want to remove. Make it + // obsolete. + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + // Unset the backend if it is active. + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } + }; + Engine.prototype.getSortedBackends = function () { + var _this = this; + if (Object.keys(this.registryFactory).length === 0) { + throw new Error('No backend found in registry.'); + } + return Object.keys(this.registryFactory).sort(function (a, b) { + // Highest priority comes first. + return _this.registryFactory[b].priority - + _this.registryFactory[a].priority; + }); + }; + Engine.prototype.initializeBackendsAndReturnBest = function () { + var sortedBackends = this.getSortedBackends(); + for (var i = 0; i < sortedBackends.length; i++) { + var backendName = sortedBackends[i]; + var _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit; + if (asyncInit || success) { + return { name: backendName, asyncInit: asyncInit }; + } + } + throw new Error("Could not initialize any backends, all backend initializations " + + "failed."); + }; + Engine.prototype.moveData = function (backend, dataId) { + var info = this.state.tensorInfo.get(dataId); + var srcBackend = info.backend; + var values = this.readSync(dataId); + var refCount = srcBackend.refCount(dataId); + // Delete the tensor from the old backend and move it to the new + // backend. + srcBackend.disposeData(dataId, true); + info.backend = backend; + backend.move(dataId, values, info.shape, info.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + // Track the number of moves during a kernel execution to correctly + // detect memory leaks. + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } + }; + Engine.prototype.tidy = function (nameOrFn, fn) { + var _this = this; + var name = null; + if (fn == null) { + // Called with only 1 argument. + if (typeof nameOrFn !== 'function') { + throw new Error('Please provide a function to tidy()'); + } + fn = nameOrFn; + } + else { + // Called with 2 arguments. + 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; + // TODO(nsthorat,smilkov): Do operation logging and performance + // profiling. + } + var result; + return this.scopedRun(function () { return _this.startScope(name); }, function () { return _this.endScope(result); }, function () { + result = fn(); + if (result instanceof Promise) { + console.error('Cannot return a Promise inside of tidy.'); + } + return result; + }); + }; + Engine.prototype.scopedRun = function (start, end, f) { + start(); + try { + var res = f(); + end(); + return res; + } + catch (ex) { + end(); + throw ex; + } + }; + Engine.prototype.nextTensorId = function () { + return Engine.nextTensorId++; + }; + Engine.prototype.nextVariableId = function () { + return Engine.nextVariableId++; + }; + /** + * This method is called instead of the public-facing tensor.clone() when + * saving a tensor for backwards pass. It makes sure to add the clone + * operation to the tape regardless of being called inside a kernel + * execution. + */ + Engine.prototype.clone = function (x) { + var y = ENGINE.runKernel(Identity, { x: x }); + var inputs = { x: x }; + var grad = function (dy) { return ({ + x: function () { + var dtype = 'float32'; + var gradInputs = { x: dy }; + var attrs = { dtype: dtype }; + return ENGINE.runKernel(Cast, gradInputs, + // tslint:disable-next-line: no-unnecessary-type-assertion + attrs); + } + }); }; + var saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {}); + return y; + }; + /** + * Execute a kernel with the given name and return the output tensor. + * + * @param kernelName The name of the kernel to execute. + * @param inputs A map of input names to tensors. + * @param attrs A map of attribute names to their values. An attribute is a + * primitive (non-tensor) input to the kernel. + * @param inputsToSave A list of tensors, inputs to save for the backprop + * computation. + * @param outputsToSave A list of booleans, specifying which output to save + * for the backprop computation. These are booleans since the output + * tensors are not visible to the user. + */ + Engine.prototype.runKernel = function (kernelName, inputs, attrs) { + if (this.backendName == null) { + // backend has not been initialized yet (backend initialization is lazy + // can be deferred until an op/ kernel is run). + // The below getter has side effects that will try to initialize the + // backend and set properties like this.backendName + // tslint:disable-next-line: no-unused-expression + this.backend; + } + var hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error("Kernel '" + kernelName + "' not registered for backend '" + this.backendName + "'"); + } + return this.runKernelFunc({ kernelName: kernelName, inputs: inputs, attrs: attrs }); + }; + Engine.prototype.shouldCheckForMemLeaks = function () { + return this.ENV.getBool('IS_TEST'); + }; + Engine.prototype.checkKernelForMemLeak = function (kernelName, numDataIdsBefore, outInfos) { + var numDataIdsAfter = this.backend.numDataIds(); + // Count the number of data ids associated with the result of the kernel. + var numOutputDataIds = 0; + outInfos.forEach(function (info) { + // Complex numbers allocate 3 data ids, one for 'real', one for + // 'imaginary', and one for the container that holds the former two. + numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1); + }); + // Account for the number of moves during kernel execution. A "data move" + // can happen in the middle of a kernel execution, placing a new (key,value) + // pair in the data storage. Since data moves have net zero effect (we + // always remove the data from the old backend), we have to cancel them out + // when detecting memory leaks. + var numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + var 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 + "'")); + } + }; + /** + * Internal helper method to execute a kernel Func + * + * Use `runKernel` to execute kernels from outside of engine. + */ + Engine.prototype.runKernelFunc = function (kernelParams) { + var _this = this; + var outputs; + var saved = []; + var isTapeOn = this.isTapeOn(); + var startingBytecount = this.state.numBytes; + var startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + var kernelFunc; + if (this.backendName == null) { + // backend has not been initialized yet (backend initialization is lazy + // can be deferred until an op/ kernel is run). + // The below getter has side effects that will try to initialize the + // backend and set properties like this.backendName + // tslint:disable-next-line: no-unused-expression + this.backend; + } + var out; + var kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? + kernelParams.kernelName : + this.state.activeScope != null ? this.state.activeScope.name : ''; + // Create the kernelFunc from either a registered kernel OR passed in + // forward/backward functions (used by custom grad). In this context a + // kernelFunc wraps a kernel implementation with some bookkeeping. + if (isRegisteredKernelInvocation(kernelParams)) { + var kernelName_1 = kernelParams.kernelName, inputs_1 = kernelParams.inputs, attrs_1 = kernelParams.attrs; + if (this.backendName == null) { + // backend has not been initialized yet (backend initialization is lazy + // can be deferred until an op/ kernel is run). + // The below getter has side effects that will try to initialize the + // backend and set properties like this.backendName + // tslint:disable-next-line: no-unused-expression + this.backend; + } + var kernel_1 = getKernel(kernelName_1, this.backendName); + assert(kernel_1 != null, function () { return "Cannot find registered kernel '" + kernelName_1 + "' for backend '" + _this.backendName + "'"; }); + kernelFunc = function () { + var numDataIdsBefore = _this.backend.numDataIds(); + out = kernel_1.kernelFunc({ inputs: inputs_1, attrs: attrs_1, backend: _this.backend }); + var outInfos = Array.isArray(out) ? out : [out]; + if (_this.shouldCheckForMemLeaks()) { + _this.checkKernelForMemLeak(kernelName_1, numDataIdsBefore, outInfos); + } + var outTensors = outInfos.map(function (outInfo) { + // todo (yassogba) remove this option (Tensor) when node backend + // methods have been modularized and they all return tensorInfo. + // TensorInfos do not have a rank attribute. + if (outInfo.rank != null) { + return outInfo; + } + var dataId = outInfo.dataId, shape = outInfo.shape, dtype = outInfo.dtype; + return _this.makeTensorFromDataId(dataId, shape, dtype); + }); + // Save any required inputs and outputs. + // Do not save unless we are recording to the tape. Otherwise it would + // cause a mem leak since there would be no backprop for these tensors + // (which would otherwise dispose them). + if (isTapeOn) { + var tensorsToSave = _this.getTensorsForGradient(kernelName_1, inputs_1, outTensors); + saved = _this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; + }; + } + else { + var forwardFunc_1 = kernelParams.forwardFunc; + // Running a customGrad op. + var saveFunc_1 = function (tensors) { + // Do not save unless we are recording to the tape. Otherwise it would + // cause a mem leak since we would never run backprop, which disposes + // the kept tensors. + if (!isTapeOn) { + return; + } + saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); }); + }; + kernelFunc = function () { + var numDataIdsBefore = _this.backend.numDataIds(); + out = _this.tidy(function () { return forwardFunc_1(_this.backend, saveFunc_1); }); + var outs = (Array.isArray(out) ? out : [out]); + if (_this.shouldCheckForMemLeaks()) { + // Scope name is used to print a more helpful error message if needed. + _this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; + }; + } + // + // Run the kernelFunc. Optionally profiling it. + // + var inputs = kernelParams.inputs, attrs = kernelParams.attrs; + var backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? + null : + kernelParams.backwardsFunc; + var kernelProfile; + this.scopedRun( + // Stop recording to a tape when running a kernel. + function () { return _this.state.kernelDepth++; }, function () { return _this.state.kernelDepth--; }, function () { + if (!_this.ENV.getBool('DEBUG') && !_this.state.profiling) { + outputs = kernelFunc(); + } + else { + kernelProfile = _this.profiler.profileKernel(kernelOrScopeName, inputs, function () { return 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(function (key) { return inputs[key] != null ? inputs[key].shape : null; }), + outputShapes: outputs.map(function (item) { return item.shape; }), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return (Array.isArray(out) ? outputs : outputs[0]); + }; + /** + * Saves tensors used in forward mode for use in backward mode. + * + * @param tensors the list of tensors to save. + */ + Engine.prototype.saveTensorsForBackwardMode = function (tensors) { + var _this = this; + var saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); }); + return saved; + }; + /** + * Returns a list of tensors to save for a given gradient calculation. + * + * @param kernelName name of kernel to look up gradient for. + * @param inputs a map of input tensors. + * @param outputs an array of output tensors from forward mode of kernel. + */ + Engine.prototype.getTensorsForGradient = function (kernelName, inputs, outputs) { + var gradConfig = getGradient(kernelName); + if (gradConfig != null) { + var inputsToSave = gradConfig.inputsToSave || []; + var outputsToSave_1 = gradConfig.outputsToSave || []; + // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs + // specified in inputsToSave will be saved. + var inputTensorsToSave = void 0; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), function () { return 'saveAllInputs is true, expected inputs to be an array.'; }); + inputTensorsToSave = Object.keys(inputs).map(function (key) { return inputs[key]; }); + } + else { + inputTensorsToSave = inputsToSave.map(function (inputName) { return inputs[inputName]; }); + } + var outputTensorsToSave = outputs.filter(function (_, i) { return outputsToSave_1[i]; }); + return inputTensorsToSave.concat(outputTensorsToSave); + } + // We return an empty list rather than throw an error because the kernel we + // are looking up may not actually be relevant to backproping through the + // overall function + // + // See 'does not error if irrelevant (pruned) ops are missing grads' test + // in gradients_test.ts for an example. + return []; + }; + /** + * Internal method used by public APIs for tensor creation. Makes a new + * tensor with the provided shape, dtype and values. It always + * creates a new data id and writes the values to the underlying backend. + */ + Engine.prototype.makeTensor = function (values, shape, dtype, backend) { + if (values == null) { + throw new Error('Values passed to engine.makeTensor() are null'); + } + dtype = dtype || 'float32'; + backend = backend || this.backend; + var backendVals = values; + if (dtype === 'string' && isString(values[0])) { + backendVals = values.map(function (d) { return encodeString(d); }); + } + var dataId = backend.write(backendVals, shape, dtype); + var t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend); + // Count bytes for string tensors. + if (dtype === 'string') { + var info = this.state.tensorInfo.get(dataId); + var newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info.bytes; + info.bytes = newBytes; + } + return t; + }; + /** + * Internal method used by backends. Makes a new tensor + * that is a wrapper around an existing data id. It doesn't create + * a new data id, only increments the ref count used in memory tracking. + */ + Engine.prototype.makeTensorFromDataId = function (dataId, shape, dtype, backend) { + dtype = dtype || 'float32'; + var t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend); + return t; + }; + Engine.prototype.makeVariable = function (initialValue, trainable, name, dtype) { + if (trainable === void 0) { trainable = true; } + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + var 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; + }; + Engine.prototype.trackTensor = function (a, backend) { + this.state.numTensors++; + if (a.dtype === 'string') { + this.state.numStringTensors++; + } + // Bytes for complex numbers are counted by their components. Bytes for + // string tensors are counted when writing values. + var 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: backend || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes: bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } + }; + // Track the tensor by dataId and increase the refCount for the dataId in the + // backend. + // TODO(pyu10055): This is currently used by makeVariable method, to increase + // refCount on the backend for the dataId. It can potentially be replaced with + // Identity op indead of calling backend directly. + Engine.prototype.incRef = function (a, backend) { + this.trackTensor(a, backend); + this.backend.incRef(a.dataId); + }; + Engine.prototype.removeDataId = function (dataId, backend) { + if (this.state.tensorInfo.has(dataId) && + this.state.tensorInfo.get(dataId).backend === backend) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } + }; + Engine.prototype.disposeTensor = function (a) { + if (!this.state.tensorInfo.has(a.dataId)) { + return; + } + var info = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === 'string') { + this.state.numStringTensors--; + this.state.numBytes -= info.bytes; + } + // Don't count bytes for complex numbers as they are counted by their + // components. + if (a.dtype !== 'complex64' && a.dtype !== 'string') { + var bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + // Remove the reference to dataId if backend dispose the data successfully + if (info.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info.backend); + } + // TODO(nsthorat): Construct an error and save the stack trace for + // debugging when in debug mode. Creating a stack trace is too expensive + // to do unconditionally. + }; + Engine.prototype.disposeVariables = function () { + for (var varName in this.state.registeredVariables) { + var v = this.state.registeredVariables[varName]; + this.disposeVariable(v); + } + }; + Engine.prototype.disposeVariable = function (v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } + }; + Engine.prototype.memory = function () { + var 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; + }; + Engine.prototype.profile = function (query) { + return __awaiter(this, void 0, void 0, function () { + var startBytes, startNumTensors, _a, _b, _c, kernel, _d, _e, e_1_1; + var e_1, _f; + return __generator(this, function (_g) { + switch (_g.label) { + case 0: + this.state.profiling = true; + startBytes = this.state.numBytes; + startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + _a = this.state.activeProfile; + return [4 /*yield*/, query()]; + case 1: + _a.result = _g.sent(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max.apply(Math, __spread(this.state.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }))); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = + this.state.numTensors - startNumTensors; + _g.label = 2; + case 2: + _g.trys.push([2, 8, 9, 10]); + _b = __values(this.state.activeProfile.kernels), _c = _b.next(); + _g.label = 3; + case 3: + if (!!_c.done) return [3 /*break*/, 7]; + kernel = _c.value; + _d = kernel; + return [4 /*yield*/, kernel.kernelTimeMs]; + case 4: + _d.kernelTimeMs = _g.sent(); + _e = kernel; + return [4 /*yield*/, kernel.extraInfo]; + case 5: + _e.extraInfo = _g.sent(); + _g.label = 6; + case 6: + _c = _b.next(); + return [3 /*break*/, 3]; + case 7: return [3 /*break*/, 10]; + case 8: + e_1_1 = _g.sent(); + e_1 = { error: e_1_1 }; + return [3 /*break*/, 10]; + case 9: + try { + if (_c && !_c.done && (_f = _b.return)) _f.call(_b); + } + finally { if (e_1) throw e_1.error; } + return [7 /*endfinally*/]; + case 10: return [2 /*return*/, this.state.activeProfile]; + } + }); + }); + }; + Engine.prototype.isTapeOn = function () { + return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; + }; + Engine.prototype.addTapeNode = function (kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + var _this = this; + var tapeNode = { id: this.state.nextTapeNodeId++, kernelName: kernelName, inputs: inputs, outputs: outputs, saved: saved }; + var gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = function (dys) { + // TODO(smilkov): To optimize back-prop, pass dys that are not used in + // the backprop graph to the user as null instead of zeros + dys = dys.map(function (dy, i) { + if (dy == null) { + var output = outputs[i]; + var vals = makeZerosTypedArray(output.size, output.dtype); + return _this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + // Grad functions of ops with single outputs expect a dy, while ops + // with multiple outputs expect dys (array of dy). + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); + }; + Engine.prototype.keep = function (result) { + result.kept = true; + return result; + }; + Engine.prototype.startTape = function () { + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; + }; + Engine.prototype.endTape = function () { + this.state.gradientDepth--; + }; + /** + * Start a scope. Use this with endScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + Engine.prototype.startScope = function (name) { + var scopeInfo = { + track: [], + name: 'unnamed scope', + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; + } + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + }; + /** + * End a scope. Use this with startScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + Engine.prototype.endScope = function (result) { + var _this = this; + var tensorsToTrackInParent = getTensorsInContainer(result); + var tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(function (t) { return t.id; })); + // Dispose the arrays tracked in this scope. + for (var i = 0; i < this.state.activeScope.track.length; i++) { + var tensor = this.state.activeScope.track[i]; + if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) { + tensor.dispose(); + } + } + var oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? + null : + this.state.scopeStack[this.state.scopeStack.length - 1]; + // Track the current result in the parent scope. + tensorsToTrackInParent.forEach(function (tensor) { + // Only track the tensor if was allocated in the inner scope and is not + // globally kept. + if (!tensor.kept && tensor.scopeId === oldScope.id) { + _this.track(tensor); + } + }); + }; + /** + * Returns gradients of `f` with respect to each of the `xs`. The gradients + * returned are of the same length as `xs`, but some might be null if `f` + * was not a function of that `x`. It also takes optional dy to multiply the + * gradient, which defaults to `1`. + */ + Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) { + var _this = this; + if (allowNoGradients === void 0) { allowNoGradients = false; } + assert(xs.length > 0, function () { return '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 + "'"); + } + var y = this.scopedRun(function () { return _this.startTape(); }, function () { return _this.endTape(); }, function () { return _this.tidy('forward', f); }); + assert(y instanceof Tensor, function () { return 'The result y returned by f() must be a tensor.'; }); + // Filter out the nodes that don't connect x => y. + var 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', function () { + var accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = (dy == null) ? ones$1(y.shape) : dy; + // Backprop gradients through the filtered nodes. + backpropagateGradients(accumulatedGradientMap, filteredTape, + // Pass the tidy function to avoid circular dep with `tape.ts`. + function (f) { return _this.tidy(f); }, + // Pass an add function to avoide a circular dep with `tape.ts`. + add$1); + var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; }); + if (_this.state.gradientDepth === 0) { + // This means that we are not computing higher-order gradients + // and can clean up the tape. + _this.state.activeTape.forEach(function (node) { + var e_2, _a; + try { + for (var _b = __values(node.saved), _c = _b.next(); !_c.done; _c = _b.next()) { + var tensor = _c.value; + tensor.dispose(); + } + } + catch (e_2_1) { e_2 = { error: e_2_1 }; } + finally { + try { + if (_c && !_c.done && (_a = _b.return)) _a.call(_b); + } + finally { if (e_2) throw e_2.error; } + } + }); + _this.state.activeTape = null; + } + return { value: y, grads: grads }; + }); + }; + Engine.prototype.customGrad = function (f) { + var _this = this; + assert(isFunction(f), function () { return 'The f passed in customGrad(f) must be a function.'; }); + return function () { + var inputs = []; + for (var _i = 0; _i < arguments.length; _i++) { + inputs[_i] = arguments[_i]; + } + assert(inputs.every(function (t) { return t instanceof Tensor; }), function () { return 'The args passed in customGrad(f)(x1, x2,...) must all be ' + + 'tensors'; }); + var res; + var inputMap = {}; + inputs.forEach(function (input, i) { + inputMap[i] = input; + }); + var forwardFunc = function (_, save) { + res = f.apply(void 0, __spread(inputs, [save])); + assert(res.value instanceof Tensor, function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.value` is a tensor'; }); + assert(isFunction(res.gradFunc), function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.gradFunc` is a function.'; }); + return res.value; + }; + var backwardsFunc = function (dy, saved) { + var gradRes = res.gradFunc(dy, saved); + var grads = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads.length === inputs.length, function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.gradFunc` is a function that returns ' + + 'the same number of tensors as inputs passed to f(...).'; }); + assert(grads.every(function (t) { return t instanceof Tensor; }), function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.gradFunc` is a function that returns ' + + 'a list of only tensors.'; }); + var gradMap = {}; + grads.forEach(function (grad, i) { + gradMap[i] = function () { return grad; }; + }); + return gradMap; + }; + return _this.runKernelFunc({ + forwardFunc: forwardFunc, + backwardsFunc: backwardsFunc, + inputs: inputMap, + }); + }; + }; + Engine.prototype.readSync = function (dataId) { + // Route the read to the correct backend. + var info = this.state.tensorInfo.get(dataId); + return info.backend.readSync(dataId); + }; + Engine.prototype.read = function (dataId) { + // Route the read to the correct backend. + var info = this.state.tensorInfo.get(dataId); + return info.backend.read(dataId); + }; + Engine.prototype.time = function (query) { + return __awaiter(this, void 0, void 0, function () { + var start, timingInfo; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + start = now(); + return [4 /*yield*/, this.backend.time(query)]; + case 1: + timingInfo = _a.sent(); + timingInfo.wallMs = now() - start; + return [2 /*return*/, timingInfo]; + } + }); + }); + }; + /** + * Tracks a Tensor in the current scope to be automatically cleaned up + * when the current scope ends, and returns the value. + * + * @param result The Tensor to track in the current scope. + */ + Engine.prototype.track = function (result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; + }; + Object.defineProperty(Engine.prototype, "registeredVariables", { + get: function () { + return this.state.registeredVariables; + }, + enumerable: true, + configurable: true + }); + /** + * Resets the engine state. Removes all backends but does not remove + * registered backend factories. + */ + Engine.prototype.reset = function () { + // Make any pending promise obsolete. + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (var backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; + }; + return Engine; + }()); + Engine.nextTensorId = 0; + Engine.nextVariableId = 0; + function ones$1(shape) { + var values = makeOnesTypedArray(sizeFromShape(shape), 'float32'); + return ENGINE.makeTensor(values, shape, 'float32'); + } + function getOrMakeEngine() { + var ns = getGlobalNamespace(); + if (ns._tfengine == null) { + var environment = new Environment(ns); + ns._tfengine = new Engine(environment); + } + setEnvironmentGlobal(ns._tfengine.ENV); + // Tell the current tensor interface that the global engine is responsible + // for tracking. + setTensorTracker(function () { return ns._tfengine; }); + return ns._tfengine; + } + var ENGINE = getOrMakeEngine(); + /** + * A implementation of the add op for use within engine and tape. + * + * This allows us to avoid a circular dependency between add.ts and engine. + * It is exported to be available in tape tests. + */ + function add$1(a, b) { + // We duplicate Add here to avoid a circular dependency with add.ts. + var inputs = { a: a, b: b }; + return ENGINE.runKernel(Add, inputs); + } + + /** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function inferShape(val, dtype) { + var firstElem = val; + if (isTypedArray(val)) { + return dtype === 'string' ? [] : [val.length]; + } + if (!Array.isArray(val)) { + return []; // Scalar. + } + var 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, function () { return "Element arr[" + indices.join('][') + "] is a primitive, " + + ("but should be an array/TypedArray of " + shape[0] + " elements"); }); + return; + } + assert(shape.length > 0, function () { return "Element arr[" + indices.join('][') + "] should be a primitive, " + + ("but is an array of " + val.length + " elements"); }); + assert(val.length === shape[0], function () { return "Element arr[" + indices.join('][') + "] should have " + shape[0] + " " + + ("elements, but has " + val.length + " elements"); }); + var subShape = shape.slice(1); + for (var 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) { + if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; } + if (x instanceof Tensor) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + var inferredDtype = inferDtype(x); + // If the user expects a bool/int/float, use that info to update the + // inferredDtype when it is not a string. + 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')) { + var type = x == null ? 'null' : x.constructor.name; + throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must be a " + + ("Tensor or TensorLike, but got '" + type + "'")); + } + var inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + var skipTypedArray = true; + var values = inferredDtype !== 'string' ? + toTypedArray(x, inferredDtype) : + flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); + } + function convertToTensorArray(arg, argName, functionName, parseAsDtype) { + if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; } + if (!Array.isArray(arg)) { + throw new Error("Argument " + argName + " passed to " + functionName + " must be a " + + '`Tensor[]` or `TensorLike[]`'); + } + var tensors = arg; + return tensors.map(function (t, i) { return convertToTensor(t, argName + "[" + i + "]", functionName, parseAsDtype); }); + } + + var OP_SCOPE_SUFFIX = '__op'; + /** + * Used for wrapping functions that perform math operations on + * Tensors. The function will be wrapped in a named scope that cleans all + * memory usage after the function is done. + */ + function op(f) { + var 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.")); + } + var opName = keys[0]; + var fn = f[opName]; + // Strip the underscore from the end of the function name. + if (opName.endsWith('_')) { + opName = opName.substring(0, opName.length - 1); + } + // add an __op suffix to distinguish ops from kernels in tf.profile + opName = opName + OP_SCOPE_SUFFIX; + // tslint:disable-next-line:no-any + var f2 = function () { + var args = []; + for (var _i = 0; _i < arguments.length; _i++) { + args[_i] = arguments[_i]; + } + ENGINE.startScope(opName); + try { + var result = fn.apply(void 0, __spread(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 }); + // tslint:disable-next-line:no-any + return f2; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes absolute value element-wise: `abs(x)` + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 4]); + * + * x.abs().print(); // or tf.abs(x) + * ``` + * @param x The input `tf.Tensor`. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function abs_(x) { + var $x = convertToTensor(x, 'x', 'abs'); + if ($x.dtype === 'complex64') { + var inputs = { x: $x }; + return ENGINE.runKernel(ComplexAbs, inputs); + } + else { + var inputs = { x: $x }; + return ENGINE.runKernel(Abs, inputs); + } + } + var abs = op({ abs_: abs_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes acos of the input `tf.Tensor` element-wise: `acos(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.acos().print(); // or tf.acos(x) + * ``` + * @param x The input tensor. + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function acos_(x) { + var $x = convertToTensor(x, 'x', 'acos'); + var inputs = { x: $x }; + return ENGINE.runKernel(Acos, inputs); + } + var acos = op({ acos_: acos_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise: + * `acosh(x)` + * + * ```js + * const x = tf.tensor1d([10, 1, 3, 5.7]); + * + * x.acosh().print(); // or tf.acosh(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function acosh_(x) { + var $x = convertToTensor(x, 'x', 'acosh'); + var inputs = { x: $x }; + return ENGINE.runKernel(Acosh, inputs); + } + var acosh = op({ acosh_: acosh_ }); + + /** + * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting. + * + * + * ```js + * const a = tf.tensor1d([1, 2, 3, 4]); + * const b = tf.tensor1d([10, 20, 30, 40]); + * + * a.add(b).print(); // or tf.add(a, b) + * ``` + * + * ```js + * // Broadcast add a with b. + * const a = tf.scalar(5); + * const b = tf.tensor1d([10, 20, 30, 40]); + * + * a.add(b).print(); // or tf.add(a, b) + * ``` + * @param a The first `tf.Tensor` to add. + * @param b The second `tf.Tensor` to add. Must have the same type as `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function add_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'add'); + var $b = convertToTensor(b, 'b', 'add'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Add, inputs); + } + var add = op({ add_: add_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype. + * + * ```js + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); + * const c = tf.tensor1d([5, 6]); + * + * tf.addN([a, b, c]).print(); + * ``` + * @param tensors A list of tensors with the same shape and dtype. + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function addN_(tensors) { + assert(Array.isArray(tensors), function () { return 'The argument passed to tf.addN() must be a list of tensors'; }); + assert(tensors.length >= 1, function () { return "Must pass at least one tensor to tf.addN(), but got " + + ("" + tensors.length); }); + var $tensors = tensors.map(function (t, i) { return convertToTensor(t, "tensors" + i, 'addN'); }); + var firstTensor = $tensors[0]; + $tensors.forEach(function (t) { + if (t.dtype !== firstTensor.dtype) { + throw new Error('All tensors passed to tf.addN() must have the same dtype'); + } + }); + $tensors.forEach(function (t) { + if (!arraysEqual(t.shape, firstTensor.shape)) { + throw new Error('All tensors passed to tf.addN() must have the same shape'); + } + }); + var inputs = $tensors; + return ENGINE.runKernel(AddN, inputs); + } + var addN = op({ addN_: addN_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the logical and of elements across dimensions of a `tf.Tensor`. + * + * Reduces the input along the dimensions given in `axes`. Unless `keepDims` + * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in + * `axes`. If `keepDims` is true, the reduced dimensions are retained with + * length 1. If `axes` has no entries, all dimensions are reduced, and an + * `tf.Tensor` with a single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 1, 1], 'bool'); + * + * x.all().print(); // or tf.all(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool'); + * + * const axis = 1; + * x.all(axis).print(); // or tf.all(x, axis) + * ``` + * + * @param x The input tensor. Must be of dtype bool. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function all_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'all', 'bool'); + var inputs = { x: $x }; + var attrs = { axis: axis, keepDims: keepDims }; + return ENGINE.runKernel(All, inputs, attrs); + } + var all = op({ all_: all_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the logical or of elements across dimensions of a `tf.Tensor`. + * + * Reduces the input along the dimensions given in `axes`. Unless `keepDims` + * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in + * `axes`. If `keepDims` is true, the reduced dimensions are retained with + * length 1. If `axes` has no entries, all dimensions are reduced, and an + * `tf.Tensor` with a single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 1, 1], 'bool'); + * + * x.any().print(); // or tf.any(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool'); + * + * const axis = 1; + * x.any(axis).print(); // or tf.any(x, axis) + * ``` + * + * @param x The input tensor. Must be of dtype bool. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function any_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'any', 'bool'); + var inputs = { x: $x }; + var attrs = { axis: axis, keepDims: keepDims }; + return ENGINE.runKernel(Any, inputs, attrs); + } + // tslint:disable-next-line:variable-name + var any = op({ any_: any_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the indices of the maximum values along an `axis`. + * + * The result has the same shape as `input` with the dimension along `axis` + * removed. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.argMax().print(); // or tf.argMax(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]); + * + * const axis = 1; + * x.argMax(axis).print(); // or tf.argMax(x, axis) + * ``` + * + * @param x The input tensor. + * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension). + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function argMax_(x, axis) { + if (axis === void 0) { axis = 0; } + var $x = convertToTensor(x, 'x', 'argMax'); + var inputs = { x: $x }; + var attrs = { axis: axis }; + return ENGINE.runKernel(ArgMax, inputs, attrs); + } + var argMax = op({ argMax_: argMax_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the indices of the minimum values along an `axis`. + * + * The result has the same shape as `input` with the dimension along `axis` + * removed. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.argMin().print(); // or tf.argMin(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]); + * + * const axis = 1; + * x.argMin(axis).print(); // or tf.argMin(x, axis) + * ``` + * + * @param x The input tensor. + * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension). + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function argMin_(x, axis) { + if (axis === void 0) { axis = 0; } + var $x = convertToTensor(x, 'x', 'argMin'); + var inputs = { x: $x }; + var attrs = { axis: axis }; + return ENGINE.runKernel(ArgMin, inputs, attrs); + } + var argMin = op({ argMin_: argMin_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes asin of the input `tf.Tensor` element-wise: `asin(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.asin().print(); // or tf.asin(x) + * ``` + * @param x The input tensor. + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function asin_(x) { + var $x = convertToTensor(x, 'x', 'asin'); + var inputs = { x: $x }; + return ENGINE.runKernel(Asin, inputs); + } + var asin = op({ asin_: asin_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise: + * `asinh(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.asinh().print(); // or tf.asinh(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function asinh_(x) { + var $x = convertToTensor(x, 'x', 'asinh'); + var inputs = { x: $x }; + return ENGINE.runKernel(Asinh, inputs); + } + var asinh = op({ asinh_: asinh_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes atan of the input `tf.Tensor` element-wise: `atan(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.atan().print(); // or tf.atan(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function atan_(x) { + var $x = convertToTensor(x, 'x', 'atan'); + var inputs = { x: $x }; + return ENGINE.runKernel(Atan, inputs); + } + var atan = op({ atan_: atan_ }); + + /** + * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`. + * Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]); + * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]); + * + * tf.atan2(a, b).print() + * ``` + * + * @param a The first tensor. + * @param b The second tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function atan2_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'atan2'); + var $b = convertToTensor(b, 'b', 'atan2'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Atan2, inputs); + } + var atan2 = op({ atan2_: atan2_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise: + * `atanh(x)` + * + * ```js + * const x = tf.tensor1d([0, .1, -.1, .7]); + * + * x.atanh().print(); // or tf.atanh(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function atanh_(x) { + var $x = convertToTensor(x, 'x', 'atanh'); + var inputs = { x: $x }; + return ENGINE.runKernel(Atanh, inputs); + } + var atanh = op({ atanh_: atanh_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Casts a `tf.Tensor` to a new dtype. + * + * ```js + * const x = tf.tensor1d([1.5, 2.5, 3]); + * tf.cast(x, 'int32').print(); + * ``` + * @param x The input tensor to be casted. + * @param dtype The dtype to cast the input tensor to. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function cast_(x, dtype) { + var $x = convertToTensor(x, 'x', 'cast'); + // Sanity checks. + 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'); + } + var inputs = { x: $x }; + var attrs = { dtype: dtype }; + return ENGINE.runKernel(Cast, inputs, attrs); + } + var cast = op({ cast_: cast_ }); + + function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) { + if (dataFormat === void 0) { dataFormat = 'channelsLast'; } + var _a = __read(parseTupleParam(filterSize), 2), filterHeight = _a[0], filterWidth = _a[1]; + var 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, pad, roundingMode, false, dataFormat); + } + /** + * Computes the information for a forward pass of a convolution/pooling + * operation. + */ + function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise, dataFormat) { + var _a, _b; + if (depthwise === void 0) { depthwise = false; } + if (dataFormat === void 0) { dataFormat = 'channelsLast'; } + var _c = __read([-1, -1, -1, -1], 4), batchSize = _c[0], inHeight = _c[1], inWidth = _c[2], inChannels = _c[3]; + if (dataFormat === 'channelsLast') { + _a = __read(inShape, 4), batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3]; + } + else if (dataFormat === 'channelsFirst') { + _b = __read(inShape, 4), batchSize = _b[0], inChannels = _b[1], inHeight = _b[2], inWidth = _b[3]; + } + else { + throw new Error("Unknown dataFormat " + dataFormat); + } + var _d = __read(filterShape, 4), filterHeight = _d[0], filterWidth = _d[1], filterChannels = _d[3]; + var _e = __read(parseTupleParam(strides), 2), strideHeight = _e[0], strideWidth = _e[1]; + var _f = __read(parseTupleParam(dilations), 2), dilationHeight = _f[0], dilationWidth = _f[1]; + var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight); + var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth); + var _g = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode, dataFormat), padInfo = _g.padInfo, outHeight = _g.outHeight, outWidth = _g.outWidth; + var outChannels = depthwise ? filterChannels * inChannels : filterChannels; + var outShape; + if (dataFormat === 'channelsFirst') { + outShape = [batchSize, outChannels, outHeight, outWidth]; + } + else if (dataFormat === 'channelsLast') { + outShape = [batchSize, outHeight, outWidth, outChannels]; + } + return { + batchSize: batchSize, + dataFormat: dataFormat, + inHeight: inHeight, + inWidth: inWidth, + inChannels: inChannels, + outHeight: outHeight, + outWidth: outWidth, + outChannels: outChannels, + padInfo: padInfo, + strideHeight: strideHeight, + strideWidth: strideWidth, + filterHeight: filterHeight, + filterWidth: filterWidth, + effectiveFilterHeight: effectiveFilterHeight, + effectiveFilterWidth: effectiveFilterWidth, + dilationHeight: dilationHeight, + dilationWidth: dilationWidth, + inShape: inShape, + outShape: outShape, + filterShape: filterShape + }; + } + function computeOutputShape2D(inShape, fieldSize, stride, zeroPad, roundingMode) { + if (zeroPad == null) { + zeroPad = computeDefaultPad(inShape, fieldSize, stride); + } + var inputRows = inShape[0]; + var inputCols = inShape[1]; + var outputRows = round$1((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); + var outputCols = round$1((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode); + return [outputRows, outputCols]; + } + function computeDefaultPad(inputShape, fieldSize, stride, dilation) { + if (dilation === void 0) { dilation = 1; } + var 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; + } + /* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d + * Atrous convolution is equivalent to standard convolution with upsampled + * filters with effective_filter_height = + * filter_height + (filter_height - 1) * (dilation - 1) + * and effective_filter_width = + * filter_width + (filter_width - 1) * (dilation - 1), + * produced by inserting dilation - 1 zeros along consecutive elements across + * the filters' spatial dimensions. + * When there is a dilation, this converts a filter dimension to the + * effective filter dimension, so it can be used in a standard convolution. + */ + function getEffectiveFilterSize(filterSize, dilation) { + if (dilation <= 1) { + return filterSize; + } + return filterSize + (filterSize - 1) * (dilation - 1); + } + function getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode, dataFormat) { + var padInfo; + var outHeight; + var outWidth; + if (typeof pad === 'number') { + var padType = (pad === 0) ? 'VALID' : 'NUMBER'; + padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType }; + var outShape = computeOutputShape2D([inHeight, inWidth], filterHeight, strideHeight, pad, roundingMode); + outHeight = outShape[0]; + outWidth = outShape[1]; + } + else if (pad === 'same') { + outHeight = Math.ceil(inHeight / strideHeight); + outWidth = Math.ceil(inWidth / strideWidth); + var padAlongHeight = Math.max(0, (outHeight - 1) * strideHeight + filterHeight - inHeight); + var padAlongWidth = Math.max(0, (outWidth - 1) * strideWidth + filterWidth - inWidth); + var top = Math.floor(padAlongHeight / 2); + var bottom = padAlongHeight - top; + var left = Math.floor(padAlongWidth / 2); + var right = padAlongWidth - left; + padInfo = { top: top, bottom: bottom, left: left, right: right, type: 'SAME' }; + } + else if (pad === '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 pad === 'object') { + var top = dataFormat === 'channelsLast' ? pad[1][0] : pad[2][0]; + var bottom = dataFormat === 'channelsLast' ? pad[1][1] : pad[2][1]; + var left = dataFormat === 'channelsLast' ? pad[2][0] : pad[3][0]; + var right = dataFormat === 'channelsLast' ? pad[2][1] : pad[3][1]; + var padType = (top === 0 && bottom === 0 && left === 0 && right === 0) ? + 'VALID' : + 'EXPLICIT'; + padInfo = { top: top, bottom: bottom, left: left, right: right, type: padType }; + outHeight = round$1((inHeight - filterHeight + top + bottom) / strideHeight + 1, roundingMode); + outWidth = round$1((inWidth - filterWidth + left + right) / strideWidth + 1, roundingMode); + } + else { + throw Error("Unknown padding parameter: " + pad); + } + return { padInfo: padInfo, outHeight: outHeight, outWidth: outWidth }; + } + /** + * Rounds a value depending on the rounding mode + * @param value + * @param roundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + */ + function round$1(value, roundingMode) { + if (!roundingMode) { + return Math.trunc(value); + } + switch (roundingMode) { + case 'round': + // used for Caffe Conv + return Math.round(value); + case 'ceil': + // used for Caffe Pool + return Math.ceil(value); + case 'floor': + return Math.floor(value); + default: + throw new Error("Unknown roundingMode " + roundingMode); + } + } + function tupleValuesAreOne(param) { + var _a = __read(parseTupleParam(param), 3), dimA = _a[0], dimB = _a[1], dimC = _a[2]; + return dimA === 1 && dimB === 1 && dimC === 1; + } + function eitherStridesOrDilationsAreOne(strides, dilations) { + return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations); + } + /** + * Check validity of pad when using dimRoundingMode. + * @param opDesc A string of op description + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid` output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * @throws unknown padding parameter + */ + function checkPadOnDimRoundingMode(opDesc, pad, dimRoundingMode) { + if (dimRoundingMode != null) { + if (typeof pad === 'string') { + throw Error("Error in " + opDesc + ": pad must be an integer when using " + + ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + ".")); + } + else if (typeof pad === 'number') { + assert(isInt(pad), function () { return "Error in " + opDesc + ": pad must be an integer when using " + + ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); }); + } + else if (typeof pad === 'object') { + pad.forEach(function (p) { + p.forEach(function (v) { + assert(isInt(v), function () { return "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: " + pad); + } + } + } + + /** + * @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. + * ============================================================================= + */ + /** + * Reshapes a `tf.Tensor` to a given shape. + * + * Given an input tensor, returns a new tensor with the same values as the + * input tensor with shape `shape`. + * + * If one component of shape is the special value -1, the size of that + * dimension is computed so that the total size remains constant. In + * particular, a shape of [-1] flattens into 1-D. At most one component of + * shape can be -1. + * + * If shape is 1-D or higher, then the operation returns a tensor with shape + * shape filled with the values of tensor. In this case, the number of + * elements implied by shape must be the same as the number of elements in + * tensor. + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * x.reshape([2, 2]).print(); + * ``` + * + * @param x The input tensor to be reshaped. + * @param shape An array of integers defining the output tensor shape. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function reshape_(x, shape) { + var $x = convertToTensor(x, 'x', 'reshape', 'string_or_numeric'); + var inputs = { x: $x }; + var attrs = { shape: shape }; + return ENGINE.runKernel(Reshape, inputs, attrs); + } + var reshape = op({ reshape_: reshape_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the 2D average pooling of an image. + * + * @param x The input tensor, of rank 4 or rank 3 of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param filterSize The filter size: `[filterHeight, filterWidth]`. If + * `filterSize` is a single number, then `filterHeight == filterWidth`. + * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If + * `strides` is a single number, then `strideHeight == strideWidth`. + * @param pad The type of padding algorithm: + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + */ + function avgPool_(x, filterSize, strides, pad, dimRoundingMode) { + var $x = convertToTensor(x, 'x', 'avgPool', 'float32'); + var dilations = 1; + assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in avgPool: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in avgPool: x must be rank 4 but got rank " + x4D.rank + "."; }); + checkPadOnDimRoundingMode('avgPool', pad, dimRoundingMode); + var inputs = { x: x4D }; + var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: avgPool_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the 3D average pooling. + * + * ```js + * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); + * const result = tf.avgPool3d(x, 2, 1, 'valid'); + * result.print(); + * ``` + * + * @param x The input tensor, of rank 5 or rank 4 of shape + * `[batch, depth, height, width, inChannels]`. + * @param filterSize The filter size: + * `[filterDepth, filterHeight, filterWidth]`. + * If `filterSize` is a single number, + * then `filterDepth == filterHeight == filterWidth`. + * @param strides The strides of the pooling: + * `[strideDepth, strideHeight, strideWidth]`. + * If `strides` is a single number, + * then `strideDepth == strideHeight == strideWidth`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1*1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to + * "NDHWC". Specify the data format of the input and output data. With the + * default format "NDHWC", the data is stored in the order of: [batch, + * depth, height, width, channels]. Only "NDHWC" is currently supported. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function avgPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat) { + if (dataFormat === void 0) { dataFormat = 'NDHWC'; } + var $x = convertToTensor(x, 'x', 'avgPool3d', 'float32'); + var x5D = $x; + var reshapedTo5D = false; + if ($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, function () { return "Error in avgPool3d: x must be rank 5 but got rank " + x5D.rank + "."; }); + assert(dataFormat === 'NDHWC', function () { return "Error in avgPool3d: Only NDHWC is currently supported, " + + ("but got dataFormat of " + dataFormat); }); + checkPadOnDimRoundingMode('avgPool3d', pad, dimRoundingMode); + var inputs = { x: x5D }; + var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dataFormat: dataFormat }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: avgPool3d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a new tensor with the same values and shape as the specified + * tensor. + * + * ```js + * const x = tf.tensor([1, 2]); + * + * x.clone().print(); + * ``` + * + * @param x The tensor to clone. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function clone_(x) { + var $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric'); + var inputs = { x: $x }; + // Note this op is called tf.identity in python. Hence the kernel name used + // here. + return ENGINE.runKernel(Identity, inputs); + } + var clone = op({ clone_: clone_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Concatenates a list of `tf.Tensor`s along a given axis. + * + * The tensors ranks and types must match, and their sizes must match in all + * dimensions except `axis`. + * + * Also available are stricter rank-specific methods that assert that + * `tensors` are of the given rank: + * - `tf.concat1d` + * - `tf.concat2d` + * - `tf.concat3d` + * - `tf.concat4d` + * + * Except `tf.concat1d` (which does not have axis param), all methods have + * same signature as this method. + * + * ```js + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); + * a.concat(b).print(); // or a.concat(b) + * ``` + * + * ```js + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); + * const c = tf.tensor1d([5, 6]); + * tf.concat([a, b, c]).print(); + * ``` + * + * ```js + * const a = tf.tensor2d([[1, 2], [10, 20]]); + * const b = tf.tensor2d([[3, 4], [30, 40]]); + * const axis = 1; + * tf.concat([a, b], axis).print(); + * ``` + * @param tensors A list of tensors to concatenate. + * @param axis The axis to concate along. Defaults to 0 (the first dim). + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function concat_(tensors, axis) { + if (axis === void 0) { axis = 0; } + assert(tensors.length >= 1, function () { return 'Pass at least one tensor to concat'; }); + var $tensors = convertToTensorArray(tensors, 'tensors', 'concat', 'string_or_numeric'); + if ($tensors[0].dtype === 'complex64') { + $tensors.forEach(function (tensor) { + if (tensor.dtype !== 'complex64') { + throw new Error("Cannot concatenate complex64 tensors with a tensor\n with dtype " + tensor.dtype + ". "); + } + }); + } + if ($tensors.length === 1) { + return clone($tensors[0]); + } + var inputs = $tensors; + var attr = { axis: axis }; + return ENGINE.runKernel(Concat, inputs, attr); + } + var concat = op({ concat_: concat_ }); + + /** + * Computes the dot product of two matrices, A * B. These must be matrices. + * + * ```js + * const a = tf.tensor2d([1, 2], [1, 2]); + * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * a.matMul(b).print(); // or tf.matMul(a, b) + * ``` + * @param a First matrix in dot product operation. + * @param b Second matrix in dot product operation. + * @param transposeA If true, `a` is transposed before multiplication. + * @param transposeB If true, `b` is transposed before multiplication. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function matMul_(a, b, transposeA, transposeB) { + var _a; + if (transposeA === void 0) { transposeA = false; } + if (transposeB === void 0) { transposeB = false; } + var $a = convertToTensor(a, 'a', 'matMul'); + var $b = convertToTensor(b, 'b', 'matMul'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + var attrs = { transposeA: transposeA, transposeB: transposeB }; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); + } + var matMul$1 = op({ matMul_: matMul_ }); + + /** + * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting. + * + * We also expose `tf.mulStrict` which has the same signature as this op and + * asserts that `a` and `b` are the same shape (does not broadcast). + * + * ```js + * const a = tf.tensor1d([1, 2, 3, 4]); + * const b = tf.tensor1d([2, 3, 4, 5]); + * + * a.mul(b).print(); // or tf.mul(a, b) + * ``` + * + * ```js + * // Broadcast mul a with b. + * const a = tf.tensor1d([1, 2, 3, 4]); + * const b = tf.scalar(5); + * + * a.mul(b).print(); // or tf.mul(a, b) + * ``` + * @param a The first tensor to multiply. + * @param b The second tensor to multiply. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function mul_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'mul'); + var $b = convertToTensor(b, 'b', 'mul'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Multiply, inputs); + } + var mul = op({ mul_: mul_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes sigmoid element-wise, `1 / (1 + exp(-x))` + * + * ```js + * const x = tf.tensor1d([0, -1, 2, -3]); + * + * x.sigmoid().print(); // or tf.sigmoid(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function sigmoid_(x) { + var $x = convertToTensor(x, 'x', 'sigmoid', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Sigmoid, inputs); + } + var sigmoid = op({ sigmoid_: sigmoid_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts a slice from a `tf.Tensor` starting at coordinates `begin` + * and is of size `size`. + * + * Also available are stricter rank-specific methods with the same signature + * as this method that assert that `x` is of the given rank: + * - `tf.slice1d` + * - `tf.slice2d` + * - `tf.slice3d` + * - `tf.slice4d` + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * + * x.slice([1], [2]).print(); + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * x.slice([1, 0], [1, 2]).print(); + * ``` + * @param x The input `tf.Tensor` to slice from. + * @param begin The coordinates to start the slice from. The length can be + * less than the rank of x - the rest of the axes will have implicit 0 as + * start. Can also be a single number, in which case it specifies the + * first axis. + * @param size The size of the slice. The length can be less than the rank of + * x - the rest of the axes will have implicit -1. A value of -1 requests + * the rest of the dimensions in the axis. Can also be a single number, + * in which case it specifies the size of the first axis. + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function slice_(x, begin, size) { + var $x = convertToTensor(x, 'x', 'slice', 'string_or_numeric'); + if ($x.rank === 0) { + throw new Error('Slicing scalar is not possible'); + } + var inputs = { x: $x }; + var attrs = { begin: begin, size: size }; + return ENGINE.runKernel(Slice, inputs, attrs); + } + var slice = op({ slice_: slice_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, 70]); + * + * x.tanh().print(); // or tf.tanh(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function tanh_(x) { + var $x = convertToTensor(x, 'x', 'tanh', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Tanh, inputs); + } + var tanh = op({ tanh_: tanh_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the next state and output of a BasicLSTMCell. + * + * Returns `[newC, newH]`. + * + * Derived from tf.contrib.rnn.BasicLSTMCell. + * + * @param forgetBias Forget bias for the cell. + * @param lstmKernel The weights for the cell. + * @param lstmBias The bias for the cell. + * @param data The input to the cell. + * @param c Previous cell state. + * @param h Previous cell output. + * + * @doc {heading: 'Operations', subheading: 'RNN'} + */ + function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) { + var $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell'); + var $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell'); + var $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell'); + var $data = convertToTensor(data, 'data', 'basicLSTMCell'); + var $c = convertToTensor(c, 'c', 'basicLSTMCell'); + var $h = convertToTensor(h, 'h', 'basicLSTMCell'); + var combined = concat([$data, $h], 1); + var weighted = matMul$1(combined, $lstmKernel); + var res = add(weighted, $lstmBias); + // i = input_gate, j = new_input, f = forget_gate, o = output_gate + var batchSize = res.shape[0]; + var sliceCols = res.shape[1] / 4; + var sliceSize = [batchSize, sliceCols]; + var i = slice(res, [0, 0], sliceSize); + var j = slice(res, [0, sliceCols], sliceSize); + var f = slice(res, [0, sliceCols * 2], sliceSize); + var o = slice(res, [0, sliceCols * 3], sliceSize); + var newC = add(mul(sigmoid(i), tanh(j)), mul($c, sigmoid(add($forgetBias, f)))); + var newH = mul(tanh(newC), sigmoid(o)); + return [newC, newH]; + } + var basicLSTMCell = op({ basicLSTMCell_: basicLSTMCell_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of + * shape `blockShape + [batch]`, interleaves these blocks back into the grid + * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with + * the same rank as the input. The spatial dimensions of this intermediate + * result are then optionally cropped according to `crops` to produce the + * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise + * description. + * + * ```js + * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]); + * const blockShape = [2, 2]; + * const crops = [[0, 0], [0, 0]]; + * + * x.batchToSpaceND(blockShape, crops).print(); + * ``` + * + * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape + + * remainingShape`, where spatialShape has `M` dimensions. + * @param blockShape A 1-D array. Must have shape `[M]`, all values must + * be >= 1. + * @param crops A 2-D array. Must have shape `[M, 2]`, all values must be >= 0. + * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input + * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required + * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]` + * + * This operation is equivalent to the following steps: + * + * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ..., + * blockShape[M-1], batch / prod(blockShape), x.shape[1], ..., + * x.shape[N-1]]` + * + * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch / + * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M], + * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]` + * + * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch / + * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] * + * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]` + * + * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted` + * according to `crops` to produce the output of shape: `[batch / + * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1], + * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] - + * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]` + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function batchToSpaceND_(x, blockShape, crops) { + var $x = convertToTensor(x, 'x', 'batchToSpaceND'); + var prod = blockShape.reduce(function (a, b) { return a * b; }); + assert($x.rank >= 1 + blockShape.length, function () { return "input rank is " + $x.rank + " but should be > than blockShape.length " + blockShape.length; }); + assert(crops.length === blockShape.length, function () { return "crops.length is " + crops.length + " but should be equal to blockShape.length " + blockShape.length; }); + assert($x.shape[0] % prod === 0, function () { return "input tensor batch is " + $x.shape[0] + " but is not divisible by the product of " + + ("the elements of blockShape " + blockShape.join(' * ') + " === " + prod); }); + var inputs = { x: $x }; + var attrs = { blockShape: blockShape, crops: crops }; + return ENGINE.runKernel(BatchToSpaceND, inputs, attrs); + } + var batchToSpaceND = op({ batchToSpaceND_: batchToSpaceND_ }); + + function xAs4D(x) { + var 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; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Batch normalization. + * + * As described in + * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167). + * + * Mean, variance, scale, and offset can be of two shapes: + * - The same shape as the input. + * - In the common case, the depth dimension is the last dimension of x, so + * the values would be an `tf.Tensor1D` of shape [depth]. + * + * Also available are stricter rank-specific methods with the same signature + * as this method that assert that parameters passed are of given rank + * - `tf.batchNorm2d` + * - `tf.batchNorm3d` + * - `tf.batchNorm4d` + * + * @param x The input Tensor. + * @param mean A mean Tensor. + * @param variance A variance Tensor. + * @param offset An offset Tensor. + * @param scale A scale Tensor. + * @param varianceEpsilon A small float number to avoid dividing by 0. + * + * @doc {heading: 'Operations', subheading: 'Normalization'} + */ + function batchNorm_(x, mean, variance, offset, scale, varianceEpsilon) { + if (varianceEpsilon == null) { + varianceEpsilon = 0.001; + } + var $x = convertToTensor(x, 'x', 'batchNorm'); + var $mean = convertToTensor(mean, 'mean', 'batchNorm'); + var $variance = convertToTensor(variance, 'variance', 'batchNorm'); + var $scale; + if (scale != null) { + $scale = convertToTensor(scale, 'scale', 'batchNorm'); + } + var $offset; + if (offset != null) { + $offset = convertToTensor(offset, 'offset', 'batchNorm'); + } + assert($mean.rank === $variance.rank, function () { return 'Batch normalization gradient requires mean and variance to have ' + + 'equal ranks.'; }); + assert($offset == null || $mean.rank === $offset.rank, function () { return 'Batch normalization gradient requires mean and offset to have ' + + 'equal ranks.'; }); + assert($scale == null || $mean.rank === $scale.rank, function () { return 'Batch normalization gradient requires mean and scale to have ' + + 'equal ranks.'; }); + var x4D = xAs4D($x); + var inputs = { + x: x4D, + scale: $scale, + offset: $offset, + mean: $mean, + variance: $variance + }; + var attrs = { varianceEpsilon: varianceEpsilon }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var res = ENGINE.runKernel(FusedBatchNorm, inputs, attrs); + return reshape(res, $x.shape); + } + var batchNorm = op({ batchNorm_: batchNorm_ }); + + /** + * Batch normalization, strictly for 2D. For the more relaxed version, see + * `tf.batchNorm`. + * + * @param x The input Tensor. + * @param mean A mean Tensor. + * @param variance A variance Tensor. + * @param offset An offset Tensor. + * @param scale A scale Tensor. + * @param varianceEpsilon A small float number to avoid dividing by 0. + */ + function batchNorm2d_(x, mean, variance, offset, scale, varianceEpsilon) { + var $x = convertToTensor(x, 'x', 'batchNorm'); + var $mean = convertToTensor(mean, 'mean', 'batchNorm'); + var $variance = convertToTensor(variance, 'variance', 'batchNorm'); + var $scale; + if (scale != null) { + $scale = convertToTensor(scale, 'scale', 'batchNorm'); + } + var $offset; + if (offset != null) { + $offset = convertToTensor(offset, 'offset', 'batchNorm'); + } + assert($x.rank === 2, function () { return "Error in batchNorm2D: x must be rank 2 but got rank " + + ($x.rank + "."); }); + assert($mean.rank === 2 || $mean.rank === 1, function () { return "Error in batchNorm2D: mean must be rank 2 or rank 1 but " + + ("got rank " + $mean.rank + "."); }); + assert($variance.rank === 2 || $variance.rank === 1, function () { return "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, function () { return "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, function () { return "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_: batchNorm2d_ }); + + /** + * Batch normalization, strictly for 3D. For the more relaxed version, see + * `tf.batchNorm`. + * + * @param x The input Tensor. + * @param mean A mean Tensor. + * @param variance A variance Tensor. + * @param offset An offset Tensor. + * @param scale A scale Tensor. + * @param varianceEpsilon A small float number to avoid dividing by 0. + */ + function batchNorm3d_(x, mean, variance, offset, scale, varianceEpsilon) { + var $x = convertToTensor(x, 'x', 'batchNorm'); + var $mean = convertToTensor(mean, 'mean', 'batchNorm'); + var $variance = convertToTensor(variance, 'variance', 'batchNorm'); + var $scale; + if (scale != null) { + $scale = convertToTensor(scale, 'scale', 'batchNorm'); + } + var $offset; + if (offset != null) { + $offset = convertToTensor(offset, 'offset', 'batchNorm'); + } + assert($x.rank === 3, function () { return "Error in batchNorm3D: x must be rank 3 but got rank " + + ($x.rank + "."); }); + assert($mean.rank === 3 || $mean.rank === 1, function () { return "Error in batchNorm3D: mean must be rank 3 or rank 1 but " + + ("got rank " + $mean.rank + "."); }); + assert($variance.rank === 3 || $variance.rank === 1, function () { return "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, function () { return "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, function () { return "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_: batchNorm3d_ }); + + /** + * Batch normalization, strictly for 4D. For the more relaxed version, see + * `tf.batchNorm`. + * + * @param x The input Tensor. + * @param mean A mean Tensor. + * @param variance A variance Tensor. + * @param offset An offset Tensor. + * @param scale A scale Tensor. + * @param varianceEpsilon A small float number to avoid dividing by 0. + */ + function batchNorm4d_(x, mean, variance, offset, scale, varianceEpsilon) { + var $x = convertToTensor(x, 'x', 'batchNorm'); + var $mean = convertToTensor(mean, 'mean', 'batchNorm'); + var $variance = convertToTensor(variance, 'variance', 'batchNorm'); + var $scale; + if (scale != null) { + $scale = convertToTensor(scale, 'scale', 'batchNorm'); + } + var $offset; + if (offset != null) { + $offset = convertToTensor(offset, 'offset', 'batchNorm'); + } + assert($x.rank === 4, function () { return "Error in batchNorm4D: x must be rank 4 but got rank " + + ($x.rank + "."); }); + assert($mean.rank === 4 || $mean.rank === 1, function () { return "Error in batchNorm4D: mean must be rank 4 or rank 1 but " + + ("got rank " + $mean.rank + "."); }); + assert($variance.rank === 4 || $variance.rank === 1, function () { return "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, function () { return "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, function () { return "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_: batchNorm4d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Outputs a vector with length `size` and the same dtype as `weights`. + * + * If `weights` are empty, then index `i` stores the number of times the value + * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the + * sum of the value in `weights` at each index where the corresponding value in + * `x` is `i`. + * + * Values in `x` outside of the range [0, size) are ignored. + * + * @param x The input int tensor, rank 1. + * @param weights The weights tensor, must have the same shape as x, or a + * length-0 Tensor, in which case it acts as all weights equal to 1. + * @param size Non-negative integer. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function bincount_(x, weights, size) { + var $x = convertToTensor(x, 'x', 'bincount'); + var $weights = convertToTensor(weights, 'weights', 'bincount'); + assert($x.dtype === 'int32', function () { return "Error in bincount: input " + + ("dtype must be int32, but got " + $x.dtype); }); + assert(size >= 0, function () { return "size must be non-negative, but got " + size + "."; }); + assert($weights.size === $x.size || $weights.size === 0, function () { return "Error in bincount: weights must have the same size as input or" + + ("0-length, but got input shape: " + $x.shape + ", weights shape: ") + + ($weights.shape + "."); }); + var inputs = { x: $x, weights: $weights }; + var attrs = { size: size }; + return ENGINE.runKernel(Bincount, inputs, attrs); + } + var bincount = op({ bincount_: bincount_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Return the shape of s0 op s1 with broadcast. + * + * compute r0, the broadcasted shape as a tensor. + * s0, s1 and r0 are all integer vectors. + * + * This function returns the shape of the result of an operation between + * two tensors of size s0 and s1 performed with broadcast. + * + * @param s0 A tensor representing a shape + * @param s1 A tensor representing a shape + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function broadcastArgs_(s0, s1) { + var shape1Input = convertToTensor(s0, 's0', 'broadcastArgs', 'int32'); + var shape2Input = convertToTensor(s1, 's1', 'broadcastArgs', 'int32'); + if (shape1Input.rank !== 1) { + throw new Error('broadcastArgs(): first input must be a vector (rank=1). ' + + ("Has rank " + shape1Input.rank)); + } + if (shape2Input.rank !== 1) { + throw new Error('broadcastArgs(): second input must be a vector (rank=1). ' + + ("Has rank " + shape2Input.rank)); + } + var inputs = { s0: shape1Input, s1: shape2Input }; + return ENGINE.runKernel(BroadcastArgs, inputs); + } + var broadcastArgs = op({ broadcastArgs_: broadcastArgs_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Broadcast an array to a compatible shape NumPy-style. + * + * The tensor's shape is compared to the broadcast shape from end to beginning. + * Ones are prepended to the tensor's shape until is has the same length as + * the broadcast shape. If input.shape[i]==shape[i], the (i+1)-th axis is + * already broadcast-compatible. If input.shape[i]==1 and shape[i]==N, then + * the input tensor is tiled N times along that axis (using tf.tile). + * + * @param input The tensor that is to be broadcasted. + * @param shape The input is to be broadcast to this shape. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function broadcastTo_(x, shape) { + var input = convertToTensor(x, 'broadcastTo', 'x'); + var xShape = input.shape; + if (shape.some(function (d) { return !(d > 0) || d % 1 !== 0; })) { + throw new Error("broadcastTo(): Invalid broadcast shape [" + shape + "]."); + } + if (shape.length < input.rank) { + throw new Error("broadcastTo(): shape.length=" + shape.length + " < input.rank=" + input.rank + "."); + } + if (shape.length > input.rank) { + var newShape = input.shape.slice(); + while (newShape.length < shape.length) { + newShape.unshift(1); + } + input = reshape(input, newShape); + } + var inputShape = input.shape; + var reps = Array.from(shape); + for (var i = shape.length - 1; i >= 0; i--) { + if (inputShape[i] === shape[i]) { + reps[i] = 1; + } + else if (input.shape[i] !== 1) { + throw new Error("broadcastTo(): [" + xShape + "] cannot be broadcast to [" + shape + "]."); + } + } + var axes = reps.map(function (n, i) { return n > 1 ? i : -1; }).filter(function (i) { return i >= 0; }); + if (axes.length === 0) { + return clone(input); + } + // TODO call broadcastTo kernel directly once backends implement broadcstTo + var inputs = { x: input }; + var attrs = { reps: reps }; + return ENGINE.runKernel(Tile, inputs, attrs); + } + var broadcastTo = op({ broadcastTo_: broadcastTo_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`. + * + * The values are stored in CPU as `TypedArray`. Fill the buffer using + * `buffer.set()`, or by modifying directly `buffer.values`. + * + * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with + * those values. + * + * ```js + * // Create a buffer and set values at particular indices. + * const buffer = tf.buffer([2, 2]); + * buffer.set(3, 0, 0); + * buffer.set(5, 1, 0); + * + * // Convert the buffer back to a tensor. + * buffer.toTensor().print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param dtype The dtype of the buffer. Defaults to 'float32'. + * @param values The values of the buffer as `TypedArray`. Defaults to + * zeros. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function buffer(shape, dtype, values) { + if (dtype === void 0) { dtype = 'float32'; } + dtype = dtype || 'float32'; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)` + * + * ```js + * const x = tf.tensor1d([.6, 1.1, -3.3]); + * + * x.ceil().print(); // or tf.ceil(x) + * ``` + * @param x The input Tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function ceil_(x) { + var $x = convertToTensor(x, 'x', 'ceil', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Ceil, inputs); + } + var ceil = op({ ceil_: ceil_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)` + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 4]); + * + * x.clipByValue(-2, 3).print(); // or tf.clipByValue(x, -2, 3) + * ``` + * @param x The input tensor. + * @param clipValueMin Lower-bound of range to be clipped to. + * @param clipValueMax Upper-bound of range to be clipped to. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function clipByValue_(x, clipValueMin, clipValueMax) { + var $x = convertToTensor(x, 'x', 'clipByValue'); + assert((clipValueMin <= clipValueMax), function () { return "Error in clip: min (" + clipValueMin + ") must be " + + ("less than or equal to max (" + clipValueMax + ")."); }); + var inputs = { x: $x }; + var attrs = { clipValueMin: clipValueMin, clipValueMax: clipValueMax }; + return ENGINE.runKernel(ClipByValue, inputs, attrs); + } + var clipByValue = op({ clipByValue_: clipByValue_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Converts two real numbers to a complex number. + * + * Given a tensor `real` representing the real part of a complex number, and a + * tensor `imag` representing the imaginary part of a complex number, this + * operation returns complex numbers elementwise of the form [r0, i0, r1, i1], + * where r represents the real part and i represents the imag part. + * + * The input tensors real and imag must have the same shape. + * + * ```js + * const real = tf.tensor1d([2.25, 3.25]); + * const imag = tf.tensor1d([4.75, 5.75]); + * const complex = tf.complex(real, imag); + * + * complex.print(); + * ``` + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function complex_(real, imag) { + var $real = convertToTensor(real, 'real', 'complex'); + var $imag = convertToTensor(imag, 'imag', 'complex'); + assertShapesMatch($real.shape, $imag.shape, "real and imag shapes, " + $real.shape + " and " + $imag.shape + ", " + + "must match in call to tf.complex()."); + var inputs = { real: $real, imag: $imag }; + return ENGINE.runKernel(Complex, inputs); + } + var complex = op({ complex_: complex_ }); + + /** + * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details. + * + * For example, if: + * A: shape(3) = |r1, g1, b1| + * B: shape(2) = |r2, g2| + * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2| + * + * @param tensors A list of`tf.Tensor`s to concatenate. + * @return The concatenated array. + */ + function concat1d_(tensors) { + return concat(tensors, 0 /* axis */); + } + var concat1d = op({ concat1d_: concat1d_ }); + + /** + * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details. + * + * For example, if: + * A: shape(2, 3) = | r1, g1, b1 | + * | r2, g2, b2 | + * + * B: shape(2, 3) = | r3, g3, b3 | + * | r4, g4, b4 | + * + * C = tf.concat2d([A, B], axis) + * + * if axis = 0: + * C: shape(4, 3) = | r1, g1, b1 | + * | r2, g2, b2 | + * | r3, g3, b3 | + * | r4, g4, b4 | + * + * if axis = 1: + * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 | + * | r2, g2, b2, r4, g4, b4 | + * + * + * @param tensors A list of `tf.Tensor`s to concatenate. + * @param axis The axis to concatenate along. + * @return The concatenated array. + */ + function concat2d_(tensors, axis) { + return concat(tensors, axis); + } + var concat2d = op({ concat2d_: concat2d_ }); + + /** + * Concatenates a list of `tf.Tensor3D`s along an axis. + * See `concat` for details. + * + * For example, if: + * A: shape(2, 1, 3) = | r1, g1, b1 | + * | r2, g2, b2 | + * + * B: shape(2, 1, 3) = | r3, g3, b3 | + * | r4, g4, b4 | + * + * C = tf.concat3d([A, B], axis) + * + * if axis = 0: + * C: shape(4, 1, 3) = | r1, g1, b1 | + * | r2, g2, b2 | + * | r3, g3, b3 | + * | r4, g4, b4 | + * + * if axis = 1: + * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 | + * | r2, g2, b2, r4, g4, b4 | + * + * if axis = 2: + * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 | + * | r2, g2, b2, r4, g4, b4 | + * + * @param tensors A list of`tf.Tensor`s to concatenate. + * @param axis The axis to concate along. + * @return The concatenated array. + */ + function concat3d_(tensors, axis) { + return concat(tensors, axis); + } + var concat3d = op({ concat3d_: concat3d_ }); + + /** + * Concatenates a list of `tf.Tensor4D`s along an axis. + * See `concat` for details. + * + * @param tensors A list of `tf.Tensor`s to concatenate. + * @param axis The axis to concate along. + * @return The concatenated array. + */ + function concat4d_(tensors, axis) { + return concat(tensors, axis); + } + var concat4d = op({ concat4d_: concat4d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes a 2D convolution over the input x. + * + * @param x The input tensor, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is + * assumed. + * @param filter The filter, rank 4, of shape + * `[filterHeight, filterWidth, inDepth, outDepth]`. + * @param strides The strides of the convolution: `[strideHeight, + * strideWidth]`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) { + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + if (dilations === void 0) { dilations = [1, 1]; } + var $x = convertToTensor(x, 'x', 'conv2d', 'float32'); + var $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32'); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + "."; }); + assert($filter.rank === 4, function () { return "Error in conv2d: filter must be rank 4, but got rank " + + ($filter.rank + "."); }); + checkPadOnDimRoundingMode('conv2d', pad, dimRoundingMode); + var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1]; + assert(inDepth === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + inDepth + ") must match " + + ("input depth for filter " + $filter.shape[2] + "."); }); + assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + var inputs = { x: x4D, filter: $filter }; + var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var res = ENGINE.runKernel(Conv2D, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; + } + var conv2d$1 = op({ conv2d_: conv2d_ }); + + /** + * Computes a 1D convolution over the input x. + * + * @param x The input tensor, of rank 3 or rank 2, of shape + * `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed. + * @param filter The filter, rank 3, of shape + * `[filterWidth, inDepth, outDepth]`. + * @param stride The number of entries by which the filter is moved right at + * each step. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC", + * the data is stored in the order of [batch, in_width, in_channels]. Only + * "NWC" is currently supported. + * @param dilation The dilation rate in which we sample input values in + * atrous convolution. Defaults to `1`. If it is greater than 1, then + * stride must be `1`. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function conv1d_(x, filter, stride, pad, dataFormat, dilation, dimRoundingMode) { + if (dataFormat === void 0) { dataFormat = 'NWC'; } + if (dilation === void 0) { dilation = 1; } + var $x = convertToTensor(x, 'x', 'conv1d'); + var $filter = convertToTensor(filter, 'filter', 'conv1d'); + var x3D = $x; + var reshapedTo3D = false; + if ($x.rank === 2) { + reshapedTo3D = true; + x3D = reshape($x, [1, $x.shape[0], $x.shape[1]]); + } + assert(x3D.rank === 3, function () { return "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + "."; }); + assert($filter.rank === 3, function () { return "Error in conv1d: filter must be rank 3, but got rank " + + ($filter.rank + "."); }); + checkPadOnDimRoundingMode('conv1d', pad, dimRoundingMode); + assert(x3D.shape[2] === $filter.shape[1], function () { return "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " + + ("input depth for filter " + $filter.shape[1] + "."); }); + assert(eitherStridesOrDilationsAreOne(stride, dilation), function () { return 'Error in conv1D: Either stride or dilation must be 1. ' + + ("Got stride " + stride + " and dilation '" + dilation + "'"); }); + assert(dataFormat === 'NWC', function () { return "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported."; }); + var filter4D = reshape($filter, [1, $filter.shape[0], $filter.shape[1], $filter.shape[2]]); + var input4D = reshape(x3D, [x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]]); + var strides = [1, stride]; + var dilations = [1, dilation]; + var conv2dDataFormat = 'NHWC'; + var res = conv2d$1(input4D, filter4D, strides, pad, 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_: conv1d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the derivative of the input of a 2D convolution. + * + * @param xShape The shape of the input: [batch, height, width, inDepth]. + * If length of 3, batch of 1 is assumed. + * @param dy The derivative of the output, of rank 4 or rank 3 of shape + * `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is + * assumed. + * @param filter The filter, rank 4, of shape + * `[filterHeight, filterWidth, inDepth, outDepth]`. + * @param strides The strides of the convolution: `[strideHeight, + * strideWidth]`. + * @param pad The type of padding algorithm used: + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + */ + function conv2DBackpropInput_(xShape, dy, filter, strides, pad, dataFormat, dimRoundingMode) { + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + assert(xShape.length === dy.rank, function () { return "Length of inShape " + + ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"); }); + var xShape4D = xShape; + var dy4D = dy; + var 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, function () { return "Error in conv2dDerInput: inShape must be length 4, but got length " + + (xShape4D.length + "."); }); + assert(dy4D.rank === 4, function () { return "Error in conv2dDerInput: dy must be rank 4, but got " + + ("rank " + dy4D.rank); }); + assert(filter.rank === 4, function () { return "Error in conv2dDerInput: filter must be rank 4, but got " + + ("rank " + filter.rank); }); + var inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1]; + var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1]; + assert(inDepth === filter.shape[2], function () { return "Error in conv2dDerInput: depth of input (" + inDepth + ") must " + + ("match input depth for filter " + filter.shape[2] + "."); }); + assert(outDepth === filter.shape[3], function () { return "Error in conv2dDerInput: depth of output (" + outDepth + ") must " + + ("match output depth for filter " + filter.shape[3] + "."); }); + checkPadOnDimRoundingMode('conv2dDerInput', pad, dimRoundingMode); + var inputs = { dy: dy4D, filter: filter }; + var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dimRoundingMode: dimRoundingMode, inputShape: xShape4D }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: conv2DBackpropInput_ }); + + /** + * Computes the transposed 2D convolution of an image, also known as a + * deconvolution. + * + * @param x The input image, of rank 4 or rank 3, of shape + * `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed. + * @param filter The filter, rank 4, of shape + * `[filterHeight, filterWidth, outDepth, inDepth]`. + * `inDepth` must match `inDepth` in `x`. + * @param outputShape Output shape, of rank 4 or rank 3: + * `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed. + * @param strides The strides of the original convolution: + * `[strideHeight, strideWidth]`. + * @param pad The type of padding algorithm used in the non-transpose version + * of the op. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) { + var $x = convertToTensor(x, 'x', 'conv2dTranspose'); + var $filter = convertToTensor(filter, 'filter', 'conv2dTranspose'); + return conv2DBackpropInput(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode); + } + var conv2dTranspose = op({ conv2dTranspose_: conv2dTranspose_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes a 3D convolution over the input x. + * + * @param x The input tensor, of rank 5 or rank 4, of shape + * `[batch, depth, height, width, channels]`. If rank 4, + * batch of 1 is assumed. + * @param filter The filter, rank 5, of shape + * `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`. + * inChannels must match between input and filter. + * @param strides The strides of the convolution: `[strideDepth, strideHeight, + * strideWidth]`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to + * "NDHWC". Specify the data format of the input and output data. With the + * default format "NDHWC", the data is stored in the order of: [batch, + * depth, height, width, channels]. Only "NDHWC" is currently supported. + * @param dilations The dilation rates: `[dilationDepth, dilationHeight, + * dilationWidth]` in which we sample input values across the height + * and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`. + * If `dilations` is a single number, then + * `dilationDepth == dilationHeight == dilationWidth`. If it is greater + * than 1, then all values of `strides` must be 1. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function conv3d_(x, filter, strides, pad, dataFormat, dilations) { + if (dataFormat === void 0) { dataFormat = 'NDHWC'; } + if (dilations === void 0) { dilations = [1, 1, 1]; } + var $x = convertToTensor(x, 'x', 'conv3d'); + var $filter = convertToTensor(filter, 'filter', 'conv3d'); + var x5D = $x; + var reshapedTo5D = false; + if ($x.rank === 4) { + reshapedTo5D = true; + x5D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]); + } + assert(x5D.rank === 5, function () { return "Error in conv3d: input must be rank 5, but got rank " + x5D.rank + "."; }); + assert($filter.rank === 5, function () { return "Error in conv3d: filter must be rank 5, but got rank " + + ($filter.rank + "."); }); + assert(x5D.shape[4] === $filter.shape[3], function () { return "Error in conv3d: depth of input (" + x5D.shape[4] + ") must match " + + ("input depth for filter " + $filter.shape[3] + "."); }); + assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv3D: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + assert(dataFormat === 'NDHWC', function () { return "Error in conv3d: got dataFormat of " + dataFormat + " but only NDHWC is currently supported."; }); + var inputs = { x: x5D, filter: $filter }; + var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: conv3d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the derivative of the input of a 3D convolution. + * + * @param xShape The shape of the input: [batch, depth, height, width, + * in_channels]. If length of 4, batch of 1 is assumed. + * @param dy The derivative of the output, of rank 5 or rank 4 of shape + * `[batch, outDepth, outHeight, outWidth, in_channels]`. + * If rank 4, batch of 1 is assumed. + * @param filter The filter, rank 5, of shape + * `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`. + * @param strides The strides of the convolution: `[strideDepth, strideHeight, + * strideWidth]`. + * @param pad The type of padding algorithm used: + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + */ + function conv3DBackpropInput_(xShape, dy, filter, strides, pad) { + assert(xShape.length === dy.rank, function () { return "Length of inShape " + + ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"); }); + var xShape5D = xShape; + var dy5D = dy; + var 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]]; + } + var inDepth = xShape5D[4]; + var outDepth = dy5D.shape[4]; + assert(xShape5D.length === 5, function () { return "Error in conv3dDerInput: inShape must be length 5, but got length " + + (xShape5D.length + "."); }); + assert(dy5D.rank === 5, function () { return "Error in conv3dDerInput: dy must be rank 5, but got " + + ("rank " + dy5D.rank); }); + assert(filter.rank === 5, function () { return "Error in conv3dDerInput: filter must be rank 5, but got " + + ("rank " + filter.rank); }); + assert(inDepth === filter.shape[3], function () { return "Error in conv3dDerInput: depth of input (" + inDepth + ") must " + + ("match input depth for filter " + filter.shape[3] + "."); }); + assert(outDepth === filter.shape[4], function () { return "Error in conv3dDerInput: depth of output (" + outDepth + ") must " + + ("match output depth for filter " + filter.shape[4] + "."); }); + var inputs = { dy: dy5D, filter: filter }; + var attrs = { pad: pad, strides: strides, inputShape: xShape5D }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: conv3DBackpropInput_ }); + + /** + * Computes the transposed 3D convolution of a volume, also known as a + * deconvolution. + * + * @param x The input image, of rank 5 or rank 4, of shape + * `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed. + * @param filter The filter, rank 4, of shape + * `[depth, filterHeight, filterWidth, outDepth, inDepth]`. + * `inDepth` must match `inDepth` in `x`. + * @param outputShape Output shape, of rank 5 or rank 4: + * `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is + * assumed. + * @param strides The strides of the original convolution: + * `[strideDepth, strideHeight, strideWidth]`. + * @param pad The type of padding algorithm used in the non-transpose version + * of the op. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function conv3dTranspose_(x, filter, outputShape, strides, pad) { + var $x = convertToTensor(x, 'x', 'conv3dTranspose'); + var $filter = convertToTensor(filter, 'filter', 'conv3dTranspose'); + return conv3DBackpropInput(outputShape, $x, $filter, strides, pad); + } + var conv3dTranspose = op({ conv3dTranspose_: conv3dTranspose_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes cos of the input `tf.Tensor` element-wise: `cos(x)` + * + * ```js + * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); + * + * x.cos().print(); // or tf.cos(x) + * ``` + * @param x The input tensor. Must be float32 type. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function cos_(x) { + var $x = convertToTensor(x, 'x', 'cos', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Cos, inputs); + } + var cos = op({ cos_: cos_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.cosh().print(); // or tf.cosh(x) + * ``` + * @param x The input tensor. Must be float32 type. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function cosh_(x) { + var $x = convertToTensor(x, 'x', 'cosh', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Cosh, inputs); + } + var cosh = op({ cosh_: cosh_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the cumulative sum of a `tf.Tensor` along `axis`. + * + * ```js + * const x = tf.tensor([1, 2, 3, 4]); + * x.cumsum().print(); + * ``` + * ```js + * const x = tf.tensor([[1, 2], [3, 4]]); + * x.cumsum().print(); + * ``` + * + * @param x The input tensor to be summed. + * @param axis The axis along which to sum. Optional. Defaults to 0. + * @param exclusive Whether to perform exclusive cumulative sum. Optional. + * Defaults to false. If set to true then the sum of each tensor entry + * does not include its own value, but only the values previous to it + * along the specified axis. + * @param reverse Whether to sum in the opposite direction. Optional. + * Defaults to false. + * + * @doc {heading: 'Operations', subheading: 'Scan'} + */ + function cumsum_(x, axis, exclusive, reverse) { + if (axis === void 0) { axis = 0; } + if (exclusive === void 0) { exclusive = false; } + if (reverse === void 0) { reverse = false; } + var $x = convertToTensor(x, 'x', 'cumsum'); + var inputs = { x: $x }; + var attrs = { axis: axis, exclusive: exclusive, reverse: reverse }; + return ENGINE.runKernel(Cumsum, inputs, attrs); + } + var cumsum = op({ cumsum_: cumsum_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Outputs a vector with length `size` and the same dtype as `weights`. + * + * If `weights` are empty, then index `i` stores the number of times the value + * `i` is counted in `x`. If `weights` are non-empty, then index `i` stores the + * sum of the value in `weights` at each index where the corresponding value in + * `x` is `i`. + * + * Values in `x` outside of the range [0, size) are ignored. + * + * @param x The input int tensor, rank 1 or rank 2. + * @param weights The weights tensor, must have the same shape as x, or a + * length-0 Tensor, in which case it acts as all weights equal to 1. + * @param size Non-negative integer. + * @param binaryOutput Optional. Whether the kernel should count the appearance + * or number of occurrences. Defaults to False. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function denseBincount_(x, weights, size, binaryOutput) { + if (binaryOutput === void 0) { binaryOutput = false; } + var $x = convertToTensor(x, 'x', 'denseBincount'); + var $weights = convertToTensor(weights, 'weights', 'denseBincount'); + assert($x.dtype === 'int32', function () { return "Error in denseBincount: input " + + ("dtype must be int32, but got " + $x.dtype); }); + assert($x.rank <= 2, function () { return "Error in denseBincount: input must be at most rank 2, but got " + + ("rank " + $x.rank + "."); }); + assert(size >= 0, function () { return "size must be non-negative, but got " + size + "."; }); + assert($weights.size === $x.size || $weights.size === 0, function () { return "Error in denseBincount: weights must have the same shape as x or " + + ("0-length, but got x shape: " + $x.shape + ", weights shape: ") + + ($weights.shape + "."); }); + var inputs = { x: $x, weights: $weights }; + var attrs = { size: size, binaryOutput: binaryOutput }; + return ENGINE.runKernel(DenseBincount, inputs, attrs); + } + var denseBincount = op({ denseBincount_: denseBincount_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Rearranges data from depth into blocks of spatial data. More specifically, + * this op outputs a copy of the input tensor where values from the `depth` + * dimension are moved in spatial blocks to the `height` and `width` dimensions. + * The attr `blockSize` indicates the input block size and how the data is + * moved. + * + * - Chunks of data of size `blockSize * blockSize` from depth are rearranged + * into non-overlapping blocks of size `blockSize x blockSize` + * + * - The width the output tensor is `inputWidth * blockSize`, whereas the + * height is `inputHeight * blockSize` + * + * - The Y, X coordinates within each block of the output image are determined + * by the high order component of the input channel index + * + * - The depth of the input tensor must be divisible by `blockSize * + * blockSize` + * + * The `dataFormat` attr specifies the layout of the input and output tensors + * with the following options: "NHWC": [ `batch, height, width, channels` ] + * "NCHW": [ `batch, channels, height, width` ] + * + * ```js + * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]); + * const blockSize = 2; + * const dataFormat = "NHWC"; + * + * tf.depthToSpace(x, blockSize, dataFormat).print(); + * ``` + * + * @param x The input tensor of rank 4 + * @param blockSIze An `int` that is `>= 2`. The size of the spatial block + * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to "NHWC" + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function depthToSpace_(x, blockSize, dataFormat) { + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + var $x = convertToTensor(x, 'x', 'depthToSpace', 'float32'); + var inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2]; + var inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3]; + var inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1]; + assert(blockSize > 1, function () { return "blockSize should be > 1 for depthToSpace, but was: " + blockSize; }); + assert(inputHeight * blockSize >= 0, function () { return "Negative dimension size caused by overflow when multiplying\n " + inputHeight + " and " + blockSize + " for depthToSpace with input shape\n " + $x.shape; }); + assert(inputWidth * blockSize >= 0, function () { return "Negative dimension size caused by overflow when multiplying\n " + inputWidth + " and " + blockSize + " for depthToSpace with input shape\n " + $x.shape; }); + assert((inputDepth % (blockSize * blockSize) === 0), function () { return "Dimension size must be evenly divisible by " + blockSize * blockSize + " but is " + inputDepth + " for depthToSpace with input shape " + $x.shape; }); + var inputs = { x: $x }; + var attrs = { blockSize: blockSize, dataFormat: dataFormat }; + return ENGINE.runKernel(DepthToSpace, inputs, attrs); + } + var depthToSpace = op({ depthToSpace_: depthToSpace_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Depthwise 2D convolution. + * + * Given a 4D `input` array and a `filter` array of shape + * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing + * `inChannels` convolutional filters of depth 1, this op applies a + * different filter to each input channel (expanding from 1 channel to + * `channelMultiplier` channels for each), then concatenates the results + * together. The output has `inChannels * channelMultiplier` channels. + * + * See + * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d]( + * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d) + * for more details. + * + * @param x The input tensor, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is + * assumed. + * @param filter The filter tensor, rank 4, of shape + * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. + * @param strides The strides of the convolution: `[strideHeight, + * strideWidth]`. If strides is a single number, then `strideHeight == + * strideWidth`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. Only "NHWC" is currently supported. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) { + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + if (dilations === void 0) { dilations = [1, 1]; } + var $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32'); + var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32'); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in depthwiseConv2d: input must be rank 4, but got " + + ("rank " + x4D.rank + "."); }); + assert($filter.rank === 4, function () { return "Error in depthwiseConv2d: filter must be rank 4, but got rank " + + ($filter.rank + "."); }); + assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in depthwiseConv2d: number of input channels " + + ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") + + ("filter " + $filter.shape[2] + "."); }); + checkPadOnDimRoundingMode('depthwiseConv2d', pad, dimRoundingMode); + var inputs = { x: x4D, filter: $filter }; + var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dilations: dilations, dimRoundingMode: dimRoundingMode }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var res = ENGINE.runKernel(DepthwiseConv2dNative, inputs, attrs); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; + } + var depthwiseConv2d$1 = op({ depthwiseConv2d_: depthwiseConv2d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns a diagonal tensor with a given diagonal values. + * + * Given a diagonal, this operation returns a tensor with the diagonal and + * everything else padded with zeros. + * + * Assume the input has dimensions `[D1,..., Dk]`, then the output is a tensor + * of rank 2k with dimensions `[D1,..., Dk, D1,..., Dk]` + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * + * tf.diag(x).print() + * ``` + * ```js + * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 6, 8], [4, 2]) + * + * tf.diag(x).print() + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function diag_(x) { + var $x = convertToTensor(x, 'x', 'diag'); + var inputs = { x: $x }; + return ENGINE.runKernel(Diag, inputs); + } + var diag = op({ diag_: diag_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the grayscale dilation over the input `x`. + * + * @param x The input tensor, rank 3 or rank 4 of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param filter The filter tensor, rank 3, of shape + * `[filterHeight, filterWidth, depth]`. + * @param strides The strides of the sliding window for each dimension of the + * input tensor: `[strideHeight, strideWidth]`. + * If `strides` is a single number, + * then `strideHeight == strideWidth`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1*1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dataFormat Specify the data format of the input and output data. + * Defaults to 'NHWC'. Only 'NHWC' is currently supported. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * for atrous morphological dilation. Defaults to `[1, 1]`. If `dilations` + * is a single number, then `dilationHeight == dilationWidth`. If it is + * greater than 1, then all values of `strides` must be 1. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function dilation2d_(x, filter, strides, pad, dilations, dataFormat) { + if (dilations === void 0) { dilations = [1, 1]; } + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + var $x = convertToTensor(x, 'x', 'dilation2d'); + var $filter = convertToTensor(filter, 'filter', 'dilation2d'); + assert($x.rank === 3 || $x.rank === 4, function () { return "Error in dilation2d: input must be rank 3 or 4, but got rank " + + ($x.rank + "."); }); + assert($filter.rank === 3, function () { return "Error in dilation2d: filter must be rank 3, but got rank " + + ($filter.rank + "."); }); + assert(dataFormat === 'NHWC', function () { return "Error in dilation2d: Only NHWC is currently supported, " + + ("but got dataFormat of " + dataFormat); }); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + reshapedTo4D = true; + } + var inputs = { x: x4D, filter: $filter }; + var attrs = { strides: strides, pad: pad, dilations: dilations }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: dilation2d_ }); + + /** + * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. + * The result is rounded with floor function. + * + * + * ```js + * const a = tf.tensor1d([1, 4, 9, 16]); + * const b = tf.tensor1d([1, 2, 3, 4]); + * + * a.floorDiv(b).print(); // or tf.div(a, b) + * ``` + * + * ```js + * // Broadcast div a with b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(2); + * + * a.floorDiv(b).print(); // or tf.floorDiv(a, b) + * ``` + * + * @param a The first tensor as the numerator. + * @param b The second tensor as the denominator. Must have the same dtype as + * `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function floorDiv_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'floorDiv'); + var $b = convertToTensor(b, 'b', 'floorDiv'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(FloorDiv, inputs); + } + var floorDiv = op({ floorDiv_: floorDiv_ }); + + /** + * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 4, 9, 16]); + * const b = tf.tensor1d([1, 2, 3, 4]); + * + * a.div(b).print(); // or tf.div(a, b) + * ``` + * + * ```js + * // Broadcast div a with b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(2); + * + * a.div(b).print(); // or tf.div(a, b) + * ``` + * + * @param a The first tensor as the numerator. + * @param b The second tensor as the denominator. Must have the same dtype as + * `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function div_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'div'); + var $b = convertToTensor(b, 'b', 'div'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + if ($a.dtype === 'int32' && $b.dtype === 'int32') { + return floorDiv($a, $b); + } + var inputs = { a: $a, b: $b }; + var attrs = {}; + // tslint:disable-next-line: no-unnecessary-type-assertion + return ENGINE.runKernel(RealDiv, inputs, attrs); + } + var div = op({ div_: div_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the axes in the output space that should be reduced to produce + * the input space. + */ + function getReductionAxes(inShape, outShape) { + var result = []; + for (var i = 0; i < outShape.length; i++) { + var inDim = inShape[inShape.length - i - 1]; + var outAxis = outShape.length - i - 1; + var outDim = outShape[outAxis]; + if (inDim == null || (inDim === 1 && outDim > 1)) { + result.unshift(outAxis); + } + } + return result; + } + function assertAndGetBroadcastShape(shapeA, shapeB) { + var result = []; + var l = Math.max(shapeA.length, shapeB.length); + for (var i = 0; i < l; i++) { + var a = shapeA[shapeA.length - i - 1]; + if (a == null) { + a = 1; + } + var 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) { + var errMsg = "Operands could not be broadcast together with shapes " + + (shapeA + " and " + shapeB + "."); + throw Error(errMsg); + } + else { + result.unshift(a); + } + } + return result; + } + + /** + * Returns the truth value of (a == b) element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([2, 2, 2]); + * + * a.equal(b).print(); + * ``` + * + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function equal_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'equal', 'string_or_numeric'); + var $b = convertToTensor(b, 'b', 'equal', 'string_or_numeric'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Equal, inputs); + } + var equal = op({ equal_: equal_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the elements, either `a` or `b` depending on the `condition`. + * + * If the condition is true, select from `a`, otherwise select from `b`. + * + * ```js + * const cond = tf.tensor1d([false, false, true], 'bool'); + * const a = tf.tensor1d([1 , 2, 3]); + * const b = tf.tensor1d([-1, -2, -3]); + * + * a.where(cond, b).print(); + * ``` + * + * @param condition The input condition. Must be of dtype bool. + * @param a If `condition` is rank 1, `a` may have a higher rank but + * its first dimension must match the size of `condition`. + * @param b A tensor with the same dtype as `a` and with shape that is + * compatible with `a`. + * @return A tensor with same dtype as `a` and `b`, and shape that is + * broadcastable from `a` and `b`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function where_(condition, a, b) { + var $a = convertToTensor(a, 'a', 'where'); + var $b = convertToTensor(b, 'b', 'where'); + var $condition = convertToTensor(condition, 'condition', 'where', 'bool'); + // TODO: move this logic to forward function when the broadcastTo op is + // implemented in WASM. + // Find the broadcastable shape for $condition, $a, and $b. + var broadcastShape = assertAndGetBroadcastShape(assertAndGetBroadcastShape($condition.shape, $a.shape), $b.shape); + var $broadcastedCondition = broadcastTo($condition, broadcastShape); + var $broadcastedA = broadcastTo($a, broadcastShape); + var $broadcastedB = broadcastTo($b, broadcastShape); + var inputs = { + condition: $broadcastedCondition, + t: $broadcastedA, + e: $broadcastedB + }; + return ENGINE.runKernel(Select, inputs); + } + var where = op({ where_: where_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the + * given tensor. + * + * ```js + * const x = tf.tensor([1, 2]); + * tf.zerosLike(x).print(); + * ``` + * + * @param x The tensor of required shape. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function zerosLike_(x) { + var $x = convertToTensor(x, 'x', 'zerosLike'); + var inputs = { x: $x }; + return ENGINE.runKernel(ZerosLike, inputs); + } + var zerosLike = op({ zerosLike_: zerosLike_ }); + + /** + * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting. Return 0 + * if denominator is 0. + * + * + * ```js + * const a = tf.tensor1d([1, 4, 9, 16]); + * const b = tf.tensor1d([1, 2, 3, 4]); + * const c = tf.tensor1d([0, 0, 0, 0]); + * + * a.divNoNan(b).print(); // or tf.divNoNan(a, b) + * a.divNoNan(c).print(); // or tf.divNoNan(a, c) + * ``` + * + * ```js + * // Broadcast div a with b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(2); + * const c = tf.scalar(0); + * + * a.divNoNan(b).print(); // or tf.divNoNan(a, b) + * a.divNoNan(c).print(); // or tf.divNoNan(a, c) + * ``` + * + * @param a The first tensor as the numerator. + * @param b The second tensor as the denominator. Must have the same dtype as + * `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function divNoNan_(a, b) { + var _a; + // TODO: Make this into its own kernel. + var $a = convertToTensor(a, 'a', 'div'); + var $b = convertToTensor(b, 'b', 'div'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var divResult = div($a, $b); + var zeros = zerosLike(divResult); + var bEqualsZero = equal($b, zeros); + return where(bEqualsZero, zeros, divResult); + } + var divNoNan = op({ divNoNan_: divNoNan_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the dot product of two matrices and/or vectors, `t1` and `t2`. + * + * ```js + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor2d([[1, 2], [3, 4]]); + * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]); + * + * a.dot(b).print(); // or tf.dot(a, b) + * b.dot(a).print(); + * b.dot(c).print(); + * ``` + * @param t1 The first tensor in the dot operation. + * @param t2 The second tensor in the dot operation. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function dot_(t1, t2) { + var $t1 = convertToTensor(t1, 't1', 'dot'); + var $t2 = convertToTensor(t2, 't2', 'dot'); + assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), function () { return "Error in dot: inputs must all be rank 1 or 2, but got ranks " + + ($t1.rank + " and " + $t2.rank + "."); }); + var t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]); + var t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]); + assert(t1Inner === t2Inner, function () { return "Error in dot: inner dimensions of inputs must match, but got " + + (t1Inner + " and " + t2Inner + "."); }); + if ($t1.rank === 1 && $t2.rank === 1) { + var t12D = reshape($t1, [1, -1]); + var t22D = reshape($t2, [-1, 1]); + var t1t2 = matMul$1(t12D, t22D); + return reshape(t1t2, []); + } + else if ($t1.rank === 1 && $t2.rank === 2) { + var t12D = reshape($t1, [1, -1]); + var t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); + var t1t2 = matMul$1(t12D, t22D); + return reshape(t1t2, [t1t2.size]); + } + else if ($t1.rank === 2 && $t2.rank === 1) { + var t22D = reshape($t2, [-1, 1]); + var t1t2 = matMul$1($t1, t22D); + return reshape(t1t2, [t1t2.size]); + } + else { + var t22D = reshape($t2, [$t2.shape[0], $t2.shape[1]]); + var t1t2 = matMul$1($t1, t22D); + return t1t2; + } + } + var dot = op({ dot_: dot_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Tensor contraction over specified indices and outer product. + * + * `einsum` allows defining Tensors by defining their element-wise computation. + * This computation is based on + * [Einstein summation](https://en.wikipedia.org/wiki/Einstein_notation). + * + * Some special cases include: + * + * Matrix multiplication: + * ```js + * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]); + * const y = tf.tensor2d([[0, 1], [2, 3], [4, 5]]); + * x.print(); + * y.print(); + * tf.einsum('ij,jk->ik', x, y).print(); + * ``` + * + * Dot product: + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * const y = tf.tensor1d([0, 1, 2]); + * x.print(); + * y.print(); + * tf.einsum('i,i->', x, y).print(); + * ``` + * + * Batch dot product: + * ```js + * const x = tf.tensor2d([[1, 2, 3], [4, 5, 6]]); + * const y = tf.tensor2d([[0, 1, 2], [3, 4, 5]]); + * x.print(); + * y.print(); + * tf.einsum('bi,bi->b', x, y).print(); + * ``` + * + * Outer prouduct: + * ```js + * const x = tf.tensor1d([1, 3, 5]); + * const y = tf.tensor1d([2, 4, 6]); + * x.print(); + * y.print(); + * tf.einsum('i,j->ij', x, y).print(); + * ``` + * + * Matrix transpose: + * ```js + * const x = tf.tensor2d([[1, 2], [3, 4]]); + * x.print(); + * tf.einsum('ij->ji', x).print(); + * ``` + * + * Batch matrix transpose: + * ```js + * const x = tf.tensor3d([[[1, 2], [3, 4]], [[-1, -2], [-3, -4]]]); + * x.print(); + * tf.einsum('bij->bji', x).print(); + * ``` + * + * Limitations: + * + * This implementation of einsum has the following limitations: + * + * - Does not support >2 input tensors. + * - Does not support duplicate axes for any given input tensor. E.g., equation + * 'ii->' is not suppoted. + * - The `...` notation is not supported. + * + * @param equation a string describing the contraction, in the same format as + * [numpy.einsum](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html). + * @param tensors the input(s) to contract (each one a Tensor), whose shapes + * should be consistent with equation. + * @returns The output tensor. + * + * @doc {heading: 'Tensors', subheading: 'Matrices'} + */ + function einsum_(equation) { + var tensors = []; + for (var _i = 1; _i < arguments.length; _i++) { + tensors[_i - 1] = arguments[_i]; + } + var $tensors = tensors.map(function (t, i) { return convertToTensor(t, "tensors" + i, 'einsum'); }); + var attrs = { equation: equation }; + return ENGINE.runKernel(Einsum, $tensors, attrs); + } + var einsum = op({ einsum_: einsum_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes exponential linear element-wise: `x > 0 ? x : (e ^ x) - 1`. + * + * ```js + * const x = tf.tensor1d([-1, 1, -3, 2]); + * + * x.elu().print(); // or tf.elu(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function elu_(x) { + var $x = convertToTensor(x, 'x', 'elu', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Elu, inputs); + } + var elu = op({ elu_: elu_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes gause error function of the input `tf.Tensor` element-wise: + * `erf(x)` + * + * ```js + * const x = tf.tensor1d([0, .1, -.1, .7]); + * + * x.erf().print(); // or tf.erf(x); + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function erf_(x) { + var $x = convertToTensor(x, 'x', 'erf'); + assert($x.dtype === 'int32' || $x.dtype === 'float32', function () { return 'Input dtype must be `int32` or `float32`.'; }); + if ($x.dtype === 'int32') { + $x = cast($x, 'float32'); + } + var inputs = { x: $x }; + return ENGINE.runKernel(Erf, inputs); + } + var erf = op({ erf_: erf_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x` + * + * ```js + * const x = tf.tensor1d([1, 2, -3]); + * + * x.exp().print(); // or tf.exp(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function exp_(x) { + var $x = convertToTensor(x, 'x', 'exp'); + var inputs = { x: $x }; + return ENGINE.runKernel(Exp, inputs); + } + var exp = op({ exp_: exp_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension + * into the tensor's shape. + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * const axis = 1; + * x.expandDims(axis).print(); + * ``` + * + * @param x The input tensor whose dimensions to be expanded. + * @param axis The dimension index at which to insert shape of `1`. Defaults + * to 0 (the first dimension). + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function expandDims_(x, axis) { + if (axis === void 0) { axis = 0; } + var $x = convertToTensor(x, 'x', 'expandDims', 'string_or_numeric'); + assert(axis <= $x.rank, function () { return 'Axis must be <= rank of the tensor'; }); + var inputs = { input: $x }; + var attrs = { dim: axis }; + return ENGINE.runKernel(ExpandDims, inputs, attrs); + } + var expandDims = op({ expandDims_: expandDims_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes exponential of the input `tf.Tensor` minus one element-wise. + * `e ^ x - 1` + * + * ```js + * const x = tf.tensor1d([1, 2, -3]); + * + * x.expm1().print(); // or tf.expm1(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function expm1_(x) { + var $x = convertToTensor(x, 'x', 'expm1'); + var inputs = { x: $x }; + return ENGINE.runKernel(Expm1, inputs); + } + var expm1 = op({ expm1_: expm1_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Construct a tensor by repeating it the number of times given by reps. + * + * This operation creates a new tensor by replicating `input` `reps` + * times. The output tensor's i'th dimension has `input.shape[i] * + * reps[i]` elements, and the values of `input` are replicated + * `reps[i]` times along the i'th dimension. For example, tiling + * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`. + * + * ```js + * const a = tf.tensor1d([1, 2]); + * + * a.tile([2]).print(); // or a.tile([2]) + * ``` + * + * ```js + * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * a.tile([1, 2]).print(); // or a.tile([1, 2]) + * ``` + * @param x The tensor to tile. + * @param reps Determines the number of replications per dimension. + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function tile_(x, reps) { + var $x = convertToTensor(x, 'x', 'tile', 'string_or_numeric'); + assert($x.rank === reps.length, function () { return "Error in transpose: rank of input " + $x.rank + " " + + ("must match length of reps " + reps + "."); }); + var inputs = { x: $x }; + var attrs = { reps: reps }; + return ENGINE.runKernel(Tile, inputs, attrs); + } + var tile = op({ tile_: tile_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Create an identity matrix. + * + * @param numRows Number of rows. + * @param numColumns Number of columns. Defaults to `numRows`. + * @param batchShape If provided, will add the batch shape to the beginning + * of the shape of the returned `tf.Tensor` by repeating the identity + * matrix. + * @param dtype Data type. + * @returns Identity matrix of the specified size and data type, possibly + * with batch repetition if `batchShape` is specified. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function eye_(numRows, numColumns, batchShape, dtype) { + if (dtype === void 0) { dtype = 'float32'; } + if (numColumns == null) { + numColumns = numRows; + } + var buff = buffer([numRows, numColumns], dtype); + var n = numRows <= numColumns ? numRows : numColumns; + for (var i = 0; i < n; ++i) { + buff.set(1, i, i); + } + var 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) { + // tslint:disable-next-line:no-unnecessary-type-assertion + return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]); + } + else if (batchShape.length === 3) { + // tslint:disable-next-line:no-unnecessary-type-assertion + 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 " + + ( + // tslint:disable-next-line:no-any + "batchShapes, but received " + batchShape.length + "D.")); + } + } + } + var eye = op({ eye_: eye_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` filled with a scalar value. + * + * ```js + * tf.fill([2, 2], 4).print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param value The scalar value to fill the tensor with. + * @param dtype The type of an element in the resulting tensor. Defaults to + * 'float'. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function fill(shape, value, dtype) { + var attrs = { shape: shape, value: value, dtype: dtype }; + return ENGINE.runKernel(Fill, {}, attrs); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes floor of input `tf.Tensor` element-wise: `floor(x)`. + * + * ```js + * const x = tf.tensor1d([.6, 1.1, -3.3]); + * + * x.floor().print(); // or tf.floor(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function floor_(x) { + var $x = convertToTensor(x, 'x', 'floor', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Floor, inputs); + } + var floor = op({ floor_: floor_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Gather slices from tensor `x`'s axis `axis` according to `indices`. + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * const indices = tf.tensor1d([1, 3, 3], 'int32'); + * + * x.gather(indices).print(); + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * const indices = tf.tensor1d([1, 1, 0], 'int32'); + * + * x.gather(indices).print(); + * ``` + * @param x The input tensor whose slices to be gathered. + * @param indices The indices of the values to extract. + * @param axis The axis over which to select values. Defaults to 0. + * @param batchDims Optional. The number of batch dimensions. It must be less + * than or equal to rank(indices). Defaults to 0. + * The output tensor will have shape of + * `x.shape[:axis] + indices.shape[batchDims:] + x.shape[axis + 1:]` + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function gather_(x, indices, axis, batchDims) { + if (axis === void 0) { axis = 0; } + if (batchDims === void 0) { batchDims = 0; } + var $x = convertToTensor(x, 'x', 'gather'); + var $indices = convertToTensor(indices, 'indices', 'gather', 'int32'); + var inputs = { x: $x, indices: $indices }; + var attrs = { axis: axis, batchDims: batchDims }; + return ENGINE.runKernel(GatherV2, inputs, attrs); + } + var gather = op({ gather_: gather_ }); + + /** + * Returns the truth value of (a > b) element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([2, 2, 2]); + * + * a.greater(b).print(); + * ``` + * + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function greater_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'greater', 'string_or_numeric'); + var $b = convertToTensor(b, 'b', 'greater', 'string_or_numeric'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Greater, inputs); + } + var greater = op({ greater_: greater_ }); + + /** + * Returns the truth value of (a >= b) element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([2, 2, 2]); + * + * a.greaterEqual(b).print(); + * ``` + * + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function greaterEqual_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'greaterEqual', 'string_or_numeric'); + var $b = convertToTensor(b, 'b', 'greaterEqual', 'string_or_numeric'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(GreaterEqual, inputs); + } + var greaterEqual = op({ greaterEqual_: greaterEqual_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the imaginary part of a complex (or real) tensor. + * + * Given a tensor input, this operation returns a tensor of type float that is + * the imaginary part of each element in input considered as a complex number. + * If input is real, a tensor of all zeros is returned. + * + * ```js + * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]); + * tf.imag(x).print(); + * ``` + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function imag_(input) { + var $input = convertToTensor(input, 'input', 'imag'); + var inputs = { input: $input }; + return ENGINE.runKernel(Imag, inputs); + } + var imag = op({ imag_: imag_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns which elements of x are finite. + * + * ```js + * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]); + * + * x.isFinite().print(); // or tf.isNaN(x) + * ``` + * @param x The input Tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function isFinite_(x) { + var $x = convertToTensor(x, 'x', 'isFinite'); + var inputs = { x: $x }; + return ENGINE.runKernel(IsFinite, inputs); + } + var isFinite$1 = op({ isFinite_: isFinite_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns which elements of x are Infinity or -Infinity. + * + * ```js + * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]); + * + * x.isInf().print(); // or tf.isNaN(x) + * ``` + * @param x The input Tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function isInf_(x) { + var $x = convertToTensor(x, 'x', 'isInf'); + var inputs = { x: $x }; + return ENGINE.runKernel(IsInf, inputs); + } + var isInf = op({ isInf_: isInf_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * RReturns which elements of x are NaN. + * + * ```js + * const x = tf.tensor1d([NaN, Infinity, -Infinity, 0, 1]); + * + * x.isNaN().print(); // or tf.isNaN(x) + * ``` + * @param x The input Tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function isNaN_(x) { + var $x = convertToTensor(x, 'x', 'isNaN'); + var inputs = { x: $x }; + return ENGINE.runKernel(IsNan, inputs); + } + var isNaN$1 = op({ isNaN_: isNaN_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes leaky rectified linear element-wise. + * + * See + * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf]( + * http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf) + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 4]); + * + * x.leakyRelu(0.1).print(); // or tf.leakyRelu(x, 0.1) + * ``` + * @param x The input tensor. + * @param alpha The scaling factor for negative values, defaults to 0.2. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function leakyRelu_(x, alpha) { + if (alpha === void 0) { alpha = 0.2; } + var $x = convertToTensor(x, 'x', 'leakyRelu'); + var inputs = { x: $x }; + var attrs = { alpha: alpha }; + return ENGINE.runKernel(LeakyRelu, inputs, attrs); + } + var leakyRelu = op({ leakyRelu_: leakyRelu_ }); + + /** + * Returns the truth value of (a < b) element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([2, 2, 2]); + * + * a.less(b).print(); + * ``` + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function less_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'less', 'string_or_numeric'); + var $b = convertToTensor(b, 'b', 'less', 'string_or_numeric'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Less, inputs); + } + var less = op({ less_: less_ }); + + /** + * Returns the truth value of (a <= b) element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([2, 2, 2]); + * + * a.lessEqual(b).print(); + * ``` + * + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function lessEqual_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'lessEqual', 'string_or_numeric'); + var $b = convertToTensor(b, 'b', 'lessEqual', 'string_or_numeric'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LessEqual, inputs); + } + var lessEqual = op({ lessEqual_: lessEqual_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Return an evenly spaced sequence of numbers over the given interval. + * + * ```js + * tf.linspace(0, 9, 10).print(); + * ``` + * @param start The start value of the sequence. + * @param stop The end value of the sequence. + * @param num The number of values to generate. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function linspace(start, stop, num) { + if (num <= 0) { + throw new Error('The number of values should be positive.'); + } + var attrs = { start: start, stop: stop, num: num }; + return ENGINE.runKernel(LinSpace, {}, attrs); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Normalizes the activation of a local neighborhood across or within + * channels. + * + * @param x The input tensor. The 4-D input tensor is treated as a 3-D array + * of 1D vectors (along the last dimension), and each vector is + * normalized independently. + * @param depthRadius The number of adjacent channels in the 1D normalization + * window. + * @param bias A constant bias term for the basis. + * @param alpha A scale factor, usually positive. + * @param beta An exponent. + * + * @doc {heading: 'Operations', subheading: 'Normalization'} + */ + function localResponseNormalization_(x, depthRadius, bias, alpha, beta) { + if (depthRadius === void 0) { depthRadius = 5; } + if (bias === void 0) { bias = 1; } + if (alpha === void 0) { alpha = 1; } + if (beta === void 0) { beta = 0.5; } + var $x = convertToTensor(x, 'x', 'localResponseNormalization'); + assert($x.rank === 4 || $x.rank === 3, function () { return "Error in localResponseNormalization: x must be rank 3 or 4 but got\n rank " + $x.rank + "."; }); + assert(isInt(depthRadius), function () { return "Error in localResponseNormalization: depthRadius must be an " + + ("integer but got depthRadius " + depthRadius + "."); }); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + var inputs = { x: x4D }; + var attrs = { depthRadius: depthRadius, bias: bias, alpha: alpha, beta: beta }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: localResponseNormalization_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)` + * + * ```js + * const x = tf.tensor1d([1, 2, Math.E]); + * + * x.log().print(); // or tf.log(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function log_(x) { + var $x = convertToTensor(x, 'x', 'log', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Log, inputs); + } + var log = op({ log_: log_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes natural logarithm of the input `tf.Tensor` plus one + * element-wise: `ln(1 + x)` + * + * ```js + * const x = tf.tensor1d([1, 2, Math.E - 1]); + * + * x.log1p().print(); // or tf.log1p(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function log1p_(x) { + var $x = convertToTensor(x, 'x', 'log1p'); + var inputs = { x: $x }; + return ENGINE.runKernel(Log1p, inputs); + } + var log1p = op({ log1p_: log1p_ }); + + /** + * Overrides the gradient computation of a function `f`. + * + * Takes a function + * `f(...inputs, save) => {value: Tensor, gradFunc: (dy, saved) => Tensor[]}` + * and returns another function `g(...inputs)` which takes the same inputs as + * `f`. When called, `g` returns `f().value`. In backward mode, custom gradients + * with respect to each input of `f` are computed using `f().gradFunc`. + * + * The `save` function passsed to `f` should be used for saving tensors needed + * in the gradient. And the `saved` passed to the `gradFunc` is a + * `NamedTensorMap`, which contains those saved tensor. + * + * ```js + * const customOp = tf.customGrad((x, save) => { + * // Save x to make sure it's available later for the gradient. + * save([x]); + * // Override gradient of our custom x ^ 2 op to be dy * abs(x); + * return { + * value: x.square(), + * // Note `saved.x` which points to the `x` we saved earlier. + * gradFunc: (dy, saved) => [dy.mul(saved[0].abs())] + * }; + * }); + * + * const x = tf.tensor1d([-1, -2, 3]); + * const dx = tf.grad(x => customOp(x)); + * + * console.log(`f(x):`); + * customOp(x).print(); + * console.log(`f'(x):`); + * dx(x).print(); + * ``` + * + * @param f The function to evaluate in forward mode, which should return + * `{value: Tensor, gradFunc: (dy, saved) => Tensor[]}`, where `gradFunc` + * returns the custom gradients of `f` with respect to its inputs. + * + * @doc {heading: 'Training', subheading: 'Gradients'} + */ + function customGrad(f) { + return ENGINE.customGrad(f); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes `-1 * x` element-wise. + * + * ```js + * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]); + * + * x.neg().print(); // or tf.neg(x) + * ``` + * + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function neg_(x) { + var $x = convertToTensor(x, 'x', 'neg'); + var inputs = { x: $x }; + return ENGINE.runKernel(Neg, inputs); + } + var neg = op({ neg_: neg_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.softplus().print(); // or tf.softplus(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function softplus_(x) { + var $x = convertToTensor(x, 'x', 'softplus'); + var inputs = { x: $x }; + return ENGINE.runKernel(Softplus, inputs); + } + var softplus = op({ softplus_: softplus_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes log sigmoid of the input `tf.Tensor` element-wise: + * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`. + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.logSigmoid().print(); // or tf.logSigmoid(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function logSigmoid_(x) { + var $x = convertToTensor(x, 'x', 'logSigmoid'); + // Use a custom gradient to maintain previous implementation. + // There is no LogSigmoid kernel in TF so we can't use engine.runKernel + // directly + var customOp = customGrad(function (x) { + // TODO(yassogba) we can remove the chained softplus call here only + // after backends have modualrized softplus at which point we can call + // engine runKernel(..., Sotfplus, ...) directly. + var value = neg(softplus(neg(x))); + var gradFunc = function (dy) { + var derX = mul(dy, sigmoid(neg(x))); + return derX; + }; + return { value: value, gradFunc: gradFunc }; + }); + return customOp($x); + } + var logSigmoid = op({ logSigmoid_: logSigmoid_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the maximum of elements across dimensions of a `tf.Tensor`. + * + * Reduces the input along the dimensions given in `axes`. Unless `keepDims` + * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in + * `axes`. If `keepDims` is true, the reduced dimensions are retained with + * length 1. If `axes` has no entries, all dimensions are reduced, and an + * `tf.Tensor` with a single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.max().print(); // or tf.max(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.max(axis).print(); // or tf.max(x, axis) + * ``` + * + * @param x The input tensor. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function max_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'max'); + var inputs = { x: $x }; + var attrs = { reductionIndices: axis, keepDims: keepDims }; + return ENGINE.runKernel(Max, inputs, attrs); + } + var max = op({ max_: max_ }); + + /** + * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([10, 20, 30, 40]); + * const b = tf.tensor1d([1, 2, 3, 4]); + * + * a.sub(b).print(); // or tf.sub(a, b) + * ``` + * + * ```js + * // Broadcast subtract a with b. + * const a = tf.tensor1d([10, 20, 30, 40]); + * const b = tf.scalar(5); + * + * a.sub(b).print(); // or tf.sub(a, b) + * ``` + * @param a The first `tf.Tensor` to subtract from. + * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as + * `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function sub_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'sub'); + var $b = convertToTensor(b, 'b', 'sub'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Sub, inputs); + } + var sub = op({ sub_: sub_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the sum of elements across dimensions of a `tf.Tensor`. + * + * Reduces the input along the dimensions given in `axes`. Unless `keepDims` + * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in + * `axes`. If `keepDims` is true, the reduced dimensions are retained with + * length 1. If axes has no entries, all dimensions are reduced, and a + * `tf.Tensor` with a single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.sum().print(); // or tf.sum(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.sum(axis).print(); // or tf.sum(x, axis) + * ``` + * + * @param x The input tensor to compute the sum over. If the dtype is `bool` + * it will be converted to `int32` and the output dtype will be `int32`. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function sum_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'sum'); + if ($x.dtype === 'bool') { + $x = cast($x, 'int32'); + } + var inputs = { x: $x }; + var attrs = { axis: axis, keepDims: keepDims }; + return ENGINE.runKernel(Sum, inputs, attrs); + } + var sum = op({ sum_: sum_ }); + + /** + * Computes the log softmax. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * + * a.logSoftmax().print(); // or tf.logSoftmax(a) + * ``` + * + * ```js + * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]); + * + * a.logSoftmax().print(); // or tf.logSoftmax(a) + * ``` + * + * @param logits The logits array. + * @param axis The dimension softmax would be performed on. Defaults to `-1` + * which indicates the last dimension. + * + * @doc {heading: 'Operations', subheading: 'Normalization'} + */ + function logSoftmax_(logits, axis) { + if (axis === void 0) { axis = -1; } + var $logits = convertToTensor(logits, 'logits', 'logSoftmax'); + if (axis === -1) { + axis = $logits.rank - 1; + } + if (axis !== $logits.rank - 1) { + throw Error('Log Softmax along a non-last dimension is not yet supported. ' + + ("Logits was rank " + $logits.rank + " and axis was " + axis)); + } + // const forward: ForwardFunc = (backend, save) => { + // const keepDims = true; + // const xMax = max(logits, axis, true); + // const shifted = sub(logits, xMax); + // const value = + // sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, + // keepDims))); + // save([value]); + // return value; + // }; + // Use a custom gradient for numerical stability. + var customOp = customGrad(function (logits, save) { + var keepDims = true; + var xMax = max(logits, axis, true); + var shifted = sub(logits, xMax); + var value = sub(cast(shifted, 'float32'), log(sum(exp(shifted), axis, keepDims))); + save([value]); + var gradFunc = function (dy, saved) { + var _a = __read(saved, 1), value = _a[0]; + var keepDims = true; + var softmax = exp(value); + return sub(dy, mul(sum(dy, axis, keepDims), softmax)); + }; + return { value: value, gradFunc: gradFunc }; + }); + return customOp($logits); + // TODO Use Engine.runKernel when CPU/WebGL/WASM backends implement this. + // const inputs: LogSoftmaxInputs = {logits: $logits}; + // const attrs: LogSoftmaxAttrs = {axis}; + // return ENGINE.runKernel( + // LogSoftmax, inputs as {} as NamedTensorMap, + // attrs as {} as NamedAttrMap); + } + var logSoftmax = op({ logSoftmax_: logSoftmax_ }); + + /** + * @license + * Copyright 2017 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function combineLocations(outputLoc, reduceLoc, axes) { + var rank = outputLoc.length + reduceLoc.length; + var loc = []; + var outIdx = 0; + var reduceIdx = 0; + for (var dim = 0; dim < rank; dim++) { + if (axes.indexOf(dim) === -1) { + loc.push(outputLoc[outIdx++]); + } + else { + loc.push(reduceLoc[reduceIdx++]); + } + } + return loc; + } + function expandShapeToKeepDim(shape, axes) { + var reduceSubShape = axes.map(function (x) { return 1; }); + return combineLocations(shape, reduceSubShape, axes); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the log(sum(exp(elements across the reduction dimensions)). + * + * Reduces the input along the dimensions given in `axis`. Unless `keepDims` + * is true, the rank of the array is reduced by 1 for each entry in `axis`. + * If `keepDims` is true, the reduced dimensions are retained with length 1. + * If `axis` has no entries, all dimensions are reduced, and an array with a + * single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.logSumExp().print(); // or tf.logSumExp(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.logSumExp(axis).print(); // or tf.logSumExp(a, axis) + * ``` + * @param x The input tensor. + * @param axis The dimension(s) to reduce. If null (the default), + * reduces all dimensions. + * @param keepDims If true, retains reduced dimensions with length + * of 1. Defaults to false. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function logSumExp_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'logSumExp'); + var axes = parseAxisParam(axis, $x.shape); + var xMax = max($x, axes, true /* keepDims */); + var a = sub($x, xMax); + var b = exp(a); + var c = sum(b, axes); + var d = log(c); + var res = add(reshape(xMax, d.shape), d); + if (keepDims) { + var newShape = expandShapeToKeepDim(res.shape, axes); + return reshape(res, newShape); + } + return res; + } + var logSumExp = op({ logSumExp_: logSumExp_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the truth value of `a AND b` element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([false, false, true, true], 'bool'); + * const b = tf.tensor1d([false, true, false, true], 'bool'); + * + * a.logicalAnd(b).print(); + * ``` + * + * @param a The first input tensor. Must be of dtype bool. + * @param b The second input tensor. Must be of dtype bool. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function logicalAnd_(a, b) { + var $a = convertToTensor(a, 'a', 'logicalAnd', 'bool'); + var $b = convertToTensor(b, 'b', 'logicalAnd', 'bool'); + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LogicalAnd, inputs); + } + var logicalAnd = op({ logicalAnd_: logicalAnd_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the truth value of `NOT x` element-wise. + * + * ```js + * const a = tf.tensor1d([false, true], 'bool'); + * + * a.logicalNot().print(); + * ``` + * + * @param x The input tensor. Must be of dtype 'bool'. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function logicalNot_(x) { + var $x = convertToTensor(x, 'x', 'logicalNot', 'bool'); + var inputs = { x: $x }; + return ENGINE.runKernel(LogicalNot, inputs); + } + var logicalNot = op({ logicalNot_: logicalNot_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the truth value of `a OR b` element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([false, false, true, true], 'bool'); + * const b = tf.tensor1d([false, true, false, true], 'bool'); + * + * a.logicalOr(b).print(); + * ``` + * @param a The first input tensor. Must be of dtype bool. + * @param b The second input tensor. Must be of dtype bool. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function logicalOr_(a, b) { + var $a = convertToTensor(a, 'a', 'logicalOr', 'bool'); + var $b = convertToTensor(b, 'b', 'logicalOr', 'bool'); + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(LogicalOr, inputs); + } + var logicalOr = op({ logicalOr_: logicalOr_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the truth value of `a XOR b` element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([false, false, true, true], 'bool'); + * const b = tf.tensor1d([false, true, false, true], 'bool'); + * + * a.logicalXor(b).print(); + * ``` + * + * @param a The first input tensor. Must be of dtype bool. + * @param b The second input tensor. Must be of dtype bool. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function logicalXor_(a, b) { + var $a = convertToTensor(a, 'a', 'logicalXor', 'bool'); + var $b = convertToTensor(b, 'b', 'logicalXor', 'bool'); + assertAndGetBroadcastShape($a.shape, $b.shape); + // x ^ y = (x | y) & ~(x & y) + return logicalAnd(logicalOr(a, b), logicalNot(logicalAnd(a, b))); + } + var logicalXor = op({ logicalXor_: logicalXor_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the 2D max pooling of an image. + * + * @param x The input tensor, of rank 4 or rank 3 of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param filterSize The filter size: `[filterHeight, filterWidth]`. If + * `filterSize` is a single number, then `filterHeight == filterWidth`. + * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If + * `strides` is a single number, then `strideHeight == strideWidth`. + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + */ + function maxPool_(x, filterSize, strides, pad, dimRoundingMode) { + var $x = convertToTensor(x, 'x', 'maxPool'); + var dilations = 1; + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in maxPool: input must be rank 4 but got rank " + x4D.rank + "."; }); + assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in maxPool: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + checkPadOnDimRoundingMode('maxPool', pad, dimRoundingMode); + var inputs = { x: x4D }; + var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: maxPool_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the 3D max pooling. + * + * ```js + * const x = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]); + * const result = tf.maxPool3d(x, 2, 1, 'valid'); + * result.print(); + * ``` + * + * @param x The input tensor, of rank 5 or rank 4 of shape + * `[batch, depth, height, width, inChannels]`. + * @param filterSize The filter size: + * `[filterDepth, filterHeight, filterWidth]`. + * If `filterSize` is a single number, + * then `filterDepth == filterHeight == filterWidth`. + * @param strides The strides of the pooling: + * `[strideDepth, strideHeight, strideWidth]`. + * If `strides` is a single number, + * then `strideDepth == strideHeight == strideWidth`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1*1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to + * "NDHWC". Specify the data format of the input and output data. With the + * default format "NDHWC", the data is stored in the order of: [batch, + * depth, height, width, channels]. Only "NDHWC" is currently supported. + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function maxPool3d_(x, filterSize, strides, pad, dimRoundingMode, dataFormat) { + if (filterSize === void 0) { filterSize = [1, 1, 1]; } + if (dataFormat === void 0) { dataFormat = 'NDHWC'; } + var $x = convertToTensor(x, 'x', 'maxPool3d'); + var x5D = $x; + var reshapedTo5D = false; + if ($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, function () { return "Error in maxPool3d: x must be rank 5 but got rank " + x5D.rank + "."; }); + assert(dataFormat === 'NDHWC', function () { return "Error in maxPool3d: Only NDHWC is currently supported, " + + ("but got dataFormat of " + dataFormat); }); + checkPadOnDimRoundingMode('maxPool3d', pad, dimRoundingMode); + var inputs = { x: x5D }; + var attrs = { filterSize: filterSize, strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dataFormat: dataFormat }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: maxPool3d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the 2D max pooling of an image with Argmax index. + * The indices in argmax are flattened, so that a maximum value at position `[b, + * y, x, c]` becomes flattened index: `(y * width + x) * channels + c` if + * include_batch_in_index is False; `((b * height + y) * width + x) * channels + * +c` if include_batch_in_index is True. + * + * The indices returned are always in `[0, height) x [0, width)` before + * flattening. + * + * @param x The input tensor, of rank 4 or rank 3 of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param filterSize The filter size: `[filterHeight, filterWidth]`. If + * `filterSize` is a single number, then `filterHeight == filterWidth`. + * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If + * `strides` is a single number, then `strideHeight == strideWidth`. + * @param dataFormat An optional string from: "NDHWC", "NCDHW". Defaults to + * "NDHWC". Specify the data format of the input and output data. With the + * default format "NDHWC", the data is stored in the order of: [batch, + * depth, height, width, channels]. Only "NDHWC" is currently supported. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param includeBatchIndex Defaults to False. Whether to include batch + * dimension in flattened index of argmax. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function maxPoolWithArgmax_(x, filterSize, strides, pad, includeBatchInIndex) { + if (includeBatchInIndex === void 0) { includeBatchInIndex = false; } + var $x = convertToTensor(x, 'x', 'maxPoolWithArgmax'); + var inputs = { x: $x }; + var attrs = { filterSize: filterSize, strides: strides, pad: pad, includeBatchInIndex: includeBatchInIndex }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var result = ENGINE.runKernel(MaxPoolWithArgmax, inputs, attrs); + return { result: result[0], indexes: result[1] }; + } + var maxPoolWithArgmax = op({ maxPoolWithArgmax_: maxPoolWithArgmax_ }); + + /** + * Returns the max of a and b (`a > b ? a : b`) element-wise. + * Supports broadcasting. + * + * We also expose `tf.maximumStrict` which has the same signature as this op and + * asserts that `a` and `b` are the same shape (does not broadcast). + * + * ```js + * const a = tf.tensor1d([1, 4, 3, 16]); + * const b = tf.tensor1d([1, 2, 9, 4]); + * + * a.maximum(b).print(); // or tf.maximum(a, b) + * ``` + * + * ```js + * // Broadcast maximum a with b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(5); + * + * a.maximum(b).print(); // or tf.maximum(a, b) + * ``` + * + * @param a The first tensor. + * @param b The second tensor. Must have the same type as `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function maximum_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'maximum'); + var $b = convertToTensor(b, 'b', 'maximum'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + if ($a.dtype === 'bool') { + $a = cast($a, 'int32'); + $b = cast($b, 'int32'); + } + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Maximum, inputs); + } + var maximum = op({ maximum_: maximum_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the mean of elements across dimensions of a `tf.Tensor`. + * + * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is + * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`. + * If `keepDims` is true, the reduced dimensions are retained with length 1. + * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with + * a single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.mean().print(); // or tf.mean(a) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.mean(axis).print(); // or tf.mean(x, axis) + * ``` + * + * @param x The input tensor. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function mean_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'mean'); + var inputs = { x: $x }; + var attrs = { axis: axis, keepDims: keepDims }; + return ENGINE.runKernel(Mean, inputs, attrs); + } + var mean = op({ mean_: mean_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with all elements set to 0. + * + * ```js + * tf.zeros([2, 2]).print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param dtype The type of an element in the resulting tensor. Can + * be 'float32', 'int32' or 'bool'. Defaults to 'float'. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function zeros(shape, dtype) { + if (dtype === void 0) { dtype = 'float32'; } + if (dtype === 'complex64') { + var real = zeros(shape, 'float32'); + var imag = zeros(shape, 'float32'); + return complex(real, imag); + } + var values = makeZerosTypedArray(sizeFromShape(shape), dtype); + return ENGINE.makeTensor(values, shape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with all elements set to 1. + * + * ```js + * tf.ones([2, 2]).print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param dtype The type of an element in the resulting tensor. Defaults to + * 'float'. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function ones(shape, dtype) { + if (dtype === void 0) { dtype = 'float32'; } + if (dtype === 'complex64') { + var real = ones(shape, 'float32'); + var imag = zeros(shape, 'float32'); + return complex(real, imag); + } + var values = makeOnesTypedArray(sizeFromShape(shape), dtype); + return ENGINE.makeTensor(values, shape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Broadcasts parameters for evaluation on an N-D grid. + * + * Given N one-dimensional coordinate arrays `*args`, returns a list `outputs` + * of N-D coordinate arrays for evaluating expressions on an N-D grid. + * + * Notes: + * `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions. + * When the `indexing` argument is set to 'xy' (the default), the broadcasting + * instructions for the first two dimensions are swapped. + * Examples: + * Calling `const [X, Y] = meshgrid(x, y)` with the tensors + * + * ```javascript + * const x = [1, 2, 3]; + * const y = [4, 5, 6]; + * const [X, Y] = tf.meshgrid(x, y); + * // X = [[1, 2, 3], + * // [1, 2, 3], + * // [1, 2, 3]] + * // Y = [[4, 4, 4], + * // [5, 5, 5], + * // [6, 6, 6]] + * ``` + * + * @param x Tensor with rank geq 1. + * @param y Tensor with rank geq 1. + * @param indexing + * + * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} + */ + function meshgrid(x, y, _a) { + var _b = (_a === void 0 ? {} : _a).indexing, indexing = _b === void 0 ? 'xy' : _b; + if (indexing !== 'xy' && indexing !== 'ij') { + throw new TypeError(indexing + " is not a valid third argument to meshgrid"); + } + if (x === undefined) { + return []; + } + var $x = convertToTensor(x, 'x', 'meshgrid', x instanceof Tensor ? x.dtype : 'float32'); + if (y === undefined) { + return [$x]; + } + var $y = convertToTensor(y, 'y', 'meshgrid', y instanceof Tensor ? y.dtype : 'float32'); + var w = sizeFromShape($x.shape); + var h = sizeFromShape($y.shape); + if (indexing === 'xy') { + $x = reshape($x, [1, -1]); + $y = reshape($y, [-1, 1]); + return [ + matMul$1(ones([h, 1], $x.dtype), $x), + matMul$1($y, ones([1, w], $y.dtype)), + ]; + } + $x = reshape($x, [-1, 1]); + $y = reshape($y, [1, -1]); + return [ + matMul$1($x, ones([1, h], $x.dtype)), + matMul$1(ones([w, 1], $y.dtype), $y), + ]; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the minimum value from the input. + * + * Reduces the input along the dimensions given in `axes`. Unless `keepDims` + * is true, the rank of the array is reduced by 1 for each entry in `axes`. + * If `keepDims` is true, the reduced dimensions are retained with length 1. + * If `axes` has no entries, all dimensions are reduced, and an array with a + * single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.min().print(); // or tf.min(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.min(axis).print(); // or tf.min(x, axis) + * ``` + * + * @param x The input Tensor. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function min_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'min'); + var inputs = { x: $x }; + var attrs = { axis: axis, keepDims: keepDims }; + // tslint:disable-next-line: no-unnecessary-type-assertion + return ENGINE.runKernel(Min, inputs, attrs); + } + var min = op({ min_: min_ }); + + /** + * Returns the min of a and b (`a < b ? a : b`) element-wise. + * Supports broadcasting. + * + * We also expose `minimumStrict` which has the same signature as this op and + * asserts that `a` and `b` are the same shape (does not broadcast). + * + * ```js + * const a = tf.tensor1d([1, 4, 3, 16]); + * const b = tf.tensor1d([1, 2, 9, 4]); + * + * a.minimum(b).print(); // or tf.minimum(a, b) + * ``` + * + * ```js + * // Broadcast minimum a with b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(5); + * + * a.minimum(b).print(); // or tf.minimum(a, b) + * ``` + * + * @param a The first tensor. + * @param b The second tensor. Must have the same type as `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function minimum_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'minimum'); + var $b = convertToTensor(b, 'b', 'minimum'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + if ($a.dtype === 'bool') { + $a = cast($a, 'int32'); + $b = cast($b, 'int32'); + } + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Minimum, inputs); + } + var minimum = op({ minimum_: minimum_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Pads a `tf.Tensor` using mirror padding. + * + * This operation implements the `REFLECT` and `SYMMETRIC` modes of pad. + * + * ```js + * const x = tf.range(0, 9).reshape([1, 1, 3, 3]); + * x.mirrorPad([[0, 0], [0, 0], [2, 2], [2, 2]], 'reflect').print(); + * ``` + * @param x The tensor to pad. + * @param paddings An array of length `R` (the rank of the tensor), where + * each element is a length-2 tuple of ints `[padBefore, padAfter]`, + * specifying how much to pad along each dimension of the tensor. + * In "reflect" mode, the padded regions do not include the borders, + * while in "symmetric" mode the padded regions do include the borders. + * For example, if the input is `[1, 2, 3]` and paddings is `[0, 2]`, + * then the output is `[1, 2, 3, 2, 1]` in "reflect" mode, and + * `[1, 2, 3, 3, 2]` in "symmetric" mode. + * If `mode` is "reflect" then both `paddings[D, 0]` and `paddings[D, 1]` + * must be no greater than `x.shape[D] - 1`. If mode is "symmetric" + * then both `paddings[D, 0]` and `paddings[D, 1]` must be no greater than + * `x.shape[D]` + * @param mode String to specify padding mode. Can be `'reflect' | 'symmetric'` + */ + /** @doc {heading: 'Tensors', subheading: 'Transformations'} */ + function mirrorPad_(x, paddings, mode) { + assert(mode === 'reflect' || mode === 'symmetric', function () { return "Invalid mode. Mode must be either reflect or symmetric. " + + ("Got " + mode + "."); }); + var $x = convertToTensor(x, 'x', 'mirrorPad'); + if ($x.rank === 0) { + throw new Error('mirrorPad(scalar) is not defined. ' + + 'Pass non-scalar to mirrorPad'); + } + assert(paddings.length === $x.rank, function () { return "Padding doesn't match input. Must be " + $x.rank + ". " + + ("Got " + paddings.length + "."); }); + var shapeOffset = mode === 'reflect' ? 1 : 0; + var _loop_1 = function (i) { + assert(paddings[i].length === 2, function () { return "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] <= $x.shape[i] - shapeOffset, function () { return "Padding in dimension " + i + " cannot be greater than or equal " + + ("to " + ($x.shape[i] - shapeOffset) + " or less than 0 for input of ") + + ("shape " + $x.shape); }); + }; + for (var i = 0; i < $x.rank; i++) { + _loop_1(i); + } + var attrs = { paddings: paddings, mode: mode }; + var inputs = { x: $x }; + return ENGINE.runKernel(MirrorPad, inputs, attrs); + } + var mirrorPad = op({ mirrorPad_: mirrorPad_ }); + + /** + * Returns the mod of a and b element-wise. + * `floor(x / y) * y + mod(x, y) = x` + * Supports broadcasting. + * + * We also expose `tf.modStrict` which has the same signature as this op and + * asserts that `a` and `b` are the same shape (does not broadcast). + * + * ```js + * const a = tf.tensor1d([1, 4, 3, 16]); + * const b = tf.tensor1d([1, 2, 9, 4]); + * + * a.mod(b).print(); // or tf.mod(a, b) + * ``` + * + * ```js + * // Broadcast a mod b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(5); + * + * a.mod(b).print(); // or tf.mod(a, b) + * ``` + * + * @param a The first tensor. + * @param b The second tensor. Must have the same type as `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function mod_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'mod'); + var $b = convertToTensor(b, 'b', 'mod'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(Mod, inputs); + } + var mod = op({ mod_: mod_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes square of `x` element-wise: `x ^ 2` + * + * ```js + * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]); + * + * x.square().print(); // or tf.square(x) + * ``` + * @param x The input Tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function square_(x) { + var $x = convertToTensor(x, 'x', 'square'); + var attrs = {}; + return ENGINE.runKernel('Square', { x: $x }, attrs); + } + var square = op({ square_: square_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Calculates the mean and variance of `x`. The mean and variance are + * calculated by aggregating the contents of `x` across `axes`. If `x` is + * 1-D and `axes = [0]` this is just the mean and variance of a vector. + * + * @param x The input tensor. + * @param axis The dimension(s) along with to compute mean and + * variance. By default it reduces all dimensions. + * @param keepDims If true, the moments have the same dimensionality as the + * input. + * @return An object with two keys: `mean` and `variance`. + * + * @doc {heading: 'Operations', subheading: 'Normalization'} + */ + function moments_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + x = convertToTensor(x, 'x', 'moments'); + var axes = parseAxisParam(axis, x.shape); + var xMean = mean(x, axes, keepDims); + var keepDimsShape = xMean.shape; + if (!keepDims) { + keepDimsShape = expandShapeToKeepDim(xMean.shape, axes); + } + var devSquared = square(sub(cast(x, 'float32'), reshape(xMean, keepDimsShape))); + var variance = mean(devSquared, axes, keepDims); + return { mean: xMean, variance: variance }; + } + var moments = op({ moments_: moments_ }); + + /** + * Computes the next states and outputs of a stack of LSTMCells. + * + * Each cell output is used as input to the next cell. + * + * Returns `[cellState, cellOutput]`. + * + * Derived from tf.contrib.rn.MultiRNNCell. + * + * @param lstmCells Array of LSTMCell functions. + * @param data The input to the cell. + * @param c Array of previous cell states. + * @param h Array of previous cell outputs. + * + * @doc {heading: 'Operations', subheading: 'RNN'} + */ + function multiRNNCell_(lstmCells, data, c, h) { + var $data = convertToTensor(data, 'data', 'multiRNNCell'); + var $c = convertToTensorArray(c, 'c', 'multiRNNCell'); + var $h = convertToTensorArray(h, 'h', 'multiRNNCell'); + var input = $data; + var newStates = []; + for (var i = 0; i < lstmCells.length; i++) { + var output = lstmCells[i](input, $c[i], $h[i]); + newStates.push(output[0]); + newStates.push(output[1]); + input = output[1]; + } + var newC = []; + var newH = []; + for (var i = 0; i < newStates.length; i += 2) { + newC.push(newStates[i]); + newH.push(newStates[i + 1]); + } + return [newC, newH]; + } + var multiRNNCell = op({ multiRNNCell_: multiRNNCell_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with values drawn from a multinomial distribution. + * + * ```js + * const probs = tf.tensor([.75, .25]); + * tf.multinomial(probs, 3).print(); + * ``` + * + * @param logits 1D array with unnormalized log-probabilities, or + * 2D array of shape `[batchSize, numOutcomes]`. See the `normalized` + * parameter. + * @param numSamples Number of samples to draw for each row slice. + * @param seed The seed number. + * @param normalized Whether the provided `logits` are normalized true + * probabilities (sum to 1). Defaults to false. + * @return 1D array of shape `[numSamples]`, or 2D array of shape + * `[batchSize, numSamples]`, depending on the rank of the input. + * + * @doc {heading: 'Tensors', subheading: 'Random'} + */ + function multinomial_(logits, numSamples, seed, normalized) { + if (normalized === void 0) { normalized = false; } + var $logits = convertToTensor(logits, 'logits', 'multinomial'); + var numOutcomes = $logits.size; + var origRank = $logits.rank; + if (numOutcomes < 2) { + throw new Error("Error in multinomial: you need at least 2 outcomes, but got " + + (numOutcomes + ".")); + } + if (origRank > 2) { + throw new Error("Rank of probabilities must be 1 or 2, but is " + origRank); + } + // TODO(lina128): Investigate correct seed behavior. The code seems not allow + // setting see to 0. + seed = seed || Math.random(); + // The kernel only accepts (and returns) rank 2 tensors. + var logits2D = origRank === 1 ? reshape($logits, [1, -1]) : $logits; + var inputs = { logits: logits2D }; + var attrs = { numSamples: numSamples, seed: seed, normalized: normalized }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var res = ENGINE.runKernel(Multinomial, inputs, attrs); + // tslint:disable-next-line:no-unnecessary-type-assertion + return origRank === 1 ? reshape(res, [res.size]) : res; + } + var multinomial = op({ multinomial_: multinomial_ }); + + /** + * Returns the truth value of (a != b) element-wise. Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([0, 2, 3]); + * + * a.notEqual(b).print(); + * ``` + * @param a The first input tensor. + * @param b The second input tensor. Must have the same dtype as `a`. + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function notEqual_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'notEqual', 'string_or_numeric'); + var $b = convertToTensor(b, 'b', 'notEqual', 'string_or_numeric'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + return ENGINE.runKernel(NotEqual, inputs); + } + var notEqual = op({ notEqual_: notEqual_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take + * value `onValue` (defaults to 1), while all other locations take value + * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank + * `R+1` with the last axis of size `depth`. + * + * ```js + * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print(); + * ``` + * + * @param indices `tf.Tensor` of indices with dtype `int32`. + * @param depth The depth of the one hot dimension. + * @param onValue A number used to fill in the output when the index matches + * the location. + * @param offValue A number used to fill in the output when the index does + * not match the location. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function oneHot_(indices, depth, onValue, offValue) { + if (onValue === void 0) { onValue = 1; } + if (offValue === void 0) { offValue = 0; } + if (depth < 2) { + throw new Error("Error in oneHot: depth must be >=2, but it is " + depth); + } + var $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32'); + var inputs = { indices: $indices }; + var attrs = { depth: depth, onValue: onValue, offValue: offValue }; + return ENGINE.runKernel(OneHot, inputs, attrs); + } + var oneHot = op({ oneHot_: oneHot_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the + * given tensor. + * + * ```js + * const x = tf.tensor([1, 2]); + * tf.onesLike(x).print(); + * ``` + * @param x A tensor. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function onesLike_(x) { + var $x = convertToTensor(x, 'x', 'onesLike'); + var inputs = { x: $x }; + return ENGINE.runKernel(OnesLike, inputs); + } + var onesLike = op({ onesLike_: onesLike_ }); + + /** + * Computes the outer product of two vectors, `v1` and `v2`. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * const b = tf.tensor1d([3, 4, 5]); + * + * tf.outerProduct(a, b).print(); + * ``` + * @param v1 The first vector in the outer product operation. + * @param v2 The second vector in the outer product operation. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function outerProduct_(v1, v2) { + var $v1 = convertToTensor(v1, 'v1', 'outerProduct'); + var $v2 = convertToTensor(v2, 'v2', 'outerProduct'); + assert($v1.rank === 1 && $v2.rank === 1, function () { return "Error in outerProduct: inputs must be rank 1, but got ranks " + + ($v1.rank + " and " + $v2.rank + "."); }); + var v12D = reshape($v1, [-1, 1]); + var v22D = reshape($v2, [1, -1]); + return matMul$1(v12D, v22D); + } + var outerProduct = op({ outerProduct_: outerProduct_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Pads a `tf.Tensor` with a given value and paddings. + * + * This operation implements `CONSTANT` mode. For `REFLECT` and `SYMMETRIC`, + * refer to `tf.mirrorPad` + * + * Also available are stricter rank-specific methods with the same signature + * as this method that assert that `paddings` is of given length. + * - `tf.pad1d` + * - `tf.pad2d` + * - `tf.pad3d` + * - `tf.pad4d` + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * x.pad([[1, 2]]).print(); + * ``` + * @param x The tensor to pad. + * @param paddings An array of length `R` (the rank of the tensor), where + * each element is a length-2 tuple of ints `[padBefore, padAfter]`, + * specifying how much to pad along each dimension of the tensor. + * @param constantValue The pad value to use. Defaults to 0. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function pad_(x, paddings, constantValue) { + if (constantValue === void 0) { constantValue = 0; } + var $x = convertToTensor(x, 'x', 'pad'); + if ($x.rank === 0) { + throw new Error('pad(scalar) is not defined. Pass non-scalar to pad'); + } + var attrs = { paddings: paddings, constantValue: constantValue }; + var inputs = { x: $x }; + return ENGINE.runKernel(PadV2, inputs, attrs); + } + var pad = op({ pad_: pad_ }); + + /** + * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details. + */ + function pad1d_(x, paddings, constantValue) { + if (constantValue === void 0) { constantValue = 0; } + assert(paddings.length === 2, function () { return 'Invalid number of paddings. Must be length of 2.'; }); + return pad(x, [paddings], constantValue); + } + var pad1d = op({ pad1d_: pad1d_ }); + + /** + * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details. + */ + function pad2d_(x, paddings, constantValue) { + if (constantValue === void 0) { constantValue = 0; } + assert(paddings.length === 2 && paddings[0].length === 2 && + paddings[1].length === 2, function () { return 'Invalid number of paddings. Must be length of 2 each.'; }); + return pad(x, paddings, constantValue); + } + var pad2d = op({ pad2d_: pad2d_ }); + + /** + * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details. + */ + function pad3d_(x, paddings, constantValue) { + if (constantValue === void 0) { constantValue = 0; } + assert(paddings.length === 3 && paddings[0].length === 2 && + paddings[1].length === 2 && paddings[2].length === 2, function () { return 'Invalid number of paddings. Must be length of 2 each.'; }); + return pad(x, paddings, constantValue); + } + var pad3d = op({ pad3d_: pad3d_ }); + + /** + * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details. + */ + function pad4d_(x, paddings, constantValue) { + if (constantValue === void 0) { constantValue = 0; } + assert(paddings.length === 4 && paddings[0].length === 2 && + paddings[1].length === 2 && paddings[2].length === 2 && + paddings[3].length === 2, function () { return 'Invalid number of paddings. Must be length of 2 each.'; }); + return pad(x, paddings, constantValue); + } + var pad4d = op({ pad4d_: pad4d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * This operation divides "spatial" dimensions `[1, ..., M]` of the input into + * a grid of blocks of shape `blockShape`, and interleaves these blocks with + * the "batch" dimension (0) such that in the output, the spatial + * dimensions `[1, ..., M]` correspond to the position within the grid, + * and the batch dimension combines both the position within a spatial block + * and the original batch position. Prior to division into blocks, + * the spatial dimensions of the input are optionally zero padded + * according to `paddings`. See below for a precise description. + * + * ```js + * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]); + * const blockShape = [2, 2]; + * const paddings = [[0, 0], [0, 0]]; + * + * x.spaceToBatchND(blockShape, paddings).print(); + * ``` + * + * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape + + * remainingShape`, where spatialShape has `M` dimensions. + * @param blockShape A 1-D array. Must have shape `[M]`, all values must + * be >= 1. + * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >= + * 0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad + * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It + * is required that + * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0` + * + * This operation is equivalent to the following steps: + * + * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input + * according to `paddings` to produce `padded` of shape paddedShape. + * + * 2. Reshape `padded` to `reshapedPadded` of shape: + * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ..., + * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape` + * + * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded` + * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ..., + * paddedShape[M] / blockShape[M-1]] + remainingShape` + * + * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the + * batch dimension, producing an output tensor of shape: + * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ..., + * paddedShape[M] / blockShape[M-1]] + remainingShape` + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function spaceToBatchND_(x, blockShape, paddings) { + var $x = convertToTensor(x, 'x', 'spaceToBatchND'); + assert($x.rank >= 1 + blockShape.length, function () { return "input rank " + $x.rank + " should be > than [blockShape] " + blockShape.length; }); + assert(paddings.length === blockShape.length, function () { return "paddings.shape[0] " + paddings.length + " must be equal to [blockShape] " + blockShape.length; }); + assert($x.shape.reduce(function (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), function () { return "input spatial dimensions " + $x.shape.slice(1) + " with paddings " + paddings.toString() + " must be divisible by blockShapes " + blockShape.toString(); }); + var inputs = { x: $x }; + var attrs = { blockShape: blockShape, paddings: paddings }; + return ENGINE.runKernel(SpaceToBatchND, inputs, attrs); + } + var spaceToBatchND = op({ spaceToBatchND_: spaceToBatchND_ }); + + /** + * Performs an N-D pooling operation + * + * @param input The input tensor, of rank 4 or rank 3 of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param windowShape The filter size: `[filterHeight, filterWidth]`. If + * `filterSize` is a single number, then `filterHeight == filterWidth`. + * @param poolingType The type of pooling, either 'max' or 'avg'. + * @param pad The type of padding algorithm: + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_guides/python/nn#Convolution]( + * https://www.tensorflow.org/api_guides/python/nn#Convolution) + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If + * `strides` is a single number, then `strideHeight == strideWidth`. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function pool_(input, windowShape, poolingType, pad, dilations, strides, dimRoundingMode) { + if (dilations == null) { + dilations = [1, 1]; + } + if (strides == null) { + strides = 1; + } + if (pad === 0) { + pad = 'valid'; + } + var $x = convertToTensor(input, 'x', 'maxPool'); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in pool: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + var convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad); + var dilation = [convInfo.dilationHeight, convInfo.dilationWidth]; + // The following implementation does batchToSpace(pool(spaceToBatch(x))) + // whenever dilation > 1 since the TF kernels do not support dilation > 1. + // tslint:disable-next-line:max-line-length + // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037 + var basePadding; + if (pad === 'same') { + basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation); + } + else { + basePadding = [[0, 0], [0, 0]]; + } + var isDilationOne = dilation[0] === 1 && dilation[1] === 1; + var _a = __read(requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), 2), adjustedPadding = _a[0], adjustedCrops = _a[1]; + var convertedPad = isDilationOne ? pad : 'valid'; + var convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding); + var forwardOp = poolingType === 'avg' ? + function () { return avgPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); } : + function () { return maxPool(convertedX, windowShape, strides, convertedPad, dimRoundingMode); }; + var y = forwardOp(); + var res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops); + if (reshapedTo4D) { + return reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return res; + } + // Helper function to compute crops and paddings for pool with dilation > 1. + // tslint:disable-next-line:max-line-length + // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184 + function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) { + var padStart = basePadding.map(function (b) { return b[0]; }); + var origPadEnd = basePadding.map(function (b) { return b[1]; }); + var fullInputShape = inputShape.concat(padStart, origPadEnd); + var padEndExtra = blockShape.map(function (b, i) { return (b - fullInputShape[i] % b) % b; }); + var padEnd = origPadEnd.map(function (s, i) { return s + padEndExtra[i]; }); + var paddings = blockShape.map(function (_, i) { return [padStart[i], padEnd[i]]; }); + var crops = blockShape.map(function (_, i) { return [0, padEndExtra[i]]; }); + return [paddings, crops]; + } + // Helper function to compute base paddings for pool with dilation > 1. + // tslint:disable-next-line:max-line-length + // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524 + function withSpaceToBatchBasePaddings(filterShape, dilation) { + // Spatial dimensions of the filters and the upsampled filters in which we + // introduce (rate - 1) zeros between consecutive filter values. + var dilatedFilterShape = filterShape.map(function (s, i) { + return s + (s - 1) * (dilation[i] - 1); + }); + var padExtraShape = dilatedFilterShape.map(function (s) { return s - 1; }); + // When padding is odd, we pad more at end, following the same + // convention as conv2d. + var padExtraStart = padExtraShape.map(function (s) { return Math.floor(s / 2); }); + var padExtraEnd = padExtraShape.map(function (s, i) { return s - padExtraStart[i]; }); + return padExtraShape.map(function (_, i) { + return [padExtraStart[i], padExtraEnd[i]]; + }); + } + var pool = op({ pool_: pool_ }); + + /** + * Computes the power of one `tf.Tensor` to another. Supports broadcasting. + * + * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for + * corresponding elements in x and y. The result's dtype will be the upcasted + * type of the `base` and `exp` dtypes. + * + * ```js + * const a = tf.tensor([[2, 3], [4, 5]]) + * const b = tf.tensor([[1, 2], [3, 0]]).toInt(); + * + * a.pow(b).print(); // or tf.pow(a, b) + * ``` + * + * ```js + * const a = tf.tensor([[1, 2], [3, 4]]) + * const b = tf.tensor(2).toInt(); + * + * a.pow(b).print(); // or tf.pow(a, b) + * ``` + * We also expose `powStrict` which has the same signature as this op and + * asserts that `base` and `exp` are the same shape (does not broadcast). + * + * @param base The base `tf.Tensor` to pow element-wise. + * @param exp The exponent `tf.Tensor` to pow element-wise. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function pow_(base, exp) { + var _a; + var $base = convertToTensor(base, 'base', 'pow'); + var $exp = convertToTensor(exp, 'exp', 'pow'); + _a = __read(makeTypesMatch($base, $exp), 2), $base = _a[0], $exp = _a[1]; + var inputs = { a: $base, b: $exp }; + return ENGINE.runKernel(Pow, inputs); + } + var pow = op({ pow_: pow_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes leaky rectified linear element-wise with parametric alphas. + * + * `x < 0 ? alpha * x : f(x) = x` + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 4]); + * const alpha = tf.scalar(0.1); + * + * x.prelu(alpha).print(); // or tf.prelu(x, alpha) + * ``` + * @param x The input tensor. + * @param alpha Scaling factor for negative values. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function prelu_(x, alpha) { + var $x = convertToTensor(x, 'x', 'prelu'); + var $alpha = convertToTensor(alpha, 'alpha', 'prelu'); + var inputs = { x: $x, alpha: $alpha }; + return ENGINE.runKernel(Prelu, inputs); + } + var prelu = op({ prelu_: prelu_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Prints information about the `tf.Tensor` including its data. + * + * ```js + * const verbose = true; + * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose); + * ``` + * @param x The tensor to be printed. + * @param verbose Whether to print verbose information about the ` Tensor`, + * including dtype and size. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function print(x, verbose) { + if (verbose === void 0) { verbose = false; } + console.log(x.toString(verbose)); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the product of elements across dimensions of a `tf.Tensor`. + * + * Reduces the input along the dimensions given in `axes`. Unless `keepDims` + * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in + * `axes`. If `keepDims` is true, the reduced dimensions are retained with + * length 1. If `axes` has no entries, all dimensions are reduced, and a + * `tf.Tensor` with a single element is returned. + * + * ```js + * const x = tf.tensor1d([1, 2, 3]); + * + * x.prod().print(); // or tf.prod(x) + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.prod(axis).print(); // or tf.prod(x, axis) + * ``` + * + * @param x The input tensor to compute the product over. If the dtype is `bool` + * it will be converted to `int32` and the output dtype will be `int32`. + * @param axis The dimension(s) to reduce. By default it reduces + * all dimensions. + * @param keepDims If true, retains reduced dimensions with size 1. + * + * @doc {heading: 'Operations', subheading: 'Reduction'} + */ + function prod_(x, axis, keepDims) { + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + var $x = convertToTensor(x, 'x', 'prod'); + if ($x.dtype === 'bool') { + // bool is not an allowed type for the underlying kernel. + $x = cast($x, 'int32'); + } + var inputs = { x: $x }; + var attrs = { axis: axis, keepDims: keepDims }; + return ENGINE.runKernel(Prod, inputs, attrs); + } + var prod = op({ prod_: prod_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with values sampled from a random number generator + * function defined by the user. + * + * @param shape An array of integers defining the output tensor shape. + * @param randFunction A random number generator function which is called + * for each element in the output tensor. + * @param dtype The data type of the output tensor. Defaults to 'float32'. + * + * @doc {heading: 'Tensors', subheading: 'Random'} + */ + function rand_(shape, randFunction, dtype) { + var size = sizeFromShape(shape); + var values = null; + if (dtype == null || dtype === 'float32') { + values = new Float32Array(size); + } + else if (dtype === 'int32') { + values = new Int32Array(size); + } + else if (dtype === 'bool') { + values = new Uint8Array(size); + } + else { + throw new Error("Unknown data type " + dtype); + } + for (var i = 0; i < size; i++) { + values[i] = randFunction(); + } + return ENGINE.makeTensor(values, shape, dtype); + } + var rand = op({ rand_: rand_ }); + + var commonjsGlobal = typeof globalThis !== 'undefined' ? globalThis : typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {}; + function getAugmentedNamespace(n) { + if (n.__esModule) + return n; + var a = Object.defineProperty({}, '__esModule', { value: true }); + Object.keys(n).forEach(function (k) { + var d = Object.getOwnPropertyDescriptor(n, k); + Object.defineProperty(a, k, d.get ? d : { + enumerable: true, + get: function () { + return n[k]; + } + }); + }); + return a; + } + function createCommonjsModule(fn) { + var module = { exports: {} }; + return fn(module, module.exports), module.exports; + } + + var alea = createCommonjsModule(function (module) { + // A port of an algorithm by Johannes Baagøe , 2010 + // http://baagoe.com/en/RandomMusings/javascript/ + // https://github.com/nquinlan/better-random-numbers-for-javascript-mirror + // Original work is under MIT license - + // Copyright (C) 2010 by Johannes Baagøe + // + // Permission is hereby granted, free of charge, to any person obtaining a copy + // of this software and associated documentation files (the "Software"), to deal + // in the Software without restriction, including without limitation the rights + // to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + // copies of the Software, and to permit persons to whom the Software is + // furnished to do so, subject to the following conditions: + // + // The above copyright notice and this permission notice shall be included in + // all copies or substantial portions of the Software. + // + // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN + // THE SOFTWARE. + (function (global, module, define) { + function Alea(seed) { + var me = this, mash = Mash(); + me.next = function () { + var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32 + me.s0 = me.s1; + me.s1 = me.s2; + return me.s2 = t - (me.c = t | 0); + }; + // Apply the seeding algorithm from Baagoe. + 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() * 0x100000000) | 0; }; + prng.double = function () { + return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53 + }; + prng.quick = prng; + if (state) { + if (typeof (state) == 'object') + copy(state, xg); + prng.state = function () { return copy(xg, {}); }; + } + return prng; + } + function Mash() { + var n = 0xefc8249d; + 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 * 0x100000000; // 2^32 + } + return (n >>> 0) * 2.3283064365386963e-10; // 2^-32 + }; + return mash; + } + if (module && module.exports) { + module.exports = impl; + } + else if (define && define.amd) { + define(function () { return impl; }); + } + else { + this.alea = impl; + } + })(commonjsGlobal, module, // present in node.js + (typeof undefined) == 'function' // present with an AMD loader + ); + }); + + var xor128 = createCommonjsModule(function (module) { + // A Javascript implementaion of the "xor128" prng algorithm by + // George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper + (function (global, module, define) { + function XorGen(seed) { + var me = this, strseed = ''; + me.x = 0; + me.y = 0; + me.z = 0; + me.w = 0; + // Set up generator function. + 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)) { + // Integer seed. + me.x = seed; + } + else { + // String seed. + strseed += seed; + } + // Mix in string seed, then discard an initial batch of 64 values. + 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) / 0x100000000; }; + prng.double = function () { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, 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 (module && module.exports) { + module.exports = impl; + } + else if (define && define.amd) { + define(function () { return impl; }); + } + else { + this.xor128 = impl; + } + })(commonjsGlobal, module, // present in node.js + (typeof undefined) == 'function' // present with an AMD loader + ); + }); + + var xorwow = createCommonjsModule(function (module) { + // A Javascript implementaion of the "xorwow" prng algorithm by + // George Marsaglia. See http://www.jstatsoft.org/v08/i14/paper + (function (global, module, define) { + function XorGen(seed) { + var me = this, strseed = ''; + // Set up generator function. + 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)) { + // Integer seed. + me.x = seed; + } + else { + // String seed. + strseed += seed; + } + // Mix in string seed, then discard an initial batch of 64 values. + 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) / 0x100000000; }; + prng.double = function () { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, 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 (module && module.exports) { + module.exports = impl; + } + else if (define && define.amd) { + define(function () { return impl; }); + } + else { + this.xorwow = impl; + } + })(commonjsGlobal, module, // present in node.js + (typeof undefined) == 'function' // present with an AMD loader + ); + }); + + var xorshift7 = createCommonjsModule(function (module) { + // A Javascript implementaion of the "xorshift7" algorithm by + // François Panneton and Pierre L'ecuyer: + // "On the Xorgshift Random Number Generators" + // http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf + (function (global, module, define) { + function XorGen(seed) { + var me = this; + // Set up generator function. + me.next = function () { + // Update xor generator. + var X = me.x, i = me.i, t, v; + 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(me, seed) { + var j, X = []; + if (seed === (seed | 0)) { + // Seed state array using a 32-bit integer. + X[0] = seed; + } + else { + // Seed state using a string. + seed = '' + seed; + for (j = 0; j < seed.length; ++j) { + X[j & 7] = (X[j & 7] << 15) ^ + (seed.charCodeAt(j) + X[(j + 1) & 7] << 13); + } + } + // Enforce an array length of 8, not all zeroes. + while (X.length < 8) + X.push(0); + for (j = 0; j < 8 && X[j] === 0; ++j) + ; + if (j == 8) + X[7] = -1; + me.x = X; + me.i = 0; + // Discard an initial 256 values. + for (j = 256; j > 0; --j) { + me.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) / 0x100000000; }; + prng.double = function () { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, 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 (module && module.exports) { + module.exports = impl; + } + else if (define && define.amd) { + define(function () { return impl; }); + } + else { + this.xorshift7 = impl; + } + })(commonjsGlobal, module, // present in node.js + (typeof undefined) == 'function' // present with an AMD loader + ); + }); + + var xor4096 = createCommonjsModule(function (module) { + // A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm. + // + // This fast non-cryptographic random number generator is designed for + // use in Monte-Carlo algorithms. It combines a long-period xorshift + // generator with a Weyl generator, and it passes all common batteries + // of stasticial tests for randomness while consuming only a few nanoseconds + // for each prng generated. For background on the generator, see Brent's + // paper: "Some long-period random number generators using shifts and xors." + // http://arxiv.org/pdf/1004.3115v1.pdf + // + // Usage: + // + // var xor4096 = require('xor4096'); + // random = xor4096(1); // Seed with int32 or string. + // assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits. + // assert.equal(random.int32(), 1806534897); // signed int32, 32 bits. + // + // For nonzero numeric keys, this impelementation provides a sequence + // identical to that by Brent's xorgens 3 implementaion in C. This + // implementation also provides for initalizing the generator with + // string seeds, or for saving and restoring the state of the generator. + // + // On Chrome, this prng benchmarks about 2.1 times slower than + // Javascript's built-in Math.random(). + (function (global, module, define) { + function XorGen(seed) { + var me = this; + // Set up generator function. + me.next = function () { + var w = me.w, X = me.X, i = me.i, t, v; + // Update Weyl generator. + me.w = w = (w + 0x61c88647) | 0; + // Update xor generator. + v = X[(i + 34) & 127]; + t = X[i = ((i + 1) & 127)]; + v ^= v << 13; + t ^= t << 17; + v ^= v >>> 15; + t ^= t >>> 12; + // Update Xor generator array state. + v = X[i] = v ^ t; + me.i = i; + // Result is the combination. + return (v + (w ^ (w >>> 16))) | 0; + }; + function init(me, seed) { + var t, v, i, j, w, X = [], limit = 128; + if (seed === (seed | 0)) { + // Numeric seeds initialize v, which is used to generates X. + v = seed; + seed = null; + } + else { + // String seeds are mixed into v and X one character at a time. + seed = seed + '\0'; + v = 0; + limit = Math.max(limit, seed.length); + } + // Initialize circular array and weyl value. + for (i = 0, j = -32; j < limit; ++j) { + // Put the unicode characters into the array, and shuffle them. + if (seed) + v ^= seed.charCodeAt((j + 32) % seed.length); + // After 32 shuffles, take v as the starting w value. + if (j === 0) + w = v; + v ^= v << 10; + v ^= v >>> 15; + v ^= v << 4; + v ^= v >>> 13; + if (j >= 0) { + w = (w + 0x61c88647) | 0; // Weyl. + t = (X[j & 127] ^= (v + w)); // Combine xor and weyl to init array. + i = (0 == t) ? i + 1 : 0; // Count zeroes. + } + } + // We have detected all zeroes; make the key nonzero. + if (i >= 128) { + X[(seed && seed.length || 0) & 127] = -1; + } + // Run the generator 512 times to further mix the state before using it. + // Factoring this as a function slows the main generator, so it is just + // unrolled here. The weyl generator is not advanced while warming up. + 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; + } + // Storing state as object members is faster than using closure variables. + me.w = w; + me.X = X; + me.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) / 0x100000000; }; + prng.double = function () { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, 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 (module && module.exports) { + module.exports = impl; + } + else if (define && define.amd) { + define(function () { return impl; }); + } + else { + this.xor4096 = impl; + } + })(commonjsGlobal, // window object or global + module, // present in node.js + (typeof undefined) == 'function' // present with an AMD loader + ); + }); + + var tychei = createCommonjsModule(function (module) { + // A Javascript implementaion of the "Tyche-i" prng algorithm by + // Samuel Neves and Filipe Araujo. + // See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf + (function (global, module, define) { + function XorGen(seed) { + var me = this, strseed = ''; + // Set up generator function. + 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; + }; + /* The following is non-inverted tyche, which has better internal + * bit diffusion, but which is about 25% slower than tyche-i in JS. + me.next = function() { + var a = me.a, b = me.b, c = me.c, d = me.d; + a = (me.a + me.b | 0) >>> 0; + d = me.d ^ a; d = d << 16 ^ d >>> 16; + c = me.c + d | 0; + b = me.b ^ c; b = b << 12 ^ d >>> 20; + me.a = a = a + b | 0; + d = d ^ a; me.d = d = d << 8 ^ d >>> 24; + me.c = c = c + d | 0; + b = b ^ c; + return me.b = (b << 7 ^ b >>> 25); + } + */ + me.a = 0; + me.b = 0; + me.c = 2654435769 | 0; + me.d = 1367130551; + if (seed === Math.floor(seed)) { + // Integer seed. + me.a = (seed / 0x100000000) | 0; + me.b = seed | 0; + } + else { + // String seed. + strseed += seed; + } + // Mix in string seed, then discard an initial batch of 64 values. + 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) / 0x100000000; }; + prng.double = function () { + do { + var top = xg.next() >>> 11, bot = (xg.next() >>> 0) / 0x100000000, 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 (module && module.exports) { + module.exports = impl; + } + else if (define && define.amd) { + define(function () { return impl; }); + } + else { + this.tychei = impl; + } + })(commonjsGlobal, module, // present in node.js + (typeof undefined) == 'function' // present with an AMD loader + ); + }); + + var _nodeResolve_empty = {}; + + var _nodeResolve_empty$1 = { + __proto__: null, + 'default': _nodeResolve_empty + }; + + var require$$0 = /*@__PURE__*/ getAugmentedNamespace(_nodeResolve_empty$1); + + /* + Copyright 2019 David Bau. + + Permission is hereby granted, free of charge, to any person obtaining + a copy of this software and associated documentation files (the + "Software"), to deal in the Software without restriction, including + without limitation the rights to use, copy, modify, merge, publish, + distribute, sublicense, and/or sell copies of the Software, and to + permit persons to whom the Software is furnished to do so, subject to + the following conditions: + + The above copyright notice and this permission notice shall be + included in all copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. + IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY + CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, + TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE + SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + */ + var seedrandom$1 = createCommonjsModule(function (module) { + (function (global, pool, math) { + // + // The following constants are related to IEEE 754 limits. + // + var width = 256, // each RC4 output is 0 <= x < 256 + chunks = 6, // at least six RC4 outputs for each double + digits = 52, // there are 52 significant digits in a double + rngname = 'random', // rngname: name for Math.random and Math.seedrandom + startdenom = math.pow(width, chunks), significance = math.pow(2, digits), overflow = significance * 2, mask = width - 1, nodecrypto; // node.js crypto module, initialized at the bottom. + // + // seedrandom() + // This is the seedrandom function described above. + // + function seedrandom(seed, options, callback) { + var key = []; + options = (options == true) ? { entropy: true } : (options || {}); + // Flatten the seed string or build one from local entropy if needed. + var shortseed = mixkey(flatten(options.entropy ? [seed, tostring(pool)] : + (seed == null) ? autoseed() : seed, 3), key); + // Use the seed to initialize an ARC4 generator. + var arc4 = new ARC4(key); + // This function returns a random double in [0, 1) that contains + // randomness in every bit of the mantissa of the IEEE 754 value. + var prng = function () { + var n = arc4.g(chunks), // Start with a numerator n < 2 ^ 48 + d = startdenom, // and denominator d = 2 ^ 48. + x = 0; // and no 'extra last byte'. + while (n < significance) { // Fill up all significant digits by + n = (n + x) * width; // shifting numerator and + d *= width; // denominator and generating a + x = arc4.g(1); // new least-significant-byte. + } + while (n >= overflow) { // To avoid rounding up, before adding + n /= 2; // last byte, shift everything + d /= 2; // right using integer math until + x >>>= 1; // we have exactly the desired bits. + } + return (n + x) / d; // Form the number within [0, 1). + }; + prng.int32 = function () { return arc4.g(4) | 0; }; + prng.quick = function () { return arc4.g(4) / 0x100000000; }; + prng.double = prng; + // Mix the randomness into accumulated entropy. + mixkey(tostring(arc4.S), pool); + // Calling convention: what to return as a function of prng, seed, is_math. + return (options.pass || callback || + function (prng, seed, is_math_call, state) { + if (state) { + // Load the arc4 state from the given state if it has an S array. + if (state.S) { + copy(state, arc4); + } + // Only provide the .state method if requested via options.state. + prng.state = function () { return copy(arc4, {}); }; + } + // If called as a method of Math (Math.seedrandom()), mutate + // Math.random because that is how seedrandom.js has worked since v1.0. + if (is_math_call) { + math[rngname] = prng; + return seed; + } + // Otherwise, it is a newer calling convention, so return the + // prng directly. + else + return prng; + })(prng, shortseed, 'global' in options ? options.global : (this == math), options.state); + } + // + // ARC4 + // + // An ARC4 implementation. The constructor takes a key in the form of + // an array of at most (width) integers that should be 0 <= x < (width). + // + // The g(count) method returns a pseudorandom integer that concatenates + // the next (count) outputs from ARC4. Its return value is a number x + // that is in the range 0 <= x < (width ^ count). + // + function ARC4(key) { + var t, keylen = key.length, me = this, i = 0, j = me.i = me.j = 0, s = me.S = []; + // The empty key [] is treated as [0]. + if (!keylen) { + key = [keylen++]; + } + // Set up S using the standard key scheduling algorithm. + 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; + } + // The "g" method returns the next (count) outputs as one number. + (me.g = function (count) { + // Using instance members instead of closure state nearly doubles speed. + var t, r = 0, i = me.i, j = me.j, s = me.S; + while (count--) { + t = s[i = mask & (i + 1)]; + r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))]; + } + me.i = i; + me.j = j; + return r; + // For robust unpredictability, the function call below automatically + // discards an initial batch of values. This is called RC4-drop[256]. + // See http://google.com/search?q=rsa+fluhrer+response&btnI + })(width); + } + // + // copy() + // Copies internal state of ARC4 to or from a plain object. + // + function copy(f, t) { + t.i = f.i; + t.j = f.j; + t.S = f.S.slice(); + return t; + } + // + // flatten() + // Converts an object tree to nested arrays of strings. + // + function flatten(obj, depth) { + var result = [], typ = (typeof obj), prop; + if (depth && typ == 'object') { + for (prop in obj) { + try { + result.push(flatten(obj[prop], depth - 1)); + } + catch (e) { } + } + } + return (result.length ? result : typ == 'string' ? obj : obj + '\0'); + } + // + // mixkey() + // Mixes a string seed into a key that is an array of integers, and + // returns a shortened string seed that is equivalent to the result key. + // + 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); + } + // + // autoseed() + // Returns an object for autoseeding, using window.crypto and Node crypto + // module if available. + // + function autoseed() { + try { + var out; + if (nodecrypto && (out = nodecrypto.randomBytes)) { + // The use of 'out' to remember randomBytes makes tight minified code. + out = out(width); + } + else { + out = new Uint8Array(width); + (global.crypto || global.msCrypto).getRandomValues(out); + } + return tostring(out); + } + catch (e) { + var browser = global.navigator, plugins = browser && browser.plugins; + return [+new Date, global, plugins, global.screen, tostring(pool)]; + } + } + // + // tostring() + // Converts an array of charcodes to a string + // + function tostring(a) { + return String.fromCharCode.apply(0, a); + } + // + // When seedrandom.js is loaded, we immediately mix a few bits + // from the built-in RNG into the entropy pool. Because we do + // not want to interfere with deterministic PRNG state later, + // seedrandom will not call math.random on its own again after + // initialization. + // + mixkey(math.random(), pool); + // + // Nodejs and AMD support: export the implementation as a module using + // either convention. + // + if (module.exports) { + module.exports = seedrandom; + // When in node.js, try using crypto package for autoseeding. + try { + nodecrypto = require$$0; + } + catch (ex) { } + } + else { + // When included as a plain script, set up Math.seedrandom global. + math['seed' + rngname] = seedrandom; + } + // End anonymous scope, and pass initial values. + })( + // global: `self` in browsers (including strict mode and web workers), + // otherwise `this` in Node and other environments + (typeof self !== 'undefined') ? self : commonjsGlobal, [], // pool: entropy pool starts empty + Math // math: package containing random, pow, and seedrandom + ); + }); + + // A library of seedable RNGs implemented in Javascript. + // + // Usage: + // + // var seedrandom = require('seedrandom'); + // var random = seedrandom(1); // or any seed. + // var x = random(); // 0 <= x < 1. Every bit is random. + // var x = random.quick(); // 0 <= x < 1. 32 bits of randomness. + // alea, a 53-bit multiply-with-carry generator by Johannes Baagøe. + // Period: ~2^116 + // Reported to pass all BigCrush tests. + // xor128, a pure xor-shift generator by George Marsaglia. + // Period: 2^128-1. + // Reported to fail: MatrixRank and LinearComp. + // xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl. + // Period: 2^192-2^32 + // Reported to fail: CollisionOver, SimpPoker, and LinearComp. + // xorshift7, by François Panneton and Pierre L'ecuyer, takes + // a different approach: it adds robustness by allowing more shifts + // than Marsaglia's original three. It is a 7-shift generator + // with 256 bits, that passes BigCrush with no systmatic failures. + // Period 2^256-1. + // No systematic BigCrush failures reported. + // xor4096, by Richard Brent, is a 4096-bit xor-shift with a + // very long period that also adds a Weyl generator. It also passes + // BigCrush with no systematic failures. Its long period may + // be useful if you have many generators and need to avoid + // collisions. + // Period: 2^4128-2^32. + // No systematic BigCrush failures reported. + // Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random + // number generator derived from ChaCha, a modern stream cipher. + // https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf + // Period: ~2^127 + // No systematic BigCrush failures reported. + // The original ARC4-based prng included in this library. + // Period: ~2^1600 + seedrandom$1.alea = alea; + seedrandom$1.xor128 = xor128; + seedrandom$1.xorwow = xorwow; + seedrandom$1.xorshift7 = xorshift7; + seedrandom$1.xor4096 = xor4096; + seedrandom$1.tychei = tychei; + var seedrandom = seedrandom$1; + + /** + * @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. + * ============================================================================= + */ + // https://en.wikipedia.org/wiki/Marsaglia_polar_method + var MPRandGauss = /** @class */ (function () { + function MPRandGauss(mean, stdDeviation, dtype, truncated, seed) { + this.mean = mean; + 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; + } + var seedValue = seed ? seed : Math.random(); + this.random = seedrandom.alea(seedValue.toString()); + } + /** Returns next sample from a Gaussian distribution. */ + MPRandGauss.prototype.nextValue = function () { + if (!isNaN(this.nextVal)) { + var value = this.nextVal; + this.nextVal = NaN; + return value; + } + var resultX, resultY; + var isValid = false; + while (!isValid) { + var v1 = void 0, v2 = void 0, s = void 0; + do { + v1 = 2 * this.random() - 1; + v2 = 2 * this.random() - 1; + s = v1 * v1 + v2 * v2; + } while (s >= 1 || s === 0); + var mul = Math.sqrt(-2.0 * Math.log(s) / s); + resultX = this.mean + this.stdDev * v1 * mul; + resultY = this.mean + this.stdDev * v2 * mul; + if (!this.truncated || this.isValidTruncated(resultX)) { + isValid = true; + } + } + if (!this.truncated || this.isValidTruncated(resultY)) { + this.nextVal = this.convertValue(resultY); + } + return this.convertValue(resultX); + }; + /** Handles proper rounding for non-floating-point numbers. */ + MPRandGauss.prototype.convertValue = function (value) { + if (this.dtype == null || this.dtype === 'float32') { + return value; + } + return Math.round(value); + }; + /** Returns true if less than 2-standard-deviations from the mean. */ + MPRandGauss.prototype.isValidTruncated = function (value) { + return value <= this.upper && value >= this.lower; + }; + return MPRandGauss; + }()); + // Marsaglia, George, and Wai Wan Tsang. 2000. "A Simple Method for Generating + // Gamma Variables." + var RandGamma = /** @class */ (function () { + function RandGamma(alpha, beta, dtype, seed) { + this.alpha = alpha; + this.beta = 1 / beta; // convert rate to scale parameter + this.dtype = dtype; + var seedValue = seed ? seed : Math.random(); + this.randu = seedrandom.alea(seedValue.toString()); + this.randn = new MPRandGauss(0, 1, dtype, false, this.randu()); + if (alpha < 1) { + this.d = alpha + (2 / 3); + } + else { + this.d = alpha - (1 / 3); + } + this.c = 1 / Math.sqrt(9 * this.d); + } + /** Returns next sample from a gamma distribution. */ + RandGamma.prototype.nextValue = function () { + var x2, v0, v1, x, u, v; + while (true) { + do { + x = this.randn.nextValue(); + v = 1 + (this.c * x); + } while (v <= 0); + v *= v * v; + x2 = x * x; + v0 = 1 - (0.331 * x2 * x2); + v1 = (0.5 * x2) + (this.d * (1 - v + Math.log(v))); + u = this.randu(); + if (u < v0 || Math.log(u) < v1) { + break; + } + } + v = (1 / this.beta) * this.d * v; + if (this.alpha < 1) { + v *= Math.pow(this.randu(), 1 / this.alpha); + } + return this.convertValue(v); + }; + /** Handles proper rounding for non-floating-point numbers. */ + RandGamma.prototype.convertValue = function (value) { + if (this.dtype === 'float32') { + return value; + } + return Math.round(value); + }; + return RandGamma; + }()); + var UniformRandom = /** @class */ (function () { + function UniformRandom(min, max, dtype, seed) { + var _this = this; + if (min === void 0) { min = 0; } + if (max === void 0) { max = 1; } + /** Handles proper rounding for non floating point numbers. */ + this.canReturnFloat = function () { return (_this.dtype == null || _this.dtype === 'float32'); }; + this.min = min; + this.range = max - min; + 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 " + min + " - " + max + " <= 1 and dtype is not float"); + } + this.random = seedrandom.alea(seed); + } + UniformRandom.prototype.convertValue = function (value) { + if (this.canReturnFloat()) { + return value; + } + return Math.round(value); + }; + UniformRandom.prototype.nextValue = function () { + return this.convertValue(this.min + this.range * this.random()); + }; + return UniformRandom; + }()); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with values sampled from a gamma distribution. + * + * ```js + * tf.randomGamma([2, 2], 1).print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param alpha The shape parameter of the gamma distribution. + * @param beta The inverse scale parameter of the gamma distribution. Defaults + * to 1. + * @param dtype The data type of the output. Defaults to float32. + * @param seed The seed for the random number generator. + * + * @doc {heading: 'Tensors', subheading: 'Random'} + */ + function randomGamma_(shape, alpha, beta, dtype, seed) { + if (beta === void 0) { beta = 1; } + if (dtype === void 0) { dtype = 'float32'; } + if (beta == null) { + beta = 1; + } + if (dtype == null) { + dtype = 'float32'; + } + if (dtype !== 'float32' && dtype !== 'int32') { + throw new Error("Unsupported data type " + dtype); + } + var rgamma = new RandGamma(alpha, beta, dtype, seed); + var res = buffer(shape, dtype); + for (var i = 0; i < res.values.length; i++) { + res.values[i] = rgamma.nextValue(); + } + return res.toTensor(); + } + var randomGamma = op({ randomGamma_: randomGamma_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with values sampled from a normal distribution. + * + * ```js + * tf.randomNormal([2, 2]).print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param mean The mean of the normal distribution. + * @param stdDev The standard deviation of the normal distribution. + * @param dtype The data type of the output. + * @param seed The seed for the random number generator. + * + * @doc {heading: 'Tensors', subheading: 'Random'} + */ + function randomNormal_(shape, mean, stdDev, dtype, seed) { + if (mean === void 0) { mean = 0; } + if (stdDev === void 0) { stdDev = 1; } + if (dtype != null && dtype === 'bool') { + throw new Error("Unsupported data type " + dtype); + } + var randGauss = new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed); + var res = buffer(shape, dtype); + for (var i = 0; i < res.values.length; i++) { + res.values[i] = randGauss.nextValue(); + } + return res.toTensor(); + } + var randomNormal = op({ randomNormal_: randomNormal_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with values sampled from a uniform distribution. + * + * The generated values follow a uniform distribution in the range [minval, + * maxval). The lower bound minval is included in the range, while the upper + * bound maxval is excluded. + * + * ```js + * tf.randomUniform([2, 2]).print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param minval The lower bound on the range of random values to generate. + * Defaults to 0. + * @param maxval The upper bound on the range of random values to generate. + * Defaults to 1. + * @param dtype The data type of the output tensor. Defaults to 'float32'. + * + * @doc {heading: 'Tensors', subheading: 'Random'} + */ + function randomUniform_(shape, minval, maxval, dtype, seed) { + if (minval === void 0) { minval = 0; } + if (maxval === void 0) { maxval = 1; } + if (dtype === void 0) { dtype = 'float32'; } + var res = buffer(shape, dtype); + var random = new UniformRandom(minval, maxval, null, seed); + for (var i = 0; i < res.values.length; i++) { + res.values[i] = random.nextValue(); + } + return res.toTensor(); + } + var randomUniform = op({ randomUniform_: randomUniform_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a new `tf.Tensor1D` filled with the numbers in the range provided. + * + * The tensor is a is half-open interval meaning it includes start, but + * excludes stop. Decrementing ranges and negative step values are also + * supported.sv + * + * + * ```js + * tf.range(0, 9, 2).print(); + * ``` + * + * @param start An integer start value + * @param stop An integer stop value + * @param step An integer increment (will default to 1 or -1) + * @param dtype The data type of the output tensor. Defaults to 'float32'. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function range(start, stop, step, dtype) { + if (step === void 0) { step = 1; } + if (dtype === void 0) { dtype = 'float32'; } + if (step === 0) { + throw new Error('Cannot have a step of zero'); + } + var attrs = { start: start, stop: stop, step: step, dtype: dtype }; + return ENGINE.runKernel(Range, {} /* inputs */, attrs); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the real part of a complex (or real) tensor. + * + * Given a tensor input, this operation returns a tensor of type float that is + * the real part of each element in input considered as a complex number. + * + * If the input is real, it simply makes a clone. + * + * ```js + * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]); + * tf.real(x).print(); + * ``` + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function real_(input) { + var $input = convertToTensor(input, 'input', 'real'); + var inputs = { input: $input }; + return ENGINE.runKernel(Real, inputs); + } + var real = op({ real_: real_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes reciprocal of x element-wise: `1 / x` + * + * ```js + * const x = tf.tensor1d([0, 1, 2]); + * + * x.reciprocal().print(); // or tf.reciprocal(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function reciprocal_(x) { + var $x = convertToTensor(x, 'x', 'reciprocal'); + var inputs = { x: $x }; + return ENGINE.runKernel(Reciprocal, inputs); + } + var reciprocal = op({ reciprocal_: reciprocal_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes rectified linear element-wise: `max(x, 0)`. + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 4]); + * + * x.relu().print(); // or tf.relu(x) + * ``` + * @param x The input tensor. If the dtype is `bool`, the output dtype will be + * `int32'. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function relu_(x) { + var $x = convertToTensor(x, 'x', 'relu'); + var inputs = { x: $x }; + return ENGINE.runKernel(Relu, inputs); + } + var relu = op({ relu_: relu_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes rectified linear 6 element-wise: `min(max(x, 0), 6)`. + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 8]); + * + * x.relu6().print(); // or tf.relu6(x) + * ``` + * @param x The input tensor. If the dtype is `bool`, the output dtype will be + * `int32'. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function relu6_(x) { + var $x = convertToTensor(x, 'x', 'relu6'); + var inputs = { x: $x }; + return ENGINE.runKernel(Relu6, inputs); + } + var relu6 = op({ relu6_: relu6_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Reverses a `tf.Tensor` along a specified axis. + * + * Also available are stricter rank-specific methods that assert that `x` is + * of the given rank: + * - `tf.reverse1d` + * - `tf.reverse2d` + * - `tf.reverse3d` + * - `tf.reverse4d` + * + * Except `tf.reverse1d` (which does not have axis param), all methods have + * same signature as this method. + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * + * x.reverse().print(); + * ``` + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * const axis = 1; + * x.reverse(axis).print(); + * ``` + * @param x The input tensor to be reversed. + * @param axis The set of dimensions to reverse. Must be in the + * range [-rank(x), rank(x)). Defaults to all axes. + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function reverse_(x, axis) { + var $x = convertToTensor(x, 'x', 'reverse'); + var inputs = { x: $x }; + var attrs = { dims: axis }; + return ENGINE.runKernel(Reverse, inputs, attrs); + } + var reverse = op({ reverse_: reverse_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Reverses a `tf.Tensor1D`. + * + * @param x The input tensor. + */ + function reverse1d_(x) { + var $x = convertToTensor(x, 'x', 'reverse'); + assert($x.rank === 1, function () { return "Error in reverse1D: x must be rank 1 but got rank " + $x.rank + "."; }); + return reverse($x, 0); + } + var reverse1d = op({ reverse1d_: reverse1d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Reverses a `tf.Tensor2D` along a specified axis. + * + * @param x The input tensor. + * @param axis The set of dimensions to reverse. Must be in the + * range [-rank(x), rank(x)). Defaults to all axes. + */ + function reverse2d_(x, axis) { + var $x = convertToTensor(x, 'x', 'reverse'); + assert($x.rank === 2, function () { return "Error in reverse2D: x must be rank 2 but got rank " + $x.rank + "."; }); + return reverse($x, axis); + } + var reverse2d = op({ reverse2d_: reverse2d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Reverses a `tf.Tensor3D` along a specified axis. + * + * @param x The input tensor. + * @param axis The set of dimensions to reverse. Must be in the + * range [-rank(x), rank(x)). Defaults to all axes. + */ + function reverse3d_(x, axis) { + var $x = convertToTensor(x, 'x', 'reverse'); + assert($x.rank === 3, function () { return "Error in reverse3D: x must be rank 3 but got rank " + $x.rank + "."; }); + return reverse($x, axis); + } + var reverse3d = op({ reverse3d_: reverse3d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Reverses a `tf.Tensor4D` along a specified axis. + * + * @param x The input tensor. + * @param axis The set of dimensions to reverse. Must be in the + * range [-rank(x), rank(x)). Defaults to all axes. + */ + function reverse4d_(x, axis) { + var $x = convertToTensor(x, 'x', 'reverse'); + assert($x.rank === 4, function () { return "Error in reverse4D: x must be rank 4 but got rank " + $x.rank + "."; }); + return reverse($x, axis); + } + var reverse4d = op({ reverse4d_: reverse4d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes round of input `tf.Tensor` element-wise: `round(x)`. + * It implements banker's rounding. + * + * ```js + * const x = tf.tensor1d([.6, 1.1, -3.3]); + * + * x.round().print(); // or tf.round(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function round_(x) { + var $x = convertToTensor(x, 'x', 'round'); + var inputs = { x: $x }; + return ENGINE.runKernel(Round, inputs); + } + var round = op({ round_: round_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes reciprocal of square root of the input `tf.Tensor` element-wise: + * `y = 1 / sqrt(x)` + * + * ```js + * const x = tf.tensor1d([1, 2, 4, -1]); + * + * x.rsqrt().print(); // or tf.rsqrt(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function rsqrt_(x) { + var $x = convertToTensor(x, 'x', 'rsqrt', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Rsqrt, inputs); + } + var rsqrt = op({ rsqrt_: rsqrt_ }); + + /** + * @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. + * ============================================================================= + */ + /** This is shared code across all tensor creation methods. */ + 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); + var providedSize_1 = sizeFromShape(shape); + var inferredSize_1 = sizeFromShape(inferredShape); + assert(providedSize_1 === inferredSize_1, function () { return "Based on the provided shape, [" + shape + "], the tensor should have " + + (providedSize_1 + " values but has " + inferredSize_1); }); + for (var i = 0; i < inferredShape.length; ++i) { + var inferred = inferredShape[i]; + var flatDimsDontMatch = i === inferredShape.length - 1 ? + inferred !== sizeFromShape(shape.slice(i)) : + true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, function () { return "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); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.scalar` as it makes the code more readable. + * + * ```js + * tf.scalar(3.14).print(); + * ``` + * + * @param value The value of the scalar. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + 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`.'); + } + var shape = []; + var inferredShape = []; + return makeTensor(value, shape, inferredShape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes scaled exponential linear element-wise. + * + * `x < 0 ? scale * alpha * (exp(x) - 1) : x` + * + * ```js + * const x = tf.tensor1d([-1, 2, -3, 4]); + * + * x.selu().print(); // or tf.selu(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function selu_(x) { + var $x = convertToTensor(x, 'x', 'selu'); + var inputs = { x: $x }; + return ENGINE.runKernel(Selu, inputs); + } + var selu = op({ selu_: selu_ }); + + /** + * 2-D convolution with separable filters. + * + * Performs a depthwise convolution that acts separately on channels followed + * by a pointwise convolution that mixes channels. Note that this is + * separability between dimensions [1, 2] and 3, not spatial separability + * between dimensions 1 and 2. + * + * See + * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d]( + * https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d) + * for more details. + * + * @param x The input tensor, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is + * assumed. + * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape + * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is + * the filter used in the first step. + * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape + * `[1, 1, inChannels * channelMultiplier, outChannels]`. This is + * the filter used in the second step. + * @param strides The strides of the convolution: `[strideHeight, + * strideWidth]`. If strides is a single number, then `strideHeight == + * strideWidth`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. Only "NHWC" is currently supported. + * + * @doc {heading: 'Operations', subheading: 'Convolution'} + */ + function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) { + if (dilation === void 0) { dilation = [1, 1]; } + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + var $x = convertToTensor(x, 'x', 'separableConv2d'); + var $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d'); + var $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d'); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + if (dataFormat === 'NCHW') { + throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' + + 'NHWC is supported'); + } + assert(x4D.rank === 4, function () { return "Error in separableConv2d: input must be rank 4, but got " + + ("rank " + x4D.rank + "."); }); + assert($depthwiseFilter.rank === 4, function () { return "Error in separableConv2d: depthwise filter must be rank 4, but " + + ("got rank " + $depthwiseFilter.rank + "."); }); + assert($pointwiseFilter.rank === 4, function () { return "Error in separableConv2d: pointwise filter must be rank 4, but " + + ("got rank " + $depthwiseFilter.rank + "."); }); + assert($pointwiseFilter.shape[0] === 1, function () { return "Error in separableConv2d: the first dimension of pointwise filter " + + (" must be 1, but got " + $pointwiseFilter.shape[0] + "."); }); + assert($pointwiseFilter.shape[1] === 1, function () { return "Error in separableConv2d: the second dimension of pointwise " + + ("filter must be 1, but got " + $pointwiseFilter.shape[1] + "."); }); + var inChannels = $depthwiseFilter.shape[2]; + var channelMultiplier = $depthwiseFilter.shape[3]; + assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, function () { return "Error in separableConv2d: the third dimension of pointwise filter " + + ("must be " + inChannels * channelMultiplier + ", ") + + ("but got " + $pointwiseFilter.shape[2] + "."); }); + var depthwise = depthwiseConv2d$1(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation); + var pointwiseStride = 1; + var res = conv2d$1(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_: separableConv2d_ }); + + /** + * Computes the difference between two lists of numbers. + * + * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out` + * that represents all values that are in `x` but not in `y`. The returned + * Tensor `out` is sorted in the same order that the numbers appear in `x` + * (duplicates are preserved). This operation also returns a Tensor indices that + * represents the position of each out element in `x`. In other words: + * + * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]` + * + * ```js + * const x = [1, 2, 3, 4, 5, 6]; + * const y = [1, 3, 5]; + * + * const [out, indices] = await tf.setdiff1dAsync(x, y); + * out.print(); // [2, 4, 6] + * indices.print(); // [1, 3, 5] + * ``` + * + * @param x 1-D Tensor. Values to keep. + * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the + * output. + * @returns Promise of Tensor tuple [out, indices]. + * out: Tensor with the same type as x. + * indices: A Tensor of type int32. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function setdiff1dAsync_(x, y) { + return __awaiter(this, void 0, void 0, function () { + var $x, $y, xVals, yVals, ySet, outputSize, i, buffer, indices, i, p; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + $x = convertToTensor(x, 'x', 'setdiff1d'); + $y = convertToTensor(y, 'y', 'setdiff1d'); + assert($x.dtype === $y.dtype, function () { return "x and y should have the same dtype, but got x (" + $x.dtype + ") and y (" + $y.dtype + ")."; }); + assert($x.rank === 1, function () { return "x should be 1D tensor, but got x (" + $x.shape + ")."; }); + assert($y.rank === 1, function () { return "y should be 1D tensor, but got y (" + $y.shape + ")."; }); + return [4 /*yield*/, $x.data()]; + case 1: + xVals = _a.sent(); + return [4 /*yield*/, $y.data()]; + case 2: + yVals = _a.sent(); + ySet = new Set(yVals); + outputSize = 0; + for (i = 0; i < xVals.length; i++) { + if (!ySet.has(xVals[i])) { + outputSize++; + } + } + buffer = new TensorBuffer([outputSize], $x.dtype); + indices = new TensorBuffer([outputSize], 'int32'); + for (i = 0, p = 0; i < xVals.length; i++) { + if (!ySet.has(xVals[i])) { + buffer.values[p] = xVals[i]; + indices.values[p] = i; + p++; + } + } + return [2 /*return*/, [buffer.toTensor(), indices.toTensor()]]; + } + }); + }); + } + var setdiff1dAsync = setdiff1dAsync_; + + /** + * @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. + * ============================================================================= + */ + /** + * Returns an element-wise indication of the sign of a number. + * + * ```js + * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]); + * + * x.sign().print(); // or tf.sign(x) + * ``` + * @param x The input Tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function sign_(x) { + var $x = convertToTensor(x, 'x', 'sign'); + var inputs = { x: $x }; + return ENGINE.runKernel(Sign, inputs); + } + var sign = op({ sign_: sign_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes sin of the input Tensor element-wise: `sin(x)` + * + * ```js + * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); + * + * x.sin().print(); // or tf.sin(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function sin_(x) { + var $x = convertToTensor(x, 'x', 'sin', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Sin, inputs); + } + var sin = op({ sin_: sin_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)` + * + * ```js + * const x = tf.tensor1d([0, 1, -1, .7]); + * + * x.sinh().print(); // or tf.sinh(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function sinh_(x) { + var $x = convertToTensor(x, 'x', 'sinh'); + var inputs = { x: $x }; + return ENGINE.runKernel(Sinh, inputs); + } + var sinh = op({ sinh_: sinh_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts a 1D slice from 1D array starting at coordinates `begin` and is + * of length `size`. See `slice` for details. + */ + function slice1d_(x, begin, size) { + var $x = convertToTensor(x, 'x', 'slice1d'); + assert($x.rank === 1, function () { return "slice1d expects a rank-1 tensor, but got a rank-" + $x.rank + " tensor"; }); + return slice($x, [begin], [size]); + } + var slice1d = op({ slice1d_: slice1d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts a 2D slice from a 2D array starting at coordinates `begin` and + * is of size `size`. See `slice` for details. + */ + function slice2d_(x, begin, size) { + var $x = convertToTensor(x, 'x', 'slice2d'); + assert($x.rank === 2, function () { return "slice2d expects a rank-2 tensor, but got a rank-" + $x.rank + " tensor"; }); + return slice($x, begin, size); + } + var slice2d = op({ slice2d_: slice2d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts a 3D slice from a 3D array starting at coordinates `begin` and + * is of size `size`. See `slice` for details. + */ + function slice3d_(x, begin, size) { + var $x = convertToTensor(x, 'x', 'slice3d'); + assert($x.rank === 3, function () { return "slice3d expects a rank-3 tensor, but got a rank-" + $x.rank + " tensor"; }); + return slice($x, begin, size); + } + var slice3d = op({ slice3d_: slice3d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts a 4D slice from a 4D array starting at coordinates `begin` and + * is of size `size`. See `slice` for details. + */ + function slice4d_(x, begin, size) { + var $x = convertToTensor(x, 'x', 'slice4d'); + assert($x.rank === 4, function () { return "slice4d expects a rank-4 tensor, but got a rank-" + $x.rank + " tensor"; }); + return slice($x, begin, size); + } + var slice4d = op({ slice4d_: slice4d_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the softmax normalized vector given the logits. + * + * ```js + * const a = tf.tensor1d([1, 2, 3]); + * + * a.softmax().print(); // or tf.softmax(a) + * ``` + * + * ```js + * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]); + * + * a.softmax().print(); // or tf.softmax(a) + * ``` + * + * @param logits The logits array. + * @param dim The dimension softmax would be performed on. Defaults to `-1` + * which indicates the last dimension. + * + * @doc {heading: 'Operations', subheading: 'Normalization'} + */ + function softmax_(logits, dim) { + if (dim === void 0) { dim = -1; } + var $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)); + } + var inputs = { logits: $logits }; + var attrs = { dim: dim }; + return ENGINE.runKernel(Softmax, inputs, attrs); + } + var softmax = op({ softmax_: softmax_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Fast Fourier transform. + * + * Computes the 1-dimensional discrete Fourier transform over the inner-most + * dimension of input. + * + * ```js + * const real = tf.tensor1d([1, 2, 3]); + * const imag = tf.tensor1d([1, 2, 3]); + * const x = tf.complex(real, imag); + * + * x.fft().print(); // tf.spectral.fft(x).print(); + * ``` + * @param input The complex input to compute an fft over. + * + * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} + */ + function fft_(input) { + assert(input.dtype === 'complex64', function () { return "The dtype for tf.spectral.fft() must be complex64 " + + ("but got " + input.dtype + "."); }); + var inputs = { input: input }; + return ENGINE.runKernel(FFT, inputs); + } + var fft = op({ fft_: fft_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Inverse fast Fourier transform. + * + * Computes the inverse 1-dimensional discrete Fourier transform over the + * inner-most dimension of input. + * + * ```js + * const real = tf.tensor1d([1, 2, 3]); + * const imag = tf.tensor1d([1, 2, 3]); + * const x = tf.complex(real, imag); + * + * x.ifft().print(); // tf.spectral.ifft(x).print(); + * ``` + * @param input The complex input to compute an ifft over. + * + * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} + */ + function ifft_(input) { + assert(input.dtype === 'complex64', function () { return "The dtype for tf.spectral.ifft() must be complex64 " + + ("but got " + input.dtype + "."); }); + var inputs = { input: input }; + return ENGINE.runKernel(IFFT, inputs); + } + var ifft = op({ ifft_: ifft_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Inversed real value input fast Fourier transform. + * + * Computes the 1-dimensional inversed discrete Fourier transform over the + * inner-most dimension of the real input. + * + * ```js + * const real = tf.tensor1d([1, 2, 3]); + * const imag = tf.tensor1d([0, 0, 0]); + * const x = tf.complex(real, imag); + * + * x.irfft().print(); + * ``` + * @param input The real value input to compute an irfft over. + * + * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} + */ + function irfft_(input) { + var innerDimensionSize = input.shape[input.shape.length - 1]; + var batch = input.size / innerDimensionSize; + var ret; + if (innerDimensionSize <= 2) { + var complexInput = reshape(input, [batch, innerDimensionSize]); + ret = ifft(complexInput); + } + else { + // The length of unique components of the DFT of a real-valued signal + // is 2 * (input_len - 1) + var outputShape = [batch, 2 * (innerDimensionSize - 1)]; + var realInput = reshape(real(input), [batch, innerDimensionSize]); + var imagInput = reshape(imag(input), [batch, innerDimensionSize]); + var realConjugate = reverse(slice(realInput, [0, 1], [batch, innerDimensionSize - 2]), 1); + var imagConjugate = mul(reverse(slice(imagInput, [0, 1], [batch, innerDimensionSize - 2]), 1), scalar(-1)); + var r = concat([realInput, realConjugate], 1); + var i = concat([imagInput, imagConjugate], 1); + var complexInput = reshape(complex(r, i), [outputShape[0], outputShape[1]]); + ret = ifft(complexInput); + } + ret = real(ret); + // reshape the result if the input is 3D tensor. + if (input.rank === 3 && input.shape[0] !== 0) { + var temp = ret; + var batch_1 = input.shape[0]; + ret = reshape(ret, [batch_1, ret.shape[0] / batch_1, ret.shape[1]]); + temp.dispose(); + } + return ret; + } + var irfft = op({ irfft_: irfft_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Splits a `tf.Tensor` into sub tensors. + * + * If `numOrSizeSplits` is a number, splits `x` along dimension `axis` + * into `numOrSizeSplits` smaller tensors. + * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`. + * + * If `numOrSizeSplits` is a number array, splits `x` into + * `numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the + * same size as `x` except along dimension `axis` where the size is + * `numOrSizeSplits[i]`. + * + * ```js + * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]); + * const [a, b] = tf.split(x, 2, 1); + * a.print(); + * b.print(); + * + * const [c, d, e] = tf.split(x, [1, 2, 1], 1); + * c.print(); + * d.print(); + * e.print(); + * ``` + * + * @param x The input tensor to split. + * @param numOrSizeSplits Either an integer indicating the number of + * splits along the axis or an array of integers containing the sizes of + * each output tensor along the axis. If a number then it must evenly divide + * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`. + * Can contain one -1 indicating that dimension is to be inferred. + * @param axis The dimension along which to split. Defaults to 0 (the first + * dim). + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function split_(x, numOrSizeSplits, axis) { + if (axis === void 0) { axis = 0; } + var $x = convertToTensor(x, 'x', 'split'); + var inputs = { x: $x }; + var attr = { numOrSizeSplits: numOrSizeSplits, axis: axis }; + return ENGINE.runKernel(SplitV, inputs, attr); + } + var split$1 = op({ split_: split_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Real value input fast Fourier transform. + * + * Computes the 1-dimensional discrete Fourier transform over the + * inner-most dimension of the real input. + * + * ```js + * const real = tf.tensor1d([1, 2, 3]); + * + * real.rfft().print(); + * ``` + * @param input The real value input to compute an rfft over. + * + * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'} + */ + function rfft_(input, fftLength) { + assert(input.dtype === 'float32', function () { return "The dtype for rfft() must be real value but got " + input.dtype; }); + var innerDimensionSize = input.shape[input.shape.length - 1]; + var batch = input.size / innerDimensionSize; + var adjustedInput; + if (fftLength != null && fftLength < innerDimensionSize) { + // Need to crop + var begin = input.shape.map(function (v) { return 0; }); + var size = input.shape.map(function (v) { return v; }); + size[input.shape.length - 1] = fftLength; + adjustedInput = slice(input, begin, size); + innerDimensionSize = fftLength; + } + else if (fftLength != null && fftLength > innerDimensionSize) { + // Need to pad with zeros + var zerosShape = input.shape.map(function (v) { return v; }); + zerosShape[input.shape.length - 1] = fftLength - innerDimensionSize; + adjustedInput = concat([input, zeros(zerosShape)], input.shape.length - 1); + innerDimensionSize = fftLength; + } + else { + adjustedInput = input; + } + // Complement the input with zero imaginary numbers. + var zerosInput = zerosLike(adjustedInput); + var complexInput = reshape(complex(adjustedInput, zerosInput), [batch, innerDimensionSize]); + var ret = fft(complexInput); + // Exclude complex conjugations. These conjugations are put symmetrically. + var half = Math.floor(innerDimensionSize / 2) + 1; + var realValues = real(ret); + var imagValues = imag(ret); + var realComplexConjugate = split$1(realValues, [half, innerDimensionSize - half], realValues.shape.length - 1); + var imagComplexConjugate = split$1(imagValues, [half, innerDimensionSize - half], imagValues.shape.length - 1); + var outputShape = adjustedInput.shape.slice(); + outputShape[adjustedInput.shape.length - 1] = half; + return reshape(complex(realComplexConjugate[0], imagComplexConjugate[0]), outputShape); + } + var rfft = op({ rfft_: rfft_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)` + * + * ```js + * const x = tf.tensor1d([1, 2, 4, -1]); + * + * x.sqrt().print(); // or tf.sqrt(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function sqrt_(x) { + var $x = convertToTensor(x, 'x', 'sqrt', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Sqrt, inputs); + } + var sqrt = op({ sqrt_: sqrt_ }); + + /** + * Returns (a - b) * (a - b) element-wise. + * Supports broadcasting. + * + * ```js + * const a = tf.tensor1d([1, 4, 3, 16]); + * const b = tf.tensor1d([1, 2, 9, 4]); + * + * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b) + * ``` + * + * ```js + * // Broadcast squared difference a with b. + * const a = tf.tensor1d([2, 4, 6, 8]); + * const b = tf.scalar(5); + * + * a.squaredDifference(b).print(); // or tf.squaredDifference(a, b) + * ``` + * + * @param a The first tensor. + * @param b The second tensor. Must have the same type as `a`. + * + * @doc {heading: 'Operations', subheading: 'Arithmetic'} + */ + function squaredDifference_(a, b) { + var _a; + var $a = convertToTensor(a, 'a', 'squaredDifference'); + var $b = convertToTensor(b, 'b', 'squaredDifference'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + assertAndGetBroadcastShape($a.shape, $b.shape); + var inputs = { a: $a, b: $b }; + var attrs = {}; + return ENGINE.runKernel(SquaredDifference, inputs, attrs); + } + var squaredDifference = op({ squaredDifference_: squaredDifference_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Removes dimensions of size 1 from the shape of a `tf.Tensor`. + * + * ```js + * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]); + * x.squeeze().print(); + * ``` + * + * @param x The input tensor to be squeezed. + * @param axis An optional list of numbers. If specified, only + * squeezes the dimensions listed. The dimension index starts at 0. It + * is an error to squeeze a dimension that is not 1. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function squeeze_(x, axis) { + var $x = convertToTensor(x, 'x', 'squeeze'); + return reshape($x, squeezeShape($x.shape, axis).newShape); + } + var squeeze = op({ squeeze_: squeeze_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`. + * + * ```js + * const a = tf.tensor1d([1, 2]); + * const b = tf.tensor1d([3, 4]); + * const c = tf.tensor1d([5, 6]); + * tf.stack([a, b, c]).print(); + * ``` + * + * @param tensors A list of tensor objects with the same shape and dtype. + * @param axis The axis to stack along. Defaults to 0 (the first dim). + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function stack_(tensors, axis) { + if (axis === void 0) { axis = 0; } + var $tensors = convertToTensorArray(tensors, 'tensors', 'stack', 'string_or_numeric'); + assert($tensors.length >= 1, function () { return 'Pass at least one tensor to tf.stack'; }); + if ($tensors.length > 0) { + assert(axis <= $tensors[0].rank, function () { return 'Axis must be <= rank of the tensor'; }); + } + var inputs = $tensors; + var attrs = { axis: axis }; + return ENGINE.runKernel(Pack, inputs, attrs); + } + var stack = op({ stack_: stack_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x` + * + * ```js + * const x = tf.tensor1d([0, 2, -1, -3]); + * + * x.step(.5).print(); // or tf.step(x, .5) + * ``` + * @param x The input tensor. + * @param alpha The gradient when input is negative. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function step_(x, alpha) { + if (alpha === void 0) { alpha = 0.0; } + var $x = convertToTensor(x, 'x', 'step'); + var inputs = { x: $x }; + var attrs = { alpha: alpha }; + return ENGINE.runKernel(Step, inputs, attrs); + } + var step = op({ step_: step_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts a strided slice of a tensor. + * + * Roughly speaking, this op extracts a slice of size (end-begin)/stride from + * the given input tensor (x). Starting at the location specified by begin the + * slice continues by adding stride to the index until all dimensions are not + * less than end. Note that a stride can be negative, which causes a reverse + * slice. + * + * ```js + * const t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6], + * [3, 2, 3]); + * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print() // [[[3, 3, 3]]] + * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print() // [[[3, 3, 3], + * // [4, 4, 4]]] + * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4], + * // [3, 3, 3]]] + * ``` + * + * @param x The tensor to stride slice. + * @param begin The coordinates to start the slice from. + * @param end: The coordinates to end the slice at. + * @param strides: The size of the slice. + * @param beginMask: If the ith bit of beginMask is set, begin[i] is ignored + * and the fullest possible range in that dimension is used instead. + * @param endMask: If the ith bit of endMask is set, end[i] is ignored + * and the fullest possible range in that dimension is used instead. + * @param shrinkAxisMask: a bitmask where bit i implies that + * the ith specification should shrink the dimensionality. begin and end must + * imply a slice of size 1 in the dimension. + * + * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} + */ + function stridedSlice_(x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) { + if (beginMask === void 0) { beginMask = 0; } + if (endMask === void 0) { endMask = 0; } + if (ellipsisMask === void 0) { ellipsisMask = 0; } + if (newAxisMask === void 0) { newAxisMask = 0; } + if (shrinkAxisMask === void 0) { shrinkAxisMask = 0; } + var $x = convertToTensor(x, 'x', 'stridedSlice', 'string_or_numeric'); + var inputs = { x: $x }; + var attrs = { + begin: begin, + end: end, + strides: strides, + beginMask: beginMask, + endMask: endMask, + ellipsisMask: ellipsisMask, + newAxisMask: newAxisMask, + shrinkAxisMask: shrinkAxisMask + }; + return ENGINE.runKernel(StridedSlice, inputs, attrs); + } + var stridedSlice = op({ stridedSlice_: stridedSlice_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes tan of the input `tf.Tensor` element-wise, `tan(x)` + * + * ```js + * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]); + * + * x.tan().print(); // or tf.tan(x) + * ``` + * @param x The input tensor. + * + * @doc {heading: 'Operations', subheading: 'Basic math'} + */ + function tan_(x) { + var $x = convertToTensor(x, 'x', 'tan', 'float32'); + var inputs = { x: $x }; + return ENGINE.runKernel(Tan, inputs); + } + var tan = op({ tan_: tan_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with the provided values, shape and dtype. + * + * ```js + * // Pass an array of values to create a vector. + * tf.tensor([1, 2, 3, 4]).print(); + * ``` + * + * ```js + * // Pass a nested array of values to make a matrix or a higher + * // dimensional tensor. + * tf.tensor([[1, 2], [3, 4]]).print(); + * ``` + * + * ```js + * // Pass a flat array and specify a shape yourself. + * tf.tensor([1, 2, 3, 4], [2, 2]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. If the values are strings, + * they will be encoded as utf-8 and kept as `Uint8Array[]`. + * @param shape The shape of the tensor. Optional. If not provided, + * it is inferred from `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor(values, shape, dtype) { + var inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor1d` as it makes the code more readable. + * + * ```js + * tf.tensor1d([1, 2, 3]).print(); + * ``` + * + * @param values The values of the tensor. Can be array of numbers, + * or a `TypedArray`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor1d(values, dtype) { + assertNonNull(values); + var inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 1) { + throw new Error('tensor1d() requires values to be a flat/TypedArray'); + } + var shape = null; + return makeTensor(values, shape, inferredShape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor2d` as it makes the code more readable. + * + * ```js + * // Pass a nested array. + * tf.tensor2d([[1, 2], [3, 4]]).print(); + * ``` + * ```js + * // Pass a flat array and specify a shape. + * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. + * @param shape The shape of the tensor. If not provided, it is inferred from + * `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor2d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 2) { + throw new Error('tensor2d() requires shape to have two numbers'); + } + var 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); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor3d` as it makes the code more readable. + * + * ```js + * // Pass a nested array. + * tf.tensor3d([[[1], [2]], [[3], [4]]]).print(); + * ``` + * ```js + * // Pass a flat array and specify a shape. + * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. + * @param shape The shape of the tensor. If not provided, it is inferred from + * `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { + throw new Error('tensor3d() requires shape to have three numbers'); + } + var 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); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor4d` as it makes the code more readable. + * + * ```js + * // Pass a nested array. + * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print(); + * ``` + * ```js + * // Pass a flat array and specify a shape. + * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. + * @param shape The shape of the tensor. Optional. If not provided, + * it is inferred from `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor4d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 4) { + throw new Error('tensor4d() requires shape to have four numbers'); + } + var inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 4 && inferredShape.length !== 1) { + throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray'); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error('tensor4d() requires shape to be provided when `values` ' + + 'are a flat array'); + } + return makeTensor(values, shape, inferredShape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor5d` as it makes the code more readable. + * + * ```js + * // Pass a nested array. + * tf.tensor5d([[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]).print(); + * ``` + * ```js + * // Pass a flat array and specify a shape. + * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. + * @param shape The shape of the tensor. Optional. If not provided, + * it is inferred from `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor5d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 5) { + throw new Error('tensor5d() requires shape to have five numbers'); + } + var inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 5 && inferredShape.length !== 1) { + throw new Error('tensor5d() requires values to be ' + + 'number[][][][][] or flat/TypedArray'); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error('tensor5d() requires shape to be provided when `values` ' + + 'are a flat array'); + } + return makeTensor(values, shape, inferredShape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor6d` as it makes the code more readable. + * + * ```js + * // Pass a nested array. + * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print(); + * ``` + * ```js + * // Pass a flat array and specify a shape. + * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. + * @param shape The shape of the tensor. Optional. If not provided, + * it is inferred from `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor6d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 6) { + throw new Error('tensor6d() requires shape to have six numbers'); + } + var inferredShape = inferShape(values, dtype); + if (inferredShape.length !== 6 && inferredShape.length !== 1) { + throw new Error('tensor6d() requires values to be number[][][][][][] or ' + + 'flat/TypedArray'); + } + if (inferredShape.length === 1 && shape == null) { + throw new Error('tensor6d() requires shape to be provided when `values` ' + + 'are a flat array'); + } + shape = shape || + inferredShape; + return makeTensor(values, shape, inferredShape, dtype); + } + + /** + * Finds the values and indices of the `k` largest entries along the last + * dimension. + * + * If the input is a vector (rank=1), finds the k largest entries in the vector + * and outputs their values and indices as vectors. Thus values[j] is the j-th + * largest entry in input, and its index is indices[j]. + * For higher rank inputs, computes the top k entries along the last dimension. + * + * If two elements are equal, the lower-index element appears first. + * + * ```js + * const a = tf.tensor2d([[1, 5], [4, 3]]); + * const {values, indices} = tf.topk(a); + * values.print(); + * indices.print(); + * ``` + * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`. + * @param k Number of top elements to look for along the last dimension. + * @param sorted If true, the resulting `k` elements will be sorted by the + * values in descending order. + * + * @doc {heading: 'Operations', subheading: 'Evaluation'} + */ + function topk_(x, k, sorted) { + if (k === void 0) { k = 1; } + if (sorted === void 0) { sorted = true; } + var $x = convertToTensor(x, 'x', 'topk'); + if ($x.rank === 0) { + throw new Error('topk() expects the input to be of rank 1 or higher'); + } + var 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)); + } + var inputs = { x: $x }; + var attrs = { k: k, sorted: sorted }; + var _a = __read(ENGINE.runKernel(TopK, inputs, attrs), 2), values = _a[0], indices = _a[1]; + return { values: values, indices: indices }; + } + var topk = op({ topk_: topk_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with values sampled from a truncated normal + * distribution. + * + * ```js + * tf.truncatedNormal([2, 2]).print(); + * ``` + * + * The generated values follow a normal distribution with specified mean and + * standard deviation, except that values whose magnitude is more than 2 + * standard deviations from the mean are dropped and re-picked. + * + * @param shape An array of integers defining the output tensor shape. + * @param mean The mean of the normal distribution. + * @param stdDev The standard deviation of the normal distribution. + * @param dtype The data type of the output tensor. + * @param seed The seed for the random number generator. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function truncatedNormal_(shape, mean, stdDev, dtype, seed) { + if (mean === void 0) { mean = 0; } + if (stdDev === void 0) { stdDev = 1; } + if (dtype != null && dtype === 'bool') { + throw new Error("Unsupported data type $ { dtype }"); + } + var randGauss = new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed); + var res = buffer(shape, dtype); + for (var i = 0; i < res.values.length; i++) { + res.values[i] = randGauss.nextValue(); + } + return res.toTensor(); + } + var truncatedNormal = op({ truncatedNormal_: truncatedNormal_ }); + + /** + * Finds unique elements along an axis of a tensor. + * + * It returns a tensor `values` containing all of the unique elements along the + * `axis` of the given tensor `x` in the same order that they occur along the + * `axis` in `x`; `x` does not need to be sorted. It also returns a tensor + * `indices` the same size as the number of the elements in `x` along the `axis` + * dimension. It contains the index in the unique output `values`. + * + * ```js + * // A 1-D tensor + * const a = tf.tensor1d([1, 1, 2, 4, 4, 4, 7, 8, 8]); + * const {values, indices} = tf.unique(a); + * values.print(); // [1, 2, 4, 7, 8,] + * indices.print(); // [0, 0, 1, 2, 2, 2, 3, 4, 4] + * ``` + * + * ```js + * // A 2-D tensor with axis=0 + * // + * // 'a' is: [[1, 0, 0], + * // [1, 0, 0], + * // [2, 0, 0]] + * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]); + * const {values, indices} = tf.unique(a, 0) + * values.print(); // [[1, 0, 0], + * // [2, 0, 0]] + * indices.print(); // [0, 0, 1] + * ``` + * + * ```js + * // A 2-D tensor with axis=1 + * // + * // 'a' is: [[1, 0, 0], + * // [1, 0, 0], + * // [2, 0, 0]] + * const a = tf.tensor2d([[1, 0, 0], [1, 0, 0], [2, 0, 0]]); + * const {values, indices} = tf.unique(a, 1) + * values.print(); // [[1, 0], + * // [1, 0], + * // [2, 0]] + * indices.print(); // [0, 1, 1] + * ``` + * @param x A tensor (int32, string, bool). + * @param axis The axis of the tensor to find the unique elements. + * @returns [uniqueElements, indices] (see above for details) + * + * @doc {heading: 'Operations', subheading: 'Evaluation'} + */ + function unique_(x, axis) { + if (axis === void 0) { axis = 0; } + var $x = convertToTensor(x, 'x', 'unique', 'string_or_numeric'); + assert($x.rank > 0, function () { return 'The input tensor must be at least 1D'; }); + var inputs = { x: $x }; + var attrs = { axis: axis }; + var _a = __read(ENGINE.runKernel(Unique, inputs, attrs), 2), values = _a[0], indices = _a[1]; + return { values: values, indices: indices }; + } + var unique = op({ unique_: unique_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the sum along segments of a `tf.Tensor`. + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32'); + * const numSegments = 3; + * + * x.unsortedSegmentSum(segmentIds, numSegments).print() + * //or tf.unsortedSegmentSum(x, segmentIds, numSegments) + * ``` + * @param x The `tf.Tensor` that will be summed along its segments. + * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s + * dimension along the `axis`. Maps each element of `x` to a segment. + * @param numSegments The number of distinct `segmentIds`. + * + * @doc {heading: 'Operations', subheading: 'Segment'} + */ + function unsortedSegmentSum_(x, segmentIds, numSegments) { + var $x = convertToTensor(x, 'x', 'unsortedSegmentSum'); + var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32'); + assert(isInt(numSegments), function () { return 'numSegments must be of dtype int'; }); + var inputs = { x: $x, segmentIds: $segmentIds }; + var attrs = { numSegments: numSegments }; + return ENGINE.runKernel(UnsortedSegmentSum, inputs, attrs); + } + var unsortedSegmentSum = op({ unsortedSegmentSum_: unsortedSegmentSum_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s. + * + * ```js + * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * tf.unstack(a).forEach(tensor => tensor.print()); + * ``` + * + * @param x A tensor object. + * @param axis The axis to unstack along. Defaults to 0 (the first dim). + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function unstack_(x, axis) { + if (axis === void 0) { axis = 0; } + var $x = convertToTensor(x, 'x', 'unstack', 'string_or_numeric'); + assert(axis >= -$x.shape.length && axis < $x.shape.length, function () { return "Axis = " + axis + " is not in [-" + $x.shape.length + ", " + $x.shape.length + ")"; }); + var inputs = { value: $x }; + var attrs = { axis: axis }; + return ENGINE.runKernel(Unpack, inputs, attrs); + } + var unstack = op({ unstack_: unstack_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a new variable with the provided initial value. + * ```js + * const x = tf.variable(tf.tensor([1, 2, 3])); + * x.assign(tf.tensor([4, 5, 6])); + * + * x.print(); + * ``` + * + * @param initialValue Initial value for the tensor. + * @param trainable If true, optimizers are allowed to update it. + * @param name Name of the variable. Defaults to a unique id. + * @param dtype If set, initialValue will be converted to the given type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function variable(initialValue, trainable, name, dtype) { + if (trainable === void 0) { trainable = true; } + return ENGINE.makeVariable(initialValue, trainable, name, dtype); + } + + /** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function whereImpl(condShape, condVals) { + var indices = []; + for (var i = 0; i < condVals.length; i++) { + if (condVals[i]) { + indices.push(i); + } + } + var inBuffer = buffer(condShape, 'int32'); + var out = buffer([indices.length, condShape.length], 'int32'); + for (var i = 0; i < indices.length; i++) { + var loc = inBuffer.indexToLoc(indices[i]); + var offset = i * condShape.length; + out.values.set(loc, offset); + } + return out.toTensor(); + } + + /** + * Returns the coordinates of true elements of condition. + * + * The coordinates are returned in a 2-D tensor where the first dimension (rows) + * represents the number of true elements, and the second dimension (columns) + * represents the coordinates of the true elements. Keep in mind, the shape of + * the output tensor can vary depending on how many true values there are in + * input. Indices are output in row-major order. The resulting tensor has the + * shape `[numTrueElems, condition.rank]`. + * + * This is analogous to calling the python `tf.where(cond)` without an x or y. + * + * ```js + * const cond = tf.tensor1d([false, false, true], 'bool'); + * const result = await tf.whereAsync(cond); + * result.print(); + * ``` + * + * @doc {heading: 'Operations', subheading: 'Logical'} + */ + function whereAsync_(condition) { + return __awaiter(this, void 0, void 0, function () { + var $condition, vals, res; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool'); + return [4 /*yield*/, $condition.data()]; + case 1: + vals = _a.sent(); + res = whereImpl($condition.shape, vals); + if (condition !== $condition) { + $condition.dispose(); + } + return [2 /*return*/, res]; + } + }); + }); + } + var whereAsync = whereAsync_; + + /** + * Apply boolean mask to tensor. + * + * ```js + * const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]); + * const mask = tf.tensor1d([1, 0, 1], 'bool'); + * const result = await tf.booleanMaskAsync(tensor, mask); + * result.print(); + * ``` + * + * @param tensor N-D tensor. + * @param mask K-D boolean tensor, K <= N and K must be known statically. + * @param axis A 0-D int Tensor representing the axis in tensor to mask from. + * By default, axis is 0 which will mask from the first dimension. + * Otherwise K + axis <= N. + * + * @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} + */ + function booleanMaskAsync_(tensor, mask, axis) { + return __awaiter(this, void 0, void 0, function () { + var $tensor, $mask, axisFrom, maskDim, tensorShape, leadingSize, i, targetTensorShape, reshapedTensor, reshapedMask, positivePositions, indices, res; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + $tensor = convertToTensor(tensor, 'tensor', 'boolMask'); + $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool'); + axisFrom = axis == null ? 0 : axis; + maskDim = $mask.rank; + tensorShape = $tensor.shape; + assert(maskDim > 0, function () { return 'mask cannot be scalar'; }); + assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, "mask's shape must match the first K dimensions of tensor's shape,"); + leadingSize = 1; + for (i = axisFrom; i < axisFrom + maskDim; i++) { + leadingSize *= tensorShape[i]; + } + targetTensorShape = tensorShape.slice(0, axisFrom) + .concat([leadingSize], tensorShape.slice(axisFrom + maskDim)); + reshapedTensor = reshape($tensor, targetTensorShape); + reshapedMask = reshape($mask, [-1]); + return [4 /*yield*/, whereAsync(reshapedMask)]; + case 1: + positivePositions = _a.sent(); + indices = squeeze(positivePositions, [1]); + res = gather(reshapedTensor, indices, axisFrom); + // Ensure no memory leak. + if (tensor !== $tensor) { + $tensor.dispose(); + } + if (mask !== $mask) { + $mask.dispose(); + } + indices.dispose(); + reshapedTensor.dispose(); + reshapedMask.dispose(); + positivePositions.dispose(); + return [2 /*return*/, res]; + } + }); + }); + } + var booleanMaskAsync = booleanMaskAsync_; + + /** + * @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. + * ============================================================================= + */ + /** + * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`. + * + * The returned `tf.Tensor`'s dimension `i` will correspond to the input + * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`, + * where `n` is the rank of the input `tf.Tensor`. Hence by default, this + * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s. + * + * ```js + * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]); + * + * a.transpose().print(); // or tf.transpose(a) + * ``` + * + * @param x The tensor to transpose. + * @param perm The permutation of the dimensions of a. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function transpose_(x, perm) { + var $x = convertToTensor(x, 'x', 'transpose'); + if (perm == null) { + perm = $x.shape.map(function (s, i) { return i; }).reverse(); + } + assert($x.rank === perm.length, function () { return "Error in transpose: rank of input " + $x.rank + " " + + ("must match length of perm " + perm + "."); }); + perm.forEach(function (axis) { + assert(axis >= 0 && axis < $x.rank, function () { return "All entries in 'perm' must be between 0 and " + ($x.rank - 1) + + (" but got " + perm); }); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + var inputs = { x: $x }; + var attrs = { perm: perm }; + return ENGINE.runKernel(Transpose, inputs, attrs); + } + var transpose = op({ transpose_: transpose_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the norm of scalar, vectors, and matrices. + * This function can compute several different vector norms (the 1-norm, the + * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0) + * and matrix norms (Frobenius, 1-norm, and inf-norm). + * + * ```js + * const x = tf.tensor1d([1, 2, 3, 4]); + * + * x.norm().print(); // or tf.norm(x) + * ``` + * + * @param x The input array. + * @param ord Optional. Order of the norm. Supported norm types are + * following: + * + * | ord | norm for matrices | norm for vectors + * |------------|---------------------------|--------------------- + * |'euclidean' |Frobenius norm |2-norm + * |'fro' |Frobenius norm | + * |Infinity |max(sum(abs(x), axis=1)) |max(abs(x)) + * |-Infinity |min(sum(abs(x), axis=1)) |min(abs(x)) + * |1 |max(sum(abs(x), axis=0)) |sum(abs(x)) + * |2 | |sum(abs(x)^2)^1/2* + * + * @param axis Optional. If axis is null (the default), the input is + * considered a vector and a single vector norm is computed over the entire + * set of values in the Tensor, i.e. norm(x, ord) is equivalent + * to norm(x.reshape([-1]), ord). If axis is a integer, the input + * is considered a batch of vectors, and axis determines the axis in x + * over which to compute vector norms. If axis is a 2-tuple of integer it is + * considered a batch of matrices and axis determines the axes in NDArray + * over which to compute a matrix norm. + * @param keepDims Optional. If true, the norm have the same dimensionality + * as the input. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function norm_(x, ord, axis, keepDims) { + if (ord === void 0) { ord = 'euclidean'; } + if (axis === void 0) { axis = null; } + if (keepDims === void 0) { keepDims = false; } + x = convertToTensor(x, 'x', 'norm'); + var norm = normImpl(x, ord, axis); + var keepDimsShape = norm.shape; + if (keepDims) { + var axes = parseAxisParam(axis, x.shape); + keepDimsShape = expandShapeToKeepDim(norm.shape, axes); + } + return reshape(norm, keepDimsShape); + } + function normImpl(x, p, axis) { + if (axis === void 0) { axis = null; } + if (x.rank === 0) { + return abs(x); + } + // consider vector when no axis is specified + if (x.rank !== 1 && axis === null) { + return normImpl(reshape(x, [-1]), p, axis); + } + // vector + if (x.rank === 1 || typeof axis === 'number' || + Array.isArray(axis) && axis.length === 1) { + if (p === 1) { + return sum(abs(x), axis); + } + if (p === Infinity) { + return max(abs(x), axis); + } + if (p === -Infinity) { + return min(abs(x), axis); + } + if (p === 'euclidean' || p === 2) { + // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2 + return sqrt(sum(pow(abs(x), scalar(2, 'int32')), axis)); + } + throw new Error("Error in norm: invalid ord value: " + p); + } + // matrix (assumption axis[0] < axis[1]) + if (Array.isArray(axis) && axis.length === 2) { + if (p === 1) { + return max(sum(abs(x), axis[0]), axis[1] - 1); + } + if (p === Infinity) { + return max(sum(abs(x), axis[1]), axis[0]); + } + if (p === -Infinity) { + return min(sum(abs(x), axis[1]), axis[0]); + } + if (p === 'fro' || p === 'euclidean') { + // norm(x) = sqrt(sum(pow(x, 2))) + return sqrt(sum(square(x), axis)); + } + throw new Error("Error in norm: invalid ord value: " + p); + } + throw new Error("Error in norm: invalid axis: " + axis); + } + var norm = op({ norm_: norm_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Compute the moving average of a variable. + * + * Without zeroDebias, the moving average operation is defined by: + * `v += delta` + * where + * `delta = (1 - decay) * (x - v)` + * + * With zeroDebias (default), the `delta` term is scaled to debias the + * effect of the (assumed) zero-initialization of `v`. + * `delta /= (1 - decay ^ step)` + * + * For more details on the zero-debiasing algorithm, see: + * https://arxiv.org/abs/1412.6980 + * + * Note that this function is completely stateless and does not keep track of + * step count. The step count needs to be maintained by the caller and passed + * in as `step`. + * + * @param v The current moving average value. + * @param x New input value, must have the same shape and dtype as `v`. + * @param decay The decay factor. Typical values are 0.95 and 0.99. + * @param step Step count. + * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`). + * @returns The new moving average value. + * + * @doc {heading: 'Operations', subheading: 'Moving Average'} + */ + function movingAverage_(v, x, decay, step, zeroDebias) { + if (zeroDebias === void 0) { zeroDebias = true; } + var $v = convertToTensor(v, 'v', 'movingAverage'); + var $x = convertToTensor(x, 'x', 'movingAverage'); + var $decay = convertToTensor(decay, 'decay', 'movingAverage'); + assertTypesMatch($v, $x); + assert(arraysEqual($v.shape, $x.shape), function () { return 'Shape mismatch in v and x'; }); + var one = scalar(1); + var oneMinusDecay = sub(one, $decay); + var update = mul(sub($x, $v), oneMinusDecay); + if (zeroDebias) { + assert(step != null, function () { return 'When using zeroDebias: true, step is required.'; }); + var $step = convertToTensor(step, 'step', 'movingAverage'); + update = div(update, sub(one, pow($decay, $step))); + } + return add($v, update); + } + var movingAverage = op({ movingAverage_: movingAverage_ }); + + /** + * Check whether updates.shape = indices.shape[:batchDim] + + * shape[sliceDim:] + * + * @param x The input tensor. + */ + function validateUpdateShape(shape, indices, updates) { + var sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1; + var batchDim = (indices.rank > 1) ? indices.rank - 1 : 1; + var 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 (var 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 (var 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] + ")")); + } + } + } + /** + * Validate scatter nd inputs. + * + * @param update The tensor contains the update values. + * @param indices The tensor contains the indices for the update values. + * @param shape The shape of the output tensor. + */ + function validateInput$1(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); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a new tensor by applying sparse updates to individual + * values or slices within a zero tensor of the given shape tensor according to + * indices. This operator is the inverse of the `tf.gatherND` operator which + * extracts values or slices from a given tensor. + * + * ```js + * const indices = tf.tensor2d([4, 3, 1, 7], [4, 1], 'int32'); + * const updates = tf.tensor1d([9, 10, 11, 12]); + * const shape = [8]; + * tf.scatterND(indices, updates, shape).print() //[0, 11, 0, 10, 9, 0, 0, 12] + * ``` + * + * @param indices The tensor contains the indices into the output tensor. + * @param updates The tensor contains the value for the indices. + * @param shape: The shape of the output tensor. + * + * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} + */ + function scatterND_(indices, updates, shape) { + var $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32'); + var $updates = convertToTensor(updates, 'updates', 'scatterND'); + validateInput$1($updates, $indices, shape); + var inputs = { indices: $indices, updates: $updates }; + var attrs = { shape: shape }; + // tslint:disable-next-line: no-unnecessary-type-assertion + return ENGINE.runKernel(ScatterNd, inputs, attrs); + } + var scatterND = op({ scatterND_: scatterND_ }); + + /** + * Validate sparseToDense inputs. + * + * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32. + * sparseIndices[i] contains the complete index where sparseValues[i] will be + * placed. + * @param sparseValues A 0-D or 1-D Tensor. Values + * corresponding to each row of sparseIndices, or a scalar value to be used for + * all sparse indices. + * @param outputShape number[]. Shape of the dense output tensor. + * @param validateIndices boolean. indice validation is not supported, error + * will be thrown if it is set. + */ + function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) { + if (sparseIndices.dtype !== 'int32') { + throw new Error('tf.sparseToDense() expects the indices to be int32 type,' + + (" but the dtype was " + sparseIndices.dtype + ".")); + } + if (sparseIndices.rank > 2) { + throw new Error('sparseIndices should be a scalar, vector, or matrix,' + + (" but got shape " + sparseIndices.shape + ".")); + } + var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1; + var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1; + if (outputShape.length !== numDims) { + throw new Error('outputShape has incorrect number of elements:,' + + (" " + outputShape.length + ", should be: " + numDims + ".")); + } + var numValues = sparseValues.size; + if (!(sparseValues.rank === 0 || + sparseValues.rank === 1 && numValues === numElems)) { + throw new Error('sparseValues has incorrect shape ' + + (sparseValues.shape + ", should be [] or [" + numElems + "]")); + } + if (sparseValues.dtype !== defaultValues.dtype) { + throw new Error('sparseValues.dtype must match defaultValues.dtype'); + } + } + + /** + * @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. + * ============================================================================= + */ + /** + * Converts a sparse representation into a dense tensor. + * + * Builds an array dense with shape outputShape such that: + * + * // If sparseIndices is scalar + * dense[i] = (i == sparseIndices ? sparseValues : defaultValue) + * + * // If sparseIndices is a vector, then for each i + * dense[sparseIndices[i]] = sparseValues[i] + * + * // If sparseIndices is an n by d matrix, then for each i in [0, n) + * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i] + * All other values in dense are set to defaultValue. If sparseValues is a + * scalar, all sparse indices are set to this single value. + * + * If indices are repeated the final value is summed over all values for those + * indices. + * + * ```js + * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32'); + * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32'); + * const shape = [8]; + * tf.sparseToDense(indices, values, shape).print(); + * ``` + * + * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32. + * sparseIndices[i] contains the complete index where sparseValues[i] will be + * placed. + * @param sparseValues A 0-D or 1-D Tensor. Values + * corresponding to each row of sparseIndices, or a scalar value to be used for + * all sparse indices. + * @param outputShape Shape of the dense output tensor. the type is inferred. + * @param defaultValue Scalar. Value to set for indices not specified in + * sparseIndices. Defaults to zero. + * + * @doc {heading: 'Operations', subheading: 'Normalization'} + */ + function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue) { + if (defaultValue === void 0) { defaultValue = 0; } + var $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32'); + var $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense'); + var $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype); + validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue); + var inputs = { + sparseIndices: $sparseIndices, + sparseValues: $sparseValues, + defaultValue: $defaultValue + }; + var attrs = { outputShape: outputShape }; + return ENGINE.runKernel(SparseToDense, inputs, attrs); + } + var sparseToDense = op({ sparseToDense_: sparseToDense_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Gather slices from input tensor into a Tensor with shape specified by + * `indices`. + * + * `indices` is an K-dimensional integer tensor, best thought of as a + * (K-1)-dimensional tensor of indices into input, where each element defines a + * slice of input: + * output[\\(i_0, ..., i_{K-2}\\)] = input[indices[\\(i_0, ..., i_{K-2}\\)]] + * + * Whereas in `tf.gather`, `indices` defines slices into the first dimension of + * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions + * of input, where N = indices.shape[-1]. + * + * The last dimension of indices can be at most the rank of input: + * indices.shape[-1] <= input.rank + * + * The last dimension of `indices` corresponds to elements + * (if indices.shape[-1] == input.rank) or slices + * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of + * input. + * The output tensor has shape + * indices.shape[:-1] + input.shape[indices.shape[-1]:] + * + * Note that on CPU, if an out of bound index is found, an error is returned. On + * GPU, if an out of bound index is found, a 0 is stored in the corresponding + * output value. + * + * ```js + * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32'); + * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]); + * tf.gatherND(input, indices).print() // [10, 11] + * ``` + * + * @param x The tensor from which to gather values. + * @param indices Index tensor, must be of type int32. + * + * @doc {heading: 'Operations', subheading: 'Slicing and Joining'} + */ + function gatherND_(x, indices) { + var $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32'); + var $x = convertToTensor(x, 'x', 'gatherND', 'string_or_numeric'); + var inputs = { params: $x, indices: $indices }; + return ENGINE.runKernel(GatherNd, inputs); + } + var gatherND = op({ gatherND_: gatherND_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Normalize noise shape based on provided tensor and noise shape. + * + * @param x Tensor. + * @param noiseShape The shape for the randomly generated keep/drop flags, as + * an array of numbers. Optional. + * @returns Normalized noise shape. + */ + function getNoiseShape(x, noiseShape) { + if (noiseShape == null) { + return x.shape.slice(); + } + if (arraysEqual(x.shape, noiseShape)) { + return noiseShape; + } + if (x.shape.length === noiseShape.length) { + var newDimension = []; + for (var 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; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Computes dropout. + * + * ```js + * const x = tf.tensor1d([1, 2, 2, 1]); + * const rate = 0.75; + * const output = tf.dropout(x, rate); + * output.print(); + * ``` + * + * @param x A floating point Tensor or TensorLike. + * @param rate A float in the range [0, 1). The probability that each element + * of x is discarded. + * @param noiseShape An array of numbers of type int32, representing the + * shape for randomly generated keep/drop flags. If the noiseShape has null + * value, it will be automatically replaced with the x's relative dimension + * size. Optional. + * @param seed Used to create random seeds. Optional. + * @returns A Tensor of the same shape of x. + * + * @doc {heading: 'Operations', subheading: 'Dropout'} + */ + function dropout_(x, rate, noiseShape, seed) { + var $x = convertToTensor(x, 'x', 'dropout'); + assert($x.dtype === 'float32', function () { return "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, function () { return "rate must be a float in the range [0, 1), but got " + rate + "."; }); + if (rate === 0) { + return x instanceof Tensor ? $x.clone() : $x; + } + var $noiseShape = getNoiseShape($x, noiseShape); + var keepProb = 1 - rate; + var multiplier = div(floor(add(randomUniform($noiseShape, 0, 1, 'float32', seed), keepProb)), keepProb); + return mul($x, multiplier); + } + var dropout = op({ dropout_: dropout_ }); + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function enclosingPowerOfTwo(value) { + // Return 2**N for integer N such that 2**N >= value. + return Math.floor(Math.pow(2, Math.ceil(Math.log(value) / Math.log(2.0)))); + } + function cosineWindow(windowLength, a, b) { + var even = 1 - windowLength % 2; + var newValues = new Float32Array(windowLength); + for (var i = 0; i < windowLength; ++i) { + var cosArg = (2.0 * Math.PI * i) / (windowLength + even - 1); + newValues[i] = a - b * Math.cos(cosArg); + } + return tensor1d(newValues, 'float32'); + } + + /** + * Returns whether the targets are in the top K predictions. + * + * ```js + * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]); + * const targets = tf.tensor1d([2, 0]); + * const precision = await tf.inTopKAsync(predictions, targets); + * precision.print(); + * ``` + * @param predictions 2-D or higher `tf.Tensor` with last dimension being + * at least `k`. + * @param targets 1-D or higher `tf.Tensor`. + * @param k Optional Number of top elements to look at for computing precision, + * default to 1. + * + * @doc {heading: 'Operations', subheading: 'Evaluation'} + */ + function inTopKAsync_(predictions, targets, k) { + if (k === void 0) { k = 1; } + return __awaiter(this, void 0, void 0, function () { + var $predictions, $targets, lastDim, predictionsVals, targetsVals, _a, batch, size, precision, b, offset, vals, valAndInd, i, i; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + $predictions = convertToTensor(predictions, 'predictions', 'inTopK'); + $targets = convertToTensor(targets, 'targets', 'inTopK'); + assert($predictions.rank > 1, function () { return 'inTopK() expects the predictions to be of rank 2 or higher, ' + + ("but got " + $predictions.rank); }); + assert($predictions.rank - 1 === $targets.rank, function () { return "predictions rank should be 1 larger than " + + "targets rank, but got predictions rank " + + ($predictions.rank + " and targets rank " + $targets.rank); }); + assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, "predictions's shape should be align with the targets' shape, " + + 'except the last dimension.'); + lastDim = $predictions.shape[$predictions.shape.length - 1]; + assert(k > 0 && k <= lastDim, function () { return "'k' passed to inTopK() must be > 0 && <= the predictions last " + + ("dimension (" + lastDim + "), but got " + k); }); + return [4 /*yield*/, $predictions.data()]; + case 1: + predictionsVals = _b.sent(); + return [4 /*yield*/, $targets.data()]; + case 2: + targetsVals = _b.sent(); + _a = __read([predictionsVals.length / lastDim, lastDim], 2), batch = _a[0], size = _a[1]; + precision = getTypedArrayFromDType('bool', batch); + for (b = 0; b < batch; b++) { + offset = b * size; + vals = predictionsVals.subarray(offset, offset + size); + valAndInd = []; + for (i = 0; i < vals.length; i++) { + valAndInd.push({ value: vals[i], index: i }); + } + valAndInd.sort(function (a, b) { return b.value - a.value; }); + precision[b] = 0; + for (i = 0; i < k; i++) { + if (valAndInd[i].index === targetsVals[b]) { + precision[b] = 1; + break; + } + } + } + if (predictions !== $predictions) { + $predictions.dispose(); + } + if (targets !== $targets) { + $targets.dispose(); + } + // Output precision has the same shape as targets. + return [2 /*return*/, tensor(precision, $targets.shape, 'bool')]; + } + }); + }); + } + var inTopKAsync = inTopKAsync_; + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the derivative of the filter of a 2D convolution. + * + * @param x The input tensor, of rank 4 or rank 3 of shape + * [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed. + * @param dy The dy image, of rank 4 or rank 3, of shape + * [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed. + * @param filterShape The shape of the filter, length 4, + * [filterHeight, filterWidth, inDepth, outDepth]. + * @param strides The strides of the convolution: [strideHeight, + * strideWidth]. + * @param pad A string from: 'same', 'valid'. The type of padding algorithm + * used in the forward prop of the op. + * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + */ + function conv2DBackpropFilter_(x, dy, filterShape, strides, pad, dataFormat, dimRoundingMode) { + if (dataFormat === void 0) { dataFormat = 'NHWC'; } + var x4D = x; + if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } + var dy4D = dy; + if (dy4D.rank === 3) { + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in conv2dDerFilter: input must be rank 4, but got shape " + + (x4D.shape + "."); }); + assert(dy4D.rank === 4, function () { return "Error in conv2dDerFilter: dy must be rank 4, but got shape " + + (dy4D.shape + "."); }); + assert(filterShape.length === 4, function () { return "Error in conv2dDerFilter: filterShape must be length 4, but got " + + (filterShape + "."); }); + var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1]; + var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1]; + assert(inDepth === filterShape[2], function () { return "Error in conv2dDerFilter: depth of input " + inDepth + ") must " + + ("match input depth in filter (" + filterShape[2] + "."); }); + assert(outDepth === filterShape[3], function () { return "Error in conv2dDerFilter: depth of dy (" + outDepth + ") must " + + ("match output depth for filter (" + filterShape[3] + ")."); }); + checkPadOnDimRoundingMode('conv2dDerFilter', pad, dimRoundingMode); + var inputs = { x: x4D, dy: dy4D }; + var attrs = { strides: strides, pad: pad, dataFormat: dataFormat, dimRoundingMode: dimRoundingMode, filterShape: filterShape }; + // tslint:disable-next-line: no-unnecessary-type-assertion + return ENGINE.runKernel(Conv2DBackpropFilter, inputs, attrs); + } + var conv2DBackpropFilter = op({ conv2DBackpropFilter_: conv2DBackpropFilter_ }); + + /** + * @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. + * ============================================================================= + */ + // Returns gradient for fused activation. + 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 + "."); + } + // Returns gradient for fused bias. + function getFusedBiasGradient(bias, dyActivation) { + var res = dyActivation; + var reduceAxes = getReductionAxes(bias.shape, dyActivation.shape); + if (reduceAxes.length > 0) { + res = sum(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 + "."); + } + // Whether we should call fused ops. + var shouldFuse = function (gradientDepth, activation) { + var gradientMode = gradientDepth > 0; + return !gradientMode || activation === 'linear'; + }; + + /** + * Computes a 2D convolution over the input x, optionally fused with adding a + * bias and applying an activation. + * + * ```js + * const inputDepth = 2; + * const inShape = [2, 2, 2, inputDepth]; + * const outputDepth = 2; + * const fSize = 1; + * const pad = 0; + * const strides = 1; + * + * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, + * 16], inShape); + * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth, + * outputDepth]); + * + * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC', + * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print(); + * ``` + * + * @param obj An object with the following properties: + * @param x The input tensor, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is + * assumed. + * @param filter The filter, rank 4, of shape + * `[filterHeight, filterWidth, inDepth, outDepth]`. + * @param strides The strides of the convolution: `[strideHeight, + * strideWidth]`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid` output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dataFormat An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. Only "NHWC" is currently supported. + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * @param bias Tensor to be added to the result. + * @param activation Name of activation kernel (defaults to `linear`) to be + * applied + * after biasAdd. + * @param preluActivationWeights Tensor of prelu weights to be applied as part + * of a `prelu` activation, typically the same shape as `x`. + * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu` + * activation. + */ + function fusedConv2d_(_a) { + var _b; + var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _c = _a.dataFormat, dataFormat = _c === void 0 ? 'NHWC' : _c, _d = _a.dilations, dilations = _d === void 0 ? [1, 1] : _d, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha; + activation = activation || 'linear'; + if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) { + var result = conv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode); + if (bias != null) { + result = add(result, bias); + } + return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha); + } + var $x = convertToTensor(x, 'x', 'conv2d', 'float32'); + var $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32'); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in fused conv2d: input must be rank 4, but got rank " + + (x4D.rank + "."); }); + assert($filter.rank === 4, function () { return "Error in fused conv2d: filter must be rank 4, but got rank " + + ($filter.rank + "."); }); + checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode); + assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " + + ("input depth for filter " + $filter.shape[2] + "."); }); + assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' + + ("Got strides " + strides + " and dilations '" + dilations + "'"); }); + assert(dataFormat === 'NHWC', function () { return "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported."; }); + var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode); + var $bias; + if (bias != null) { + $bias = convertToTensor(bias, 'bias', 'fused conv2d'); + _b = __read(makeTypesMatch($bias, $x), 1), $bias = _b[0]; + assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); + } + var $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d'); + } + var grad = function (dy, saved) { + var _a = __read(saved, 4), $filter = _a[0], x4D = _a[1], y = _a[2], $bias = _a[3]; + var dyActivation = getFusedDyActivation(dy, y, activation); + assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused conv2D: ' + + "dilation rates greater than 1 " + + ("are not yet supported in gradients. Got dilations '" + dilations + "'"); }); + var xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad); + var filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad); + var der = [xDer, filterDer]; + if ($bias != null) { + var biasDer = getFusedBiasGradient($bias, dyActivation); + der.push(biasDer); + } + return der; + }; + var inputs = { + x: x4D, + filter: $filter, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + var attrs = { + strides: strides, + pad: pad, + dataFormat: dataFormat, + dilations: dilations, + dimRoundingMode: dimRoundingMode, + activation: activation, + leakyreluAlpha: leakyreluAlpha + }; + // Depending on the the params passed in we will have different number of + // inputs and thus a a different number of elements in the gradient. + if (bias == null) { + var customOp = customGrad(function (x4D, filter, save) { + var res = + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(FusedConv2D, inputs, attrs); + save([filter, x4D, res]); + if (reshapedTo4D) { + // tslint:disable-next-line: no-unnecessary-type-assertion + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad }; + }); + return customOp(x4D, $filter); + } + else { + var customOpWithBias = customGrad(function (x4D, filter, bias, save) { + var res = ENGINE.runKernel(FusedConv2D, inputs, attrs); + save([filter, x4D, res, bias]); + if (reshapedTo4D) { + // tslint:disable-next-line: no-unnecessary-type-assertion + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad }; + }); + return customOpWithBias(x4D, $filter, $bias); + } + } + var conv2d = op({ fusedConv2d_: fusedConv2d_ }); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function depthwiseConv2dNativeBackpropFilter_(x, dy, filterShape, strides, pad, dilations, dimRoundingMode) { + if (dilations === void 0) { dilations = [1, 1]; } + var x4D = x; + if (x.rank === 3) { + x4D = reshape(x, [1, x.shape[0], x.shape[1], x.shape[2]]); + } + var dy4D = dy; + if (dy4D.rank === 3) { + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + var inputs = { x: x4D, dy: dy4D }; + var attrs = { strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dilations: dilations, filterShape: filterShape }; + // tslint:disable-next-line: no-unnecessary-type-assertion + return ENGINE.runKernel(DepthwiseConv2dNativeBackpropFilter, inputs, attrs); + } + var depthwiseConv2dNativeBackpropFilter = op({ depthwiseConv2dNativeBackpropFilter_: depthwiseConv2dNativeBackpropFilter_ }); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function depthwiseConv2dNativeBackpropInput_(xShape, dy, filter, strides, pad, dilations, dimRoundingMode) { + if (dilations === void 0) { dilations = [1, 1]; } + var dy4D = dy; + var reshapedTo4D = false; + if (dy.rank === 3) { + reshapedTo4D = true; + dy4D = reshape(dy, [1, dy.shape[0], dy.shape[1], dy.shape[2]]); + } + var inputs = { dy: dy4D, filter: filter }; + var attrs = { strides: strides, pad: pad, dimRoundingMode: dimRoundingMode, dilations: dilations, inputShape: xShape }; + var res = + // tslint:disable-next-line: no-unnecessary-type-assertion + 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_: depthwiseConv2dNativeBackpropInput_ }); + + /** + * Computes depthwise 2D convolution, optionally fused with adding a + * bias and applying an activation. + * + * Given a 4D `input` array and a `filter` array of shape + * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing + * `inChannels` convolutional filters of depth 1, this op applies a + * different filter to each input channel (expanding from 1 channel to + * `channelMultiplier` channels for each), then concatenates the results + * together. The output has `inChannels * channelMultiplier` channels. + * + * See + * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d]( + * https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d) + * for more details. + * + * @param obj An object with the following properties: + * @param x The input tensor, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is + * assumed. + * @param filter The filter tensor, rank 4, of shape + * `[filterHeight, filterWidth, inChannels, channelMultiplier]`. + * @param strides The strides of the convolution: `[strideHeight, + * strideWidth]`. If strides is a single number, then `strideHeight == + * strideWidth`. + * @param pad The type of padding algorithm. + * - `same` and stride 1: output will be of same size as input, + * regardless of filter size. + * - `valid`: output will be smaller than input if filter is larger + * than 1x1. + * - For more info, see this guide: + * [https://www.tensorflow.org/api_docs/python/tf/nn/convolution]( + * https://www.tensorflow.org/api_docs/python/tf/nn/convolution) + * @param dilations The dilation rates: `[dilationHeight, dilationWidth]` + * in which we sample input values across the height and width dimensions + * in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single + * number, then `dilationHeight == dilationWidth`. If it is greater than + * 1, then all values of `strides` must be 1. + * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to + * "NHWC". Specify the data format of the input and output data. With the + * default format "NHWC", the data is stored in the order of: [batch, + * height, width, channels]. Only "NHWC" is currently supported. + * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is + * provided, it will default to truncate. + * @param bias Tensor to be added to the result. + * @param activation Name of activation kernel (defaults to `linear`). + * @param preluActivationWeights Tensor of prelu weights to be applied as part + * of a `prelu` activation, typically the same shape as `x`. + * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu` + * activation. + */ + function fusedDepthwiseConv2d_(_a) { + var _b; + var x = _a.x, filter = _a.filter, strides = _a.strides, pad = _a.pad, _c = _a.dataFormat, dataFormat = _c === void 0 ? 'NHWC' : _c, _d = _a.dilations, dilations = _d === void 0 ? [1, 1] : _d, dimRoundingMode = _a.dimRoundingMode, bias = _a.bias, _e = _a.activation, activation = _e === void 0 ? 'linear' : _e, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha; + if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) { + var result = depthwiseConv2d$1(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode); + if (bias != null) { + result = add(result, bias); + } + return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha); + } + var $x = convertToTensor(x, 'x', 'depthwiseConv2d', 'float32'); + var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d', 'float32'); + var x4D = $x; + var reshapedTo4D = false; + if ($x.rank === 3) { + reshapedTo4D = true; + x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]); + } + assert(x4D.rank === 4, function () { return "Error in fused depthwiseConv2d: input must be rank 4, but got " + + ("rank " + x4D.rank + "."); }); + assert($filter.rank === 4, function () { return "Error in fused depthwiseConv2d: filter must be rank 4, " + + ("but got rank " + $filter.rank + "."); }); + assert(x4D.shape[3] === $filter.shape[2], function () { return "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), function () { return 'Error in fused depthwiseConv2d: Either strides or dilations must ' + + ("be 1. Got strides " + strides + " and dilations '" + dilations + "'"); }); + checkPadOnDimRoundingMode('fused depthwiseConv2d', pad, dimRoundingMode); + var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */); + var $bias; + if (bias != null) { + $bias = convertToTensor(bias, 'bias', 'fused conv2d'); + _b = __read(makeTypesMatch($bias, $x), 1), $bias = _b[0]; + assertAndGetBroadcastShape(convInfo.outShape, $bias.shape); + } + var $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d'); + } + var grad = function (dy, saved) { + assert(tupleValuesAreOne(dilations), function () { return 'Error in gradient of fused depthwiseConv2d: dilation rates ' + + "greater than 1 are not yet supported. Got dilations " + + ("'" + dilations + "'"); }); + var _a = __read(saved, 4), $filter = _a[0], x4D = _a[1], y = _a[2], bias = _a[3]; + var dyActivation = getFusedDyActivation(dy, y, activation); + var xDer = depthwiseConv2dNativeBackpropInput(x4D.shape, dyActivation, $filter, strides, pad, dilations, dimRoundingMode); + var filterDer = depthwiseConv2dNativeBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad, dilations, dimRoundingMode); + if (bias != null) { + var biasDer = getFusedBiasGradient($bias, dyActivation); + return [xDer, filterDer, biasDer]; + } + return [xDer, filterDer]; + }; + var inputs = { + x: x4D, + filter: $filter, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + var attrs = { + strides: strides, + pad: pad, + dataFormat: dataFormat, + dilations: dilations, + dimRoundingMode: dimRoundingMode, + activation: activation, + leakyreluAlpha: leakyreluAlpha + }; + // Depending on the the params passed in we will have different number of + // inputs and thus a a different number of elements in the gradient. + if (bias == null) { + var customOp = customGrad(function (x4D, filter, save) { + // tslint:disable-next-line: no-unnecessary-type-assertion + var res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); + save([filter, x4D, res]); + if (reshapedTo4D) { + // tslint:disable-next-line: no-unnecessary-type-assertion + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad }; + }); + return customOp(x4D, $filter); + } + else { + var customOpWithBias = customGrad(function (x4D, filter, bias, save) { + // tslint:disable-next-line: no-unnecessary-type-assertion + var res = ENGINE.runKernel(FusedDepthwiseConv2D, inputs, attrs); + save([filter, x4D, res, bias]); + if (reshapedTo4D) { + // tslint:disable-next-line: no-unnecessary-type-assertion + res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]); + } + return { value: res, gradFunc: grad }; + }); + return customOpWithBias(x4D, $filter, $bias); + } + } + var depthwiseConv2d = op({ fusedDepthwiseConv2d_: fusedDepthwiseConv2d_ }); + + /** + * Computes the dot product of two matrices with optional activation and bias. + * + * ```js + * const a = tf.tensor2d([-1, -2], [1, 2]); + * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * const bias = tf.tensor2d([1, 2], [1, 2]); + * + * tf.fused.matMul({a, b, bias, activation: 'relu'}).print(); + * ``` + * + * @param obj An object with the following properties: + * - `a` First matrix in dot product operation. + * - `b` Second matrix in dot product operation. + * - `transposeA` If true, `a` is transposed before multiplication. + * - `transposeB` If true, `b` is transposed before multiplication. + * - `bias` Matrix to be added to the result. + * - `activation` Name of activation kernel (defaults to `linear`). + * - `preluActivationWeights` Tensor of prelu weights. + * - `leakyreluAlpha` Alpha of leakyrelu. + */ + function fusedMatMul_(_a) { + var _b, _c; + var a = _a.a, b = _a.b, _d = _a.transposeA, transposeA = _d === void 0 ? false : _d, _e = _a.transposeB, transposeB = _e === void 0 ? false : _e, bias = _a.bias, _f = _a.activation, activation = _f === void 0 ? 'linear' : _f, preluActivationWeights = _a.preluActivationWeights, leakyreluAlpha = _a.leakyreluAlpha; + if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) { + var result = matMul$1(a, b, transposeA, transposeB); + if (bias != null) { + result = add(result, bias); + } + return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha); + } + var $a = convertToTensor(a, 'a', 'fused matMul'); + var $b = convertToTensor(b, 'b', 'fused matMul'); + _b = __read(makeTypesMatch($a, $b), 2), $a = _b[0], $b = _b[1]; + var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1]; + var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2]; + var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2]; + var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1]; + var outerDimsA = $a.shape.slice(0, -2); + var outerDimsB = $b.shape.slice(0, -2); + var batchDimA = sizeFromShape(outerDimsA); + var batchDimB = sizeFromShape(outerDimsB); + assert(innerShapeA === innerShapeB, function () { return "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."); }); + var outShapeOuterDims = assertAndGetBroadcastShape($a.shape.slice(0, -2), $b.shape.slice(0, -2)); + var outShape = outShapeOuterDims.concat([outerShapeA, outerShapeB]); + var a3D = transposeA ? + reshape($a, [batchDimA, innerShapeA, outerShapeA]) : + reshape($a, [batchDimA, outerShapeA, innerShapeA]); + var b3D = transposeB ? + reshape($b, [batchDimB, outerShapeB, innerShapeB]) : + reshape($b, [batchDimB, innerShapeB, outerShapeB]); + var $bias; + if (bias != null) { + $bias = convertToTensor(bias, 'bias', 'fused matMul'); + _c = __read(makeTypesMatch($bias, $a), 1), $bias = _c[0]; + assertAndGetBroadcastShape(outShape, $bias.shape); + } + var $preluActivationWeights; + if (preluActivationWeights != null) { + $preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused matMul'); + } + var grad = function (dy, saved) { + var _a = __read(saved, 4), a3D = _a[0], b3D = _a[1], y = _a[2], $bias = _a[3]; + // we reshape dy because the result of the forward is not + // necessarily going to be a 3d tensor due to a reshape done at the end of + // the customOp. + var dyActivation = getFusedDyActivation(reshape(dy, y.shape), y, activation); + var aDer; + var bDer; + if (!transposeA && !transposeB) { + aDer = matMul$1(dyActivation, b3D, false, true); + bDer = matMul$1(a3D, dyActivation, true, false); + } + else if (!transposeA && transposeB) { + aDer = matMul$1(dyActivation, b3D, false, false); + bDer = matMul$1(dyActivation, a3D, true, false); + } + else if (transposeA && !transposeB) { + aDer = matMul$1(b3D, dyActivation, false, true); + bDer = matMul$1(a3D, dyActivation, false, false); + } + else { + aDer = matMul$1(b3D, dyActivation, true, true); + bDer = matMul$1(dyActivation, a3D, true, true); + } + if (bias != null) { + var biasDer = getFusedBiasGradient($bias, dyActivation); + return [aDer, bDer, biasDer]; + } + else { + return [aDer, bDer]; + } + }; + var inputs = { + a: a3D, + b: b3D, + bias: $bias, + preluActivationWeights: $preluActivationWeights + }; + var attrs = { transposeA: transposeA, transposeB: transposeB, activation: activation, leakyreluAlpha: leakyreluAlpha }; + // Depending on the the params passed in we will have different number of + // inputs and thus a a different number of elements in the gradient. + if (bias == null) { + var customOp = customGrad(function (a3D, b3D, save) { + var res = + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(_FusedMatMul, inputs, attrs); + save([a3D, b3D, res]); + return { value: reshape(res, outShape), gradFunc: grad }; + }); + return customOp(a3D, b3D); + } + else { + var customOpWithBias = customGrad(function (a3D, b3D, $bias, save) { + var res = + // tslint:disable-next-line: no-unnecessary-type-assertion + ENGINE.runKernel(_FusedMatMul, inputs, attrs); + save([a3D, b3D, res, $bias]); + return { value: reshape(res, outShape), gradFunc: grad }; + }); + return customOpWithBias(a3D, b3D, $bias); + } + } + var matMul = op({ fusedMatMul_: fusedMatMul_ }); + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + + var fused_ops = { + __proto__: null, + conv2d: conv2d, + depthwiseConv2d: depthwiseConv2d, + matMul: matMul + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Generate a hamming window. + * + * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows + * + * ```js + * tf.signal.hammingWindow(10).print(); + * ``` + * @param The length of window + * + * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} + */ + function hammingWindow_(windowLength) { + return cosineWindow(windowLength, 0.54, 0.46); + } + var hammingWindow = op({ hammingWindow_: hammingWindow_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Generate a Hann window. + * + * See: https://en.wikipedia.org/wiki/Window_function#Hann_and_Hamming_windows + * + * ```js + * tf.signal.hannWindow(10).print(); + * ``` + * @param The length of window + * + * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} + */ + function hannWindow_(windowLength) { + return cosineWindow(windowLength, 0.5, 0.5); + } + var hannWindow = op({ hannWindow_: hannWindow_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Expands input into frames of frameLength. + * Slides a window size with frameStep. + * + * ```js + * tf.signal.frame([1, 2, 3], 2, 1).print(); + * ``` + * @param signal The input tensor to be expanded + * @param frameLength Length of each frame + * @param frameStep The frame hop size in samples. + * @param padEnd Whether to pad the end of signal with padValue. + * @param padValue An number to use where the input signal does + * not exist when padEnd is True. + * + * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} + */ + function frame_(signal, frameLength, frameStep, padEnd, padValue) { + if (padEnd === void 0) { padEnd = false; } + if (padValue === void 0) { padValue = 0; } + var start = 0; + var output = []; + while (start + frameLength <= signal.size) { + output.push(slice(signal, start, frameLength)); + start += frameStep; + } + if (padEnd) { + while (start < signal.size) { + var padLen = (start + frameLength) - signal.size; + var pad = concat([ + slice(signal, start, frameLength - padLen), fill([padLen], padValue) + ]); + output.push(pad); + start += frameStep; + } + } + if (output.length === 0) { + return tensor2d([], [0, frameLength]); + } + return reshape(concat(output), [output.length, frameLength]); + } + var frame = op({ frame_: frame_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the Short-time Fourier Transform of signals + * See: https://en.wikipedia.org/wiki/Short-time_Fourier_transform + * + * ```js + * const input = tf.tensor1d([1, 1, 1, 1, 1]) + * tf.signal.stft(input, 3, 1).print(); + * ``` + * @param signal 1-dimensional real value tensor. + * @param frameLength The window length of samples. + * @param frameStep The number of samples to step. + * @param fftLength The size of the FFT to apply. + * @param windowFn A callable that takes a window length and returns 1-d tensor. + * + * @doc {heading: 'Operations', subheading: 'Signal', namespace: 'signal'} + */ + function stft_(signal, frameLength, frameStep, fftLength, windowFn) { + if (windowFn === void 0) { windowFn = hannWindow; } + if (fftLength == null) { + fftLength = enclosingPowerOfTwo(frameLength); + } + var framedSignal = frame(signal, frameLength, frameStep); + var windowedSignal = mul(framedSignal, windowFn(frameLength)); + return rfft(windowedSignal, fftLength); + } + var stft = op({ stft_: stft_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Extracts crops from the input image tensor and resizes them using bilinear + * sampling or nearest neighbor sampling (possibly with aspect ratio change) + * to a common output size specified by cropSize. + * + * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`, + * where imageHeight and imageWidth must be positive, specifying the + * batch of images from which to take crops + * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized + * coordinates of the box in the boxInd[i]'th image in the batch + * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range + * `[0, batch)` that specifies the image that the `i`-th box refers to. + * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]` + * specifying the size to which all crops are resized to. + * @param method Optional string from `'bilinear' | 'nearest'`, + * defaults to bilinear, which specifies the sampling method for resizing + * @param extrapolationValue A threshold for deciding when to remove boxes based + * on score. Defaults to 0. + * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]` + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) { + if (method === void 0) { method = 'bilinear'; } + if (extrapolationValue === void 0) { extrapolationValue = 0; } + var $image = convertToTensor(image, 'image', 'cropAndResize'); + var $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32'); + var $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32'); + var numBoxes = $boxes.shape[0]; + assert($image.rank === 4, function () { return 'Error in cropAndResize: image must be rank 4,' + + ("but got rank " + $image.rank + "."); }); + assert($boxes.rank === 2 && $boxes.shape[1] === 4, function () { return "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " + + ("but had shape " + $boxes.shape + "."); }); + assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, function () { return "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " + + ("but had shape " + $boxes.shape + "."); }); + assert(cropSize.length === 2, function () { return "Error in cropAndResize: cropSize must be of length 2, but got " + + ("length " + cropSize.length + "."); }); + assert(cropSize[0] >= 1 && cropSize[1] >= 1, function () { return "cropSize must be atleast [1,1], but was " + cropSize; }); + assert(method === 'bilinear' || method === 'nearest', function () { return "method must be bilinear or nearest, but was " + method; }); + var inputs = { image: $image, boxes: $boxes, boxInd: $boxInd }; + var attrs = { method: method, extrapolationValue: extrapolationValue, cropSize: cropSize }; + var res = ENGINE.runKernel(CropAndResize, inputs, attrs); + return res; + } + var cropAndResize = op({ cropAndResize_: cropAndResize_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Flips the image left to right. Currently available in the CPU, WebGL, and + * WASM backends. + * + * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`. + */ + /** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */ + function flipLeftRight_(image) { + var $image = convertToTensor(image, 'image', 'flipLeftRight', 'float32'); + assert($image.rank === 4, function () { return 'Error in flipLeftRight: image must be rank 4,' + + ("but got rank " + $image.rank + "."); }); + var inputs = { image: $image }; + var res = ENGINE.runKernel(FlipLeftRight, inputs, {}); + return res; + } + var flipLeftRight = op({ flipLeftRight_: flipLeftRight_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Converts images from grayscale to RGB format. + * + * @param image A grayscale tensor to convert. The `image`'s last dimension must + * be size 1 with at least a two-dimensional shape. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function grayscaleToRGB_(image) { + var $image = convertToTensor(image, 'image', 'grayscaleToRGB'); + var lastDimsIdx = $image.rank - 1; + var lastDims = $image.shape[lastDimsIdx]; + assert($image.rank >= 2, function () { return 'Error in grayscaleToRGB: images must be at least rank 2, ' + + ("but got rank " + $image.rank + "."); }); + assert(lastDims === 1, function () { return 'Error in grayscaleToRGB: last dimension of a grayscale image ' + + ("should be size 1, but got size " + lastDims + "."); }); + var reps = new Array($image.rank); + reps.fill(1, 0, lastDimsIdx); + reps[lastDimsIdx] = 3; + return tile($image, reps); + } + var grayscaleToRGB = op({ grayscaleToRGB_: grayscaleToRGB_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Rotates the input image tensor counter-clockwise with an optional offset + * center of rotation. Currently available in the CPU, WebGL, and WASM backends. + * + * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`. + * @param radians The amount of rotation. + * @param fillValue The value to fill in the empty space leftover + * after rotation. Can be either a single grayscale value (0-255), or an + * array of three numbers `[red, green, blue]` specifying the red, green, + * and blue channels. Defaults to `0` (black). + * @param center The center of rotation. Can be either a single value (0-1), or + * an array of two numbers `[centerX, centerY]`. Defaults to `0.5` (rotates + * the image around its center). + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function rotateWithOffset_(image, radians, fillValue, center) { + if (fillValue === void 0) { fillValue = 0; } + if (center === void 0) { center = 0.5; } + var $image = convertToTensor(image, 'image', 'rotateWithOffset', 'float32'); + assert($image.rank === 4, function () { return 'Error in rotateWithOffset: image must be rank 4,' + + ("but got rank " + $image.rank + "."); }); + var inputs = { image: $image }; + var attrs = { radians: radians, fillValue: fillValue, center: center }; + var res = ENGINE.runKernel(RotateWithOffset, inputs, attrs); + return res; + } + var rotateWithOffset = op({ rotateWithOffset_: rotateWithOffset_ }); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + if (iouThreshold == null) { + iouThreshold = 0.5; + } + if (scoreThreshold == null) { + scoreThreshold = Number.NEGATIVE_INFINITY; + } + if (softNmsSigma == null) { + softNmsSigma = 0.0; + } + var numBoxes = boxes.shape[0]; + maxOutputSize = Math.min(maxOutputSize, numBoxes); + assert(0 <= iouThreshold && iouThreshold <= 1, function () { return "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'"; }); + assert(boxes.rank === 2, function () { return "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'"; }); + assert(boxes.shape[1] === 4, function () { return "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1]; }); + assert(scores.rank === 1, function () { return 'scores must be a 1D tensor'; }); + assert(scores.shape[0] === numBoxes, function () { return "scores has incompatible shape with boxes. Expected " + numBoxes + ", " + + ("but was " + scores.shape[0]); }); + assert(0 <= softNmsSigma && softNmsSigma <= 1, function () { return "softNmsSigma must be in [0, 1], but was '" + softNmsSigma + "'"; }); + return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma }; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Performs non maximum suppression of bounding boxes based on + * iou (intersection over union). + * + * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of + * the bounding box. + * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. + * @param maxOutputSize The maximum number of boxes to be selected. + * @param iouThreshold A float representing the threshold for deciding whether + * boxes overlap too much with respect to IOU. Must be between [0, 1]. + * Defaults to 0.5 (50% box overlap). + * @param scoreThreshold A threshold for deciding when to remove boxes based + * on score. Defaults to -inf, which means any score is accepted. + * @return A 1D tensor with the selected box indices. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + if (iouThreshold === void 0) { iouThreshold = 0.5; } + if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; } + var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression', 'float32'); + var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression', 'float32'); + var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); + maxOutputSize = inputs.maxOutputSize; + iouThreshold = inputs.iouThreshold; + scoreThreshold = inputs.scoreThreshold; + var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold }; + return ENGINE.runKernel(NonMaxSuppressionV3, { boxes: $boxes, scores: $scores }, attrs); + } + var nonMaxSuppression = op({ nonMaxSuppression_: nonMaxSuppression_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Inserts a value into a sorted array. This method allows duplicate, meaning it + * allows inserting duplicate value, in which case, the element will be inserted + * at the lowest index of the value. + * @param arr The array to modify. + * @param element The element to insert. + * @param comparator Optional. If no comparator is specified, elements are + * compared using array_util.defaultComparator, which is suitable for Strings + * and Numbers in ascending arrays. If the array contains multiple instances of + * the target value, the left-most instance will be returned. To provide a + * comparator, it should take 2 arguments to compare and return a negative, + * zero, or a positive number. + */ + function binaryInsert(arr, element, comparator) { + var index = binarySearch(arr, element, comparator); + var insertionPoint = index < 0 ? -(index + 1) : index; + arr.splice(insertionPoint, 0, element); + } + /** + * Searches the array for the target using binary search, returns the index + * of the found element, or position to insert if element not found. If no + * comparator is specified, elements are compared using array_ + * util.defaultComparator, which is suitable for Strings and Numbers in + * ascending arrays. If the array contains multiple instances of the target + * value, the left-most instance will be returned. + * @param arr The array to be searched in. + * @param target The target to be searched for. + * @param comparator Should take 2 arguments to compare and return a negative, + * zero, or a positive number. + * @return Lowest index of the target value if found, otherwise the insertion + * point where the target should be inserted, in the form of + * (-insertionPoint - 1). + */ + function binarySearch(arr, target, comparator) { + return binarySearch_(arr, target, comparator || defaultComparator); + } + /** + * Compares its two arguments for order. + * @param a The first element to be compared. + * @param b The second element to be compared. + * @return A negative number, zero, or a positive number as the first + * argument is less than, equal to, or greater than the second. + */ + function defaultComparator(a, b) { + return a > b ? 1 : a < b ? -1 : 0; + } + function binarySearch_(arr, target, comparator) { + var left = 0; + var right = arr.length; + var middle = 0; + var found = false; + while (left < right) { + middle = left + ((right - left) >>> 1); + var compareResult = comparator(target, arr[middle]); + if (compareResult > 0) { + left = middle + 1; + } + else { + right = middle; + // If compareResult is 0, the value is found. We record it is found, + // and then keep looking because there may be duplicate. + found = !compareResult; + } + } + return found ? left : -left - 1; + } + + function nonMaxSuppressionV3Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */); + } + function nonMaxSuppressionV4Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) { + return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, 0 /* softNmsSigma */, false /* returnScoresTensor */, padToMaxOutputSize /* padToMaxOutputSize */, true + /* returnValidOutputs */ ); + } + function nonMaxSuppressionV5Impl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + return nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, true /* returnScoresTensor */); + } + function nonMaxSuppressionImpl_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, returnScoresTensor, padToMaxOutputSize, returnValidOutputs) { + if (returnScoresTensor === void 0) { returnScoresTensor = false; } + if (padToMaxOutputSize === void 0) { padToMaxOutputSize = false; } + if (returnValidOutputs === void 0) { returnValidOutputs = false; } + // The list is sorted in ascending order, so that we can always pop the + // candidate with the largest score in O(1) time. + var candidates = []; + for (var i = 0; i < scores.length; i++) { + if (scores[i] > scoreThreshold) { + candidates.push({ score: scores[i], boxIndex: i, suppressBeginIndex: 0 }); + } + } + candidates.sort(ascendingComparator); + // If softNmsSigma is 0, the outcome of this algorithm is exactly same as + // before. + var scale = softNmsSigma > 0 ? (-0.5 / softNmsSigma) : 0.0; + var selectedIndices = []; + var selectedScores = []; + while (selectedIndices.length < maxOutputSize && candidates.length > 0) { + var candidate = candidates.pop(); + var originalScore = candidate.score, boxIndex = candidate.boxIndex, suppressBeginIndex = candidate.suppressBeginIndex; + if (originalScore < scoreThreshold) { + break; + } + // Overlapping boxes are likely to have similar scores, therefore we + // iterate through the previously selected boxes backwards in order to + // see if candidate's score should be suppressed. We use + // suppressBeginIndex to track and ensure a candidate can be suppressed + // by a selected box no more than once. Also, if the overlap exceeds + // iouThreshold, we simply ignore the candidate. + var ignoreCandidate = false; + for (var j = selectedIndices.length - 1; j >= suppressBeginIndex; --j) { + var iou = intersectionOverUnion(boxes, boxIndex, selectedIndices[j]); + if (iou >= iouThreshold) { + ignoreCandidate = true; + break; + } + candidate.score = + candidate.score * suppressWeight(iouThreshold, scale, iou); + if (candidate.score <= scoreThreshold) { + break; + } + } + // At this point, if `candidate.score` has not dropped below + // `scoreThreshold`, then we know that we went through all of the + // previous selections and can safely update `suppressBeginIndex` to the + // end of the selected array. Then we can re-insert the candidate with + // the updated score and suppressBeginIndex back in the candidate list. + // If on the other hand, `candidate.score` has dropped below the score + // threshold, we will not add it back to the candidates list. + candidate.suppressBeginIndex = selectedIndices.length; + if (!ignoreCandidate) { + // Candidate has passed all the tests, and is not suppressed, so + // select the candidate. + if (candidate.score === originalScore) { + selectedIndices.push(boxIndex); + selectedScores.push(candidate.score); + } + else if (candidate.score > scoreThreshold) { + // Candidate's score is suppressed but is still high enough to be + // considered, so add back to the candidates list. + binaryInsert(candidates, candidate, ascendingComparator); + } + } + } + // NonMaxSuppressionV4 feature: padding output to maxOutputSize. + var validOutputs = selectedIndices.length; + var elemsToPad = maxOutputSize - validOutputs; + if (padToMaxOutputSize && elemsToPad > 0) { + selectedIndices.push.apply(selectedIndices, __spread(new Array(elemsToPad).fill(0))); + selectedScores.push.apply(selectedScores, __spread(new Array(elemsToPad).fill(0.0))); + } + var result = { selectedIndices: selectedIndices }; + if (returnScoresTensor) { + result['selectedScores'] = selectedScores; + } + if (returnValidOutputs) { + result['validOutputs'] = validOutputs; + } + return result; + } + function intersectionOverUnion(boxes, i, j) { + var iCoord = boxes.subarray(i * 4, i * 4 + 4); + var jCoord = boxes.subarray(j * 4, j * 4 + 4); + var yminI = Math.min(iCoord[0], iCoord[2]); + var xminI = Math.min(iCoord[1], iCoord[3]); + var ymaxI = Math.max(iCoord[0], iCoord[2]); + var xmaxI = Math.max(iCoord[1], iCoord[3]); + var yminJ = Math.min(jCoord[0], jCoord[2]); + var xminJ = Math.min(jCoord[1], jCoord[3]); + var ymaxJ = Math.max(jCoord[0], jCoord[2]); + var xmaxJ = Math.max(jCoord[1], jCoord[3]); + var areaI = (ymaxI - yminI) * (xmaxI - xminI); + var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ); + if (areaI <= 0 || areaJ <= 0) { + return 0.0; + } + var intersectionYmin = Math.max(yminI, yminJ); + var intersectionXmin = Math.max(xminI, xminJ); + var intersectionYmax = Math.min(ymaxI, ymaxJ); + var intersectionXmax = Math.min(xmaxI, xmaxJ); + var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) * + Math.max(intersectionXmax - intersectionXmin, 0.0); + return intersectionArea / (areaI + areaJ - intersectionArea); + } + // A Gaussian penalty function, this method always returns values in [0, 1]. + // The weight is a function of similarity, the more overlap two boxes are, the + // smaller the weight is, meaning highly overlapping boxe will be significantly + // penalized. On the other hand, a non-overlapping box will not be penalized. + function suppressWeight(iouThreshold, scale, iou) { + var weight = Math.exp(scale * iou * iou); + return iou <= iouThreshold ? weight : 0.0; + } + function ascendingComparator(c1, c2) { + // For objects with same scores, we make the object with the larger index go + // first. In an array that pops from the end, this means that the object with + // the smaller index will be popped first. This ensures the same output as + // the TensorFlow python version. + return (c1.score - c2.score) || + ((c1.score === c2.score) && (c2.boxIndex - c1.boxIndex)); + } + + /** + * Performs non maximum suppression of bounding boxes based on + * iou (intersection over union). + * + * This is the async version of `nonMaxSuppression` + * + * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of + * the bounding box. + * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. + * @param maxOutputSize The maximum number of boxes to be selected. + * @param iouThreshold A float representing the threshold for deciding whether + * boxes overlap too much with respect to IOU. Must be between [0, 1]. + * Defaults to 0.5 (50% box overlap). + * @param scoreThreshold A threshold for deciding when to remove boxes based + * on score. Defaults to -inf, which means any score is accepted. + * @return A 1D tensor with the selected box indices. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) { + if (iouThreshold === void 0) { iouThreshold = 0.5; } + if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; } + return __awaiter(this, void 0, void 0, function () { + var $boxes, $scores, inputs, boxesAndScores, boxesVals, scoresVals, selectedIndices; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync'); + $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync'); + inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); + maxOutputSize = inputs.maxOutputSize; + iouThreshold = inputs.iouThreshold; + scoreThreshold = inputs.scoreThreshold; + return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])]; + case 1: + boxesAndScores = _a.sent(); + boxesVals = boxesAndScores[0]; + scoresVals = boxesAndScores[1]; + selectedIndices = nonMaxSuppressionV3Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold).selectedIndices; + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return [2 /*return*/, tensor1d(selectedIndices, 'int32')]; + } + }); + }); + } + var nonMaxSuppressionAsync = nonMaxSuppressionAsync_; + + /** + * @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. + * ============================================================================= + */ + /** + * Performs non maximum suppression of bounding boxes based on + * iou (intersection over union). + * + * This op also supports a Soft-NMS mode (c.f. + * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score + * of other overlapping boxes, therefore favoring different regions of the image + * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma` + * parameter to be larger than 0. + * + * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of + * the bounding box. + * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. + * @param maxOutputSize The maximum number of boxes to be selected. + * @param iouThreshold A float representing the threshold for deciding whether + * boxes overlap too much with respect to IOU. Must be between [0, 1]. + * Defaults to 0.5 (50% box overlap). + * @param scoreThreshold A threshold for deciding when to remove boxes based + * on score. Defaults to -inf, which means any score is accepted. + * @param softNmsSigma A float representing the sigma parameter for Soft NMS. + * When sigma is 0, it falls back to nonMaxSuppression. + * @return A map with the following properties: + * - selectedIndices: A 1D tensor with the selected box indices. + * - selectedScores: A 1D tensor with the corresponding scores for each + * selected box. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function nonMaxSuppressionWithScore_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + if (iouThreshold === void 0) { iouThreshold = 0.5; } + if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; } + if (softNmsSigma === void 0) { softNmsSigma = 0.0; } + var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression'); + var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression'); + var params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + maxOutputSize = params.maxOutputSize; + iouThreshold = params.iouThreshold; + scoreThreshold = params.scoreThreshold; + softNmsSigma = params.softNmsSigma; + var inputs = { boxes: $boxes, scores: $scores }; + var attrs = { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold, softNmsSigma: softNmsSigma }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var result = ENGINE.runKernel(NonMaxSuppressionV5, inputs, attrs); + return { selectedIndices: result[0], selectedScores: result[1] }; + } + var nonMaxSuppressionWithScore = op({ nonMaxSuppressionWithScore_: nonMaxSuppressionWithScore_ }); + + /** + * Asynchronously performs non maximum suppression of bounding boxes based on + * iou (intersection over union). + * + * This op also supports a Soft-NMS mode (c.f. + * Bodla et al, https://arxiv.org/abs/1704.04503) where boxes reduce the score + * of other overlapping boxes, therefore favoring different regions of the image + * with high scores. To enable this Soft-NMS mode, set the `softNmsSigma` + * parameter to be larger than 0. + * + * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of + * the bounding box. + * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. + * @param maxOutputSize The maximum number of boxes to be selected. + * @param iouThreshold A float representing the threshold for deciding whether + * boxes overlap too much with respect to IOU. Must be between [0, 1]. + * Defaults to 0.5 (50% box overlap). + * @param scoreThreshold A threshold for deciding when to remove boxes based + * on score. Defaults to -inf, which means any score is accepted. + * @param softNmsSigma A float representing the sigma parameter for Soft NMS. + * When sigma is 0, it falls back to nonMaxSuppression. + * @return A map with the following properties: + * - selectedIndices: A 1D tensor with the selected box indices. + * - selectedScores: A 1D tensor with the corresponding scores for each + * selected box. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function nonMaxSuppressionWithScoreAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma) { + if (iouThreshold === void 0) { iouThreshold = 0.5; } + if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; } + if (softNmsSigma === void 0) { softNmsSigma = 0.0; } + return __awaiter(this, void 0, void 0, function () { + var $boxes, $scores, params, boxesAndScores, boxesVals, scoresVals, _a, selectedIndices, selectedScores; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync'); + $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync'); + params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma); + maxOutputSize = params.maxOutputSize; + iouThreshold = params.iouThreshold; + scoreThreshold = params.scoreThreshold; + softNmsSigma = params.softNmsSigma; + return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])]; + case 1: + boxesAndScores = _b.sent(); + boxesVals = boxesAndScores[0]; + scoresVals = boxesAndScores[1]; + _a = nonMaxSuppressionV5Impl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma), selectedIndices = _a.selectedIndices, selectedScores = _a.selectedScores; + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return [2 /*return*/, { + selectedIndices: tensor1d(selectedIndices, 'int32'), + selectedScores: tensor1d(selectedScores) + }]; + } + }); + }); + } + var nonMaxSuppressionWithScoreAsync = nonMaxSuppressionWithScoreAsync_; + + /** + * @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. + * ============================================================================= + */ + /** + * Asynchronously performs non maximum suppression of bounding boxes based on + * iou (intersection over union), with an option to pad results. + * + * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of + * the bounding box. + * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. + * @param maxOutputSize The maximum number of boxes to be selected. + * @param iouThreshold A float representing the threshold for deciding whether + * boxes overlap too much with respect to IOU. Must be between [0, 1]. + * Defaults to 0.5 (50% box overlap). + * @param scoreThreshold A threshold for deciding when to remove boxes based + * on score. Defaults to -inf, which means any score is accepted. + * @param padToMaxOutputSize Defalts to false. If true, size of output + * `selectedIndices` is padded to maxOutputSize. + * @return A map with the following properties: + * - selectedIndices: A 1D tensor with the selected box indices. + * - validOutputs: A scalar denoting how many elements in `selectedIndices` + * are valid. Valid elements occur first, then padding. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function nonMaxSuppressionPadded_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) { + if (iouThreshold === void 0) { iouThreshold = 0.5; } + if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; } + if (padToMaxOutputSize === void 0) { padToMaxOutputSize = false; } + var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression'); + var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression'); + var params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */); + var $maxOutputSize = params.maxOutputSize; + var $iouThreshold = params.iouThreshold; + var $scoreThreshold = params.scoreThreshold; + var inputs = { boxes: $boxes, scores: $scores }; + var attrs = { + maxOutputSize: $maxOutputSize, + iouThreshold: $iouThreshold, + scoreThreshold: $scoreThreshold, + padToMaxOutputSize: padToMaxOutputSize + }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var result = ENGINE.runKernel(NonMaxSuppressionV4, inputs, attrs); + return { selectedIndices: result[0], validOutputs: result[1] }; + } + var nonMaxSuppressionPadded = op({ nonMaxSuppressionPadded_: nonMaxSuppressionPadded_ }); + + /** + * Asynchronously performs non maximum suppression of bounding boxes based on + * iou (intersection over union), with an option to pad results. + * + * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is + * `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of + * the bounding box. + * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`. + * @param maxOutputSize The maximum number of boxes to be selected. + * @param iouThreshold A float representing the threshold for deciding whether + * boxes overlap too much with respect to IOU. Must be between [0, 1]. + * Defaults to 0.5 (50% box overlap). + * @param scoreThreshold A threshold for deciding when to remove boxes based + * on score. Defaults to -inf, which means any score is accepted. + * @param padToMaxOutputSize Defalts to false. If true, size of output + * `selectedIndices` is padded to maxOutputSize. + * @return A map with the following properties: + * - selectedIndices: A 1D tensor with the selected box indices. + * - validOutputs: A scalar denoting how many elements in `selectedIndices` + * are valid. Valid elements occur first, then padding. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function nonMaxSuppressionPaddedAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize) { + if (iouThreshold === void 0) { iouThreshold = 0.5; } + if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; } + if (padToMaxOutputSize === void 0) { padToMaxOutputSize = false; } + return __awaiter(this, void 0, void 0, function () { + var $boxes, $scores, params, $maxOutputSize, $iouThreshold, $scoreThreshold, _a, boxesVals, scoresVals, _b, selectedIndices, validOutputs; + return __generator(this, function (_c) { + switch (_c.label) { + case 0: + $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync'); + $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync'); + params = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold, null /* softNmsSigma */); + $maxOutputSize = params.maxOutputSize; + $iouThreshold = params.iouThreshold; + $scoreThreshold = params.scoreThreshold; + return [4 /*yield*/, Promise.all([$boxes.data(), $scores.data()])]; + case 1: + _a = __read.apply(void 0, [_c.sent(), 2]), boxesVals = _a[0], scoresVals = _a[1]; + _b = nonMaxSuppressionV4Impl(boxesVals, scoresVals, $maxOutputSize, $iouThreshold, $scoreThreshold, padToMaxOutputSize), selectedIndices = _b.selectedIndices, validOutputs = _b.validOutputs; + if ($boxes !== boxes) { + $boxes.dispose(); + } + if ($scores !== scores) { + $scores.dispose(); + } + return [2 /*return*/, { + selectedIndices: tensor1d(selectedIndices, 'int32'), + validOutputs: scalar(validOutputs, 'int32') + }]; + } + }); + }); + } + var nonMaxSuppressionPaddedAsync = nonMaxSuppressionPaddedAsync_; + + /** + * Bilinear resize a single 3D image or a batch of 3D images to a new shape. + * + * @param images The images, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param size The new shape `[newHeight, newWidth]` to resize the + * images to. Each channel is resized individually. + * @param alignCorners Defaults to `false`. If true, rescale + * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4 + * corners of images and resized images. If false, rescale by + * `new_height / height`. Treat similarly the width dimension. + * @param halfPixelCenters Defaults to `false`. Whether to assume pixel centers + * are at 0.5, which would make the floating point coordinates of the top + * left pixel 0.5, 0.5. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function resizeBilinear_(images, size, alignCorners, halfPixelCenters) { + if (alignCorners === void 0) { alignCorners = false; } + if (halfPixelCenters === void 0) { halfPixelCenters = false; } + var $images = convertToTensor(images, 'images', 'resizeBilinear'); + assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeBilinear: x must be rank 3 or 4, but got " + + ("rank " + $images.rank + "."); }); + assert(size.length === 2, function () { return "Error in resizeBilinear: new shape must 2D, but got shape " + + (size + "."); }); + assert(halfPixelCenters === false || alignCorners === false, function () { return "Error in resizeBilinear: If halfPixelCenters is true, " + + "alignCorners must be false."; }); + var batchImages = $images; + var reshapedTo4D = false; + if ($images.rank === 3) { + reshapedTo4D = true; + batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); + } + __read(size, 0); + var inputs = { images: batchImages }; + var attrs = { alignCorners: alignCorners, halfPixelCenters: halfPixelCenters, size: size }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: resizeBilinear_ }); + + /** + * NearestNeighbor resize a batch of 3D images to a new shape. + * + * @param images The images, of rank 4 or rank 3, of shape + * `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed. + * @param size The new shape `[newHeight, newWidth]` to resize the + * images to. Each channel is resized individually. + * @param alignCorners Defaults to False. If true, rescale + * input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4 + * corners of images and resized images. If false, rescale by + * `new_height / height`. Treat similarly the width dimension. + * @param halfPixelCenters Defaults to `false`. Whether to assumes pixels are of + * half the actual dimensions, and yields more accurate resizes. This flag + * would also make the floating point coordinates of the top left pixel + * 0.5, 0.5. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function resizeNearestNeighbor_(images, size, alignCorners, halfPixelCenters) { + if (alignCorners === void 0) { alignCorners = false; } + if (halfPixelCenters === void 0) { halfPixelCenters = false; } + var $images = convertToTensor(images, 'images', 'resizeNearestNeighbor'); + assert($images.rank === 3 || $images.rank === 4, function () { return "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " + + ("rank " + $images.rank + "."); }); + assert(size.length === 2, function () { return "Error in resizeNearestNeighbor: new shape must 2D, but got shape " + + (size + "."); }); + assert($images.dtype === 'float32' || $images.dtype === 'int32', function () { return '`images` must have `int32` or `float32` as dtype'; }); + assert(halfPixelCenters === false || alignCorners === false, function () { return "Error in resizeNearestNeighbor: If halfPixelCenters is true, " + + "alignCorners must be false."; }); + var batchImages = $images; + var reshapedTo4D = false; + if ($images.rank === 3) { + reshapedTo4D = true; + batchImages = reshape($images, [1, $images.shape[0], $images.shape[1], $images.shape[2]]); + } + __read(size, 0); + var inputs = { images: batchImages }; + var attrs = { alignCorners: alignCorners, halfPixelCenters: halfPixelCenters, size: size }; + // tslint:disable-next-line: no-unnecessary-type-assertion + var 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_: resizeNearestNeighbor_ }); + + /** + * Performs image binarization with corresponding threshold + * (depends on the method)value, which creates a binary image from a grayscale. + * @param image 3d tensor of shape [imageHeight,imageWidth, depth], + * where imageHeight and imageWidth must be positive.The image color + * range should be [0, 255]. + * @param method Optional string from `'binary' | 'otsu'` + * which specifies the method for thresholding. Defaults to 'binary'. + * @param inverted Optional boolean whichspecifies + * if colours should be inverted. Defaults to false. + * @param threshValue Optional number which defines threshold value from 0 to 1. + * Defaults to 0.5. + * @return A 3d tensor of shape [imageHeight,imageWidth, depth], which + * contains binarized image. + */ + function threshold_(image, method, inverted, threshValue) { + var _a; + if (method === void 0) { method = 'binary'; } + if (inverted === void 0) { inverted = false; } + if (threshValue === void 0) { threshValue = 0.5; } + var $image = convertToTensor(image, 'image', 'threshold'); + /* 0.2989, 0.5870, 0.1140 are represent luma coefficients in CCIR601. + Reference for converting between RGB and grayscale: https://en.wikipedia.org/wiki/Luma_%28video%29 */ + var RED_INTENCITY_COEF = 0.2989; + var GREEN_INTENCITY_COEF = 0.5870; + var BLUE_INTENCITY_COEF = 0.1140; + var totalPixelsInImage = $image.shape[0] * $image.shape[1]; + var $threshold = mul(tensor1d([threshValue]), 255); + var r, g, b, grayscale; + assert($image.rank === 3, function () { return 'Error in threshold: image must be rank 3,' + + ("but got rank " + $image.rank + "."); }); + assert($image.shape[2] === 3 || $image.shape[2] === 1, function () { return 'Error in threshold: ' + + 'image color channel must be equal to 3 or 1' + + ("but got " + $image.shape[2] + "."); }); + assert($image.dtype === 'int32' || $image.dtype === 'float32', function () { return 'Error in dtype: image dtype must be int32 or float32,' + + ("but got dtype " + $image.dtype + "."); }); + assert(method === 'otsu' || method === 'binary', function () { return "Method must be binary or otsu, but was " + method; }); + if ($image.shape[2] === 3) { + _a = __read(split$1($image, [1, 1, 1], -1), 3), r = _a[0], g = _a[1], b = _a[2]; + var $r = mul(r, RED_INTENCITY_COEF); + var $g = mul(g, GREEN_INTENCITY_COEF); + var $b = mul(b, BLUE_INTENCITY_COEF); + grayscale = add(add($r, $g), $b); + } + else { + grayscale = image; + } + if (method === 'otsu') { + var $histogram = bincount(cast(round(grayscale), 'int32'), tensor([]), 256); + $threshold = otsu($histogram, totalPixelsInImage); + } + var invCondition = inverted ? + lessEqual(grayscale, $threshold) : greater(grayscale, $threshold); + var result = cast(mul(invCondition, 255), 'int32'); + return result; + } + function otsu(histogram, total) { + var bestThresh = tensor1d([-1]); + var bestInBetVar = tensor1d([0]); + var cInBetVar = tensor1d([0]); + var classFirst, classSecond, meanFirst, meanSec, weightForeground, weightBack; + for (var index = 0; index < histogram.size - 1; index++) { + classFirst = slice(histogram, 0, index + 1); + classSecond = slice(histogram, index + 1); + weightForeground = div(sum(classFirst), total); + weightBack = div(sum(classSecond), total); + var meanFirstDivA = sum(mul(classFirst, range(0, classFirst.size))); + meanFirst = div(meanFirstDivA, sum(classFirst)); + var meanSecFill = fill(classSecond.shape, classFirst.size); + var meanSecAdd = add(range(0, classSecond.size), meanSecFill); + var meanSecMul = mul(classSecond, (meanSecAdd)); + meanSec = div(sum(meanSecMul), sum(classSecond)); + var cInBetVarSubA = sub(meanFirst, meanSec); + var cInBetVarSubB = sub(meanFirst, meanSec); + var cInBetVarMul = mul(weightForeground, weightBack); + cInBetVar = mul(mul(cInBetVarMul, cInBetVarSubA), cInBetVarSubB); + var condition = greater(cInBetVar, bestInBetVar); + bestInBetVar = where(condition, cInBetVar, bestInBetVar); + bestThresh = where(condition, tensor1d([index]), bestThresh); + } + return bestThresh; + } + var threshold = op({ threshold_: threshold_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Applies the given transform(s) to the image(s). + * + * @param image 4d tensor of shape `[batch, imageHeight, imageWidth, depth]`. + * @param transforms Projective transform matrix/matrices. A tensor1d of length + * 8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0 + * b1, b2, c0, c1], then it maps the output point (x, y) to a transformed + * input point (x', y') = ((a0 x + a1 y + a2) / k, (b0 x + b1 y + b2) / k), + * where k = c0 x + c1 y + 1. The transforms are inverted compared to the + * transform mapping input points to output points. + * @param interpolation Interpolation mode. + * Supported values: 'nearest', 'bilinear'. Default to 'nearest'. + * @param fillMode Points outside the boundaries of the input are filled + * according to the given mode, one of 'constant', 'reflect', 'wrap', + * 'nearest'. Default to 'constant'. + * 'reflect': (d c b a | a b c d | d c b a ) The input is extended by + * reflecting about the edge of the last pixel. + * 'constant': (k k k k | a b c d | k k k k) The input is extended by + * filling all values beyond the edge with the same constant value k. + * 'wrap': (a b c d | a b c d | a b c d) The input is extended by + * wrapping around to the opposite edge. + * 'nearest': (a a a a | a b c d | d d d d) The input is extended by + * the nearest pixel. + * @param fillValue A float represents the value to be filled outside the + * boundaries when fillMode is 'constant'. + * @param Output dimension after the transform, [height, width]. If undefined, + * output is the same size as input image. + * + * @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} + */ + function transform_(image, transforms, interpolation, fillMode, fillValue, outputShape) { + if (interpolation === void 0) { interpolation = 'nearest'; } + if (fillMode === void 0) { fillMode = 'constant'; } + if (fillValue === void 0) { fillValue = 0; } + var $image = convertToTensor(image, 'image', 'transform', 'float32'); + var $transforms = convertToTensor(transforms, 'transforms', 'transform', 'float32'); + assert($image.rank === 4, function () { return '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, function () { return "Error in transform: Input transform should be batch x 8 or 1 x 8"; }); + assert(outputShape == null || outputShape.length === 2, function () { return 'Error in transform: outputShape must be [height, width] or null, ' + + ("but got " + outputShape + "."); }); + var inputs = { image: $image, transforms: $transforms }; + var attrs = { interpolation: interpolation, fillMode: fillMode, fillValue: fillValue, outputShape: outputShape }; + return ENGINE.runKernel(Transform, inputs, attrs); + } + var transform = op({ transform_: transform_ }); + + /** + * Copy a tensor setting everything outside a central band in each innermost + * matrix to zero. + * + * The band part is computed as follows: Assume input has `k` dimensions + * `[I, J, K, ..., M, N]`, then the output is a tensor with the same shape where + * `band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]`. + * The indicator function + * `in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower))` + * `&& (num_upper < 0 || (n-m) <= num_upper)` + * + * ```js + * const x = tf.tensor2d([[ 0, 1, 2, 3], + * [-1, 0, 1, 2], + * [-2, -1, 0, 1], + * [-3, -2, -1, 0]]); + * let y = tf.linalg.bandPart(x, 1, -1); + * y.print(); // [[ 0, 1, 2, 3], + * // [-1, 0, 1, 2], + * // [ 0, -1, 0, 1], + * // [ 0, 0 , -1, 0]] + * let z = tf.linalg.bandPart(x, 2, 1); + * z.print(); // [[ 0, 1, 0, 0], + * // [-1, 0, 1, 0], + * // [-2, -1, 0, 1], + * // [ 0, -2, -1, 0]] + * ``` + * + * @param x Rank `k` tensor + * @param numLower Number of subdiagonals to keep. + * If negative, keep entire lower triangle. + * @param numUpper Number of subdiagonals to keep. + * If negative, keep entire upper triangle. + * @returns Rank `k` tensor of the same shape as input. + * The extracted banded tensor. + * + * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'} + */ + function bandPart_(a, numLower, numUpper) { + assert(numLower % 1 === 0, function () { return "bandPart(): numLower must be an integer, got " + numLower + "."; }); + assert(numUpper % 1 === 0, function () { return "bandPart(): numUpper must be an integer, got " + numUpper + "."; }); + var $a = convertToTensor(a, 'a', 'bandPart'); + assert($a.rank >= 2, function () { return "bandPart(): Rank must be at least 2, got " + $a.rank + "."; }); + var shape = $a.shape; + var _a = __read($a.shape.slice(-2), 2), M = _a[0], N = _a[1]; + 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; + } + var i = reshape(range(0, M, 1, 'int32'), [-1, 1]); + var j = range(0, N, 1, 'int32'); + var ij = sub(i, j); + var inBand = logicalAnd(lessEqual(ij, scalar(+numLower, 'int32')), greaterEqual(ij, scalar(-numUpper, 'int32'))); + var zero = zeros([M, N], $a.dtype); + return reshape(stack(unstack(reshape($a, [-1, M, N])) + .map(function (mat) { return where(inBand, mat, zero); })), shape); + } + var bandPart = op({ bandPart_: bandPart_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Gram-Schmidt orthogonalization. + * + * ```js + * const x = tf.tensor2d([[1, 2], [3, 4]]); + * let y = tf.linalg.gramSchmidt(x); + * y.print(); + * console.log('Othogonalized:'); + * y.dot(y.transpose()).print(); // should be nearly the identity matrix. + * console.log('First row direction maintained:'); + * const data = await y.array(); + * console.log(data[0][1] / data[0][0]); // should be nearly 2. + * ``` + * + * @param xs The vectors to be orthogonalized, in one of the two following + * formats: + * - An Array of `tf.Tensor1D`. + * - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows + * of `xs`. + * In each case, all the vectors must have the same length and the length + * must be greater than or equal to the number of vectors. + * @returns The orthogonalized and normalized vectors or matrix. + * Orthogonalization means that the vectors or the rows of the matrix + * are orthogonal (zero inner products). Normalization means that each + * vector or each row of the matrix has an L2 norm that equals `1`. + * + * @doc {heading:'Operations', subheading:'Linear Algebra', namespace:'linalg'} + */ + function gramSchmidt_(xs) { + var inputIsTensor2D; + if (Array.isArray(xs)) { + inputIsTensor2D = false; + assert(xs != null && xs.length > 0, function () { return 'Gram-Schmidt process: input must not be null, undefined, or ' + + 'empty'; }); + var dim_1 = xs[0].shape[0]; + var _loop_1 = function (i) { + assert(xs[i].shape[0] === dim_1, function () { return 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' + + ("(" + xs[i].shape[0] + " vs. " + dim_1 + ")"); }); + }; + for (var i = 1; i < xs.length; ++i) { + _loop_1(i); + } + } + else { + inputIsTensor2D = true; + xs = split$1(xs, xs.shape[0], 0).map(function (x) { return squeeze(x, [0]); }); + } + assert(xs.length <= xs[0].shape[0], function () { return "Gram-Schmidt: Number of vectors (" + xs.length + ") exceeds " + + ("number of dimensions (" + xs[0].shape[0] + ")."); }); + var ys = []; + var xs1d = xs; + var _loop_2 = function (i) { + ys.push(ENGINE.tidy(function () { + var x = xs1d[i]; + if (i > 0) { + for (var j = 0; j < i; ++j) { + var proj = mul(sum(mul(ys[j], x)), ys[j]); + x = sub(x, proj); + } + } + return div(x, norm(x, 'euclidean')); + })); + }; + for (var i = 0; i < xs.length; ++i) { + _loop_2(i); + } + if (inputIsTensor2D) { + return stack(ys, 0); + } + else { + return ys; + } + } + var gramSchmidt = op({ gramSchmidt_: gramSchmidt_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Disposes any `tf.Tensor`s found within the provided object. + * + * @param container an object that may be a `tf.Tensor` or may directly + * contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If + * the object is not a `tf.Tensor` or does not contain `Tensors`, nothing + * happens. In general it is safe to pass any object here, except that + * `Promise`s are not supported. + * + * @doc {heading: 'Performance', subheading: 'Memory'} + */ + function dispose(container) { + var tensors = getTensorsInContainer(container); + tensors.forEach(function (tensor) { return tensor.dispose(); }); + } + + /** + * Compute QR decomposition of m-by-n matrix using Householder transformation. + * + * Implementation based on + * [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf] + * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf) + * + * ```js + * const a = tf.tensor2d([[1, 2], [3, 4]]); + * let [q, r] = tf.linalg.qr(a); + * console.log('Q'); + * q.print(); + * console.log('R'); + * r.print(); + * console.log('Orthogonalized'); + * q.dot(q.transpose()).print() // should be nearly the identity matrix. + * console.log('Reconstructed'); + * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]]; + * ``` + * + * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose + * it has the shape `[..., M, N]`. + * @param fullMatrices An optional boolean parameter. Defaults to `false`. + * If `true`, compute full-sized `Q`. If `false` (the default), + * compute only the leading N columns of `Q` and `R`. + * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix, + * i.e., its columns all have unit norm and are mutually orthogonal. + * If `M >= N`, + * If `fullMatrices` is `false` (default), + * - `Q` has a shape of `[..., M, N]`, + * - `R` has a shape of `[..., N, N]`. + * If `fullMatrices` is `true` (default), + * - `Q` has a shape of `[..., M, M]`, + * - `R` has a shape of `[..., M, N]`. + * If `M < N`, + * - `Q` has a shape of `[..., M, M]`, + * - `R` has a shape of `[..., M, N]`. + * @throws If the rank of `x` is less than 2. + * + * @doc {heading:'Operations', + * subheading:'Linear Algebra', + * namespace:'linalg'} + */ + function qr_(x, fullMatrices) { + if (fullMatrices === void 0) { fullMatrices = false; } + assert(x.rank >= 2, function () { return "qr() requires input tensor to have a rank >= 2, but got rank " + x.rank; }); + if (x.rank === 2) { + return qr2d(x, fullMatrices); + } + else { + // Rank > 2. + // TODO(cais): Below we split the input into individual 2D tensors, + // perform QR decomposition on them and then stack the results back + // together. We should explore whether this can be parallelized. + var outerDimsProd = x.shape.slice(0, x.shape.length - 2) + .reduce(function (value, prev) { return value * prev; }); + var x2ds = unstack(reshape(x, [ + outerDimsProd, x.shape[x.shape.length - 2], + x.shape[x.shape.length - 1] + ]), 0); + var q2ds_1 = []; + var r2ds_1 = []; + x2ds.forEach(function (x2d) { + var _a = __read(qr2d(x2d, fullMatrices), 2), q2d = _a[0], r2d = _a[1]; + q2ds_1.push(q2d); + r2ds_1.push(r2d); + }); + var q = reshape(stack(q2ds_1, 0), x.shape); + var r = reshape(stack(r2ds_1, 0), x.shape); + return [q, r]; + } + } + function qr2d(x, fullMatrices) { + if (fullMatrices === void 0) { fullMatrices = false; } + return ENGINE.tidy(function () { + assert(x.shape.length === 2, function () { return "qr2d() requires a 2D Tensor, but got a " + x.shape.length + "D Tensor."; }); + var m = x.shape[0]; + var n = x.shape[1]; + var q = eye(m); // Orthogonal transform so far. + var r = clone(x); // Transformed matrix so far. + var one2D = tensor2d([[1]], [1, 1]); + var w = clone(one2D); + var iters = m >= n ? n : m; + var _loop_1 = function (j) { + var _a; + // This tidy within the for-loop ensures we clean up temporary + // tensors as soon as they are no longer needed. + var rTemp = r; + var wTemp = w; + var qTemp = q; + _a = __read(ENGINE.tidy(function () { + // Find H = I - tau * w * w', to put zeros below R(j, j). + var rjEnd1 = slice(r, [j, j], [m - j, 1]); + var normX = norm(rjEnd1); + var rjj = slice(r, [j, j], [1, 1]); + // The sign() function returns 0 on 0, which causes division by zero. + var s = where(greater(rjj, 0), tensor2d([[-1]]), tensor2d([[1]])); + var u1 = sub(rjj, mul(s, normX)); + var 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); + } + var tau = neg(div(matMul$1(s, u1), normX)); + // -- R := HR, Q := QH. + var rjEndAll = slice(r, [j, 0], [m - j, n]); + var tauTimesW = mul(tau, w); + var wT = transpose(w); + if (j === 0) { + r = sub(rjEndAll, matMul$1(tauTimesW, matMul$1(wT, rjEndAll))); + } + else { + var rTimesTau = sub(rjEndAll, matMul$1(tauTimesW, matMul$1(wT, rjEndAll))); + r = concat([slice(r, [0, 0], [j, n]), rTimesTau], 0); + } + var tawTimesWT = transpose(tauTimesW); + var qAllJEnd = slice(q, [0, j], [m, q.shape[1] - j]); + if (j === 0) { + q = sub(qAllJEnd, matMul$1(matMul$1(qAllJEnd, w), tawTimesWT)); + } + else { + var qTimesTau = sub(qAllJEnd, matMul$1(matMul$1(qAllJEnd, w), tawTimesWT)); + q = concat([slice(q, [0, 0], [m, j]), qTimesTau], 1); + } + return [w, r, q]; + }), 3), w = _a[0], r = _a[1], q = _a[2]; + dispose([rTemp, wTemp, qTemp]); + }; + for (var j = 0; j < iters; ++j) { + _loop_1(j); + } + 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_: qr_ }); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var Reduction; + (function (Reduction) { + Reduction[Reduction["NONE"] = 0] = "NONE"; + Reduction[Reduction["MEAN"] = 1] = "MEAN"; + Reduction[Reduction["SUM"] = 2] = "SUM"; + Reduction[Reduction["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS"; + })(Reduction || (Reduction = {})); + + /** + * Computes the weighted loss between two tensors. + * + * @param losses Tensor of shape `[batch_size, d1, ... dN]`. + * @param weights Tensor whose rank is either 0, or the same rank as + * `losses`, and must be broadcastable to `losses` (i.e., all + * dimensions must be either `1`, or the same as the corresponding + * `losses` dimension). + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function computeWeightedLoss_(losses, weights, reduction) { + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss'); + } + var weightedLoss = ($weights == null) ? $losses : mul($losses, $weights); + if (reduction === Reduction.NONE) { + return weightedLoss; + } + if (reduction === Reduction.SUM) { + return sum(weightedLoss); + } + if (reduction === Reduction.MEAN) { + if ($weights == null) { + return mean(weightedLoss); + } + else { + var broadcastFactor = $losses.size / $weights.size; + var result = div(sum(weightedLoss), sum($weights)); + return broadcastFactor > 1 ? div(result, scalar(broadcastFactor)) : + result; + } + } + if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) { + if ($weights == null) { + return div(sum(weightedLoss), scalar($losses.size)); + } + else { + var broadcastedWeights = mul($weights, ones($losses.shape)); + var numNonZeros = cast(sum(notEqual(broadcastedWeights, scalar(0))), 'float32'); + return div(sum(weightedLoss), numNonZeros); + } + } + throw Error("Unknown reduction: " + reduction); + } + var computeWeightedLoss = op({ computeWeightedLoss_: computeWeightedLoss_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the absolute difference loss between two tensors. + * + * @param labels The ground truth output tensor, same dimensions as + * 'predictions'. + * @param predictions The predicted outputs. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function absoluteDifference_(labels, predictions, weights, reduction) { + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $labels = convertToTensor(labels, 'labels', 'absoluteDifference'); + var $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'absoluteDifference'); + } + assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: '); + var losses = abs(sub($labels, $predictions)); + return computeWeightedLoss(losses, $weights, reduction); + } + var absoluteDifference = op({ absoluteDifference_: absoluteDifference_ }); + + /** + * Computes the cosine distance loss between two tensors. + * + * @param labels The ground truth output tensor, same dimensions as + * 'predictions'. + * @param predictions The predicted outputs. + * @param axis The dimension along which the cosine distance is computed. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function cosineDistance_(labels, predictions, axis, weights, reduction) { + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $labels = convertToTensor(labels, 'labels', 'cosineDistance'); + var $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'cosineDistance'); + } + assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: '); + var one = scalar(1); + var losses = sub(one, sum(mul($labels, $predictions), axis, true)); + return computeWeightedLoss(losses, $weights, reduction); + } + var cosineDistance = op({ cosineDistance_: cosineDistance_ }); + + /** + * Computes the Hinge loss between two tensors. + * + * @param labels The ground truth output tensor, same dimensions as + * 'predictions'. + * @param predictions The predicted outputs. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function hingeLoss_(labels, predictions, weights, reduction) { + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $labels = convertToTensor(labels, 'labels', 'hingeLoss'); + var $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'hingeLoss'); + } + assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: '); + var one = scalar(1); + // Convert binary labels to (-1, 1) + $labels = sub(mul(scalar(2), $labels), one); + var losses = relu(sub(one, mul($labels, $predictions))); + return computeWeightedLoss(losses, $weights, reduction); + } + var hingeLoss = op({ hingeLoss_: hingeLoss_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the huber loss between two tensors. + * + * @param labels The ground truth output tensor, same dimensions as + * 'predictions'. + * @param predictions The predicted outputs. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param delta Point where huber loss changes from quadratic to linear. + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction`. + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function huberLoss_(labels, predictions, weights, delta, reduction) { + if (delta === void 0) { delta = 1.0; } + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $labels = convertToTensor(labels, 'labels', 'huberLoss'); + var $predictions = convertToTensor(predictions, 'predictions', 'huberLoss'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'huberLoss'); + } + assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: '); + var deltaScalar = scalar(delta); + var error = abs(sub($predictions, $labels)); + var quadratic = minimum(error, deltaScalar); + var linear = sub(error, quadratic); + var losses = add(mul(scalar(0.5), square(quadratic)), mul(deltaScalar, linear)); + return computeWeightedLoss(losses, $weights, reduction); + } + var huberLoss = op({ huberLoss_: huberLoss_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the log loss between two tensors. + * + * @param labels The ground truth output tensor, same dimensions as + * 'predictions'. + * @param predictions The predicted outputs. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param epsilon A small increment to avoid taking log of zero + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function logLoss_(labels, predictions, weights, epsilon, reduction) { + if (epsilon === void 0) { epsilon = 1e-7; } + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $labels = convertToTensor(labels, 'labels', 'logLoss'); + var $predictions = convertToTensor(predictions, 'predictions', 'logLoss'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'logLoss'); + } + assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: '); + var one = scalar(1); + var epsilonScalar = scalar(epsilon); + var l1 = neg(mul($labels, log(add($predictions, epsilonScalar)))); + var l2 = mul(sub(one, $labels), log(add(sub(one, $predictions), epsilonScalar))); + var losses = sub(l1, l2); + return computeWeightedLoss(losses, $weights, reduction); + } + var logLoss = op({ logLoss_: logLoss_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the mean squared error between two tensors. + * + * @param labels The ground truth output tensor, same dimensions as + * 'predictions'. + * @param predictions The predicted outputs. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} + */ + function meanSquaredError_(labels, predictions, weights, reduction) { + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $labels = convertToTensor(labels, 'labels', 'meanSquaredError'); + var $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'meanSquaredError'); + } + assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: '); + var losses = squaredDifference($labels, $predictions); + return computeWeightedLoss(losses, $weights, reduction); + } + var meanSquaredError = op({ meanSquaredError_: meanSquaredError_ }); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function sigmoidCrossEntropyWithLogits_(labels, logits) { + var $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits'); + var $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits'); + assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: '); + /** + * Implementation Details: + * + * For brevity, let `x = logits`, `z = labels`. The logistic loss is + * z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) + * = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x))) + * = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x))) + * = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x)) + * = (1 - z) * x + log(1 + exp(-x)) + * = x - x * z + log(1 + exp(-x)) + * + * For x < 0, to avoid overflow in exp(-x), we reformulate the above + * x - x * z + log(1 + exp(-x)) + * = log(exp(x)) - x * z + log(1 + exp(-x)) + * = - x * z + log(1 + exp(x)) + * + * Hence, to ensure stability and avoid overflow, the implementation uses + * this equivalent formulation: + * max(x, 0) - x * z + log(1 + exp(-abs(x))) + */ + var maxOutput = relu($logits); + var outputXTarget = mul($logits, $labels); + var sigmoidOutput = log1p(exp(neg(abs($logits)))); + return add(sub(maxOutput, outputXTarget), sigmoidOutput); + } + /** + * Computes the sigmoid cross entropy loss between two tensors. + * + * If labelSmoothing is nonzero, smooth the labels towards 1/2: + * + * newMulticlassLabels = multiclassLabels * (1 - labelSmoothing) + * + 0.5 * labelSmoothing + * + * @param multiClassLabels The ground truth output tensor of shape + * [batch_size, num_classes], same dimensions as 'predictions'. + * @param logits The predicted outputs. + * @param weights Tensor whose rank is either 0, or the same rank as + * `labels`, and must be broadcastable to `labels` (i.e., all dimensions + * must be either `1`, or the same as the corresponding `losses` + * dimension). + * @param labelSmoothing If greater than 0, then smooth the labels. + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } + */ + function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing, reduction) { + if (labelSmoothing === void 0) { labelSmoothing = 0; } + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy'); + var $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy'); + } + assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: '); + if (labelSmoothing > 0) { + var labelSmoothingScalar = scalar(labelSmoothing); + var one = scalar(1); + var half = scalar(0.5); + $multiClassLabels = + add(mul($multiClassLabels, sub(one, labelSmoothingScalar)), mul(half, labelSmoothingScalar)); + } + var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits); + return computeWeightedLoss(losses, $weights, reduction); + } + var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ }); + + /** + * Computes softmax cross entropy between logits and labels. + * + * Measures the probability error in discrete classification tasks in which + * the classes are mutually exclusive (each entry is in exactly one class). + * For example, each CIFAR-10 image is labeled with one and only one label: an + * image can be a dog or a truck, but not both. + * + * `NOTE`: While the classes are mutually exclusive, their probabilities need + * not be. All that is required is that each row of labels is a valid + * probability distribution. If they are not, the computation of the gradient + * will be incorrect. + * + * `WARNING`: This op expects unscaled logits, since it performs a softmax on + * logits internally for efficiency. Do not call this op with the output of + * softmax, as it will produce incorrect results. + * + * logits and labels must have the same shape, e.g. [batch_size, num_classes] + * and the same dtype. + * @param labels The labels array. + * @param logits The logits array. + * @param dim The dimension softmax would be performed on. Defaults to `-1` + * which indicates the last dimension. + */ + function softmaxCrossEntropyWithLogits_(labels, logits, dim) { + if (dim === void 0) { dim = -1; } + if (dim === -1) { + dim = logits.rank - 1; + } + if (dim !== logits.rank - 1) { + throw Error("Softmax cross entropy along a non-last dimension is not yet " + + ("supported. Labels / logits was rank " + logits.rank + " ") + + ("and dim was " + dim)); + } + // Use a custom gradient for numerical stability. + var customOp = customGrad(function (labels, logits, save) { + // Reference: + // 1. http://cs231n.github.io/linear-classify/#softmax + // 2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/ + var keepDims = true; + var lse = logSumExp(logits, [dim], keepDims); + var logResult = sub(cast(logits, 'float32'), lse); + save([labels, logResult]); + var costVector = neg(mul(logResult, labels)); + var value = sum(costVector, [dim]); + var gradFunc = function (dy, saved) { + var _a = __read(saved, 2), labels = _a[0], logResult = _a[1]; + var dyShape = expandShapeToKeepDim(dy.shape, [dim]); + return [ + mul(reshape(dy, dyShape), sub(cast(labels, 'float32'), exp(logResult))), + mul(reshape(dy, dyShape), sub(exp(logResult), cast(labels, 'float32'))), + ]; + }; + return { value: value, gradFunc: gradFunc }; + }); + return customOp(labels, logits); + } + /** + * Computes the softmax cross entropy loss between two tensors. + * + * If labelSmoothing is nonzero, smooth the labels towards 1/2: + * + * newOnehotLabels = onehotLabels * (1 - labelSmoothing) + * + labelSmoothing / numClasses + * + * @param onehotLabels One hot encoded labels + * [batch_size, num_classes], same dimensions as 'predictions'. + * @param logits The predicted outputs. + * @param weights Tensor whose rank is either 0, or 1, and must be + * broadcastable to `loss` of shape [batch_size] + * @param labelSmoothing If greater than 0, then smooth the labels. + * @param reduction Type of reduction to apply to loss. Should be of type + * `Reduction` + * + * @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } + */ + function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing, reduction) { + if (labelSmoothing === void 0) { labelSmoothing = 0; } + if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; } + var $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy'); + var $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy'); + var $weights = null; + if (weights != null) { + $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy'); + } + assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: '); + if (labelSmoothing > 0) { + var labelSmoothingScalar = scalar(labelSmoothing); + var one = scalar(1); + var numClasses = scalar($onehotLabels.shape[1]); + $onehotLabels = + add(mul($onehotLabels, sub(one, labelSmoothingScalar)), div(labelSmoothingScalar, numClasses)); + } + var losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits); + return computeWeightedLoss(losses, $weights, reduction); + } + var softmaxCrossEntropy = op({ softmaxCrossEntropy_: softmaxCrossEntropy_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * The input SparseTensor is represented via the map of inputs {`indices`, + * `values`, `denseShape`}. The output SparseTensor has the same `denseShape` + * but with indices `outputIndices` and values `outputValues`. This op inserts a + * single entry for every row that doesn't have any values. The index is created + * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`. + * + * For example, suppose `spInput` has shape [5, 6] and non-empty values: + * [0, 1]: a + * [0, 3]: b + * [2, 0]: c + * [3, 1]: d + * + * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values: + * [0, 1]: a + * [0, 3]: b + * [1, 0]: `defaultValue` + * [2, 0]: c + * [3, 1]: d + * [4, 0]: `defaultValue` + * + * The output SparseTensor will be in row-major order and will have the same + * shape as the input. + * + * This op also returns an indicator vector shaped [dense_shape[0]] such that + * emptyRowIndicator[i] = True iff row i was an empty row. + * + * And a reverse index map vector shaped [indices.shape[0]] that is used during + * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] == + * outputIndices[outi, j] for all j + * + * ```js + * const result = tf.sparse.sparseFillEmptyRows( + * [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]], + * [0, 10, 13, 14, 32, 33], [5, 6], -1); + * console.log(result); + * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4], + * // [2, 0], [3, 2], [3, 3], [4, 0]] + * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1] + * result['emptyRowIndicator'].print(); // [false, false, true, false, true] + * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6] + * ``` + * @param indices: 2-D. the indices of the sparse tensor. + * @param values: 1-D. the values of the sparse tensor. + * @param denseShape: 1-D. the shape of the sparse tensor. + * @param defaultValue: 0-D. default value to insert into location [row, 0, ..., + * 0] for rows missing from the input sparse tensor. + * @return A map with the following properties: + * - outputIndices + * - outputValues: 1-D. the values of the filled sparse tensor. + * - emptyRowIndicator: 1-D. whether the dense row was missing in the input + * sparse tensor. + * - reverseIndexMap: 1-D. a map from the input indices to the output + * indices. + * @doc {heading: 'Operations', subheading: 'Sparse'} + */ + function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) { + var $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32'); + var $values = convertToTensor(values, 'values', 'sparseFillEmptyRows'); + var $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32'); + var $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype); + if ($indices.rank !== 2) { + throw new Error("Indices should be Tensor2D but received shape\n " + $indices.shape); + } + if ($values.rank !== 1) { + throw new Error("Values should be Tensor1D but received shape " + $values.shape); + } + if ($denseShape.rank !== 1) { + throw new Error("Dense shape should be Tensor1D but received shape " + $denseShape.shape); + } + if ($defaultValue.rank !== 0) { + throw new Error("Default value should be a scalar but received shape " + $defaultValue.shape); + } + var inputs = { + indices: $indices, + values: $values, + denseShape: $denseShape, + defaultValue: $defaultValue + }; + var result = ENGINE.runKernel(SparseFillEmptyRows, inputs); + return { + outputIndices: result[0], + outputValues: result[1], + emptyRowIndicator: result[2], + reverseIndexMap: result[3] + }; + } + var sparseFillEmptyRows = op({ sparseFillEmptyRows_: sparseFillEmptyRows_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * This operation has the same semantics as reshape on the represented dense + * tensor. The `inputIndices` are recomputed based on the requested `newShape`. + * If one component of `newShape` is the special value -1, the size of that + * dimension is computed so that the total dense size remains constant. At most + * one component of `newShape` can be -1. The number of dense elements implied + * by `newShape` must be the same as the number of dense elements originally + * implied by `inputShape`. Reshaping does not affect the order of values in the + * SparseTensor. If the input tensor has rank R_in and N non-empty values, and + * `newShape` has length R_out, then `inputIndices` has shape [N, R_in], + * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and + * `outputShape` has length R_out. + * + * ```js + * const result = tf.sparse.sparseReshape( + * [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]], + * [2, 3, 6], [9, -1]); + * console.log(result); + * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]] + * result['outputShape'].print(); // [9, 4] + * ``` + * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty + * values in a SparseTensor. + * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense + * shape. + * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape. + * @return A map with the following properties: + * - outputIndices: 2-D. N x R_out matrix with the updated indices of + * non-empty values in the output SparseTensor. + * - outputShape: 1-D. R_out vector with the full dense shape of the output + * SparseTensor. This is the same as newShape but with any -1 dimensions + * filled in. + * @doc {heading: 'Operations', subheading: 'Sparse'} + */ + function sparseReshape_(inputIndices, inputShape, newShape) { + var $inputIndices = convertToTensor(inputIndices, 'inputIndices', 'sparseReshape', 'int32'); + var $inputShape = convertToTensor(inputShape, 'inputShape', 'sparseReshape', 'int32'); + var $newShape = convertToTensor(newShape, 'newShape', 'sparseReshape', 'int32'); + if ($inputIndices.rank !== 2) { + throw new Error("Input indices should be Tensor2D but received shape\n " + $inputIndices.shape); + } + if ($inputShape.rank !== 1) { + throw new Error("Input shape should be Tensor1D but received shape " + $inputShape.shape); + } + if ($newShape.rank !== 1) { + throw new Error("New shape should be Tensor1D but received shape " + $newShape.shape); + } + var inputs = { + inputIndices: $inputIndices, + inputShape: $inputShape, + newShape: $newShape + }; + var result = ENGINE.runKernel(SparseReshape, inputs); + return { outputIndices: result[0], outputShape: result[1] }; + } + var sparseReshape = op({ sparseReshape_: sparseReshape_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the mean along sparse segments of a tensor. + * + * ```js + * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [6,7,8,9]]); + * // Select two rows, one segment. + * const result1 = tf.sparse.sparseSegmentMean(c, + * tf.tensor1d([0, 1], 'int32'), + * tf.tensor1d([0, 0], 'int32')); + * result1.print(); // [[0, 0, 0, 0]] + * + * // Select two rows, two segments. + * const result2 = tf.sparse.sparseSegmentMean(c, + * tf.tensor1d([0, 1], 'int32'), + * tf.tensor1d([0, 1], 'int32')); + * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]] + * + * // Select all rows, two segments. + * const result3 = tf.sparse.sparseSegmentMean(c, + * tf.tensor1d([0, 1, 2], 'int32'), + * tf.tensor1d([0, 1, 1], 'int32')); + * result3.print(); // [[1.0, 2.0, 3.0, 4.0], [2.5, 2.5, 2.5, 2.5]] + * ``` + * @param data: A Tensor of at least one dimension with data that will be + * assembled in the output. + * @param indices: A 1-D Tensor with indices into data. Has same rank as + * segmentIds. + * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values + * should be sorted and can be repeated. + * @return Has same shape as data, except for dimension 0 which has equal to + * the number of segments. + * + * @doc {heading: 'Operations', subheading: 'Sparse'} + */ + function sparseSegmentMean_(data, indices, segmentIds) { + var $data = convertToTensor(data, 'data', 'sparseSegmentMean'); + var $indices = convertToTensor(indices, 'indices', 'sparseSegmentMean', 'int32'); + var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentMean', 'int32'); + if ($data.rank < 1) { + throw new Error("Data should be at least 1 dimensional but received scalar"); + } + if ($indices.rank !== 1) { + throw new Error("Indices should be Tensor1D but received shape\n " + $indices.shape); + } + if ($segmentIds.rank !== 1) { + throw new Error("Segment ids should be Tensor1D but received shape\n " + $segmentIds.shape); + } + var inputs = { + data: $data, + indices: $indices, + segmentIds: $segmentIds + }; + return ENGINE.runKernel(SparseSegmentMean, inputs); + } + var sparseSegmentMean = op({ sparseSegmentMean_: sparseSegmentMean_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the sum along sparse segments of a tensor. + * + * ```js + * const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]); + * // Select two rows, one segment. + * const result1 = tf.sparse.sparseSegmentSum(c, + * tf.tensor1d([0, 1], 'int32'), + * tf.tensor1d([0, 0], 'int32')); + * result1.print(); // [[0, 0, 0, 0]] + * + * // Select two rows, two segment. + * const result2 = tf.sparse.sparseSegmentSum(c, + * tf.tensor1d([0, 1], 'int32'), + * tf.tensor1d([0, 1], 'int32')); + * result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]] + * + * // Select all rows, two segments. + * const result3 = tf.sparse.sparseSegmentSum(c, + * tf.tensor1d([0, 1, 2], 'int32'), + * tf.tensor1d([0, 0, 1], 'int32')); + * result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]] + * ``` + * @param data: A Tensor of at least one dimension with data that will be + * assembled in the output. + * @param indices: A 1-D Tensor with indices into data. Has same rank as + * segmentIds. + * @param segmentIds: A 1-D Tensor with indices into the output Tensor. Values + * should be sorted and can be repeated. + * @return Has same shape as data, except for dimension 0 which has equal to + * the number of segments. + * + * @doc {heading: 'Operations', subheading: 'Sparse'} + */ + function sparseSegmentSum_(data, indices, segmentIds) { + var $data = convertToTensor(data, 'data', 'sparseSegmentSum'); + var $indices = convertToTensor(indices, 'indices', 'sparseSegmentSum', 'int32'); + var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'sparseSegmentSum', 'int32'); + if ($data.rank < 1) { + throw new Error("Data should be at least 1 dimensional but received scalar"); + } + if ($indices.rank !== 1) { + throw new Error("Indices should be Tensor1D but received shape\n " + $indices.shape); + } + if ($segmentIds.rank !== 1) { + throw new Error("Segment ids should be Tensor1D but received shape\n " + $segmentIds.shape); + } + var inputs = { + data: $data, + indices: $indices, + segmentIds: $segmentIds + }; + return ENGINE.runKernel(SparseSegmentSum, inputs); + } + var sparseSegmentSum = op({ sparseSegmentSum_: sparseSegmentSum_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates ngrams from ragged string data. + * + * This op accepts a ragged tensor with 1 ragged dimension containing only + * strings and outputs a ragged tensor with 1 ragged dimension containing ngrams + * of that string, joined along the innermost axis. + * + * ```js + * const result = tf.string.stringNGrams( + * ['a', 'b', 'c', 'd'], tf.tensor1d([0, 2, 4], 'int32'), + * '|', [1, 2], 'LP', 'RP', -1, false); + * result['nGrams'].print(); // ['a', 'b', 'LP|a', 'a|b', 'b|RP', + * // 'c', 'd', 'LP|c', 'c|d', 'd|RP'] + * result['nGramsSplits'].print(); // [0, 5, 10] + * ``` + * @param data: The values tensor of the ragged string tensor to make ngrams out + * of. Must be a 1D string tensor. + * @param dataSplits: The splits tensor of the ragged string tensor to make + * ngrams out of. + * @param separator: The string to append between elements of the token. Use "" + * for no separator. + * @param nGramWidths: The sizes of the ngrams to create. + * @param leftPad: The string to use to pad the left side of the ngram sequence. + * Only used if pad_width !== 0. + * @param rightPad: The string to use to pad the right side of the ngram + * sequence. Only used if pad_width !== 0. + * @param padWidth: The number of padding elements to add to each side of each + * sequence. Note that padding will never be greater than `nGramWidths`-1 + * regardless of this value. If `padWidth`=-1 , then add max(`nGramWidths)-1 + * elements. + * @param preserveShortSequences: If true, then ensure that at least one ngram + * is generated for each input sequence. In particular, if an input sequence + * is shorter than min(ngramWidth) + 2*padWidth, then generate a single + * ngram containing the entire sequence. If false, then no ngrams are + * generated for these short input sequences. + * @return A map with the following properties: + * - nGrams: The values tensor of the output ngrams ragged tensor. + * - nGramsSplits: The splits tensor of the output ngrams ragged tensor. + * + * @doc {heading: 'Operations', subheading: 'String'} + */ + function stringNGrams_(data, dataSplits, separator, nGramWidths, leftPad, rightPad, padWidth, preserveShortSequences) { + var $data = convertToTensor(data, 'data', 'stringNGrams', 'string'); + if ($data.dtype !== 'string') { + throw new Error('Data must be of datatype string'); + } + if ($data.shape.length !== 1) { + throw new Error("Data must be a vector, saw: " + $data.shape); + } + var $dataSplits = convertToTensor(dataSplits, 'dataSplits', 'stringNGrams'); + if ($dataSplits.dtype !== 'int32') { + throw new Error('Data splits must be of datatype int32'); + } + var attrs = { + separator: separator, + nGramWidths: nGramWidths, + leftPad: leftPad, + rightPad: rightPad, + padWidth: padWidth, + preserveShortSequences: preserveShortSequences + }; + var inputs = { data: $data, dataSplits: $dataSplits }; + var result = ENGINE.runKernel(StringNGrams, inputs, attrs); + return { nGrams: result[0], nGramsSplits: result[1] }; + } + var stringNGrams = op({ stringNGrams_: stringNGrams_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Split elements of `input` based on `delimiter` into a SparseTensor . + * + * Let N be the size of source (typically N will be the batch size). Split each + * element of `input` based on `delimiter` and return a SparseTensor containing + * the splitted tokens. Empty tokens are ignored if `skipEmpty` is set to True. + * + * `delimiter` can be empty, or a string of split characters. If `delimiter` is + * an empty string, each element of `input` is split into individual + * character strings. Otherwise every character of `delimiter` is a potential + * split point. + * + * ```js + * const result = tf.string.stringSplit(['hello world', 'a b c'], ' '); + * result['indices'].print(); // [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]] + * result['values'].print(); // ['hello', 'world', 'a', 'b', 'c'] + * result['shape'].print(); // [2, 3] + * ``` + * @param input: 1-D. Strings to split. + * @param delimiter: 0-D. Delimiter characters, or empty string. + * @param skipEmpty: Optional. If true, skip the empty strings from the result. + * Defaults to true. + * @return A map with the following properties: + * - indices: A dense matrix of int32 representing the indices of the sparse + * tensor. + * - values: A vector of strings corresponding to the splited values. + * - shape: a length-2 vector of int32 representing the shape of the sparse + * tensor, where the first value is N and the second value is the maximum number + * of tokens in a single input entry. + * + * @doc {heading: 'Operations', subheading: 'String'} + */ + function stringSplit_(input, delimiter, skipEmpty) { + if (skipEmpty === void 0) { skipEmpty = true; } + var $input = convertToTensor(input, 'input', 'stringSplit', 'string'); + var $delimiter = convertToTensor(delimiter, 'delimiter', 'stringSplit', 'string'); + if ($input.rank !== 1) { + throw new Error("Input should be Tensor1D but received shape " + $input.shape); + } + if ($delimiter.rank !== 0) { + throw new Error("Delimiter should be a scalar but received shape " + $delimiter.shape); + } + var attrs = { skipEmpty: skipEmpty }; + var inputs = { input: $input, delimiter: $delimiter }; + var result = ENGINE.runKernel(StringSplit, inputs, attrs); + return { indices: result[0], values: result[1], shape: result[2] }; + } + var stringSplit = op({ stringSplit_: stringSplit_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Converts each string in the input Tensor to its hash mod by a number of + * buckets. + * + * The hash function is deterministic on the content of the string within the + * process and will never change. However, it is not suitable for cryptography. + * This function may be used when CPU time is scarce and inputs are trusted or + * unimportant. There is a risk of adversaries constructing inputs that all hash + * to the same bucket. + * + * ```js + * const result = tf.string.stringToHashBucketFast( + * ['Hello', 'TensorFlow', '2.x'], 3); + * result.print(); // [0, 2, 2] + * ``` + * @param input: The strings to assign a hash bucket. + * @param numBuckets: The number of buckets. + * @return A Tensor of the same shape as the input tensor. + * + * @doc {heading: 'Operations', subheading: 'String'} + */ + function stringToHashBucketFast_(input, numBuckets) { + var $input = convertToTensor(input, 'input', 'stringToHashBucketFast', 'string'); + var attrs = { numBuckets: numBuckets }; + if (numBuckets <= 0) { + throw new Error("Number of buckets must be at least 1"); + } + var inputs = { input: $input }; + return ENGINE.runKernel(StringToHashBucketFast, inputs, attrs); + } + var stringToHashBucketFast = op({ stringToHashBucketFast_: stringToHashBucketFast_ }); + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var spectral = { + fft: fft, + ifft: ifft, + rfft: rfft, + irfft: irfft + }; + var signal = { + hammingWindow: hammingWindow, + hannWindow: hannWindow, + frame: frame, + stft: stft, + }; + var image = { + flipLeftRight: flipLeftRight, + grayscaleToRGB: grayscaleToRGB, + resizeNearestNeighbor: resizeNearestNeighbor, + resizeBilinear: resizeBilinear, + rotateWithOffset: rotateWithOffset, + cropAndResize: cropAndResize, + nonMaxSuppression: nonMaxSuppression, + nonMaxSuppressionAsync: nonMaxSuppressionAsync, + nonMaxSuppressionWithScore: nonMaxSuppressionWithScore, + nonMaxSuppressionWithScoreAsync: nonMaxSuppressionWithScoreAsync, + nonMaxSuppressionPadded: nonMaxSuppressionPadded, + nonMaxSuppressionPaddedAsync: nonMaxSuppressionPaddedAsync, + threshold: threshold, + transform: transform + }; + var linalg = { + bandPart: bandPart, + gramSchmidt: gramSchmidt, + qr: qr + }; + var losses = { + absoluteDifference: absoluteDifference, + computeWeightedLoss: computeWeightedLoss, + cosineDistance: cosineDistance, + hingeLoss: hingeLoss, + huberLoss: huberLoss, + logLoss: logLoss, + meanSquaredError: meanSquaredError, + sigmoidCrossEntropy: sigmoidCrossEntropy, + softmaxCrossEntropy: softmaxCrossEntropy + }; + var sparse = { + sparseFillEmptyRows: sparseFillEmptyRows, + sparseReshape: sparseReshape, + sparseSegmentMean: sparseSegmentMean, + sparseSegmentSum: sparseSegmentSum + }; + // tslint:disable-next-line:variable-name + var string = { + stringNGrams: stringNGrams, + stringSplit: stringSplit, + stringToHashBucketFast: stringToHashBucketFast + }; + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + + var tfOps = { + __proto__: null, + abs: abs, + acos: acos, + acosh: acosh, + add: add, + addN: addN, + all: all, + any: any, + argMax: argMax, + argMin: argMin, + asin: asin, + asinh: asinh, + atan: atan, + atan2: atan2, + atanh: atanh, + avgPool: avgPool, + avgPool3d: avgPool3d, + basicLSTMCell: basicLSTMCell, + batchToSpaceND: batchToSpaceND, + batchNorm: batchNorm, + batchNorm2d: batchNorm2d, + batchNorm3d: batchNorm3d, + batchNorm4d: batchNorm4d, + bincount: bincount, + broadcastArgs: broadcastArgs, + broadcastTo: broadcastTo, + buffer: buffer, + cast: cast, + ceil: ceil, + clipByValue: clipByValue, + clone: clone, + complex: complex, + concat: concat, + concat1d: concat1d, + concat2d: concat2d, + concat3d: concat3d, + concat4d: concat4d, + conv1d: conv1d, + conv2d: conv2d$1, + conv2dTranspose: conv2dTranspose, + conv3d: conv3d, + conv3dTranspose: conv3dTranspose, + cos: cos, + cosh: cosh, + cumsum: cumsum, + denseBincount: denseBincount, + depthToSpace: depthToSpace, + depthwiseConv2d: depthwiseConv2d$1, + diag: diag, + dilation2d: dilation2d, + div: div, + divNoNan: divNoNan, + dot: dot, + einsum: einsum, + elu: elu, + equal: equal, + erf: erf, + exp: exp, + expandDims: expandDims, + expm1: expm1, + eye: eye, + fill: fill, + floor: floor, + floorDiv: floorDiv, + gather: gather, + greater: greater, + greaterEqual: greaterEqual, + imag: imag, + isFinite: isFinite$1, + isInf: isInf, + isNaN: isNaN$1, + leakyRelu: leakyRelu, + less: less, + lessEqual: lessEqual, + linspace: linspace, + localResponseNormalization: localResponseNormalization, + log: log, + log1p: log1p, + logSigmoid: logSigmoid, + logSoftmax: logSoftmax, + logSumExp: logSumExp, + logicalAnd: logicalAnd, + logicalNot: logicalNot, + logicalOr: logicalOr, + logicalXor: logicalXor, + matMul: matMul$1, + max: max, + maxPool: maxPool, + maxPool3d: maxPool3d, + maxPoolWithArgmax: maxPoolWithArgmax, + maximum: maximum, + mean: mean, + meshgrid: meshgrid, + min: min, + minimum: minimum, + mirrorPad: mirrorPad, + mod: mod, + moments: moments, + mul: mul, + multiRNNCell: multiRNNCell, + multinomial: multinomial, + neg: neg, + notEqual: notEqual, + oneHot: oneHot, + ones: ones, + onesLike: onesLike, + outerProduct: outerProduct, + pad: pad, + pad1d: pad1d, + pad2d: pad2d, + pad3d: pad3d, + pad4d: pad4d, + pool: pool, + pow: pow, + prelu: prelu, + print: print, + prod: prod, + rand: rand, + randomGamma: randomGamma, + randomNormal: randomNormal, + randomUniform: randomUniform, + range: range, + real: real, + reciprocal: reciprocal, + relu: relu, + relu6: relu6, + reshape: reshape, + reverse: reverse, + reverse1d: reverse1d, + reverse2d: reverse2d, + reverse3d: reverse3d, + reverse4d: reverse4d, + round: round, + rsqrt: rsqrt, + scalar: scalar, + selu: selu, + separableConv2d: separableConv2d, + setdiff1dAsync: setdiff1dAsync, + sigmoid: sigmoid, + sign: sign, + sin: sin, + sinh: sinh, + slice: slice, + slice1d: slice1d, + slice2d: slice2d, + slice3d: slice3d, + slice4d: slice4d, + softmax: softmax, + softplus: softplus, + spaceToBatchND: spaceToBatchND, + fft: fft, + ifft: ifft, + irfft: irfft, + rfft: rfft, + split: split$1, + sqrt: sqrt, + square: square, + squaredDifference: squaredDifference, + squeeze: squeeze, + stack: stack, + step: step, + stridedSlice: stridedSlice, + sub: sub, + sum: sum, + tan: tan, + tanh: tanh, + tensor: tensor, + tensor1d: tensor1d, + tensor2d: tensor2d, + tensor3d: tensor3d, + tensor4d: tensor4d, + tensor5d: tensor5d, + tensor6d: tensor6d, + tile: tile, + topk: topk, + truncatedNormal: truncatedNormal, + unique: unique, + unsortedSegmentSum: unsortedSegmentSum, + unstack: unstack, + variable: variable, + where: where, + whereAsync: whereAsync, + zeros: zeros, + zerosLike: zerosLike, + op: op, + OP_SCOPE_SUFFIX: OP_SCOPE_SUFFIX, + booleanMaskAsync: booleanMaskAsync, + transpose: transpose, + norm: norm, + movingAverage: movingAverage, + scatterND: scatterND, + sparseToDense: sparseToDense, + gatherND: gatherND, + dropout: dropout, + enclosingPowerOfTwo: enclosingPowerOfTwo, + cosineWindow: cosineWindow, + inTopKAsync: inTopKAsync, + image: image, + linalg: linalg, + losses: losses, + spectral: spectral, + fused: fused_ops, + signal: signal, + sparse: sparse, + string: string + }; + + /** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var executeOp$j = function (node, tensorMap, context) { + switch (node.op) { + case 'BiasAdd': + case 'AddV2': + case 'Add': { + return [add(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'AddN': { + return [addN(getParamValue('tensors', node, tensorMap, context))]; + } + case 'FloorMod': + case 'Mod': + return [mod(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + case 'Mul': + return [mul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + case 'RealDiv': + case 'Div': { + return [div(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'DivNoNan': { + return [divNoNan(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'FloorDiv': { + return [floorDiv(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Sub': { + return [sub(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Minimum': { + return [minimum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Maximum': { + return [maximum(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Pow': { + return [pow(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'SquaredDifference': { + return [squaredDifference(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$i = function (node, tensorMap, context) { + switch (node.op) { + case 'Abs': + case 'ComplexAbs': + return [abs(getParamValue('x', node, tensorMap, context))]; + case 'Acos': + return [acos(getParamValue('x', node, tensorMap, context))]; + case 'Acosh': + return [acosh(getParamValue('x', node, tensorMap, context))]; + case 'Asin': + return [asin(getParamValue('x', node, tensorMap, context))]; + case 'Asinh': + return [asinh(getParamValue('x', node, tensorMap, context))]; + case 'Atan': + return [atan(getParamValue('x', node, tensorMap, context))]; + case 'Atan2': + return [atan2(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))]; + case 'Atanh': + return [atanh(getParamValue('x', node, tensorMap, context))]; + case 'Ceil': + return [ceil(getParamValue('x', node, tensorMap, context))]; + case 'Complex': + return [complex(getParamValue('real', node, tensorMap, context), getParamValue('imag', node, tensorMap, context))]; + case 'Cos': + return [cos(getParamValue('x', node, tensorMap, context))]; + case 'Cosh': + return [cosh(getParamValue('x', node, tensorMap, context))]; + case 'Elu': + return [elu(getParamValue('x', node, tensorMap, context))]; + case 'Erf': + return [erf(getParamValue('x', node, tensorMap, context))]; + case 'Exp': + return [exp(getParamValue('x', node, tensorMap, context))]; + case 'Expm1': { + return [expm1(getParamValue('x', node, tensorMap, context))]; + } + case 'Floor': + return [floor(getParamValue('x', node, tensorMap, context))]; + case 'Log': + return [log(getParamValue('x', node, tensorMap, context))]; + case 'Log1p': { + return [log1p(getParamValue('x', node, tensorMap, context))]; + } + case 'Imag': + return [imag(getParamValue('x', node, tensorMap, context))]; + case 'Neg': + return [neg(getParamValue('x', node, tensorMap, context))]; + case 'Reciprocal': { + return [reciprocal(getParamValue('x', node, tensorMap, context))]; + } + case 'Real': + return [real(getParamValue('x', node, tensorMap, context))]; + case 'Relu': + return [relu(getParamValue('x', node, tensorMap, context))]; + case 'Round': { + return [round(getParamValue('x', node, tensorMap, context))]; + } + case 'Selu': + return [selu(getParamValue('x', node, tensorMap, context))]; + case 'Sigmoid': + return [sigmoid(getParamValue('x', node, tensorMap, context))]; + case 'Sin': + return [sin(getParamValue('x', node, tensorMap, context))]; + case 'Sign': { + return [sign(getParamValue('x', node, tensorMap, context))]; + } + case 'Sinh': { + return [sinh(getParamValue('x', node, tensorMap, context))]; + } + case 'Softplus': { + return [softplus(getParamValue('x', node, tensorMap, context))]; + } + case 'Sqrt': { + return [sqrt(getParamValue('x', node, tensorMap, context))]; + } + case 'Square': { + return [square(getParamValue('x', node, tensorMap, context))]; + } + case 'Tanh': { + return [tanh(getParamValue('x', node, tensorMap, context))]; + } + case 'Tan': + return [tan(getParamValue('x', node, tensorMap, context))]; + case 'ClipByValue': + return [clipByValue(getParamValue('x', node, tensorMap, context), getParamValue('clipValueMin', node, tensorMap, context), getParamValue('clipValueMax', node, tensorMap, context))]; + case 'Relu6': + return [relu6(getParamValue('x', node, tensorMap, context))]; + case 'Rsqrt': + return [rsqrt(getTensor(node.inputNames[0], tensorMap, context))]; + case 'Prod': + return [prod(getParamValue('x', node, tensorMap, context), getParamValue('axes', node, tensorMap, context))]; + case 'LeakyRelu': + return [leakyRelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))]; + case 'Prelu': + return [prelu(getParamValue('x', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context))]; + case 'IsNan': + return [isNaN$1(getTensor(node.inputNames[0], tensorMap, context))]; + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Used by TensorList and TensorArray to verify if elementShape matches, support + * negative value as the dim shape. + * @param shapeA + * @param shapeB + * @param errorMessagePrefix + */ + function assertShapesMatchAllowUndefinedSize(shapeA, shapeB, errorMessagePrefix) { + if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; } + // constant shape means unknown rank + if (typeof shapeA === 'number' || typeof shapeB === 'number') { + return; + } + tfc.util.assert(shapeA.length === shapeB.length, function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); }); + for (var i = 0; i < shapeA.length; i++) { + var dim0 = shapeA[i]; + var dim1 = shapeB[i]; + tfc.util.assert(dim0 < 0 || dim1 < 0 || dim0 === dim1, function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); }); + } + } + function fullDefinedShape(elementShape) { + if (typeof elementShape === 'number' || elementShape.some(function (dim) { return dim < 0; })) { + return false; + } + return true; + } + /** + * Generate the output element shape from the list elementShape, list tensors + * and input param. + * @param listElementShape + * @param tensors + * @param elementShape + */ + function inferElementShape(listElementShape, tensors, elementShape) { + var partialShape = mergeElementShape(listElementShape, elementShape); + var notfullDefinedShape = !fullDefinedShape(partialShape); + if (notfullDefinedShape && tensors.length === 0) { + throw new Error("Tried to calculate elements of an empty list" + + (" with non-fully-defined elementShape: " + partialShape)); + } + if (notfullDefinedShape) { + tensors.forEach(function (tensor) { + partialShape = mergeElementShape(tensor.shape, partialShape); + }); + } + if (!fullDefinedShape(partialShape)) { + throw new Error("Non-fully-defined elementShape: " + partialShape); + } + return partialShape; + } + function mergeElementShape(elementShapeA, elementShapeB) { + if (typeof elementShapeA === 'number') { + return elementShapeB; + } + if (typeof elementShapeB === 'number') { + return elementShapeA; + } + if (elementShapeA.length !== elementShapeB.length) { + throw new Error("Incompatible ranks during merge: " + elementShapeA + " vs. " + elementShapeB); + } + var result = []; + for (var i = 0; i < elementShapeA.length; ++i) { + var dim0 = elementShapeA[i]; + var dim1 = elementShapeB[i]; + if (dim0 >= 0 && dim1 >= 0 && dim0 !== dim1) { + throw new Error("Incompatible shape during merge: " + elementShapeA + " vs. " + elementShapeB); + } + result[i] = dim0 >= 0 ? dim0 : dim1; + } + return result; + } + + /** + * The TensorArray object keeps an array of Tensors. It + * allows reading from the array and writing to the array. + */ + var TensorArray = /** @class */ (function () { + function TensorArray(name, dtype, maxSize, elementShape, identicalElementShapes, dynamicSize, clearAfterRead) { + this.name = name; + this.dtype = dtype; + this.maxSize = maxSize; + this.elementShape = elementShape; + this.identicalElementShapes = identicalElementShapes; + this.dynamicSize = dynamicSize; + this.clearAfterRead = clearAfterRead; + this.tensors = []; + this.closed_ = false; + this.idTensor = tfc.scalar(0); + tfc.keep(this.idTensor); + } + Object.defineProperty(TensorArray.prototype, "id", { + get: function () { + return this.idTensor.id; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(TensorArray.prototype, "closed", { + get: function () { + return this.closed_; + }, + enumerable: true, + configurable: true + }); + /** + * Dispose the tensors and idTensor and mark the TensoryArray as closed. + */ + TensorArray.prototype.clearAndClose = function (keepIds) { + this.tensors.forEach(function (tensor) { + if (keepIds == null || !keepIds.has(tensor.tensor.id)) { + tensor.tensor.dispose(); + } + }); + this.tensors = []; + this.closed_ = true; + this.idTensor.dispose(); + }; + TensorArray.prototype.size = function () { + return this.tensors.length; + }; + /** + * Read the value at location index in the TensorArray. + * @param index Number the index to read from. + */ + TensorArray.prototype.read = function (index) { + if (this.closed_) { + throw new Error("TensorArray " + this.name + " has already been closed."); + } + if (index < 0 || index >= this.size()) { + throw new Error("Tried to read from index " + index + ", but array size is: " + this.size()); + } + var tensorWithState = this.tensors[index]; + if (tensorWithState.cleared) { + throw new Error("TensorArray " + this.name + ": Could not read index " + index + " twice because it was cleared after a previous read " + + "(perhaps try setting clear_after_read = false?)."); + } + if (this.clearAfterRead) { + tensorWithState.cleared = true; + } + tensorWithState.read = true; + return tensorWithState.tensor; + }; + /** + * Helper method to read multiple tensors from the specified indices. + */ + TensorArray.prototype.readMany = function (indices) { + var _this = this; + return indices.map(function (index) { return _this.read(index); }); + }; + /** + * Write value into the index of the TensorArray. + * @param index number the index to write to. + * @param tensor + */ + TensorArray.prototype.write = function (index, tensor) { + if (this.closed_) { + throw new Error("TensorArray " + this.name + " has already been closed."); + } + if (index < 0 || !this.dynamicSize && index >= this.maxSize) { + throw new Error("Tried to write to index " + index + ", but array is not resizeable and size is: " + this.maxSize); + } + var t = this.tensors[index] || {}; + if (tensor.dtype !== this.dtype) { + throw new Error("TensorArray " + this.name + ": Could not write to TensorArray index " + index + ",\n because the value dtype is " + tensor.dtype + ", but TensorArray dtype is " + this.dtype + "."); + } + // Set the shape for the first time write to unknow shape tensor array + if (this.size() === 0 && + (this.elementShape == null || this.elementShape.length === 0)) { + this.elementShape = tensor.shape; + } + assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, "TensorArray " + this.name + ": Could not write to TensorArray index " + index + "."); + if (t.read) { + throw new Error("TensorArray " + this.name + ": Could not write to TensorArray index " + index + ", because it has already been read."); + } + if (t.written) { + throw new Error("TensorArray " + this.name + ": Could not write to TensorArray index " + index + ", because it has already been written."); + } + t.tensor = tensor; + tfc.keep(tensor); + t.written = true; + this.tensors[index] = t; + }; + /** + * Helper method to write multiple tensors to the specified indices. + */ + TensorArray.prototype.writeMany = function (indices, tensors) { + var _this = this; + if (indices.length !== tensors.length) { + throw new Error("TensorArray " + this.name + ": could not write multiple tensors," + + ("because the index size: " + indices.length + " is not the same as tensors size: " + tensors.length + ".")); + } + indices.forEach(function (i, index) { return _this.write(i, tensors[index]); }); + }; + /** + * Return selected values in the TensorArray as a packed Tensor. All of + * selected values must have been written and their shapes must all match. + * @param [indices] number[] Optional. Taking values in [0, max_value). If the + * TensorArray is not dynamic, max_value=size(). If not specified returns + * all tensors in the original order. + * @param [dtype] + */ + TensorArray.prototype.gather = function (indices, dtype) { + if (!!dtype && dtype !== this.dtype) { + throw new Error("TensorArray dtype is " + this.dtype + " but gather requested dtype " + dtype); + } + if (!indices) { + indices = []; + for (var i = 0; i < this.size(); i++) { + indices.push(i); + } + } + else { + indices = indices.slice(0, this.size()); + } + if (indices.length === 0) { + return tfc.tensor([], [0].concat(this.elementShape)); + } + // Read all the PersistentTensors into a vector to keep track of + // their memory. + var tensors = this.readMany(indices); + assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, 'TensorArray shape mismatch: '); + return tfc.stack(tensors, 0); + }; + /** + * Return the values in the TensorArray as a concatenated Tensor. + */ + TensorArray.prototype.concat = function (dtype) { + if (!!dtype && dtype !== this.dtype) { + throw new Error("TensorArray dtype is " + this.dtype + " but concat requested dtype " + dtype); + } + if (this.size() === 0) { + return tfc.tensor([], [0].concat(this.elementShape)); + } + var indices = []; + for (var i = 0; i < this.size(); i++) { + indices.push(i); + } + // Collect all the tensors from the tensors array. + var tensors = this.readMany(indices); + assertShapesMatchAllowUndefinedSize(this.elementShape, tensors[0].shape, "TensorArray shape mismatch: tensor array shape (" + this.elementShape + ") vs first tensor shape (" + tensors[0].shape + ")"); + return tfc.concat(tensors, 0); + }; + /** + * Scatter the values of a Tensor in specific indices of a TensorArray. + * @param indices nummber[] values in [0, max_value). If the + * TensorArray is not dynamic, max_value=size(). + * @param tensor Tensor input tensor. + */ + TensorArray.prototype.scatter = function (indices, tensor) { + if (tensor.dtype !== this.dtype) { + throw new Error("TensorArray dtype is " + this.dtype + " but tensor has dtype " + tensor.dtype); + } + if (indices.length !== tensor.shape[0]) { + throw new Error("Expected len(indices) == tensor.shape[0], but saw: " + indices.length + " vs. " + tensor.shape[0]); + } + var maxIndex = Math.max.apply(Math, __spread(indices)); + if (!this.dynamicSize && maxIndex >= this.maxSize) { + throw new Error("Max index must be < array size (" + maxIndex + " vs. " + this.maxSize + ")"); + } + this.writeMany(indices, tfc.unstack(tensor, 0)); + }; + /** + * Split the values of a Tensor into the TensorArray. + * @param length number[] with the lengths to use when splitting value along + * its first dimension. + * @param tensor Tensor, the tensor to split. + */ + TensorArray.prototype.split = function (length, tensor) { + var _this = this; + if (tensor.dtype !== this.dtype) { + throw new Error("TensorArray dtype is " + this.dtype + " but tensor has dtype " + tensor.dtype); + } + var totalLength = 0; + var cumulativeLengths = length.map(function (len) { + totalLength += len; + return totalLength; + }); + if (totalLength !== tensor.shape[0]) { + throw new Error("Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n " + totalLength + ", and tensor's shape is: " + tensor.shape); + } + if (!this.dynamicSize && length.length !== this.maxSize) { + throw new Error("TensorArray's size is not equal to the size of lengths (" + this.maxSize + " vs. " + length.length + "), " + + 'and the TensorArray is not marked as dynamically resizeable'); + } + var elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength; + var tensors = []; + tfc.tidy(function () { + tensor = tfc.reshape(tensor, [1, totalLength, elementPerRow]); + for (var i = 0; i < length.length; ++i) { + var previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1]; + var indices_1 = [0, previousLength, 0]; + var sizes = [1, length[i], elementPerRow]; + tensors[i] = tfc.reshape(tfc.slice(tensor, indices_1, sizes), _this.elementShape); + } + return tensors; + }); + var indices = []; + for (var i = 0; i < length.length; i++) { + indices[i] = i; + } + this.writeMany(indices, tensors); + }; + return TensorArray; + }()); + + /** + * TensorList stores a container of `tf.Tensor` objects, which are accessible + * via tensors field. + * + * In order to get a copy of the underlying list, use the copy method: + * ``` + * TensorList b = a.copy(); + * b.tensors().pushBack(t); // This does not modify a.tensors(). + * ``` + * + * Note that this is not a deep copy: the memory locations of the underlying + * tensors will still point to the same locations of the corresponding tensors + * in the original. + */ + var TensorList = /** @class */ (function () { + /** + * + * @param tensors list of tensors + * @param elementShape shape of each tensor, this can be a single number (any + * shape is allowed) or partial shape (dim = -1). + * @param elementDtype data type of each tensor + * @param maxNumElements The maximum allowed size of `tensors`. Defaults to -1 + * meaning that the size of `tensors` is unbounded. + */ + function TensorList(tensors, elementShape, elementDtype, maxNumElements) { + if (maxNumElements === void 0) { maxNumElements = -1; } + this.tensors = tensors; + this.elementShape = elementShape; + this.elementDtype = elementDtype; + if (tensors != null) { + tensors.forEach(function (tensor) { + if (elementDtype !== tensor.dtype) { + throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + tensor.dtype); + } + assertShapesMatchAllowUndefinedSize(elementShape, tensor.shape, 'TensorList shape mismatch: '); + tfc.keep(tensor); + }); + } + this.idTensor = tfc.scalar(0); + this.maxNumElements = maxNumElements; + tfc.keep(this.idTensor); + } + Object.defineProperty(TensorList.prototype, "id", { + get: function () { + return this.idTensor.id; + }, + enumerable: true, + configurable: true + }); + /** + * Get a new TensorList containing a copy of the underlying tensor container. + */ + TensorList.prototype.copy = function () { + return new TensorList(__spread(this.tensors), this.elementShape, this.elementDtype); + }; + /** + * Dispose the tensors and idTensor and clear the tensor list. + */ + TensorList.prototype.clearAndClose = function (keepIds) { + this.tensors.forEach(function (tensor) { + if (keepIds == null || !keepIds.has(tensor.id)) { + tensor.dispose(); + } + }); + this.tensors.length = 0; + this.idTensor.dispose(); + }; + /** + * The size of the tensors in the tensor list. + */ + TensorList.prototype.size = function () { + return this.tensors.length; + }; + /** + * Return a tensor that stacks a list of rank-R tf.Tensors into one rank-(R+1) + * tf.Tensor. + * @param elementShape shape of each tensor + * @param elementDtype data type of each tensor + * @param numElements the number of elements to stack + */ + TensorList.prototype.stack = function (elementShape, elementDtype, numElements) { + var _this = this; + if (numElements === void 0) { numElements = -1; } + if (elementDtype !== this.elementDtype) { + throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype); + } + if (numElements !== -1 && this.tensors.length !== numElements) { + throw new Error("Operation expected a list with " + numElements + " elements but got a list with " + this.tensors.length + " elements."); + } + assertShapesMatchAllowUndefinedSize(elementShape, this.elementShape, 'TensorList shape mismatch: '); + var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + return tfc.tidy(function () { + var reshapedTensors = _this.tensors.map(function (tensor) { return tfc.reshape(tensor, outputElementShape); }); + return tfc.stack(reshapedTensors, 0); + }); + }; + /** + * Pop a tensor from the end of the list. + * @param elementShape shape of the tensor + * @param elementDtype data type of the tensor + */ + TensorList.prototype.popBack = function (elementShape, elementDtype) { + if (elementDtype !== this.elementDtype) { + throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype); + } + if (this.size() === 0) { + throw new Error('Trying to pop from an empty list.'); + } + var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + var tensor = this.tensors.pop(); + assertShapesMatchAllowUndefinedSize(tensor.shape, elementShape, 'TensorList shape mismatch: '); + return tfc.reshape(tensor, outputElementShape); + }; + /** + * Push a tensor to the end of the list. + * @param tensor Tensor to be pushed. + */ + TensorList.prototype.pushBack = function (tensor) { + if (tensor.dtype !== this.elementDtype) { + throw new Error("Invalid data types; op elements " + tensor.dtype + ", but list elements " + this.elementDtype); + } + assertShapesMatchAllowUndefinedSize(tensor.shape, this.elementShape, 'TensorList shape mismatch: '); + if (this.maxNumElements === this.size()) { + throw new Error("Trying to push element into a full list."); + } + tfc.keep(tensor); + this.tensors.push(tensor); + }; + /** + * Update the size of the list. + * @param size the new size of the list. + */ + TensorList.prototype.resize = function (size) { + if (size < 0) { + throw new Error("TensorListResize expects size to be non-negative. Got: " + size); + } + if (this.maxNumElements !== -1 && size > this.maxNumElements) { + throw new Error("TensorListResize input size " + size + " is greater maxNumElement " + this.maxNumElements + "."); + } + this.tensors.length = size; + }; + /** + * Retrieve the element at the provided index + * @param elementShape shape of the tensor + * @param elementDtype dtype of the tensor + * @param elementIndex index of the tensor + */ + TensorList.prototype.getItem = function (elementIndex, elementShape, elementDtype) { + if (elementDtype !== this.elementDtype) { + throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype); + } + if (elementIndex < 0 || elementIndex > this.tensors.length) { + throw new Error("Trying to access element " + elementIndex + " in a list with " + this.tensors.length + " elements."); + } + if (this.tensors[elementIndex] == null) { + throw new Error("element at index " + elementIndex + " is null."); + } + assertShapesMatchAllowUndefinedSize(this.tensors[elementIndex].shape, elementShape, 'TensorList shape mismatch: '); + var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + return tfc.reshape(this.tensors[elementIndex], outputElementShape); + }; + /** + * Set the tensor at the index + * @param elementIndex index of the tensor + * @param tensor the tensor to be inserted into the list + */ + TensorList.prototype.setItem = function (elementIndex, tensor) { + if (tensor.dtype !== this.elementDtype) { + throw new Error("Invalid data types; op elements " + tensor.dtype + ", but list elements " + this.elementDtype); + } + if (elementIndex < 0 || + this.maxNumElements !== -1 && elementIndex >= this.maxNumElements) { + throw new Error("Trying to set element " + elementIndex + " in a list with max " + this.maxNumElements + " elements."); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, tensor.shape, 'TensorList shape mismatch: '); + tfc.keep(tensor); + this.tensors[elementIndex] = tensor; + }; + /** + * Return selected values in the TensorList as a stacked Tensor. All of + * selected values must have been written and their shapes must all match. + * @param indices indices of tensors to gather + * @param elementDtype output tensor dtype + * @param elementShape output tensor element shape + */ + TensorList.prototype.gather = function (indices, elementDtype, elementShape) { + var _this = this; + if (elementDtype !== this.elementDtype) { + throw new Error("Invalid data types; op elements " + elementDtype + ", but list elements " + this.elementDtype); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: '); + // When indices is greater than the size of the list, indices beyond the + // size of the list are ignored. + indices = indices.slice(0, this.size()); + var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + if (indices.length === 0) { + return tfc.tensor([], [0].concat(outputElementShape)); + } + return tfc.tidy(function () { + var tensors = indices.map(function (i) { return tfc.reshape(_this.tensors[i], outputElementShape); }); + return tfc.stack(tensors, 0); + }); + }; + /** + * Return the values in the TensorList as a concatenated Tensor. + * @param elementDtype output tensor dtype + * @param elementShape output tensor element shape + */ + TensorList.prototype.concat = function (elementDtype, elementShape) { + var _this = this; + if (!!elementDtype && elementDtype !== this.elementDtype) { + throw new Error("TensorList dtype is " + this.elementDtype + " but concat requested dtype " + elementDtype); + } + assertShapesMatchAllowUndefinedSize(this.elementShape, elementShape, 'TensorList shape mismatch: '); + var outputElementShape = inferElementShape(this.elementShape, this.tensors, elementShape); + if (this.size() === 0) { + return tfc.tensor([], [0].concat(outputElementShape)); + } + return tfc.tidy(function () { + var tensors = _this.tensors.map(function (t) { return tfc.reshape(t, outputElementShape); }); + return tfc.concat(tensors, 0); + }); + }; + return TensorList; + }()); + /** + * Creates a TensorList which, when stacked, has the value of tensor. + * @param tensor from tensor + * @param elementShape output tensor element shape + */ + function fromTensor(tensor, elementShape, elementDtype) { + var dtype = tensor.dtype; + if (tensor.shape.length < 1) { + throw new Error("Tensor must be at least a vector, but saw shape: " + tensor.shape); + } + if (tensor.dtype !== elementDtype) { + throw new Error("Invalid data types; op elements " + tensor.dtype + ", but list elements " + elementDtype); + } + var tensorElementShape = tensor.shape.slice(1); + assertShapesMatchAllowUndefinedSize(tensorElementShape, elementShape, 'TensorList shape mismatch: '); + var tensorList = tfc.unstack(tensor); + return new TensorList(tensorList, elementShape, dtype); + } + /** + * Return a TensorList of the given size with empty elements. + * @param elementShape the shape of the future elements of the list + * @param elementDtype the desired type of elements in the list + * @param numElements the number of elements to reserve + */ + function reserve(elementShape, elementDtype, numElements) { + return new TensorList([], elementShape, elementDtype, numElements); + } + /** + * Put tensors at specific indices of a stacked tensor into a TensorList. + * @param indices list of indices on how to scatter the tensor. + * @param tensor input tensor. + * @param elementShape the shape of the future elements of the list + * @param numElements the number of elements to scatter + */ + function scatter(tensor, indices, elementShape, numElements) { + if (indices.length !== tensor.shape[0]) { + throw new Error("Expected len(indices) == tensor.shape[0], but saw: " + indices.length + " vs. " + tensor.shape[0]); + } + var maxIndex = Math.max.apply(Math, __spread(indices)); + if (numElements != null && numElements !== -1 && maxIndex >= numElements) { + throw new Error("Max index must be < array size (" + maxIndex + " vs. " + numElements + ")"); + } + var list = new TensorList([], elementShape, tensor.dtype, numElements); + var tensors = tfc.unstack(tensor, 0); + indices.forEach(function (value, index) { + list.setItem(value, tensors[index]); + }); + return list; + } + /** + * Split the values of a Tensor into a TensorList. + * @param length the lengths to use when splitting value along + * its first dimension. + * @param tensor the tensor to split. + * @param elementShape the shape of the future elements of the list + */ + function split(tensor, length, elementShape) { + var totalLength = 0; + var cumulativeLengths = length.map(function (len) { + totalLength += len; + return totalLength; + }); + if (totalLength !== tensor.shape[0]) { + throw new Error("Expected sum of lengths to be equal to\n tensor.shape[0], but sum of lengths is\n " + totalLength + ", and tensor's shape is: " + tensor.shape); + } + var shapeWithoutFirstDim = tensor.shape.slice(1); + var outputElementShape = mergeElementShape(shapeWithoutFirstDim, elementShape); + var elementPerRow = totalLength === 0 ? 0 : tensor.size / totalLength; + var tensors = tfc.tidy(function () { + var tensors = []; + tensor = tfc.reshape(tensor, [1, totalLength, elementPerRow]); + for (var i = 0; i < length.length; ++i) { + var previousLength = (i === 0) ? 0 : cumulativeLengths[i - 1]; + var indices = [0, previousLength, 0]; + var sizes = [1, length[i], elementPerRow]; + tensors[i] = tfc.reshape(tfc.slice(tensor, indices, sizes), outputElementShape); + } + tensor.dispose(); + return tensors; + }); + var list = new TensorList([], elementShape, tensor.dtype, length.length); + for (var i = 0; i < tensors.length; i++) { + list.setItem(i, tensors[i]); + } + return list; + } + + var _this$2 = undefined; + var executeOp$h = function (node, tensorMap, context) { return __awaiter(_this$2, void 0, void 0, function () { + var _a, thenFunc, elseFunc, cond, args, condValue, bodyFunc, condFunc, args, condResult, argIds_1, condValue, result, _loop_1, pred, pred, data, inputName, data, frameId, data, data, data, size, dtype, elementShape, dynamicSize, clearAfterRead, identicalElementShapes, name, tensorArray, id, index, writeTensor, writeTensorArray, readId, readIndex, readTensorArray, gatherId, gatherIndices, gatherDtype, gatherTensorArray, scatterId, scatterIndices, scatterTensor, scatterTensorArray, concatId, concatTensorArray, concatDtype, splitId, splitTensor, lengths, splitTensorArray, sizeId, sizeTensorArray, closeId, closeTensorArray, idTensor, index, writeTensor, tensorList, idTensor, readIndex, elementShape, elementDType, tensorList, scatterIndices, scatterTensor, elementShape, numElements, tensorList, elementShape, elementDtype, numElementsParam, numElements, tensorList, gatherId, gatherIndices, elementShape, elementDtype, tensorList, idTensor, elementShape, elementDtype, numElements, tensorList, tensor, elementShape, elementDtype, tensorList, concatId, tensorList, concatDtype, elementShape, idTensor, writeTensor, tensorList, idTensor, elementShape, elementDType, tensorList, splitTensor, elementShape, lengths, tensorList; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + _a = node.op; + switch (_a) { + case 'If': return [3 /*break*/, 1]; + case 'StatelessIf': return [3 /*break*/, 1]; + case 'While': return [3 /*break*/, 3]; + case 'StatelessWhile': return [3 /*break*/, 3]; + case 'LoopCond': return [3 /*break*/, 9]; + case 'Switch': return [3 /*break*/, 10]; + case 'Merge': return [3 /*break*/, 12]; + case 'Enter': return [3 /*break*/, 13]; + case 'Exit': return [3 /*break*/, 14]; + case 'NextIteration': return [3 /*break*/, 15]; + case 'TensorArrayV3': return [3 /*break*/, 16]; + case 'TensorArrayWriteV3': return [3 /*break*/, 17]; + case 'TensorArrayReadV3': return [3 /*break*/, 18]; + case 'TensorArrayGatherV3': return [3 /*break*/, 19]; + case 'TensorArrayScatterV3': return [3 /*break*/, 20]; + case 'TensorArrayConcatV3': return [3 /*break*/, 21]; + case 'TensorArraySplitV3': return [3 /*break*/, 22]; + case 'TensorArraySizeV3': return [3 /*break*/, 23]; + case 'TensorArrayCloseV3': return [3 /*break*/, 24]; + case 'TensorListSetItem': return [3 /*break*/, 25]; + case 'TensorListGetItem': return [3 /*break*/, 26]; + case 'TensorListScatterV2': return [3 /*break*/, 27]; + case 'TensorListScatter': return [3 /*break*/, 27]; + case 'TensorListReserve': return [3 /*break*/, 28]; + case 'EmptyTensorList': return [3 /*break*/, 28]; + case 'TensorListGather': return [3 /*break*/, 29]; + case 'TensorListStack': return [3 /*break*/, 30]; + case 'TensorListFromTensor': return [3 /*break*/, 31]; + case 'TensorListConcat': return [3 /*break*/, 32]; + case 'TensorListPushBack': return [3 /*break*/, 33]; + case 'TensorListPopBack': return [3 /*break*/, 34]; + case 'TensorListSplit': return [3 /*break*/, 35]; + } + return [3 /*break*/, 36]; + case 1: + thenFunc = getParamValue('thenBranch', node, tensorMap, context); + elseFunc = getParamValue('elseBranch', node, tensorMap, context); + cond = getParamValue('cond', node, tensorMap, context); + args = getParamValue('args', node, tensorMap, context); + return [4 /*yield*/, cond.data()]; + case 2: + condValue = _b.sent(); + if (condValue[0]) { + return [2 /*return*/, context.functionMap[thenFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)]; + } + else { + return [2 /*return*/, context.functionMap[elseFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)]; + } + case 3: + bodyFunc = getParamValue('body', node, tensorMap, context); + condFunc = getParamValue('cond', node, tensorMap, context); + args = getParamValue('args', node, tensorMap, context); + return [4 /*yield*/, context.functionMap[condFunc].executeFunctionAsync(args, context.tensorArrayMap, context.tensorListMap)]; + case 4: + condResult = (_b.sent()); + argIds_1 = args.map(function (tensor) { return tensor.id; }); + return [4 /*yield*/, condResult[0].data()]; + case 5: + condValue = _b.sent(); + // Dispose the intermediate tensors for condition function + condResult.forEach(function (tensor) { + if (!tensor.kept && argIds_1.indexOf(tensor.id) === -1) { + tensor.dispose(); + } + }); + result = args; + _loop_1 = function () { + var origResult, resultIds, condResult_1; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + origResult = result; + return [4 /*yield*/, context.functionMap[bodyFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap)]; + case 1: + // Execution the body of the loop + result = _a.sent(); + resultIds = result.map(function (tensor) { return tensor.id; }); + // Dispose the intermediate tensor for body function that is not global + // kept, not input/output of the body function + origResult.forEach(function (tensor) { + if (!tensor.kept && argIds_1.indexOf(tensor.id) === -1 && + resultIds.indexOf(tensor.id) === -1) { + tensor.dispose(); + } + }); + return [4 /*yield*/, context.functionMap[condFunc].executeFunctionAsync(result, context.tensorArrayMap, context.tensorListMap)]; + case 2: + condResult_1 = (_a.sent()); + return [4 /*yield*/, condResult_1[0].data()]; + case 3: + condValue = _a.sent(); + // Dispose the intermediate tensors for condition function + condResult_1.forEach(function (tensor) { + if (!tensor.kept && argIds_1.indexOf(tensor.id) === -1 && + resultIds.indexOf(tensor.id) === -1) { + tensor.dispose(); + } + }); + return [2 /*return*/]; + } + }); + }; + _b.label = 6; + case 6: + if (!condValue[0]) return [3 /*break*/, 8]; + return [5 /*yield**/, _loop_1()]; + case 7: + _b.sent(); + return [3 /*break*/, 6]; + case 8: return [2 /*return*/, result]; + case 9: + { + pred = getParamValue('pred', node, tensorMap, context); + return [2 /*return*/, [cloneTensor(pred)]]; + } + case 10: + pred = getParamValue('pred', node, tensorMap, context); + data = getParamValue('data', node, tensorMap, context); + if (!data.kept) { + data = cloneTensor(data); + } + return [4 /*yield*/, pred.data()]; + case 11: + // Outputs nodes :0 => false, :1 => true + return [2 /*return*/, (_b.sent())[0] ? [undefined, data] : [data, undefined]]; + case 12: + { + inputName = node.inputNames.find(function (name) { return getTensor(name, tensorMap, context) !== undefined; }); + if (inputName) { + data = getTensor(inputName, tensorMap, context); + return [2 /*return*/, [cloneTensor(data)]]; + } + return [2 /*return*/, undefined]; + } + case 13: + { + frameId = getParamValue('frameName', node, tensorMap, context); + data = getParamValue('tensor', node, tensorMap, context); + context.enterFrame(frameId); + return [2 /*return*/, [cloneTensor(data)]]; + } + case 14: + { + data = getParamValue('tensor', node, tensorMap, context); + context.exitFrame(); + return [2 /*return*/, [cloneTensor(data)]]; + } + case 15: + { + data = getParamValue('tensor', node, tensorMap, context); + context.nextIteration(); + return [2 /*return*/, [cloneTensor(data)]]; + } + case 16: + { + size = getParamValue('size', node, tensorMap, context); + dtype = getParamValue('dtype', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + dynamicSize = getParamValue('dynamicSize', node, tensorMap, context); + clearAfterRead = getParamValue('clearAfterRead', node, tensorMap, context); + identicalElementShapes = getParamValue('identicalElementShapes', node, tensorMap, context); + name = getParamValue('name', node, tensorMap, context); + tensorArray = new TensorArray(name, dtype, size, elementShape, identicalElementShapes, dynamicSize, clearAfterRead); + context.addTensorArray(tensorArray); + return [2 /*return*/, [tensorArray.idTensor, tfc.scalar(1.0)]]; + } + case 17: + { + id = getParamValue('tensorArrayId', node, tensorMap, context); + index = getParamValue('index', node, tensorMap, context); + writeTensor = getParamValue('tensor', node, tensorMap, context); + writeTensorArray = context.getTensorArray(id.id); + writeTensorArray.write(index, writeTensor); + return [2 /*return*/, [writeTensorArray.idTensor]]; + } + case 18: + { + readId = getParamValue('tensorArrayId', node, tensorMap, context); + readIndex = getParamValue('index', node, tensorMap, context); + readTensorArray = context.getTensorArray(readId.id); + return [2 /*return*/, [readTensorArray.read(readIndex)]]; + } + case 19: + { + gatherId = getParamValue('tensorArrayId', node, tensorMap, context); + gatherIndices = getParamValue('indices', node, tensorMap, context); + gatherDtype = getParamValue('dtype', node, tensorMap, context); + gatherTensorArray = context.getTensorArray(gatherId.id); + return [2 /*return*/, [gatherTensorArray.gather(gatherIndices, gatherDtype)]]; + } + case 20: + { + scatterId = getParamValue('tensorArrayId', node, tensorMap, context); + scatterIndices = getParamValue('indices', node, tensorMap, context); + scatterTensor = getParamValue('tensor', node, tensorMap, context); + scatterTensorArray = context.getTensorArray(scatterId.id); + scatterTensorArray.scatter(scatterIndices, scatterTensor); + return [2 /*return*/, [scatterTensorArray.idTensor]]; + } + case 21: + { + concatId = getParamValue('tensorArrayId', node, tensorMap, context); + concatTensorArray = context.getTensorArray(concatId.id); + concatDtype = getParamValue('dtype', node, tensorMap, context); + return [2 /*return*/, [concatTensorArray.concat(concatDtype)]]; + } + case 22: + { + splitId = getParamValue('tensorArrayId', node, tensorMap, context); + splitTensor = getParamValue('tensor', node, tensorMap, context); + lengths = getParamValue('lengths', node, tensorMap, context); + splitTensorArray = context.getTensorArray(splitId.id); + splitTensorArray.split(lengths, splitTensor); + return [2 /*return*/, [splitTensorArray.idTensor]]; + } + case 23: + { + sizeId = getParamValue('tensorArrayId', node, tensorMap, context); + sizeTensorArray = context.getTensorArray(sizeId.id); + return [2 /*return*/, [tfc.scalar(sizeTensorArray.size(), 'int32')]]; + } + case 24: + { + closeId = getParamValue('tensorArrayId', node, tensorMap, context); + closeTensorArray = context.getTensorArray(closeId.id); + closeTensorArray.clearAndClose(); + return [2 /*return*/, [closeTensorArray.idTensor]]; + } + case 25: + { + idTensor = getParamValue('tensorListId', node, tensorMap, context); + index = getParamValue('index', node, tensorMap, context); + writeTensor = getParamValue('tensor', node, tensorMap, context); + tensorList = context.getTensorList(idTensor.id); + tensorList.setItem(index, writeTensor); + return [2 /*return*/, [tensorList.idTensor]]; + } + case 26: + { + idTensor = getParamValue('tensorListId', node, tensorMap, context); + readIndex = getParamValue('index', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + elementDType = getParamValue('elementDType', node, tensorMap, context); + tensorList = context.getTensorList(idTensor.id); + return [2 /*return*/, [tensorList.getItem(readIndex, elementShape, elementDType)]]; + } + case 27: + { + scatterIndices = getParamValue('indices', node, tensorMap, context); + scatterTensor = getParamValue('tensor', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + numElements = getParamValue('numElements', node, tensorMap, context); + tensorList = scatter(scatterTensor, scatterIndices, elementShape, numElements); + context.addTensorList(tensorList); + return [2 /*return*/, [tensorList.idTensor]]; + } + case 28: + { + elementShape = getParamValue('elementShape', node, tensorMap, context); + elementDtype = getParamValue('elementDType', node, tensorMap, context); + numElementsParam = void 0; + if (node.op === 'TensorListReserve') { + numElementsParam = 'numElements'; + } + else { + numElementsParam = 'maxNumElements'; + } + numElements = getParamValue(numElementsParam, node, tensorMap, context); + tensorList = reserve(elementShape, elementDtype, numElements); + context.addTensorList(tensorList); + return [2 /*return*/, [tensorList.idTensor]]; + } + case 29: + { + gatherId = getParamValue('tensorListId', node, tensorMap, context); + gatherIndices = getParamValue('indices', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + elementDtype = getParamValue('elementDType', node, tensorMap, context); + tensorList = context.getTensorList(gatherId.id); + return [2 /*return*/, [tensorList.gather(gatherIndices, elementDtype, elementShape)]]; + } + case 30: + { + idTensor = getParamValue('tensorListId', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + elementDtype = getParamValue('elementDType', node, tensorMap, context); + numElements = getParamValue('numElements', node, tensorMap, context); + tensorList = context.getTensorList(idTensor.id); + return [2 /*return*/, [tensorList.stack(elementShape, elementDtype, numElements)]]; + } + case 31: + { + tensor = getParamValue('tensor', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + elementDtype = getParamValue('elementDType', node, tensorMap, context); + tensorList = fromTensor(tensor, elementShape, elementDtype); + context.addTensorList(tensorList); + return [2 /*return*/, [tensorList.idTensor]]; + } + case 32: + { + concatId = getParamValue('tensorListId', node, tensorMap, context); + tensorList = context.getTensorList(concatId.id); + concatDtype = getParamValue('dtype', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + return [2 /*return*/, [tensorList.concat(concatDtype, elementShape)]]; + } + case 33: + { + idTensor = getParamValue('tensorListId', node, tensorMap, context); + writeTensor = getParamValue('tensor', node, tensorMap, context); + tensorList = context.getTensorList(idTensor.id); + tensorList.pushBack(writeTensor); + return [2 /*return*/, [tensorList.idTensor]]; + } + case 34: + { + idTensor = getParamValue('tensorListId', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + elementDType = getParamValue('elementDType', node, tensorMap, context); + tensorList = context.getTensorList(idTensor.id); + return [2 /*return*/, [tensorList.popBack(elementShape, elementDType)]]; + } + case 35: + { + splitTensor = getParamValue('tensor', node, tensorMap, context); + elementShape = getParamValue('elementShape', node, tensorMap, context); + lengths = getParamValue('lengths', node, tensorMap, context); + tensorList = split(splitTensor, lengths, elementShape); + context.addTensorList(tensorList); + return [2 /*return*/, [tensorList.idTensor]]; + } + case 36: throw TypeError("Node type " + node.op + " is not implemented"); + } + }); + }); }; + + function fusedConvAndDepthWiseParams(node, tensorMap, context) { + var _a = __read(getParamValue('fusedOps', node, tensorMap, context), 2), extraOp = _a[0], activationFunc = _a[1]; + var isBiasAdd = extraOp === 'biasadd'; + var noBiasAdd = !isBiasAdd; + var isPrelu = activationFunc === 'prelu'; + var isBatchNorm = extraOp === 'fusedbatchnorm'; + var numArgs = getParamValue('numArgs', node, tensorMap, context); + if (isBiasAdd) { + if (isPrelu && numArgs !== 2) { + throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' + + 'must have two extra arguments: bias and alpha.'); + } + if (!isPrelu && isBiasAdd && numArgs !== 1) { + throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' + + 'one extra argument: bias.'); + } + } + if (isBatchNorm) { + throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported'); + } + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getPadding(node, tensorMap, context); + var dataFormat = getParamValue('dataFormat', node, tensorMap, context) + .toUpperCase(); + var dilations = getParamValue('dilations', node, tensorMap, context); + var _b = __read(getParamValue('args', node, tensorMap, context), 2), biasArg = _b[0], preluArg = _b[1]; + if (noBiasAdd) { + preluArg = biasArg; + biasArg = undefined; + } + var leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context); + return { + stride: stride, + pad: pad, + dataFormat: dataFormat, + dilations: dilations, + biasArg: biasArg, + preluArg: preluArg, + activationFunc: activationFunc, + leakyreluAlpha: leakyreluAlpha + }; + } + var executeOp$g = function (node, tensorMap, context) { + switch (node.op) { + case 'Conv1D': { + var stride = getParamValue('stride', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var dataFormat = getParamValue('dataFormat', node, tensorMap, context) + .toUpperCase(); + var dilation = getParamValue('dilation', node, tensorMap, context); + return [conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)]; + } + case 'Conv2D': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getPadding(node, tensorMap, context); + var dataFormat = getParamValue('dataFormat', node, tensorMap, context) + .toUpperCase(); + var dilations = getParamValue('dilations', node, tensorMap, context); + return [conv2d$1(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])]; + } + case '_FusedConv2D': { + var _a = fusedConvAndDepthWiseParams(node, tensorMap, context), stride = _a.stride, pad = _a.pad, dataFormat = _a.dataFormat, dilations = _a.dilations, biasArg = _a.biasArg, preluArg = _a.preluArg, activationFunc = _a.activationFunc, leakyreluAlpha = _a.leakyreluAlpha; + return [conv2d({ + x: getParamValue('x', node, tensorMap, context), + filter: getParamValue('filter', node, tensorMap, context), + strides: [stride[1], stride[2]], + pad: pad, + dataFormat: dataFormat, + dilations: [dilations[1], dilations[2]], + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha: leakyreluAlpha + })]; + } + case 'FusedDepthwiseConv2dNative': { + var _b = fusedConvAndDepthWiseParams(node, tensorMap, context), stride = _b.stride, pad = _b.pad, dataFormat = _b.dataFormat, dilations = _b.dilations, biasArg = _b.biasArg, preluArg = _b.preluArg, activationFunc = _b.activationFunc, leakyreluAlpha = _b.leakyreluAlpha; + return [depthwiseConv2d({ + x: getParamValue('x', node, tensorMap, context), + filter: getParamValue('filter', node, tensorMap, context), + strides: [stride[1], stride[2]], + pad: pad, + dataFormat: dataFormat, + dilations: [dilations[1], dilations[2]], + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha: leakyreluAlpha + })]; + } + case 'Conv2DBackpropInput': + case 'Conv2dTranspose': { + var shape = getParamValue('outputShape', node, tensorMap, context); + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getPadding(node, tensorMap, context); + return [conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)]; + } + case 'DepthwiseConv2dNative': + case 'DepthwiseConv2d': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getPadding(node, tensorMap, context); + var dilations = getParamValue('dilations', node, tensorMap, context); + var dataFormat = getParamValue('dataFormat', node, tensorMap, context) + .toUpperCase(); + return [depthwiseConv2d$1(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])]; + } + case 'Conv3D': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var dataFormat = getParamValue('dataFormat', node, tensorMap, context) + .toUpperCase(); + var dilations = getParamValue('dilations', node, tensorMap, context); + return [conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])]; + } + case 'AvgPool': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var kernelSize = getParamValue('kernelSize', node, tensorMap, context); + return [avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)]; + } + case 'MaxPool': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var kernelSize = getParamValue('kernelSize', node, tensorMap, context); + return [maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)]; + } + case 'MaxPoolWithArgmax': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var kernelSize = getParamValue('kernelSize', node, tensorMap, context); + var includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context); + var _c = maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex), result = _c.result, indexes = _c.indexes; + return [result, indexes]; + } + case 'AvgPool3D': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var kernelSize = getParamValue('kernelSize', node, tensorMap, context); + return [avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)]; + } + case 'MaxPool3D': { + var stride = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var kernelSize = getParamValue('kernelSize', node, tensorMap, context); + return [maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)]; + } + case 'Dilation2D': { + var strides = getParamValue('strides', node, tensorMap, context); + var pad = getParamValue('pad', node, tensorMap, context); + var dilations = getParamValue('dilations', node, tensorMap, context); + // strides: [1, stride_height, stride_width, 1]. + var strideHeight = strides[1]; + var strideWidth = strides[2]; + // dilations: [1, dilation_height, dilation_width, 1]. + var dilationHeight = dilations[1]; + var dilationWidth = dilations[2]; + return [dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$f = function (node, tensorMap, context) { + switch (node.op) { + case 'Fill': { + var shape = getParamValue('shape', node, tensorMap, context); + var dtype = getParamValue('dtype', node, tensorMap, context); + var value = getParamValue('value', node, tensorMap, context); + return [fill(shape, value, dtype)]; + } + case 'LinSpace': { + var start = getParamValue('start', node, tensorMap, context); + var stop = getParamValue('stop', node, tensorMap, context); + var num = getParamValue('num', node, tensorMap, context); + return [linspace(start, stop, num)]; + } + case 'Multinomial': { + var logits = getParamValue('logits', node, tensorMap, context); + var numSamples = getParamValue('numSamples', node, tensorMap, context); + var seed = getParamValue('seed', node, tensorMap, context); + return [multinomial(logits, numSamples, seed)]; + } + case 'OneHot': { + var indices = getParamValue('indices', node, tensorMap, context); + var depth = getParamValue('depth', node, tensorMap, context); + var onValue = getParamValue('onValue', node, tensorMap, context); + var offValue = getParamValue('offValue', node, tensorMap, context); + return [oneHot(indices, depth, onValue, offValue)]; + } + case 'Ones': { + return [ones(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; + } + case 'OnesLike': { + return [onesLike(getParamValue('x', node, tensorMap, context))]; + } + case 'RandomUniform': { + return [randomUniform( + // tslint:disable-next-line:no-any + getParamValue('shape', node, tensorMap, context), getParamValue('minval', node, tensorMap, context), getParamValue('maxval', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; + } + case 'Range': { + var start = getParamValue('start', node, tensorMap, context); + var stop = getParamValue('stop', node, tensorMap, context); + var step = getParamValue('step', node, tensorMap, context); + return [range(start, stop, step, getParamValue('dtype', node, tensorMap, context))]; + } + case 'TruncatedNormal': { + var shape = getParamValue('shape', node, tensorMap, context); + var mean = getParamValue('mean', node, tensorMap, context); + var stdDev = getParamValue('stdDev', node, tensorMap, context); + var seed = getParamValue('seed', node, tensorMap, context); + return [truncatedNormal(shape, mean, stdDev, getParamValue('dtype', node, tensorMap, context), seed)]; + } + case 'Zeros': { + return [zeros(getParamValue('shape', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; + } + case 'ZerosLike': { + return [zerosLike(getParamValue('x', node, tensorMap, context))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + var _this$1 = undefined; + function nmsParams(node, tensorMap, context) { + var boxes = getParamValue('boxes', node, tensorMap, context); + var scores = getParamValue('scores', node, tensorMap, context); + var maxOutputSize = getParamValue('maxOutputSize', node, tensorMap, context); + var iouThreshold = getParamValue('iouThreshold', node, tensorMap, context); + var scoreThreshold = getParamValue('scoreThreshold', node, tensorMap, context); + var softNmsSigma = getParamValue('softNmsSigma', node, tensorMap, context); + return { + boxes: boxes, + scores: scores, + maxOutputSize: maxOutputSize, + iouThreshold: iouThreshold, + scoreThreshold: scoreThreshold, + softNmsSigma: softNmsSigma + }; + } + var executeOp$e = function (node, tensorMap, context) { return __awaiter(_this$1, void 0, void 0, function () { + var _a, _b, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma, result, _c, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize, result, _d, boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, condition, result; + return __generator(this, function (_e) { + switch (_e.label) { + case 0: + _a = node.op; + switch (_a) { + case 'NonMaxSuppressionV5': return [3 /*break*/, 1]; + case 'NonMaxSuppressionV4': return [3 /*break*/, 3]; + case 'NonMaxSuppressionV3': return [3 /*break*/, 5]; + case 'NonMaxSuppressionV2': return [3 /*break*/, 5]; + case 'Where': return [3 /*break*/, 7]; + case 'ListDiff': return [3 /*break*/, 9]; + } + return [3 /*break*/, 10]; + case 1: + _b = nmsParams(node, tensorMap, context), boxes = _b.boxes, scores = _b.scores, maxOutputSize = _b.maxOutputSize, iouThreshold = _b.iouThreshold, scoreThreshold = _b.scoreThreshold, softNmsSigma = _b.softNmsSigma; + return [4 /*yield*/, image.nonMaxSuppressionWithScoreAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, softNmsSigma)]; + case 2: + result = _e.sent(); + return [2 /*return*/, [result.selectedIndices, result.selectedScores]]; + case 3: + _c = nmsParams(node, tensorMap, context), boxes = _c.boxes, scores = _c.scores, maxOutputSize = _c.maxOutputSize, iouThreshold = _c.iouThreshold, scoreThreshold = _c.scoreThreshold; + padToMaxOutputSize = getParamValue('padToMaxOutputSize', node, tensorMap, context); + return [4 /*yield*/, image.nonMaxSuppressionPaddedAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize)]; + case 4: + result = _e.sent(); + return [2 /*return*/, [result.selectedIndices, result.validOutputs]]; + case 5: + _d = nmsParams(node, tensorMap, context), boxes = _d.boxes, scores = _d.scores, maxOutputSize = _d.maxOutputSize, iouThreshold = _d.iouThreshold, scoreThreshold = _d.scoreThreshold; + return [4 /*yield*/, image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)]; + case 6: return [2 /*return*/, [_e.sent()]]; + case 7: + condition = cast(getParamValue('condition', node, tensorMap, context), 'bool'); + return [4 /*yield*/, whereAsync(condition)]; + case 8: + result = [_e.sent()]; + condition.dispose(); + return [2 /*return*/, result]; + case 9: + { + return [2 /*return*/, setdiff1dAsync(getParamValue('x', node, tensorMap, context), getParamValue('y', node, tensorMap, context))]; + } + case 10: throw TypeError("Node type " + node.op + " is not implemented"); + } + }); + }); }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$d = function (node, tensorMap, context) { + switch (node.op) { + case 'TopKV2': { + var x = getParamValue('x', node, tensorMap, context); + var k = getParamValue('k', node, tensorMap, context); + var sorted = getParamValue('sorted', node, tensorMap, context); + var result = topk(x, k, sorted); + return [result.values, result.indices]; + } + case 'Unique': { + var x = getParamValue('x', node, tensorMap, context); + var result = unique(x); + return [result.values, result.indices]; + } + case 'UniqueV2': { + var x = getParamValue('x', node, tensorMap, context); + var axis = getParamValue('axis', node, tensorMap, context); + var result = unique(x, axis); + return [result.values, result.indices]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$c = function (node, tensorMap, context) { + switch (node.op) { + case 'Const': { + return tensorMap[node.name]; + } + case 'PlaceholderWithDefault': + var def = getParamValue('default', node, tensorMap, context); + return [getTensor(node.name, tensorMap, context) || def]; + case 'Placeholder': + return [getTensor(node.name, tensorMap, context)]; + case 'Identity': + case 'StopGradient': + case 'FakeQuantWithMinMaxVars': { // This op is currently ignored. + var data_1 = getParamValue('x', node, tensorMap, context); + return [cloneTensor(data_1)]; + } + case 'IdentityN': + return getParamValue('x', node, tensorMap, context) + .map(function (t) { return cloneTensor(t); }); + case 'Snapshot': + var snapshot = getParamValue('x', node, tensorMap, context); + return [cloneTensor(snapshot)]; + case 'Shape': + return [tensor1d(getParamValue('x', node, tensorMap, context).shape, 'int32')]; + case 'ShapeN': + return getParamValue('x', node, tensorMap, context) + .map(function (t) { return tensor1d(t.shape); }); + case 'Size': + return [scalar(getParamValue('x', node, tensorMap, context).size, 'int32')]; + case 'Rank': + return [scalar(getParamValue('x', node, tensorMap, context).rank, 'int32')]; + case 'NoOp': + return [scalar(1)]; + case 'Print': + var input = getParamValue('x', node, tensorMap, context); + var data = getParamValue('data', node, tensorMap, context); + var message = getParamValue('message', node, tensorMap, context); + var summarize = getParamValue('summarize', node, tensorMap, context); + console.warn('The graph has a tf.print() operation,' + + 'usually used for debugging, which slows down performance.'); + console.log(message); + for (var i = 0; i < data.length; i++) { + console.log(Array.prototype.slice.call(data[i].dataSync()) + .slice(0, summarize)); + } + return [input]; + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * Hashtable contains a set of tensors, which can be accessed by key. + */ + var HashTable = /** @class */ (function () { + /** + * Constructor of HashTable. Creates a hash table. + * + * @param keyDType `dtype` of the table keys. + * @param valueDType `dtype` of the table values. + */ + function HashTable(keyDType, valueDType) { + this.keyDType = keyDType; + this.valueDType = valueDType; + this.handle = tfc.scalar(0); + // tslint:disable-next-line: no-any + this.tensorMap = new Map(); + tfc.keep(this.handle); + } + Object.defineProperty(HashTable.prototype, "id", { + get: function () { + return this.handle.id; + }, + enumerable: true, + configurable: true + }); + /** + * Dispose the tensors and handle and clear the hashtable. + */ + HashTable.prototype.clearAndClose = function () { + this.tensorMap.forEach(function (value) { return value.dispose(); }); + this.tensorMap.clear(); + this.handle.dispose(); + }; + /** + * The number of items in the hash table. + */ + HashTable.prototype.size = function () { + return this.tensorMap.size; + }; + /** + * The number of items in the hash table as a rank-0 tensor. + */ + HashTable.prototype.tensorSize = function () { + return scalar(this.size(), 'int32'); + }; + /** + * Replaces the contents of the table with the specified keys and values. + * @param keys Keys to store in the hashtable. + * @param values Values to store in the hashtable. + */ + HashTable.prototype.import = function (keys, values) { + return __awaiter(this, void 0, void 0, function () { + var $keys; + var _this = this; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + this.checkKeyAndValueTensor(keys, values); + return [4 /*yield*/, keys.data()]; + case 1: + $keys = _a.sent(); + // Clear the hashTable before inserting new values. + this.tensorMap.forEach(function (value) { return value.dispose(); }); + this.tensorMap.clear(); + return [2 /*return*/, tfc.tidy(function () { + var $values = tfc.unstack(values); + var keysLength = $keys.length; + var valuesLength = $values.length; + tfc.util.assert(keysLength === valuesLength, function () { return "The number of elements doesn't match, keys has " + + (keysLength + " elements, the values has " + valuesLength + " ") + + "elements."; }); + for (var i = 0; i < keysLength; i++) { + var key = $keys[i]; + var value = $values[i]; + tfc.keep(value); + _this.tensorMap.set(key, value); + } + return _this.handle; + })]; + } + }); + }); + }; + /** + * Looks up keys in a hash table, outputs the corresponding values. + * + * Performs batch lookups, for every element in the key tensor, `find` + * stacks the corresponding value into the return tensor. + * + * If an element is not present in the table, the given `defaultValue` is + * used. + * + * @param keys Keys to look up. Must have the same type as the keys of the + * table. + * @param defaultValue The scalar `defaultValue` is the value output for keys + * not present in the table. It must also be of the same type as the + * table values. + */ + HashTable.prototype.find = function (keys, defaultValue) { + return __awaiter(this, void 0, void 0, function () { + var $keys; + var _this = this; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + this.checkKeyAndValueTensor(keys, defaultValue); + return [4 /*yield*/, keys.data()]; + case 1: + $keys = _a.sent(); + return [2 /*return*/, tfc.tidy(function () { + var result = []; + for (var i = 0; i < $keys.length; i++) { + var key = $keys[i]; + var value = _this.findWithDefault(key, defaultValue); + result.push(value); + } + return tfc.stack(result); + })]; + } + }); + }); + }; + // tslint:disable-next-line: no-any + HashTable.prototype.findWithDefault = function (key, defaultValue) { + var result = this.tensorMap.get(key); + return result != null ? result : defaultValue; + }; + HashTable.prototype.checkKeyAndValueTensor = function (key, value) { + if (key.dtype !== this.keyDType) { + throw new Error("Expect key dtype " + this.keyDType + ", but got " + + ("" + key.dtype)); + } + if (value.dtype !== this.valueDType) { + throw new Error("Expect value dtype " + this.valueDType + ", but got " + + ("" + value.dtype)); + } + }; + return HashTable; + }()); + + var _this = undefined; + var executeOp$b = function (node, tensorMap, context, resourceManager) { return __awaiter(_this, void 0, void 0, function () { + var _a, keyDType, valueDType, hashTable, handle, keys, values, hashTable, handle, keys, defaultValue, hashTable, handle, hashTable; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + _a = node.op; + switch (_a) { + case 'HashTable': return [3 /*break*/, 1]; + case 'HashTableV2': return [3 /*break*/, 1]; + case 'LookupTableImport': return [3 /*break*/, 2]; + case 'LookupTableImportV2': return [3 /*break*/, 2]; + case 'LookupTableFind': return [3 /*break*/, 4]; + case 'LookupTableFindV2': return [3 /*break*/, 4]; + case 'LookupTableSize': return [3 /*break*/, 6]; + case 'LookupTableSizeV2': return [3 /*break*/, 6]; + } + return [3 /*break*/, 7]; + case 1: + { + keyDType = getParamValue('keyDType', node, tensorMap, context); + valueDType = getParamValue('valueDType', node, tensorMap, context); + hashTable = new HashTable(keyDType, valueDType); + resourceManager.addHashTable(node.name, hashTable); + return [2 /*return*/, [hashTable.handle]]; + } + case 2: + handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager); + keys = getParamValue('keys', node, tensorMap, context); + values = getParamValue('values', node, tensorMap, context); + hashTable = resourceManager.getHashTableById(handle.id); + return [4 /*yield*/, hashTable.import(keys, values)]; + case 3: return [2 /*return*/, [_b.sent()]]; + case 4: + handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager); + keys = getParamValue('keys', node, tensorMap, context); + defaultValue = getParamValue('defaultValue', node, tensorMap, context); + hashTable = resourceManager.getHashTableById(handle.id); + return [4 /*yield*/, hashTable.find(keys, defaultValue)]; + case 5: return [2 /*return*/, [_b.sent()]]; + case 6: + { + handle = getParamValue('tableHandle', node, tensorMap, context, resourceManager); + hashTable = resourceManager.getHashTableById(handle.id); + return [2 /*return*/, [hashTable.tensorSize()]]; + } + case 7: throw TypeError("Node type " + node.op + " is not implemented"); + } + }); + }); }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$a = function (node, tensorMap, context) { + switch (node.op) { + case 'ResizeBilinear': { + var images = getParamValue('images', node, tensorMap, context); + var size = getParamValue('size', node, tensorMap, context); + var alignCorners = getParamValue('alignCorners', node, tensorMap, context); + var halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context); + return [image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; + } + case 'ResizeNearestNeighbor': { + var images = getParamValue('images', node, tensorMap, context); + var size = getParamValue('size', node, tensorMap, context); + var alignCorners = getParamValue('alignCorners', node, tensorMap, context); + var halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context); + return [image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)]; + } + case 'CropAndResize': { + var image$1 = getParamValue('image', node, tensorMap, context); + var boxes = getParamValue('boxes', node, tensorMap, context); + var boxInd = getParamValue('boxInd', node, tensorMap, context); + var cropSize = getParamValue('cropSize', node, tensorMap, context); + var method = getParamValue('method', node, tensorMap, context); + var extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context); + return [image.cropAndResize(image$1, boxes, boxInd, cropSize, method, extrapolationValue)]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$9 = function (node, tensorMap, context) { + switch (node.op) { + case 'Equal': { + return [equal(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'NotEqual': { + return [notEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Greater': { + return [greater(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'GreaterEqual': { + return [greaterEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Less': { + return [less(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'LessEqual': { + return [lessEqual(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'LogicalAnd': { + return [logicalAnd(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'LogicalNot': { + return [logicalNot(getParamValue('a', node, tensorMap, context))]; + } + case 'LogicalOr': { + return [logicalOr(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + case 'Select': + case 'SelectV2': { + return [where(getParamValue('condition', node, tensorMap, context), getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + var executeOp$8 = function (node, tensorMap, context) { + switch (node.op) { + case 'BatchMatMul': + case 'BatchMatMulV2': + case 'MatMul': + return [matMul$1(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))]; + case 'Einsum': + return [einsum.apply(tfOps, __spread([getParamValue('equation', node, tensorMap, context)], getParamValue('tensors', node, tensorMap, context)))]; + case 'Transpose': + return [transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))]; + case '_FusedMatMul': + var _a = __read(getParamValue('fusedOps', node, tensorMap, context), 2), extraOp = _a[0], activationFunc = _a[1]; + var isBiasAdd = extraOp === 'biasadd'; + var isPrelu = activationFunc === 'prelu'; + var numArgs = getParamValue('numArgs', node, tensorMap, context); + var leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context); + if (isBiasAdd) { + if (isPrelu && numArgs !== 2) { + throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' + + 'extra arguments: bias and alpha.'); + } + if (!isPrelu && numArgs !== 1) { + throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.'); + } + } + var _b = __read(getParamValue('args', node, tensorMap, context), 2), biasArg = _b[0], preluArg = _b[1]; + return [matMul({ + a: getParamValue('a', node, tensorMap, context), + b: getParamValue('b', node, tensorMap, context), + transposeA: getParamValue('transposeA', node, tensorMap, context), + transposeB: getParamValue('transposeB', node, tensorMap, context), + bias: biasArg, + activation: activationFunc, + preluActivationWeights: preluArg, + leakyreluAlpha: leakyreluAlpha + })]; + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$7 = function (node, tensorMap, context) { + switch (node.op) { + case 'FusedBatchNorm': + case 'FusedBatchNormV2': { + return [batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))]; + } + case 'FusedBatchNormV3': { + return [batchNorm(getParamValue('x', node, tensorMap, context), getParamValue('mean', node, tensorMap, context), getParamValue('variance', node, tensorMap, context), getParamValue('offset', node, tensorMap, context), getParamValue('scale', node, tensorMap, context), getParamValue('epsilon', node, tensorMap, context))]; + } + case 'LRN': { + return [localResponseNormalization(getParamValue('x', node, tensorMap, context), getParamValue('radius', node, tensorMap, context), getParamValue('bias', node, tensorMap, context), getParamValue('alpha', node, tensorMap, context), getParamValue('beta', node, tensorMap, context))]; + } + case 'Softmax': { + return [softmax(getParamValue('x', node, tensorMap, context))]; + } + case 'LogSoftmax': { + return [logSoftmax(getParamValue('x', node, tensorMap, context))]; + } + case 'SparseToDense': { + return [sparseToDense(getParamValue('sparseIndices', node, tensorMap, context), getParamValue('outputShape', node, tensorMap, context), getParamValue('sparseValues', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$6 = function (node, tensorMap, context) { + switch (node.op) { + case 'Max': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [max(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'Mean': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [mean(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'Min': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [min(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'Sum': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [sum(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'All': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [all(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'Any': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [any(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'ArgMax': { + var axis = getParamValue('axis', node, tensorMap, context); + return [argMax(getParamValue('x', node, tensorMap, context), axis)]; + } + case 'ArgMin': { + var axis = getParamValue('axis', node, tensorMap, context); + return [argMin(getParamValue('x', node, tensorMap, context), axis)]; + } + case 'Prod': { + var axis = getParamValue('axis', node, tensorMap, context); + var keepDims = getParamValue('keepDims', node, tensorMap, context); + return [prod(getParamValue('x', node, tensorMap, context), axis, keepDims)]; + } + case 'Cumsum': { + var axis = getParamValue('axis', node, tensorMap, context); + var exclusive = getParamValue('exclusive', node, tensorMap, context); + var reverse = getParamValue('reverse', node, tensorMap, context); + return [cumsum(getParamValue('x', node, tensorMap, context), axis, exclusive, reverse)]; + } + case 'Bincount': + var x = getParamValue('x', node, tensorMap, context); + var weights = getParamValue('weights', node, tensorMap, context); + var size = getParamValue('size', node, tensorMap, context); + return [bincount(x, weights, size)]; + case 'DenseBincount': { + var x_1 = getParamValue('x', node, tensorMap, context); + var weights_1 = getParamValue('weights', node, tensorMap, context); + var size_1 = getParamValue('size', node, tensorMap, context); + var binaryOutput = getParamValue('binaryOutput', node, tensorMap, context); + return [denseBincount(x_1, weights_1, size_1, binaryOutput)]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$5 = function (node, tensorMap, context) { + switch (node.op) { + case 'ConcatV2': + case 'Concat': { + var n = getParamValue('n', node, tensorMap, context); + var axis = getParamValue('axis', node, tensorMap, context); + var inputs = getParamValue('tensors', node, tensorMap, context); + inputs = inputs.slice(0, n); + return [concat(inputs, axis)]; + } + case 'Gather': { + var input = getParamValue('x', node, tensorMap, context); + var indices = getParamValue('indices', node, tensorMap, context); + return [gather(input, cast(indices, 'int32'), 0)]; + } + case 'GatherV2': { + var axis = getParamValue('axis', node, tensorMap, context); + var batchDims = getParamValue('batchDims', node, tensorMap, context); + var input = getParamValue('x', node, tensorMap, context); + var indices = getParamValue('indices', node, tensorMap, context); + return [gather(input, cast(indices, 'int32'), axis, batchDims)]; + } + case 'Reverse': { + var dims = getParamValue('dims', node, tensorMap, context); + var axis = []; + for (var i = 0; i < dims.length; i++) { + if (dims[i]) { + axis.push(i); + } + } + var input = getParamValue('x', node, tensorMap, context); + return [reverse(input, axis)]; + } + case 'ReverseV2': { + var axis = getParamValue('axis', node, tensorMap, context); + var input = getParamValue('x', node, tensorMap, context); + return [reverse(input, axis)]; + } + case 'Slice': { + // tslint:disable-next-line:no-any + var begin = getParamValue('begin', node, tensorMap, context); + // tslint:disable-next-line:no-any + var size = getParamValue('size', node, tensorMap, context); + return [slice(getParamValue('x', node, tensorMap, context), begin, size)]; + } + case 'StridedSlice': { + var begin = getParamValue('begin', node, tensorMap, context); + var end = getParamValue('end', node, tensorMap, context); + var strides = getParamValue('strides', node, tensorMap, context); + var beginMask = getParamValue('beginMask', node, tensorMap, context); + var endMask = getParamValue('endMask', node, tensorMap, context); + var ellipsisMask = getParamValue('ellipsisMask', node, tensorMap, context); + var newAxisMask = getParamValue('newAxisMask', node, tensorMap, context); + var shrinkAxisMask = getParamValue('shrinkAxisMask', node, tensorMap, context); + var tensor = getParamValue('x', node, tensorMap, context); + return [stridedSlice(tensor, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask)]; + } + case 'Pack': { + return tfc.tidy(function () { + var axis = getParamValue('axis', node, tensorMap, context); + var tensors = getParamValue('tensors', node, tensorMap, context); + // Reshape the tensors to the first tensor's shape if they don't + // match. + var shape = tensors[0].shape; + var squeezedShape = squeeze(tensors[0]).shape; + var mapped = tensors.map(function (tensor) { + var sameShape = tfc.util.arraysEqual(tensor.shape, shape); + if (!sameShape && + !tfc.util.arraysEqual(squeeze(tensor).shape, squeezedShape)) { + throw new Error('the input tensors shape does not match'); + } + return sameShape ? tensor : reshape(tensor, shape); + }); + return [stack(mapped, axis)]; + }); + } + case 'Unpack': { + var axis = getParamValue('axis', node, tensorMap, context); + var tensor = getParamValue('tensor', node, tensorMap, context); + return unstack(tensor, axis); + } + case 'Tile': { + var reps = getParamValue('reps', node, tensorMap, context); + return [tile(getParamValue('x', node, tensorMap, context), reps)]; + } + case 'Split': + case 'SplitV': { + var axis = getParamValue('axis', node, tensorMap, context); + var numOrSizeSplits = getParamValue('numOrSizeSplits', node, tensorMap, context); + var tensor = getParamValue('x', node, tensorMap, context); + return split$1(tensor, numOrSizeSplits, axis); + } + case 'ScatterNd': { + var indices = getParamValue('indices', node, tensorMap, context); + var values = getParamValue('values', node, tensorMap, context); + var shape = getParamValue('shape', node, tensorMap, context); + return [scatterND(indices, values, shape)]; + } + case 'GatherNd': { + var x = getParamValue('x', node, tensorMap, context); + var indices = getParamValue('indices', node, tensorMap, context); + return [gatherND(x, indices)]; + } + case 'SparseToDense': { + var indices = getParamValue('sparseIndices', node, tensorMap, context); + var shape = getParamValue('outputShape', node, tensorMap, context); + var sparseValues = getParamValue('sparseValues', node, tensorMap, context); + var defaultValue = getParamValue('defaultValue', node, tensorMap, context); + return [sparseToDense(indices, sparseValues, shape, sparseValues.dtype === defaultValue.dtype ? + defaultValue : + cast(defaultValue, sparseValues.dtype))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$4 = function (node, tensorMap, context) { + switch (node.op) { + case 'SparseFillEmptyRows': { + var _a = sparse.sparseFillEmptyRows(getParamValue('indices', node, tensorMap, context), getParamValue('values', node, tensorMap, context), getParamValue('denseShape', node, tensorMap, context), getParamValue('defaultValue', node, tensorMap, context)), outputIndices = _a.outputIndices, outputValues = _a.outputValues, emptyRowIndicator = _a.emptyRowIndicator, reverseIndexMap = _a.reverseIndexMap; + return [ + outputIndices, outputValues, emptyRowIndicator, reverseIndexMap + ]; + } + case 'SparseReshape': { + var _b = sparse.sparseReshape(getParamValue('inputIndices', node, tensorMap, context), getParamValue('inputShape', node, tensorMap, context), getParamValue('newShape', node, tensorMap, context)), outputIndices = _b.outputIndices, outputShape = _b.outputShape; + return [outputIndices, outputShape]; + } + case 'SparseSegmentMean': { + var outputData = sparse.sparseSegmentMean(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context)); + return [outputData]; + } + case 'SparseSegmentSum': { + var outputData = sparse.sparseSegmentSum(getParamValue('data', node, tensorMap, context), getParamValue('indices', node, tensorMap, context), getParamValue('segmentIds', node, tensorMap, context)); + return [outputData]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$3 = function (node, tensorMap, context) { + switch (node.op) { + case 'FFT': { + return [fft(getParamValue('x', node, tensorMap, context))]; + } + case 'IFFT': { + return [ifft(getParamValue('x', node, tensorMap, context))]; + } + case 'RFFT': { + return [rfft(getParamValue('x', node, tensorMap, context))]; + } + case 'IRFFT': { + return [irfft(getParamValue('x', node, tensorMap, context))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$2 = function (node, tensorMap, context) { + switch (node.op) { + case 'StringNGrams': { + var _a = string.stringNGrams(getParamValue('data', node, tensorMap, context), getParamValue('dataSplits', node, tensorMap, context), getParamValue('separator', node, tensorMap, context), getParamValue('nGramWidths', node, tensorMap, context), getParamValue('leftPad', node, tensorMap, context), getParamValue('rightPad', node, tensorMap, context), getParamValue('padWidth', node, tensorMap, context), getParamValue('preserveShortSequences', node, tensorMap, context)), nGrams = _a.nGrams, nGramsSplits = _a.nGramsSplits; + return [nGrams, nGramsSplits]; + } + case 'StringSplit': { + var _b = string.stringSplit(getParamValue('input', node, tensorMap, context), getParamValue('delimiter', node, tensorMap, context), getParamValue('skipEmpty', node, tensorMap, context)), indices = _b.indices, values = _b.values, shape = _b.shape; + return [indices, values, shape]; + } + case 'StringToHashBucketFast': { + var output = string.stringToHashBucketFast(getParamValue('input', node, tensorMap, context), getParamValue('numBuckets', node, tensorMap, context)); + return [output]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + var executeOp$1 = function (node, tensorMap, context) { + switch (node.op) { + case 'Cast': { + return [cast(getParamValue('x', node, tensorMap, context), getParamValue('dtype', node, tensorMap, context))]; + } + case 'ExpandDims': { + var axis = getParamValue('axis', node, tensorMap, context); + return [expandDims(getParamValue('x', node, tensorMap, context), axis)]; + } + case 'Squeeze': { + var axis = getParamValue('axis', node, tensorMap, context); + return [squeeze(getParamValue('x', node, tensorMap, context), axis)]; + } + case 'Reshape': { + return [reshape(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))]; + } + case 'MirrorPad': { + return [mirrorPad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('mode', node, tensorMap, context))]; + } + case 'PadV2': + case 'Pad': { + return [pad(getParamValue('x', node, tensorMap, context), getParamValue('padding', node, tensorMap, context), getParamValue('constantValue', node, tensorMap, context))]; + } + case 'SpaceToBatchND': { + var blockShape = getParamValue('blockShape', node, tensorMap, context); + var paddings = getParamValue('paddings', node, tensorMap, context); + return [spaceToBatchND(getParamValue('x', node, tensorMap, context), blockShape, paddings)]; + } + case 'BatchToSpaceND': { + var blockShape = getParamValue('blockShape', node, tensorMap, context); + var crops = getParamValue('crops', node, tensorMap, context); + return [batchToSpaceND(getParamValue('x', node, tensorMap, context), blockShape, crops)]; + } + case 'DepthToSpace': { + var blockSize = getParamValue('blockSize', node, tensorMap, context); + var dataFormat = getParamValue('dataFormat', node, tensorMap, context).toUpperCase(); + return [depthToSpace(getParamValue('x', node, tensorMap, context), blockSize, dataFormat)]; + } + case 'BroadcastTo': { + return [broadcastTo(getParamValue('x', node, tensorMap, context), getParamValue('shape', node, tensorMap, context))]; + } + case 'BroadcastArgs': { + return [broadcastArgs(getParamValue('s0', node, tensorMap, context), getParamValue('s1', node, tensorMap, context))]; + } + default: + throw TypeError("Node type " + node.op + " is not implemented"); + } + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Executes the op defined by the node object. + * @param node + * @param tensorMap contains tensors for executed nodes and weights + * @param context contains tensors and information for running the current node. + * @param resourceManager Optional. Contains global resources of the model. + */ + function executeOp(node, tensorMap, context, resourceManager) { + var value = (function (node, tensorMap, context) { + switch (node.category) { + case 'arithmetic': + return tfc__namespace.tidy(function () { return executeOp$j(node, tensorMap, context); }); + case 'basic_math': + return tfc__namespace.tidy(function () { return executeOp$i(node, tensorMap, context); }); + case 'control': + return executeOp$h(node, tensorMap, context); + case 'convolution': + return tfc__namespace.tidy(function () { return executeOp$g(node, tensorMap, context); }); + case 'creation': + return tfc__namespace.tidy(function () { return executeOp$f(node, tensorMap, context); }); + case 'dynamic': + return executeOp$e(node, tensorMap, context); + case 'evaluation': + return tfc__namespace.tidy(function () { return executeOp$d(node, tensorMap, context); }); + case 'image': + return tfc__namespace.tidy(function () { return executeOp$a(node, tensorMap, context); }); + case 'graph': + return tfc__namespace.tidy(function () { return executeOp$c(node, tensorMap, context); }); + case 'logical': + return tfc__namespace.tidy(function () { return executeOp$9(node, tensorMap, context); }); + case 'matrices': + return tfc__namespace.tidy(function () { return executeOp$8(node, tensorMap, context); }); + case 'normalization': + return tfc__namespace.tidy(function () { return executeOp$7(node, tensorMap, context); }); + case 'reduction': + return tfc__namespace.tidy(function () { return executeOp$6(node, tensorMap, context); }); + case 'slice_join': + return tfc__namespace.tidy(function () { return executeOp$5(node, tensorMap, context); }); + case 'sparse': + return tfc__namespace.tidy(function () { return executeOp$4(node, tensorMap, context); }); + case 'spectral': + return tfc__namespace.tidy(function () { return executeOp$3(node, tensorMap, context); }); + case 'string': + return tfc__namespace.tidy(function () { return executeOp$2(node, tensorMap, context); }); + case 'transformation': + return tfc__namespace.tidy(function () { return executeOp$1(node, tensorMap, context); }); + case 'hash_table': + return executeOp$b(node, tensorMap, context, resourceManager); + case 'custom': + var opMapper = getRegisteredOp(node.op); + if (opMapper && opMapper.customExecutor) { + return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context)); + } + else { + throw TypeError("Custom op " + node.op + " is not registered."); + } + default: + throw TypeError("Unknown op '" + node.op + "'. File an issue at " + + "https://github.com/tensorflow/tfjs/issues so we can add it" + + ", or register a custom execution with tf.registerOp()"); + } + })(node, tensorMap, context); + if (tfc__namespace.util.isPromise(value)) { + return value.then(function (data) { return [].concat(data); }); + } + return [].concat(value); + } + + /** + * ExecutionContext captures the runtime environment of the node. It keeps + * track of the current frame and iteration for the control flow ops. + * + * For example, typical Dynamic RNN model may contain loops, for which + * TensorFlow will generate graphs with Enter/Exit nodes to control the + * current execution frame, and NextIteration Nodes for iteration id increment. + * For model with branch logic, TensorFLow will generate Switch/Merge ops. + */ + var ExecutionContext = /** @class */ (function () { + function ExecutionContext(weightMap, tensorArrayMap, tensorListMap, functionMap) { + if (weightMap === void 0) { weightMap = {}; } + if (tensorArrayMap === void 0) { tensorArrayMap = {}; } + if (tensorListMap === void 0) { tensorListMap = {}; } + if (functionMap === void 0) { functionMap = {}; } + this.weightMap = weightMap; + this.tensorArrayMap = tensorArrayMap; + this.tensorListMap = tensorListMap; + this.functionMap = functionMap; + this.rootContext = { id: 0, frameName: '', iterationId: 0 }; + this.contexts = [this.rootContext]; + this.lastId = 0; + this.generateCurrentContextIds(); + } + ExecutionContext.prototype.newFrame = function (id, frameName) { + return { id: id, frameName: frameName, iterationId: 0 }; + }; + Object.defineProperty(ExecutionContext.prototype, "currentContext", { + get: function () { + return this.contexts; + }, + /** + * Set the current context + * @param contexts: ExecutionContextInfo[] the current path of execution + * frames + */ + set: function (contexts) { + if (this.contexts !== contexts) { + this.contexts = contexts; + this.generateCurrentContextIds(); + } + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(ExecutionContext.prototype, "currentContextId", { + /** + * Returns the current context in string format. + */ + get: function () { + return this._currentContextIds[0]; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(ExecutionContext.prototype, "currentContextIds", { + /** + * Returns the current context and all parent contexts in string format. + * This allow access to the nodes in the current and parent frames. + */ + get: function () { + return this._currentContextIds; + }, + enumerable: true, + configurable: true + }); + ExecutionContext.prototype.generateCurrentContextIds = function () { + var names = []; + for (var i = 0; i < this.contexts.length - 1; i++) { + var contexts = this.contexts.slice(0, this.contexts.length - i); + names.push(this.contextIdforContexts(contexts)); + } + names.push(''); + this._currentContextIds = names; + }; + ExecutionContext.prototype.contextIdforContexts = function (contexts) { + return contexts ? + contexts + .map(function (context) { return (context.id === 0 && context.iterationId === 0) ? + '' : + context.frameName + "-" + context.iterationId; }) + .join('/') : + ''; + }; + /** + * Enter a new frame, a new context is pushed on the current context list. + * @param frameId new frame id + */ + ExecutionContext.prototype.enterFrame = function (frameId) { + if (this.contexts) { + this.lastId++; + this.contexts = this.contexts.slice(); + this.contexts.push(this.newFrame(this.lastId, frameId)); + this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)); + } + }; + /** + * Exit the current frame, the last context is removed from the current + * context list. + */ + ExecutionContext.prototype.exitFrame = function () { + if (this.contexts && this.contexts.length > 1) { + this.contexts = this.contexts.slice(); + this.contexts.splice(-1); + this.currentContextIds.shift(); + } + else { + throw new Error('Cannot exit frame, the context is empty'); + } + }; + /** + * Enter the next iteration of a loop, the iteration id of last context is + * increased. + */ + ExecutionContext.prototype.nextIteration = function () { + if (this.contexts && this.contexts.length > 0) { + this.contexts = this.contexts.slice(); + this.lastId++; + var context = Object.assign({}, this.contexts[this.contexts.length - 1]); + context.iterationId += 1; + context.id = this.lastId; + this.contexts.splice(-1, 1, context); + this._currentContextIds.splice(0, 1, this.contextIdforContexts(this.contexts)); + } + else { + throw new Error('Cannot increase frame iteration, the context is empty'); + } + }; + ExecutionContext.prototype.getWeight = function (name) { + return this.weightMap[name]; + }; + ExecutionContext.prototype.addTensorArray = function (tensorArray) { + this.tensorArrayMap[tensorArray.id] = tensorArray; + }; + ExecutionContext.prototype.getTensorArray = function (id) { + return this.tensorArrayMap[id]; + }; + ExecutionContext.prototype.addTensorList = function (tensorList) { + this.tensorListMap[tensorList.id] = tensorList; + }; + ExecutionContext.prototype.getTensorList = function (id) { + return this.tensorListMap[id]; + }; + ExecutionContext.prototype.dispose = function (keepIds) { + for (var key in this.tensorArrayMap) { + this.tensorArrayMap[key].clearAndClose(keepIds); + } + for (var key in this.tensorListMap) { + this.tensorListMap[key].clearAndClose(keepIds); + } + }; + return ExecutionContext; + }()); + + /** + * Given graph inputs and desired outputs, find the minimal set of nodes + * to execute in order to compute the outputs. In addition return other useful + * info such: + * - Missing inputs needed to compute the output. + * - Whether the subgraph contains dynamic ops (control flow, dynamic shape). + * - Alternative inputs in order to avoid async (dynamic op) execution. + */ + function getExecutionSubgraph(inputs, outputs, weightMap, initNodes) { + var usedNodes = new Set(); + var missingInputs = []; + var dynamicNode = null; + var syncInputs = null; + // Start with the outputs, going backwards and find all the nodes that are + // needed to compute those outputs. + var seen = new Set(); + var inputNodeNames = Object.keys(inputs).map(function (name) { return parseNodeName(name)[0]; }); + var initNodeNames = []; + if (initNodes != null) { + initNodeNames = initNodes.map(function (node) { return parseNodeName(node.name)[0]; }); + } + var frontier = __spread(outputs); + while (frontier.length > 0) { + var node = frontier.pop(); + if (isControlFlow(node) || isDynamicShape(node) || isHashTable(node)) { + if (dynamicNode == null) { + dynamicNode = node; + syncInputs = dynamicNode.children.map(function (child) { return child.name; }) + .filter(function (name) { return usedNodes.has(name); }); + } + } + usedNodes.add(node.name); + // Weights are dead end since we already have their values. + if (weightMap[node.name] != null) { + continue; + } + // This node is a dead end since it's one of the user-provided inputs. + if (inputNodeNames.indexOf(node.name) !== -1) { + continue; + } + // This node is a dead end since it doesn't have any inputs. + if (initNodeNames.indexOf(node.name) !== -1) { + continue; + } + if (node.inputs.length === 0) { + missingInputs.push(node.name); + continue; + } + node.inputs.forEach(function (input) { + // Don't add to the frontier if it is already there. + if (seen.has(input.name)) { + return; + } + seen.add(input.name); + frontier.push(input); + }); + } + return { inputs: inputs, outputs: outputs, usedNodes: usedNodes, missingInputs: missingInputs, dynamicNode: dynamicNode, syncInputs: syncInputs }; + } + /** + * Given the execution info, return a list of nodes in topological order that + * need to be executed to compute the output. + */ + function getNodesInTopologicalOrder(graph, weightMap, executionInfo) { + var usedNodes = executionInfo.usedNodes, inputs = executionInfo.inputs; + var frontier = []; + var inputNodes = Object.keys(inputs) + .map(function (name) { return parseNodeName(name)[0]; }) + .map(function (name) { return graph.nodes[name]; }); + var initNodes = graph.initNodes; + inputNodes.forEach(function (input) { + if (usedNodes.has(input.name)) { + frontier.push(input); + } + }); + graph.weights.forEach(function (weight) { + if (usedNodes.has(weight.name)) { + frontier.push(weight); + } + }); + if (initNodes != null) { + initNodes.forEach(function (node) { + if (usedNodes.has(node.name)) { + frontier.push(node); + } + }); + } + var seen = new Set(); + var orderedNodes = []; + while (frontier.length > 0) { + var node = frontier.pop(); + seen.add(node.name); + if (!weightMap[node.name]) { + orderedNodes.push(node); + } + node.children.forEach(function (child) { + if (!seen.has(child.name) && usedNodes.has(child.name) && + child.inputs.every(function (input) { return seen.has(input.name); })) { + frontier.push(child); + } + }); + } + return orderedNodes; + } + var CONTROL_FLOW_OPS = [ + 'Switch', 'Merge', 'Enter', 'Exit', 'NextIteration', 'StatelessIf', + 'StatelessWhile', 'if', 'While' + ]; + var DYNAMIC_SHAPE_OPS = [ + 'NonMaxSuppressionV2', 'NonMaxSuppressionV3', 'NonMaxSuppressionV5', 'Where' + ]; + var HASH_TABLE_OPS = [ + 'HashTable', 'HashTableV2', 'LookupTableImport', 'LookupTableImportV2', + 'LookupTableFind', 'LookupTableFindV2', 'LookupTableSize', 'LookupTableSizeV2' + ]; + function isControlFlow(node) { + return CONTROL_FLOW_OPS.indexOf(node.op) >= 0; + } + function isDynamicShape(node) { + return DYNAMIC_SHAPE_OPS.indexOf(node.op) >= 0; + } + function isHashTable(node) { + return HASH_TABLE_OPS.indexOf(node.op) >= 0; + } + + var GraphExecutor = /** @class */ (function () { + /** + * + * @param graph Graph the model or function graph to be executed. + * @param parent When building function exector you need to set the parent + * executor. Since the weights and function executor maps are set at parant + * level, that function executor can access the function maps and weight maps + * through the parent. + */ + function GraphExecutor(graph, parent) { + var _this = this; + this.graph = graph; + this.parent = parent; + this.compiledMap = new Map(); + this._weightMap = {}; + this.SEPERATOR = ','; + this._functions = {}; + this._functionExecutorMap = {}; + this.intermediateTensors = {}; + this.keepTensorForDebug = false; + this._outputs = graph.outputs; + this._inputs = graph.inputs; + this._initNodes = graph.initNodes; + this._signature = graph.signature; + this._functions = graph.functions; + // create sub-graph executors + if (graph.functions != null) { + Object.keys(graph.functions).forEach(function (name) { + _this._functionExecutorMap[name] = + new GraphExecutor(graph.functions[name], _this); + }); + } + } + Object.defineProperty(GraphExecutor.prototype, "weightIds", { + get: function () { + return this.parent ? this.parent.weightIds : this._weightIds; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "functionExecutorMap", { + get: function () { + return this.parent ? this.parent.functionExecutorMap : + this._functionExecutorMap; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "weightMap", { + get: function () { + return this.parent ? this.parent.weightMap : this._weightMap; + }, + set: function (weightMap) { + var weightIds = Object.keys(weightMap).map(function (key) { return weightMap[key].map(function (tensor) { return tensor.id; }); }); + this._weightIds = [].concat.apply([], __spread(weightIds)); + this._weightMap = weightMap; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "resourceManager", { + /** + * Set `ResourceManager` shared by executors of a model. + * @param resourceManager: `ResourceManager` of the `GraphModel`. + */ + set: function (resourceManager) { + this._resourceManager = resourceManager; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "inputs", { + get: function () { + return this._inputs.map(function (node) { + return { + name: node.name, + shape: node.attrParams['shape'] ? + node.attrParams['shape'].value : + undefined, + dtype: node.attrParams['dtype'] ? + node.attrParams['dtype'].value : + undefined + }; + }); + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "outputs", { + get: function () { + return this._outputs.map(function (node) { + return { + name: node.name, + shape: node.attrParams['shape'] ? + node.attrParams['shape'].value : + undefined, + dtype: node.attrParams['dtype'] ? + node.attrParams['dtype'].value : + undefined + }; + }); + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "inputNodes", { + get: function () { + return this._inputs.map(function (node) { return node.signatureKey || node.name; }); + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "outputNodes", { + get: function () { + return this._outputs.map(function (node) { + var name = node.signatureKey || node.name; + return node.defaultOutput ? (name + ":" + node.defaultOutput) : name; + }); + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphExecutor.prototype, "functions", { + get: function () { + var _this = this; + return Object.keys(this._functions).reduce(function (map, key) { + map[key] = _this._functions[key].signature; + return map; + }, {}); + }, + enumerable: true, + configurable: true + }); + GraphExecutor.prototype.getCompilationKey = function (inputs, outputs) { + var sortedInputs = inputs.map(function (node) { return node.name; }).sort(); + var sortedOutputs = outputs.map(function (node) { return node.name; }).sort(); + return sortedInputs.join(this.SEPERATOR) + '--' + + sortedOutputs.join(this.SEPERATOR); + }; + /** + * Compiles the inference graph and returns the minimal set of nodes that are + * required for execution, in the correct execution order. + */ + GraphExecutor.prototype.compile = function (inputs, outputs) { + var executionInfo = getExecutionSubgraph(inputs, outputs, this.weightMap, this._initNodes); + var missingInputs = executionInfo.missingInputs, dynamicNode = executionInfo.dynamicNode, syncInputs = executionInfo.syncInputs; + if (dynamicNode != null) { + throw new Error("This execution contains the node '" + dynamicNode.name + "', which has " + + ("the dynamic op '" + dynamicNode.op + "'. Please use ") + + "model.executeAsync() instead. Alternatively, to avoid the " + + ("dynamic ops, specify the inputs [" + syncInputs + "]")); + } + if (missingInputs.length > 0) { + var outNames = outputs.map(function (n) { return n.name; }); + var inNames = Object.keys(inputs); + throw new Error("Cannot compute the outputs [" + outNames + "] from the provided inputs " + + ("[" + inNames + "]. Missing the following inputs: [" + missingInputs + "]")); + } + return getNodesInTopologicalOrder(this.graph, this.weightMap, executionInfo); + }; + /** + * Executes the inference for given input tensors. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs Optional. output node name from the Tensorflow model, if + * no outputs are specified, the default outputs of the model would be used. + * You can inspect intermediate nodes of the model by adding them to the + * outputs array. + */ + GraphExecutor.prototype.execute = function (inputs, outputs) { + var _this = this; + inputs = this.mapInputs(inputs); + var names = Object.keys(inputs).sort(); + this.checkInputs(inputs); + this.checkInputShapeAndType(inputs); + outputs = this.mapOutputs(outputs); + this.checkOutputs(outputs); + var inputNodes = names.map(function (name) { return _this.graph.nodes[parseNodeName(name)[0]]; }); + var outputNodeNames = outputs.map(function (name) { return parseNodeName(name)[0]; }); + var outputNodes = outputNodeNames.map(function (name) { return _this.graph.nodes[name]; }); + this.resetIntermediateTensors(); + // If no outputs are specified, then use the default outputs of the model. + if (outputNodes.length === 0) { + outputNodes = this._outputs; + } + var compilationKey = this.getCompilationKey(inputNodes, outputNodes); + // Do nothing if the compiled graph cache contains the input. + var orderedNodes = this.compiledMap.get(compilationKey); + if (orderedNodes == null) { + orderedNodes = this.compile(inputs, outputNodes); + this.compiledMap.set(compilationKey, orderedNodes); + } + var tensorArrayMap = {}; + var tensorListMap = {}; + return tfc.tidy(function () { + var context = new ExecutionContext(_this.weightMap, tensorArrayMap, tensorListMap, _this.functionExecutorMap); + var tensorsMap = Object.assign({}, _this.weightMap); + Object.keys(inputs).forEach(function (name) { + var _a = __read(parseNodeName(name), 2), nodeName = _a[0], index = _a[1]; + var tensors = []; + tensors[index] = inputs[name]; + tensorsMap[nodeName] = tensors; + }); + var tensorsToKeep = _this.getFrozenTensorIds(tensorsMap); + var intermediateTensorConsumerCount = {}; + for (var i = 0; i < orderedNodes.length; i++) { + var node = orderedNodes[i]; + if (!tensorsMap[node.name]) { + var tensors = executeOp(node, tensorsMap, context, _this._resourceManager); + if (tfc.util.isPromise(tensors)) { + throw new Error("The execution of the op '" + node.op + "' returned a promise. " + + "Please use model.executeAsync() instead."); + } + tensorsMap[node.name] = tensors; + _this.checkTensorForDisposal(node.name, node, tensorsMap, context, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount); + } + } + // dispose the context for the root executor + if (_this.parent == null) { + context.dispose(tensorsToKeep); + } + return outputs.map(function (name) { return getTensor(name, tensorsMap, context); }); + }); + }; + GraphExecutor.prototype.getFrozenTensorIds = function (tensorMap) { + var ids = [].concat.apply([], Object.keys(tensorMap) + .map(function (key) { return tensorMap[key]; }) + .map(function (tensors) { return tensors.map(function (tensor) { return tensor.id; }); })); + return new Set(ids); + }; + GraphExecutor.prototype.checkTensorForDisposal = function (nodeName, node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount) { + var _this = this; + // Skip output nodes and any control flow nodes, since its dependency is + // tricky to track correctly. + if (node.category === 'control' || outputNames.indexOf(nodeName) !== -1) { + return; + } + tensorMap[nodeName].forEach(function (tensor) { + if (tensor != null) { + intermediateTensorConsumerCount[tensor.id] = + (intermediateTensorConsumerCount[tensor.id] || 0) + + node.children.length; + } + }); + node.inputs.forEach(function (input) { + // Skip any control flow nodes, since its dependency is tricky to track + // correctly. + if (input.category !== 'control') { + var tensors = getTensorsForCurrentContenxt(input.name, tensorMap, context); + if (tensors != null) { + tensors.forEach(function (tensor) { + if (tensor && !tensor.kept && !tensorsToKeep.has(tensor.id)) { + var count = intermediateTensorConsumerCount[tensor.id]; + if (count === 1) { + if (!_this.keepTensorForDebug) { + tensor.dispose(); + } + else { + var _a = __read(getNodeNameAndIndex(node.name, context), 2), nodeName_1 = _a[0], index = _a[1]; + if (_this.intermediateTensors[nodeName_1]) { + _this.intermediateTensors[nodeName_1][index] = tensor; + } + else { + _this.intermediateTensors[nodeName_1] = []; + _this.intermediateTensors[nodeName_1][index] = tensor; + } + } + delete intermediateTensorConsumerCount[tensor.id]; + } + else if (count != null) { + // only intermediate nodes has count set, inputs and weights are + // not. + intermediateTensorConsumerCount[tensor.id]--; + } + } + }); + } + } + }); + }; + /** + * Executes the inference for given input tensors in Async fashion. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs output node name from the Tensorflow model, if no outputs + * are specified, the default outputs of the model would be used. You can + * inspect intermediate nodes of the model by adding them to the outputs + * array. + */ + GraphExecutor.prototype.executeAsync = function (inputs, outputs) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + return [2 /*return*/, this._executeAsync(inputs, outputs)]; + }); + }); + }; + GraphExecutor.prototype.disposeIntermediateTensors = function () { + var _this = this; + if (!this.intermediateTensors) { + return; + } + Object.keys(this.intermediateTensors) + .forEach(function (key) { return _this.intermediateTensors[key].forEach(function (tensor) { return tensor.dispose(); }); }); + this.disposeTensorsMap(); + }; + GraphExecutor.prototype.disposeTensorsMap = function () { + var _this = this; + if (!this.tensorsMap) { + return; + } + Object.keys(this.tensorsMap).forEach(function (key) { + var tensorArray = _this.tensorsMap[key]; + tensorArray.forEach(function (tensor) { + if (tensor && !tensor.kept && !tensor.isDisposed && + !_this.keepIds.has(tensor.id)) { + tensor.dispose(); + } + }); + }); + }; + GraphExecutor.prototype.getIntermediateTensors = function () { + return this.tensorsMap; + }; + GraphExecutor.prototype.resetIntermediateTensors = function () { + for (var key in this.intermediateTensors) { + this.intermediateTensors[key].forEach(function (tensor) { return tensor.dispose(); }); + delete this.intermediateTensors[key]; + } + }; + /** + * Executes the inference for given input tensors in Async fashion. + * @param inputs Tensor map for the model inputs, keyed by the input node + * names. + * @param outputs Optional. output node name from the Tensorflow model, + * if no outputs are specified, the default outputs of the model would be + * used. You can inspect intermediate nodes of the model by adding them to the + * outputs array. + * @param isFunctionExecution Optional. Flag for executing a function. + * @param tensorArrayMap Optional, global TensorArray map by id. Used for + * function execution. + * @param tensorArrayMap Optinal global TensorList map by id. Used for + * function execution. + */ + GraphExecutor.prototype._executeAsync = function (inputs, outputs, isFunctionExecution, tensorArrayMap, tensorListMap) { + if (isFunctionExecution === void 0) { isFunctionExecution = false; } + if (tensorArrayMap === void 0) { tensorArrayMap = {}; } + if (tensorListMap === void 0) { tensorListMap = {}; } + return __awaiter(this, void 0, void 0, function () { + var context, _a, results, outputIds, inputIds; + var _this = this; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + if (!isFunctionExecution) { + inputs = this.mapInputs(inputs); + this.checkInputs(inputs); + this.checkInputShapeAndType(inputs); + outputs = this.mapOutputs(outputs); + this.checkOutputs(outputs); + } + // For model debug. + try { + this.keepTensorForDebug = tfc.env().getBool('KEEP_INTERMEDIATE_TENSORS'); + } + catch (e) { + console.warn(e.message); + } + this.resetIntermediateTensors(); + context = new ExecutionContext(this.weightMap, tensorArrayMap, tensorListMap, this.functionExecutorMap); + // Graph with control flow op requires runtime evaluation of the execution + // order, while without control flow the execution order is pre-determined + // in the compile method. + _a = this; + return [4 /*yield*/, this.executeWithControlFlow(inputs, context, outputs, isFunctionExecution)]; + case 1: + // Graph with control flow op requires runtime evaluation of the execution + // order, while without control flow the execution order is pre-determined + // in the compile method. + _a.tensorsMap = _b.sent(); + results = outputs.map(function (name) { return getTensor(name, _this.tensorsMap, context); }); + outputIds = results.map(function (t) { return t.id; }); + inputIds = Object.keys(inputs).map(function (name) { return inputs[name].id; }); + this.keepIds = + new Set(__spread(outputIds, inputIds, this.weightIds)); + if (!this.keepTensorForDebug) { + this.disposeTensorsMap(); + } + // dispose the context for the root executor + if (this.parent == null) { + context.dispose(this.keepIds); + } + return [2 /*return*/, results]; + } + }); + }); + }; + GraphExecutor.prototype.executeFunctionAsync = function (inputs, tensorArrayMap, tensorListMap) { + return __awaiter(this, void 0, void 0, function () { + var mappedInputs; + var _this = this; + return __generator(this, function (_a) { + mappedInputs = inputs.reduce(function (map, tensor, index) { + map[_this.inputs[index].name] = tensor; + return map; + }, {}); + return [2 /*return*/, this._executeAsync(mappedInputs, this.outputNodes, true, tensorArrayMap, tensorListMap)]; + }); + }); + }; + /** + * When there are control flow nodes in the graph, the graph execution use + * ExecutionContext to keep track of the frames and loop iterators. + * @param inputs placeholder tensors for the graph. + * @param context the execution context object for current execution. + * @param outputNames Optional. output node name from the Tensorflow model, + * if no outputs are specified, the default outputs of the model would be + * used. You can inspect intermediate nodes of the model by adding them to the + * outputs array. + * @param isFunctionExecution Flag for executing a function. + */ + GraphExecutor.prototype.executeWithControlFlow = function (inputs, context, outputNames, isFunctionExecution) { + return __awaiter(this, void 0, void 0, function () { + var names, inputNodes, outputNodeNames, outputNodes, _a, usedNodes, missingInputs, dynamicNode, syncInputs, stack, tensorsMap, intermediateTensorConsumerCount, tensorsToKeep, added, promises, missingOutputs, alternativeMsg; + var _this = this; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + names = Object.keys(inputs); + inputNodes = names.map(function (name) { return _this.graph.nodes[parseNodeName(name)[0]]; }); + outputNodeNames = outputNames.map(function (name) { return parseNodeName(name)[0]; }); + outputNodes = outputNodeNames.map(function (name) { return _this.graph.nodes[name]; }); + // If no outputs are specified, then use the default outputs of the model. + if (outputNodes.length === 0) { + outputNodes = this._outputs; + } + _a = getExecutionSubgraph(inputs, outputNodes, this.weightMap, this._initNodes), usedNodes = _a.usedNodes, missingInputs = _a.missingInputs, dynamicNode = _a.dynamicNode, syncInputs = _a.syncInputs; + stack = __spread(inputNodes, this.graph.weights, (this._initNodes || [])).map(function (node) { + return { node: node, contexts: context.currentContext }; + }); + tensorsMap = Object.assign({}, this.weightMap); + Object.keys(inputs).forEach(function (name) { + var _a = __read(parseNodeName(name), 2), nodeName = _a[0], index = _a[1]; + var tensors = []; + tensors[index] = inputs[name]; + tensorsMap[nodeName] = tensors; + }); + intermediateTensorConsumerCount = {}; + tensorsToKeep = this.getFrozenTensorIds(tensorsMap); + added = {}; + _b.label = 1; + case 1: + if (!(stack.length > 0)) return [3 /*break*/, 3]; + promises = this.processStack(inputNodes, stack, context, tensorsMap, added, tensorsToKeep, outputNodeNames, intermediateTensorConsumerCount, usedNodes); + return [4 /*yield*/, Promise.all(promises)]; + case 2: + _b.sent(); + return [3 /*break*/, 1]; + case 3: + if (dynamicNode == null && !isFunctionExecution) { + console.warn("This model execution did not contain any nodes with control flow " + + "or dynamic output shapes. You can use model.execute() instead."); + } + missingOutputs = outputNodes + .filter(function (node) { return !isControlFlow(node) && + !getTensor(node.name, tensorsMap, context); }) + .map(function (node) { return node.name; }); + if (missingOutputs.length > 0) { + alternativeMsg = ''; + if (dynamicNode != null) { + alternativeMsg = + "Alternatively, to avoid the dynamic ops, use model.execute() " + + ("and specify the inputs [" + syncInputs + "]"); + } + throw new Error("Cannot compute the outputs [" + missingOutputs + "] from the provided " + + ("inputs [" + names + "]. Consider providing the following inputs: ") + + ("[" + missingInputs + "]. " + alternativeMsg)); + } + return [2 /*return*/, tensorsMap]; + } + }); + }); + }; + GraphExecutor.prototype.processStack = function (inputNodes, stack, context, tensorMap, added, tensorsToKeep, outputNames, intermediateTensorConsumerCount, usedNodes) { + var _this = this; + var promises = []; + var _loop_1 = function () { + var _a, _b; + var item = stack.pop(); + context.currentContext = item.contexts; + var nodeName = ''; + // The tensor of the Enter op with isConstant set should be set + // in the parent scope, so it will be available as constant for the + // whole loop. + if (item.node.op === 'Enter' && + getParamValue('isConstant', item.node, tensorMap, context)) { + _a = __read(getNodeNameAndIndex(item.node.name, context), 1), nodeName = _a[0]; + } + // only process nodes that are not in the tensorMap yet, this include + // inputNodes and internal initNodes. + if (tensorMap[item.node.name] == null) { + var tensors = executeOp(item.node, tensorMap, context, this_1._resourceManager); + if (!nodeName) { + _b = __read(getNodeNameAndIndex(item.node.name, context), 1), nodeName = _b[0]; + } + var currentContext_1 = context.currentContext; + if (tfc.util.isPromise(tensors)) { + promises.push(tensors.then(function (t) { + tensorMap[nodeName] = t; + context.currentContext = currentContext_1; + _this.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount); + _this.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes); + return t; + })); + } + else { + tensorMap[nodeName] = tensors; + this_1.checkTensorForDisposal(nodeName, item.node, tensorMap, context, tensorsToKeep, outputNames, intermediateTensorConsumerCount); + this_1.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes); + } + } + else { + this_1.processChildNodes(item.node, stack, context, tensorMap, added, usedNodes); + } + }; + var this_1 = this; + while (stack.length > 0) { + _loop_1(); + } + return promises; + }; + GraphExecutor.prototype.processChildNodes = function (node, stack, context, tensorMap, added, usedNodes) { + node.children.forEach(function (childNode) { + var _a = __read(getNodeNameAndIndex(childNode.name, context), 1), nodeName = _a[0]; + if (added[nodeName] || !usedNodes.has(childNode.name)) { + return; + } + // Merge op can be pushed if any of its inputs has value. + if (childNode.op === 'Merge') { + if (childNode.inputNames.some(function (name) { + return !!getTensor(name, tensorMap, context); + })) { + added[nodeName] = true; + stack.push({ contexts: context.currentContext, node: childNode }); + } + } + else // Otherwise all inputs must to have value. + if (childNode.inputNames.every(function (name) { + return !!getTensor(name, tensorMap, context); + })) { + added[nodeName] = true; + stack.push({ contexts: context.currentContext, node: childNode }); + } + }); + }; + /** + * Releases the memory used by the weight tensors. + */ + GraphExecutor.prototype.dispose = function () { + var _this = this; + Object.keys(this.weightMap) + .forEach(function (key) { return _this.weightMap[key].forEach(function (tensor) { return tensor.dispose(); }); }); + }; + GraphExecutor.prototype.checkInputShapeAndType = function (inputs) { + var _this = this; + Object.keys(inputs).forEach(function (name) { + var input = inputs[name]; + var _a = __read(parseNodeName(name), 1), nodeName = _a[0]; + var node = _this.graph.nodes[nodeName]; + if (node.attrParams['shape'] && node.attrParams['shape'].value) { + var shape_1 = node.attrParams['shape'].value; + var match = shape_1.length === input.shape.length && + input.shape.every(function (dim, index) { return shape_1[index] === -1 || shape_1[index] === dim; }); + tfc.util.assert(match, function () { return "The shape of dict['" + node.name + "'] provided in " + + ("model.execute(dict) must be [" + shape_1 + "], but was ") + + ("[" + input.shape + "]"); }); + } + if (node.attrParams['dtype'] && node.attrParams['dtype'].value) { + tfc.util.assert(input.dtype === node.attrParams['dtype'].value, function () { return "The dtype of dict['" + node.name + "'] provided in " + + "model.execute(dict) must be " + + (node.attrParams['dtype'].value + ", but was " + input.dtype); }); + } + }); + }; + GraphExecutor.prototype.mapInputs = function (inputs) { + var result = {}; + for (var inputName in inputs) { + if (this._signature != null && this._signature.inputs != null && + this._signature.inputs[inputName] != null) { + var tensor = this._signature.inputs[inputName]; + result[tensor.name] = inputs[inputName]; + } + else { + result[inputName] = inputs[inputName]; + } + } + return result; + }; + GraphExecutor.prototype.checkInputs = function (inputs) { + var _this = this; + var notInGraph = Object.keys(inputs).filter(function (name) { + var _a = __read(parseNodeName(name), 1), nodeName = _a[0]; + return _this.graph.nodes[nodeName] == null; + }); + if (notInGraph.length > 0) { + throw new Error("The dict provided in model.execute(dict) has " + + ("keys: [" + notInGraph + "] that are not part of graph")); + } + }; + GraphExecutor.prototype.mapOutputs = function (outputs) { + var _this = this; + return outputs.map(function (name) { + if (_this._signature != null && _this._signature.outputs != null && + _this._signature.outputs[name] != null) { + var tensor = _this._signature.outputs[name]; + return tensor.name; + } + return name; + }, {}); + }; + GraphExecutor.prototype.checkOutputs = function (outputs) { + var _this = this; + outputs.forEach(function (name) { + var _a = __read(parseNodeName(name), 1), normalizedName = _a[0]; + if (!_this.graph.nodes[normalizedName]) { + throw new Error("The output '" + name + "' is not found in the graph"); + } + }); + }; + return GraphExecutor; + }()); + + /** + * Contains global resources of a model. + */ + var ResourceManager = /** @class */ (function () { + function ResourceManager(hashTableNameToHandle, hashTableMap) { + if (hashTableNameToHandle === void 0) { hashTableNameToHandle = {}; } + if (hashTableMap === void 0) { hashTableMap = {}; } + this.hashTableNameToHandle = hashTableNameToHandle; + this.hashTableMap = hashTableMap; + } + /** + * Register a `HashTable` in the resource manager. + * + * The `HashTable` can be retrieved by `resourceManager.getHashTableById`, + * where id is the table handle tensor's id. + * + * @param name Op node name that creates the `HashTable`. + * @param hashTable The `HashTable` to be added to resource manager. + */ + ResourceManager.prototype.addHashTable = function (name, hashTable) { + this.hashTableNameToHandle[name] = hashTable.handle; + this.hashTableMap[hashTable.id] = hashTable; + }; + /** + * Get the table handle by node name. + * @param name Op node name that creates the `HashTable`. This name is also + * used in the inputs list of lookup and import `HashTable` ops. + */ + ResourceManager.prototype.getHashTableHandleByName = function (name) { + return this.hashTableNameToHandle[name]; + }; + /** + * Get the actual `HashTable` by its handle tensor's id. + * @param id The id of the handle tensor. + */ + ResourceManager.prototype.getHashTableById = function (id) { + return this.hashTableMap[id]; + }; + /** + * Dispose `ResourceManager`, including its hashTables and tensors in them. + */ + ResourceManager.prototype.dispose = function () { + for (var key in this.hashTableMap) { + this.hashTableMap[key].clearAndClose(); + delete this.hashTableMap[key]; + } + for (var name in this.hashTableNameToHandle) { + this.hashTableNameToHandle[name].dispose(); + delete this.hashTableNameToHandle[name]; + } + }; + return ResourceManager; + }()); + + var TFHUB_SEARCH_PARAM = '?tfjs-format=file'; + var DEFAULT_MODEL_NAME = 'model.json'; + /** + * A `tf.GraphModel` is a directed, acyclic graph built from a + * SavedModel GraphDef and allows inference execution. + * + * A `tf.GraphModel` can only be created by loading from a model converted from + * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using + * the command line converter tool and loaded via `tf.loadGraphModel`. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + var GraphModel = /** @class */ (function () { + /** + * @param modelUrl url for the model, or an `io.IOHandler`. + * @param weightManifestUrl url for the weight file generated by + * scripts/convert.py script. + * @param requestOption options for Request, which allows to send credentials + * and custom headers. + * @param onProgress Optional, progress callback function, fired periodically + * before the load is completed. + */ + function GraphModel(modelUrl, loadOptions) { + if (loadOptions === void 0) { loadOptions = {}; } + this.modelUrl = modelUrl; + this.loadOptions = loadOptions; + this.version = 'n/a'; + if (loadOptions == null) { + this.loadOptions = {}; + } + this.resourceManager = new ResourceManager(); + } + Object.defineProperty(GraphModel.prototype, "modelVersion", { + // Returns the version information for the tensorflow model GraphDef. + get: function () { + return this.version; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "inputNodes", { + get: function () { + return this.executor.inputNodes; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "outputNodes", { + get: function () { + return this.executor.outputNodes; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "inputs", { + get: function () { + return this.executor.inputs; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "outputs", { + get: function () { + return this.executor.outputs; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "weights", { + get: function () { + return this.executor.weightMap; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "metadata", { + get: function () { + return this.artifacts.userDefinedMetadata; + }, + enumerable: true, + configurable: true + }); + Object.defineProperty(GraphModel.prototype, "modelSignature", { + get: function () { + return this.signature; + }, + enumerable: true, + configurable: true + }); + GraphModel.prototype.findIOHandler = function () { + var path = this.modelUrl; + if (path.load != null) { + // Path is an IO Handler. + this.handler = path; + } + else if (this.loadOptions.requestInit != null) { + this.handler = tfc.io.browserHTTPRequest(path, this.loadOptions); + } + else { + var handlers = tfc.io.getLoadHandlers(path, this.loadOptions); + if (handlers.length === 0) { + // For backward compatibility: if no load handler can be found, + // assume it is a relative http path. + handlers.push(tfc.io.browserHTTPRequest(path, this.loadOptions)); + } + else if (handlers.length > 1) { + throw new Error("Found more than one (" + handlers.length + ") load handlers for " + + ("URL '" + [path] + "'")); + } + this.handler = handlers[0]; + } + }; + /** + * Loads the model and weight files, construct the in memory weight map and + * compile the inference graph. + */ + GraphModel.prototype.load = function () { + return __awaiter(this, void 0, void 0, function () { + var artifacts; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + this.findIOHandler(); + if (this.handler.load == null) { + throw new Error('Cannot proceed with model loading because the IOHandler provided ' + + 'does not have the `load` method implemented.'); + } + return [4 /*yield*/, this.handler.load()]; + case 1: + artifacts = _a.sent(); + return [2 /*return*/, this.loadSync(artifacts)]; + } + }); + }); + }; + /** + * Synchronously construct the in memory weight map and + * compile the inference graph. Also initialize hashtable if any. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + GraphModel.prototype.loadSync = function (artifacts) { + this.artifacts = artifacts; + var graph = this.artifacts.modelTopology; + var signature; + if (this.artifacts.userDefinedMetadata != null && + this.artifacts.userDefinedMetadata.signature != null) { + signature = // tslint:disable-next-line:no-any + this.artifacts.userDefinedMetadata.signature; + } + else { + signature = this.artifacts.signature; + } + this.signature = signature; + this.version = graph.versions.producer + "." + graph.versions.minConsumer; + var weightMap = tfc.io.decodeWeights(this.artifacts.weightData, this.artifacts.weightSpecs); + this.executor = new GraphExecutor(OperationMapper.Instance.transformGraph(graph, this.signature)); + this.executor.weightMap = this.convertTensorMapToTensorsMap(weightMap); + // Attach a model-level resourceManager to each executor to share resources, + // such as `HashTable`. + this.executor.resourceManager = this.resourceManager; + if (artifacts.modelInitializer != null && + artifacts.modelInitializer.node != null) { + var initializer = OperationMapper.Instance.transformGraph(artifacts.modelInitializer); + this.initializer = new GraphExecutor(initializer); + this.initializer.weightMap = this.executor.weightMap; + // Attach a model-level resourceManager to the initializer, the + // hashTables created from when executing the initializer will be stored + // in the resourceManager. + this.initializer.resourceManager = this.resourceManager; + this.initializer.executeAsync({}, []); + } + return true; + }; + /** + * Save the configuration and/or weights of the GraphModel. + * + * An `IOHandler` is an object that has a `save` method of the proper + * signature defined. The `save` method manages the storing or + * transmission of serialized data ("artifacts") that represent the + * model's topology and weights onto or via a specific medium, such as + * file downloads, local storage, IndexedDB in the web browser and HTTP + * requests to a server. TensorFlow.js provides `IOHandler` + * implementations for a number of frequently used saving mediums, such as + * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io` + * for more details. + * + * This method also allows you to refer to certain types of `IOHandler`s + * as URL-like string shortcuts, such as 'localstorage://' and + * 'indexeddb://'. + * + * Example 1: Save `model`'s topology and weights to browser [local + * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage); + * then load it back. + * + * ```js + * const modelUrl = + * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; + * const model = await tf.loadGraphModel(modelUrl); + * const zeros = tf.zeros([1, 224, 224, 3]); + * model.predict(zeros).print(); + * + * const saveResults = await model.save('localstorage://my-model-1'); + * + * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1'); + * console.log('Prediction from loaded model:'); + * model.predict(zeros).print(); + * ``` + * + * @param handlerOrURL An instance of `IOHandler` or a URL-like, + * scheme-based string shortcut for `IOHandler`. + * @param config Options for saving the model. + * @returns A `Promise` of `SaveResult`, which summarizes the result of + * the saving, such as byte sizes of the saved artifacts for the model's + * topology and weight values. + * + * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true} + */ + GraphModel.prototype.save = function (handlerOrURL, config) { + return __awaiter(this, void 0, void 0, function () { + var handlers; + return __generator(this, function (_a) { + if (typeof handlerOrURL === 'string') { + handlers = tfc.io.getSaveHandlers(handlerOrURL); + if (handlers.length === 0) { + throw new Error("Cannot find any save handlers for URL '" + handlerOrURL + "'"); + } + else if (handlers.length > 1) { + throw new Error("Found more than one (" + handlers.length + ") save handlers for " + + ("URL '" + handlerOrURL + "'")); + } + handlerOrURL = handlers[0]; + } + if (handlerOrURL.save == null) { + throw new Error('GraphModel.save() cannot proceed because the IOHandler ' + + 'provided does not have the `save` attribute defined.'); + } + return [2 /*return*/, handlerOrURL.save(this.artifacts)]; + }); + }); + }; + /** + * Execute the inference for the input tensors. + * + * @param input The input tensors, when there is single input for the model, + * inputs param should be a `tf.Tensor`. For models with mutliple inputs, + * inputs params should be in either `tf.Tensor`[] if the input order is + * fixed, or otherwise NamedTensorMap format. + * + * For model with multiple inputs, we recommend you use NamedTensorMap as the + * input type, if you use `tf.Tensor`[], the order of the array needs to + * follow the + * order of inputNodes array. @see {@link GraphModel.inputNodes} + * + * You can also feed any intermediate nodes using the NamedTensorMap as the + * input type. For example, given the graph + * InputNode => Intermediate => OutputNode, + * you can execute the subgraph Intermediate => OutputNode by calling + * model.execute('IntermediateNode' : tf.tensor(...)); + * + * This is useful for models that uses tf.dynamic_rnn, where the intermediate + * state needs to be fed manually. + * + * For batch inference execution, the tensors for each input need to be + * concatenated together. For example with mobilenet, the required input shape + * is [1, 244, 244, 3], which represents the [batch, height, width, channel]. + * If we are provide a batched data of 100 images, the input tensor should be + * in the shape of [100, 244, 244, 3]. + * + * @param config Prediction configuration for specifying the batch size and + * output node names. Currently the batch size option is ignored for graph + * model. + * + * @returns Inference result tensors. The output would be single `tf.Tensor` + * if model has single output node, otherwise Tensor[] or NamedTensorMap[] + * will be returned for model with multiple outputs. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + GraphModel.prototype.predict = function (inputs, config) { + return this.execute(inputs, this.outputNodes); + }; + GraphModel.prototype.normalizeInputs = function (inputs) { + if (!(inputs instanceof tfc.Tensor) && !Array.isArray(inputs)) { + // The input is already a NamedTensorMap. + return inputs; + } + inputs = Array.isArray(inputs) ? inputs : [inputs]; + if (inputs.length !== this.inputNodes.length) { + throw new Error('Input tensor count mismatch,' + + ("the graph model has " + this.inputNodes.length + " placeholders, ") + + ("while there are " + inputs.length + " input tensors.")); + } + return this.inputNodes.reduce(function (map, inputName, i) { + map[inputName] = inputs[i]; + return map; + }, {}); + }; + GraphModel.prototype.normalizeOutputs = function (outputs) { + outputs = outputs || this.outputNodes; + return !Array.isArray(outputs) ? [outputs] : outputs; + }; + /** + * Executes inference for the model for given input tensors. + * @param inputs tensor, tensor array or tensor map of the inputs for the + * model, keyed by the input node names. + * @param outputs output node name from the Tensorflow model, if no + * outputs are specified, the default outputs of the model would be used. + * You can inspect intermediate nodes of the model by adding them to the + * outputs array. + * + * @returns A single tensor if provided with a single output or no outputs + * are provided and there is only one default output, otherwise return a + * tensor array. The order of the tensor array is the same as the outputs + * if provided, otherwise the order of outputNodes attribute of the model. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + GraphModel.prototype.execute = function (inputs, outputs) { + inputs = this.normalizeInputs(inputs); + outputs = this.normalizeOutputs(outputs); + var result = this.executor.execute(inputs, outputs); + return result.length > 1 ? result : result[0]; + }; + /** + * Executes inference for the model for given input tensors in async + * fashion, use this method when your model contains control flow ops. + * @param inputs tensor, tensor array or tensor map of the inputs for the + * model, keyed by the input node names. + * @param outputs output node name from the Tensorflow model, if no outputs + * are specified, the default outputs of the model would be used. You can + * inspect intermediate nodes of the model by adding them to the outputs + * array. + * + * @returns A Promise of single tensor if provided with a single output or + * no outputs are provided and there is only one default output, otherwise + * return a tensor map. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + GraphModel.prototype.executeAsync = function (inputs, outputs) { + return __awaiter(this, void 0, void 0, function () { + var result; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + inputs = this.normalizeInputs(inputs); + outputs = this.normalizeOutputs(outputs); + return [4 /*yield*/, this.executor.executeAsync(inputs, outputs)]; + case 1: + result = _a.sent(); + return [2 /*return*/, result.length > 1 ? result : result[0]]; + } + }); + }); + }; + /** + * Get intermediate tensors for model debugging mode (flag + * KEEP_INTERMEDIATE_TENSORS is true). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + GraphModel.prototype.getIntermediateTensors = function () { + return this.executor.getIntermediateTensors(); + }; + /** + * Dispose intermediate tensors for model debugging mode (flag + * KEEP_INTERMEDIATE_TENSORS is true). + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + GraphModel.prototype.disposeIntermediateTensors = function () { + this.executor.disposeIntermediateTensors(); + }; + GraphModel.prototype.convertTensorMapToTensorsMap = function (map) { + return Object.keys(map).reduce(function (newMap, key) { + newMap[key] = [map[key]]; + return newMap; + }, {}); + }; + /** + * Releases the memory used by the weight tensors and resourceManager. + * + * @doc {heading: 'Models', subheading: 'Classes'} + */ + GraphModel.prototype.dispose = function () { + this.executor.dispose(); + if (this.initializer) { + this.initializer.dispose(); + } + this.resourceManager.dispose(); + }; + return GraphModel; + }()); + /** + * Load a graph model given a URL to the model definition. + * + * Example of loading MobileNetV2 from a URL and making a prediction with a + * zeros input: + * + * ```js + * const modelUrl = + * 'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json'; + * const model = await tf.loadGraphModel(modelUrl); + * const zeros = tf.zeros([1, 224, 224, 3]); + * model.predict(zeros).print(); + * ``` + * + * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with + * a zeros input: + * + * ```js + * const modelUrl = + * 'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2'; + * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true}); + * const zeros = tf.zeros([1, 224, 224, 3]); + * model.predict(zeros).print(); + * ``` + * @param modelUrl The url or an `io.IOHandler` that loads the model. + * @param options Options for the HTTP request, which allows to send credentials + * and custom headers. + * + * @doc {heading: 'Models', subheading: 'Loading'} + */ + function loadGraphModel(modelUrl, options) { + if (options === void 0) { options = {}; } + return __awaiter(this, void 0, void 0, function () { + var model; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + if (modelUrl == null) { + throw new Error('modelUrl in loadGraphModel() cannot be null. Please provide a url ' + + 'or an IOHandler that loads the model'); + } + if (options == null) { + options = {}; + } + if (options.fromTFHub) { + if (modelUrl.load == null) { + if (!modelUrl.endsWith('/')) { + modelUrl = modelUrl + '/'; + } + modelUrl = "" + modelUrl + DEFAULT_MODEL_NAME + TFHUB_SEARCH_PARAM; + } + } + model = new GraphModel(modelUrl, options); + return [4 /*yield*/, model.load()]; + case 1: + _a.sent(); + return [2 /*return*/, model]; + } + }); + }); + } + + /** @license See the LICENSE file. */ + // This code is auto-generated, do not modify this file! + var version = '3.12.0'; + + exports.GraphModel = GraphModel; + exports.deregisterOp = deregisterOp; + exports.loadGraphModel = loadGraphModel; + exports.registerOp = registerOp; + exports.version_converter = version; + + Object.defineProperty(exports, '__esModule', { value: true }); + +}))); +//# sourceMappingURL=tf-converter.js.map diff --git a/js/tf-core.js b/js/tf-core.js new file mode 100644 index 0000000..75711cc --- /dev/null +++ b/js/tf-core.js @@ -0,0 +1,27144 @@ +/** + * @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. + * ============================================================================= + */ +(function (global, factory) { + typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports, require('crypto')) : + typeof define === 'function' && define.amd ? define(['exports', 'crypto'], factory) : + (global = typeof globalThis !== 'undefined' ? globalThis : global || self, factory(global.tf = global.tf || {}, global.require$$0)); +}(this, (function (exports, require$$0) { 'use strict'; + + function _interopDefaultLegacy (e) { return e && typeof e === 'object' && 'default' in e ? e : { 'default': e }; } + + var require$$0__default = /*#__PURE__*/_interopDefaultLegacy(require$$0); + + /*! ***************************************************************************** + Copyright (c) Microsoft Corporation. + + Permission to use, copy, modify, and/or distribute this software for any + purpose with or without fee is hereby granted. + + THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH + REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY + AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, + INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM + LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR + OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR + PERFORMANCE OF THIS SOFTWARE. + ***************************************************************************** */ + /* global Reflect, Promise */ + var extendStatics = function (d, b) { + extendStatics = Object.setPrototypeOf || + ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) || + function (d, b) { for (var p in b) + if (b.hasOwnProperty(p)) + d[p] = b[p]; }; + return extendStatics(d, b); + }; + function __extends(d, b) { + extendStatics(d, b); + function __() { this.constructor = d; } + d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __()); + } + function __awaiter(thisArg, _arguments, P, generator) { + function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } + return new (P || (P = Promise))(function (resolve, reject) { + function fulfilled(value) { try { + step(generator.next(value)); + } + catch (e) { + reject(e); + } } + function rejected(value) { try { + step(generator["throw"](value)); + } + catch (e) { + reject(e); + } } + function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } + step((generator = generator.apply(thisArg, _arguments || [])).next()); + }); + } + function __generator(thisArg, body) { + var _ = { label: 0, sent: function () { if (t[0] & 1) + throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g; + return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function () { return this; }), g; + function verb(n) { return function (v) { return step([n, v]); }; } + function step(op) { + if (f) + throw new TypeError("Generator is already executing."); + while (_) + try { + if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) + return t; + if (y = 0, t) + op = [op[0] & 2, t.value]; + switch (op[0]) { + case 0: + case 1: + t = op; + break; + case 4: + _.label++; + return { value: op[1], done: false }; + case 5: + _.label++; + y = op[1]; + op = [0]; + continue; + case 7: + op = _.ops.pop(); + _.trys.pop(); + continue; + default: + if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { + _ = 0; + continue; + } + if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { + _.label = op[1]; + break; + } + if (op[0] === 6 && _.label < t[1]) { + _.label = t[1]; + t = op; + break; + } + if (t && _.label < t[2]) { + _.label = t[2]; + _.ops.push(op); + break; + } + if (t[2]) + _.ops.pop(); + _.trys.pop(); + continue; + } + op = body.call(thisArg, _); + } + catch (e) { + op = [6, e]; + y = 0; + } + finally { + f = t = 0; + } + if (op[0] & 5) + throw op[1]; + return { value: op[0] ? op[1] : void 0, done: true }; + } + } + function __values(o) { + var s = typeof Symbol === "function" && Symbol.iterator, m = s && o[s], i = 0; + if (m) + return m.call(o); + if (o && typeof o.length === "number") + return { + next: function () { + if (o && i >= o.length) + o = void 0; + return { value: o && o[i++], done: !o }; + } + }; + throw new TypeError(s ? "Object is not iterable." : "Symbol.iterator is not defined."); + } + function __read(o, n) { + var m = typeof Symbol === "function" && o[Symbol.iterator]; + if (!m) + return o; + var i = m.call(o), r, ar = [], e; + try { + while ((n === void 0 || n-- > 0) && !(r = i.next()).done) + ar.push(r.value); + } + catch (error) { + e = { error: error }; + } + finally { + try { + if (r && !r.done && (m = i["return"])) + m.call(i); + } + finally { + if (e) + throw e.error; + } + } + return ar; + } + function __spread() { + for (var ar = [], i = 0; i < arguments.length; i++) + ar = ar.concat(__read(arguments[i])); + return ar; + } + + /** + * @license + * Copyright 2020 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var EPSILON_FLOAT32 = 1e-7; + var EPSILON_FLOAT16 = 1e-4; + /** Convenient class for storing tensor-related data. */ + var DataStorage = /** @class */ (function () { + function DataStorage(backend, dataMover) { + this.backend = backend; + this.dataMover = dataMover; + this.data = new WeakMap(); + this.dataIdsCount = 0; + } + DataStorage.prototype.get = function (dataId) { + if (!this.data.has(dataId)) { + this.dataMover.moveData(this.backend, dataId); + } + return this.data.get(dataId); + }; + DataStorage.prototype.set = function (dataId, value) { + this.dataIdsCount++; + this.data.set(dataId, value); + }; + DataStorage.prototype.has = function (dataId) { + return this.data.has(dataId); + }; + DataStorage.prototype.delete = function (dataId) { + this.dataIdsCount--; + return this.data.delete(dataId); + }; + DataStorage.prototype.numDataIds = function () { + return this.dataIdsCount; + }; + return DataStorage; + }()); + /** + * The interface that defines the kernels that should be implemented when + * adding a new backend. New backends don't need to implement every one of the + * methods, this can be done gradually (throw an error for unimplemented + * methods). + */ + var KernelBackend = /** @class */ (function () { + function KernelBackend() { + } + KernelBackend.prototype.refCount = function (dataId) { + return notYetImplemented('refCount'); + }; + KernelBackend.prototype.incRef = function (dataId) { + return notYetImplemented('incRef'); + }; + KernelBackend.prototype.timerAvailable = function () { + return true; + }; + KernelBackend.prototype.time = function (f) { + return notYetImplemented('time'); + }; + KernelBackend.prototype.read = function (dataId) { + return notYetImplemented('read'); + }; + KernelBackend.prototype.readSync = function (dataId) { + return notYetImplemented('readSync'); + }; + KernelBackend.prototype.numDataIds = function () { + return notYetImplemented('numDataIds'); + }; + KernelBackend.prototype.disposeData = function (dataId, force) { + return notYetImplemented('disposeData'); + }; + KernelBackend.prototype.write = function (values, shape, dtype) { + return notYetImplemented('write'); + }; + KernelBackend.prototype.move = function (dataId, values, shape, dtype, refCount) { + return notYetImplemented('move'); + }; + KernelBackend.prototype.memory = function () { + return notYetImplemented('memory'); + }; + /** Returns the highest precision for floats in bits (e.g. 16 or 32) */ + KernelBackend.prototype.floatPrecision = function () { + return notYetImplemented('floatPrecision'); + }; + /** Returns the smallest representable number. */ + KernelBackend.prototype.epsilon = function () { + return this.floatPrecision() === 32 ? EPSILON_FLOAT32 : EPSILON_FLOAT16; + }; + KernelBackend.prototype.dispose = function () { + return notYetImplemented('dispose'); + }; + return KernelBackend; + }()); + 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"); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Shuffles the array in-place using Fisher-Yates algorithm. + * + * ```js + * const a = [1, 2, 3, 4, 5]; + * tf.util.shuffle(a); + * console.log(a); + * ``` + * + * @param array The array to shuffle in-place. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + // tslint:disable-next-line:no-any + function shuffle(array) { + var counter = array.length; + var index = 0; + // While there are elements in the array + while (counter > 0) { + // Pick a random index + index = (Math.random() * counter) | 0; + // Decrease counter by 1 + counter--; + // And swap the last element with it + swap(array, counter, index); + } + } + /** + * Shuffles two arrays in-place the same way using Fisher-Yates algorithm. + * + * ```js + * const a = [1,2,3,4,5]; + * const b = [11,22,33,44,55]; + * tf.util.shuffleCombo(a, b); + * console.log(a, b); + * ``` + * + * @param array The first array to shuffle in-place. + * @param array2 The second array to shuffle in-place with the same permutation + * as the first array. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function shuffleCombo( + // tslint:disable-next-line:no-any + array, + // tslint:disable-next-line:no-any + array2) { + if (array.length !== array2.length) { + throw new Error("Array sizes must match to be shuffled together " + + ("First array length was " + array.length) + + ("Second array length was " + array2.length)); + } + var counter = array.length; + var index = 0; + // While there are elements in the array + while (counter > 0) { + // Pick a random index + index = (Math.random() * counter) | 0; + // Decrease counter by 1 + counter--; + // And swap the last element of each array with it + swap(array, counter, index); + swap(array2, counter, index); + } + } + /** Clamps a value to a specified range. */ + function clamp(min, x, max) { + return Math.max(min, Math.min(x, max)); + } + function nearestLargerEven(val) { + return val % 2 === 0 ? val : val + 1; + } + function swap(object, left, right) { + var temp = object[left]; + object[left] = object[right]; + object[right] = temp; + } + function sum$1(arr) { + var sum = 0; + for (var i = 0; i < arr.length; i++) { + sum += arr[i]; + } + return sum; + } + /** + * Returns a sample from a uniform [a, b) distribution. + * + * @param a The minimum support (inclusive). + * @param b The maximum support (exclusive). + * @return A pseudorandom number on the half-open interval [a,b). + */ + function randUniform(a, b) { + var r = Math.random(); + return (b * r) + (1 - r) * a; + } + /** Returns the squared Euclidean distance between two vectors. */ + function distSquared(a, b) { + var result = 0; + for (var i = 0; i < a.length; i++) { + var diff = Number(a[i]) - Number(b[i]); + result += diff * diff; + } + return result; + } + /** + * Asserts that the expression is true. Otherwise throws an error with the + * provided message. + * + * ```js + * const x = 2; + * tf.util.assert(x === 2, 'x is not 2'); + * ``` + * + * @param expr The expression to assert (as a boolean). + * @param msg A function that returns the message to report when throwing an + * error. We use a function for performance reasons. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function assert(expr, msg) { + if (!expr) { + throw new Error(typeof msg === 'string' ? msg : msg()); + } + } + function assertShapesMatch(shapeA, shapeB, errorMessagePrefix) { + if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; } + assert(arraysEqual(shapeA, shapeB), function () { return errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"); }); + } + function assertNonNull(a) { + assert(a != null, function () { return "The input to the tensor constructor must be a non-null value."; }); + } + // NOTE: We explicitly type out what T extends instead of any so that + // util.flatten on a nested array of number doesn't try to infer T as a + // number[][], causing us to explicitly type util.flatten(). + /** + * Flattens an arbitrarily nested array. + * + * ```js + * const a = [[1, 2], [3, 4], [5, [6, [7]]]]; + * const flat = tf.util.flatten(a); + * console.log(flat); + * ``` + * + * @param arr The nested array to flatten. + * @param result The destination array which holds the elements. + * @param skipTypedArray If true, avoids flattening the typed arrays. Defaults + * to false. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function flatten(arr, result, skipTypedArray) { + if (result === void 0) { result = []; } + if (skipTypedArray === void 0) { skipTypedArray = false; } + if (result == null) { + result = []; + } + if (Array.isArray(arr) || isTypedArray(arr) && !skipTypedArray) { + for (var i = 0; i < arr.length; ++i) { + flatten(arr[i], result, skipTypedArray); + } + } + else { + result.push(arr); + } + return result; + } + /** + * Returns the size (number of elements) of the tensor given its shape. + * + * ```js + * const shape = [3, 4, 2]; + * const size = tf.util.sizeFromShape(shape); + * console.log(size); + * ``` + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function sizeFromShape(shape) { + if (shape.length === 0) { + // Scalar. + return 1; + } + var size = shape[0]; + for (var 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 (var i = 0; i < n1.length; i++) { + if (n1[i] !== n2[i]) { + return false; + } + } + return true; + } + function isInt(a) { + return a % 1 === 0; + } + function tanh$1(x) { + // tslint:disable-next-line:no-any + if (Math.tanh != null) { + // tslint:disable-next-line:no-any + return Math.tanh(x); + } + if (x === Infinity) { + return 1; + } + else if (x === -Infinity) { + return -1; + } + else { + var e2x = Math.exp(2 * x); + return (e2x - 1) / (e2x + 1); + } + } + function sizeToSquarishShape(size) { + var width = Math.ceil(Math.sqrt(size)); + return [width, Math.ceil(size / width)]; + } + /** + * Creates a new array with randomized indicies to a given quantity. + * + * ```js + * const randomTen = tf.util.createShuffledIndices(10); + * console.log(randomTen); + * ``` + * + * @param number Quantity of how many shuffled indicies to create. + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function createShuffledIndices(n) { + var shuffledIndices = new Uint32Array(n); + for (var 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, maxCounter) { + if (delayFn === void 0) { delayFn = function (counter) { return 0; }; } + return new Promise(function (resolve, reject) { + var tryCount = 0; + var tryFn = function () { + if (checkFn()) { + resolve(); + return; + } + tryCount++; + var nextBackoff = delayFn(tryCount); + if (maxCounter != null && tryCount >= maxCounter) { + reject(); + return; + } + setTimeout(tryFn, nextBackoff); + }; + tryFn(); + }); + } + /** + * Given the full size of the array and a shape that may contain -1 as the + * implicit dimension, returns the inferred shape where -1 is replaced. + * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3]. + * + * @param shape The shape, which may contain -1 in some dimension. + * @param size The full size (number of elements) of the array. + * @return The inferred shape where -1 is replaced with the inferred size. + */ + function inferFromImplicitShape(shape, size) { + var shapeProd = 1; + var implicitIdx = -1; + for (var 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)); + } + var newShape = shape.slice(); + newShape[implicitIdx] = size / shapeProd; + return newShape; + } + function parseAxisParam(axis, shape) { + var rank = shape.length; + // Normalize input + axis = axis == null ? shape.map(function (s, i) { return i; }) : [].concat(axis); + // Check for valid range + assert(axis.every(function (ax) { return ax >= -rank && ax < rank; }), function () { return "All values in axis param must be in range [-" + rank + ", " + rank + ") but " + + ("got axis " + axis); }); + // Check for only integers + assert(axis.every(function (ax) { return isInt(ax); }), function () { return "All values in axis param must be integers but " + + ("got axis " + axis); }); + // Handle negative axis. + return axis.map(function (a) { return a < 0 ? rank + a : a; }); + } + /** Reduces the shape by removing all dimensions of shape 1. */ + function squeezeShape(shape, axis) { + var newShape = []; + var keptDims = []; + var isEmptyArray = axis != null && Array.isArray(axis) && axis.length === 0; + var axes = (axis == null || isEmptyArray) ? + null : + parseAxisParam(axis, shape).sort(); + var j = 0; + for (var 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: newShape, keptDims: keptDims }; + } + function getTypedArrayFromDType(dtype, size) { + var 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) { + var 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 (var i = 0; i < vals.length; i++) { + var num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + throw Error("A tensor of type " + dtype + " being uploaded contains " + num + "."); + } + } + } + /** Returns true if the dtype is valid. */ + function isValidDtype(dtype) { + return dtype === 'bool' || dtype === 'complex64' || dtype === 'float32' || + dtype === 'int32' || dtype === 'string'; + } + /** + * Returns true if the new type can't encode the old type without loss of + * precision. + */ + 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); + } + } + /** + * Returns the approximate number of bytes allocated in the string array - 2 + * bytes per character. Computing the exact bytes for a native string in JS is + * not possible since it depends on the encoding of the html page that serves + * the website. + */ + function bytesFromStringArray(arr) { + if (arr == null) { + return 0; + } + var bytes = 0; + arr.forEach(function (x) { return bytes += x.length; }); + return bytes; + } + /** Returns true if the value is a string. */ + 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 (var i = start; i < size; ++i) { + if (size % i === 0) { + return i; + } + } + return size; + } + function computeStrides(shape) { + var rank = shape.length; + if (rank < 2) { + return []; + } + // Last dimension has implicit stride of 1, thus having D-1 (instead of D) + // strides. + var strides = new Array(rank - 1); + strides[rank - 2] = shape[rank - 1]; + for (var i = rank - 3; i >= 0; --i) { + strides[i] = strides[i + 1] * shape[i + 1]; + } + return strides; + } + function createNestedArray(offset, shape, a, isComplex) { + if (isComplex === void 0) { isComplex = false; } + var ret = new Array(); + if (shape.length === 1) { + var d = shape[0] * (isComplex ? 2 : 1); + for (var i = 0; i < d; i++) { + ret[i] = a[offset + i]; + } + } + else { + var d = shape[0]; + var rest = shape.slice(1); + var len = rest.reduce(function (acc, c) { return acc * c; }) * (isComplex ? 2 : 1); + for (var i = 0; i < d; i++) { + ret[i] = createNestedArray(offset + i * len, rest, a, isComplex); + } + } + return ret; + } + // Provide a nested array of TypedArray in given shape. + function toNestedArray(shape, a, isComplex) { + if (isComplex === void 0) { isComplex = false; } + if (shape.length === 0) { + // Scalar type should return a single number. + return a[0]; + } + var size = shape.reduce(function (acc, c) { return acc * c; }) * (isComplex ? 2 : 1); + if (size === 0) { + // A tensor with shape zero should be turned into empty list. + 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) { + var array = makeZerosTypedArray(size, dtype); + for (var i = 0; i < array.length; i++) { + array[i] = 1; + } + return array; + } + 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); + } + } + /** + * Make nested `TypedArray` filled with zeros. + * @param shape The shape information for the nested array. + * @param dtype dtype of the array element. + */ + function makeZerosNestedTypedArray(shape, dtype) { + var size = shape.reduce(function (prev, curr) { return 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(function (dimSize) { + assert(Number.isInteger(dimSize) && dimSize >= 0, function () { return "Tensor must have a shape comprised of positive integers but got " + + ("shape [" + shape + "]."); }); + }); + } + /** + * Computes flat index for a given location (multidimentionsal index) in a + * Tensor/multidimensional array. + * + * @param locs Location in the tensor. + * @param rank Rank of the tensor. + * @param strides Tensor strides. + */ + function locToIndex(locs, rank, strides) { + if (rank === 0) { + return 0; + } + else if (rank === 1) { + return locs[0]; + } + var index = locs[locs.length - 1]; + for (var i = 0; i < locs.length - 1; ++i) { + index += strides[i] * locs[i]; + } + return index; + } + /** + * Computes the location (multidimensional index) in a tensor/multidimentional + * array for a given flat index. + * + * @param index Index in flat array. + * @param rank Rank of tensor. + * @param strides Strides of tensor. + */ + function indexToLoc(index, rank, strides) { + if (rank === 0) { + return []; + } + else if (rank === 1) { + return [index]; + } + var locs = new Array(rank); + for (var 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; + } + /** + * This method asserts whether an object is a Promise instance. + * @param object + */ + // tslint:disable-next-line: no-any + function isPromise(object) { + // We chose to not use 'obj instanceOf Promise' for two reasons: + // 1. It only reliably works for es6 Promise, not other Promise + // implementations. + // 2. It doesn't work with framework that uses zone.js. zone.js monkey patch + // the async calls, so it is possible the obj (patched) is comparing to a + // pre-patched Promise. + return object && object.then && typeof object.then === 'function'; + } + + // Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true. + var TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags'; + /** + * The environment contains evaluated flags as well as the registered platform. + * This is always used as a global singleton and can be retrieved with + * `tf.env()`. + * + * @doc {heading: 'Environment'} + */ + var Environment = /** @class */ (function () { + // tslint:disable-next-line: no-any + function Environment(global) { + this.global = global; + this.flags = {}; + this.flagRegistry = {}; + this.urlFlags = {}; + // Jasmine spies on this in 'environment_test.ts' + this.getQueryParams = getQueryParams; + this.populateURLFlags(); + } + Environment.prototype.setPlatform = function (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 " + platform + ".")); + } + } + this.platformName = platformName; + this.platform = platform; + }; + Environment.prototype.registerFlag = function (flagName, evaluationFn, setHook) { + this.flagRegistry[flagName] = { evaluationFn: evaluationFn, setHook: setHook }; + // Override the flag value from the URL. This has to happen here because + // the environment is initialized before flags get registered. + if (this.urlFlags[flagName] != null) { + var 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); + } + }; + Environment.prototype.getAsync = function (flagName) { + return __awaiter(this, void 0, void 0, function () { + var _a, _b; + return __generator(this, function (_c) { + switch (_c.label) { + case 0: + if (flagName in this.flags) { + return [2 /*return*/, this.flags[flagName]]; + } + _a = this.flags; + _b = flagName; + return [4 /*yield*/, this.evaluateFlag(flagName)]; + case 1: + _a[_b] = _c.sent(); + return [2 /*return*/, this.flags[flagName]]; + } + }); + }); + }; + Environment.prototype.get = function (flagName) { + if (flagName in this.flags) { + return this.flags[flagName]; + } + var 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]; + }; + Environment.prototype.getNumber = function (flagName) { + return this.get(flagName); + }; + Environment.prototype.getBool = function (flagName) { + return this.get(flagName); + }; + Environment.prototype.getFlags = function () { + return this.flags; + }; + Object.defineProperty(Environment.prototype, "features", { + // For backwards compatibility. + get: function () { + return this.flags; + }, + enumerable: true, + configurable: true + }); + Environment.prototype.set = function (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); + } + }; + Environment.prototype.evaluateFlag = function (flagName) { + if (this.flagRegistry[flagName] == null) { + throw new Error("Cannot evaluate flag '" + flagName + "': no evaluation function found."); + } + return this.flagRegistry[flagName].evaluationFn(); + }; + Environment.prototype.setFlags = function (flags) { + this.flags = Object.assign({}, flags); + }; + Environment.prototype.reset = function () { + this.flags = {}; + this.urlFlags = {}; + this.populateURLFlags(); + }; + Environment.prototype.populateURLFlags = function () { + var _this = this; + if (typeof this.global === 'undefined' || + typeof this.global.location === 'undefined' || + typeof this.global.location.search === 'undefined') { + return; + } + var urlParams = this.getQueryParams(this.global.location.search); + if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) { + var keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(','); + keyValues.forEach(function (keyValue) { + var _a = __read(keyValue.split(':'), 2), key = _a[0], value = _a[1]; + _this.urlFlags[key] = parseValue(key, value); + }); + } + }; + return Environment; + }()); + function getQueryParams(queryString) { + var params = {}; + queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (s) { + var t = []; + for (var _i = 1; _i < arguments.length; _i++) { + t[_i - 1] = arguments[_i]; + } + 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 + "."); + } + /** + * Returns the current environment (a global singleton). + * + * The environment object contains the evaluated feature values as well as the + * active platform. + * + * @doc {heading: 'Environment'} + */ + function env() { + return exports.ENV; + } + exports.ENV = null; + function setEnvironmentGlobal(environment) { + exports.ENV = environment; + } + + /** + * @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. + * ============================================================================= + */ + // Note that the identifier globalNameSpace is scoped to this module, but will + // always resolve to the same global object regardless of how the module is + // resolved. + // tslint:disable-next-line:no-any + var globalNameSpace; + // tslint:disable-next-line:no-any + function getGlobalNamespace() { + if (globalNameSpace == null) { + // tslint:disable-next-line:no-any + var ns = void 0; + if (typeof (window) !== 'undefined') { + ns = window; + } + else if (typeof (global) !== 'undefined') { + ns = global; + } + else if (typeof (process) !== 'undefined') { + ns = process; + } + else if (typeof (self) !== 'undefined') { + ns = self; + } + else { + throw new Error('Could not find a global object'); + } + globalNameSpace = ns; + } + return globalNameSpace; + } + // tslint:disable-next-line:no-any + function getGlobalMap() { + var ns = getGlobalNamespace(); + if (ns._tfGlobals == null) { + ns._tfGlobals = new Map(); + } + return ns._tfGlobals; + } + /** + * Returns a globally accessible 'singleton' object. + * + * @param key the name of the object + * @param init a function to initialize to initialize this object + * the first time it is fetched. + */ + function getGlobal(key, init) { + var globalMap = getGlobalMap(); + if (globalMap.has(key)) { + return globalMap.get(key); + } + else { + var singleton = init(); + globalMap.set(key, singleton); + return globalMap.get(key); + } + } + + 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 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 Pool = 'Pool'; + 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 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'; + /** + * TensorFlow.js-only kernels + */ + var Step = 'Step'; + var FromPixels = 'FromPixels'; + var RotateWithOffset = 'RotateWithOffset'; + var _FusedMatMul = '_FusedMatMul'; + var FusedConv2D = 'FusedConv2D'; + var FusedDepthwiseConv2D = 'FusedDepthwiseConv2D'; + + function warn() { + var msg = []; + for (var _i = 0; _i < arguments.length; _i++) { + msg[_i] = arguments[_i]; + } + if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) { + console.warn.apply(console, __spread(msg)); + } + } + function log$1() { + var msg = []; + for (var _i = 0; _i < arguments.length; _i++) { + msg[_i] = arguments[_i]; + } + if (!(env().getBool('IS_TEST') || env().getBool('PROD'))) { + console.log.apply(console, __spread(msg)); + } + } + + var kernelRegistry = getGlobal('kernelRegistry', function () { return new Map(); }); + var gradRegistry = getGlobal('gradRegistry', function () { return new Map(); }); + /** + * Returns the kernel function (code) associated with the provided names. + * + * @param kernelName The official name of the kernel. + * @param backendName The official name of the backend. + */ + function getKernel(kernelName, backendName) { + var key = makeKey(kernelName, backendName); + return kernelRegistry.get(key); + } + /** + * Returns the registered gradient info associated with the provided kernel. + * @param kernelName The official TF kernel name. + */ + function getGradient(kernelName) { + return gradRegistry.get(kernelName); + } + function getKernelsForBackend(backendName) { + var it = kernelRegistry.entries(); + var result = []; + while (true) { + var _a = it.next(), done = _a.done, value = _a.value; + if (done) { + break; + } + var _b = __read(value, 2), key = _b[0], config = _b[1]; + var _c = __read(key.split('_'), 1), backend = _c[0]; + if (backend === backendName) { + result.push(config); + } + } + return result; + } + /** + * Registers the function (forward pass) for the kernel in a global registry. + * + * @param config A config object with the following properties: + * - `kernelName` The official name of the kernel. + * - `backendName` The official name of the backend. + * - `kernelFunc` The function to run during the forward pass of the kernel. + * - `setupFunc` Optional. Gets called once, after the backend initializes. + * - `disposeFunc` Optional. Gets called once, right before the backend is + * disposed. + */ + function registerKernel(config) { + var kernelName = config.kernelName, backendName = config.backendName; + var key = makeKey(kernelName, backendName); + if (kernelRegistry.has(key)) { + warn("The kernel '" + kernelName + "' for backend " + + ("'" + backendName + "' is already registered")); + } + kernelRegistry.set(key, config); + } + /** + * Registers a gradient function for a given kernel in the global registry, + * to be used during the back-propagation of that kernel. + * + * @param config An object with the following properties: + * - `kernelName` The name of the kernel that the gradient function is for. + * - `gradFunc` The function to run during back-propagation. + */ + function registerGradient(config) { + var kernelName = config.kernelName; + if (gradRegistry.has(kernelName)) { + // TODO (yassogba) after 3.0 assess whether we need to keep this gated + // to debug mode. + if (env().getBool('DEBUG')) { + warn("Overriding the gradient for '" + kernelName + "'"); + } + } + gradRegistry.set(kernelName, config); + } + /** + * Removes the kernel function from the registry. + * + * @param kernelName The official name of the kernel. + * @param backendName The official name of the backend. + * + */ + function unregisterKernel(kernelName, backendName) { + var key = makeKey(kernelName, backendName); + if (!kernelRegistry.has(key)) { + throw new Error("The kernel '" + kernelName + "' for backend " + + ("'" + backendName + "' is not registered")); + } + kernelRegistry.delete(key); + } + /** Removes the registered gradient from the global registry. */ + function unregisterGradient(kernelName) { + if (!gradRegistry.has(kernelName)) { + throw new Error("The gradient '" + kernelName + "' for backend is not registered"); + } + gradRegistry.delete(kernelName); + } + /** + * Finds kernels that have already been registered to a backend and re-registers + * them for a new backend. Useful for registering custom backends. + * @param registeredBackendName Already registered backend. + * @param newBackendName New backend. + */ + function copyRegisteredKernels(registeredBackendName, newBackendName) { + var kernels = getKernelsForBackend(registeredBackendName); + kernels.forEach(function (kernelConfig) { + var newKernelConfig = Object.assign({}, kernelConfig, { backendName: newBackendName }); + registerKernel(newKernelConfig); + }); + } + function makeKey(kernelName, backendName) { + return backendName + "_" + kernelName; + } + + var long = Long$1; + /** + * wasm optimizations, to do native i64 multiplication and divide + */ + var wasm = null; + try { + wasm = new WebAssembly.Instance(new WebAssembly.Module(new Uint8Array([ + 0, 97, 115, 109, 1, 0, 0, 0, 1, 13, 2, 96, 0, 1, 127, 96, 4, 127, 127, 127, 127, 1, 127, 3, 7, 6, 0, 1, 1, 1, 1, 1, 6, 6, 1, 127, 1, 65, 0, 11, 7, 50, 6, 3, 109, 117, 108, 0, 1, 5, 100, 105, 118, 95, 115, 0, 2, 5, 100, 105, 118, 95, 117, 0, 3, 5, 114, 101, 109, 95, 115, 0, 4, 5, 114, 101, 109, 95, 117, 0, 5, 8, 103, 101, 116, 95, 104, 105, 103, 104, 0, 0, 10, 191, 1, 6, 4, 0, 35, 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, 132, 127, 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, 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, 132, 32, 2, 173, 32, 3, 173, 66, 32, 134, 132, 129, 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, 132, 130, 34, 4, 66, 32, 135, 167, 36, 0, 32, 4, 167, 11 + ])), {}).exports; + } + catch (e) { + // no wasm support :( + } + /** + * Constructs a 64 bit two's-complement integer, given its low and high 32 bit values as *signed* integers. + * See the from* functions below for more convenient ways of constructing Longs. + * @exports Long + * @class A Long class for representing a 64 bit two's-complement integer value. + * @param {number} low The low (signed) 32 bits of the long + * @param {number} high The high (signed) 32 bits of the long + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @constructor + */ + function Long$1(low, high, unsigned) { + /** + * The low 32 bits as a signed value. + * @type {number} + */ + this.low = low | 0; + /** + * The high 32 bits as a signed value. + * @type {number} + */ + this.high = high | 0; + /** + * Whether unsigned or not. + * @type {boolean} + */ + this.unsigned = !!unsigned; + } + // The internal representation of a long is the two given signed, 32-bit values. + // We use 32-bit pieces because these are the size of integers on which + // Javascript performs bit-operations. For operations like addition and + // multiplication, we split each number into 16 bit pieces, which can easily be + // multiplied within Javascript's floating-point representation without overflow + // or change in sign. + // + // In the algorithms below, we frequently reduce the negative case to the + // positive case by negating the input(s) and then post-processing the result. + // Note that we must ALWAYS check specially whether those values are MIN_VALUE + // (-2^63) because -MIN_VALUE == MIN_VALUE (since 2^63 cannot be represented as + // a positive number, it overflows back into a negative). Not handling this + // case would often result in infinite recursion. + // + // Common constant values ZERO, ONE, NEG_ONE, etc. are defined below the from* + // methods on which they depend. + /** + * An indicator used to reliably determine if an object is a Long or not. + * @type {boolean} + * @const + * @private + */ + Long$1.prototype.__isLong__; + Object.defineProperty(Long$1.prototype, "__isLong__", { value: true }); + /** + * @function + * @param {*} obj Object + * @returns {boolean} + * @inner + */ + function isLong(obj) { + return (obj && obj["__isLong__"]) === true; + } + /** + * Tests if the specified object is a Long. + * @function + * @param {*} obj Object + * @returns {boolean} + */ + Long$1.isLong = isLong; + /** + * A cache of the Long representations of small integer values. + * @type {!Object} + * @inner + */ + var INT_CACHE = {}; + /** + * A cache of the Long representations of small unsigned integer values. + * @type {!Object} + * @inner + */ + var UINT_CACHE = {}; + /** + * @param {number} value + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + 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; + } + } + /** + * Returns a Long representing the given 32 bit integer value. + * @function + * @param {number} value The 32 bit integer in question + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} The corresponding Long value + */ + Long$1.fromInt = fromInt; + /** + * @param {number} value + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + 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); + } + /** + * Returns a Long representing the given value, provided that it is a finite number. Otherwise, zero is returned. + * @function + * @param {number} value The number in question + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} The corresponding Long value + */ + Long$1.fromNumber = fromNumber; + /** + * @param {number} lowBits + * @param {number} highBits + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + function fromBits(lowBits, highBits, unsigned) { + return new Long$1(lowBits, highBits, unsigned); + } + /** + * Returns a Long representing the 64 bit integer that comes by concatenating the given low and high bits. Each is + * assumed to use 32 bits. + * @function + * @param {number} lowBits The low 32 bits + * @param {number} highBits The high 32 bits + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} The corresponding Long value + */ + Long$1.fromBits = fromBits; + /** + * @function + * @param {number} base + * @param {number} exponent + * @returns {number} + * @inner + */ + var pow_dbl = Math.pow; // Used 4 times (4*8 to 15+4) + /** + * @param {string} str + * @param {(boolean|number)=} unsigned + * @param {number=} radix + * @returns {!Long} + * @inner + */ + 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') { + // For goog.math.long compatibility + radix = unsigned, + unsigned = false; + } + else { + unsigned = !!unsigned; + } + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError('radix'); + var p; + if ((p = str.indexOf('-')) > 0) + throw Error('interior hyphen'); + else if (p === 0) { + return fromString(str.substring(1), unsigned, radix).neg(); + } + // Do several (8) digits each time through the loop, so as to + // minimize the calls to the very expensive emulated div. + 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; + } + /** + * Returns a Long representation of the given string, written using the specified radix. + * @function + * @param {string} str The textual representation of the Long + * @param {(boolean|number)=} unsigned Whether unsigned or not, defaults to signed + * @param {number=} radix The radix in which the text is written (2-36), defaults to 10 + * @returns {!Long} The corresponding Long value + */ + Long$1.fromString = fromString; + /** + * @function + * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val + * @param {boolean=} unsigned + * @returns {!Long} + * @inner + */ + function fromValue(val, unsigned) { + if (typeof val === 'number') + return fromNumber(val, unsigned); + if (typeof val === 'string') + return fromString(val, unsigned); + // Throws for non-objects, converts non-instanceof Long: + return fromBits(val.low, val.high, typeof unsigned === 'boolean' ? unsigned : val.unsigned); + } + /** + * Converts the specified value to a Long using the appropriate from* function for its type. + * @function + * @param {!Long|number|string|!{low: number, high: number, unsigned: boolean}} val Value + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {!Long} + */ + Long$1.fromValue = fromValue; + // NOTE: the compiler should inline these constant values below and then remove these variables, so there should be + // no runtime penalty for these. + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_16_DBL = 1 << 16; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_24_DBL = 1 << 24; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_32_DBL = TWO_PWR_16_DBL * TWO_PWR_16_DBL; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_64_DBL = TWO_PWR_32_DBL * TWO_PWR_32_DBL; + /** + * @type {number} + * @const + * @inner + */ + var TWO_PWR_63_DBL = TWO_PWR_64_DBL / 2; + /** + * @type {!Long} + * @const + * @inner + */ + var TWO_PWR_24 = fromInt(TWO_PWR_24_DBL); + /** + * @type {!Long} + * @inner + */ + var ZERO = fromInt(0); + /** + * Signed zero. + * @type {!Long} + */ + Long$1.ZERO = ZERO; + /** + * @type {!Long} + * @inner + */ + var UZERO = fromInt(0, true); + /** + * Unsigned zero. + * @type {!Long} + */ + Long$1.UZERO = UZERO; + /** + * @type {!Long} + * @inner + */ + var ONE = fromInt(1); + /** + * Signed one. + * @type {!Long} + */ + Long$1.ONE = ONE; + /** + * @type {!Long} + * @inner + */ + var UONE = fromInt(1, true); + /** + * Unsigned one. + * @type {!Long} + */ + Long$1.UONE = UONE; + /** + * @type {!Long} + * @inner + */ + var NEG_ONE = fromInt(-1); + /** + * Signed negative one. + * @type {!Long} + */ + Long$1.NEG_ONE = NEG_ONE; + /** + * @type {!Long} + * @inner + */ + var MAX_VALUE = fromBits(0xFFFFFFFF | 0, 0x7FFFFFFF | 0, false); + /** + * Maximum signed value. + * @type {!Long} + */ + Long$1.MAX_VALUE = MAX_VALUE; + /** + * @type {!Long} + * @inner + */ + var MAX_UNSIGNED_VALUE = fromBits(0xFFFFFFFF | 0, 0xFFFFFFFF | 0, true); + /** + * Maximum unsigned value. + * @type {!Long} + */ + Long$1.MAX_UNSIGNED_VALUE = MAX_UNSIGNED_VALUE; + /** + * @type {!Long} + * @inner + */ + var MIN_VALUE = fromBits(0, 0x80000000 | 0, false); + /** + * Minimum signed value. + * @type {!Long} + */ + Long$1.MIN_VALUE = MIN_VALUE; + /** + * @alias Long.prototype + * @inner + */ + var LongPrototype = Long$1.prototype; + /** + * Converts the Long to a 32 bit integer, assuming it is a 32 bit integer. + * @returns {number} + */ + LongPrototype.toInt = function toInt() { + return this.unsigned ? this.low >>> 0 : this.low; + }; + /** + * Converts the Long to a the nearest floating-point representation of this value (double, 53 bit mantissa). + * @returns {number} + */ + 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); + }; + /** + * Converts the Long to a string written in the specified radix. + * @param {number=} radix Radix (2-36), defaults to 10 + * @returns {string} + * @override + * @throws {RangeError} If `radix` is out of range + */ + LongPrototype.toString = function toString(radix) { + radix = radix || 10; + if (radix < 2 || 36 < radix) + throw RangeError('radix'); + if (this.isZero()) + return '0'; + if (this.isNegative()) { // Unsigned Longs are never negative + if (this.eq(MIN_VALUE)) { + // We need to change the Long value before it can be negated, so we remove + // the bottom-most digit in this base and then recurse to do the rest. + var radixLong = fromNumber(radix), div = this.div(radixLong), rem1 = div.mul(radixLong).sub(this); + return div.toString(radix) + rem1.toInt().toString(radix); + } + else + return '-' + this.neg().toString(radix); + } + // Do several (6) digits each time through the loop, so as to + // minimize the calls to the very expensive emulated div. + 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; + } + } + }; + /** + * Gets the high 32 bits as a signed integer. + * @returns {number} Signed high bits + */ + LongPrototype.getHighBits = function getHighBits() { + return this.high; + }; + /** + * Gets the high 32 bits as an unsigned integer. + * @returns {number} Unsigned high bits + */ + LongPrototype.getHighBitsUnsigned = function getHighBitsUnsigned() { + return this.high >>> 0; + }; + /** + * Gets the low 32 bits as a signed integer. + * @returns {number} Signed low bits + */ + LongPrototype.getLowBits = function getLowBits() { + return this.low; + }; + /** + * Gets the low 32 bits as an unsigned integer. + * @returns {number} Unsigned low bits + */ + LongPrototype.getLowBitsUnsigned = function getLowBitsUnsigned() { + return this.low >>> 0; + }; + /** + * Gets the number of bits needed to represent the absolute value of this Long. + * @returns {number} + */ + LongPrototype.getNumBitsAbs = function getNumBitsAbs() { + if (this.isNegative()) // Unsigned Longs are never negative + 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; + }; + /** + * Tests if this Long's value equals zero. + * @returns {boolean} + */ + LongPrototype.isZero = function isZero() { + return this.high === 0 && this.low === 0; + }; + /** + * Tests if this Long's value equals zero. This is an alias of {@link Long#isZero}. + * @returns {boolean} + */ + LongPrototype.eqz = LongPrototype.isZero; + /** + * Tests if this Long's value is negative. + * @returns {boolean} + */ + LongPrototype.isNegative = function isNegative() { + return !this.unsigned && this.high < 0; + }; + /** + * Tests if this Long's value is positive. + * @returns {boolean} + */ + LongPrototype.isPositive = function isPositive() { + return this.unsigned || this.high >= 0; + }; + /** + * Tests if this Long's value is odd. + * @returns {boolean} + */ + LongPrototype.isOdd = function isOdd() { + return (this.low & 1) === 1; + }; + /** + * Tests if this Long's value is even. + * @returns {boolean} + */ + LongPrototype.isEven = function isEven() { + return (this.low & 1) === 0; + }; + /** + * Tests if this Long's value equals the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + 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; + }; + /** + * Tests if this Long's value equals the specified's. This is an alias of {@link Long#equals}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.eq = LongPrototype.equals; + /** + * Tests if this Long's value differs from the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.notEquals = function notEquals(other) { + return !this.eq(/* validates */ other); + }; + /** + * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.neq = LongPrototype.notEquals; + /** + * Tests if this Long's value differs from the specified's. This is an alias of {@link Long#notEquals}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.ne = LongPrototype.notEquals; + /** + * Tests if this Long's value is less than the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lessThan = function lessThan(other) { + return this.comp(/* validates */ other) < 0; + }; + /** + * Tests if this Long's value is less than the specified's. This is an alias of {@link Long#lessThan}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lt = LongPrototype.lessThan; + /** + * Tests if this Long's value is less than or equal the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lessThanOrEqual = function lessThanOrEqual(other) { + return this.comp(/* validates */ other) <= 0; + }; + /** + * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.lte = LongPrototype.lessThanOrEqual; + /** + * Tests if this Long's value is less than or equal the specified's. This is an alias of {@link Long#lessThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.le = LongPrototype.lessThanOrEqual; + /** + * Tests if this Long's value is greater than the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.greaterThan = function greaterThan(other) { + return this.comp(/* validates */ other) > 0; + }; + /** + * Tests if this Long's value is greater than the specified's. This is an alias of {@link Long#greaterThan}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.gt = LongPrototype.greaterThan; + /** + * Tests if this Long's value is greater than or equal the specified's. + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.greaterThanOrEqual = function greaterThanOrEqual(other) { + return this.comp(/* validates */ other) >= 0; + }; + /** + * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.gte = LongPrototype.greaterThanOrEqual; + /** + * Tests if this Long's value is greater than or equal the specified's. This is an alias of {@link Long#greaterThanOrEqual}. + * @function + * @param {!Long|number|string} other Other value + * @returns {boolean} + */ + LongPrototype.ge = LongPrototype.greaterThanOrEqual; + /** + * Compares this Long's value with the specified's. + * @param {!Long|number|string} other Other value + * @returns {number} 0 if they are the same, 1 if the this is greater and -1 + * if the given one is greater + */ + 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; + // At this point the sign bits are the same + if (!this.unsigned) + return this.sub(other).isNegative() ? -1 : 1; + // Both are positive if at least one is unsigned + return (other.high >>> 0) > (this.high >>> 0) || (other.high === this.high && (other.low >>> 0) > (this.low >>> 0)) ? -1 : 1; + }; + /** + * Compares this Long's value with the specified's. This is an alias of {@link Long#compare}. + * @function + * @param {!Long|number|string} other Other value + * @returns {number} 0 if they are the same, 1 if the this is greater and -1 + * if the given one is greater + */ + LongPrototype.comp = LongPrototype.compare; + /** + * Negates this Long's value. + * @returns {!Long} Negated Long + */ + LongPrototype.negate = function negate() { + if (!this.unsigned && this.eq(MIN_VALUE)) + return MIN_VALUE; + return this.not().add(ONE); + }; + /** + * Negates this Long's value. This is an alias of {@link Long#negate}. + * @function + * @returns {!Long} Negated Long + */ + LongPrototype.neg = LongPrototype.negate; + /** + * Returns the sum of this and the specified Long. + * @param {!Long|number|string} addend Addend + * @returns {!Long} Sum + */ + LongPrototype.add = function add(addend) { + if (!isLong(addend)) + addend = fromValue(addend); + // Divide each number into 4 chunks of 16 bits, and then sum the chunks. + var a48 = this.high >>> 16; + var a32 = this.high & 0xFFFF; + var a16 = this.low >>> 16; + var a00 = this.low & 0xFFFF; + var b48 = addend.high >>> 16; + var b32 = addend.high & 0xFFFF; + var b16 = addend.low >>> 16; + var b00 = addend.low & 0xFFFF; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 + b00; + c16 += c00 >>> 16; + c00 &= 0xFFFF; + c16 += a16 + b16; + c32 += c16 >>> 16; + c16 &= 0xFFFF; + c32 += a32 + b32; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c48 += a48 + b48; + c48 &= 0xFFFF; + return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned); + }; + /** + * Returns the difference of this and the specified Long. + * @param {!Long|number|string} subtrahend Subtrahend + * @returns {!Long} Difference + */ + LongPrototype.subtract = function subtract(subtrahend) { + if (!isLong(subtrahend)) + subtrahend = fromValue(subtrahend); + return this.add(subtrahend.neg()); + }; + /** + * Returns the difference of this and the specified Long. This is an alias of {@link Long#subtract}. + * @function + * @param {!Long|number|string} subtrahend Subtrahend + * @returns {!Long} Difference + */ + LongPrototype.sub = LongPrototype.subtract; + /** + * Returns the product of this and the specified Long. + * @param {!Long|number|string} multiplier Multiplier + * @returns {!Long} Product + */ + LongPrototype.multiply = function multiply(multiplier) { + if (this.isZero()) + return ZERO; + if (!isLong(multiplier)) + multiplier = fromValue(multiplier); + // use wasm support if present + 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 both longs are small, use float multiplication + if (this.lt(TWO_PWR_24) && multiplier.lt(TWO_PWR_24)) + return fromNumber(this.toNumber() * multiplier.toNumber(), this.unsigned); + // Divide each long into 4 chunks of 16 bits, and then add up 4x4 products. + // We can skip products that would overflow. + var a48 = this.high >>> 16; + var a32 = this.high & 0xFFFF; + var a16 = this.low >>> 16; + var a00 = this.low & 0xFFFF; + var b48 = multiplier.high >>> 16; + var b32 = multiplier.high & 0xFFFF; + var b16 = multiplier.low >>> 16; + var b00 = multiplier.low & 0xFFFF; + var c48 = 0, c32 = 0, c16 = 0, c00 = 0; + c00 += a00 * b00; + c16 += c00 >>> 16; + c00 &= 0xFFFF; + c16 += a16 * b00; + c32 += c16 >>> 16; + c16 &= 0xFFFF; + c16 += a00 * b16; + c32 += c16 >>> 16; + c16 &= 0xFFFF; + c32 += a32 * b00; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c32 += a16 * b16; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c32 += a00 * b32; + c48 += c32 >>> 16; + c32 &= 0xFFFF; + c48 += a48 * b00 + a32 * b16 + a16 * b32 + a00 * b48; + c48 &= 0xFFFF; + return fromBits((c16 << 16) | c00, (c48 << 16) | c32, this.unsigned); + }; + /** + * Returns the product of this and the specified Long. This is an alias of {@link Long#multiply}. + * @function + * @param {!Long|number|string} multiplier Multiplier + * @returns {!Long} Product + */ + LongPrototype.mul = LongPrototype.multiply; + /** + * Returns this Long divided by the specified. The result is signed if this Long is signed or + * unsigned if this Long is unsigned. + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Quotient + */ + LongPrototype.divide = function divide(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + if (divisor.isZero()) + throw Error('division by zero'); + // use wasm support if present + if (wasm) { + // guard against signed division overflow: the largest + // negative number / -1 would be 1 larger than the largest + // positive number, due to two's complement. + if (!this.unsigned && + this.high === -0x80000000 && + divisor.low === -1 && divisor.high === -1) { + // be consistent with non-wasm code path + 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) { + // This section is only relevant for signed longs and is derived from the + // closure library as a whole. + if (this.eq(MIN_VALUE)) { + if (divisor.eq(ONE) || divisor.eq(NEG_ONE)) + return MIN_VALUE; // recall that -MIN_VALUE == MIN_VALUE + else if (divisor.eq(MIN_VALUE)) + return ONE; + else { + // At this point, we have |other| >= 2, so |this/other| < |MIN_VALUE|. + 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 { + // The algorithm below has not been made for unsigned longs. It's therefore + // required to take special care of the MSB prior to running it. + if (!divisor.unsigned) + divisor = divisor.toUnsigned(); + if (divisor.gt(this)) + return UZERO; + if (divisor.gt(this.shru(1))) // 15 >>> 1 = 7 ; with divisor = 8 ; true + return UONE; + res = UZERO; + } + // Repeat the following until the remainder is less than other: find a + // floating-point that approximates remainder / other *from below*, add this + // into the result, and subtract it from the remainder. It is critical that + // the approximate value is less than or equal to the real value so that the + // remainder never becomes negative. + rem = this; + while (rem.gte(divisor)) { + // Approximate the result of division. This may be a little greater or + // smaller than the actual value. + approx = Math.max(1, Math.floor(rem.toNumber() / divisor.toNumber())); + // We will tweak the approximate result by changing it in the 48-th digit or + // the smallest non-fractional digit, whichever is larger. + var log2 = Math.ceil(Math.log(approx) / Math.LN2), delta = (log2 <= 48) ? 1 : pow_dbl(2, log2 - 48), + // Decrease the approximation until it is smaller than the remainder. Note + // that if it is too large, the product overflows and is negative. + approxRes = fromNumber(approx), approxRem = approxRes.mul(divisor); + while (approxRem.isNegative() || approxRem.gt(rem)) { + approx -= delta; + approxRes = fromNumber(approx, this.unsigned); + approxRem = approxRes.mul(divisor); + } + // We know the answer can't be zero... and actually, zero would cause + // infinite recursion since we would make no progress. + if (approxRes.isZero()) + approxRes = ONE; + res = res.add(approxRes); + rem = rem.sub(approxRem); + } + return res; + }; + /** + * Returns this Long divided by the specified. This is an alias of {@link Long#divide}. + * @function + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Quotient + */ + LongPrototype.div = LongPrototype.divide; + /** + * Returns this Long modulo the specified. + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Remainder + */ + LongPrototype.modulo = function modulo(divisor) { + if (!isLong(divisor)) + divisor = fromValue(divisor); + // use wasm support if present + 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)); + }; + /** + * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}. + * @function + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Remainder + */ + LongPrototype.mod = LongPrototype.modulo; + /** + * Returns this Long modulo the specified. This is an alias of {@link Long#modulo}. + * @function + * @param {!Long|number|string} divisor Divisor + * @returns {!Long} Remainder + */ + LongPrototype.rem = LongPrototype.modulo; + /** + * Returns the bitwise NOT of this Long. + * @returns {!Long} + */ + LongPrototype.not = function not() { + return fromBits(~this.low, ~this.high, this.unsigned); + }; + /** + * Returns the bitwise AND of this Long and the specified. + * @param {!Long|number|string} other Other Long + * @returns {!Long} + */ + LongPrototype.and = function and(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low & other.low, this.high & other.high, this.unsigned); + }; + /** + * Returns the bitwise OR of this Long and the specified. + * @param {!Long|number|string} other Other Long + * @returns {!Long} + */ + LongPrototype.or = function or(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low | other.low, this.high | other.high, this.unsigned); + }; + /** + * Returns the bitwise XOR of this Long and the given one. + * @param {!Long|number|string} other Other Long + * @returns {!Long} + */ + LongPrototype.xor = function xor(other) { + if (!isLong(other)) + other = fromValue(other); + return fromBits(this.low ^ other.low, this.high ^ other.high, this.unsigned); + }; + /** + * Returns this Long with bits shifted to the left by the given amount. + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + 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); + }; + /** + * Returns this Long with bits shifted to the left by the given amount. This is an alias of {@link Long#shiftLeft}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shl = LongPrototype.shiftLeft; + /** + * Returns this Long with bits arithmetically shifted to the right by the given amount. + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + 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); + }; + /** + * Returns this Long with bits arithmetically shifted to the right by the given amount. This is an alias of {@link Long#shiftRight}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shr = LongPrototype.shiftRight; + /** + * Returns this Long with bits logically shifted to the right by the given amount. + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + 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); + } + }; + /** + * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shru = LongPrototype.shiftRightUnsigned; + /** + * Returns this Long with bits logically shifted to the right by the given amount. This is an alias of {@link Long#shiftRightUnsigned}. + * @function + * @param {number|!Long} numBits Number of bits + * @returns {!Long} Shifted Long + */ + LongPrototype.shr_u = LongPrototype.shiftRightUnsigned; + /** + * Converts this Long to signed. + * @returns {!Long} Signed long + */ + LongPrototype.toSigned = function toSigned() { + if (!this.unsigned) + return this; + return fromBits(this.low, this.high, false); + }; + /** + * Converts this Long to unsigned. + * @returns {!Long} Unsigned long + */ + LongPrototype.toUnsigned = function toUnsigned() { + if (this.unsigned) + return this; + return fromBits(this.low, this.high, true); + }; + /** + * Converts this Long to its byte representation. + * @param {boolean=} le Whether little or big endian, defaults to big endian + * @returns {!Array.} Byte representation + */ + LongPrototype.toBytes = function toBytes(le) { + return le ? this.toBytesLE() : this.toBytesBE(); + }; + /** + * Converts this Long to its little endian byte representation. + * @returns {!Array.} Little endian byte representation + */ + LongPrototype.toBytesLE = function toBytesLE() { + var hi = this.high, lo = this.low; + return [ + lo & 0xff, + lo >>> 8 & 0xff, + lo >>> 16 & 0xff, + lo >>> 24, + hi & 0xff, + hi >>> 8 & 0xff, + hi >>> 16 & 0xff, + hi >>> 24 + ]; + }; + /** + * Converts this Long to its big endian byte representation. + * @returns {!Array.} Big endian byte representation + */ + LongPrototype.toBytesBE = function toBytesBE() { + var hi = this.high, lo = this.low; + return [ + hi >>> 24, + hi >>> 16 & 0xff, + hi >>> 8 & 0xff, + hi & 0xff, + lo >>> 24, + lo >>> 16 & 0xff, + lo >>> 8 & 0xff, + lo & 0xff + ]; + }; + /** + * Creates a Long from its byte representation. + * @param {!Array.} bytes Byte representation + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @param {boolean=} le Whether little or big endian, defaults to big endian + * @returns {Long} The corresponding Long value + */ + Long$1.fromBytes = function fromBytes(bytes, unsigned, le) { + return le ? Long$1.fromBytesLE(bytes, unsigned) : Long$1.fromBytesBE(bytes, unsigned); + }; + /** + * Creates a Long from its little endian byte representation. + * @param {!Array.} bytes Little endian byte representation + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {Long} The corresponding Long value + */ + Long$1.fromBytesLE = function fromBytesLE(bytes, unsigned) { + return new Long$1(bytes[0] | + bytes[1] << 8 | + bytes[2] << 16 | + bytes[3] << 24, bytes[4] | + bytes[5] << 8 | + bytes[6] << 16 | + bytes[7] << 24, unsigned); + }; + /** + * Creates a Long from its big endian byte representation. + * @param {!Array.} bytes Big endian byte representation + * @param {boolean=} unsigned Whether unsigned or not, defaults to signed + * @returns {Long} The corresponding Long value + */ + Long$1.fromBytesBE = function fromBytesBE(bytes, unsigned) { + return new Long$1(bytes[4] << 24 | + bytes[5] << 16 | + bytes[6] << 8 | + bytes[7], bytes[0] << 24 | + bytes[1] << 16 | + bytes[2] << 8 | + bytes[3], unsigned); + }; + + var LongExports = /*#__PURE__*/Object.assign(/*#__PURE__*/Object.create(null), long, { + 'default': long + }); + + // tslint:disable-next-line + var Long = + // tslint:disable-next-line + long || LongExports; + function hexToLong(hex) { + return Long.fromString(hex, true, 16); + } + // Some primes between 2^63 and 2^64 for various uses. + // Hex 0xc3a5c85c97cb3127 + var k0 = hexToLong('c3a5c85c97cb3127'); + // Hex 0xb492b66fbe98f273 + var k1 = hexToLong('b492b66fbe98f273'); + // Hex 0x9ae16a3b2f90404f + var k2 = hexToLong('9ae16a3b2f90404f'); + function shiftMix(val) { + return val.xor(val.shru(47)); + } + function fetch$2(s, offset, numBytes) { + var bytes = s.slice(offset, offset + numBytes); + return Long.fromBytes(Array.from(bytes), true, true); + } + function fetch64(s, offset) { + return fetch$2(s, offset, 8); + } + function fetch32(s, offset) { + return fetch$2(s, offset, 4); + } + function rotate64(val, shift) { + // Avoid shifting by 64: doing so yields an undefined result. + return shift === 0 ? val : val.shru(shift).or(val.shl(64 - shift)); + } + function hashLen16(u, v, mul) { + if (mul === void 0) { mul = hexToLong('9ddfea08eb382d69'); } + // Murmur-inspired hashing. + var a = u.xor(v).mul(mul); + a = a.xor(a.shru(47)); + var b = v.xor(a).mul(mul); + b = b.xor(b.shru(47)); + b = b.mul(mul); + return b; + } + // Return a 16-byte hash for 48 bytes. Quick and dirty. + // Callers do best to use "random-looking" values for a and b. + function weakHashLen32WithSeeds(w, x, y, z, a, b) { + a = a.add(w); + b = rotate64(b.add(a).add(z), 21); + var 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) { + if (len === void 0) { len = s.length; } + if (len >= 8) { + var mul = k2.add(len * 2); + var a = fetch64(s, 0).add(k2); + var b = fetch64(s, len - 8); + var c = rotate64(b, 37).mul(mul).add(a); + var d = rotate64(a, 25).add(b).mul(mul); + return hashLen16(c, d, mul); + } + if (len >= 4) { + var mul = k2.add(len * 2); + var a = fetch32(s, 0); + return hashLen16(a.shl(3).add(len), fetch32(s, len - 4), mul); + } + if (len > 0) { + var a = s[0]; + var b = s[len >> 1]; + var c = s[len - 1]; + var y = a + (b << 8); + var z = len + (c << 2); + return shiftMix(k2.mul(y).xor(k0.mul(z))).mul(k2); + } + return k2; + } + function hashLen17to32(s, len) { + if (len === void 0) { len = s.length; } + var mul = k2.add(len * 2); + var a = fetch64(s, 0).mul(k1); + var b = fetch64(s, 8); + var c = fetch64(s, len - 8).mul(mul); + var 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), mul); + } + function hashLen33to64(s, len) { + if (len === void 0) { len = s.length; } + var mul = k2.add(len * 2); + var a = fetch64(s, 0).mul(k2); + var b = fetch64(s, 8); + var c = fetch64(s, len - 8).mul(mul); + var d = fetch64(s, len - 16).mul(k2); + var y = rotate64(a.add(b), 43).add(rotate64(c, 30)).add(d); + var z = hashLen16(y, a.add(rotate64(b.add(k2), 18)).add(c), mul); + var e = fetch64(s, 16).mul(mul); + var f = fetch64(s, 24); + var g = y.add(fetch64(s, len - 32)).mul(mul); + var h = z.add(fetch64(s, len - 24)).mul(mul); + return hashLen16(rotate64(e.add(f), 43).add(rotate64(g, 30)).add(h), e.add(rotate64(f.add(a), 18)).add(g), mul); + } + function fingerPrint64(s, len) { + var _a, _b; + if (len === void 0) { len = s.length; } + var 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); + } + // For strings over 64 bytes we loop. Internal state consists of + // 56 bytes: v, w, x, y, and z. + var x = seed; + var y = seed.mul(k1).add(113); + var z = shiftMix(y.mul(k2).add(113)).mul(k2); + var v = [Long.UZERO, Long.UZERO]; + var w = [Long.UZERO, Long.UZERO]; + x = x.mul(k2).add(fetch64(s, 0)); + var offset = 0; + // Set end so that after the loop we have 1 to 64 bytes left to process. + var end = ((len - 1) >> 6) * 64; + var 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))); + _a = __read([x, z], 2), z = _a[0], x = _a[1]; + offset += 64; + } while (offset !== end); + var mul = k1.add(z.and(0xff).shl(1)); + // Point to the last 64 bytes of input. + 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(mul); + y = rotate64(y.add(v[1]).add(fetch64(s, offset + 48)), 42).mul(mul); + 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(mul); + v = weakHashLen32WithSeedsStr(s, offset, v[1].mul(mul), x.add(w[0])); + w = weakHashLen32WithSeedsStr(s, offset + 32, z.add(w[1]), y.add(fetch64(s, offset + 16))); + _b = __read([x, z], 2), z = _b[0], x = _b[1]; + return hashLen16(hashLen16(v[0], w[0], mul).add(shiftMix(y).mul(k0)).add(z), hashLen16(v[1], w[1], mul).add(x), mul); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Create typed array for scalar value. Used for storing in `DataStorage`. + */ + 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') { + var bool = new Uint8Array(a.length); + for (var 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); + } + } + /** + * Returns the current high-resolution time in milliseconds relative to an + * arbitrary time in the past. It works across different platforms (node.js, + * browsers). + * + * ```js + * console.log(tf.util.now()); + * ``` + * + * @doc {heading: 'Util', namespace: 'util'} + */ + function now() { + return env().platform.now(); + } + /** + * Returns a platform-specific implementation of + * [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API). + * + * If `fetch` is defined on the global object (`window`, `process`, etc.), + * `tf.util.fetch` returns that function. + * + * If not, `tf.util.fetch` returns a platform-specific solution. + * + * ```js + * const resource = await tf.util.fetch('https://unpkg.com/@tensorflow/tfjs'); + * // handle response + * ``` + * + * @doc {heading: 'Util'} + */ + function fetch$1(path, requestInits) { + return env().platform.fetch(path, requestInits); + } + /** + * Encodes the provided string into bytes using the provided encoding scheme. + * + * @param s The string to encode. + * @param encoding The encoding scheme. Defaults to utf-8. + * + * @doc {heading: 'Util'} + */ + function encodeString(s, encoding) { + if (encoding === void 0) { encoding = 'utf-8'; } + encoding = encoding || 'utf-8'; + return env().platform.encode(s, encoding); + } + /** + * Decodes the provided bytes into a string using the provided encoding scheme. + * @param bytes The bytes to decode. + * + * @param encoding The encoding scheme. Defaults to utf-8. + * + * @doc {heading: 'Util'} + */ + function decodeString(bytes, encoding) { + if (encoding === void 0) { encoding = 'utf-8'; } + encoding = encoding || 'utf-8'; + return env().platform.decode(bytes, encoding); + } + + var util = { + __proto__: null, + createScalarValue: createScalarValue, + toTypedArray: toTypedArray, + now: now, + fetch: fetch$1, + encodeString: encodeString, + decodeString: decodeString, + shuffle: shuffle, + shuffleCombo: shuffleCombo, + clamp: clamp, + nearestLargerEven: nearestLargerEven, + swap: swap, + sum: sum$1, + randUniform: randUniform, + distSquared: distSquared, + assert: assert, + assertShapesMatch: assertShapesMatch, + assertNonNull: assertNonNull, + flatten: flatten, + sizeFromShape: sizeFromShape, + isScalarShape: isScalarShape, + arraysEqual: arraysEqual, + isInt: isInt, + tanh: tanh$1, + sizeToSquarishShape: sizeToSquarishShape, + createShuffledIndices: createShuffledIndices, + rightPad: rightPad, + repeatedTry: repeatedTry, + inferFromImplicitShape: inferFromImplicitShape, + parseAxisParam: parseAxisParam, + squeezeShape: squeezeShape, + getTypedArrayFromDType: getTypedArrayFromDType, + getArrayFromDType: getArrayFromDType, + checkConversionForErrors: checkConversionForErrors, + isValidDtype: isValidDtype, + hasEncodingLoss: hasEncodingLoss, + isTypedArray: isTypedArray, + bytesPerElement: bytesPerElement, + bytesFromStringArray: bytesFromStringArray, + isString: isString, + isBoolean: isBoolean, + isNumber: isNumber, + inferDtype: inferDtype, + isFunction: isFunction, + nearestDivisor: nearestDivisor, + computeStrides: computeStrides, + toNestedArray: toNestedArray, + makeOnesTypedArray: makeOnesTypedArray, + makeZerosTypedArray: makeZerosTypedArray, + makeZerosNestedTypedArray: makeZerosNestedTypedArray, + assertNonNegativeIntegerDimensions: assertNonNegativeIntegerDimensions, + locToIndex: locToIndex, + indexToLoc: indexToLoc, + isPromise: isPromise, + hexToLong: hexToLong, + fingerPrint64: fingerPrint64 + }; + + var Profiler = /** @class */ (function () { + function Profiler(backendTimer, logger) { + this.backendTimer = backendTimer; + this.logger = logger; + if (logger == null) { + this.logger = new Logger(); + } + } + Profiler.prototype.profileKernel = function (kernelName, inputs, f) { + var e_1, _a; + var outputs; + var holdResultWrapperFn = function () { + outputs = f(); + }; + var timer; + var start = now(); + if (this.backendTimer.timerAvailable()) { + timer = this.backendTimer.time(holdResultWrapperFn); + } + else { + holdResultWrapperFn(); + try { + for (var outputs_1 = __values(outputs), outputs_1_1 = outputs_1.next(); !outputs_1_1.done; outputs_1_1 = outputs_1.next()) { + var output = outputs_1_1.value; + output.dataSync(); + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (outputs_1_1 && !outputs_1_1.done && (_a = outputs_1.return)) _a.call(outputs_1); + } + finally { if (e_1) throw e_1.error; } + } + timer = Promise.resolve({ kernelMs: now() - start }); + } + if (env().getBool('CHECK_COMPUTATION_FOR_ERRORS')) { + var _loop_1 = function (i) { + var output = outputs[i]; + // Dangling promise here because we don't want to propagate up + // asynchronicity. + output.data().then(function (tensorVals) { + checkComputationForErrors(tensorVals, output.dtype, kernelName); + }); + }; + for (var i = 0; i < outputs.length; i++) { + _loop_1(i); + } + } + var kernelProfile = { + kernelName: kernelName, + outputs: outputs, + inputs: inputs, + timeMs: timer.then(function (timing) { return timing.kernelMs; }), + extraInfo: timer.then(function (timing) { return timing.getExtraProfileInfo != null ? + timing.getExtraProfileInfo() : + ''; }) + }; + return kernelProfile; + }; + Profiler.prototype.logKernelProfile = function (kernelProfile) { + var _this = this; + var kernelName = kernelProfile.kernelName, outputs = kernelProfile.outputs, timeMs = kernelProfile.timeMs, inputs = kernelProfile.inputs, extraInfo = kernelProfile.extraInfo; + outputs.forEach(function (result) { + Promise.all([result.data(), timeMs, extraInfo]).then(function (valueContainer) { + _this.logger.logKernelProfile(kernelName, result, valueContainer[0], valueContainer[1], inputs, valueContainer[2]); + }); + }); + }; + return Profiler; + }()); + function checkComputationForErrors(vals, dtype, kernelName) { + if (dtype !== 'float32') { + // Only floating point computations will generate NaN values + return false; + } + for (var i = 0; i < vals.length; i++) { + var num = vals[i]; + if (isNaN(num) || !isFinite(num)) { + // Throwing custom exception so behavior is testable. + console.warn("Found " + num + " in the result of '" + kernelName + "'"); + return true; + } + } + return false; + } + var Logger = /** @class */ (function () { + function Logger() { + } + Logger.prototype.logKernelProfile = function (name, result, vals, timeMs, inputs, extraInfo) { + var time = typeof timeMs === 'number' ? rightPad(timeMs + "ms", 9) : + timeMs['error']; + var paddedName = rightPad(name, 25); + var rank = result.rank; + var size = result.size; + var shape = rightPad(result.shape.toString(), 14); + var inputShapesDescription = ''; + for (var name_1 in inputs) { + var input = inputs[name_1]; + if (input != null) { + // The input might be a non-tensor (e.g HTMLImageElement), in which case + // we claim the output shape as input shape. + var inputShape = input.shape || result.shape; + var inputRank = inputShape.length; + inputShapesDescription += + name_1 + ": " + inputRank + "D " + (inputRank > 0 ? inputShape : '') + " "; + } + } + console.log("%c" + paddedName + "\t%c" + time + "\t%c" + rank + "D " + shape + "\t%c" + size + "\t%c" + inputShapesDescription + "\t%c" + extraInfo, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green', 'color: steelblue'); + }; + return Logger; + }()); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes a list of TapeNodes that connect x to y, filtering everything else + * out and preserving the order of the original tape elements. + * + * @param tape The tape elements to filter. + * @param xs The input Tensors. + * @param y The output Tensor. + */ + function getFilteredNodesXToY(tape, xs, y) { + // Forward pass to compute all the nodes and Tensors that are transitively a + // function of x. + var tensorsFromX = {}; + var nodesFromX = {}; + for (var i = 0; i < xs.length; i++) { + tensorsFromX[xs[i].id] = true; + } + for (var i = 0; i < tape.length; i++) { + var node = tape[i]; + var nodeInputs = node.inputs; + for (var inputName in nodeInputs) { + var input = nodeInputs[inputName]; + var anyInputFromX = false; + for (var j = 0; j < xs.length; j++) { + if (tensorsFromX[input.id]) { + node.outputs.forEach(function (output) { return tensorsFromX[output.id] = true; }); + anyInputFromX = true; + nodesFromX[node.id] = true; + break; + } + } + if (anyInputFromX) { + break; + } + } + } + // Backward pass to find all of the nodes and Tensors that lead to y. + var tensorsLeadToY = {}; + tensorsLeadToY[y.id] = true; + var nodesToY = {}; + for (var i = tape.length - 1; i >= 0; i--) { + var node = tape[i]; + var nodeInputs = node.inputs; + // If any of the outputs lead to y, mark all of the inputs as leading to y. + for (var j = 0; j < node.outputs.length; j++) { + if (tensorsLeadToY[node.outputs[j].id]) { + for (var inputName in nodeInputs) { + tensorsLeadToY[nodeInputs[inputName].id] = true; + nodesToY[node.id] = true; + } + break; + } + } + } + // Return the paths that come from x and lead to y. + var filteredTape = []; + for (var i = 0; i < tape.length; i++) { + var node = tape[i]; + if (nodesFromX[node.id] && nodesToY[node.id]) { + // Prune the inputs from the node that aren't a function of x. + var prunedInputs = {}; + for (var inputName in node.inputs) { + var nodeInput = node.inputs[inputName]; + if (tensorsFromX[nodeInput.id]) { + prunedInputs[inputName] = nodeInput; + } + } + // Copy the node and overwrite inputsAndArgs to the pruned version. + var prunedNode = Object.assign({}, node); + prunedNode.inputs = prunedInputs; + prunedNode.outputs = node.outputs; + filteredTape.push(prunedNode); + } + } + return filteredTape; + } + /** + * Backpropagate gradients through the filtered TapeNodes. + * + * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map + * is mutated by this method. + * @param filteredTape The filtered TapeNodes to backprop through. + */ + function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape, tidy, add) { + var _loop_1 = function (i) { + var node = filteredTape[i]; + var dys = []; + node.outputs.forEach(function (o) { + var gradTensor = tensorAccumulatedGradientMap[o.id]; + if (gradTensor != null) { + dys.push(gradTensor); + } + else { + // This particular output is not in the back-propagation subgraph, so it + // does not affect the final output, thus we put null for its dy. + dys.push(null); + } + }); + if (node.gradient == null) { + throw new Error("Cannot compute gradient: gradient function not found " + + ("for " + node.kernelName + ".")); + } + // Backprop dy through this node and accumulate gradients over the inputs. + var inputGradients = node.gradient(dys); + var _loop_2 = function (inputName) { + if (!(inputName in inputGradients)) { + throw new Error("Cannot backprop through input " + inputName + ". " + + ("Available gradients found: " + Object.keys(inputGradients) + ".")); + } + // Call the gradient function. + var dx = tidy(function () { return 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 + "'")); + } + var 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 { + var curGradient = tensorAccumulatedGradientMap[x.id]; + tensorAccumulatedGradientMap[x.id] = add(curGradient, dx); + curGradient.dispose(); + } + }; + for (var inputName in node.inputs) { + _loop_2(inputName); + } + }; + // Walk the tape backward and keep a map of Tensor to its gradient. + for (var i = filteredTape.length - 1; i >= 0; i--) { + _loop_1(i); + } + } + + // Maximum number of values before we decide to show ellipsis. + var FORMAT_LIMIT_NUM_VALS = 20; + // Number of first and last values to show when displaying a, b,...,y, z. + var FORMAT_NUM_FIRST_LAST_VALS = 3; + // Number of significant digits to show. + var FORMAT_NUM_SIG_DIGITS = 7; + function tensorToString(vals, shape, dtype, verbose) { + var strides = computeStrides(shape); + var padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides); + var rank = shape.length; + var valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol); + var lines = ['Tensor']; + if (verbose) { + lines.push(" dtype: " + dtype); + lines.push(" rank: " + rank); + lines.push(" shape: [" + shape + "]"); + lines.push(" values:"); + } + lines.push(valsLines.map(function (l) { return ' ' + l; }).join('\n')); + return lines.join('\n'); + } + function computeMaxSizePerColumn(vals, shape, dtype, strides) { + var n = sizeFromShape(shape); + var numCols = strides[strides.length - 1]; + var padPerCol = new Array(numCols).fill(0); + var rank = shape.length; + var valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals; + if (rank > 1) { + for (var row = 0; row < n / numCols; row++) { + var offset = row * numCols; + for (var j = 0; j < numCols; j++) { + padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0, dtype).length); + } + } + } + return padPerCol; + } + function valToString(val, pad, dtype) { + var 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, pad); + } + function boolNumToString(v) { + return v === 0 ? 'false' : 'true'; + } + function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast) { + if (isLast === void 0) { isLast = true; } + var storagePerElement = dtype === 'complex64' ? 2 : 1; + var size = shape[0]; + var rank = shape.length; + if (rank === 0) { + if (dtype === 'complex64') { + var 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) { + var firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement; + var firstVals = Array.from(vals.slice(0, firstValsSize)); + var 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(function (x, i) { return valToString(x, padPerCol[i], dtype); }) + .join(', ') + + ', ..., ' + + lastVals + .map(function (x, i) { return valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i], dtype); }) + .join(', ') + + ']' + ]; + } + var displayVals = dtype === 'complex64' ? createComplexTuples(vals) : + Array.from(vals); + return [ + '[' + + displayVals.map(function (x, i) { return valToString(x, padPerCol[i], dtype); }) + .join(', ') + + ']' + ]; + } + // The array is rank 2 or more. + var subshape = shape.slice(1); + var substrides = strides.slice(1); + var stride = strides[0] * storagePerElement; + var lines = []; + if (size > FORMAT_LIMIT_NUM_VALS) { + for (var i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) { + var start = i * stride; + var end = start + stride; + lines.push.apply(lines, __spread(subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false /* isLast */))); + } + lines.push('...'); + for (var i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) { + var start = i * stride; + var end = start + stride; + lines.push.apply(lines, __spread(subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */))); + } + } + else { + for (var i = 0; i < size; i++) { + var start = i * stride; + var end = start + stride; + lines.push.apply(lines, __spread(subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1 /* isLast */))); + } + } + var sep = rank === 2 ? ',' : ''; + lines[0] = '[' + lines[0] + sep; + for (var i = 1; i < lines.length - 1; i++) { + lines[i] = ' ' + lines[i] + sep; + } + var newLineSep = ',\n'; + for (var i = 2; i < rank; i++) { + newLineSep += '\n'; + } + lines[lines.length - 1] = + ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep); + return lines; + } + function createComplexTuples(vals) { + var complexTuples = []; + for (var i = 0; i < vals.length; i += 2) { + complexTuples.push([vals[i], vals[i + 1]]); + } + return complexTuples; + } + + /** + * A mutable object, similar to `tf.Tensor`, that allows users to set values + * at locations before converting to an immutable `tf.Tensor`. + * + * See `tf.buffer` for creating a tensor buffer. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + var TensorBuffer = /** @class */ (function () { + function TensorBuffer(shape, dtype, values) { + var _this = this; + this.dtype = dtype; + this.shape = shape.slice(); + this.size = sizeFromShape(shape); + if (values != null) { + var n_1 = values.length; + assert(n_1 === this.size, function () { return "Length of values '" + n_1 + "' 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); + } + /** + * Sets a value in the buffer at a given location. + * + * @param value The value to set. + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + TensorBuffer.prototype.set = function (value) { + var _this = this; + var locs = []; + for (var _i = 1; _i < arguments.length; _i++) { + locs[_i - 1] = arguments[_i]; + } + if (locs.length === 0) { + locs = [0]; + } + assert(locs.length === this.rank, function () { return "The number of provided coordinates (" + locs.length + ") must " + + ("match the rank (" + _this.rank + ")"); }); + var index = this.locToIndex(locs); + this.values[index] = value; + }; + /** + * Returns the value in the buffer at the provided location. + * + * @param locs The location indices. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + TensorBuffer.prototype.get = function () { + var e_1, _b; + var locs = []; + for (var _i = 0; _i < arguments.length; _i++) { + locs[_i] = arguments[_i]; + } + if (locs.length === 0) { + locs = [0]; + } + var i = 0; + try { + for (var locs_1 = __values(locs), locs_1_1 = locs_1.next(); !locs_1_1.done; locs_1_1 = locs_1.next()) { + var loc = locs_1_1.value; + if (loc < 0 || loc >= this.shape[i]) { + var msg = "Requested out of range element at " + locs + ". " + + (" Buffer shape=" + this.shape); + throw new Error(msg); + } + i++; + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (locs_1_1 && !locs_1_1.done && (_b = locs_1.return)) _b.call(locs_1); + } + finally { if (e_1) throw e_1.error; } + } + var index = locs[locs.length - 1]; + for (var i_1 = 0; i_1 < locs.length - 1; ++i_1) { + index += this.strides[i_1] * locs[i_1]; + } + return this.values[index]; + }; + TensorBuffer.prototype.locToIndex = function (locs) { + if (this.rank === 0) { + return 0; + } + else if (this.rank === 1) { + return locs[0]; + } + var index = locs[locs.length - 1]; + for (var i = 0; i < locs.length - 1; ++i) { + index += this.strides[i] * locs[i]; + } + return index; + }; + TensorBuffer.prototype.indexToLoc = function (index) { + if (this.rank === 0) { + return []; + } + else if (this.rank === 1) { + return [index]; + } + var locs = new Array(this.shape.length); + for (var 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; + }; + Object.defineProperty(TensorBuffer.prototype, "rank", { + get: function () { + return this.shape.length; + }, + enumerable: true, + configurable: true + }); + /** + * Creates an immutable `tf.Tensor` object from the buffer. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + TensorBuffer.prototype.toTensor = function () { + return trackerFn().makeTensor(this.values, this.shape, this.dtype); + }; + return TensorBuffer; + }()); + // For tracking tensor creation and disposal. + var trackerFn = null; + // Used by chaining methods to call into ops. + var opHandler$1 = null; + /** + * An external consumer can register itself as the tensor tracker. This way + * the Tensor class can notify the tracker for every tensor created and + * disposed. + */ + function setTensorTracker(fn) { + trackerFn = fn; + } + /** + * An external consumer can register itself as the op handler. This way the + * Tensor class can have chaining methods that call into ops via the op + * handler. + */ + function setOpHandler(handler) { + opHandler$1 = handler; + } + /** + * A `tf.Tensor` object represents an immutable, multidimensional array of + * numbers that has a shape and a data type. + * + * For performance reasons, functions that create tensors do not necessarily + * perform a copy of the data passed to them (e.g. if the data is passed as a + * `Float32Array`), and changes to the data will change the tensor. This is not + * a feature and is not supported. To avoid this behavior, use the tensor before + * changing the input data or create a copy with `copy = tf.add(yourTensor, 0)`. + * + * See `tf.tensor` for details on how to create a `tf.Tensor`. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + var Tensor = /** @class */ (function () { + function Tensor(shape, dtype, dataId, id) { + /** Whether this tensor has been globally kept. */ + 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'); + } + Object.defineProperty(Tensor.prototype, "rank", { + get: function () { + return this.shape.length; + }, + enumerable: true, + configurable: true + }); + /** + * Returns a promise of `tf.TensorBuffer` that holds the underlying data. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.buffer = function () { + return __awaiter(this, void 0, void 0, function () { + var vals; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: return [4 /*yield*/, this.data()]; + case 1: + vals = _b.sent(); + return [2 /*return*/, opHandler$1.buffer(this.shape, this.dtype, vals)]; + } + }); + }); + }; + /** + * Returns a `tf.TensorBuffer` that holds the underlying data. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.bufferSync = function () { + return opHandler$1.buffer(this.shape, this.dtype, this.dataSync()); + }; + /** + * Returns the tensor data as a nested array. The transfer of data is done + * asynchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.array = function () { + return __awaiter(this, void 0, void 0, function () { + var vals; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: return [4 /*yield*/, this.data()]; + case 1: + vals = _b.sent(); + return [2 /*return*/, toNestedArray(this.shape, vals, this.dtype === 'complex64')]; + } + }); + }); + }; + /** + * Returns the tensor data as a nested array. The transfer of data is done + * synchronously. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.arraySync = function () { + return toNestedArray(this.shape, this.dataSync(), this.dtype === 'complex64'); + }; + /** + * Asynchronously downloads the values from the `tf.Tensor`. Returns a + * promise of `TypedArray` that resolves when the computation has finished. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.data = function () { + return __awaiter(this, void 0, void 0, function () { + var data, bytes; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + this.throwIfDisposed(); + data = trackerFn().read(this.dataId); + if (!(this.dtype === 'string')) return [3 /*break*/, 2]; + return [4 /*yield*/, data]; + case 1: + bytes = _b.sent(); + try { + return [2 /*return*/, bytes.map(function (b) { return decodeString(b); })]; + } + catch (_a) { + throw new Error('Failed to decode the string bytes into utf-8. ' + + 'To get the original bytes, call tensor.bytes().'); + } + _b.label = 2; + case 2: return [2 /*return*/, data]; + } + }); + }); + }; + /** + * Synchronously downloads the values from the `tf.Tensor`. This blocks the + * UI thread until the values are ready, which can cause performance issues. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.dataSync = function () { + this.throwIfDisposed(); + var data = trackerFn().readSync(this.dataId); + if (this.dtype === 'string') { + try { + return data.map(function (b) { return 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; + }; + /** Returns the underlying bytes of the tensor's data. */ + Tensor.prototype.bytes = function () { + return __awaiter(this, void 0, void 0, function () { + var data; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + this.throwIfDisposed(); + return [4 /*yield*/, trackerFn().read(this.dataId)]; + case 1: + data = _b.sent(); + if (this.dtype === 'string') { + return [2 /*return*/, data]; + } + else { + return [2 /*return*/, new Uint8Array(data.buffer)]; + } + } + }); + }); + }; + /** + * Disposes `tf.Tensor` from memory. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.dispose = function () { + if (this.isDisposed) { + return; + } + trackerFn().disposeTensor(this); + this.isDisposedInternal = true; + }; + Object.defineProperty(Tensor.prototype, "isDisposed", { + get: function () { + return this.isDisposedInternal; + }, + enumerable: true, + configurable: true + }); + Tensor.prototype.throwIfDisposed = function () { + if (this.isDisposed) { + throw new Error("Tensor is disposed."); + } + }; + /** + * Prints the `tf.Tensor`. See `tf.print` for details. + * + * @param verbose Whether to print verbose information about the tensor, + * including dtype and size. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.print = function (verbose) { + if (verbose === void 0) { verbose = false; } + return opHandler$1.print(this, verbose); + }; + /** + * Returns a copy of the tensor. See `tf.clone` for details. + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.clone = function () { + this.throwIfDisposed(); + return opHandler$1.clone(this); + }; + /** + * Returns a human-readable description of the tensor. Useful for logging. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Tensor.prototype.toString = function (verbose) { + if (verbose === void 0) { verbose = false; } + var vals = this.dataSync(); + return tensorToString(vals, this.shape, this.dtype, verbose); + }; + Tensor.prototype.cast = function (dtype) { + this.throwIfDisposed(); + return opHandler$1.cast(this, dtype); + }; + Tensor.prototype.variable = function (trainable, name, dtype) { + if (trainable === void 0) { trainable = true; } + this.throwIfDisposed(); + return trackerFn().makeVariable(this, trainable, name, dtype); + }; + return Tensor; + }()); + Object.defineProperty(Tensor, Symbol.hasInstance, { + value: function (instance) { + // Implementation note: we should use properties of the object that will be + // defined before the constructor body has finished executing (methods). + // This is because when this code is transpiled by babel, babel will call + // classCallCheck before the constructor body is run. + // See https://github.com/tensorflow/tfjs/issues/3384 for backstory. + return !!instance && instance.data != null && instance.dataSync != null && + instance.throwIfDisposed != null; + } + }); + function getGlobalTensorClass() { + // Use getGlobal so that we can augment the Tensor class across package + // boundaries becase the node resolution alg may result in different modules + // being returned for this file depending on the path they are loaded from. + return getGlobal('Tensor', function () { + return Tensor; + }); + } + // Global side effect. Cache global reference to Tensor class + getGlobalTensorClass(); + /** + * A mutable `tf.Tensor`, useful for persisting state, e.g. for training. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + var Variable = /** @class */ (function (_super) { + __extends(Variable, _super); + function Variable(initialValue, trainable, name, tensorId) { + var _this = _super.call(this, initialValue.shape, initialValue.dtype, initialValue.dataId, tensorId) || this; + _this.trainable = trainable; + _this.name = name; + return _this; + } + /** + * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have + * the same shape and dtype as the old `tf.Tensor`. + * + * @param newValue New tensor to be assigned to this variable. + * + * @doc {heading: 'Tensors', subheading: 'Classes'} + */ + Variable.prototype.assign = function (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 /* backend */); + }; + Variable.prototype.dispose = function () { + trackerFn().disposeVariable(this); + this.isDisposedInternal = true; + }; + return Variable; + }(Tensor)); + Object.defineProperty(Variable, Symbol.hasInstance, { + value: function (instance) { + return instance instanceof Tensor && instance.assign != null && + instance.assign instanceof Function; + } + }); + + /** + * @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. + * ============================================================================= + */ + exports.Rank = void 0; + (function (Rank) { + Rank["R0"] = "R0"; + Rank["R1"] = "R1"; + Rank["R2"] = "R2"; + Rank["R3"] = "R3"; + Rank["R4"] = "R4"; + Rank["R5"] = "R5"; + Rank["R6"] = "R6"; + })(exports.Rank || (exports.Rank = {})); + // Looks for upcasting types. Used, for example, in operations with mixed dtype + // inputs. + var UpcastInt32AndMap; + (function (UpcastInt32AndMap) { + UpcastInt32AndMap["float32"] = "float32"; + UpcastInt32AndMap["int32"] = "int32"; + UpcastInt32AndMap["bool"] = "int32"; + UpcastInt32AndMap["complex64"] = "complex64"; + })(UpcastInt32AndMap || (UpcastInt32AndMap = {})); + var UpcastBoolAndMap; + (function (UpcastBoolAndMap) { + UpcastBoolAndMap["float32"] = "float32"; + UpcastBoolAndMap["int32"] = "int32"; + UpcastBoolAndMap["bool"] = "bool"; + UpcastBoolAndMap["complex64"] = "complex64"; + })(UpcastBoolAndMap || (UpcastBoolAndMap = {})); + var UpcastFloat32AndMap; + (function (UpcastFloat32AndMap) { + UpcastFloat32AndMap["float32"] = "float32"; + UpcastFloat32AndMap["int32"] = "float32"; + UpcastFloat32AndMap["bool"] = "float32"; + UpcastFloat32AndMap["complex64"] = "complex64"; + })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {})); + var UpcastComplex64AndMap; + (function (UpcastComplex64AndMap) { + UpcastComplex64AndMap["float32"] = "complex64"; + UpcastComplex64AndMap["int32"] = "complex64"; + UpcastComplex64AndMap["bool"] = "complex64"; + UpcastComplex64AndMap["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]; + } + /** Returns the output type after summation. */ + function sumOutType(type) { + return upcastType(type, 'int32'); + } + + /** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function makeTypesMatch(a, b) { + if (a.dtype === b.dtype) { + return [a, b]; + } + var dtype = upcastType(a.dtype, b.dtype); + return [a.cast(dtype), b.cast(dtype)]; + } + function assertTypesMatch(a, b) { + assert(a.dtype === b.dtype, function () { return "The dtypes of the first(" + a.dtype + ") and" + + (" second(" + b.dtype + ") input must match"); }); + } + function isTensorInList(tensor, tensorList) { + return tensorList.some(function (x) { return x.id === tensor.id; }); + } + /** + * Extracts any `Tensor`s found within the provided object. + * + * @param container an object that may be a `Tensor` or may directly contain + * `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it + * is safe to pass any object here, except that `Promise`s are not + * supported. + * @returns An array of `Tensors` found within the passed object. If the + * argument is simply a `Tensor', a list containing that `Tensor` is + * returned. If the object is not a `Tensor` or does not + * contain `Tensors`, an empty list is returned. + */ + function getTensorsInContainer(result) { + var list = []; + var seen = 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; + } + // Iteration over keys works also for arrays. + var iterable = container; + for (var k in iterable) { + var val = iterable[k]; + if (!seen.has(val)) { + seen.add(val); + walkTensorContainer(val, list, seen); + } + } + } + // tslint:disable-next-line:no-any + function isIterable(obj) { + return Array.isArray(obj) || typeof obj === 'object'; + } + + var tensor_util = { + __proto__: null, + makeTypesMatch: makeTypesMatch, + assertTypesMatch: assertTypesMatch, + isTensorInList: isTensorInList, + getTensorsInContainer: getTensorsInContainer + }; + + function isRegisteredKernelInvocation(kernelInvocation) { + return kernelInvocation.kernelName != null; + } + var EngineState = /** @class */ (function () { + function EngineState() { + // Public since optimizers will use it. + this.registeredVariables = {}; + this.nextTapeNodeId = 0; + this.numBytes = 0; + this.numTensors = 0; + this.numStringTensors = 0; + this.numDataBuffers = 0; + // Number of nested tf.grad() statements when computing higher-order + // gradients. E.g. `1` for first-order gradients and `2` for second-order + // gradients. Used to track if the tape should be removed after a backprop. + this.gradientDepth = 0; + // Number of nested kernel calls. When kernel depth is greater than 1, we turn + // off the tape. + this.kernelDepth = 0; + this.scopeStack = []; + /** + * Keeps track of the number of data moves during a kernel execution. We + * maintain a stack since kernels can call other kernels, recursively. + */ + this.numDataMovesStack = []; + this.nextScopeId = 0; + this.tensorInfo = 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(function (k) { return k.name; }))); + } + }; + } + EngineState.prototype.dispose = function () { + for (var variableName in this.registeredVariables) { + this.registeredVariables[variableName].dispose(); + } + }; + return EngineState; + }()); + var Engine = /** @class */ (function () { + function Engine(ENV) { + this.ENV = ENV; + this.registry = {}; + this.registryFactory = {}; + this.pendingBackendInitId = 0; + this.state = new EngineState(); + } + Engine.prototype.ready = function () { + return __awaiter(this, void 0, void 0, function () { + var sortedBackends, i, backendName, success; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + if (this.pendingBackendInit != null) { + return [2 /*return*/, this.pendingBackendInit.then(function () { })]; + } + if (this.backendInstance != null) { + return [2 /*return*/]; + } + sortedBackends = this.getSortedBackends(); + i = 0; + _a.label = 1; + case 1: + if (!(i < sortedBackends.length)) return [3 /*break*/, 5]; + backendName = sortedBackends[i]; + return [4 /*yield*/, this.initializeBackend(backendName).success]; + case 2: + success = _a.sent(); + if (!success) return [3 /*break*/, 4]; + return [4 /*yield*/, this.setBackend(backendName)]; + case 3: + _a.sent(); + return [2 /*return*/]; + case 4: + i++; + return [3 /*break*/, 1]; + case 5: throw new Error("Could not initialize any backends, all backend initializations " + + "failed."); + } + }); + }); + }; + Object.defineProperty(Engine.prototype, "backend", { + get: function () { + 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) { + var _a = this.initializeBackendsAndReturnBest(), name = _a.name, asyncInit = _a.asyncInit; + 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; + }, + enumerable: true, + configurable: true + }); + Engine.prototype.backendNames = function () { + return Object.keys(this.registryFactory); + }; + Engine.prototype.findBackend = function (backendName) { + if (!(backendName in this.registry)) { + // If the backend hasn't been initialized but we have a registry entry for + // it, initialize it and return it. + if (backendName in this.registryFactory) { + var asyncInit = this.initializeBackend(backendName).asyncInit; + if (asyncInit) { + // Backend is not ready yet. + return null; + } + } + else { + return null; + } + } + return this.registry[backendName]; + }; + Engine.prototype.findBackendFactory = function (backendName) { + if (!(backendName in this.registryFactory)) { + return null; + } + return this.registryFactory[backendName].factory; + }; + Engine.prototype.registerBackend = function (backendName, factory, priority) { + if (priority === void 0) { priority = 1; } + if (backendName in this.registryFactory) { + warn(backendName + " backend was already registered. " + + "Reusing existing backend factory."); + return false; + } + this.registryFactory[backendName] = { factory: factory, priority: priority }; + return true; + }; + Engine.prototype.setBackend = function (backendName) { + return __awaiter(this, void 0, void 0, function () { + var _a, success, asyncInit, result, _b; + return __generator(this, function (_c) { + switch (_c.label) { + case 0: + if (this.registryFactory[backendName] == null) { + throw new Error("Backend name '" + backendName + "' not found in registry"); + } + this.backendName = backendName; + if (!(this.registry[backendName] == null)) return [3 /*break*/, 4]; + this.backendInstance = null; + _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit; + if (!asyncInit) return [3 /*break*/, 2]; + return [4 /*yield*/, success]; + case 1: + _b = _c.sent(); + return [3 /*break*/, 3]; + case 2: + _b = success; + _c.label = 3; + case 3: + result = _b; + if (!result) { + return [2 /*return*/, false]; + } + _c.label = 4; + case 4: + this.backendInstance = this.registry[backendName]; + this.setupRegisteredKernels(); + // Reset the profiler. + this.profiler = new Profiler(this.backendInstance); + return [2 /*return*/, true]; + } + }); + }); + }; + Engine.prototype.setupRegisteredKernels = function () { + var _this = this; + var kernels = getKernelsForBackend(this.backendName); + kernels.forEach(function (kernel) { + if (kernel.setupFunc != null) { + kernel.setupFunc(_this.backendInstance); + } + }); + }; + Engine.prototype.disposeRegisteredKernels = function (backendName) { + var _this = this; + var kernels = getKernelsForBackend(backendName); + kernels.forEach(function (kernel) { + if (kernel.disposeFunc != null) { + kernel.disposeFunc(_this.registry[backendName]); + } + }); + }; + /** + * Initializes a backend by looking up the backend name in the factory + * registry and calling the factory method. Returns a boolean representing + * whether the initialization of the backend suceeded. Throws an error if + * there is no backend in the factory registry. + */ + Engine.prototype.initializeBackend = function (backendName) { + var _this = this; + var registryFactoryEntry = this.registryFactory[backendName]; + if (registryFactoryEntry == null) { + throw new Error("Cannot initialize backend " + backendName + ", no registration found."); + } + try { + var backend = registryFactoryEntry.factory(); + /* Test if the factory returns a promise. + Done in a more liberal way than + previous 'Promise.resolve(backend)===backend' + as we needed to account for custom Promise + implementations (e.g. Angular) */ + if (backend && !(backend instanceof KernelBackend) && + typeof backend.then === 'function') { + var promiseId_1 = ++this.pendingBackendInitId; + var success = backend + .then(function (backendInstance) { + // Outdated promise. Another backend was set in the meantime. + if (promiseId_1 < _this.pendingBackendInitId) { + return false; + } + _this.registry[backendName] = backendInstance; + _this.pendingBackendInit = null; + return true; + }) + .catch(function (err) { + // Outdated promise. Another backend was set in the meantime. + if (promiseId_1 < _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: success, asyncInit: true }; + } + else { + this.registry[backendName] = backend; + return { success: true, asyncInit: false }; + } + } + catch (err) { + warn("Initialization of backend " + backendName + " failed"); + warn(err.stack || err.message); + return { success: false, asyncInit: false }; + } + }; + Engine.prototype.removeBackend = function (backendName) { + if (!(backendName in this.registryFactory)) { + throw new Error(backendName + " backend not found in registry"); + } + if (this.backendName === backendName && this.pendingBackendInit != null) { + // There is a pending promise of the backend we want to remove. Make it + // obsolete. + this.pendingBackendInitId++; + } + if (backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + delete this.registryFactory[backendName]; + // Unset the backend if it is active. + if (this.backendName === backendName) { + this.pendingBackendInit = null; + this.backendName = null; + this.backendInstance = null; + } + }; + Engine.prototype.getSortedBackends = function () { + var _this = this; + if (Object.keys(this.registryFactory).length === 0) { + throw new Error('No backend found in registry.'); + } + return Object.keys(this.registryFactory).sort(function (a, b) { + // Highest priority comes first. + return _this.registryFactory[b].priority - + _this.registryFactory[a].priority; + }); + }; + Engine.prototype.initializeBackendsAndReturnBest = function () { + var sortedBackends = this.getSortedBackends(); + for (var i = 0; i < sortedBackends.length; i++) { + var backendName = sortedBackends[i]; + var _a = this.initializeBackend(backendName), success = _a.success, asyncInit = _a.asyncInit; + if (asyncInit || success) { + return { name: backendName, asyncInit: asyncInit }; + } + } + throw new Error("Could not initialize any backends, all backend initializations " + + "failed."); + }; + Engine.prototype.moveData = function (backend, dataId) { + var info = this.state.tensorInfo.get(dataId); + var srcBackend = info.backend; + var values = this.readSync(dataId); + var refCount = srcBackend.refCount(dataId); + // Delete the tensor from the old backend and move it to the new + // backend. + srcBackend.disposeData(dataId, true); + info.backend = backend; + backend.move(dataId, values, info.shape, info.dtype, refCount); + if (this.shouldCheckForMemLeaks()) { + // Track the number of moves during a kernel execution to correctly + // detect memory leaks. + this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]++; + } + }; + Engine.prototype.tidy = function (nameOrFn, fn) { + var _this = this; + var name = null; + if (fn == null) { + // Called with only 1 argument. + if (typeof nameOrFn !== 'function') { + throw new Error('Please provide a function to tidy()'); + } + fn = nameOrFn; + } + else { + // Called with 2 arguments. + 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; + // TODO(nsthorat,smilkov): Do operation logging and performance + // profiling. + } + var result; + return this.scopedRun(function () { return _this.startScope(name); }, function () { return _this.endScope(result); }, function () { + result = fn(); + if (result instanceof Promise) { + console.error('Cannot return a Promise inside of tidy.'); + } + return result; + }); + }; + Engine.prototype.scopedRun = function (start, end, f) { + start(); + try { + var res = f(); + end(); + return res; + } + catch (ex) { + end(); + throw ex; + } + }; + Engine.prototype.nextTensorId = function () { + return Engine.nextTensorId++; + }; + Engine.prototype.nextVariableId = function () { + return Engine.nextVariableId++; + }; + /** + * This method is called instead of the public-facing tensor.clone() when + * saving a tensor for backwards pass. It makes sure to add the clone + * operation to the tape regardless of being called inside a kernel + * execution. + */ + Engine.prototype.clone = function (x) { + var y = ENGINE.runKernel(Identity, { x: x }); + var inputs = { x: x }; + var grad = function (dy) { return ({ + x: function () { + var dtype = 'float32'; + var gradInputs = { x: dy }; + var attrs = { dtype: dtype }; + return ENGINE.runKernel(Cast, gradInputs, + // tslint:disable-next-line: no-unnecessary-type-assertion + attrs); + } + }); }; + var saved = []; + this.addTapeNode(this.state.activeScope.name, inputs, [y], grad, saved, {}); + return y; + }; + /** + * Execute a kernel with the given name and return the output tensor. + * + * @param kernelName The name of the kernel to execute. + * @param inputs A map of input names to tensors. + * @param attrs A map of attribute names to their values. An attribute is a + * primitive (non-tensor) input to the kernel. + * @param inputsToSave A list of tensors, inputs to save for the backprop + * computation. + * @param outputsToSave A list of booleans, specifying which output to save + * for the backprop computation. These are booleans since the output + * tensors are not visible to the user. + */ + Engine.prototype.runKernel = function (kernelName, inputs, attrs) { + if (this.backendName == null) { + // backend has not been initialized yet (backend initialization is lazy + // can be deferred until an op/ kernel is run). + // The below getter has side effects that will try to initialize the + // backend and set properties like this.backendName + // tslint:disable-next-line: no-unused-expression + this.backend; + } + var hasKernel = getKernel(kernelName, this.backendName) != null; + if (!hasKernel) { + throw new Error("Kernel '" + kernelName + "' not registered for backend '" + this.backendName + "'"); + } + return this.runKernelFunc({ kernelName: kernelName, inputs: inputs, attrs: attrs }); + }; + Engine.prototype.shouldCheckForMemLeaks = function () { + return this.ENV.getBool('IS_TEST'); + }; + Engine.prototype.checkKernelForMemLeak = function (kernelName, numDataIdsBefore, outInfos) { + var numDataIdsAfter = this.backend.numDataIds(); + // Count the number of data ids associated with the result of the kernel. + var numOutputDataIds = 0; + outInfos.forEach(function (info) { + // Complex numbers allocate 3 data ids, one for 'real', one for + // 'imaginary', and one for the container that holds the former two. + numOutputDataIds += (info.dtype === 'complex64' ? 3 : 1); + }); + // Account for the number of moves during kernel execution. A "data move" + // can happen in the middle of a kernel execution, placing a new (key,value) + // pair in the data storage. Since data moves have net zero effect (we + // always remove the data from the old backend), we have to cancel them out + // when detecting memory leaks. + var numMoves = this.state.numDataMovesStack[this.state.numDataMovesStack.length - 1]; + var 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 + "'")); + } + }; + /** + * Internal helper method to execute a kernel Func + * + * Use `runKernel` to execute kernels from outside of engine. + */ + Engine.prototype.runKernelFunc = function (kernelParams) { + var _this = this; + var outputs; + var saved = []; + var isTapeOn = this.isTapeOn(); + var startingBytecount = this.state.numBytes; + var startingNumTensors = this.state.numTensors; + if (this.shouldCheckForMemLeaks()) { + this.state.numDataMovesStack.push(0); + } + var kernelFunc; + if (this.backendName == null) { + // backend has not been initialized yet (backend initialization is lazy + // can be deferred until an op/ kernel is run). + // The below getter has side effects that will try to initialize the + // backend and set properties like this.backendName + // tslint:disable-next-line: no-unused-expression + this.backend; + } + var out; + var kernelOrScopeName = isRegisteredKernelInvocation(kernelParams) ? + kernelParams.kernelName : + this.state.activeScope != null ? this.state.activeScope.name : ''; + // Create the kernelFunc from either a registered kernel OR passed in + // forward/backward functions (used by custom grad). In this context a + // kernelFunc wraps a kernel implementation with some bookkeeping. + if (isRegisteredKernelInvocation(kernelParams)) { + var kernelName_1 = kernelParams.kernelName, inputs_1 = kernelParams.inputs, attrs_1 = kernelParams.attrs; + if (this.backendName == null) { + // backend has not been initialized yet (backend initialization is lazy + // can be deferred until an op/ kernel is run). + // The below getter has side effects that will try to initialize the + // backend and set properties like this.backendName + // tslint:disable-next-line: no-unused-expression + this.backend; + } + var kernel_1 = getKernel(kernelName_1, this.backendName); + assert(kernel_1 != null, function () { return "Cannot find registered kernel '" + kernelName_1 + "' for backend '" + _this.backendName + "'"; }); + kernelFunc = function () { + var numDataIdsBefore = _this.backend.numDataIds(); + out = kernel_1.kernelFunc({ inputs: inputs_1, attrs: attrs_1, backend: _this.backend }); + var outInfos = Array.isArray(out) ? out : [out]; + if (_this.shouldCheckForMemLeaks()) { + _this.checkKernelForMemLeak(kernelName_1, numDataIdsBefore, outInfos); + } + var outTensors = outInfos.map(function (outInfo) { + // todo (yassogba) remove this option (Tensor) when node backend + // methods have been modularized and they all return tensorInfo. + // TensorInfos do not have a rank attribute. + if (outInfo.rank != null) { + return outInfo; + } + var dataId = outInfo.dataId, shape = outInfo.shape, dtype = outInfo.dtype; + return _this.makeTensorFromDataId(dataId, shape, dtype); + }); + // Save any required inputs and outputs. + // Do not save unless we are recording to the tape. Otherwise it would + // cause a mem leak since there would be no backprop for these tensors + // (which would otherwise dispose them). + if (isTapeOn) { + var tensorsToSave = _this.getTensorsForGradient(kernelName_1, inputs_1, outTensors); + saved = _this.saveTensorsForBackwardMode(tensorsToSave); + } + return outTensors; + }; + } + else { + var forwardFunc_1 = kernelParams.forwardFunc; + // Running a customGrad op. + var saveFunc_1 = function (tensors) { + // Do not save unless we are recording to the tape. Otherwise it would + // cause a mem leak since we would never run backprop, which disposes + // the kept tensors. + if (!isTapeOn) { + return; + } + saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); }); + }; + kernelFunc = function () { + var numDataIdsBefore = _this.backend.numDataIds(); + out = _this.tidy(function () { return forwardFunc_1(_this.backend, saveFunc_1); }); + var outs = (Array.isArray(out) ? out : [out]); + if (_this.shouldCheckForMemLeaks()) { + // Scope name is used to print a more helpful error message if needed. + _this.checkKernelForMemLeak(kernelOrScopeName, numDataIdsBefore, outs); + } + return outs; + }; + } + // + // Run the kernelFunc. Optionally profiling it. + // + var inputs = kernelParams.inputs, attrs = kernelParams.attrs; + var backwardsFunc = isRegisteredKernelInvocation(kernelParams) ? + null : + kernelParams.backwardsFunc; + var kernelProfile; + this.scopedRun( + // Stop recording to a tape when running a kernel. + function () { return _this.state.kernelDepth++; }, function () { return _this.state.kernelDepth--; }, function () { + if (!_this.ENV.getBool('DEBUG') && !_this.state.profiling) { + outputs = kernelFunc(); + } + else { + kernelProfile = _this.profiler.profileKernel(kernelOrScopeName, inputs, function () { return 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(function (key) { return inputs[key] != null ? inputs[key].shape : null; }), + outputShapes: outputs.map(function (item) { return item.shape; }), + kernelTimeMs: kernelProfile.timeMs, + extraInfo: kernelProfile.extraInfo + }); + } + return (Array.isArray(out) ? outputs : outputs[0]); + }; + /** + * Saves tensors used in forward mode for use in backward mode. + * + * @param tensors the list of tensors to save. + */ + Engine.prototype.saveTensorsForBackwardMode = function (tensors) { + var _this = this; + var saved = tensors.map(function (tensor) { return _this.keep(_this.clone(tensor)); }); + return saved; + }; + /** + * Returns a list of tensors to save for a given gradient calculation. + * + * @param kernelName name of kernel to look up gradient for. + * @param inputs a map of input tensors. + * @param outputs an array of output tensors from forward mode of kernel. + */ + Engine.prototype.getTensorsForGradient = function (kernelName, inputs, outputs) { + var gradConfig = getGradient(kernelName); + if (gradConfig != null) { + var inputsToSave = gradConfig.inputsToSave || []; + var outputsToSave_1 = gradConfig.outputsToSave || []; + // If saveAllInputs is true, all inputs will be saved. Otherwise, inputs + // specified in inputsToSave will be saved. + var inputTensorsToSave = void 0; + if (gradConfig.saveAllInputs) { + assert(Array.isArray(inputs), function () { return 'saveAllInputs is true, expected inputs to be an array.'; }); + inputTensorsToSave = Object.keys(inputs).map(function (key) { return inputs[key]; }); + } + else { + inputTensorsToSave = inputsToSave.map(function (inputName) { return inputs[inputName]; }); + } + var outputTensorsToSave = outputs.filter(function (_, i) { return outputsToSave_1[i]; }); + return inputTensorsToSave.concat(outputTensorsToSave); + } + // We return an empty list rather than throw an error because the kernel we + // are looking up may not actually be relevant to backproping through the + // overall function + // + // See 'does not error if irrelevant (pruned) ops are missing grads' test + // in gradients_test.ts for an example. + return []; + }; + /** + * Internal method used by public APIs for tensor creation. Makes a new + * tensor with the provided shape, dtype and values. It always + * creates a new data id and writes the values to the underlying backend. + */ + Engine.prototype.makeTensor = function (values, shape, dtype, backend) { + if (values == null) { + throw new Error('Values passed to engine.makeTensor() are null'); + } + dtype = dtype || 'float32'; + backend = backend || this.backend; + var backendVals = values; + if (dtype === 'string' && isString(values[0])) { + backendVals = values.map(function (d) { return encodeString(d); }); + } + var dataId = backend.write(backendVals, shape, dtype); + var t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend); + // Count bytes for string tensors. + if (dtype === 'string') { + var info = this.state.tensorInfo.get(dataId); + var newBytes = bytesFromStringArray(backendVals); + this.state.numBytes += newBytes - info.bytes; + info.bytes = newBytes; + } + return t; + }; + /** + * Internal method used by backends. Makes a new tensor + * that is a wrapper around an existing data id. It doesn't create + * a new data id, only increments the ref count used in memory tracking. + */ + Engine.prototype.makeTensorFromDataId = function (dataId, shape, dtype, backend) { + dtype = dtype || 'float32'; + var t = new Tensor(shape, dtype, dataId, this.nextTensorId()); + this.trackTensor(t, backend); + return t; + }; + Engine.prototype.makeVariable = function (initialValue, trainable, name, dtype) { + if (trainable === void 0) { trainable = true; } + name = name || this.nextVariableId().toString(); + if (dtype != null && dtype !== initialValue.dtype) { + initialValue = initialValue.cast(dtype); + } + var 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; + }; + Engine.prototype.trackTensor = function (a, backend) { + this.state.numTensors++; + if (a.dtype === 'string') { + this.state.numStringTensors++; + } + // Bytes for complex numbers are counted by their components. Bytes for + // string tensors are counted when writing values. + var 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: backend || this.backend, + dtype: a.dtype, + shape: a.shape, + bytes: bytes + }); + } + if (!(a instanceof Variable)) { + this.track(a); + } + }; + // Track the tensor by dataId and increase the refCount for the dataId in the + // backend. + // TODO(pyu10055): This is currently used by makeVariable method, to increase + // refCount on the backend for the dataId. It can potentially be replaced with + // Identity op indead of calling backend directly. + Engine.prototype.incRef = function (a, backend) { + this.trackTensor(a, backend); + this.backend.incRef(a.dataId); + }; + Engine.prototype.removeDataId = function (dataId, backend) { + if (this.state.tensorInfo.has(dataId) && + this.state.tensorInfo.get(dataId).backend === backend) { + this.state.tensorInfo.delete(dataId); + this.state.numDataBuffers--; + } + }; + Engine.prototype.disposeTensor = function (a) { + if (!this.state.tensorInfo.has(a.dataId)) { + return; + } + var info = this.state.tensorInfo.get(a.dataId); + this.state.numTensors--; + if (a.dtype === 'string') { + this.state.numStringTensors--; + this.state.numBytes -= info.bytes; + } + // Don't count bytes for complex numbers as they are counted by their + // components. + if (a.dtype !== 'complex64' && a.dtype !== 'string') { + var bytes = a.size * bytesPerElement(a.dtype); + this.state.numBytes -= bytes; + } + // Remove the reference to dataId if backend dispose the data successfully + if (info.backend.disposeData(a.dataId)) { + this.removeDataId(a.dataId, info.backend); + } + // TODO(nsthorat): Construct an error and save the stack trace for + // debugging when in debug mode. Creating a stack trace is too expensive + // to do unconditionally. + }; + Engine.prototype.disposeVariables = function () { + for (var varName in this.state.registeredVariables) { + var v = this.state.registeredVariables[varName]; + this.disposeVariable(v); + } + }; + Engine.prototype.disposeVariable = function (v) { + this.disposeTensor(v); + if (this.state.registeredVariables[v.name] != null) { + delete this.state.registeredVariables[v.name]; + } + }; + Engine.prototype.memory = function () { + var 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; + }; + Engine.prototype.profile = function (query) { + return __awaiter(this, void 0, void 0, function () { + var startBytes, startNumTensors, _a, _b, _c, kernel, _d, _e, e_1_1; + var e_1, _f; + return __generator(this, function (_g) { + switch (_g.label) { + case 0: + this.state.profiling = true; + startBytes = this.state.numBytes; + startNumTensors = this.state.numTensors; + this.state.activeProfile.kernels = []; + _a = this.state.activeProfile; + return [4 /*yield*/, query()]; + case 1: + _a.result = _g.sent(); + this.state.profiling = false; + this.state.activeProfile.peakBytes = Math.max.apply(Math, __spread(this.state.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }))); + this.state.activeProfile.newBytes = this.state.numBytes - startBytes; + this.state.activeProfile.newTensors = + this.state.numTensors - startNumTensors; + _g.label = 2; + case 2: + _g.trys.push([2, 8, 9, 10]); + _b = __values(this.state.activeProfile.kernels), _c = _b.next(); + _g.label = 3; + case 3: + if (!!_c.done) return [3 /*break*/, 7]; + kernel = _c.value; + _d = kernel; + return [4 /*yield*/, kernel.kernelTimeMs]; + case 4: + _d.kernelTimeMs = _g.sent(); + _e = kernel; + return [4 /*yield*/, kernel.extraInfo]; + case 5: + _e.extraInfo = _g.sent(); + _g.label = 6; + case 6: + _c = _b.next(); + return [3 /*break*/, 3]; + case 7: return [3 /*break*/, 10]; + case 8: + e_1_1 = _g.sent(); + e_1 = { error: e_1_1 }; + return [3 /*break*/, 10]; + case 9: + try { + if (_c && !_c.done && (_f = _b.return)) _f.call(_b); + } + finally { if (e_1) throw e_1.error; } + return [7 /*endfinally*/]; + case 10: return [2 /*return*/, this.state.activeProfile]; + } + }); + }); + }; + Engine.prototype.isTapeOn = function () { + return this.state.gradientDepth > 0 && this.state.kernelDepth === 0; + }; + Engine.prototype.addTapeNode = function (kernelName, inputs, outputs, gradientsFunc, saved, attrs) { + var _this = this; + var tapeNode = { id: this.state.nextTapeNodeId++, kernelName: kernelName, inputs: inputs, outputs: outputs, saved: saved }; + var gradConfig = getGradient(kernelName); + if (gradConfig != null) { + gradientsFunc = gradConfig.gradFunc; + } + if (gradientsFunc != null) { + tapeNode.gradient = function (dys) { + // TODO(smilkov): To optimize back-prop, pass dys that are not used in + // the backprop graph to the user as null instead of zeros + dys = dys.map(function (dy, i) { + if (dy == null) { + var output = outputs[i]; + var vals = makeZerosTypedArray(output.size, output.dtype); + return _this.makeTensor(vals, output.shape, output.dtype); + } + return dy; + }); + // Grad functions of ops with single outputs expect a dy, while ops + // with multiple outputs expect dys (array of dy). + return gradientsFunc(dys.length > 1 ? dys : dys[0], saved, attrs); + }; + } + this.state.activeTape.push(tapeNode); + }; + Engine.prototype.keep = function (result) { + result.kept = true; + return result; + }; + Engine.prototype.startTape = function () { + if (this.state.gradientDepth === 0) { + this.state.activeTape = []; + } + this.state.gradientDepth++; + }; + Engine.prototype.endTape = function () { + this.state.gradientDepth--; + }; + /** + * Start a scope. Use this with endScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + Engine.prototype.startScope = function (name) { + var scopeInfo = { + track: [], + name: 'unnamed scope', + id: this.state.nextScopeId++ + }; + if (name) { + scopeInfo.name = name; + } + this.state.scopeStack.push(scopeInfo); + this.state.activeScope = scopeInfo; + }; + /** + * End a scope. Use this with startScope() to achieve the same functionality + * as scope() without the need for a function closure. + */ + Engine.prototype.endScope = function (result) { + var _this = this; + var tensorsToTrackInParent = getTensorsInContainer(result); + var tensorsToTrackInParentSet = new Set(tensorsToTrackInParent.map(function (t) { return t.id; })); + // Dispose the arrays tracked in this scope. + for (var i = 0; i < this.state.activeScope.track.length; i++) { + var tensor = this.state.activeScope.track[i]; + if (!tensor.kept && !tensorsToTrackInParentSet.has(tensor.id)) { + tensor.dispose(); + } + } + var oldScope = this.state.scopeStack.pop(); + this.state.activeScope = this.state.scopeStack.length === 0 ? + null : + this.state.scopeStack[this.state.scopeStack.length - 1]; + // Track the current result in the parent scope. + tensorsToTrackInParent.forEach(function (tensor) { + // Only track the tensor if was allocated in the inner scope and is not + // globally kept. + if (!tensor.kept && tensor.scopeId === oldScope.id) { + _this.track(tensor); + } + }); + }; + /** + * Returns gradients of `f` with respect to each of the `xs`. The gradients + * returned are of the same length as `xs`, but some might be null if `f` + * was not a function of that `x`. It also takes optional dy to multiply the + * gradient, which defaults to `1`. + */ + Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) { + var _this = this; + if (allowNoGradients === void 0) { allowNoGradients = false; } + assert(xs.length > 0, function () { return '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 + "'"); + } + var y = this.scopedRun(function () { return _this.startTape(); }, function () { return _this.endTape(); }, function () { return _this.tidy('forward', f); }); + assert(y instanceof Tensor, function () { return 'The result y returned by f() must be a tensor.'; }); + // Filter out the nodes that don't connect x => y. + var 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', function () { + var accumulatedGradientMap = {}; + accumulatedGradientMap[y.id] = (dy == null) ? ones$1(y.shape) : dy; + // Backprop gradients through the filtered nodes. + backpropagateGradients(accumulatedGradientMap, filteredTape, + // Pass the tidy function to avoid circular dep with `tape.ts`. + function (f) { return _this.tidy(f); }, + // Pass an add function to avoide a circular dep with `tape.ts`. + add$1); + var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; }); + if (_this.state.gradientDepth === 0) { + // This means that we are not computing higher-order gradients + // and can clean up the tape. + _this.state.activeTape.forEach(function (node) { + var e_2, _a; + try { + for (var _b = __values(node.saved), _c = _b.next(); !_c.done; _c = _b.next()) { + var tensor = _c.value; + tensor.dispose(); + } + } + catch (e_2_1) { e_2 = { error: e_2_1 }; } + finally { + try { + if (_c && !_c.done && (_a = _b.return)) _a.call(_b); + } + finally { if (e_2) throw e_2.error; } + } + }); + _this.state.activeTape = null; + } + return { value: y, grads: grads }; + }); + }; + Engine.prototype.customGrad = function (f) { + var _this = this; + assert(isFunction(f), function () { return 'The f passed in customGrad(f) must be a function.'; }); + return function () { + var inputs = []; + for (var _i = 0; _i < arguments.length; _i++) { + inputs[_i] = arguments[_i]; + } + assert(inputs.every(function (t) { return t instanceof Tensor; }), function () { return 'The args passed in customGrad(f)(x1, x2,...) must all be ' + + 'tensors'; }); + var res; + var inputMap = {}; + inputs.forEach(function (input, i) { + inputMap[i] = input; + }); + var forwardFunc = function (_, save) { + res = f.apply(void 0, __spread(inputs, [save])); + assert(res.value instanceof Tensor, function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.value` is a tensor'; }); + assert(isFunction(res.gradFunc), function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.gradFunc` is a function.'; }); + return res.value; + }; + var backwardsFunc = function (dy, saved) { + var gradRes = res.gradFunc(dy, saved); + var grads = Array.isArray(gradRes) ? gradRes : [gradRes]; + assert(grads.length === inputs.length, function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.gradFunc` is a function that returns ' + + 'the same number of tensors as inputs passed to f(...).'; }); + assert(grads.every(function (t) { return t instanceof Tensor; }), function () { return 'The function f passed in customGrad(f) must return an ' + + 'object where `obj.gradFunc` is a function that returns ' + + 'a list of only tensors.'; }); + var gradMap = {}; + grads.forEach(function (grad, i) { + gradMap[i] = function () { return grad; }; + }); + return gradMap; + }; + return _this.runKernelFunc({ + forwardFunc: forwardFunc, + backwardsFunc: backwardsFunc, + inputs: inputMap, + }); + }; + }; + Engine.prototype.readSync = function (dataId) { + // Route the read to the correct backend. + var info = this.state.tensorInfo.get(dataId); + return info.backend.readSync(dataId); + }; + Engine.prototype.read = function (dataId) { + // Route the read to the correct backend. + var info = this.state.tensorInfo.get(dataId); + return info.backend.read(dataId); + }; + Engine.prototype.time = function (query) { + return __awaiter(this, void 0, void 0, function () { + var start, timingInfo; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + start = now(); + return [4 /*yield*/, this.backend.time(query)]; + case 1: + timingInfo = _a.sent(); + timingInfo.wallMs = now() - start; + return [2 /*return*/, timingInfo]; + } + }); + }); + }; + /** + * Tracks a Tensor in the current scope to be automatically cleaned up + * when the current scope ends, and returns the value. + * + * @param result The Tensor to track in the current scope. + */ + Engine.prototype.track = function (result) { + if (this.state.activeScope != null) { + result.scopeId = this.state.activeScope.id; + this.state.activeScope.track.push(result); + } + return result; + }; + Object.defineProperty(Engine.prototype, "registeredVariables", { + get: function () { + return this.state.registeredVariables; + }, + enumerable: true, + configurable: true + }); + /** + * Resets the engine state. Removes all backends but does not remove + * registered backend factories. + */ + Engine.prototype.reset = function () { + // Make any pending promise obsolete. + this.pendingBackendInitId++; + this.state.dispose(); + this.ENV.reset(); + this.state = new EngineState(); + for (var backendName in this.registry) { + this.disposeRegisteredKernels(backendName); + this.registry[backendName].dispose(); + delete this.registry[backendName]; + } + this.backendName = null; + this.backendInstance = null; + this.pendingBackendInit = null; + }; + return Engine; + }()); + Engine.nextTensorId = 0; + Engine.nextVariableId = 0; + function ones$1(shape) { + var values = makeOnesTypedArray(sizeFromShape(shape), 'float32'); + return ENGINE.makeTensor(values, shape, 'float32'); + } + function getOrMakeEngine() { + var ns = getGlobalNamespace(); + if (ns._tfengine == null) { + var environment = new Environment(ns); + ns._tfengine = new Engine(environment); + } + setEnvironmentGlobal(ns._tfengine.ENV); + // Tell the current tensor interface that the global engine is responsible + // for tracking. + setTensorTracker(function () { return ns._tfengine; }); + return ns._tfengine; + } + var ENGINE = getOrMakeEngine(); + /** + * A implementation of the add op for use within engine and tape. + * + * This allows us to avoid a circular dependency between add.ts and engine. + * It is exported to be available in tape tests. + */ + function add$1(a, b) { + // We duplicate Add here to avoid a circular dependency with add.ts. + var inputs = { a: a, b: b }; + return ENGINE.runKernel(Add, inputs); + } + + /** + * @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. + * ============================================================================= + */ + // tslint:disable-next-line:no-any + function _isNavigatorDefined() { + return typeof navigator !== 'undefined' && navigator != null; + } + var isMobileMockValue; + function mockIsMobile(value) { + isMobileMockValue = value; + } + function isMobile(nav) { + if (isMobileMockValue !== undefined) { + return isMobileMockValue; + } + if (nav || _isNavigatorDefined()) { + if (!nav) { + nav = navigator; + } + if (nav.product === 'ReactNative') { + return true; + } + var a = nav.userAgent || nav.vendor || + // tslint:disable-next-line:no-any + (typeof window !== 'undefined' ? window.opera : ''); + // Use `navigator.userAgentData.mobile` as fallback. + if (!a) { + // tslint:disable-next-line:no-any + var navAny = nav; + return navAny.userAgentData && navAny.userAgentData.mobile; + } + // tslint:disable-next-line:max-line-length + 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) || + // tslint:disable-next-line:max-line-length + /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) || + //@ts-ignore + (typeof WorkerGlobalScope !== 'undefined'); + } + + var device_util = { + __proto__: null, + mockIsMobile: mockIsMobile, + isMobile: isMobile, + isBrowser: isBrowser + }; + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var ENV = env(); + /** + * This file contains environment-related flag registrations. + */ + /** Whether to enable debug mode. */ + ENV.registerFlag('DEBUG', function () { return false; }, function (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.'); + } + }); + /** Whether we are in a browser (as versus, say, node.js) environment. */ + ENV.registerFlag('IS_BROWSER', function () { return isBrowser(); }); + /** Whether we are in a browser (as versus, say, node.js) environment. */ + ENV.registerFlag('IS_NODE', function () { return (typeof process !== 'undefined') && + (typeof process.versions !== 'undefined') && + (typeof process.versions.node !== 'undefined'); }); + /** Whether this browser is Chrome. */ + ENV.registerFlag('IS_CHROME', function () { return typeof navigator !== 'undefined' && navigator != null && + navigator.userAgent != null && /Chrome/.test(navigator.userAgent) && + /Google Inc/.test(navigator.vendor); }); + /** + * True when the environment is "production" where we disable safety checks + * to gain performance. + */ + ENV.registerFlag('PROD', function () { return false; }); + /** + * Whether to do sanity checks when inferring a shape from user-provided + * values, used when creating a new tensor. + */ + ENV.registerFlag('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', function () { return ENV.getBool('DEBUG'); }); + /** Whether deprecation warnings are enabled. */ + ENV.registerFlag('DEPRECATION_WARNINGS_ENABLED', function () { return true; }); + /** True if running unit tests. */ + ENV.registerFlag('IS_TEST', function () { return false; }); + /** Whether to check computation result for errors. */ + ENV.registerFlag('CHECK_COMPUTATION_FOR_ERRORS', function () { return true; }); + /** Whether the backend needs to wrap input to imageBitmap. */ + ENV.registerFlag('WRAP_TO_IMAGEBITMAP', function () { return false; }); + + /** + * @license + * Copyright 2018 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + function inferShape(val, dtype) { + var firstElem = val; + if (isTypedArray(val)) { + return dtype === 'string' ? [] : [val.length]; + } + if (!Array.isArray(val)) { + return []; // Scalar. + } + var 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, function () { return "Element arr[" + indices.join('][') + "] is a primitive, " + + ("but should be an array/TypedArray of " + shape[0] + " elements"); }); + return; + } + assert(shape.length > 0, function () { return "Element arr[" + indices.join('][') + "] should be a primitive, " + + ("but is an array of " + val.length + " elements"); }); + assert(val.length === shape[0], function () { return "Element arr[" + indices.join('][') + "] should have " + shape[0] + " " + + ("elements, but has " + val.length + " elements"); }); + var subShape = shape.slice(1); + for (var 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) { + if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; } + if (x instanceof Tensor) { + assertDtype(parseAsDtype, x.dtype, argName, functionName); + return x; + } + var inferredDtype = inferDtype(x); + // If the user expects a bool/int/float, use that info to update the + // inferredDtype when it is not a string. + 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')) { + var type = x == null ? 'null' : x.constructor.name; + throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must be a " + + ("Tensor or TensorLike, but got '" + type + "'")); + } + var inferredShape = inferShape(x, inferredDtype); + if (!isTypedArray(x) && !Array.isArray(x)) { + x = [x]; + } + var skipTypedArray = true; + var values = inferredDtype !== 'string' ? + toTypedArray(x, inferredDtype) : + flatten(x, [], skipTypedArray); + return ENGINE.makeTensor(values, inferredShape, inferredDtype); + } + function convertToTensorArray(arg, argName, functionName, parseAsDtype) { + if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; } + if (!Array.isArray(arg)) { + throw new Error("Argument " + argName + " passed to " + functionName + " must be a " + + '`Tensor[]` or `TensorLike[]`'); + } + var tensors = arg; + return tensors.map(function (t, i) { return convertToTensor(t, argName + "[" + i + "]", functionName, parseAsDtype); }); + } + + var OP_SCOPE_SUFFIX = '__op'; + /** + * Used for wrapping functions that perform math operations on + * Tensors. The function will be wrapped in a named scope that cleans all + * memory usage after the function is done. + */ + function op(f) { + var 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.")); + } + var opName = keys[0]; + var fn = f[opName]; + // Strip the underscore from the end of the function name. + if (opName.endsWith('_')) { + opName = opName.substring(0, opName.length - 1); + } + // add an __op suffix to distinguish ops from kernels in tf.profile + opName = opName + OP_SCOPE_SUFFIX; + // tslint:disable-next-line:no-any + var f2 = function () { + var args = []; + for (var _i = 0; _i < arguments.length; _i++) { + args[_i] = arguments[_i]; + } + ENGINE.startScope(opName); + try { + var result = fn.apply(void 0, __spread(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 }); + // tslint:disable-next-line:no-any + return f2; + } + + /** + * @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. + * ============================================================================= + */ + /** + * Converts two real numbers to a complex number. + * + * Given a tensor `real` representing the real part of a complex number, and a + * tensor `imag` representing the imaginary part of a complex number, this + * operation returns complex numbers elementwise of the form [r0, i0, r1, i1], + * where r represents the real part and i represents the imag part. + * + * The input tensors real and imag must have the same shape. + * + * ```js + * const real = tf.tensor1d([2.25, 3.25]); + * const imag = tf.tensor1d([4.75, 5.75]); + * const complex = tf.complex(real, imag); + * + * complex.print(); + * ``` + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function complex_(real, imag) { + var $real = convertToTensor(real, 'real', 'complex'); + var $imag = convertToTensor(imag, 'imag', 'complex'); + assertShapesMatch($real.shape, $imag.shape, "real and imag shapes, " + $real.shape + " and " + $imag.shape + ", " + + "must match in call to tf.complex()."); + var inputs = { real: $real, imag: $imag }; + return ENGINE.runKernel(Complex, inputs); + } + var complex = op({ complex_: complex_ }); + + /** + * @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. + * ============================================================================= + */ + /** This is shared code across all tensor creation methods. */ + 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); + var providedSize_1 = sizeFromShape(shape); + var inferredSize_1 = sizeFromShape(inferredShape); + assert(providedSize_1 === inferredSize_1, function () { return "Based on the provided shape, [" + shape + "], the tensor should have " + + (providedSize_1 + " values but has " + inferredSize_1); }); + for (var i = 0; i < inferredShape.length; ++i) { + var inferred = inferredShape[i]; + var flatDimsDontMatch = i === inferredShape.length - 1 ? + inferred !== sizeFromShape(shape.slice(i)) : + true; + assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, function () { return "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); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a `tf.Tensor` with the provided values, shape and dtype. + * + * ```js + * // Pass an array of values to create a vector. + * tf.tensor([1, 2, 3, 4]).print(); + * ``` + * + * ```js + * // Pass a nested array of values to make a matrix or a higher + * // dimensional tensor. + * tf.tensor([[1, 2], [3, 4]]).print(); + * ``` + * + * ```js + * // Pass a flat array and specify a shape yourself. + * tf.tensor([1, 2, 3, 4], [2, 2]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. If the values are strings, + * they will be encoded as utf-8 and kept as `Uint8Array[]`. + * @param shape The shape of the tensor. Optional. If not provided, + * it is inferred from `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor(values, shape, dtype) { + var inferredShape = inferShape(values, dtype); + return makeTensor(values, shape, inferredShape, dtype); + } + + /** + * @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. + * ============================================================================= + */ + /* Type definitions for exporting and importing of models. */ + /** + * A map from Tensor dtype to number of bytes per element of the Tensor. + */ + var DTYPE_VALUE_SIZE_MAP = { + 'float32': 4, + 'float16': 2, + 'int32': 4, + 'uint16': 2, + 'uint8': 1, + 'bool': 1, + 'complex64': 8 + }; + + /** Number of bytes reserved for the length of the string. (32bit integer). */ + var NUM_BYTES_STRING_LENGTH = 4; + /** + * Encode a map from names to weight values as an ArrayBuffer, along with an + * `Array` of `WeightsManifestEntry` as specification of the encoded weights. + * + * This function does not perform sharding. + * + * This function is the reverse of `decodeWeights`. + * + * @param tensors A map ("dict") from names to tensors. + * @param group Group to which the weights belong (optional). + * @returns A `Promise` of + * - A flat `ArrayBuffer` with all the binary values of the `Tensor`s + * concatenated. + * - An `Array` of `WeightManifestEntry`s, carrying information including + * tensor names, `dtype`s and shapes. + * @throws Error: on unsupported tensor `dtype`. + */ + function encodeWeights(tensors, group) { + return __awaiter(this, void 0, void 0, function () { + var specs, dataPromises, names, _loop_1, i, tensorValues; + var _this = this; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + specs = []; + dataPromises = []; + names = Array.isArray(tensors) ? + tensors.map(function (tensor) { return tensor.name; }) : + Object.keys(tensors); + _loop_1 = function (i) { + var name = names[i]; + var 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); + } + var spec = { name: name, shape: t.shape, dtype: t.dtype }; + if (t.dtype === 'string') { + var utf8bytes = new Promise(function (resolve) { return __awaiter(_this, void 0, void 0, function () { + var vals, totalNumBytes, bytes, offset, i_1, val, bytesOfLength; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: return [4 /*yield*/, t.bytes()]; + case 1: + vals = _a.sent(); + totalNumBytes = vals.reduce(function (p, c) { return p + c.length; }, 0) + + NUM_BYTES_STRING_LENGTH * vals.length; + bytes = new Uint8Array(totalNumBytes); + offset = 0; + for (i_1 = 0; i_1 < vals.length; i_1++) { + val = vals[i_1]; + 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); + return [2 /*return*/]; + } + }); + }); }); + dataPromises.push(utf8bytes); + } + else { + dataPromises.push(t.data()); + } + if (group != null) { + spec.group = group; + } + specs.push(spec); + }; + for (i = 0; i < names.length; ++i) { + _loop_1(i); + } + return [4 /*yield*/, Promise.all(dataPromises)]; + case 1: + tensorValues = _a.sent(); + return [2 /*return*/, { data: concatenateTypedArrays(tensorValues), specs: specs }]; + } + }); + }); + } + /** + * Decode flat ArrayBuffer as weights. + * + * This function does not handle sharding. + * + * This function is the reverse of `encodeWeights`. + * + * @param buffer A flat ArrayBuffer carrying the binary values of the tensors + * concatenated in the order specified in `specs`. + * @param specs Specifications of the names, dtypes and shapes of the tensors + * whose value are encoded by `buffer`. + * @return A map from tensor name to tensor value, with the names corresponding + * to names in `specs`. + * @throws Error, if any of the tensors has unsupported dtype. + */ + function decodeWeights(buffer, specs) { + var e_1, _a; + // TODO(adarob, cais): Support quantization. + var out = {}; + var float16Decode; + var offset = 0; + try { + for (var specs_1 = __values(specs), specs_1_1 = specs_1.next(); !specs_1_1.done; specs_1_1 = specs_1.next()) { + var spec = specs_1_1.value; + var name = spec.name; + var dtype = spec.dtype; + var shape = spec.shape; + var size = sizeFromShape(shape); + var values = void 0; + if ('quantization' in spec) { + var 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'."); + } + var quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype]; + var byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor); + var 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 (var i = 0; i < quantizedArray.length; i++) { + var v = quantizedArray[i]; + values[i] = v * quantization.scale + quantization.min; + } + } + else if (quantization.dtype === 'float16') { + if (float16Decode === undefined) { + 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 (var i = 0; i < quantizedArray.length; i++) { + var 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') { + var size_1 = sizeFromShape(spec.shape); + values = []; + for (var i = 0; i < size_1; i++) { + var byteLength = new Uint32Array(buffer.slice(offset, offset + NUM_BYTES_STRING_LENGTH))[0]; + offset += NUM_BYTES_STRING_LENGTH; + var bytes = new Uint8Array(buffer.slice(offset, offset + byteLength)); + values.push(bytes); + offset += byteLength; + } + } + else { + var dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype]; + var byteBuffer = buffer.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); + var real = new Float32Array(values.length / 2); + var image = new Float32Array(values.length / 2); + for (var i = 0; i < real.length; i++) { + real[i] = values[i * 2]; + image[i] = values[i * 2 + 1]; + } + var realTensor = tensor(real, shape, 'float32'); + var imageTensor = tensor(image, 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] = tensor(values, shape, dtype); + } + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (specs_1_1 && !specs_1_1.done && (_a = specs_1.return)) _a.call(specs_1); + } + finally { if (e_1) throw e_1.error; } + } + return out; + } + /** + * Concatenate TypedArrays into an ArrayBuffer. + */ + function concatenateTypedArrays(xs) { + // TODO(adarob, cais): Support quantization. + if (xs === null) { + throw new Error("Invalid input value: " + JSON.stringify(xs)); + } + var totalByteLength = 0; + // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer' + // can have a different byte length from that of the `TypedArray` itself, + // for example, when the `TypedArray` is created from an offset in an + // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match + // the `TypedArray` in byte length. If an element of `xs` does not show + // this property, a new `TypedArray` that satisfy this property will be + // constructed and pushed into `normalizedXs`. + var normalizedXs = []; + xs.forEach(function (x) { + totalByteLength += x.byteLength; + // tslint:disable:no-any + 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); + } + // tslint:enable:no-any + }); + var y = new Uint8Array(totalByteLength); + var offset = 0; + normalizedXs.forEach(function (x) { + y.set(new Uint8Array(x.buffer), offset); + offset += x.byteLength; + }); + return y.buffer; + } + // Use Buffer on Node.js instead of Blob/atob/btoa + var useNodeBuffer = typeof Buffer !== 'undefined' && + (typeof Blob === 'undefined' || typeof atob === 'undefined' || + typeof btoa === 'undefined'); + /** + * Calculate the byte length of a JavaScript string. + * + * Note that a JavaScript string can contain wide characters, therefore the + * length of the string is not necessarily equal to the byte length. + * + * @param str Input string. + * @returns Byte length. + */ + function stringByteLength(str) { + if (useNodeBuffer) { + return Buffer.byteLength(str); + } + return new Blob([str]).size; + } + /** + * Encode an ArrayBuffer as a base64 encoded string. + * + * @param buffer `ArrayBuffer` to be converted. + * @returns A string that base64-encodes `buffer`. + */ + function arrayBufferToBase64String(buffer) { + if (useNodeBuffer) { + return Buffer.from(buffer).toString('base64'); + } + var buf = new Uint8Array(buffer); + var s = ''; + for (var i = 0, l = buf.length; i < l; i++) { + s += String.fromCharCode(buf[i]); + } + return btoa(s); + } + /** + * Decode a base64 string as an ArrayBuffer. + * + * @param str Base64 string. + * @returns Decoded `ArrayBuffer`. + */ + function base64StringToArrayBuffer(str) { + if (useNodeBuffer) { + var buf = Buffer.from(str, 'base64'); + return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength); + } + var s = atob(str); + var buffer = new Uint8Array(s.length); + for (var i = 0; i < s.length; ++i) { + buffer.set([s.charCodeAt(i)], i); + } + return buffer.buffer; + } + /** + * Concatenate a number of ArrayBuffers into one. + * + * @param buffers A number of array buffers to concatenate. + * @returns Result of concatenating `buffers` in order. + */ + function concatenateArrayBuffers(buffers) { + if (buffers.length === 1) { + return buffers[0]; + } + var totalByteLength = 0; + buffers.forEach(function (buffer) { + totalByteLength += buffer.byteLength; + }); + var temp = new Uint8Array(totalByteLength); + var offset = 0; + buffers.forEach(function (buffer) { + temp.set(new Uint8Array(buffer), offset); + offset += buffer.byteLength; + }); + return temp.buffer; + } + /** + * Get the basename of a path. + * + * Behaves in a way analogous to Linux's basename command. + * + * @param path + */ + function basename(path) { + var SEPARATOR = '/'; + path = path.trim(); + while (path.endsWith(SEPARATOR)) { + path = path.slice(0, path.length - 1); + } + var items = path.split(SEPARATOR); + return items[items.length - 1]; + } + /** + * Create `ModelJSON` from `ModelArtifacts`. + * + * @param artifacts Model artifacts, describing the model and its weights. + * @param manifest Weight manifest, describing where the weights of the + * `ModelArtifacts` are stored, and some metadata about them. + * @returns Object representing the `model.json` file describing the model + * artifacts and weights + */ + function getModelJSONForModelArtifacts(artifacts, manifest) { + var 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; + } + /** + * Create `ModelArtifacts` from a JSON file. + * + * @param modelJSON Object containing the parsed JSON of `model.json` + * @param loadWeights Function that takes the JSON file's weights manifest, + * reads weights from the listed path(s), and returns a Promise of the + * weight manifest entries along with the weights data. + * @returns A Promise of the `ModelArtifacts`, as described by the JSON file. + */ + function getModelArtifactsForJSON(modelJSON, loadWeights) { + return __awaiter(this, void 0, void 0, function () { + var modelArtifacts, _a, weightSpecs, weightData; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + 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)) return [3 /*break*/, 2]; + return [4 /*yield*/, loadWeights(modelJSON.weightsManifest)]; + case 1: + _a = __read.apply(void 0, [_b.sent(), 2]), weightSpecs = _a[0], weightData = _a[1]; + modelArtifacts.weightSpecs = weightSpecs; + modelArtifacts.weightData = weightData; + _b.label = 2; + case 2: + 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 [2 /*return*/, modelArtifacts]; + } + }); + }); + } + /** + * Populate ModelArtifactsInfo fields for a model with JSON topology. + * @param modelArtifacts + * @returns A ModelArtifactsInfo object. + */ + 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, + }; + } + /** + * Computes mantisa table for casting Float16 to Float32 + * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + * + * @returns Uint32Array, 2048 mantissa lookup values. + */ + function computeFloat16MantisaTable() { + var convertMantissa = function (i) { + var m = i << 13; + var e = 0; + while ((m & 0x00800000) === 0) { + e -= 0x00800000; + m <<= 1; + } + m &= ~0x00800000; + e += 0x38800000; + return m | e; + }; + var mantisaTable = new Uint32Array(2048); + mantisaTable[0] = 0; + for (var i = 1; i < 1024; i++) { + mantisaTable[i] = convertMantissa(i); + } + for (var i = 1024; i < 2048; i++) { + mantisaTable[i] = 0x38000000 + ((i - 1024) << 13); + } + return mantisaTable; + } + /** + * Computes exponent table for casting Float16 to Float32 + * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + * + * @returns Uint32Array, 64 exponent lookup values. + */ + function computeFloat16ExponentTable() { + var exponentTable = new Uint32Array(64); + exponentTable[0] = 0; + exponentTable[31] = 0x47800000; + exponentTable[32] = 0x80000000; + exponentTable[63] = 0xc7800000; + for (var i = 1; i < 31; i++) { + exponentTable[i] = i << 23; + } + for (var i = 33; i < 63; i++) { + exponentTable[i] = 0x80000000 + ((i - 32) << 23); + } + return exponentTable; + } + /** + * Computes offset table for casting Float16 to Float32 + * See http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + * + * @returns Uint32Array, 6d offset values. + */ + function computeFloat16OffsetTable() { + var offsetTable = new Uint32Array(64); + for (var i = 0; i < 64; i++) { + offsetTable[i] = 1024; + } + offsetTable[0] = offsetTable[32] = 0; + return offsetTable; + } + /** + * Retrieve a Float16 decoder which will decode a ByteArray of Float16 values + * to a Float32Array. + * + * @returns Function (buffer: Uint16Array) => Float32Array which decodes + * the Uint16Array of Float16 bytes to a Float32Array. + */ + function getFloat16Decoder() { + // Algorithm is based off of + // http://www.fox-toolkit.org/ftp/fasthalffloatconversion.pdf + // Cache lookup tables + var mantisaTable = computeFloat16MantisaTable(); + var exponentTable = computeFloat16ExponentTable(); + var offsetTable = computeFloat16OffsetTable(); + return function (quantizedArray) { + var buffer = new ArrayBuffer(4 * quantizedArray.length); + var bufferUint32View = new Uint32Array(buffer); + for (var index = 0; index < quantizedArray.length; index++) { + var float16Bits = quantizedArray[index]; + var float32Bits = mantisaTable[offsetTable[float16Bits >> 10] + (float16Bits & 0x3ff)] + + exponentTable[float16Bits >> 10]; + bufferUint32View[index] = float32Bits; + } + return new Float32Array(buffer); + }; + } + + /** + * @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. + * ============================================================================= + */ + var IORouterRegistry = /** @class */ (function () { + function IORouterRegistry() { + this.saveRouters = []; + this.loadRouters = []; + } + IORouterRegistry.getInstance = function () { + if (IORouterRegistry.instance == null) { + IORouterRegistry.instance = new IORouterRegistry(); + } + return IORouterRegistry.instance; + }; + /** + * Register a save-handler router. + * + * @param saveRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `save` method defined or `null`. + */ + IORouterRegistry.registerSaveRouter = function (saveRouter) { + IORouterRegistry.getInstance().saveRouters.push(saveRouter); + }; + /** + * Register a load-handler router. + * + * @param loadRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `load` method defined or `null`. + */ + IORouterRegistry.registerLoadRouter = function (loadRouter) { + IORouterRegistry.getInstance().loadRouters.push(loadRouter); + }; + /** + * Look up IOHandler for saving, given a URL-like string. + * + * @param url + * @returns If only one match is found, an instance of IOHandler with the + * `save` method defined. If no match is found, `null`. + * @throws Error, if more than one match is found. + */ + IORouterRegistry.getSaveHandlers = function (url) { + return IORouterRegistry.getHandlers(url, 'save'); + }; + /** + * Look up IOHandler for loading, given a URL-like string. + * + * @param url + * @param loadOptions Optional, custom load options. + * @returns All valid handlers for `url`, given the currently registered + * handler routers. + */ + IORouterRegistry.getLoadHandlers = function (url, loadOptions) { + return IORouterRegistry.getHandlers(url, 'load', loadOptions); + }; + IORouterRegistry.getHandlers = function (url, handlerType, loadOptions) { + var validHandlers = []; + var routers = handlerType === 'load' ? + IORouterRegistry.getInstance().loadRouters : + IORouterRegistry.getInstance().saveRouters; + routers.forEach(function (router) { + var handler = router(url, loadOptions); + if (handler !== null) { + validHandlers.push(handler); + } + }); + return validHandlers; + }; + return IORouterRegistry; + }()); + var registerSaveRouter = function (loudRouter) { return IORouterRegistry.registerSaveRouter(loudRouter); }; + var registerLoadRouter = function (loudRouter) { return IORouterRegistry.registerLoadRouter(loudRouter); }; + var getSaveHandlers = function (url) { return IORouterRegistry.getSaveHandlers(url); }; + var getLoadHandlers = function (url, loadOptions) { return IORouterRegistry.getLoadHandlers(url, loadOptions); }; + + var DATABASE_NAME = 'tensorflowjs'; + var DATABASE_VERSION = 1; + // Model data and ModelArtifactsInfo (metadata) are stored in two separate + // stores for efficient access of the list of stored models and their metadata. + // 1. The object store for model data: topology, weights and weight manifests. + var MODEL_STORE_NAME = 'models_store'; + // 2. The object store for ModelArtifactsInfo, including meta-information such + // as the type of topology (JSON vs binary), byte size of the topology, byte + // size of the weights, etc. + var INFO_STORE_NAME = 'model_info_store'; + function getIndexedDBFactory() { + if (!env().getBool('IS_BROWSER')) { + // TODO(cais): Add more info about what IOHandler subtypes are available. + // Maybe point to a doc page on the web and/or automatically determine + // the available IOHandlers and print them in the error message. + throw new Error('Failed to obtain IndexedDB factory because the current environment' + + 'is not a web browser.'); + } + // tslint:disable-next-line:no-any + var theWindow = typeof window === 'undefined' ? self : window; + var 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) { + var db = openRequest.result; + db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' }); + db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' }); + } + /** + * IOHandler subclass: Browser IndexedDB. + * + * See the doc string of `browserIndexedDB` for more details. + */ + var BrowserIndexedDB = /** @class */ (function () { + function BrowserIndexedDB(modelPath) { + this.indexedDB = getIndexedDBFactory(); + if (modelPath == null || !modelPath) { + throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.'); + } + this.modelPath = modelPath; + } + BrowserIndexedDB.prototype.save = function (modelArtifacts) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + // TODO(cais): Support saving GraphDef models. + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error('BrowserLocalStorage.save() does not support saving model topology ' + + 'in binary formats yet.'); + } + return [2 /*return*/, this.databaseAction(this.modelPath, modelArtifacts)]; + }); + }); + }; + BrowserIndexedDB.prototype.load = function () { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + return [2 /*return*/, this.databaseAction(this.modelPath)]; + }); + }); + }; + /** + * Perform database action to put model artifacts into or read model artifacts + * from IndexedDB object store. + * + * Whether the action is put or get depends on whether `modelArtifacts` is + * specified. If it is specified, the action will be put; otherwise the action + * will be get. + * + * @param modelPath A unique string path for the model. + * @param modelArtifacts If specified, it will be the model artifacts to be + * stored in IndexedDB. + * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise` + * of `ModelArtifacts`, if the action is get. + */ + BrowserIndexedDB.prototype.databaseAction = function (modelPath, modelArtifacts) { + var _this = this; + return new Promise(function (resolve, reject) { + var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); }; + openRequest.onsuccess = function () { + var db = openRequest.result; + if (modelArtifacts == null) { + // Read model out from object store. + var modelTx = db.transaction(MODEL_STORE_NAME, 'readonly'); + var modelStore = modelTx.objectStore(MODEL_STORE_NAME); + var getRequest_1 = modelStore.get(_this.modelPath); + getRequest_1.onsuccess = function () { + if (getRequest_1.result == null) { + db.close(); + return reject(new Error("Cannot find model with path '" + _this.modelPath + "' " + + "in IndexedDB.")); + } + else { + resolve(getRequest_1.result.modelArtifacts); + } + }; + getRequest_1.onerror = function (error) { + db.close(); + return reject(getRequest_1.error); + }; + modelTx.oncomplete = function () { return db.close(); }; + } + else { + // Put model into object store. + var modelArtifactsInfo_1 = getModelArtifactsInfoForJSON(modelArtifacts); + // First, put ModelArtifactsInfo into info store. + var infoTx_1 = db.transaction(INFO_STORE_NAME, 'readwrite'); + var infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME); + var putInfoRequest_1 = infoStore_1.put({ modelPath: _this.modelPath, modelArtifactsInfo: modelArtifactsInfo_1 }); + var modelTx_1; + putInfoRequest_1.onsuccess = function () { + // Second, put model data into model store. + modelTx_1 = db.transaction(MODEL_STORE_NAME, 'readwrite'); + var modelStore = modelTx_1.objectStore(MODEL_STORE_NAME); + var putModelRequest = modelStore.put({ + modelPath: _this.modelPath, + modelArtifacts: modelArtifacts, + modelArtifactsInfo: modelArtifactsInfo_1 + }); + putModelRequest.onsuccess = function () { return resolve({ modelArtifactsInfo: modelArtifactsInfo_1 }); }; + putModelRequest.onerror = function (error) { + // If the put-model request fails, roll back the info entry as + // well. + infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME); + var deleteInfoRequest = infoStore_1.delete(_this.modelPath); + deleteInfoRequest.onsuccess = function () { + db.close(); + return reject(putModelRequest.error); + }; + deleteInfoRequest.onerror = function (error) { + db.close(); + return reject(putModelRequest.error); + }; + }; + }; + putInfoRequest_1.onerror = function (error) { + db.close(); + return reject(putInfoRequest_1.error); + }; + infoTx_1.oncomplete = function () { + if (modelTx_1 == null) { + db.close(); + } + else { + modelTx_1.oncomplete = function () { return db.close(); }; + } + }; + } + }; + openRequest.onerror = function (error) { return reject(openRequest.error); }; + }); + }; + return BrowserIndexedDB; + }()); + BrowserIndexedDB.URL_SCHEME = 'indexeddb://'; + var indexedDBRouter = function (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); + /** + * Creates a browser IndexedDB IOHandler for saving and loading models. + * + * ```js + * const model = tf.sequential(); + * model.add( + * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'})); + * + * const saveResult = await model.save('indexeddb://MyModel')); + * console.log(saveResult); + * ``` + * + * @param modelPath A unique identifier for the model to be saved. Must be a + * non-empty string. + * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`), + * which can be used with, e.g., `tf.Model.save`. + */ + function browserIndexedDB(modelPath) { + return new BrowserIndexedDB(modelPath); + } + function maybeStripScheme$1(key) { + return key.startsWith(BrowserIndexedDB.URL_SCHEME) ? + key.slice(BrowserIndexedDB.URL_SCHEME.length) : + key; + } + var BrowserIndexedDBManager = /** @class */ (function () { + function BrowserIndexedDBManager() { + this.indexedDB = getIndexedDBFactory(); + } + BrowserIndexedDBManager.prototype.listModels = function () { + return __awaiter(this, void 0, void 0, function () { + var _this = this; + return __generator(this, function (_a) { + return [2 /*return*/, new Promise(function (resolve, reject) { + var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); }; + openRequest.onsuccess = function () { + var db = openRequest.result; + var tx = db.transaction(INFO_STORE_NAME, 'readonly'); + var store = tx.objectStore(INFO_STORE_NAME); + // tslint:disable:max-line-length + // Need to cast `store` as `any` here because TypeScript's DOM + // library does not have the `getAll()` method even though the + // method is supported in the latest version of most mainstream + // browsers: + // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll + // tslint:enable:max-line-length + // tslint:disable-next-line:no-any + var getAllInfoRequest = store.getAll(); + getAllInfoRequest.onsuccess = function () { + var e_1, _a; + var out = {}; + try { + for (var _b = __values(getAllInfoRequest.result), _c = _b.next(); !_c.done; _c = _b.next()) { + var item = _c.value; + out[item.modelPath] = item.modelArtifactsInfo; + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (_c && !_c.done && (_a = _b.return)) _a.call(_b); + } + finally { if (e_1) throw e_1.error; } + } + resolve(out); + }; + getAllInfoRequest.onerror = function (error) { + db.close(); + return reject(getAllInfoRequest.error); + }; + tx.oncomplete = function () { return db.close(); }; + }; + openRequest.onerror = function (error) { return reject(openRequest.error); }; + })]; + }); + }); + }; + BrowserIndexedDBManager.prototype.removeModel = function (path) { + return __awaiter(this, void 0, void 0, function () { + var _this = this; + return __generator(this, function (_a) { + path = maybeStripScheme$1(path); + return [2 /*return*/, new Promise(function (resolve, reject) { + var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION); + openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); }; + openRequest.onsuccess = function () { + var db = openRequest.result; + var infoTx = db.transaction(INFO_STORE_NAME, 'readwrite'); + var infoStore = infoTx.objectStore(INFO_STORE_NAME); + var getInfoRequest = infoStore.get(path); + var modelTx; + getInfoRequest.onsuccess = function () { + if (getInfoRequest.result == null) { + db.close(); + return reject(new Error("Cannot find model with path '" + path + "' " + + "in IndexedDB.")); + } + else { + // First, delete the entry in the info store. + var deleteInfoRequest = infoStore.delete(path); + var deleteModelData_1 = function () { + // Second, delete the entry in the model store. + modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite'); + var modelStore = modelTx.objectStore(MODEL_STORE_NAME); + var deleteModelRequest = modelStore.delete(path); + deleteModelRequest.onsuccess = function () { return resolve(getInfoRequest.result.modelArtifactsInfo); }; + deleteModelRequest.onerror = function (error) { return reject(getInfoRequest.error); }; + }; + // Proceed with deleting model data regardless of whether deletion + // of info data succeeds or not. + deleteInfoRequest.onsuccess = deleteModelData_1; + deleteInfoRequest.onerror = function (error) { + deleteModelData_1(); + db.close(); + return reject(getInfoRequest.error); + }; + } + }; + getInfoRequest.onerror = function (error) { + db.close(); + return reject(getInfoRequest.error); + }; + infoTx.oncomplete = function () { + if (modelTx == null) { + db.close(); + } + else { + modelTx.oncomplete = function () { return db.close(); }; + } + }; + }; + openRequest.onerror = function (error) { return reject(openRequest.error); }; + })]; + }); + }); + }; + return BrowserIndexedDBManager; + }()); + + 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) { + var e_1, _a; + try { + for (var _b = __values(Object.values(keys)), _c = _b.next(); !_c.done; _c = _b.next()) { + var key = _c.value; + window.localStorage.removeItem(key); + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (_c && !_c.done && (_a = _b.return)) _a.call(_b); + } + finally { if (e_1) throw e_1.error; } + } + } + /** + * Get model path from a local-storage key. + * + * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1' + * + * @param key + */ + function getModelPathFromKey(key) { + var 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 maybeStripScheme(key) { + return key.startsWith(BrowserLocalStorage.URL_SCHEME) ? + key.slice(BrowserLocalStorage.URL_SCHEME.length) : + key; + } + /** + * IOHandler subclass: Browser Local Storage. + * + * See the doc string to `browserLocalStorage` for more details. + */ + var BrowserLocalStorage = /** @class */ (function () { + function BrowserLocalStorage(modelPath) { + if (!env().getBool('IS_BROWSER') || typeof window === 'undefined' || + typeof window.localStorage === 'undefined') { + // TODO(cais): Add more info about what IOHandler subtypes are + // available. + // Maybe point to a doc page on the web and/or automatically determine + // the available IOHandlers and print them in the error message. + 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); + } + /** + * Save model artifacts to browser local storage. + * + * See the documentation to `browserLocalStorage` for details on the saved + * artifacts. + * + * @param modelArtifacts The model artifacts to be stored. + * @returns An instance of SaveResult. + */ + BrowserLocalStorage.prototype.save = function (modelArtifacts) { + return __awaiter(this, void 0, void 0, function () { + var topology, weightSpecs, modelArtifactsInfo, metadata; + return __generator(this, function (_a) { + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error('BrowserLocalStorage.save() does not support saving model topology ' + + 'in binary formats yet.'); + } + else { + topology = JSON.stringify(modelArtifacts.modelTopology); + weightSpecs = JSON.stringify(modelArtifacts.weightSpecs); + 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)); + metadata = { + format: modelArtifacts.format, + generatedBy: modelArtifacts.generatedBy, + convertedBy: modelArtifacts.convertedBy, + signature: modelArtifacts.signature != null ? + modelArtifacts.signature : + undefined, + userDefinedMetadata: modelArtifacts.userDefinedMetadata != null ? + modelArtifacts.userDefinedMetadata : + undefined, + modelInitializer: modelArtifacts.modelInitializer != null ? + modelArtifacts.modelInitializer : + undefined, + trainingConfig: modelArtifacts.trainingConfig != null ? + modelArtifacts.trainingConfig : + undefined + }; + this.LS.setItem(this.keys.modelMetadata, JSON.stringify(metadata)); + return [2 /*return*/, { modelArtifactsInfo: modelArtifactsInfo }]; + } + catch (err) { + // If saving failed, clean up all items saved so far. + 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 + ".")); + } + } + return [2 /*return*/]; + }); + }); + }; + /** + * Load a model from local storage. + * + * See the documentation to `browserLocalStorage` for details on the saved + * artifacts. + * + * @returns The loaded model (if loading succeeds). + */ + BrowserLocalStorage.prototype.load = function () { + return __awaiter(this, void 0, void 0, function () { + var info, out, topology, weightSpecs, metadataString, metadata, weightDataBase64; + return __generator(this, function (_a) { + 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.'); + } + out = {}; + 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; + 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; + metadataString = this.LS.getItem(this.keys.modelMetadata); + if (metadataString != null) { + 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; + } + } + 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 [2 /*return*/, out]; + }); + }); + }; + return BrowserLocalStorage; + }()); + BrowserLocalStorage.URL_SCHEME = 'localstorage://'; + var localStorageRouter = function (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); + /** + * Factory function for local storage IOHandler. + * + * This `IOHandler` supports both `save` and `load`. + * + * For each model's saved artifacts, four items are saved to local storage. + * - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the + * model, such as date saved, type of the topology, size in bytes, etc. + * - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras- + * style models, this is a stringized JSON. + * - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the + * model, can be used to decode the saved binary weight values (see + * item below). + * - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary + * weight values, stored as a base64-encoded string. + * + * Saving may throw an `Error` if the total size of the artifacts exceed the + * browser-specific quota. + * + * @param modelPath A unique identifier for the model to be saved. Must be a + * non-empty string. + * @returns An instance of `IOHandler`, which can be used with, e.g., + * `tf.Model.save`. + */ + function browserLocalStorage(modelPath) { + return new BrowserLocalStorage(modelPath); + } + var BrowserLocalStorageManager = /** @class */ (function () { + function BrowserLocalStorageManager() { + assert(env().getBool('IS_BROWSER'), function () { return 'Current environment is not a web browser'; }); + assert(typeof window === 'undefined' || + typeof window.localStorage !== 'undefined', function () { return 'Current browser does not appear to support localStorage'; }); + this.LS = window.localStorage; + } + BrowserLocalStorageManager.prototype.listModels = function () { + return __awaiter(this, void 0, void 0, function () { + var out, prefix, suffix, i, key, modelPath; + return __generator(this, function (_a) { + out = {}; + prefix = PATH_PREFIX + PATH_SEPARATOR; + suffix = PATH_SEPARATOR + INFO_SUFFIX; + for (i = 0; i < this.LS.length; ++i) { + key = this.LS.key(i); + if (key.startsWith(prefix) && key.endsWith(suffix)) { + modelPath = getModelPathFromKey(key); + out[modelPath] = JSON.parse(this.LS.getItem(key)); + } + } + return [2 /*return*/, out]; + }); + }); + }; + BrowserLocalStorageManager.prototype.removeModel = function (path) { + return __awaiter(this, void 0, void 0, function () { + var keys, info; + return __generator(this, function (_a) { + path = maybeStripScheme(path); + keys = getModelKeys(path); + if (this.LS.getItem(keys.info) == null) { + throw new Error("Cannot find model at path '" + path + "'"); + } + info = JSON.parse(this.LS.getItem(keys.info)); + removeItems(keys); + return [2 /*return*/, info]; + }); + }); + }; + return BrowserLocalStorageManager; + }()); + + var URL_SCHEME_SUFFIX = '://'; + var ModelStoreManagerRegistry = /** @class */ (function () { + function ModelStoreManagerRegistry() { + this.managers = {}; + } + ModelStoreManagerRegistry.getInstance = function () { + if (ModelStoreManagerRegistry.instance == null) { + ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry(); + } + return ModelStoreManagerRegistry.instance; + }; + /** + * Register a save-handler router. + * + * @param saveRouter A function that maps a URL-like string onto an instance + * of `IOHandler` with the `save` method defined or `null`. + */ + ModelStoreManagerRegistry.registerManager = function (scheme, manager) { + assert(scheme != null, function () { return '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, function () { return 'scheme must not be an empty string.'; }); + var registry = ModelStoreManagerRegistry.getInstance(); + assert(registry.managers[scheme] == null, function () { return "A model store manager is already registered for scheme '" + scheme + "'."; }); + registry.managers[scheme] = manager; + }; + ModelStoreManagerRegistry.getManager = function (scheme) { + var manager = this.getInstance().managers[scheme]; + if (manager == null) { + throw new Error("Cannot find model manager for scheme '" + scheme + "'"); + } + return manager; + }; + ModelStoreManagerRegistry.getSchemes = function () { + return Object.keys(this.getInstance().managers); + }; + return ModelStoreManagerRegistry; + }()); + /** + * Helper method for parsing a URL string into a scheme and a path. + * + * @param url E.g., 'localstorage://my-model' + * @returns A dictionary with two fields: scheme and path. + * Scheme: e.g., 'localstorage' in the example above. + * Path: e.g., 'my-model' in the example above. + */ + 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], + }; + } + function cloneModelInternal(sourceURL, destURL, deleteSource) { + if (deleteSource === void 0) { deleteSource = false; } + return __awaiter(this, void 0, void 0, function () { + var loadHandlers, loadHandler, saveHandlers, saveHandler, sourceScheme, sourcePath, sameMedium, modelArtifacts, saveResult; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + assert(sourceURL !== destURL, function () { return "Old path and new path are the same: '" + sourceURL + "'"; }); + loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL); + assert(loadHandlers.length > 0, function () { return "Copying failed because no load handler is found for source URL " + sourceURL + "."; }); + assert(loadHandlers.length < 2, function () { return "Copying failed because more than one (" + loadHandlers.length + ") " + + ("load handlers for source URL " + sourceURL + "."); }); + loadHandler = loadHandlers[0]; + saveHandlers = IORouterRegistry.getSaveHandlers(destURL); + assert(saveHandlers.length > 0, function () { return "Copying failed because no save handler is found for destination " + + ("URL " + destURL + "."); }); + assert(saveHandlers.length < 2, function () { return "Copying failed because more than one (" + loadHandlers.length + ") " + + ("save handlers for destination URL " + destURL + "."); }); + saveHandler = saveHandlers[0]; + sourceScheme = parseURL(sourceURL).scheme; + sourcePath = parseURL(sourceURL).path; + sameMedium = sourceScheme === parseURL(sourceURL).scheme; + return [4 /*yield*/, loadHandler.load()]; + case 1: + modelArtifacts = _a.sent(); + if (!(deleteSource && sameMedium)) return [3 /*break*/, 3]; + return [4 /*yield*/, ModelStoreManagerRegistry.getManager(sourceScheme) + .removeModel(sourcePath)]; + case 2: + _a.sent(); + _a.label = 3; + case 3: return [4 /*yield*/, saveHandler.save(modelArtifacts)]; + case 4: + saveResult = _a.sent(); + if (!(deleteSource && !sameMedium)) return [3 /*break*/, 6]; + return [4 /*yield*/, ModelStoreManagerRegistry.getManager(sourceScheme) + .removeModel(sourcePath)]; + case 5: + _a.sent(); + _a.label = 6; + case 6: return [2 /*return*/, saveResult.modelArtifactsInfo]; + } + }); + }); + } + /** + * List all models stored in registered storage mediums. + * + * For a web browser environment, the registered mediums are Local Storage and + * IndexedDB. + * + * ```js + * // First create and save a model. + * const model = tf.sequential(); + * model.add(tf.layers.dense( + * {units: 1, inputShape: [10], activation: 'sigmoid'})); + * await model.save('localstorage://demo/management/model1'); + * + * // Then list existing models. + * console.log(JSON.stringify(await tf.io.listModels())); + * + * // Delete the model. + * await tf.io.removeModel('localstorage://demo/management/model1'); + * + * // List models again. + * console.log(JSON.stringify(await tf.io.listModels())); + * ``` + * + * @returns A `Promise` of a dictionary mapping URLs of existing models to + * their model artifacts info. URLs include medium-specific schemes, e.g., + * 'indexeddb://my/model/1'. Model artifacts info include type of the + * model's topology, byte sizes of the topology, weights, etc. + * + * @doc { + * heading: 'Models', + * subheading: 'Management', + * namespace: 'io', + * ignoreCI: true + * } + */ + function listModels() { + return __awaiter(this, void 0, void 0, function () { + var schemes, out, schemes_1, schemes_1_1, scheme, schemeOut, path, url, e_1_1; + var e_1, _a; + return __generator(this, function (_b) { + switch (_b.label) { + case 0: + schemes = ModelStoreManagerRegistry.getSchemes(); + out = {}; + _b.label = 1; + case 1: + _b.trys.push([1, 6, 7, 8]); + schemes_1 = __values(schemes), schemes_1_1 = schemes_1.next(); + _b.label = 2; + case 2: + if (!!schemes_1_1.done) return [3 /*break*/, 5]; + scheme = schemes_1_1.value; + return [4 /*yield*/, ModelStoreManagerRegistry.getManager(scheme).listModels()]; + case 3: + schemeOut = _b.sent(); + for (path in schemeOut) { + url = scheme + URL_SCHEME_SUFFIX + path; + out[url] = schemeOut[path]; + } + _b.label = 4; + case 4: + schemes_1_1 = schemes_1.next(); + return [3 /*break*/, 2]; + case 5: return [3 /*break*/, 8]; + case 6: + e_1_1 = _b.sent(); + e_1 = { error: e_1_1 }; + return [3 /*break*/, 8]; + case 7: + try { + if (schemes_1_1 && !schemes_1_1.done && (_a = schemes_1.return)) _a.call(schemes_1); + } + finally { if (e_1) throw e_1.error; } + return [7 /*endfinally*/]; + case 8: return [2 /*return*/, out]; + } + }); + }); + } + /** + * Remove a model specified by URL from a reigstered storage medium. + * + * ```js + * // First create and save a model. + * const model = tf.sequential(); + * model.add(tf.layers.dense( + * {units: 1, inputShape: [10], activation: 'sigmoid'})); + * await model.save('localstorage://demo/management/model1'); + * + * // Then list existing models. + * console.log(JSON.stringify(await tf.io.listModels())); + * + * // Delete the model. + * await tf.io.removeModel('localstorage://demo/management/model1'); + * + * // List models again. + * console.log(JSON.stringify(await tf.io.listModels())); + * ``` + * + * @param url A URL to a stored model, with a scheme prefix, e.g., + * 'localstorage://my-model-1', 'indexeddb://my/model/2'. + * @returns ModelArtifactsInfo of the deleted model (if and only if deletion + * is successful). + * @throws Error if deletion fails, e.g., if no model exists at `path`. + * + * @doc { + * heading: 'Models', + * subheading: 'Management', + * namespace: 'io', + * ignoreCI: true + * } + */ + function removeModel(url) { + return __awaiter(this, void 0, void 0, function () { + var schemeAndPath, manager; + return __generator(this, function (_a) { + schemeAndPath = parseURL(url); + manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme); + return [2 /*return*/, manager.removeModel(schemeAndPath.path)]; + }); + }); + } + /** + * Copy a model from one URL to another. + * + * This function supports: + * + * 1. Copying within a storage medium, e.g., + * `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')` + * 2. Copying between two storage mediums, e.g., + * `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')` + * + * ```js + * // First create and save a model. + * const model = tf.sequential(); + * model.add(tf.layers.dense( + * {units: 1, inputShape: [10], activation: 'sigmoid'})); + * await model.save('localstorage://demo/management/model1'); + * + * // Then list existing models. + * console.log(JSON.stringify(await tf.io.listModels())); + * + * // Copy the model, from Local Storage to IndexedDB. + * await tf.io.copyModel( + * 'localstorage://demo/management/model1', + * 'indexeddb://demo/management/model1'); + * + * // List models again. + * console.log(JSON.stringify(await tf.io.listModels())); + * + * // Remove both models. + * await tf.io.removeModel('localstorage://demo/management/model1'); + * await tf.io.removeModel('indexeddb://demo/management/model1'); + * ``` + * + * @param sourceURL Source URL of copying. + * @param destURL Destination URL of copying. + * @returns ModelArtifactsInfo of the copied model (if and only if copying + * is successful). + * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or + * if `oldPath` and `newPath` are identical. + * + * @doc { + * heading: 'Models', + * subheading: 'Management', + * namespace: 'io', + * ignoreCI: true + * } + */ + function copyModel(sourceURL, destURL) { + return __awaiter(this, void 0, void 0, function () { + var deleteSource; + return __generator(this, function (_a) { + deleteSource = false; + return [2 /*return*/, cloneModelInternal(sourceURL, destURL, deleteSource)]; + }); + }); + } + /** + * Move a model from one URL to another. + * + * This function supports: + * + * 1. Moving within a storage medium, e.g., + * `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')` + * 2. Moving between two storage mediums, e.g., + * `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')` + * + * ```js + * // First create and save a model. + * const model = tf.sequential(); + * model.add(tf.layers.dense( + * {units: 1, inputShape: [10], activation: 'sigmoid'})); + * await model.save('localstorage://demo/management/model1'); + * + * // Then list existing models. + * console.log(JSON.stringify(await tf.io.listModels())); + * + * // Move the model, from Local Storage to IndexedDB. + * await tf.io.moveModel( + * 'localstorage://demo/management/model1', + * 'indexeddb://demo/management/model1'); + * + * // List models again. + * console.log(JSON.stringify(await tf.io.listModels())); + * + * // Remove the moved model. + * await tf.io.removeModel('indexeddb://demo/management/model1'); + * ``` + * + * @param sourceURL Source URL of moving. + * @param destURL Destination URL of moving. + * @returns ModelArtifactsInfo of the copied model (if and only if copying + * is successful). + * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or + * if `oldPath` and `newPath` are identical. + * + * @doc { + * heading: 'Models', + * subheading: 'Management', + * namespace: 'io', + * ignoreCI: true + * } + */ + function moveModel(sourceURL, destURL) { + return __awaiter(this, void 0, void 0, function () { + var deleteSource; + return __generator(this, function (_a) { + deleteSource = true; + return [2 /*return*/, cloneModelInternal(sourceURL, destURL, deleteSource)]; + }); + }); + } + + /** + * @license + * Copyright 2019 Google LLC. All Rights Reserved. + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + * ============================================================================= + */ + var PlatformBrowser = /** @class */ (function () { + function PlatformBrowser() { + } + PlatformBrowser.prototype.fetch = function (path, init) { + return fetch(path, init); + }; + PlatformBrowser.prototype.now = function () { + return performance.now(); + }; + PlatformBrowser.prototype.encode = function (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); + }; + PlatformBrowser.prototype.decode = function (bytes, encoding) { + return new TextDecoder(encoding).decode(bytes); + }; + return PlatformBrowser; + }()); + if (env().get('IS_BROWSER')) { + env().setPlatform('browser', new PlatformBrowser()); + // Register LocalStorage IOHandler + try { + ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager()); + } + catch (err) { + } + // Register IndexedDB IOHandler + try { + ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager()); + } + catch (err) { + } + } + + /** + * @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. + * ============================================================================= + */ + // We are wrapping this within an object so it can be stubbed by Jasmine. + var getNodeFetch = { + // tslint:disable-next-line:no-require-imports + importFetch: function () { return require('node-fetch'); } + }; + var systemFetch; + var PlatformNode = /** @class */ (function () { + function PlatformNode() { + // tslint:disable-next-line:no-require-imports + this.util = require('util'); + // According to the spec, the built-in encoder can do only UTF-8 encoding. + // https://developer.mozilla.org/en-US/docs/Web/API/TextEncoder/TextEncoder + this.textEncoder = new this.util.TextEncoder(); + } + PlatformNode.prototype.fetch = function (path, requestInits) { + if (env().global.fetch != null) { + return env().global.fetch(path, requestInits); + } + if (systemFetch == null) { + systemFetch = getNodeFetch.importFetch(); + } + return systemFetch(path, requestInits); + }; + PlatformNode.prototype.now = function () { + var time = process.hrtime(); + return time[0] * 1000 + time[1] / 1000000; + }; + PlatformNode.prototype.encode = function (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); + }; + PlatformNode.prototype.decode = function (bytes, encoding) { + if (bytes.length === 0) { + return ''; + } + return new this.util.TextDecoder(encoding).decode(bytes); + }; + return PlatformNode; + }()); + if (env().get('IS_NODE')) { + env().setPlatform('node', new PlatformNode()); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`. + * + * The values are stored in CPU as `TypedArray`. Fill the buffer using + * `buffer.set()`, or by modifying directly `buffer.values`. + * + * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with + * those values. + * + * ```js + * // Create a buffer and set values at particular indices. + * const buffer = tf.buffer([2, 2]); + * buffer.set(3, 0, 0); + * buffer.set(5, 1, 0); + * + * // Convert the buffer back to a tensor. + * buffer.toTensor().print(); + * ``` + * + * @param shape An array of integers defining the output tensor shape. + * @param dtype The dtype of the buffer. Defaults to 'float32'. + * @param values The values of the buffer as `TypedArray`. Defaults to + * zeros. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function buffer(shape, dtype, values) { + if (dtype === void 0) { dtype = 'float32'; } + dtype = dtype || 'float32'; + assertNonNegativeIntegerDimensions(shape); + return new TensorBuffer(shape, dtype, values); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Casts a `tf.Tensor` to a new dtype. + * + * ```js + * const x = tf.tensor1d([1.5, 2.5, 3]); + * tf.cast(x, 'int32').print(); + * ``` + * @param x The input tensor to be casted. + * @param dtype The dtype to cast the input tensor to. + * + * @doc {heading: 'Tensors', subheading: 'Transformations'} + */ + function cast_(x, dtype) { + var $x = convertToTensor(x, 'x', 'cast'); + // Sanity checks. + 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'); + } + var inputs = { x: $x }; + var attrs = { dtype: dtype }; + return ENGINE.runKernel(Cast, inputs, attrs); + } + var cast = op({ cast_: cast_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a new tensor with the same values and shape as the specified + * tensor. + * + * ```js + * const x = tf.tensor([1, 2]); + * + * x.clone().print(); + * ``` + * + * @param x The tensor to clone. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function clone_(x) { + var $x = convertToTensor(x, 'x', 'clone', 'string_or_numeric'); + var inputs = { x: $x }; + // Note this op is called tf.identity in python. Hence the kernel name used + // here. + return ENGINE.runKernel(Identity, inputs); + } + var clone = op({ clone_: clone_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Prints information about the `tf.Tensor` including its data. + * + * ```js + * const verbose = true; + * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose); + * ``` + * @param x The tensor to be printed. + * @param verbose Whether to print verbose information about the ` Tensor`, + * including dtype and size. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function print(x, verbose) { + if (verbose === void 0) { verbose = false; } + console.log(x.toString(verbose)); + } + + /** + * @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. + * ============================================================================= + */ + getOrMakeEngine(); + var opHandler = { + buffer: buffer, + cast: cast, + clone: clone, + print: print + }; + setOpHandler(opHandler); + + 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(function (resolve) { return setTimeout(resolve); }).then(f); + } + var BrowserDownloads = /** @class */ (function () { + function BrowserDownloads(fileNamePrefix) { + if (!env().getBool('IS_BROWSER')) { + // TODO(cais): Provide info on what IOHandlers are available under the + // current environment. + 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; + } + BrowserDownloads.prototype.save = function (modelArtifacts) { + return __awaiter(this, void 0, void 0, function () { + var weightsURL, weightsManifest, modelJSON, modelJsonURL, jsonAnchor_1, weightDataAnchor_1; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + if (typeof (document) === 'undefined') { + throw new Error('Browser downloads are not supported in ' + + 'this environment since `document` is not present'); + } + weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' })); + if (!(modelArtifacts.modelTopology instanceof ArrayBuffer)) return [3 /*break*/, 1]; + throw new Error('BrowserDownloads.save() does not support saving model topology ' + + 'in binary formats yet.'); + case 1: + weightsManifest = [{ + paths: ['./' + this.weightDataFileName], + weights: modelArtifacts.weightSpecs + }]; + modelJSON = getModelJSONForModelArtifacts(modelArtifacts, weightsManifest); + modelJsonURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelJSON)], { type: 'application/json' })); + jsonAnchor_1 = this.modelJsonAnchor == null ? + document.createElement('a') : + this.modelJsonAnchor; + jsonAnchor_1.download = this.modelJsonFileName; + jsonAnchor_1.href = modelJsonURL; + // Trigger downloads by evoking a click event on the download anchors. + // When multiple downloads are started synchronously, Firefox will only + // save the last one. + return [4 /*yield*/, defer(function () { return jsonAnchor_1.dispatchEvent(new MouseEvent('click')); })]; + case 2: + // Trigger downloads by evoking a click event on the download anchors. + // When multiple downloads are started synchronously, Firefox will only + // save the last one. + _a.sent(); + if (!(modelArtifacts.weightData != null)) return [3 /*break*/, 4]; + weightDataAnchor_1 = this.weightDataAnchor == null ? + document.createElement('a') : + this.weightDataAnchor; + weightDataAnchor_1.download = this.weightDataFileName; + weightDataAnchor_1.href = weightsURL; + return [4 /*yield*/, defer(function () { return weightDataAnchor_1.dispatchEvent(new MouseEvent('click')); })]; + case 3: + _a.sent(); + _a.label = 4; + case 4: return [2 /*return*/, { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }]; + } + }); + }); + }; + return BrowserDownloads; + }()); + BrowserDownloads.URL_SCHEME = 'downloads://'; + var BrowserFiles = /** @class */ (function () { + function BrowserFiles(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); + } + BrowserFiles.prototype.load = function () { + return __awaiter(this, void 0, void 0, function () { + var _this = this; + return __generator(this, function (_a) { + return [2 /*return*/, new Promise(function (resolve, reject) { + var jsonReader = new FileReader(); + jsonReader.onload = function (event) { + // tslint:disable-next-line:no-any + var modelJSON = JSON.parse(event.target.result); + var modelTopology = modelJSON.modelTopology; + if (modelTopology == null) { + reject(new Error("modelTopology field is missing from file " + _this.jsonFile.name)); + return; + } + var 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: modelTopology }); + return; + } + var modelArtifactsPromise = getModelArtifactsForJSON(modelJSON, function (weightsManifest) { return _this.loadWeights(weightsManifest); }); + resolve(modelArtifactsPromise); + }; + jsonReader.onerror = function (error) { return 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); + })]; + }); + }); + }; + BrowserFiles.prototype.loadWeights = function (weightsManifest) { + var e_1, _a; + var _this = this; + var weightSpecs = []; + var paths = []; + try { + for (var weightsManifest_1 = __values(weightsManifest), weightsManifest_1_1 = weightsManifest_1.next(); !weightsManifest_1_1.done; weightsManifest_1_1 = weightsManifest_1.next()) { + var entry = weightsManifest_1_1.value; + weightSpecs.push.apply(weightSpecs, __spread(entry.weights)); + paths.push.apply(paths, __spread(entry.paths)); + } + } + catch (e_1_1) { e_1 = { error: e_1_1 }; } + finally { + try { + if (weightsManifest_1_1 && !weightsManifest_1_1.done && (_a = weightsManifest_1.return)) _a.call(weightsManifest_1); + } + finally { if (e_1) throw e_1.error; } + } + var pathToFile = this.checkManifestAndWeightFiles(weightsManifest); + var promises = paths.map(function (path) { return _this.loadWeightsFile(path, pathToFile[path]); }); + return Promise.all(promises).then(function (buffers) { return [weightSpecs, concatenateArrayBuffers(buffers)]; }); + }; + BrowserFiles.prototype.loadWeightsFile = function (path, file) { + return new Promise(function (resolve, reject) { + var weightFileReader = new FileReader(); + weightFileReader.onload = function (event) { + // tslint:disable-next-line:no-any + var weightData = event.target.result; + resolve(weightData); + }; + weightFileReader.onerror = function (error) { return reject("Failed to weights data from file of path '" + path + "'."); }; + weightFileReader.readAsArrayBuffer(file); + }); + }; + /** + * Check the compatibility between weights manifest and weight files. + */ + BrowserFiles.prototype.checkManifestAndWeightFiles = function (manifest) { + var e_2, _a; + var _this = this; + var basenames = []; + var fileNames = this.weightsFiles.map(function (file) { return basename(file.name); }); + var pathToFile = {}; + try { + for (var manifest_1 = __values(manifest), manifest_1_1 = manifest_1.next(); !manifest_1_1.done; manifest_1_1 = manifest_1.next()) { + var group = manifest_1_1.value; + group.paths.forEach(function (path) { + var 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)]; + } + }); + } + } + catch (e_2_1) { e_2 = { error: e_2_1 }; } + finally { + try { + if (manifest_1_1 && !manifest_1_1.done && (_a = manifest_1.return)) _a.call(manifest_1); + } + finally { if (e_2) throw e_2.error; } + } + 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; + }; + return BrowserFiles; + }()); + var browserDownloadsRouter = function (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); + /** + * Creates an IOHandler that triggers file downloads from the browser. + * + * The returned `IOHandler` instance can be used as model exporting methods such + * as `tf.Model.save` and supports only saving. + * + * ```js + * const model = tf.sequential(); + * model.add(tf.layers.dense( + * {units: 1, inputShape: [10], activation: 'sigmoid'})); + * const saveResult = await model.save('downloads://mymodel'); + * // This will trigger downloading of two files: + * // 'mymodel.json' and 'mymodel.weights.bin'. + * console.log(saveResult); + * ``` + * + * @param fileNamePrefix Prefix name of the files to be downloaded. For use with + * `tf.Model`, `fileNamePrefix` should follow either of the following two + * formats: + * 1. `null` or `undefined`, in which case the default file + * names will be used: + * - 'model.json' for the JSON file containing the model topology and + * weights manifest. + * - 'model.weights.bin' for the binary file containing the binary weight + * values. + * 2. A single string or an Array of a single string, as the file name prefix. + * For example, if `'foo'` is provided, the downloaded JSON + * file and binary weights file will be named 'foo.json' and + * 'foo.weights.bin', respectively. + * @param config Additional configuration for triggering downloads. + * @returns An instance of `BrowserDownloads` `IOHandler`. + * + * @doc { + * heading: 'Models', + * subheading: 'Loading', + * namespace: 'io', + * ignoreCI: true + * } + */ + function browserDownloads(fileNamePrefix) { + if (fileNamePrefix === void 0) { fileNamePrefix = 'model'; } + return new BrowserDownloads(fileNamePrefix); + } + /** + * Creates an IOHandler that loads model artifacts from user-selected files. + * + * This method can be used for loading from files such as user-selected files + * in the browser. + * When used in conjunction with `tf.loadLayersModel`, an instance of + * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts. + * + * ```js + * // Note: This code snippet won't run properly without the actual file input + * // elements in the HTML DOM. + * + * // Suppose there are two HTML file input (``) + * // elements. + * const uploadJSONInput = document.getElementById('upload-json'); + * const uploadWeightsInput = document.getElementById('upload-weights'); + * const model = await tf.loadLayersModel(tf.io.browserFiles( + * [uploadJSONInput.files[0], uploadWeightsInput.files[0]])); + * ``` + * + * @param files `File`s to load from. Currently, this function supports only + * loading from files that contain Keras-style models (i.e., `tf.Model`s), for + * which an `Array` of `File`s is expected (in that order): + * - A JSON file containing the model topology and weight manifest. + * - Optionally, One or more binary files containing the binary weights. + * These files must have names that match the paths in the `weightsManifest` + * contained by the aforementioned JSON file, or errors will be thrown + * during loading. These weights files have the same format as the ones + * generated by `tensorflowjs_converter` that comes with the `tensorflowjs` + * Python PIP package. If no weights files are provided, only the model + * topology will be loaded from the JSON file above. + * @returns An instance of `Files` `IOHandler`. + * + * @doc { + * heading: 'Models', + * subheading: 'Loading', + * namespace: 'io', + * ignoreCI: true + * } + */ + function browserFiles(files) { + return new BrowserFiles(files); + } + + /** + * @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. + * ============================================================================= + */ + /** + * Monitor Promise.all progress, fire onProgress callback function. + * + * @param promises Promise list going to be monitored + * @param onProgress Callback function. Fired when a promise resolved. + * @param startFraction Optional fraction start. Default to 0. + * @param endFraction Optional fraction end. Default to 1. + */ + function monitorPromisesProgress(promises, onProgress, startFraction, endFraction) { + checkPromises(promises); + startFraction = startFraction == null ? 0 : startFraction; + endFraction = endFraction == null ? 1 : endFraction; + checkFraction(startFraction, endFraction); + var resolvedPromise = 0; + var registerMonitor = function (promise) { + promise.then(function (value) { + var fraction = startFraction + + ++resolvedPromise / promises.length * (endFraction - startFraction); + // pass fraction as parameter to callback function. + onProgress(fraction); + return value; + }); + return promise; + }; + function checkPromises(promises) { + assert(promises != null && Array.isArray(promises) && promises.length > 0, function () { return 'promises must be a none empty array'; }); + } + function checkFraction(startFraction, endFraction) { + assert(startFraction >= 0 && startFraction <= 1, function () { return "Progress fraction must be in range [0, 1], but " + + ("got startFraction " + startFraction); }); + assert(endFraction >= 0 && endFraction <= 1, function () { return "Progress fraction must be in range [0, 1], but " + + ("got endFraction " + endFraction); }); + assert(endFraction >= startFraction, function () { return "startFraction must be no more than endFraction, but " + + ("got startFraction " + startFraction + " and endFraction ") + + ("" + endFraction); }); + } + return Promise.all(promises.map(registerMonitor)); + } + + /** + * Reads binary weights data from a number of URLs. + * + * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls. + * @param requestOptions RequestInit (options) for the HTTP requests. + * @param fetchFunc Optional overriding value for the `window.fetch` function. + * @param onProgress Optional, progress callback function, fired periodically + * before the load is completed. + * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same + * length as `fetchURLs`. + */ + function loadWeightsAsArrayBuffer(fetchURLs, loadOptions) { + return __awaiter(this, void 0, void 0, function () { + var fetchFunc, requests, fetchStartFraction, fetchEndFraction, responses, _a, bufferPromises, bufferStartFraction, bufferEndFraction, buffers, _b; + return __generator(this, function (_c) { + switch (_c.label) { + case 0: + if (loadOptions == null) { + loadOptions = {}; + } + fetchFunc = loadOptions.fetchFunc == null ? env().platform.fetch : + loadOptions.fetchFunc; + requests = fetchURLs.map(function (fetchURL) { return fetchFunc(fetchURL, loadOptions.requestInit, { isBinary: true }); }); + fetchStartFraction = 0; + fetchEndFraction = 0.5; + if (!(loadOptions.onProgress == null)) return [3 /*break*/, 2]; + return [4 /*yield*/, Promise.all(requests)]; + case 1: + _a = _c.sent(); + return [3 /*break*/, 4]; + case 2: return [4 /*yield*/, monitorPromisesProgress(requests, loadOptions.onProgress, fetchStartFraction, fetchEndFraction)]; + case 3: + _a = _c.sent(); + _c.label = 4; + case 4: + responses = _a; + bufferPromises = responses.map(function (response) { return response.arrayBuffer(); }); + bufferStartFraction = 0.5; + bufferEndFraction = 1; + if (!(loadOptions.onProgress == null)) return [3 /*break*/, 6]; + return [4 /*yield*/, Promise.all(bufferPromises)]; + case 5: + _b = _c.sent(); + return [3 /*break*/, 8]; + case 6: return [4 /*yield*/, monitorPromisesProgress(bufferPromises, loadOptions.onProgress, bufferStartFraction, bufferEndFraction)]; + case 7: + _b = _c.sent(); + _c.label = 8; + case 8: + buffers = _b; + return [2 /*return*/, buffers]; + } + }); + }); + } + /** + * Reads a weights manifest JSON configuration, fetches the weights and + * returns them as `Tensor`s. + * + * @param manifest The weights manifest JSON. + * @param filePathPrefix The path prefix for filenames given in the manifest. + * Defaults to the empty string. + * @param weightNames The names of the weights to be fetched. + */ + function loadWeights(manifest, filePathPrefix, weightNames, requestInit) { + if (filePathPrefix === void 0) { filePathPrefix = ''; } + return __awaiter(this, void 0, void 0, function () { + var fetchWeights, loadWeights; + return __generator(this, function (_a) { + fetchWeights = function (fetchUrls) { return loadWeightsAsArrayBuffer(fetchUrls, { requestInit: requestInit }); }; + loadWeights = weightsLoaderFactory(fetchWeights); + return [2 /*return*/, loadWeights(manifest, filePathPrefix, weightNames)]; + }); + }); + } + /** + * Creates a function, which reads a weights manifest JSON configuration, + * fetches the weight files using the specified function and returns them as + * `Tensor`s. + * + * ```js + * // example for creating a nodejs weight loader, which reads the weight files + * // from disk using fs.readFileSync + * + * import * as fs from 'fs' + * + * const fetchWeightsFromDisk = (filePaths: string[]) => + * filePaths.map(filePath => fs.readFileSync(filePath).buffer) + * + * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk) + * + * const manifest = JSON.parse( + * fs.readFileSync('./my_model-weights_manifest').toString() + * ) + * const weightMap = await loadWeights(manifest, './') + * ``` + * @param fetchWeightsFunction The function used for fetching the weight files. + * @returns Weight loading function. + */ + function weightsLoaderFactory(fetchWeightsFunction) { + var _this = this; + return function (manifest, filePathPrefix, weightNames) { + if (filePathPrefix === void 0) { filePathPrefix = ''; } + return __awaiter(_this, void 0, void 0, function () { + var groupIndicesToFetchMap, groupWeightsToFetch, weightsFound, allManifestWeightNames, weightsNotFound, groupIndicesToFetch, fetchUrls, buffers, weightsTensorMap, bufferIndexOffset; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + groupIndicesToFetchMap = manifest.map(function () { return false; }); + groupWeightsToFetch = {}; + weightsFound = weightNames != null ? weightNames.map(function () { return false; }) : []; + allManifestWeightNames = []; + manifest.forEach(function (manifestGroupConfig, groupIndex) { + var groupOffset = 0; + manifestGroupConfig.weights.forEach(function (weightsEntry) { + var rawDtype = ('quantization' in weightsEntry) ? + weightsEntry.quantization.dtype : + weightsEntry.dtype; + var weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] * + sizeFromShape(weightsEntry.shape); + var enqueueWeightsForFetchingFn = function () { + groupIndicesToFetchMap[groupIndex] = true; + if (groupWeightsToFetch[groupIndex] == null) { + groupWeightsToFetch[groupIndex] = []; + } + groupWeightsToFetch[groupIndex].push({ + manifestEntry: weightsEntry, + groupOffset: groupOffset, + sizeBytes: weightsBytes + }); + }; + if (weightNames != null) { + weightNames.forEach(function (weightName, weightIndex) { + if (weightName === weightsEntry.name) { + enqueueWeightsForFetchingFn(); + weightsFound[weightIndex] = true; + } + }); + } + else { + enqueueWeightsForFetchingFn(); + } + allManifestWeightNames.push(weightsEntry.name); + groupOffset += weightsBytes; + }); + }); + if (!weightsFound.every(function (found) { return found; })) { + weightsNotFound = weightNames.filter(function (_, i) { return !weightsFound[i]; }); + throw new Error("Could not find weights in manifest with names: " + + (weightsNotFound.join(', ') + ". \n") + + "Manifest JSON has weights with names: " + + (allManifestWeightNames.join(', ') + ".")); + } + groupIndicesToFetch = groupIndicesToFetchMap.reduce(function (accumulator, shouldFetch, i) { + if (shouldFetch) { + accumulator.push(i); + } + return accumulator; + }, []); + fetchUrls = []; + groupIndicesToFetch.forEach(function (i) { + manifest[i].paths.forEach(function (filepath) { + var fetchUrl = filePathPrefix + + (!filePathPrefix.endsWith('/') ? '/' : '') + filepath; + fetchUrls.push(fetchUrl); + }); + }); + return [4 /*yield*/, fetchWeightsFunction(fetchUrls)]; + case 1: + buffers = _a.sent(); + weightsTensorMap = {}; + bufferIndexOffset = 0; + groupIndicesToFetch.forEach(function (i) { + var numBuffers = manifest[i].paths.length; + var groupBytes = 0; + for (var i_1 = 0; i_1 < numBuffers; i_1++) { + groupBytes += buffers[bufferIndexOffset + i_1].byteLength; + } + // Create a buffer for the whole group. + var groupBuffer = new ArrayBuffer(groupBytes); + var groupByteBuffer = new Uint8Array(groupBuffer); + var groupBufferOffset = 0; + for (var i_2 = 0; i_2 < numBuffers; i_2++) { + var buffer = new Uint8Array(buffers[bufferIndexOffset + i_2]); + groupByteBuffer.set(buffer, groupBufferOffset); + groupBufferOffset += buffer.byteLength; + } + var weightsEntries = groupWeightsToFetch[i]; + weightsEntries.forEach(function (weightsEntry) { + var byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes); + var nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]); + for (var name in nameToTensorMap) { + weightsTensorMap[name] = nameToTensorMap[name]; + } + }); + bufferIndexOffset += numBuffers; + }); + return [2 /*return*/, weightsTensorMap]; + } + }); + }); + }; + } + + var OCTET_STREAM_MIME_TYPE = 'application/octet-stream'; + var JSON_TYPE = 'application/json'; + var HTTPRequest = /** @class */ (function () { + function HTTPRequest(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', function () { return '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, function () { return 'URL path for http must not be null, undefined or ' + + 'empty.'; }); + if (Array.isArray(path)) { + assert(path.length === 2, function () { return '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 || {}; + } + HTTPRequest.prototype.save = function (modelArtifacts) { + return __awaiter(this, void 0, void 0, function () { + var init, weightsManifest, modelTopologyAndWeightManifest, response; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: + if (modelArtifacts.modelTopology instanceof ArrayBuffer) { + throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' + + 'in binary formats yet.'); + } + init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit); + init.body = new FormData(); + weightsManifest = [{ + paths: ['./model.weights.bin'], + weights: modelArtifacts.weightSpecs, + }]; + 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'); + } + return [4 /*yield*/, this.fetch(this.path, init)]; + case 1: + response = _a.sent(); + if (response.ok) { + return [2 /*return*/, { + modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts), + responses: [response], + }]; + } + else { + throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status " + + (response.status + ".")); + } + } + }); + }); + }; + /** + * Load model artifacts via HTTP request(s). + * + * See the documentation to `tf.io.http` for details on the saved + * artifacts. + * + * @returns The loaded model artifacts (if loading succeeds). + */ + HTTPRequest.prototype.load = function () { + return __awaiter(this, void 0, void 0, function () { + var modelConfigRequest, modelJSON, message, modelTopology, weightsManifest; + var _this = this; + return __generator(this, function (_a) { + switch (_a.label) { + case 0: return [4 /*yield*/, this.fetch(this.path, this.requestInit)]; + case 1: + modelConfigRequest = _a.sent(); + 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."); + } + _a.label = 2; + case 2: + _a.trys.push([2, 4, , 5]); + return [4 /*yield*/, modelConfigRequest.json()]; + case 3: + modelJSON = _a.sent(); + return [3 /*break*/, 5]; + case 4: + _a.sent(); + message = "Failed to parse model JSON of response from " + this.path + "."; + // TODO(nsthorat): Remove this after some time when we're comfortable that + // .pb files are mostly gone. + 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); + case 5: + modelTopology = modelJSON.modelTopology; + 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 [2 /*return*/, getModelArtifactsForJSON(modelJSON, function (weightsManifest) { return _this.loadWeights(weightsManifest); })]; + } + }); + }); + }; + HTTPRequest.prototype.loadWeights = function (weightsManifest) { + return __awaiter(this, void 0, void 0, function () { + var weightPath, _a, prefix, suffix, pathPrefix, weightSpecs, weightsManifest_1, weightsManifest_1_1, entry, fetchURLs, urlPromises, weightsManifest_2, weightsManifest_2_1, weightsGroup, _b, _c, path, _d, _e, _f, buffers; + var e_2, _g, e_3, _h, e_4, _j; + return __generator(this, function (_k) { + switch (_k.label) { + case 0: + weightPath = Array.isArray(this.path) ? this.path[1] : this.path; + _a = __read(parseUrl(weightPath), 2), prefix = _a[0], suffix = _a[1]; + pathPrefix = this.weightPathPrefix || prefix; + weightSpecs = []; + try { + for (weightsManifest_1 = __values(weightsManifest), weightsManifest_1_1 = weightsManifest_1.next(); !weightsManifest_1_1.done; weightsManifest_1_1 = weightsManifest_1.next()) { + entry = weightsManifest_1_1.value; + weightSpecs.push.apply(weightSpecs, __spread(entry.weights)); + } + } + catch (e_2_1) { e_2 = { error: e_2_1 }; } + finally { + try { + if (weightsManifest_1_1 && !weightsManifest_1_1.done && (_g = weightsManifest_1.return)) _g.call(weightsManifest_1); + } + finally { if (e_2) throw e_2.error; } + } + fetchURLs = []; + urlPromises = []; + try { + for (weightsManifest_2 = __values(weightsManifest), weightsManifest_2_1 = weightsManifest_2.next(); !weightsManifest_2_1.done; weightsManifest_2_1 = weightsManifest_2.next()) { + weightsGroup = weightsManifest_2_1.value; + try { + for (_b = (e_4 = void 0, __values(weightsGroup.paths)), _c = _b.next(); !_c.done; _c = _b.next()) { + path = _c.value; + if (this.weightUrlConverter != null) { + urlPromises.push(this.weightUrlConverter(path)); + } + else { + fetchURLs.push(pathPrefix + path + suffix); + } + } + } + catch (e_4_1) { e_4 = { error: e_4_1 }; } + finally { + try { + if (_c && !_c.done && (_j = _b.return)) _j.call(_b); + } + finally { if (e_4) throw e_4.error; } + } + } + } + catch (e_3_1) { e_3 = { error: e_3_1 }; } + finally { + try { + if (weightsManifest_2_1 && !weightsManifest_2_1.done && (_h = weightsManifest_2.return)) _h.call(weightsManifest_2); + } + finally { if (e_3) throw e_3.error; } + } + if (!this.weightUrlConverter) return [3 /*break*/, 2]; + _e = (_d = fetchURLs.push).apply; + _f = [fetchURLs]; + return [4 /*yield*/, Promise.all(urlPromises)]; + case 1: + _e.apply(_d, _f.concat([__spread.apply(void 0, [_k.sent()])])); + _k.label = 2; + case 2: return [4 /*yield*/, loadWeightsAsArrayBuffer(fetchURLs, { + requestInit: this.requestInit, + fetchFunc: this.fetch, + onProgress: this.onProgress + })]; + case 3: + buffers = _k.sent(); + return [2 /*return*/, [weightSpecs, concatenateArrayBuffers(buffers)]]; + } + }); + }); + }; + return HTTPRequest; + }()); + HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//; + /** + * Extract the prefix and suffix of the url, where the prefix is the path before + * the last file, and suffix is the search params after the last file. + * ``` + * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file' + * [prefix, suffix] = parseUrl(url) + * // prefix = 'http://tfhub.dev/model/1/' + * // suffix = '?tfjs-format=file' + * ``` + * @param url the model url to be parsed. + */ + function parseUrl(url) { + var lastSlash = url.lastIndexOf('/'); + var lastSearchParam = url.lastIndexOf('?'); + var prefix = url.substring(0, lastSlash); + var suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : ''; + return [prefix + '/', suffix]; + } + function isHTTPScheme(url) { + return url.match(HTTPRequest.URL_SCHEME_REGEX) != null; + } + var httpRouter = function (url, loadOptions) { + if (typeof fetch === 'undefined' && + (loadOptions == null || loadOptions.fetchFunc == null)) { + // `http` uses `fetch` or `node-fetch`, if one wants to use it in + // an environment that is not the browser or node they have to setup a + // global fetch polyfill. + return null; + } + else { + var isHTTP = true; + if (Array.isArray(url)) { + isHTTP = url.every(function (urlItem) { return isHTTPScheme(urlItem); }); + } + else { + isHTTP = isHTTPScheme(url); + } + if (isHTTP) { + return http(url, loadOptions); + } + } + return null; + }; + IORouterRegistry.registerSaveRouter(httpRouter); + IORouterRegistry.registerLoadRouter(httpRouter); + /** + * Creates an IOHandler subtype that sends model artifacts to HTTP server. + * + * An HTTP request of the `multipart/form-data` mime type will be sent to the + * `path` URL. The form data includes artifacts that represent the topology + * and/or weights of the model. In the case of Keras-style `tf.Model`, two + * blobs (files) exist in form-data: + * - A JSON file consisting of `modelTopology` and `weightsManifest`. + * - A binary weights file consisting of the concatenated weight values. + * These files are in the same format as the one generated by + * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html). + * + * The following code snippet exemplifies the client-side code that uses this + * function: + * + * ```js + * const model = tf.sequential(); + * model.add( + * tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'})); + * + * const saveResult = await model.save(tf.io.http( + * 'http://model-server:5000/upload', {requestInit: {method: 'PUT'}})); + * console.log(saveResult); + * ``` + * + * If the default `POST` method is to be used, without any custom parameters + * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`: + * + * ```js + * const saveResult = await model.save('http://model-server:5000/upload'); + * ``` + * + * The following GitHub Gist + * https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864 + * implements a server based on [flask](https://github.com/pallets/flask) that + * can receive the request. Upon receiving the model artifacts via the requst, + * this particular server reconsistutes instances of [Keras + * Models](https://keras.io/models/model/) in memory. + * + * + * @param path A URL path to the model. + * Can be an absolute HTTP path (e.g., + * 'http://localhost:8000/model-upload)') or a relative path (e.g., + * './model-upload'). + * @param requestInit Request configurations to be used when sending + * HTTP request to server using `fetch`. It can contain fields such as + * `method`, `credentials`, `headers`, `mode`, etc. See + * https://developer.mozilla.org/en-US/docs/Web/API/Request/Request + * for more information. `requestInit` must not have a body, because the + * body will be set by TensorFlow.js. File blobs representing the model + * topology (filename: 'model.json') and the weights of the model (filename: + * 'model.weights.bin') will be appended to the body. If `requestInit` has a + * `body`, an Error will be thrown. + * @param loadOptions Optional configuration for the loading. It includes the + * following fields: + * - weightPathPrefix Optional, this specifies the path prefix for weight + * files, by default this is calculated from the path param. + * - fetchFunc Optional, custom `fetch` function. E.g., in Node.js, + * the `fetch` from node-fetch can be used here. + * - onProgress Optional, progress callback function, fired periodically + * before the load is completed. + * @returns An instance of `IOHandler`. + * + * @doc { + * heading: 'Models', + * subheading: 'Loading', + * namespace: 'io', + * ignoreCI: true + * } + */ + function http(path, loadOptions) { + return new HTTPRequest(path, loadOptions); + } + /** + * Deprecated. Use `tf.io.http`. + * @param path + * @param loadOptions + */ + function browserHTTPRequest(path, loadOptions) { + return http(path, loadOptions); + } + + /** + * @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. + * ============================================================================= + */ + var PassthroughLoader = /** @class */ (function () { + function PassthroughLoader(modelArtifacts) { + this.modelArtifacts = modelArtifacts; + } + PassthroughLoader.prototype.load = function () { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + return [2 /*return*/, this.modelArtifacts]; + }); + }); + }; + return PassthroughLoader; + }()); + var PassthroughSaver = /** @class */ (function () { + function PassthroughSaver(saveHandler) { + this.saveHandler = saveHandler; + } + PassthroughSaver.prototype.save = function (modelArtifacts) { + return __awaiter(this, void 0, void 0, function () { + return __generator(this, function (_a) { + return [2 /*return*/, this.saveHandler(modelArtifacts)]; + }); + }); + }; + return PassthroughSaver; + }()); + /** + * Creates an IOHandler that loads model artifacts from memory. + * + * When used in conjunction with `tf.loadLayersModel`, an instance of + * `tf.LayersModel` (Keras-style) can be constructed from the loaded artifacts. + * + * ```js + * const model = await tf.loadLayersModel(tf.io.fromMemory( + * modelTopology, weightSpecs, weightData)); + * ``` + * + * @param modelArtifacts a object containing model topology (i.e., parsed from + * the JSON format). + * @param weightSpecs An array of `WeightsManifestEntry` objects describing the + * names, shapes, types, and quantization of the weight data. + * @param weightData A single `ArrayBuffer` containing the weight data, + * concatenated in the order described by the weightSpecs. + * @param trainingConfig Model training configuration. Optional. + * + * @returns A passthrough `IOHandler` that simply loads the provided data. + */ + function fromMemory(modelArtifacts, weightSpecs, weightData, trainingConfig) { + if (arguments.length === 1) { + var isModelArtifacts = modelArtifacts.modelTopology != null || + modelArtifacts.weightSpecs != null; + if (isModelArtifacts) { + return new PassthroughLoader(modelArtifacts); + } + else { + // Legacy support: with only modelTopology. + // TODO(cais): Remove this deprecated API. + 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 { + // Legacy support. + // TODO(cais): Remove this deprecated API. + 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: weightSpecs, + weightData: weightData, + trainingConfig: trainingConfig + }); + } + } + /** + * Creates an IOHandler that passes saved model artifacts to a callback. + * + * ```js + * function handleSave(artifacts) { + * // ... do something with the artifacts ... + * return {modelArtifactsInfo: {...}, ...}; + * } + * + * const saveResult = model.save(tf.io.withSaveHandler(handleSave)); + * ``` + * + * @param saveHandler A function that accepts a `ModelArtifacts` and returns a + * `SaveResult`. + */ + function withSaveHandler(saveHandler) { + return new PassthroughSaver(saveHandler); + } + + /** + * @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. + * ============================================================================= + */ + + var io = { + __proto__: null, + browserFiles: browserFiles, + browserHTTPRequest: browserHTTPRequest, + concatenateArrayBuffers: concatenateArrayBuffers, + decodeWeights: decodeWeights, + encodeWeights: encodeWeights, + fromMemory: fromMemory, + getLoadHandlers: getLoadHandlers, + getModelArtifactsForJSON: getModelArtifactsForJSON, + getModelArtifactsInfoForJSON: getModelArtifactsInfoForJSON, + getSaveHandlers: getSaveHandlers, + http: http, + isHTTPScheme: isHTTPScheme, + loadWeights: loadWeights, + registerLoadRouter: registerLoadRouter, + registerSaveRouter: registerSaveRouter, + weightsLoaderFactory: weightsLoaderFactory, + withSaveHandler: withSaveHandler, + copyModel: copyModel, + listModels: listModels, + moveModel: moveModel, + removeModel: removeModel + }; + + /** + * Computes the dot product of two matrices, A * B. These must be matrices. + * + * ```js + * const a = tf.tensor2d([1, 2], [1, 2]); + * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]); + * + * a.matMul(b).print(); // or tf.matMul(a, b) + * ``` + * @param a First matrix in dot product operation. + * @param b Second matrix in dot product operation. + * @param transposeA If true, `a` is transposed before multiplication. + * @param transposeB If true, `b` is transposed before multiplication. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function matMul_(a, b, transposeA, transposeB) { + var _a; + if (transposeA === void 0) { transposeA = false; } + if (transposeB === void 0) { transposeB = false; } + var $a = convertToTensor(a, 'a', 'matMul'); + var $b = convertToTensor(b, 'b', 'matMul'); + _a = __read(makeTypesMatch($a, $b), 2), $a = _a[0], $b = _a[1]; + var inputs = { a: $a, b: $b }; + var attrs = { transposeA: transposeA, transposeB: transposeB }; + return ENGINE.runKernel(BatchMatMul, inputs, attrs); + } + var matMul$1 = op({ matMul_: matMul_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take + * value `onValue` (defaults to 1), while all other locations take value + * `offValue` (defaults to 0). If `indices` is rank `R`, the output has rank + * `R+1` with the last axis of size `depth`. + * + * ```js + * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print(); + * ``` + * + * @param indices `tf.Tensor` of indices with dtype `int32`. + * @param depth The depth of the one hot dimension. + * @param onValue A number used to fill in the output when the index matches + * the location. + * @param offValue A number used to fill in the output when the index does + * not match the location. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function oneHot_(indices, depth, onValue, offValue) { + if (onValue === void 0) { onValue = 1; } + if (offValue === void 0) { offValue = 0; } + if (depth < 2) { + throw new Error("Error in oneHot: depth must be >=2, but it is " + depth); + } + var $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32'); + var inputs = { indices: $indices }; + var attrs = { depth: depth, onValue: onValue, offValue: offValue }; + return ENGINE.runKernel(OneHot, inputs, attrs); + } + var oneHot = op({ oneHot_: oneHot_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`. + * + * The returned `tf.Tensor`'s dimension `i` will correspond to the input + * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`, + * where `n` is the rank of the input `tf.Tensor`. Hence by default, this + * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s. + * + * ```js + * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]); + * + * a.transpose().print(); // or tf.transpose(a) + * ``` + * + * @param x The tensor to transpose. + * @param perm The permutation of the dimensions of a. + * + * @doc {heading: 'Operations', subheading: 'Matrices'} + */ + function transpose_(x, perm) { + var $x = convertToTensor(x, 'x', 'transpose'); + if (perm == null) { + perm = $x.shape.map(function (s, i) { return i; }).reverse(); + } + assert($x.rank === perm.length, function () { return "Error in transpose: rank of input " + $x.rank + " " + + ("must match length of perm " + perm + "."); }); + perm.forEach(function (axis) { + assert(axis >= 0 && axis < $x.rank, function () { return "All entries in 'perm' must be between 0 and " + ($x.rank - 1) + + (" but got " + perm); }); + }); + if ($x.rank <= 1) { + return $x.clone(); + } + var inputs = { x: $x }; + var attrs = { perm: perm }; + return ENGINE.runKernel(Transpose, inputs, attrs); + } + var transpose = op({ transpose_: transpose_ }); + + /** + * @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. + * ============================================================================= + */ + /** + * Computes the confusion matrix from true labels and predicted labels. + * + * ```js + * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32'); + * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32'); + * const numClasses = 3; + * const out = tf.math.confusionMatrix(labels, predictions, numClasses); + * out.print(); + * // Expected output matrix: + * // [[2, 0, 0], + * // [0, 1, 1], + * // [0, 0, 1]] + * ``` + * + * @param labels The target labels, assumed to be 0-based integers + * for the classes. The shape is `[numExamples]`, where + * `numExamples` is the number of examples included. + * @param predictions The predicted classes, assumed to be + * 0-based integers for the classes. Must have the same shape as `labels`. + * @param numClasses Number of all classes, as an integer. + * Its value must be larger than the largest element in `labels` and + * `predictions`. + * @returns The confusion matrix as a int32-type 2D tensor. The value at + * row `r` and column `c` is the number of times examples of actual class + * `r` were predicted as class `c`. + * + * @doc {heading: 'Operations', subheading: 'Evaluation'} + */ + function confusionMatrix_(labels, predictions, numClasses) { + var $labels = convertToTensor(labels, 'labels', 'confusionMatrix'); + var $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix'); + assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), function () { return "If provided, numClasses must be a positive integer, " + + ("but got " + numClasses); }); + assert($labels.rank === 1, function () { return "Expected the rank of labels to be 1, but got " + $labels.rank; }); + assert($predictions.rank === 1, function () { return "Expected the rank of predictions to be 1, " + + ("but got " + $predictions.rank); }); + assert($labels.shape[0] === $predictions.shape[0], function () { return "Mismatch in the number of examples: " + + ($labels.shape[0] + " vs. " + $predictions.shape[0] + ". ") + + "Labels and predictions should have the same number of elements."; }); + assert(numClasses > 0 && Number.isInteger(numClasses), function () { return "numClasses is required to be a positive integer, but got " + + ("" + numClasses); }); + // TODO(cais): In the future, if oneHot supports tensors inputs for + // `numClasses`, `confusionMatrix` can make `numClasses` optional. + var oneHotLabels = oneHot(cast($labels, 'int32'), numClasses); + var oneHotPredictions = oneHot(cast($predictions, 'int32'), numClasses); + var oneHotLabelsT = transpose(oneHotLabels); + var product = matMul$1(oneHotLabelsT, oneHotPredictions); + return cast(product, 'int32'); + } + var confusionMatrix = op({ confusionMatrix_: confusionMatrix_ }); + + /** + * @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. + * ============================================================================= + */ + + var math = { + __proto__: null, + confusionMatrix: confusionMatrix + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Returns the dimensions in the input shape that are broadcasted to + * produce the provided output shape. + * + * The returned dimensions are 0-indexed and sorted. An example: + * inShape = [4, 1, 3] + * outShape = [5, 4, 3, 3] + * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3. + */ + function getBroadcastDims(inShape, outShape) { + var inRank = inShape.length; + var dims = []; + for (var i = 0; i < inRank; i++) { + var dim = inRank - 1 - i; + var a = inShape[dim] || 1; + var b = outShape[outShape.length - 1 - i] || 1; + if (b > 1 && a === 1) { + dims.unshift(dim); + } + } + return dims; + } + /** + * Returns the axes in the output space that should be reduced to produce + * the input space. + */ + function getReductionAxes(inShape, outShape) { + var result = []; + for (var i = 0; i < outShape.length; i++) { + var inDim = inShape[inShape.length - i - 1]; + var outAxis = outShape.length - i - 1; + var outDim = outShape[outAxis]; + if (inDim == null || (inDim === 1 && outDim > 1)) { + result.unshift(outAxis); + } + } + return result; + } + function assertAndGetBroadcastShape(shapeA, shapeB) { + var result = []; + var l = Math.max(shapeA.length, shapeB.length); + for (var i = 0; i < l; i++) { + var a = shapeA[shapeA.length - i - 1]; + if (a == null) { + a = 1; + } + var 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) { + var errMsg = "Operands could not be broadcast together with shapes " + + (shapeA + " and " + shapeB + "."); + throw Error(errMsg); + } + else { + result.unshift(a); + } + } + return result; + } + + var broadcast_util = { + __proto__: null, + getBroadcastDims: getBroadcastDims, + getReductionAxes: getReductionAxes, + assertAndGetBroadcastShape: assertAndGetBroadcastShape + }; + + /** + * @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. + * ============================================================================= + */ + /** + * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype. + * + * The same functionality can be achieved with `tf.tensor`, but in general + * we recommend using `tf.tensor3d` as it makes the code more readable. + * + * ```js + * // Pass a nested array. + * tf.tensor3d([[[1], [2]], [[3], [4]]]).print(); + * ``` + * ```js + * // Pass a flat array and specify a shape. + * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print(); + * ``` + * + * @param values The values of the tensor. Can be nested array of numbers, + * or a flat array, or a `TypedArray`. + * @param shape The shape of the tensor. If not provided, it is inferred from + * `values`. + * @param dtype The data type. + * + * @doc {heading: 'Tensors', subheading: 'Creation'} + */ + function tensor3d(values, shape, dtype) { + assertNonNull(values); + if (shape != null && shape.length !== 3) { + throw new Error('tensor3d() requires shape to have three numbers'); + } + var 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); + } + + var fromPixels2DContext; + /** + * Creates a `tf.Tensor` from an image. + * + * ```js + * const image = new ImageData(1, 1); + * image.data[0] = 100; + * image.data[1] = 150; + * image.data[2] = 200; + * image.data[3] = 255; + * + * tf.browser.fromPixels(image).print(); + * ``` + * + * @param pixels The input image to construct the tensor from. The + * supported image types are all 4-channel. You can also pass in an image + * object with following attributes: + * `{data: Uint8Array; width: number; height: number}` + * @param numChannels The number of channels of the output tensor. A + * numChannels value less than 4 allows you to ignore channels. Defaults to + * 3 (ignores alpha channel of input image). + * + * @returns A Tensor3D with the shape `[height, width, numChannels]`. + * + * Note: fromPixels can be lossy in some cases, same image may result in + * slightly different tensor values, if rendered by different rendering + * engines. This means that results from different browsers, or even same + * browser with CPU and GPU rendering engines can be different. See discussion + * in details: + * https://github.com/tensorflow/tfjs/issues/5482 + * + * @doc {heading: 'Browser', namespace: 'browser', ignoreCI: true} + */ + function fromPixels_(pixels, numChannels) { + if (numChannels === void 0) { numChannels = 3; } + // Sanity checks. + 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'); + } + var isPixelData = false; + var isImageData = false; + var isVideo = false; + var isImage = false; + var isCanvasLike = false; + var isImageBitmap = false; + if (pixels.data instanceof Uint8Array) { + isPixelData = true; + } + else if (typeof (ImageData) !== 'undefined' && pixels instanceof ImageData) { + isImageData = true; + } + else if (typeof (HTMLVideoElement) !== 'undefined' && + pixels instanceof HTMLVideoElement) { + isVideo = true; + } + else if (typeof (HTMLImageElement) !== 'undefined' && + pixels instanceof HTMLImageElement) { + isImage = true; + // tslint:disable-next-line: no-any + } + 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) { + var 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