refactor predict with execute

2021-11-02 11:07:11 -04:00 · 2021-11-02 11:07:11 -04:00 · 12507c58c2
parent f271030965
commit 12507c58c2
30 changed files with 992 additions and 1387 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@ -9,8 +9,13 @@
  
 ## Changelog
  
-### **HEAD -> main** 2021/10/30 mandic00@live.com
+### **HEAD -> main** 2021/10/31 mandic00@live.com

+
+### **origin/main** 2021/10/31 mandic00@live.com
+
+- patch tfjs type defs
+- start 2.5 major version
 - build and docs cleanup
 - fix firefox bug

--- a/demo/index.html
+++ b/demo/index.html
@ -67,7 +67,7 @@
      .hint { opacity: 0; transition-duration: 0.5s; transition-property: opacity; font-style: italic; position: fixed; top: 5rem; padding: 8px; margin: 8px; box-shadow: 0 0 2px 2px #303030; }
      .input-file { align-self: center; width: 5rem; }

-      .results { position: absolute; left: 0; top: 6rem; background: #303030; width: 20rem; height: 90%; font-size: 0.8rem; overflow-y: auto; display: none }
+      .results { position: absolute; left: 0; top: 5rem; background: #303030; width: 20rem; height: 90%; font-size: 0.8rem; overflow-y: auto; display: none }
      .results::-webkit-scrollbar { background-color: #303030; }
      .results::-webkit-scrollbar-thumb { background: black; border-radius: 10px; }
      .json-line { margin: 4px 0; display: flex; justify-content: flex-start; }
--- a/dist/human.esm-nobundle.js
+++ b/dist/human.esm-nobundle.js
@ -1328,7 +1328,7 @@ async function predict(image25, config3, idx, count2) {
  skipped2 = 0;
  return new Promise(async (resolve) => {
    const resize = tfjs_esm_exports.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
-    const res = model2?.predict(resize);
+    const res = model2?.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count2;
@ -4985,7 +4985,7 @@ var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
  const t = {};
  t.input = await prepareImage(input);
-  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await models[1]?.execute(t.input, outputNodes);
+  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
  const poseScoreRaw = (await t.poseflag.data())[0];
  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
  const points = await t.ld.data();
@ -5293,7 +5293,7 @@ async function predict4(image25, config3) {
    });
    let resT;
    if (config3.body.enabled)
-      resT = await model5?.predict(tensor3);
+      resT = model5?.execute(tensor3);
    lastTime4 = now();
    tfjs_esm_exports.dispose(tensor3);
    if (resT) {
@ -5401,7 +5401,7 @@ async function predict5(image25, config3, idx, count2) {
      tfjs_esm_exports.dispose(blueNorm);
      const normalize = tfjs_esm_exports.tidy(() => tfjs_esm_exports.mul(tfjs_esm_exports.sub(grayscale, 0.5), 2));
      tfjs_esm_exports.dispose(grayscale);
-      const emotionT = await model6?.predict(normalize);
+      const emotionT = model6?.execute(normalize);
      lastTime5 = now();
      const data = await emotionT.data();
      tfjs_esm_exports.dispose(emotionT);
@ -5526,7 +5526,7 @@ async function augmentIris(rawCoords, face5, config3, meshSize) {
  const combined = tfjs_esm_exports.concat([leftEyeCrop, rightEyeCrop]);
  tfjs_esm_exports.dispose(leftEyeCrop);
  tfjs_esm_exports.dispose(rightEyeCrop);
-  const eyePredictions = model7.predict(combined);
+  const eyePredictions = model7.execute(combined);
  tfjs_esm_exports.dispose(combined);
  const eyePredictionsData = await eyePredictions.data();
  tfjs_esm_exports.dispose(eyePredictions);
@ -5730,7 +5730,7 @@ async function predict7(image25, config3, idx, count2) {
    };
    if (config3.face.description?.enabled) {
      const enhanced = enhance(image25);
-      const resT = await model9?.predict(enhanced);
+      const resT = model9?.execute(enhanced);
      lastTime7 = now();
      tfjs_esm_exports.dispose(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
@ -8852,7 +8852,7 @@ var HandDetector = class {
  }
  async getBoxes(input, config3) {
    const t = {};
-    t.batched = this.model.predict(input);
+    t.batched = this.model.execute(input);
    t.predictions = tfjs_esm_exports.squeeze(t.batched);
    t.scores = tfjs_esm_exports.tidy(() => tfjs_esm_exports.squeeze(tfjs_esm_exports.sigmoid(tfjs_esm_exports.slice(t.predictions, [0, 0], [-1, 1]))));
    const scores = await t.scores.data();
@ -8993,7 +8993,7 @@ var HandPipeline = class {
        const handImage = tfjs_esm_exports.div(croppedInput, 255);
        tfjs_esm_exports.dispose(croppedInput);
        tfjs_esm_exports.dispose(rotatedImage);
-        const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime8 = now();
        tfjs_esm_exports.dispose(handImage);
        const confidence = (await confidenceT.data())[0];
@ -10020,7 +10020,7 @@ async function predict10(input, config3) {
    const t = {};
    skipped10 = 0;
    t.input = padInput(input, inputSize7);
-    t.res = await model10?.predict(t.input);
+    t.res = model10?.execute(t.input);
    cache5.last = now();
    const res = await t.res.array();
    cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10133,7 +10133,7 @@ async function predict11(image25, config3) {
    tfjs_esm_exports.dispose(resize);
    let objectT;
    if (config3.object.enabled)
-      objectT = await model11.predict(transpose);
+      objectT = model11.execute(transpose);
    lastTime10 = now();
    tfjs_esm_exports.dispose(transpose);
    const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
@ -10510,7 +10510,7 @@ async function process5(input, background, config3) {
  t.resize = tfjs_esm_exports.image.resizeBilinear(inputImage.tensor, [model13.inputs[0].shape ? model13.inputs[0].shape[1] : 0, model13.inputs[0].shape ? model13.inputs[0].shape[2] : 0], false);
  tfjs_esm_exports.dispose(inputImage.tensor);
  t.norm = tfjs_esm_exports.div(t.resize, 255);
-  t.res = model13.predict(t.norm);
+  t.res = model13.execute(t.norm);
  t.squeeze = tfjs_esm_exports.squeeze(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = tfjs_esm_exports.softmax(t.squeeze);
--- a/dist/human.esm-nobundle.js.map
+++ b/dist/human.esm-nobundle.js.map
--- a/dist/human.esm.js
+++ b/dist/human.esm.js
@ -48128,7 +48128,8 @@ function compileProgram(gpgpu, program, inputs, output) {
    flatOffset: null
  };
  const source = makeShader(inputInfos, outShapeInfo, program);
-  const webGLProgram = gpgpu.createProgram(source);
+  const fragmentShader = createFragmentShader(gpgpu.gl, source);
+  const webGLProgram = gpgpu.createProgram(fragmentShader);
  let infLoc = null;
  const nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
  if (env().getNumber("WEBGL_VERSION") === 1) {
@ -48163,6 +48164,7 @@ function compileProgram(gpgpu, program, inputs, output) {
  }
  return {
    program,
+    fragmentShader,
    source,
    webGLProgram,
    uniformLocations,
@ -48895,10 +48897,9 @@ var GPGPUContext = class {
  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
  }
-  createProgram(fragmentShaderSource) {
+  createProgram(fragmentShader) {
    this.throwIfDisposed();
    const gl = this.gl;
-    const fragmentShader = createFragmentShader(gl, fragmentShaderSource);
    if (this.vertexShader == null) {
      this.vertexShader = createVertexShader2(gl);
    }
@ -65442,13 +65443,14 @@ var ScatterOptimizedProgram = class {
    }
    const updatesSnippet = `getUpdates(${updatesString})`;
    const atomicAddSnippet = this.type === "int32" ? `ignore(atomicAdd(&(result.numbers[flatIndex]), i32(updateValue)));` : `
-     var oldI32 = atomicLoad(&(result.numbers[flatIndex]));
-     var assumed = oldI32 - 1;
-     for (; assumed != oldI32;) {
-       assumed = oldI32;
+     var assumed = atomicLoad(&(result.numbers[flatIndex]));
+     var success = 0;
+     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
-       oldI32 = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32)[0];
+       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
+       assumed = resValue[0];
+       success = resValue[1];
     }
     `;
    const userCode = `
@ -65859,7 +65861,7 @@ function stridedSlice4(args) {
    const size2 = slice_util_exports.computeOutShape($begin, $end, $strides);
    const sliced = slice4({ inputs: { x }, backend: backend22, attrs: { begin: $begin, size: size2 } });
    result = reshape5({ inputs: { x: sliced }, backend: backend22, attrs: { shape: finalShape } });
-    backend22.disposeData(sliced);
+    backend22.disposeData(sliced.dataId);
  } else {
    const shouldExecuteOnCPU = backend22.shouldExecuteOnCPU([x]);
    if (shouldExecuteOnCPU) {
@ -65870,7 +65872,9 @@ function stridedSlice4(args) {
    } else {
      const program = new StridedSliceProgram2(finalShapeSparse);
      const uniformData = [{ type: "int32", data: $begin }, { type: "int32", data: $strides }];
-      result = backend22.runWebGPUProgram(program, [x], x.dtype, uniformData);
+      const resultValues = backend22.runWebGPUProgram(program, [x], x.dtype, uniformData);
+      result = reshape5({ inputs: { x: resultValues }, backend: backend22, attrs: { shape: finalShape } });
+      backend22.disposeData(resultValues.dataId);
    }
  }
  return result;
@ -70572,7 +70576,7 @@ registerBackend("wasm", async () => {
  const { wasm } = await init();
  return new BackendWasm(wasm);
 }, WASM_PRIORITY);
-var externalVersion = "3.11.0-20211031";
+var externalVersion = "3.11.0-20211102";
 var version8 = {
  tfjs: externalVersion,
  "tfjs-core": externalVersion,
@ -71655,7 +71659,7 @@ async function predict(image7, config3, idx, count3) {
  skipped2 = 0;
  return new Promise(async (resolve) => {
    const resize = image.resizeBilinear(image7, [model3?.inputs[0].shape ? model3.inputs[0].shape[2] : 0, model3?.inputs[0].shape ? model3.inputs[0].shape[1] : 0], false);
-    const res = model3?.predict(resize);
+    const res = model3?.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count3;
@ -75312,7 +75316,7 @@ var sigmoid6 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input2, config3, outputSize2) {
  const t = {};
  t.input = await prepareImage(input2);
-  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await models[1]?.execute(t.input, outputNodes);
+  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
  const poseScoreRaw = (await t.poseflag.data())[0];
  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
  const points = await t.ld.data();
@ -75620,7 +75624,7 @@ async function predict4(image7, config3) {
    });
    let resT;
    if (config3.body.enabled)
-      resT = await model6?.predict(tensor2);
+      resT = model6?.execute(tensor2);
    lastTime4 = now();
    dispose(tensor2);
    if (resT) {
@ -75728,7 +75732,7 @@ async function predict5(image7, config3, idx, count3) {
      dispose(blueNorm);
      const normalize = tidy(() => mul(sub(grayscale, 0.5), 2));
      dispose(grayscale);
-      const emotionT = await model7?.predict(normalize);
+      const emotionT = model7?.execute(normalize);
      lastTime5 = now();
      const data = await emotionT.data();
      dispose(emotionT);
@ -75853,7 +75857,7 @@ async function augmentIris(rawCoords, face5, config3, meshSize) {
  const combined = concat([leftEyeCrop, rightEyeCrop]);
  dispose(leftEyeCrop);
  dispose(rightEyeCrop);
-  const eyePredictions = model8.predict(combined);
+  const eyePredictions = model8.execute(combined);
  dispose(combined);
  const eyePredictionsData = await eyePredictions.data();
  dispose(eyePredictions);
@ -76057,7 +76061,7 @@ async function predict7(image7, config3, idx, count3) {
    };
    if (config3.face.description?.enabled) {
      const enhanced = enhance(image7);
-      const resT = await model10?.predict(enhanced);
+      const resT = model10?.execute(enhanced);
      lastTime7 = now();
      dispose(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
@ -79179,7 +79183,7 @@ var HandDetector = class {
  }
  async getBoxes(input2, config3) {
    const t = {};
-    t.batched = this.model.predict(input2);
+    t.batched = this.model.execute(input2);
    t.predictions = squeeze(t.batched);
    t.scores = tidy(() => squeeze(sigmoid(slice(t.predictions, [0, 0], [-1, 1]))));
    const scores = await t.scores.data();
@ -79320,7 +79324,7 @@ var HandPipeline = class {
        const handImage = div(croppedInput, 255);
        dispose(croppedInput);
        dispose(rotatedImage);
-        const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime8 = now();
        dispose(handImage);
        const confidence = (await confidenceT.data())[0];
@ -80347,7 +80351,7 @@ async function predict10(input2, config3) {
    const t = {};
    skipped10 = 0;
    t.input = padInput(input2, inputSize7);
-    t.res = await model11?.predict(t.input);
+    t.res = model11?.execute(t.input);
    cache5.last = now();
    const res = await t.res.array();
    cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input2, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input2, [0, 0, 1, 1]);
@ -80460,7 +80464,7 @@ async function predict11(image7, config3) {
    dispose(resize);
    let objectT;
    if (config3.object.enabled)
-      objectT = await model12.predict(transpose6);
+      objectT = model12.execute(transpose6);
    lastTime10 = now();
    dispose(transpose6);
    const obj = await process4(objectT, model12.inputSize, outputSize2, config3);
@ -80837,7 +80841,7 @@ async function process5(input2, background, config3) {
  t.resize = image.resizeBilinear(inputImage.tensor, [model14.inputs[0].shape ? model14.inputs[0].shape[1] : 0, model14.inputs[0].shape ? model14.inputs[0].shape[2] : 0], false);
  dispose(inputImage.tensor);
  t.norm = div(t.resize, 255);
-  t.res = model14.predict(t.norm);
+  t.res = model14.execute(t.norm);
  t.squeeze = squeeze(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = softmax(t.squeeze);
--- a/dist/human.esm.js.map
+++ b/dist/human.esm.js.map
--- a/dist/human.js
+++ b/dist/human.js
--- a/dist/human.node-gpu.js
+++ b/dist/human.node-gpu.js
@ -1343,7 +1343,7 @@ async function predict(image25, config3, idx, count2) {
  skipped2 = 0;
  return new Promise(async (resolve) => {
    const resize = tf4.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
-    const res = model2?.predict(resize);
+    const res = model2?.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count2;
@ -5009,7 +5009,7 @@ var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
  const t = {};
  t.input = await prepareImage(input);
-  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await models[1]?.execute(t.input, outputNodes);
+  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
  const poseScoreRaw = (await t.poseflag.data())[0];
  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
  const points = await t.ld.data();
@ -5323,7 +5323,7 @@ async function predict4(image25, config3) {
    });
    let resT;
    if (config3.body.enabled)
-      resT = await model5?.predict(tensor3);
+      resT = model5?.execute(tensor3);
    lastTime4 = now();
    tf9.dispose(tensor3);
    if (resT) {
@ -5432,7 +5432,7 @@ async function predict5(image25, config3, idx, count2) {
      tf10.dispose(blueNorm);
      const normalize = tf10.tidy(() => tf10.mul(tf10.sub(grayscale, 0.5), 2));
      tf10.dispose(grayscale);
-      const emotionT = await model6?.predict(normalize);
+      const emotionT = model6?.execute(normalize);
      lastTime5 = now();
      const data = await emotionT.data();
      tf10.dispose(emotionT);
@ -5561,7 +5561,7 @@ async function augmentIris(rawCoords, face5, config3, meshSize) {
  const combined = tf11.concat([leftEyeCrop, rightEyeCrop]);
  tf11.dispose(leftEyeCrop);
  tf11.dispose(rightEyeCrop);
-  const eyePredictions = model7.predict(combined);
+  const eyePredictions = model7.execute(combined);
  tf11.dispose(combined);
  const eyePredictionsData = await eyePredictions.data();
  tf11.dispose(eyePredictions);
@ -5766,7 +5766,7 @@ async function predict7(image25, config3, idx, count2) {
    };
    if (config3.face.description?.enabled) {
      const enhanced = enhance(image25);
-      const resT = await model9?.predict(enhanced);
+      const resT = model9?.execute(enhanced);
      lastTime7 = now();
      tf13.dispose(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
@ -8895,7 +8895,7 @@ var HandDetector = class {
  }
  async getBoxes(input, config3) {
    const t = {};
-    t.batched = this.model.predict(input);
+    t.batched = this.model.execute(input);
    t.predictions = tf15.squeeze(t.batched);
    t.scores = tf15.tidy(() => tf15.squeeze(tf15.sigmoid(tf15.slice(t.predictions, [0, 0], [-1, 1]))));
    const scores = await t.scores.data();
@ -9037,7 +9037,7 @@ var HandPipeline = class {
        const handImage = tf16.div(croppedInput, 255);
        tf16.dispose(croppedInput);
        tf16.dispose(rotatedImage);
-        const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime8 = now();
        tf16.dispose(handImage);
        const confidence = (await confidenceT.data())[0];
@ -10069,7 +10069,7 @@ async function predict10(input, config3) {
    const t = {};
    skipped10 = 0;
    t.input = padInput(input, inputSize7);
-    t.res = await model10?.predict(t.input);
+    t.res = model10?.execute(t.input);
    cache5.last = now();
    const res = await t.res.array();
    cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10183,7 +10183,7 @@ async function predict11(image25, config3) {
    tf21.dispose(resize);
    let objectT;
    if (config3.object.enabled)
-      objectT = await model11.predict(transpose);
+      objectT = model11.execute(transpose);
    lastTime10 = now();
    tf21.dispose(transpose);
    const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
@ -10564,7 +10564,7 @@ async function process5(input, background, config3) {
  t.resize = tf23.image.resizeBilinear(inputImage.tensor, [model13.inputs[0].shape ? model13.inputs[0].shape[1] : 0, model13.inputs[0].shape ? model13.inputs[0].shape[2] : 0], false);
  tf23.dispose(inputImage.tensor);
  t.norm = tf23.div(t.resize, 255);
-  t.res = model13.predict(t.norm);
+  t.res = model13.execute(t.norm);
  t.squeeze = tf23.squeeze(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = tf23.softmax(t.squeeze);
--- a/dist/human.node-wasm.js
+++ b/dist/human.node-wasm.js
@ -1344,7 +1344,7 @@ async function predict(image25, config3, idx, count2) {
  skipped2 = 0;
  return new Promise(async (resolve) => {
    const resize = tf4.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
-    const res = model2?.predict(resize);
+    const res = model2?.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count2;
@ -5010,7 +5010,7 @@ var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
  const t = {};
  t.input = await prepareImage(input);
-  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await models[1]?.execute(t.input, outputNodes);
+  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
  const poseScoreRaw = (await t.poseflag.data())[0];
  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
  const points = await t.ld.data();
@ -5324,7 +5324,7 @@ async function predict4(image25, config3) {
    });
    let resT;
    if (config3.body.enabled)
-      resT = await model5?.predict(tensor3);
+      resT = model5?.execute(tensor3);
    lastTime4 = now();
    tf9.dispose(tensor3);
    if (resT) {
@ -5433,7 +5433,7 @@ async function predict5(image25, config3, idx, count2) {
      tf10.dispose(blueNorm);
      const normalize = tf10.tidy(() => tf10.mul(tf10.sub(grayscale, 0.5), 2));
      tf10.dispose(grayscale);
-      const emotionT = await model6?.predict(normalize);
+      const emotionT = model6?.execute(normalize);
      lastTime5 = now();
      const data = await emotionT.data();
      tf10.dispose(emotionT);
@ -5562,7 +5562,7 @@ async function augmentIris(rawCoords, face5, config3, meshSize) {
  const combined = tf11.concat([leftEyeCrop, rightEyeCrop]);
  tf11.dispose(leftEyeCrop);
  tf11.dispose(rightEyeCrop);
-  const eyePredictions = model7.predict(combined);
+  const eyePredictions = model7.execute(combined);
  tf11.dispose(combined);
  const eyePredictionsData = await eyePredictions.data();
  tf11.dispose(eyePredictions);
@ -5767,7 +5767,7 @@ async function predict7(image25, config3, idx, count2) {
    };
    if (config3.face.description?.enabled) {
      const enhanced = enhance(image25);
-      const resT = await model9?.predict(enhanced);
+      const resT = model9?.execute(enhanced);
      lastTime7 = now();
      tf13.dispose(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
@ -8896,7 +8896,7 @@ var HandDetector = class {
  }
  async getBoxes(input, config3) {
    const t = {};
-    t.batched = this.model.predict(input);
+    t.batched = this.model.execute(input);
    t.predictions = tf15.squeeze(t.batched);
    t.scores = tf15.tidy(() => tf15.squeeze(tf15.sigmoid(tf15.slice(t.predictions, [0, 0], [-1, 1]))));
    const scores = await t.scores.data();
@ -9038,7 +9038,7 @@ var HandPipeline = class {
        const handImage = tf16.div(croppedInput, 255);
        tf16.dispose(croppedInput);
        tf16.dispose(rotatedImage);
-        const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime8 = now();
        tf16.dispose(handImage);
        const confidence = (await confidenceT.data())[0];
@ -10070,7 +10070,7 @@ async function predict10(input, config3) {
    const t = {};
    skipped10 = 0;
    t.input = padInput(input, inputSize7);
-    t.res = await model10?.predict(t.input);
+    t.res = model10?.execute(t.input);
    cache5.last = now();
    const res = await t.res.array();
    cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10184,7 +10184,7 @@ async function predict11(image25, config3) {
    tf21.dispose(resize);
    let objectT;
    if (config3.object.enabled)
-      objectT = await model11.predict(transpose);
+      objectT = model11.execute(transpose);
    lastTime10 = now();
    tf21.dispose(transpose);
    const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
@ -10565,7 +10565,7 @@ async function process5(input, background, config3) {
  t.resize = tf23.image.resizeBilinear(inputImage.tensor, [model13.inputs[0].shape ? model13.inputs[0].shape[1] : 0, model13.inputs[0].shape ? model13.inputs[0].shape[2] : 0], false);
  tf23.dispose(inputImage.tensor);
  t.norm = tf23.div(t.resize, 255);
-  t.res = model13.predict(t.norm);
+  t.res = model13.execute(t.norm);
  t.squeeze = tf23.squeeze(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = tf23.softmax(t.squeeze);
--- a/dist/human.node.js
+++ b/dist/human.node.js
@ -1343,7 +1343,7 @@ async function predict(image25, config3, idx, count2) {
  skipped2 = 0;
  return new Promise(async (resolve) => {
    const resize = tf4.image.resizeBilinear(image25, [model2?.inputs[0].shape ? model2.inputs[0].shape[2] : 0, model2?.inputs[0].shape ? model2.inputs[0].shape[1] : 0], false);
-    const res = model2?.predict(resize);
+    const res = model2?.execute(resize);
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count2;
@ -5009,7 +5009,7 @@ var sigmoid2 = (x) => 1 - 1 / (1 + Math.exp(x));
 async function detectParts(input, config3, outputSize2) {
  const t = {};
  t.input = await prepareImage(input);
-  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = await models[1]?.execute(t.input, outputNodes);
+  [t.ld, t.segmentation, t.heatmap, t.world, t.poseflag] = models[1]?.execute(t.input, outputNodes);
  const poseScoreRaw = (await t.poseflag.data())[0];
  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8));
  const points = await t.ld.data();
@ -5323,7 +5323,7 @@ async function predict4(image25, config3) {
    });
    let resT;
    if (config3.body.enabled)
-      resT = await model5?.predict(tensor3);
+      resT = model5?.execute(tensor3);
    lastTime4 = now();
    tf9.dispose(tensor3);
    if (resT) {
@ -5432,7 +5432,7 @@ async function predict5(image25, config3, idx, count2) {
      tf10.dispose(blueNorm);
      const normalize = tf10.tidy(() => tf10.mul(tf10.sub(grayscale, 0.5), 2));
      tf10.dispose(grayscale);
-      const emotionT = await model6?.predict(normalize);
+      const emotionT = model6?.execute(normalize);
      lastTime5 = now();
      const data = await emotionT.data();
      tf10.dispose(emotionT);
@ -5561,7 +5561,7 @@ async function augmentIris(rawCoords, face5, config3, meshSize) {
  const combined = tf11.concat([leftEyeCrop, rightEyeCrop]);
  tf11.dispose(leftEyeCrop);
  tf11.dispose(rightEyeCrop);
-  const eyePredictions = model7.predict(combined);
+  const eyePredictions = model7.execute(combined);
  tf11.dispose(combined);
  const eyePredictionsData = await eyePredictions.data();
  tf11.dispose(eyePredictions);
@ -5766,7 +5766,7 @@ async function predict7(image25, config3, idx, count2) {
    };
    if (config3.face.description?.enabled) {
      const enhanced = enhance(image25);
-      const resT = await model9?.predict(enhanced);
+      const resT = model9?.execute(enhanced);
      lastTime7 = now();
      tf13.dispose(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1);
@ -8895,7 +8895,7 @@ var HandDetector = class {
  }
  async getBoxes(input, config3) {
    const t = {};
-    t.batched = this.model.predict(input);
+    t.batched = this.model.execute(input);
    t.predictions = tf15.squeeze(t.batched);
    t.scores = tf15.tidy(() => tf15.squeeze(tf15.sigmoid(tf15.slice(t.predictions, [0, 0], [-1, 1]))));
    const scores = await t.scores.data();
@ -9037,7 +9037,7 @@ var HandPipeline = class {
        const handImage = tf16.div(croppedInput, 255);
        tf16.dispose(croppedInput);
        tf16.dispose(rotatedImage);
-        const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage);
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage);
        lastTime8 = now();
        tf16.dispose(handImage);
        const confidence = (await confidenceT.data())[0];
@ -10069,7 +10069,7 @@ async function predict10(input, config3) {
    const t = {};
    skipped10 = 0;
    t.input = padInput(input, inputSize7);
-    t.res = await model10?.predict(t.input);
+    t.res = model10?.execute(t.input);
    cache5.last = now();
    const res = await t.res.array();
    cache5.bodies = t.res.shape[2] === 17 ? await parseSinglePose(res, config3, input, [0, 0, 1, 1]) : await parseMultiPose(res, config3, input, [0, 0, 1, 1]);
@ -10183,7 +10183,7 @@ async function predict11(image25, config3) {
    tf21.dispose(resize);
    let objectT;
    if (config3.object.enabled)
-      objectT = await model11.predict(transpose);
+      objectT = model11.execute(transpose);
    lastTime10 = now();
    tf21.dispose(transpose);
    const obj = await process4(objectT, model11.inputSize, outputSize2, config3);
@ -10564,7 +10564,7 @@ async function process5(input, background, config3) {
  t.resize = tf23.image.resizeBilinear(inputImage.tensor, [model13.inputs[0].shape ? model13.inputs[0].shape[1] : 0, model13.inputs[0].shape ? model13.inputs[0].shape[2] : 0], false);
  tf23.dispose(inputImage.tensor);
  t.norm = tf23.div(t.resize, 255);
-  t.res = model13.predict(t.norm);
+  t.res = model13.execute(t.norm);
  t.squeeze = tf23.squeeze(t.res, 0);
  if (t.squeeze.shape[2] === 2) {
    t.softmax = tf23.softmax(t.squeeze);
--- a/dist/tfjs.esm.js
+++ b/dist/tfjs.esm.js
@ -47447,7 +47447,8 @@ function compileProgram(gpgpu, program, inputs, output) {
    flatOffset: null
  };
  const source = makeShader(inputInfos, outShapeInfo, program);
-  const webGLProgram = gpgpu.createProgram(source);
+  const fragmentShader = createFragmentShader(gpgpu.gl, source);
+  const webGLProgram = gpgpu.createProgram(fragmentShader);
  let infLoc = null;
  const nanLoc = gpgpu.getUniformLocation(webGLProgram, "NAN", false);
  if (env().getNumber("WEBGL_VERSION") === 1) {
@ -47482,6 +47483,7 @@ function compileProgram(gpgpu, program, inputs, output) {
  }
  return {
    program,
+    fragmentShader,
    source,
    webGLProgram,
    uniformLocations,
@ -48214,10 +48216,9 @@ var GPGPUContext = class {
  downloadMatrixFromPackedTexture(texture, physicalRows, physicalCols) {
    return this.downloadMatrixDriver(texture, () => downloadMatrixFromPackedOutputTexture(this.gl, physicalRows, physicalCols));
  }
-  createProgram(fragmentShaderSource) {
+  createProgram(fragmentShader) {
    this.throwIfDisposed();
    const gl = this.gl;
-    const fragmentShader = createFragmentShader(gl, fragmentShaderSource);
    if (this.vertexShader == null) {
      this.vertexShader = createVertexShader2(gl);
    }
@ -64761,13 +64762,14 @@ var ScatterOptimizedProgram = class {
    }
    const updatesSnippet = `getUpdates(${updatesString})`;
    const atomicAddSnippet = this.type === "int32" ? `ignore(atomicAdd(&(result.numbers[flatIndex]), i32(updateValue)));` : `
-     var oldI32 = atomicLoad(&(result.numbers[flatIndex]));
-     var assumed = oldI32 - 1;
-     for (; assumed != oldI32;) {
-       assumed = oldI32;
+     var assumed = atomicLoad(&(result.numbers[flatIndex]));
+     var success = 0;
+     for (; success == 0;) {
       let new = bitcast<f32>(assumed) + updateValue;
       let newI32 = bitcast<i32>(new);
-       oldI32 = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32)[0];
+       let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
+       assumed = resValue[0];
+       success = resValue[1];
     }
     `;
    const userCode = `
@ -65178,7 +65180,7 @@ function stridedSlice4(args) {
    const size = slice_util_exports.computeOutShape($begin, $end, $strides);
    const sliced = slice4({ inputs: { x }, backend: backend2, attrs: { begin: $begin, size } });
    result = reshape5({ inputs: { x: sliced }, backend: backend2, attrs: { shape: finalShape } });
-    backend2.disposeData(sliced);
+    backend2.disposeData(sliced.dataId);
  } else {
    const shouldExecuteOnCPU = backend2.shouldExecuteOnCPU([x]);
    if (shouldExecuteOnCPU) {
@ -65189,7 +65191,9 @@ function stridedSlice4(args) {
    } else {
      const program = new StridedSliceProgram2(finalShapeSparse);
      const uniformData = [{ type: "int32", data: $begin }, { type: "int32", data: $strides }];
-      result = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+      const resultValues = backend2.runWebGPUProgram(program, [x], x.dtype, uniformData);
+      result = reshape5({ inputs: { x: resultValues }, backend: backend2, attrs: { shape: finalShape } });
+      backend2.disposeData(resultValues.dataId);
    }
  }
  return result;
@ -69891,7 +69895,7 @@ registerBackend("wasm", async () => {
  const { wasm } = await init();
  return new BackendWasm(wasm);
 }, WASM_PRIORITY);
-var externalVersion = "3.11.0-20211031";
+var externalVersion = "3.11.0-20211102";
 var version8 = {
  tfjs: externalVersion,
  "tfjs-core": externalVersion,
--- a/models/README.md
+++ b/models/README.md
@ -3,3 +3,285 @@
 For details see Wiki:

 - [**List of Models & Credits**](https://github.com/vladmandic/human/wiki/Models)
+
+## Model signatures:
+
+```js
+INFO:  graph model: /home/vlado/dev/human/models/iris.json
+INFO:  created on: 2020-10-12T18:46:47.060Z
+INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 1, 1, 228 ] }
+INFO:  tensors: 191
+DATA:  weights: {
+  files: [ 'iris.bin' ],
+  size: { disk: 2599092, memory: 2599092 },
+  count: { total: 191, float32: 189, int32: 2 },
+  quantized: { none: 191 },
+  values: { total: 649773, float32: 649764, int32: 9 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity' ],
+  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
+  arithmetic: [ 'AddV2' ],
+  basic_math: [ 'Prelu' ],
+  transformation: [ 'Pad' ],
+  slice_join: [ 'ConcatV2' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/facemesh.json
+INFO:  created on: 2020-10-12T18:46:46.944Z
+INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 192, 192, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 266 ] }
+ { id: 1, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
+ { id: 2, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1404 ] }
+INFO:  tensors: 118
+DATA:  weights: {
+  files: [ 'facemesh.bin' ],
+  size: { disk: 2955780, memory: 2955780 },
+  count: { total: 118, float32: 114, int32: 4 },
+  quantized: { none: 118 },
+  values: { total: 738945, float32: 738919, int32: 26 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Placeholder', 'Const', 'NoOp', 'Identity' ],
+  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
+  arithmetic: [ 'AddV2' ],
+  basic_math: [ 'Prelu', 'Sigmoid' ],
+  transformation: [ 'Pad', 'Reshape' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/emotion.json
+INFO:  created on: 2020-11-05T20:11:29.740Z
+INFO:  metadata: { generatedBy: 'https://github.com/oarriaga/face_classification', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ -1, 64, 64, 1 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ -1, 7 ] }
+INFO:  tensors: 23
+DATA:  weights: {
+  files: [ 'emotion.bin' ],
+  size: { disk: 820516, memory: 820516 },
+  count: { total: 23, float32: 22, int32: 1 },
+  quantized: { none: 23 },
+  values: { total: 205129, float32: 205127, int32: 2 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity' ],
+  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
+  arithmetic: [ 'AddV2' ],
+  basic_math: [ 'Relu' ],
+  reduction: [ 'Mean' ],
+  normalization: [ 'Softmax' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/faceres.json
+INFO:  created on: 2021-03-21T14:12:59.863Z
+INFO:  metadata: { generatedBy: 'https://github.com/HSE-asavchenko/HSE_FaceRec_tf', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'input_1', dtype: 'DT_FLOAT', shape: [ -1, 224, 224, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'gender_pred/Sigmoid:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
+ { id: 1, name: 'global_pooling/Mean', dytpe: 'DT_FLOAT', shape: [ 1, 1024 ] }
+ { id: 2, name: 'age_pred/Softmax:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
+INFO:  tensors: 128
+DATA:  weights: {
+  files: [ 'faceres.bin' ],
+  size: { disk: 6978814, memory: 13957620 },
+  count: { total: 128, float32: 127, int32: 1 },
+  quantized: { float16: 127, none: 1 },
+  values: { total: 3489405, float32: 3489403, int32: 2 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder' ],
+  convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
+  arithmetic: [ 'Add', 'Minimum', 'Maximum', 'Mul' ],
+  basic_math: [ 'Relu', 'Sigmoid' ],
+  reduction: [ 'Mean' ],
+  matrices: [ '_FusedMatMul' ],
+  normalization: [ 'Softmax' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/blazeface.json
+INFO:  created on: 2020-10-15T19:57:26.419Z
+INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'input:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 16 ] }
+ { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 1 ] }
+ { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 384, 1 ] }
+ { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 512, 16 ] }
+INFO:  tensors: 112
+DATA:  weights: {
+  files: [ 'blazeface.bin' ],
+  size: { disk: 538928, memory: 538928 },
+  count: { total: 112, float32: 106, int32: 6 },
+  quantized: { none: 112 },
+  values: { total: 134732, float32: 134704, int32: 28 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity' ],
+  convolution: [ '_FusedConv2D', 'DepthwiseConv2dNative', 'MaxPool' ],
+  arithmetic: [ 'AddV2' ],
+  basic_math: [ 'Relu' ],
+  transformation: [ 'Pad', 'Reshape' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/mb3-centernet.json
+INFO:  created on: 2021-05-19T11:50:13.013Z
+INFO:  metadata: { generatedBy: 'https://github.com/610265158/mobilenetv3_centernet', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'tower_0/images', dtype: 'DT_FLOAT', shape: [ 1, 512, 512, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'tower_0/wh', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 4 ] }
+ { id: 1, name: 'tower_0/keypoints', dytpe: 'DT_FLOAT', shape: [ 1, 128, 128, 80 ] }
+ { id: 2, name: 'tower_0/detections', dytpe: 'DT_FLOAT', shape: [ 1, 100, 6 ] }
+INFO:  tensors: 267
+DATA:  weights: {
+  files: [ 'mb3-centernet.bin' ],
+  size: { disk: 4030290, memory: 8060260 },
+  count: { total: 267, float32: 227, int32: 40 },
+  quantized: { float16: 227, none: 40 },
+  values: { total: 2015065, float32: 2014985, int32: 80 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity' ],
+  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative', 'Conv2D', 'MaxPool' ],
+  arithmetic: [ 'Mul', 'Add', 'FloorDiv', 'FloorMod', 'Sub' ],
+  basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
+  reduction: [ 'Mean' ],
+  image: [ 'ResizeBilinear' ],
+  slice_join: [ 'ConcatV2', 'GatherV2', 'StridedSlice' ],
+  transformation: [ 'Reshape', 'Cast', 'ExpandDims' ],
+  logical: [ 'Equal' ],
+  evaluation: [ 'TopKV2' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/movenet-lightning.json
+INFO:  created on: 2021-05-29T12:26:32.994Z
+INFO:  metadata: { generatedBy: 'https://tfhub.dev/google/movenet/singlepose/lightning/4', convertedBy: 'https://github.com/vladmandic', version: undefined }
+INFO:  model inputs based on signature
+ { name: 'input:0', dtype: 'DT_INT32', shape: [ 1, 192, 192, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 1, 17, 3 ] }
+INFO:  tensors: 180
+DATA:  weights: {
+  files: [ 'movenet-lightning.bin' ],
+  size: { disk: 4650216, memory: 9300008 },
+  count: { total: 180, int32: 31, float32: 149 },
+  quantized: { none: 31, float16: 149 },
+  values: { total: 2325002, int32: 106, float32: 2324896 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity' ],
+  transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape' ],
+  slice_join: [ 'Unpack', 'Pack', 'GatherNd', 'ConcatV2' ],
+  arithmetic: [ 'Sub', 'Mul', 'AddV2', 'FloorDiv', 'SquaredDifference', 'RealDiv' ],
+  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
+  image: [ 'ResizeBilinear' ],
+  basic_math: [ 'Sigmoid', 'Sqrt' ],
+  reduction: [ 'ArgMax' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/selfie.json
+INFO:  created on: 2021-06-04T13:46:56.904Z
+INFO:  metadata: { generatedBy: 'https://github.com/PINTO0309/PINTO_model_zoo/tree/main/109_Selfie_Segmentation', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
+INFO:  model inputs based on signature
+ { name: 'input_1:0', dtype: 'DT_FLOAT', shape: [ 1, 256, 256, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'activation_10:0', dytpe: 'DT_FLOAT', shape: [ 1, 256, 256, 1 ] }
+INFO:  tensors: 136
+DATA:  weights: {
+  files: [ 'selfie.bin' ],
+  size: { disk: 212886, memory: 425732 },
+  count: { total: 136, int32: 4, float32: 132 },
+  quantized: { none: 4, float16: 132 },
+  values: { total: 106433, int32: 10, float32: 106423 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder' ],
+  convolution: [ 'Conv2D', 'DepthwiseConv2dNative', 'AvgPool', 'Conv2DBackpropInput' ],
+  arithmetic: [ 'Add', 'Mul', 'AddV2', 'AddN' ],
+  basic_math: [ 'Relu6', 'Relu', 'Sigmoid' ],
+  image: [ 'ResizeBilinear' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/handtrack.json
+INFO:  created on: 2021-09-21T12:09:47.583Z
+INFO:  metadata: { generatedBy: 'https://github.com/victordibia/handtracking', convertedBy: 'https://github.com/vladmandic', version: '561.undefined' }
+INFO:  model inputs based on signature
+ { name: 'input_tensor:0', dtype: 'DT_UINT8', shape: [ 1, 320, 320, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
+ { id: 1, name: 'Identity_4:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
+ { id: 2, name: 'Identity_6:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 4 ] }
+ { id: 3, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 4 ] }
+ { id: 4, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 100, 8 ] }
+ { id: 5, name: 'Identity_5:0', dytpe: 'DT_FLOAT', shape: [ 1 ] }
+ { id: 6, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 100 ] }
+ { id: 7, name: 'Identity_7:0', dytpe: 'DT_FLOAT', shape: [ 1, 12804, 8 ] }
+INFO:  tensors: 619
+DATA:  weights: {
+  files: [ 'handtrack.bin' ],
+  size: { disk: 2964837, memory: 11846016 },
+  count: { total: 619, int32: 347, float32: 272 },
+  quantized: { none: 347, uint8: 272 },
+  values: { total: 2961504, int32: 1111, float32: 2960393 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity', 'Shape', 'NoOp' ],
+  control: [ 'TensorListReserve', 'Enter', 'TensorListFromTensor', 'Merge', 'LoopCond', 'Switch', 'Exit', 'TensorListStack', 'NextIteration', 'TensorListSetItem', 'TensorListGetItem' ],
+  logical: [ 'Less', 'LogicalAnd', 'Select', 'Greater', 'GreaterEqual' ],
+  convolution: [ '_FusedConv2D', 'FusedDepthwiseConv2dNative', 'DepthwiseConv2dNative' ],
+  arithmetic: [ 'AddV2', 'Mul', 'Sub', 'Minimum', 'Maximum' ],
+  transformation: [ 'Cast', 'ExpandDims', 'Squeeze', 'Reshape', 'Pad' ],
+  slice_join: [ 'Unpack', 'StridedSlice', 'Pack', 'ConcatV2', 'Slice', 'GatherV2', 'Split' ],
+  image: [ 'ResizeBilinear' ],
+  basic_math: [ 'Reciprocal', 'Sigmoid', 'Exp' ],
+  matrices: [ 'Transpose' ],
+  dynamic: [ 'NonMaxSuppressionV5', 'Where' ],
+  creation: [ 'Fill', 'Range' ],
+  evaluation: [ 'TopKV2' ],
+  reduction: [ 'Sum' ]
+}
+INFO:  graph model: /home/vlado/dev/human/models/antispoof.json
+INFO:  created on: 2021-10-13T14:20:27.100Z
+INFO:  metadata: { generatedBy: 'https://www.kaggle.com/anku420/fake-face-detection', convertedBy: 'https://github.com/vladmandic', version: '716.undefined' }
+INFO:  model inputs based on signature
+ { name: 'conv2d_input', dtype: 'DT_FLOAT', shape: [ -1, 128, 128, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'activation_4', dytpe: 'DT_FLOAT', shape: [ -1, 1 ] }
+INFO:  tensors: 11
+DATA:  weights: {
+  files: [ 'antispoof.bin' ],
+  size: { disk: 853098, memory: 1706188 },
+  count: { total: 11, float32: 10, int32: 1 },
+  quantized: { float16: 10, none: 1 },
+  values: { total: 426547, float32: 426545, int32: 2 }
+}
+DATA:  kernel ops: { graph: [ 'Const', 'Placeholder', 'Identity' ], convolution: [ '_FusedConv2D', 'MaxPool' ], basic_math: [ 'Relu', 'Sigmoid' ], transformation: [ 'Reshape' ], matrices: [ '_FusedMatMul' ] }
+INFO:  graph model: /home/vlado/dev/human/models/handlandmark-full.json
+INFO:  created on: 2021-10-31T12:27:49.343Z
+INFO:  metadata: { generatedBy: 'https://github.com/google/mediapipe', convertedBy: 'https://github.com/vladmandic', version: '808.undefined' }
+INFO:  model inputs based on signature
+ { name: 'input_1', dtype: 'DT_FLOAT', shape: [ 1, 224, 224, 3 ] }
+INFO:  model outputs based on signature
+ { id: 0, name: 'Identity_3:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
+ { id: 1, name: 'Identity:0', dytpe: 'DT_FLOAT', shape: [ 1, 63 ] }
+ { id: 2, name: 'Identity_1:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
+ { id: 3, name: 'Identity_2:0', dytpe: 'DT_FLOAT', shape: [ 1, 1 ] }
+INFO:  tensors: 103
+DATA:  weights: {
+  files: [ 'handlandmark-full.bin' ],
+  size: { disk: 5431368, memory: 10862728 },
+  count: { total: 103, float32: 102, int32: 1 },
+  quantized: { float16: 102, none: 1 },
+  values: { total: 2715682, float32: 2715680, int32: 2 }
+}
+DATA:  kernel ops: {
+  graph: [ 'Const', 'Placeholder', 'Identity' ],
+  convolution: [ 'Conv2D', 'DepthwiseConv2dNative' ],
+  arithmetic: [ 'AddV2', 'AddN' ],
+  basic_math: [ 'Relu6', 'Sigmoid' ],
+  reduction: [ 'Mean' ],
+  matrices: [ '_FusedMatMul' ]
+}
+```
--- a/package.json
+++ b/package.json
@ -67,10 +67,10 @@
    "@tensorflow/tfjs-node": "^3.11.0",
    "@tensorflow/tfjs-node-gpu": "^3.11.0",
    "@types/node": "^16.11.6",
-    "@typescript-eslint/eslint-plugin": "^5.2.0",
-    "@typescript-eslint/parser": "^5.2.0",
+    "@typescript-eslint/eslint-plugin": "^5.3.0",
+    "@typescript-eslint/parser": "^5.3.0",
    "@vladmandic/build": "^0.6.3",
-    "@vladmandic/pilogger": "^0.3.3",
+    "@vladmandic/pilogger": "^0.3.5",
    "canvas": "^2.8.0",
    "dayjs": "^1.10.7",
    "esbuild": "^0.13.12",
--- a/src/body/blazepose.ts
+++ b/src/body/blazepose.ts
@ -102,7 +102,7 @@ async function detectParts(input: Tensor, config: Config, outputSize: [number, n
   * t.world: 39 keypoints [x,y,z] normalized to -1..1
   * t.poseflag: body score
   */
-  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = await models[1]?.execute(t.input, outputNodes) as Tensor[]; // run model
+  [t.ld/* 1,195(39*5) */, t.segmentation/* 1,256,256,1 */, t.heatmap/* 1,64,64,39 */, t.world/* 1,117(39*3) */, t.poseflag/* 1,1 */] = models[1]?.execute(t.input, outputNodes) as Tensor[]; // run model
  const poseScoreRaw = (await t.poseflag.data())[0];
  const poseScore = Math.max(0, (poseScoreRaw - 0.8) / (1 - 0.8)); // blow up score variance 5x
  const points = await t.ld.data();
--- a/src/body/efficientpose.ts
+++ b/src/body/efficientpose.ts
@ -67,7 +67,7 @@ export async function predict(image: Tensor, config: Config): Promise<BodyResult
    });

    let resT;
-    if (config.body.enabled) resT = await model?.predict(tensor);
+    if (config.body.enabled) resT = model?.execute(tensor);
    lastTime = now();
    tf.dispose(tensor);

--- a/src/body/movenet.ts
+++ b/src/body/movenet.ts
@ -150,7 +150,7 @@ export async function predict(input: Tensor, config: Config): Promise<BodyResult
        t.crop = tf.image.cropAndResize(input, [cache.boxes[i]], [0], [inputSize, inputSize], 'bilinear');
        t.cast = tf.cast(t.crop, 'int32');
        // t.input = prepareImage(input);
-        t.res = await model?.predict(t.cast) as Tensor;
+        t.res = model?.execute(t.cast) as Tensor;
        const res = await t.res.array();
        const newBodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, cache.boxes[i]) : await parseMultiPose(res, config, input, cache.boxes[i]);
        cache.bodies = cache.bodies.concat(newBodies);
@ -159,7 +159,7 @@ export async function predict(input: Tensor, config: Config): Promise<BodyResult
    }
    if (cache.bodies.length !== config.body.maxDetected) { // did not find enough bodies based on cached boxes so run detection on full frame
      t.input = prepareImage(input);
-      t.res = await model?.predict(t.input) as Tensor;
+      t.res = model?.execute(t.input) as Tensor;
      const res = await t.res.array();
      cache.bodies = (t.res.shape[2] === 17) ? await parseSinglePose(res, config, input, [0, 0, 1, 1]) : await parseMultiPose(res, config, input, [0, 0, 1, 1]);
      for (const body of cache.bodies) rescaleBody(body, [input.shape[2] || 1, input.shape[1] || 1]);
@ -177,7 +177,7 @@ export async function predict(input: Tensor, config: Config): Promise<BodyResult

    // run detection on squared input and no cached boxes
    t.input = fix.padInput(input, inputSize);
-    t.res = await model?.predict(t.input) as Tensor;
+    t.res = model?.execute(t.input) as Tensor;
    cache.last = now();
    const res = await t.res.array();
    cache.bodies = (t.res.shape[2] === 17)
--- a/src/face/antispoof.ts
+++ b/src/face/antispoof.ts
@ -35,7 +35,7 @@ export async function predict(image: Tensor, config: Config, idx, count) {
  skipped = 0;
  return new Promise(async (resolve) => {
    const resize = tf.image.resizeBilinear(image, [model?.inputs[0].shape ? model.inputs[0].shape[2] : 0, model?.inputs[0].shape ? model.inputs[0].shape[1] : 0], false);
-    const res = model?.predict(resize) as Tensor;
+    const res = model?.execute(resize) as Tensor;
    const num = (await res.data())[0];
    cached[idx] = Math.round(100 * num) / 100;
    lastCount = count;
--- a/src/face/embedding.ts
+++ b/src/face/embedding.ts
@ -72,31 +72,28 @@ export async function predict(input, config): Promise<number[]> {
    let data: Array<number> = [];
    if (config.face.embedding.enabled) {
      const image = enhance(input);
+      const dataT = model?.execute(image) as Tensor;
+      /*
      const dataT = tf.tidy(() => {
        /*
        // if needed convert from NHWC to NCHW
        const nchw = image.transpose([3, 0, 1, 2]);
-        */

-        const res = model?.predict(image);
+        const res = model.execute(image);

-        /*
        // optionally do it twice with flipped image and average results
-        const res1 = model.predict(image);
+        const res1 = model.execute(image);
        const flipped = tf.image.flipLeftRight(image);
-        const res2 = model.predict(flipped);
+        const res2 = model.execute(flipped);
        const merge = tf.stack([res1, res2], 2).squeeze();
        const res = reshape.logSumExp(1);
-        */

-        /*
        // optional normalize outputs with l2 normalization
        const scaled = tf.tidy(() => {
          const l2 = res.norm('euclidean');
          const scale = res.div(l2);
          return scale;
        });
-        */

        // optional reduce feature vector complexity
        const reshape = tf.reshape(res, [128, 2]); // split 256 vectors into 128 x 2
@ -104,8 +101,9 @@ export async function predict(input, config): Promise<number[]> {

        return reduce;
      });
-      const output: Array<number> = await dataT.data();
-      data = [...output]; // convert typed array to simple array
+      */
+      const output = await dataT.data();
+      data = Array.from(output); // convert typed array to simple array
      tf.dispose(dataT);
      tf.dispose(image);
    }
--- a/src/face/faceres.ts
+++ b/src/face/faceres.ts
@ -108,7 +108,7 @@ export async function predict(image: Tensor, config: Config, idx, count) {

    if (config.face.description?.enabled) {
      const enhanced = enhance(image);
-      const resT = await model?.predict(enhanced) as Tensor[];
+      const resT = model?.execute(enhanced) as Tensor[];
      lastTime = now();
      tf.dispose(enhanced);
      const genderT = await resT.find((t) => t.shape[1] === 1) as Tensor;
@ -129,7 +129,6 @@ export async function predict(image: Tensor, config: Config, idx, count) {
      // const reshape = desc.reshape([128, 8]); // reshape large 1024-element descriptor to 128 x 8
      // const reduce = reshape.logSumExp(1); // reduce 2nd dimension by calculating logSumExp on it which leaves us with 128-element descriptor
      const descriptor = desc ? await desc.data() : <number[]>[];
-      // obj.descriptor = [...descriptor];
      obj.descriptor = Array.from(descriptor);
      resT.forEach((t) => tf.dispose(t));
    }
--- a/src/face/iris.ts
+++ b/src/face/iris.ts
@ -124,7 +124,7 @@ export async function augmentIris(rawCoords, face, config, meshSize) {
  const combined = tf.concat([leftEyeCrop, rightEyeCrop]);
  tf.dispose(leftEyeCrop);
  tf.dispose(rightEyeCrop);
-  const eyePredictions = model.predict(combined) as Tensor;
+  const eyePredictions = model.execute(combined) as Tensor;
  tf.dispose(combined);
  const eyePredictionsData = await eyePredictions.data(); // inside tf.tidy
  tf.dispose(eyePredictions);
--- a/src/gear/emotion.ts
+++ b/src/gear/emotion.ts
@ -59,7 +59,7 @@ export async function predict(image: Tensor, config: Config, idx, count) {
      tf.dispose(blueNorm);
      const normalize = tf.tidy(() => tf.mul(tf.sub(grayscale, 0.5), 2));
      tf.dispose(grayscale);
-      const emotionT = await model?.predict(normalize) as Tensor; // result is already in range 0..1, no need for additional activation
+      const emotionT = model?.execute(normalize) as Tensor; // result is already in range 0..1, no need for additional activation
      lastTime = now();
      const data = await emotionT.data();
      tf.dispose(emotionT);
--- a/src/gear/gear-agegenderrace.ts
+++ b/src/gear/gear-agegenderrace.ts
@ -54,7 +54,7 @@ export async function predict(image: Tensor, config: Config) {
    const obj = { age: 0 };

    // @ts-ignore array definition unavailable at compile time
-    if (config.face.agegenderrace.enabled) [ageT, genderT, raceT] = await model.execute(resize, ['age_output', 'gender_output', 'race_output']);
+    if (config.face.agegenderrace.enabled) [ageT, genderT, raceT] = model.execute(resize, ['age_output', 'gender_output', 'race_output']);
    lastTime = now();
    tf.dispose(resize);
    // tf.dispose(enhance);
--- a/src/gear/ssrnet-age.ts
+++ b/src/gear/ssrnet-age.ts
@ -49,7 +49,7 @@ export async function predict(image: Tensor, config: Config | any) {
    let ageT;
    const obj = { age: 0 };

-    if (config.face.age.enabled) ageT = await model.predict(enhance);
+    if (config.face.age.enabled) ageT = model.execute(enhance);
    lastTime = now();
    tf.dispose(enhance);

--- a/src/gear/ssrnet-gender.ts
+++ b/src/gear/ssrnet-gender.ts
@ -65,7 +65,7 @@ export async function predict(image: Tensor, config: Config | any) {
    let genderT;
    const obj = { gender: '', confidence: 0 };

-    if (config.face.gender.enabled) genderT = await model.predict(enhance);
+    if (config.face.gender.enabled) genderT = model.execute(enhance);
    lastTime = now();
    tf.dispose(enhance);

--- a/src/hand/handposedetector.ts
+++ b/src/hand/handposedetector.ts
@ -46,7 +46,7 @@ export class HandDetector {

  async getBoxes(input, config) {
    const t: Record<string, Tensor> = {};
-    t.batched = this.model.predict(input) as Tensor;
+    t.batched = this.model.execute(input) as Tensor;
    t.predictions = tf.squeeze(t.batched);
    t.scores = tf.tidy(() => tf.squeeze(tf.sigmoid(tf.slice(t.predictions, [0, 0], [-1, 1]))));
    const scores = await t.scores.data();
--- a/src/hand/handposepipeline.ts
+++ b/src/hand/handposepipeline.ts
@ -122,7 +122,7 @@ export class HandPipeline {
        const handImage = tf.div(croppedInput, 255);
        tf.dispose(croppedInput);
        tf.dispose(rotatedImage);
-        const [confidenceT, keypoints] = await this.handPoseModel.predict(handImage) as Array<Tensor>;
+        const [confidenceT, keypoints] = this.handPoseModel.execute(handImage) as Array<Tensor>;
        lastTime = now();
        tf.dispose(handImage);
        const confidence = (await confidenceT.data())[0];
--- a/src/object/nanodet.ts
+++ b/src/object/nanodet.ts
@ -124,7 +124,7 @@ export async function predict(image: Tensor, config: Config): Promise<ObjectResu
    tf.dispose(resize);

    let objectT;
-    if (config.object.enabled) objectT = await model.predict(transpose);
+    if (config.object.enabled) objectT = model.execute(transpose);
    lastTime = now();
    tf.dispose(transpose);

--- a/src/segmentation/segmentation.ts
+++ b/src/segmentation/segmentation.ts
@ -40,7 +40,7 @@ export async function process(input: Input, background: Input | undefined, confi
  t.resize = tf.image.resizeBilinear(inputImage.tensor, [model.inputs[0].shape ? model.inputs[0].shape[1] : 0, model.inputs[0].shape ? model.inputs[0].shape[2] : 0], false);
  tf.dispose(inputImage.tensor);
  t.norm = tf.div(t.resize, 255);
-  t.res = model.predict(t.norm) as Tensor;
+  t.res = model.execute(t.norm) as Tensor;

  t.squeeze = tf.squeeze(t.res, 0); // meet.shape:[1,256,256,1], selfie.shape:[1,144,256,2]
  if (t.squeeze.shape[2] === 2) {
--- a/test/build.log
+++ b/test/build.log
@ -1,713 +1,25 @@
-2021-10-31 07:56:56 [36mINFO: [39m @vladmandic/human version 2.5.0 
-2021-10-31 07:56:56 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v17.0.1 
-2021-10-31 07:56:56 [36mINFO: [39m Application: {"name":"@vladmandic/human","version":"2.5.0"} 
-2021-10-31 07:56:56 [36mINFO: [39m Environment: {"profile":"production","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
-2021-10-31 07:56:56 [36mINFO: [39m Toolchain: {"build":"0.6.3","esbuild":"0.13.12","typescript":"4.4.4","typedoc":"0.22.7","eslint":"8.1.0"} 
-2021-10-31 07:56:56 [36mINFO: [39m Build: {"profile":"production","steps":["clean","compile","typings","typedoc","lint","changelog"]} 
-2021-10-31 07:56:56 [35mSTATE:[39m Clean: {"locations":["dist/*","types/*","typedoc/*"]} 
-2021-10-31 07:56:56 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/cpu","format":"cjs","platform":"node","input":"tfjs/tf-node.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":102,"outputBytes":1275} 
-2021-10-31 07:56:56 [35mSTATE:[39m Compile: {"name":"human/nodejs/cpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node.js","files":55,"inputBytes":516456,"outputBytes":432592} 
-2021-10-31 07:56:56 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/gpu","format":"cjs","platform":"node","input":"tfjs/tf-node-gpu.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":110,"outputBytes":1283} 
-2021-10-31 07:56:56 [35mSTATE:[39m Compile: {"name":"human/nodejs/gpu","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-gpu.js","files":55,"inputBytes":516464,"outputBytes":432596} 
-2021-10-31 07:56:56 [35mSTATE:[39m Compile: {"name":"tfjs/nodejs/wasm","format":"cjs","platform":"node","input":"tfjs/tf-node-wasm.ts","output":"dist/tfjs.esm.js","files":1,"inputBytes":149,"outputBytes":1350} 
-2021-10-31 07:56:57 [35mSTATE:[39m Compile: {"name":"human/nodejs/wasm","format":"cjs","platform":"node","input":"src/human.ts","output":"dist/human.node-wasm.js","files":55,"inputBytes":516531,"outputBytes":432668} 
-2021-10-31 07:56:57 [35mSTATE:[39m Compile: {"name":"tfjs/browser/version","format":"esm","platform":"browser","input":"tfjs/tf-version.ts","output":"dist/tfjs.version.js","files":1,"inputBytes":1063,"outputBytes":1652} 
-2021-10-31 07:56:57 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/nobundle","format":"esm","platform":"browser","input":"tfjs/tf-browser.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2323,"outputBytes":973} 
-2021-10-31 07:56:57 [35mSTATE:[39m Compile: {"name":"human/browser/esm/nobundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm-nobundle.js","files":55,"inputBytes":516154,"outputBytes":434523} 
-2021-10-31 07:56:57 [35mSTATE:[39m Compile: {"name":"tfjs/browser/esm/custom","format":"esm","platform":"browser","input":"tfjs/tf-custom.ts","output":"dist/tfjs.esm.js","files":2,"inputBytes":2564128,"outputBytes":2499347} 
-2021-10-31 07:56:57 [35mSTATE:[39m Compile: {"name":"human/browser/iife/bundle","format":"iife","platform":"browser","input":"src/human.ts","output":"dist/human.js","files":55,"inputBytes":3014528,"outputBytes":1607633} 
-2021-10-31 07:56:58 [35mSTATE:[39m Compile: {"name":"human/browser/esm/bundle","format":"esm","platform":"browser","input":"src/human.ts","output":"dist/human.esm.js","files":55,"inputBytes":3014528,"outputBytes":2935933} 
-2021-10-31 07:57:14 [35mSTATE:[39m Typings: {"input":"src/human.ts","output":"types","files":48} 
-2021-10-31 07:57:20 [35mSTATE:[39m TypeDoc: {"input":"src/human.ts","output":"typedoc","objects":48,"generated":true} 
-2021-10-31 07:57:20 [35mSTATE:[39m Compile: {"name":"demo/browser","format":"esm","platform":"browser","input":"demo/typescript/index.ts","output":"demo/typescript/index.js","files":1,"inputBytes":3468,"outputBytes":3331} 
-2021-10-31 07:57:50 [35mSTATE:[39m Lint: {"locations":["*.json","src/**/*.ts","test/**/*.js","demo/**/*.js"],"files":92,"errors":0,"warnings":0} 
-2021-10-31 07:57:51 [35mSTATE:[39m ChangeLog: {"repository":"https://github.com/vladmandic/human","branch":"main","output":"CHANGELOG.md"} 
-2021-10-31 07:57:51 [36mINFO: [39m Done... 
-2021-10-31 08:28:13 [36mINFO: [39m @vladmandic/human version 2.5.0 
-2021-10-31 08:28:13 [36mINFO: [39m User: vlado Platform: linux Arch: x64 Node: v17.0.1 
-2021-10-31 08:28:13 [36mINFO: [39m Application: {"name":"@vladmandic/human","version":"2.5.0"} 
-2021-10-31 08:28:13 [36mINFO: [39m Environment: {"profile":"development","config":".build.json","package":"package.json","tsconfig":true,"eslintrc":true,"git":true} 
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