fix centernet & update blazeface

CHANGELOG.md

@@ -9,7 +9,7 @@ Repository: **<git+https://github.com/vladmandic/human.git>**
 
 ## Changelog
 
-### **HEAD -> main** 2021/08/06 mandic00@live.com
+### **HEAD -> main** 2021/08/09 mandic00@live.com
 
 - minor update
 - replace movenet with lightning-v4

demo/index.js

@@ -40,15 +40,15 @@ let userConfig = {
     enabled: false,
     flip: false,
   },
-  face: { enabled: false,
+  face: { enabled: true,
-    detector: { return: true },
+    detector: { return: false },
     mesh: { enabled: true },
     iris: { enabled: false },
     description: { enabled: false },
     emotion: { enabled: false },
   },
   object: { enabled: false },
-  gesture: { enabled: true },
+  gesture: { enabled: false },
   hand: { enabled: false },
   body: { enabled: false },
   // body: { enabled: true, modelPath: 'posenet.json' },
@@ -164,7 +164,8 @@ function status(msg) {
     div.innerText = msg;
   } else {
     const video = document.getElementById('video');
-    document.getElementById('play').style.display = (video.srcObject !== null) && !video.paused ? 'none' : 'block';
+    const playing = (video.srcObject !== null) && !video.paused;
+    document.getElementById('play').style.display = playing ? 'none' : 'block';
     document.getElementById('loader').style.display = 'none';
     div.innerText = '';
   }
@@ -259,9 +260,10 @@ async function drawResults(input) {
   const avgDraw = ui.drawFPS.length > 0 ? Math.trunc(10 * ui.drawFPS.reduce((a, b) => a + b, 0) / ui.drawFPS.length) / 10 : 0;
   const warning = (ui.detectFPS.length > 5) && (avgDetect < 2) ? '<font color="lightcoral">warning: your performance is low: try switching to higher performance backend, lowering resolution or disabling some models</font>' : '';
   const fps = avgDetect > 0 ? `FPS process:${avgDetect} refresh:${avgDraw}` : '';
+  const backend = engine.state.numTensors > 0 ? `backend: ${human.tf.getBackend()} | ${memory}` : 'running in web worker';
   document.getElementById('log').innerHTML = `
     video: ${ui.camera.name} | facing: ${ui.camera.facing} | screen: ${window.innerWidth} x ${window.innerHeight} camera: ${ui.camera.width} x ${ui.camera.height} ${processing}<br>
-    backend: ${human.tf.getBackend()} | ${memory}<br>
+    backend: ${backend}<br>
     performance: ${str(lastDetectedResult.performance)}ms ${fps}<br>
     ${warning}<br>
   `;
@@ -363,6 +365,7 @@ async function setupCamera() {
       // eslint-disable-next-line no-use-before-define
       if (live && !ui.detectThread) runHumanDetect(video, canvas);
       ui.busy = false;
+      status();
       resolve();
     };
   });
@@ -597,6 +600,7 @@ async function detectVideo() {
       document.getElementById('btnStartText').innerHTML = 'pause video';
       await video.play();
       runHumanDetect(video, canvas);
+      status();
     } else {
       status(cameraError);
     }
@@ -878,6 +882,7 @@ async function pwaRegister() {
 }
 
 async function main() {
+  /*
   window.addEventListener('unhandledrejection', (evt) => {
     // eslint-disable-next-line no-console
     console.error(evt.reason || evt);
@@ -885,6 +890,7 @@ async function main() {
     status('exception error');
     evt.preventDefault();
   });
+  */
 
   log('demo starting ...');
 

dist/human.node-gpu.js

@@ -517,7 +517,7 @@ var BlazeFaceModel = class {
     });
     this.config = mergeDeep(this.config, userConfig);
     const nmsTensor = await tf3.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence);
-    const nms = nmsTensor.arraySync();
+    const nms = await nmsTensor.array();
     tf3.dispose(nmsTensor);
     const annotatedBoxes = [];
     for (let i = 0; i < nms.length; i++) {
@@ -3955,7 +3955,10 @@ var Pipeline = class {
       this.storedBoxes = [];
       this.detectedFaces = 0;
       for (const possible of detector.boxes) {
-        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks.arraySync(), confidence: possible.confidence });
+        const startPoint = await possible.box.startPoint.data();
+        const endPoint = await possible.box.endPoint.data();
+        const landmarks = await possible.landmarks.array();
+        this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });
       }
       if (this.storedBoxes.length > 0)
         useFreshBox = true;
@@ -4118,7 +4121,6 @@ async function predict(input, config3) {
       mesh: prediction.mesh,
       meshRaw,
       annotations: annotations3,
-      image: prediction.image,
       tensor: prediction.image
     });
     if (prediction.coords)
@@ -7707,7 +7709,7 @@ var HandDetector = class {
     const boxes = this.normalizeBoxes(rawBoxes);
     tf10.dispose(rawBoxes);
     const filteredT = await tf10.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
-    const filtered = filteredT.arraySync();
+    const filtered = await filteredT.array();
     tf10.dispose(scoresT);
     tf10.dispose(filteredT);
     const hands = [];
@@ -7737,7 +7739,7 @@ var HandDetector = class {
       const boxes = prediction.box.dataSync();
       const startPoint = boxes.slice(0, 2);
       const endPoint = boxes.slice(2, 4);
-      const palmLandmarks = prediction.palmLandmarks.arraySync();
+      const palmLandmarks = await prediction.palmLandmarks.array();
       tf10.dispose(prediction.box);
       tf10.dispose(prediction.palmLandmarks);
       hands.push(scaleBoxCoordinates2({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));
@@ -7911,7 +7913,7 @@ var HandPipeline = class {
         tf11.dispose(confidenceT);
         if (confidence >= config3.hand.minConfidence) {
           const keypointsReshaped = tf11.reshape(keypoints3, [-1, 3]);
-          const rawCoords = keypointsReshaped.arraySync();
+          const rawCoords = await keypointsReshaped.array();
           tf11.dispose(keypoints3);
           tf11.dispose(keypointsReshaped);
           const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
@@ -8310,7 +8312,7 @@ async function predict8(image18, config3) {
     tf15.dispose(tensor2);
     if (resT) {
       keypoints2.length = 0;
-      const res = resT.arraySync();
+      const res = await resT.array();
       tf15.dispose(resT);
       const kpt3 = res[0][0];
       for (let id = 0; id < kpt3.length; id++) {
@@ -8463,14 +8465,14 @@ async function process2(res, inputSize, outputShape, config3) {
   let id = 0;
   let results = [];
   for (const strideSize of [1, 2, 4]) {
-    tf16.tidy(() => {
+    tf16.tidy(async () => {
       var _a, _b;
       const baseSize = strideSize * 13;
       const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
       const featuresT = (_b = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)) == null ? void 0 : _b.squeeze();
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
-      const boxIdx = boxesMax.argMax(2).arraySync();
+      const boxIdx = await boxesMax.argMax(2).array();
-      const scores = scoresT.arraySync();
+      const scores = await scoresT.array();
       for (let i = 0; i < scoresT.shape[0]; i++) {
         for (let j = 0; j < scoresT.shape[1]; j++) {
           const score3 = scores[i][j];
@@ -8567,15 +8569,15 @@ async function process3(res, inputSize, outputShape, config3) {
   if (!res)
     return [];
   const results = [];
-  const detections = res.arraySync();
+  const detections = await res.array();
   const squeezeT = tf17.squeeze(res);
   tf17.dispose(res);
   const arr = tf17.split(squeezeT, 6, 1);
   tf17.dispose(squeezeT);
   const stackT = tf17.stack([arr[1], arr[0], arr[3], arr[2]], 1);
-  const boxesT = stackT.squeeze();
+  const boxesT = tf17.squeeze(stackT);
-  const scoresT = arr[4].squeeze();
+  const scoresT = tf17.squeeze(arr[4]);
-  const classesT = arr[5].squeeze();
+  const classesT = tf17.squeeze(arr[5]);
   arr.forEach((t) => tf17.dispose(t));
   const nmsT = await tf17.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
   tf17.dispose(boxesT);
@@ -9765,28 +9767,28 @@ var detectFace = async (parent, input) => {
     return [];
   for (let i = 0; i < faces.length; i++) {
     parent.analyze("Get Face");
-    if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
+    if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) {
-      log("Face object is disposed:", faces[i].image);
+      log("Face object is disposed:", faces[i].tensor);
       continue;
     }
     const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
     parent.analyze("Start Emotion:");
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = "run:emotion";
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Emotion:");
     parent.analyze("Start Description:");
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? predict2(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? predict2(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = "run:description";
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await predict2(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await predict2(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Description:");
@@ -9799,6 +9801,10 @@ var detectFace = async (parent, input) => {
       delete faces[i].annotations.rightEyeIris;
     }
     const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor2 = parent.config.face.detector.return ? tf20.squeeze(faces[i].tensor) : null;
+    tf20.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
     faceRes.push({
       ...faces[i],
       id: i,
@@ -9809,11 +9815,8 @@ var detectFace = async (parent, input) => {
       emotion: emotionRes,
       iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
       rotation,
-      tensor: parent.config.face.detector.return ? tf20.squeeze(faces[i].image) : null
+      tensor: tensor2
     });
-    tf20.dispose(faces[i].image);
-    if (faces[i].image)
-      delete faces[i].image;
     parent.analyze("End Face");
   }
   parent.analyze("End FaceMesh:");

dist/human.node-wasm.js

@@ -518,7 +518,7 @@ var BlazeFaceModel = class {
     });
     this.config = mergeDeep(this.config, userConfig);
     const nmsTensor = await tf3.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence);
-    const nms = nmsTensor.arraySync();
+    const nms = await nmsTensor.array();
     tf3.dispose(nmsTensor);
     const annotatedBoxes = [];
     for (let i = 0; i < nms.length; i++) {
@@ -3956,7 +3956,10 @@ var Pipeline = class {
       this.storedBoxes = [];
       this.detectedFaces = 0;
       for (const possible of detector.boxes) {
-        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks.arraySync(), confidence: possible.confidence });
+        const startPoint = await possible.box.startPoint.data();
+        const endPoint = await possible.box.endPoint.data();
+        const landmarks = await possible.landmarks.array();
+        this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });
       }
       if (this.storedBoxes.length > 0)
         useFreshBox = true;
@@ -4119,7 +4122,6 @@ async function predict(input, config3) {
       mesh: prediction.mesh,
       meshRaw,
       annotations: annotations3,
-      image: prediction.image,
       tensor: prediction.image
     });
     if (prediction.coords)
@@ -7708,7 +7710,7 @@ var HandDetector = class {
     const boxes = this.normalizeBoxes(rawBoxes);
     tf10.dispose(rawBoxes);
     const filteredT = await tf10.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
-    const filtered = filteredT.arraySync();
+    const filtered = await filteredT.array();
     tf10.dispose(scoresT);
     tf10.dispose(filteredT);
     const hands = [];
@@ -7738,7 +7740,7 @@ var HandDetector = class {
       const boxes = prediction.box.dataSync();
       const startPoint = boxes.slice(0, 2);
       const endPoint = boxes.slice(2, 4);
-      const palmLandmarks = prediction.palmLandmarks.arraySync();
+      const palmLandmarks = await prediction.palmLandmarks.array();
       tf10.dispose(prediction.box);
       tf10.dispose(prediction.palmLandmarks);
       hands.push(scaleBoxCoordinates2({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));
@@ -7912,7 +7914,7 @@ var HandPipeline = class {
         tf11.dispose(confidenceT);
         if (confidence >= config3.hand.minConfidence) {
           const keypointsReshaped = tf11.reshape(keypoints3, [-1, 3]);
-          const rawCoords = keypointsReshaped.arraySync();
+          const rawCoords = await keypointsReshaped.array();
           tf11.dispose(keypoints3);
           tf11.dispose(keypointsReshaped);
           const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
@@ -8311,7 +8313,7 @@ async function predict8(image18, config3) {
     tf15.dispose(tensor2);
     if (resT) {
       keypoints2.length = 0;
-      const res = resT.arraySync();
+      const res = await resT.array();
       tf15.dispose(resT);
       const kpt3 = res[0][0];
       for (let id = 0; id < kpt3.length; id++) {
@@ -8464,14 +8466,14 @@ async function process2(res, inputSize, outputShape, config3) {
   let id = 0;
   let results = [];
   for (const strideSize of [1, 2, 4]) {
-    tf16.tidy(() => {
+    tf16.tidy(async () => {
       var _a, _b;
       const baseSize = strideSize * 13;
       const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
       const featuresT = (_b = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)) == null ? void 0 : _b.squeeze();
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
-      const boxIdx = boxesMax.argMax(2).arraySync();
+      const boxIdx = await boxesMax.argMax(2).array();
-      const scores = scoresT.arraySync();
+      const scores = await scoresT.array();
       for (let i = 0; i < scoresT.shape[0]; i++) {
         for (let j = 0; j < scoresT.shape[1]; j++) {
           const score3 = scores[i][j];
@@ -8568,15 +8570,15 @@ async function process3(res, inputSize, outputShape, config3) {
   if (!res)
     return [];
   const results = [];
-  const detections = res.arraySync();
+  const detections = await res.array();
   const squeezeT = tf17.squeeze(res);
   tf17.dispose(res);
   const arr = tf17.split(squeezeT, 6, 1);
   tf17.dispose(squeezeT);
   const stackT = tf17.stack([arr[1], arr[0], arr[3], arr[2]], 1);
-  const boxesT = stackT.squeeze();
+  const boxesT = tf17.squeeze(stackT);
-  const scoresT = arr[4].squeeze();
+  const scoresT = tf17.squeeze(arr[4]);
-  const classesT = arr[5].squeeze();
+  const classesT = tf17.squeeze(arr[5]);
   arr.forEach((t) => tf17.dispose(t));
   const nmsT = await tf17.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
   tf17.dispose(boxesT);
@@ -9766,28 +9768,28 @@ var detectFace = async (parent, input) => {
     return [];
   for (let i = 0; i < faces.length; i++) {
     parent.analyze("Get Face");
-    if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
+    if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) {
-      log("Face object is disposed:", faces[i].image);
+      log("Face object is disposed:", faces[i].tensor);
       continue;
     }
     const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
     parent.analyze("Start Emotion:");
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = "run:emotion";
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Emotion:");
     parent.analyze("Start Description:");
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? predict2(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? predict2(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = "run:description";
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await predict2(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await predict2(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Description:");
@@ -9800,6 +9802,10 @@ var detectFace = async (parent, input) => {
       delete faces[i].annotations.rightEyeIris;
     }
     const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor2 = parent.config.face.detector.return ? tf20.squeeze(faces[i].tensor) : null;
+    tf20.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
     faceRes.push({
       ...faces[i],
       id: i,
@@ -9810,11 +9816,8 @@ var detectFace = async (parent, input) => {
       emotion: emotionRes,
       iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
       rotation,
-      tensor: parent.config.face.detector.return ? tf20.squeeze(faces[i].image) : null
+      tensor: tensor2
     });
-    tf20.dispose(faces[i].image);
-    if (faces[i].image)
-      delete faces[i].image;
     parent.analyze("End Face");
   }
   parent.analyze("End FaceMesh:");

dist/human.node.js

@@ -517,7 +517,7 @@ var BlazeFaceModel = class {
     });
     this.config = mergeDeep(this.config, userConfig);
     const nmsTensor = await tf3.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence);
-    const nms = nmsTensor.arraySync();
+    const nms = await nmsTensor.array();
     tf3.dispose(nmsTensor);
     const annotatedBoxes = [];
     for (let i = 0; i < nms.length; i++) {
@@ -3955,7 +3955,10 @@ var Pipeline = class {
       this.storedBoxes = [];
       this.detectedFaces = 0;
       for (const possible of detector.boxes) {
-        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks.arraySync(), confidence: possible.confidence });
+        const startPoint = await possible.box.startPoint.data();
+        const endPoint = await possible.box.endPoint.data();
+        const landmarks = await possible.landmarks.array();
+        this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });
       }
       if (this.storedBoxes.length > 0)
         useFreshBox = true;
@@ -4118,7 +4121,6 @@ async function predict(input, config3) {
       mesh: prediction.mesh,
       meshRaw,
       annotations: annotations3,
-      image: prediction.image,
       tensor: prediction.image
     });
     if (prediction.coords)
@@ -7707,7 +7709,7 @@ var HandDetector = class {
     const boxes = this.normalizeBoxes(rawBoxes);
     tf10.dispose(rawBoxes);
     const filteredT = await tf10.image.nonMaxSuppressionAsync(boxes, scores, config3.hand.maxDetected, config3.hand.iouThreshold, config3.hand.minConfidence);
-    const filtered = filteredT.arraySync();
+    const filtered = await filteredT.array();
     tf10.dispose(scoresT);
     tf10.dispose(filteredT);
     const hands = [];
@@ -7737,7 +7739,7 @@ var HandDetector = class {
       const boxes = prediction.box.dataSync();
       const startPoint = boxes.slice(0, 2);
       const endPoint = boxes.slice(2, 4);
-      const palmLandmarks = prediction.palmLandmarks.arraySync();
+      const palmLandmarks = await prediction.palmLandmarks.array();
       tf10.dispose(prediction.box);
       tf10.dispose(prediction.palmLandmarks);
       hands.push(scaleBoxCoordinates2({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));
@@ -7911,7 +7913,7 @@ var HandPipeline = class {
         tf11.dispose(confidenceT);
         if (confidence >= config3.hand.minConfidence) {
           const keypointsReshaped = tf11.reshape(keypoints3, [-1, 3]);
-          const rawCoords = keypointsReshaped.arraySync();
+          const rawCoords = await keypointsReshaped.array();
           tf11.dispose(keypoints3);
           tf11.dispose(keypointsReshaped);
           const coords3 = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);
@@ -8310,7 +8312,7 @@ async function predict8(image18, config3) {
     tf15.dispose(tensor2);
     if (resT) {
       keypoints2.length = 0;
-      const res = resT.arraySync();
+      const res = await resT.array();
       tf15.dispose(resT);
       const kpt3 = res[0][0];
       for (let id = 0; id < kpt3.length; id++) {
@@ -8463,14 +8465,14 @@ async function process2(res, inputSize, outputShape, config3) {
   let id = 0;
   let results = [];
   for (const strideSize of [1, 2, 4]) {
-    tf16.tidy(() => {
+    tf16.tidy(async () => {
       var _a, _b;
       const baseSize = strideSize * 13;
       const scoresT = (_a = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] === labels.length)) == null ? void 0 : _a.squeeze();
       const featuresT = (_b = res.find((a) => a.shape[1] === baseSize ** 2 && a.shape[2] < labels.length)) == null ? void 0 : _b.squeeze();
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]);
-      const boxIdx = boxesMax.argMax(2).arraySync();
+      const boxIdx = await boxesMax.argMax(2).array();
-      const scores = scoresT.arraySync();
+      const scores = await scoresT.array();
       for (let i = 0; i < scoresT.shape[0]; i++) {
         for (let j = 0; j < scoresT.shape[1]; j++) {
           const score3 = scores[i][j];
@@ -8567,15 +8569,15 @@ async function process3(res, inputSize, outputShape, config3) {
   if (!res)
     return [];
   const results = [];
-  const detections = res.arraySync();
+  const detections = await res.array();
   const squeezeT = tf17.squeeze(res);
   tf17.dispose(res);
   const arr = tf17.split(squeezeT, 6, 1);
   tf17.dispose(squeezeT);
   const stackT = tf17.stack([arr[1], arr[0], arr[3], arr[2]], 1);
-  const boxesT = stackT.squeeze();
+  const boxesT = tf17.squeeze(stackT);
-  const scoresT = arr[4].squeeze();
+  const scoresT = tf17.squeeze(arr[4]);
-  const classesT = arr[5].squeeze();
+  const classesT = tf17.squeeze(arr[5]);
   arr.forEach((t) => tf17.dispose(t));
   const nmsT = await tf17.image.nonMaxSuppressionAsync(boxesT, scoresT, config3.object.maxDetected, config3.object.iouThreshold, config3.object.minConfidence);
   tf17.dispose(boxesT);
@@ -9765,28 +9767,28 @@ var detectFace = async (parent, input) => {
     return [];
   for (let i = 0; i < faces.length; i++) {
     parent.analyze("Get Face");
-    if (!faces[i].image || faces[i].image["isDisposedInternal"]) {
+    if (!faces[i].tensor || faces[i].tensor["isDisposedInternal"]) {
-      log("Face object is disposed:", faces[i].image);
+      log("Face object is disposed:", faces[i].tensor);
       continue;
     }
     const rotation = calculateFaceAngle(faces[i], [input.shape[2], input.shape[1]]);
     parent.analyze("Start Emotion:");
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? predict3(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = "run:emotion";
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await predict3(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Emotion:");
     parent.analyze("Start Description:");
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? predict2(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? predict2(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = "run:description";
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await predict2(faces[i].image || tf20.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await predict2(faces[i].tensor || tf20.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze("End Description:");
@@ -9799,6 +9801,10 @@ var detectFace = async (parent, input) => {
       delete faces[i].annotations.rightEyeIris;
     }
     const irisSize = ((_e = faces[i].annotations) == null ? void 0 : _e.leftEyeIris) && ((_f = faces[i].annotations) == null ? void 0 : _f.rightEyeIris) ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2] : 0;
+    const tensor2 = parent.config.face.detector.return ? tf20.squeeze(faces[i].tensor) : null;
+    tf20.dispose(faces[i].tensor);
+    if (faces[i].tensor)
+      delete faces[i].tensor;
     faceRes.push({
       ...faces[i],
       id: i,
@@ -9809,11 +9815,8 @@ var detectFace = async (parent, input) => {
       emotion: emotionRes,
       iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
       rotation,
-      tensor: parent.config.face.detector.return ? tf20.squeeze(faces[i].image) : null
+      tensor: tensor2
     });
-    tf20.dispose(faces[i].image);
-    if (faces[i].image)
-      delete faces[i].image;
     parent.analyze("End Face");
   }
   parent.analyze("End FaceMesh:");

package.json

@@ -59,15 +59,16 @@
     "@tensorflow/tfjs-backend-cpu": "^3.8.0",
     "@tensorflow/tfjs-backend-wasm": "^3.8.0",
     "@tensorflow/tfjs-backend-webgl": "^3.8.0",
+    "@tensorflow/tfjs-backend-webgpu": "^0.0.1-alpha.7",
     "@tensorflow/tfjs-converter": "^3.8.0",
     "@tensorflow/tfjs-core": "^3.8.0",
     "@tensorflow/tfjs-data": "^3.8.0",
     "@tensorflow/tfjs-layers": "^3.8.0",
     "@tensorflow/tfjs-node": "^3.8.0",
     "@tensorflow/tfjs-node-gpu": "^3.8.0",
-    "@types/node": "^16.4.13",
+    "@types/node": "^16.4.14",
-    "@typescript-eslint/eslint-plugin": "^4.29.0",
+    "@typescript-eslint/eslint-plugin": "^4.29.1",
-    "@typescript-eslint/parser": "^4.29.0",
+    "@typescript-eslint/parser": "^4.29.1",
     "@vladmandic/pilogger": "^0.2.18",
     "canvas": "^2.8.0",
     "chokidar": "^3.5.2",

server/build.log

@@ -1,22 +1,22 @@
-2021-08-09 08:50:50 INFO:  @vladmandic/human version 2.1.2 
+2021-08-11 18:57:35 INFO:  @vladmandic/human version 2.1.2 
-2021-08-09 08:50:50 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.5.0 
+2021-08-11 18:57:35 INFO:  User: vlado Platform: linux Arch: x64 Node: v16.5.0 
-2021-08-09 08:50:50 INFO:  Toolchain:  {"tfjs":"3.8.0","esbuild":"0.12.19","typescript":"4.3.5","typedoc":"0.21.5","eslint":"7.32.0"} 
+2021-08-11 18:57:35 INFO:  Toolchain:  {"tfjs":"3.8.0","esbuild":"0.12.19","typescript":"4.3.5","typedoc":"0.21.5","eslint":"7.32.0"} 
-2021-08-09 08:50:50 INFO:  Clean: ["dist/*","types/*","typedoc/*"] 
+2021-08-11 18:57:35 INFO:  Clean: ["dist/*","types/*","typedoc/*"] 
-2021-08-09 08:50:50 INFO:  Build: file startup all type: production config: {"minifyWhitespace":true,"minifyIdentifiers":true,"minifySyntax":true} 
+2021-08-11 18:57:35 INFO:  Build: file startup all type: production config: {"minifyWhitespace":true,"minifyIdentifiers":true,"minifySyntax":true} 
-2021-08-09 08:50:50 STATE:  target: node type: tfjs: {"imports":1,"importBytes":102,"outputBytes":1303,"outputFiles":"dist/tfjs.esm.js"} 
+2021-08-11 18:57:35 STATE:  target: node type: tfjs: {"imports":1,"importBytes":102,"outputBytes":1303,"outputFiles":"dist/tfjs.esm.js"} 
-2021-08-09 08:50:50 STATE:  target: node type: node: {"imports":42,"importBytes":435865,"outputBytes":377825,"outputFiles":"dist/human.node.js"} 
+2021-08-11 18:57:35 STATE:  target: node type: node: {"imports":42,"importBytes":436067,"outputBytes":377952,"outputFiles":"dist/human.node.js"} 
-2021-08-09 08:50:50 STATE:  target: nodeGPU type: tfjs: {"imports":1,"importBytes":110,"outputBytes":1311,"outputFiles":"dist/tfjs.esm.js"} 
+2021-08-11 18:57:35 STATE:  target: nodeGPU type: tfjs: {"imports":1,"importBytes":110,"outputBytes":1311,"outputFiles":"dist/tfjs.esm.js"} 
-2021-08-09 08:50:51 STATE:  target: nodeGPU type: node: {"imports":42,"importBytes":435873,"outputBytes":377829,"outputFiles":"dist/human.node-gpu.js"} 
+2021-08-11 18:57:35 STATE:  target: nodeGPU type: node: {"imports":42,"importBytes":436075,"outputBytes":377956,"outputFiles":"dist/human.node-gpu.js"} 
-2021-08-09 08:50:51 STATE:  target: nodeWASM type: tfjs: {"imports":1,"importBytes":149,"outputBytes":1378,"outputFiles":"dist/tfjs.esm.js"} 
+2021-08-11 18:57:35 STATE:  target: nodeWASM type: tfjs: {"imports":1,"importBytes":149,"outputBytes":1378,"outputFiles":"dist/tfjs.esm.js"} 
-2021-08-09 08:50:51 STATE:  target: nodeWASM type: node: {"imports":42,"importBytes":435940,"outputBytes":377901,"outputFiles":"dist/human.node-wasm.js"} 
+2021-08-11 18:57:35 STATE:  target: nodeWASM type: node: {"imports":42,"importBytes":436142,"outputBytes":378028,"outputFiles":"dist/human.node-wasm.js"} 
-2021-08-09 08:50:51 STATE:  target: browserNoBundle type: tfjs: {"imports":1,"importBytes":2111,"outputBytes":1242,"outputFiles":"dist/tfjs.esm.js"} 
+2021-08-11 18:57:35 STATE:  target: browserNoBundle type: tfjs: {"imports":1,"importBytes":2168,"outputBytes":1242,"outputFiles":"dist/tfjs.esm.js"} 
-2021-08-09 08:50:51 STATE:  target: browserNoBundle type: esm: {"imports":42,"importBytes":435804,"outputBytes":247953,"outputFiles":"dist/human.esm-nobundle.js"} 
+2021-08-11 18:57:35 STATE:  target: browserNoBundle type: esm: {"imports":42,"importBytes":436006,"outputBytes":248005,"outputFiles":"dist/human.esm-nobundle.js"} 
-2021-08-09 08:50:51 STATE:  target: browserBundle type: tfjs: {"modules":1170,"moduleBytes":4145868,"imports":7,"importBytes":2111,"outputBytes":2334701,"outputFiles":"dist/tfjs.esm.js"} 
+2021-08-11 18:57:36 STATE:  target: browserBundle type: tfjs: {"modules":1170,"moduleBytes":4145868,"imports":7,"importBytes":2168,"outputBytes":2334701,"outputFiles":"dist/tfjs.esm.js"} 
-2021-08-09 08:50:51 STATE:  target: browserBundle type: iife: {"imports":42,"importBytes":2769263,"outputBytes":1378416,"outputFiles":"dist/human.js"} 
+2021-08-11 18:57:36 STATE:  target: browserBundle type: iife: {"imports":42,"importBytes":2769465,"outputBytes":1378447,"outputFiles":"dist/human.js"} 
-2021-08-09 08:50:52 STATE:  target: browserBundle type: esm: {"imports":42,"importBytes":2769263,"outputBytes":1378408,"outputFiles":"dist/human.esm.js"} 
+2021-08-11 18:57:36 STATE:  target: browserBundle type: esm: {"imports":42,"importBytes":2769465,"outputBytes":1378439,"outputFiles":"dist/human.esm.js"} 
-2021-08-09 08:50:52 INFO:  Running Linter: ["server/","src/","tfjs/","test/","demo/"] 
+2021-08-11 18:57:36 INFO:  Running Linter: ["server/","src/","tfjs/","test/","demo/"] 
-2021-08-09 08:51:14 INFO:  Linter complete: files: 75 errors: 0 warnings: 0 
+2021-08-11 18:57:59 INFO:  Linter complete: files: 75 errors: 0 warnings: 0 
-2021-08-09 08:51:14 INFO:  Generate ChangeLog: ["/home/vlado/dev/human/CHANGELOG.md"] 
+2021-08-11 18:57:59 INFO:  Generate ChangeLog: ["/home/vlado/dev/human/CHANGELOG.md"] 
-2021-08-09 08:51:14 INFO:  Generate Typings: ["src/human.ts"] outDir: ["types"] 
+2021-08-11 18:57:59 INFO:  Generate Typings: ["src/human.ts"] outDir: ["types"] 
-2021-08-09 08:51:28 INFO:  Generate TypeDocs: ["src/human.ts"] outDir: ["typedoc"] 
+2021-08-11 18:58:13 INFO:  Generate TypeDocs: ["src/human.ts"] outDir: ["typedoc"] 
-2021-08-09 08:51:42 INFO:  Documentation generated at /home/vlado/dev/human/typedoc 1 
+2021-08-11 18:58:27 INFO:  Documentation generated at /home/vlado/dev/human/typedoc 1 

src/blazeface/blazeface.ts

@@ -64,7 +64,7 @@ export class BlazeFaceModel {
     this.config = mergeDeep(this.config, userConfig) as Config;
 
     const nmsTensor = await tf.image.nonMaxSuppressionAsync(boxes, scores, this.config.face.detector.maxDetected, this.config.face.detector.iouThreshold, this.config.face.detector.minConfidence);
-    const nms = nmsTensor.arraySync();
+    const nms = await nmsTensor.array();
     tf.dispose(nmsTensor);
     const annotatedBoxes: Array<{ box: { startPoint: Tensor, endPoint: Tensor }, landmarks: Tensor, anchor: number[], confidence: number }> = [];
     for (let i = 0; i < nms.length; i++) {

src/blazeface/facemesh.ts

@@ -51,7 +51,6 @@ export async function predict(input: Tensor, config: Config): Promise<Face[]> {
       mesh: prediction.mesh,
       meshRaw,
       annotations,
-      image: prediction.image,
       tensor: prediction.image,
     });
     if (prediction.coords) tf.dispose(prediction.coords);

src/blazeface/facepipeline.ts

@@ -168,7 +168,10 @@ export class Pipeline {
       this.storedBoxes = [];
       this.detectedFaces = 0;
       for (const possible of detector.boxes) {
-        this.storedBoxes.push({ startPoint: possible.box.startPoint.dataSync(), endPoint: possible.box.endPoint.dataSync(), landmarks: possible.landmarks.arraySync(), confidence: possible.confidence });
+        const startPoint = await possible.box.startPoint.data();
+        const endPoint = await possible.box.endPoint.data();
+        const landmarks = await possible.landmarks.array();
+        this.storedBoxes.push({ startPoint, endPoint, landmarks, confidence: possible.confidence });
       }
       if (this.storedBoxes.length > 0) useFreshBox = true;
     }

src/face.ts

@@ -163,8 +163,8 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
 
     // is something went wrong, skip the face
     // @ts-ignore possibly undefined
-    if (!faces[i].image || faces[i].image['isDisposedInternal']) {
+    if (!faces[i].tensor || faces[i].tensor['isDisposedInternal']) {
-      log('Face object is disposed:', faces[i].image);
+      log('Face object is disposed:', faces[i].tensor);
       continue;
     }
 
@@ -173,11 +173,11 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
     // run emotion, inherits face from blazeface
     parent.analyze('Start Emotion:');
     if (parent.config.async) {
-      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = 'run:emotion';
       timeStamp = now();
-      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : {};
+      emotionRes = parent.config.face.emotion.enabled ? await emotion.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze('End Emotion:');
@@ -186,11 +186,11 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
     /*
     parent.analyze('Start GEAR:');
     if (parent.config.async) {
-      gearRes = parent.config.face.agegenderrace.enabled ? agegenderrace.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : {};
+      gearRes = parent.config.face.agegenderrace.enabled ? agegenderrace.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};
     } else {
       parent.state = 'run:gear';
       timeStamp = now();
-      gearRes = parent.config.face.agegenderrace.enabled ? await agegenderrace.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : {};
+      gearRes = parent.config.face.agegenderrace.enabled ? await agegenderrace.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : {};
       parent.performance.emotion = Math.trunc(now() - timeStamp);
     }
     parent.analyze('End GEAR:');
@@ -199,11 +199,11 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
     // run emotion, inherits face from blazeface
     parent.analyze('Start Description:');
     if (parent.config.async) {
-      descRes = parent.config.face.description.enabled ? faceres.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : [];
     } else {
       parent.state = 'run:description';
       timeStamp = now();
-      descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].image || tf.tensor([]), parent.config, i, faces.length) : [];
+      descRes = parent.config.face.description.enabled ? await faceres.predict(faces[i].tensor || tf.tensor([]), parent.config, i, faces.length) : [];
       parent.performance.embedding = Math.trunc(now() - timeStamp);
     }
     parent.analyze('End Description:');
@@ -226,6 +226,12 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
       ? Math.max(Math.abs(faces[i].annotations.leftEyeIris[3][0] - faces[i].annotations.leftEyeIris[1][0]), Math.abs(faces[i].annotations.rightEyeIris[4][1] - faces[i].annotations.rightEyeIris[2][1])) / input.shape[2]
       : 0;
 
+    // optionally return tensor
+    const tensor = parent.config.face.detector.return ? tf.squeeze(faces[i].tensor) : null;
+    // dispose original face tensor
+    tf.dispose(faces[i].tensor);
+    // delete temp face image
+    if (faces[i].tensor) delete faces[i].tensor;
     // combine results
     faceRes.push({
       ...faces[i],
@@ -237,12 +243,8 @@ export const detectFace = async (parent /* instance of human */, input: Tensor):
       emotion: emotionRes,
       iris: irisSize !== 0 ? Math.trunc(500 / irisSize / 11.7) / 100 : 0,
       rotation,
-      tensor: parent.config.face.detector.return ? tf.squeeze(faces[i].image) : null,
+      tensor,
     });
-    // dispose original face tensor
-    tf.dispose(faces[i].image);
-    // delete temp face image
-    if (faces[i].image) delete faces[i].image;
 
     parent.analyze('End Face');
   }

src/handpose/handdetector.ts

@@ -50,7 +50,7 @@ export class HandDetector {
     const boxes = this.normalizeBoxes(rawBoxes);
     tf.dispose(rawBoxes);
     const filteredT = await tf.image.nonMaxSuppressionAsync(boxes, scores, config.hand.maxDetected, config.hand.iouThreshold, config.hand.minConfidence);
-    const filtered = filteredT.arraySync();
+    const filtered = await filteredT.array();
 
     tf.dispose(scoresT);
     tf.dispose(filteredT);
@@ -81,7 +81,7 @@ export class HandDetector {
       const boxes = prediction.box.dataSync();
       const startPoint = boxes.slice(0, 2);
       const endPoint = boxes.slice(2, 4);
-      const palmLandmarks = prediction.palmLandmarks.arraySync();
+      const palmLandmarks = await prediction.palmLandmarks.array();
       tf.dispose(prediction.box);
       tf.dispose(prediction.palmLandmarks);
       hands.push(box.scaleBoxCoordinates({ startPoint, endPoint, palmLandmarks, confidence: prediction.confidence }, [inputWidth / this.inputSize, inputHeight / this.inputSize]));

src/handpose/handpipeline.ts

@@ -122,7 +122,7 @@ export class HandPipeline {
         tf.dispose(confidenceT);
         if (confidence >= config.hand.minConfidence) {
           const keypointsReshaped = tf.reshape(keypoints, [-1, 3]);
-          const rawCoords = keypointsReshaped.arraySync();
+          const rawCoords = await keypointsReshaped.array();
           tf.dispose(keypoints);
           tf.dispose(keypointsReshaped);
           const coords = this.transformRawCoords(rawCoords, newBox, angle, rotationMatrix);

src/movenet/movenet.ts

@@ -50,7 +50,7 @@ export async function predict(image: Tensor, config: Config): Promise<Body[]> {
 
     if (resT) {
       keypoints.length = 0;
-      const res = resT.arraySync();
+      const res = await resT.array();
       tf.dispose(resT);
       const kpt = res[0][0];
       for (let id = 0; id < kpt.length; id++) {

src/object/centernet.ts

@@ -28,15 +28,15 @@ export async function load(config: Config): Promise<GraphModel> {
 async function process(res: Tensor, inputSize, outputShape, config: Config) {
   if (!res) return [];
   const results: Array<Item> = [];
-  const detections = res.arraySync();
+  const detections = await res.array();
   const squeezeT = tf.squeeze(res);
   tf.dispose(res);
   const arr = tf.split(squeezeT, 6, 1); // x1, y1, x2, y2, score, class
   tf.dispose(squeezeT);
   const stackT = tf.stack([arr[1], arr[0], arr[3], arr[2]], 1); // reorder dims as tf.nms expects y, x
-  const boxesT = stackT.squeeze();
+  const boxesT = tf.squeeze(stackT);
-  const scoresT = arr[4].squeeze();
+  const scoresT = tf.squeeze(arr[4]);
-  const classesT = arr[5].squeeze();
+  const classesT = tf.squeeze(arr[5]);
   arr.forEach((t) => tf.dispose(t));
   const nmsT = await tf.image.nonMaxSuppressionAsync(boxesT, scoresT, config.object.maxDetected, config.object.iouThreshold, config.object.minConfidence);
   tf.dispose(boxesT);

src/object/nanodet.ts

@@ -32,14 +32,14 @@ async function process(res, inputSize, outputShape, config) {
   let results: Array<Item> = [];
   for (const strideSize of [1, 2, 4]) { // try each stride size as it detects large/medium/small objects
     // find scores, boxes, classes
-    tf.tidy(() => { // wrap in tidy to automatically deallocate temp tensors
+    tf.tidy(async () => { // wrap in tidy to automatically deallocate temp tensors
       const baseSize = strideSize * 13; // 13x13=169, 26x26=676, 52x52=2704
       // find boxes and scores output depending on stride
       const scoresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] === labels.length))?.squeeze();
       const featuresT = res.find((a) => (a.shape[1] === (baseSize ** 2) && a.shape[2] < labels.length))?.squeeze();
       const boxesMax = featuresT.reshape([-1, 4, featuresT.shape[1] / 4]); // reshape [output] to [4, output / 4] where number is number of different features inside each stride
-      const boxIdx = boxesMax.argMax(2).arraySync(); // what we need is indexes of features with highest scores, not values itself
+      const boxIdx = await boxesMax.argMax(2).array(); // what we need is indexes of features with highest scores, not values itself
-      const scores = scoresT.arraySync(); // optionally use exponential scores or just as-is
+      const scores = await scoresT.array(); // optionally use exponential scores or just as-is
       for (let i = 0; i < scoresT.shape[0]; i++) { // total strides (x * y matrix)
         for (let j = 0; j < scoresT.shape[1]; j++) { // one score for each class
           const score = scores[i][j]; // get score for current position

src/result.ts

@@ -53,8 +53,7 @@ export interface Face {
     matrix: [number, number, number, number, number, number, number, number, number],
     gaze: { bearing: number, strength: number },
   }
-  image?: Tensor;
+  tensor?: Tensor,
-  tensor: Tensor,
 }
 
 /** Body results

tfjs/tf-browser.ts

@@ -3,7 +3,6 @@
  * @external
  */
 
-// import from src
 // get versions of all packages
 import { version as tfjsVersion } from '@tensorflow/tfjs/package.json';
 import { version as tfjsCoreVersion } from '@tensorflow/tfjs-core/package.json';
@@ -14,7 +13,7 @@ import { version as tfjsBackendCPUVersion } from '@tensorflow/tfjs-backend-cpu/p
 import { version as tfjsBackendWebGLVersion } from '@tensorflow/tfjs-backend-webgl/package.json';
 import { version as tfjsBackendWASMVersion } from '@tensorflow/tfjs-backend-wasm/package.json';
 
-// export all
+// export all from sources
 // requires treeShaking:ignore-annotations due to tfjs misconfiguration
 /*
 export * from '@tensorflow/tfjs-core/src/index';
@@ -26,7 +25,7 @@ export * from '@tensorflow/tfjs-backend-webgl/src/index';
 export * from '@tensorflow/tfjs-backend-wasm/src/index';
 */
 
-// export all
+// export all from build
 export * from '@tensorflow/tfjs-core/dist/index.js';
 export * from '@tensorflow/tfjs-layers/dist/index.js';
 export * from '@tensorflow/tfjs-converter/dist/index.js';
@@ -34,6 +33,7 @@ export * as data from '@tensorflow/tfjs-data/dist/index.js';
 export * from '@tensorflow/tfjs-backend-cpu/dist/index.js';
 export * from '@tensorflow/tfjs-backend-webgl/dist/index.js';
 export * from '@tensorflow/tfjs-backend-wasm/dist/index.js';
+// export * from '@tensorflow/tfjs-backend-webgpu/dist/index.js'; // experimental
 
 // export versions
 export const version = {
@@ -46,4 +46,3 @@ export const version = {
   'tfjs-backend-webgl': tfjsBackendWebGLVersion,
   'tfjs-backend-wasm': tfjsBackendWASMVersion,
 };
-// export const version = {};

typedoc/interfaces/Face.html

@@ -122,7 +122,6 @@
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#gender" class="tsd-kind-icon">gender</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#genderScore" class="tsd-kind-icon">gender<wbr>Score</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#id" class="tsd-kind-icon">id</a></li>
-								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#image" class="tsd-kind-icon">image</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#iris" class="tsd-kind-icon">iris</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#mesh" class="tsd-kind-icon">mesh</a></li>
 								<li class="tsd-kind-property tsd-parent-kind-interface"><a href="Face.html#meshRaw" class="tsd-kind-icon">mesh<wbr>Raw</a></li>
@@ -213,13 +212,6 @@
 					<aside class="tsd-sources">
 					</aside>
 				</section>
-				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
-					<a name="image" class="tsd-anchor"></a>
-					<h3><span class="tsd-flag ts-flagOptional">Optional</span> image</h3>
-					<div class="tsd-signature tsd-kind-icon">image<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Tensor</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">&gt;</span></div>
-					<aside class="tsd-sources">
-					</aside>
-				</section>
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
 					<a name="iris" class="tsd-anchor"></a>
 					<h3><span class="tsd-flag ts-flagOptional">Optional</span> iris</h3>
@@ -290,7 +282,7 @@
 				</section>
 				<section class="tsd-panel tsd-member tsd-kind-property tsd-parent-kind-interface">
 					<a name="tensor" class="tsd-anchor"></a>
-					<h3>tensor</h3>
+					<h3><span class="tsd-flag ts-flagOptional">Optional</span> tensor</h3>
 					<div class="tsd-signature tsd-kind-icon">tensor<span class="tsd-signature-symbol">:</span> <span class="tsd-signature-type">Tensor</span><span class="tsd-signature-symbol">&lt;</span><span class="tsd-signature-type">Rank</span><span class="tsd-signature-symbol">&gt;</span></div>
 					<aside class="tsd-sources">
 					</aside>
@@ -360,9 +352,6 @@
 							<li class=" tsd-kind-property tsd-parent-kind-interface">
 								<a href="Face.html#id" class="tsd-kind-icon">id</a>
 							</li>
-							<li class=" tsd-kind-property tsd-parent-kind-interface">
-								<a href="Face.html#image" class="tsd-kind-icon">image</a>
-							</li>
 							<li class=" tsd-kind-property tsd-parent-kind-interface">
 								<a href="Face.html#iris" class="tsd-kind-icon">iris</a>
 							</li>

types/src/result.d.ts

@@ -61,8 +61,7 @@ export interface Face {
             strength: number;
         };
     };
-    image?: Tensor;
+    tensor?: Tensor;
-    tensor: Tensor;
 }
 /** Body results
  *

wiki

@@ -1 +1 @@
-Subproject commit 2135debf198b5b0ecb670896bef837cbb45fe32e
+Subproject commit bdc4077a3df07abdf4a2d5b2d2beadf2e573e8d8