check in retrained tiny 68 point landmark model

src/faceLandmarkNet/FaceLandmark68TinyNet.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, normalize } from 'tfjs-image-recognition-base';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
+
+import { depthwiseSeparableConv } from './depthwiseSeparableConv';
+import { extractParamsTiny } from './extractParamsTiny';
+import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
+import { fullyConnectedLayer } from './fullyConnectedLayer';
+import { loadQuantizedParamsTiny } from './loadQuantizedParamsTiny';
+import { DenseBlockParams, TinyNetParams } from './types';
+
+function denseBlock(
+  x: tf.Tensor4D,
+  denseBlockParams: DenseBlockParams,
+  isFirstLayer: boolean = false
+): tf.Tensor4D {
+  return tf.tidy(() => {
+    const out1 = tf.relu(
+      isFirstLayer
+        ? tf.add(
+          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),
+          denseBlockParams.conv0.bias
+        )
+        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])
+    ) as tf.Tensor4D
+    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])
+
+    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D
+    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])
+
+    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D
+  })
+}
+
+export class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyNetParams> {
+
+  constructor() {
+    super('FaceLandmark68TinyNet')
+  }
+
+  public runNet(input: NetInput): tf.Tensor2D {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('FaceLandmark68TinyNet - load model before inference')
+    }
+
+    return tf.tidy(() => {
+      const batchTensor = input.toBatchTensor(112, true)
+      const meanRgb = [122.782, 117.001, 104.298]
+      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
+
+      let out = denseBlock(normalized, params.dense0, true)
+      out = denseBlock(out, params.dense1)
+      out = denseBlock(out, params.dense2)
+      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')
+
+      return fullyConnectedLayer(out.as2D(out.shape[0], -1), params.fc)
+    })
+  }
+
+  protected loadQuantizedParams(uri: string | undefined) {
+    return loadQuantizedParamsTiny(uri)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParamsTiny(weights)
+  }
+}
\ No newline at end of file

src/faceLandmarkNet/depthwiseSeparableConv.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
+
+export function depthwiseSeparableConv(
+  x: tf.Tensor4D,
+  params: SeparableConvParams,
+  stride: [number, number]
+): tf.Tensor4D {
+  return tf.tidy(() => {
+    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')
+    out = tf.add(out, params.bias)
+    return out
+  })
+}
\ No newline at end of file

src/faceLandmarkNet/extractParamsTiny.ts

+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+import { extractConvParamsFactory, FCParams } from 'tfjs-tiny-yolov2';
+import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
+
+import { DenseBlockParams, TinyNetParams } from './types';
+
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  function extractSeparableConvParams(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {
+    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])
+    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/depthwise_filter` },
+      { paramPath: `${mappedPrefix}/pointwise_filter` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return new SeparableConvParams(
+      depthwise_filter,
+      pointwise_filter,
+      bias
+    )
+  }
+
+  function extractFCParams(channelsIn: number, channelsOut: number, mappedPrefix: string): FCParams {
+    const weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/weights` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return {
+      weights,
+      bias
+    }
+  }
+
+  return {
+    extractSeparableConvParams,
+    extractFCParams
+  }
+
+}
+
+export function extractParamsTiny(weights: Float32Array): { params: TinyNetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const {
+    extractSeparableConvParams,
+    extractFCParams
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)
+
+  function extractDenseBlockParams(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlockParams {
+
+    const conv0 = isFirstLayer
+      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)
+      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)
+    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)
+    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)
+
+    return { conv0, conv1, conv2 }
+  }
+
+  const dense0 = extractDenseBlockParams(3, 32, 'dense0', true)
+  const dense1 = extractDenseBlockParams(32, 64, 'dense1')
+  const dense2 = extractDenseBlockParams(64, 128, 'dense2')
+  const fc = extractFCParams(128, 136, 'fc')
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  return {
+    paramMappings,
+    params: { dense0, dense1, dense2, fc }
+  }
+}
\ No newline at end of file

src/faceLandmarkNet/index.ts

 import { FaceLandmark68Net } from './FaceLandmark68Net';
 
 export * from './FaceLandmark68Net';
+export * from './FaceLandmark68TinyNet';
 
 export class FaceLandmarkNet extends FaceLandmark68Net {}
 

src/faceLandmarkNet/loadQuantizedParamsTiny.ts

+import * as tf from '@tensorflow/tfjs-core';
+import {
+  disposeUnusedWeightTensors,
+  extractWeightEntryFactory,
+  loadWeightMap,
+  ParamMapping,
+} from 'tfjs-image-recognition-base';
+import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
+import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
+
+import { DenseBlockParams, TinyNetParams } from './types';
+
+const DEFAULT_MODEL_NAME = 'face_landmark_68_tiny_model'
+
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractConvParams(prefix: string): ConvParams {
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return { filters, bias }
+  }
+
+  function extractSeparableConvParams(prefix: string): SeparableConvParams {
+    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)
+    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return new SeparableConvParams(
+      depthwise_filter,
+      pointwise_filter,
+      bias
+    )
+  }
+
+  function extractDenseBlockParams(prefix: string, isFirstLayer: boolean = false): DenseBlockParams {
+    const conv0 = isFirstLayer
+      ? extractConvParams(`${prefix}/conv0`)
+      : extractSeparableConvParams(`${prefix}/conv0`)
+    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)
+    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)
+
+    return { conv0, conv1, conv2 }
+  }
+
+  function extractFcParams(prefix: string): FCParams {
+    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return { weights, bias }
+  }
+
+  return {
+    extractDenseBlockParams,
+    extractFcParams
+  }
+}
+
+export async function loadQuantizedParamsTiny(
+  uri: string | undefined
+): Promise<{ params: TinyNetParams, paramMappings: ParamMapping[] }> {
+
+  const weightMap = await loadWeightMap(uri, DEFAULT_MODEL_NAME)
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractDenseBlockParams,
+    extractFcParams
+  } = extractorsFactory(weightMap, paramMappings)
+
+  const params = {
+    dense0: extractDenseBlockParams('dense0', true),
+    dense1: extractDenseBlockParams('dense1'),
+    dense2: extractDenseBlockParams('dense2'),
+    fc: extractFcParams('fc')
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file

src/faceLandmarkNet/types.ts

+import * as tf from '@tensorflow/tfjs-core';
 import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
+import { SeparableConvParams } from 'tfjs-tiny-yolov2/build/tinyYolov2/types';
 
 export type NetParams = {
   conv0: ConvParams
@@ -11,4 +13,49 @@ export type NetParams = {
   conv7: ConvParams
   fc0: FCParams
   fc1: FCParams
-}
+}
\ No newline at end of file
+
+export type ConvWithBatchNormParams = BatchNormParams & {
+  filter: tf.Tensor4D
+}
+
+export type BatchNormParams = {
+  mean: tf.Tensor1D
+  variance: tf.Tensor1D
+  scale: tf.Tensor1D
+  offset: tf.Tensor1D
+}
+
+export type SeparableConvWithBatchNormParams = {
+  depthwise: ConvWithBatchNormParams
+  pointwise: ConvWithBatchNormParams
+}
+
+export declare type FCWithBatchNormParams = BatchNormParams & {
+    weights: tf.Tensor2D
+}
+
+export type DenseBlockParams = {
+  conv0: SeparableConvParams | ConvParams
+  conv1: SeparableConvParams
+  conv2: SeparableConvParams
+  //conv3: SeparableConvParams
+}
+
+export type TinyNetParams = {
+  dense0: DenseBlockParams
+  dense1: DenseBlockParams
+  dense2: DenseBlockParams
+  fc: FCParams
+}
+
+export type MobileResnetParams = {
+  conv0: SeparableConvParams
+  conv1: SeparableConvParams
+  conv2: SeparableConvParams
+  conv3: SeparableConvParams
+  conv4: SeparableConvParams
+  conv5: SeparableConvParams
+  fc: FCParams
+}
+

tools/quantize/views/quantizeModel.html

@@ -10,9 +10,9 @@
   <script>
     tf = faceapi.tf
 
-    const modelName = 'tiny_yolov2_separable_conv'
-    const uncompressedWeightsUri = `tiny_yolov2_separable_conv_model_v1.weights`
-    const net = new faceapi.TinyYolov2(true)
+    const modelName = 'face_landmark_68_tiny_model'
+    const uncompressedWeightsUri = `face_landmark_68_tiny_model.weights`
+    const net = new faceapi.FaceLandmark68TinyNet()
 
     async function loadNetWeights(uri) {
       return new Float32Array(await (await fetch(uri)).arrayBuffer())

tools/train/faceLandmarks/faceLandmarksTrain.js

-async function trainStep(batchCreators) {
-  await promiseSequential(batchCreators.map((batchCreator, dataIdx) => async () => {
-
-    const { batchInput, landmarksBatchTensor } = await batchCreator()
-
-    let ts = Date.now()
-    const cost = optimizer.minimize(() => {
-      const out = window.trainNet.forwardInput(batchInput.managed())
-      const loss = lossFunction(
-        landmarksBatchTensor,
-        out
-      )
-      return tf.sum(out)
-    }, true)
-
-    ts = Date.now() - ts
-    console.log(`loss[${dataIdx}]: ${await cost.data()}, ${ts} ms (${ts / batchInput.batchSize} ms / batch element)`)
-
-    landmarksBatchTensor.dispose()
-    cost.dispose()
-
-    await tf.nextFrame()
-    console.log(tf.memory())
-  }))
-}
-
-function createBatchCreators(data, batchSize) {
-  if (batchSize < 1) {
-    throw new Error('invalid batch size: ' + batchSize)
-  }
-
-  const batches = []
-  const pushToBatch = (remaining) => {
-    if (remaining.length) {
-      batches.push(remaining.slice(0, batchSize))
-      pushToBatch(remaining.slice(batchSize))
-    }
-    return batches
-  }
-
-  pushToBatch(data)
-
-  const batchCreators = batches.map(dataForBatch => async () => {
-    const imgs = dataForBatch.map(d => d.img)
-    const allLandmarks = dataForBatch.map(d => landmarkPositionsToArray(d.landmarks))
-
-    const batchInput = await faceapi.toNetInput(imgs)
-    const landmarksBatchTensor = tf.tidy(() => {
-      const landmarkTensors = allLandmarks.map(arr => tf.tensor2d(arr, [1, 136]))
-      return tf.stack(landmarkTensors, 0).as2D(-1, 136)
-    })
-
-    return {
-      batchInput,
-      landmarksBatchTensor
-    }
-  })
-
-  return batchCreators
-}
-
-function landmarkPositionsToArray(landmarks) {
-  return landmarks.getRelativePositions().map(pt => [pt.x, pt.y])
-    .reduce((flat, arr) => flat.concat(arr))
-}
-
-function toFaceLandmarks(landmarks, { naturalWidth, naturalHeight }) {
-  return new faceapi.FaceLandmarks68(
-    landmarks.map(l => new faceapi.Point(l.x / naturalWidth, l.y / naturalHeight)),
-    { width: naturalWidth, height: naturalHeight }
-  )
-}
-
-async function loadImagesInBatch(allLandmarks, offset = 0) {
-  return Promise.all(allLandmarks.map(async (landmarks, i) => {
-    const imgUri = `/train_images/${i + offset}.png`
-    const img = await faceapi.bufferToImage(await fetchImage(imgUri))
-    return {
-      imgUri,
-      img,
-      landmarks: toFaceLandmarks(landmarks, img)
-    }
-  }))
-}
-
-async function getTrainData() {
-  const landmarksJson = (await (await fetch('/train_landmarks.json')).json())
-
-  const numLandmarks = Object.keys(landmarksJson).length
-  const allLandmarks = Array.from(
-    new Array(numLandmarks),
-    (_, i) => landmarksJson[i]
-  )
-
-  return await loadImagesInBatch(allLandmarks.slice(0, 100))
-/**
-  const batch1 = await loadImagesInBatch(allLandmarks.slice(0, 4000))
-  const batch2 = await loadImagesInBatch(allLandmarks.slice(4000), 4000)
-
-  return batch1.concat(batch2)
-  */
-}
\ No newline at end of file

tools/train/faceLandmarks/faceLandmarksUi.js

-function onKeyDown(e) {
-  e.target.value = (
-    parseInt(e.target.value) + (e.keyCode === 38 ? 1 : (e.keyCode === 40 ? -1 : 0))
-  ) || e.target.value || 0
-
-  const uri = '/train_images/' + e.target.value + '.png'
-
-  console.log(uri)
-  onSelectionChanged(uri)
-}
-
-function onChangeDrawLines(e) {
-  window.drawLines = $(e.target).prop('checked')
-  redraw()
-}
-
-async function onSelectionChanged(uri) {
-  const imgBuf = await fetchImage(uri)
-  window.currentImg = await faceapi.bufferToImage(imgBuf)
-  window.landmarks = await Promise.all(window.nets.map(
-    async net => net.detectLandmarks(window.currentImg)
-  ))
-  redraw(uri)
-}
-
-function drawLandmarkCanvas(img, landmarks, drawOpts) {
-  const canvas = faceapi.createCanvasFromMedia(img)
-  $('#faceContainer').append(canvas)
-
-  faceapi.drawLandmarks(
-    canvas,
-    landmarks,
-    drawOpts
-  )
-}
-
-function redraw(uri) {
-  $('#faceContainer').empty()
-
-  const drawOpts = { lineWidth: window.drawLines ? 2 : 4, drawLines: window.drawLines }
-  window.landmarks.forEach(landmarks => {
-    drawLandmarkCanvas(window.currentImg, landmarks, drawOpts)
-  })
-
-  const trainImgAndLandmarks = window.trainData.find(t => t.imgUri === uri)
-  if (trainImgAndLandmarks) {
-    drawLandmarkCanvas(trainImgAndLandmarks.img, trainImgAndLandmarks.landmarks, drawOpts)
-  }
-}
-
-async function loadNet(file) {
-  const res = await fetch(file)
-  const weights = new Float32Array(await res.arrayBuffer())
-  return faceapi.faceLandmarkNet(weights)
-}
-
-async function init() {
-  //await faceapi.loadFaceLandmarkModel('/')
-  //window.nets.push(faceapi.landmarkNet)
-  //window.nets.push(await loadNet('retrained/landmarks_v0.weights'))
-  //window.nets.push(await loadNet('retrained/landmarks_v2.weights'))
-
-  window.trainNet = await loadNet('/tmp/retrained/landmarks_v9.weights')
-  window.nets.push(trainNet)
-
-  $('#loader').hide()
-  await run()
-  await onSelectionChanged($('#selectList select').val())
-}
-
-$(document).ready(function() {
-  $('#imgByNr').keydown(onKeyDown);
-  renderFaceImageSelectList(
-    '#selectList',
-    onSelectionChanged,
-    { className: 'sheldon', imageIdx: 1 }
-  )
-  init()
-})
\ No newline at end of file

tools/train/faceLandmarks/initWeightsDensenet.html

+<!DOCTYPE html>
+<html>
+<head>
+  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.12.0"> </script>
+  <script src="../node_modules/file-saver/FileSaver.js"></script>
+</head>
+<body>
+
+  <div class="row side-by-side">
+    <button
+      class="waves-effect waves-light btn"
+      onclick="save()"
+    >
+      Save
+    </button>
+    <button
+      class="waves-effect waves-light btn"
+      onclick="save(true)"
+    >
+      Save Tiny
+    </button>
+  </div>
+
+  <script>
+    function toDataArray(tensor) {
+      return Array.from(tensor.dataSync())
+    }
+
+    function flatten(arrs) {
+      return arrs.reduce((flat, arr) => flat.concat(arr))
+    }
+
+    function initWeights(initializer, isTiny) {
+      function initConvWeights(inChannels, outChannels) {
+        return flatten(
+          [
+            // filter
+            initializer.apply([3, 3, inChannels, outChannels]),
+            // bias
+            tf.zeros([outChannels])
+          ]
+            .map(toDataArray)
+        )
+      }
+
+      function initSeparableConvWeights(inChannels, outChannels) {
+        return flatten(
+          [
+            // depthwise filter
+            initializer.apply([3, 3, inChannels, 1]),
+            // pointwise filter
+            initializer.apply([1, 1, inChannels, outChannels]),
+            // bias
+            tf.zeros([outChannels])
+          ]
+            .map(toDataArray)
+        )
+      }
+
+      const separableConvWeights = isTiny
+        ? flatten([
+          initConvWeights(3, 32),
+          initSeparableConvWeights(32, 32),
+          initSeparableConvWeights(32, 32),
+          initSeparableConvWeights(32, 64),
+          initSeparableConvWeights(64, 64),
+          initSeparableConvWeights(64, 64),
+          initSeparableConvWeights(64, 128),
+          initSeparableConvWeights(128, 128),
+          initSeparableConvWeights(128, 128)
+        ])
+        : flatten([
+          initConvWeights(3, 32),
+          initSeparableConvWeights(32, 32),
+          initSeparableConvWeights(32, 32),
+          initSeparableConvWeights(32, 32),
+          initSeparableConvWeights(32, 64),
+          initSeparableConvWeights(64, 64),
+          initSeparableConvWeights(64, 64),
+          initSeparableConvWeights(64, 64),
+          initSeparableConvWeights(64, 128),
+          initSeparableConvWeights(128, 128),
+          initSeparableConvWeights(128, 128),
+          initSeparableConvWeights(128, 128),
+          initSeparableConvWeights(256, 256),
+          initSeparableConvWeights(256, 256),
+          initSeparableConvWeights(256, 256),
+          initSeparableConvWeights(256, 256)
+        ])
+
+      const fc = flatten(
+        [
+          initializer.apply([1, 1, isTiny ? 128 : 256, 136]),
+          // bias
+          tf.zeros([136])
+        ]
+          .map(toDataArray)
+      )
+      return new Float32Array(separableConvWeights.concat(fc))
+    }
+
+    function save(isTiny = false) {
+      const initialWeights = initWeights(
+        tf.initializers.glorotNormal(),
+        isTiny
+      )
+      saveAs(new Blob([initialWeights]), `initial_glorot.weights`)
+    }
+
+  </script>
+</body>
+</html>
\ No newline at end of file

tools/train/faceLandmarks/js/indexedDb.js

+const  indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB || window.shimIndexedDB
+
+let jsonsDb = null
+let jpgsDb = null
+
+const openJsons = indexedDB.open('jsons', 1)
+const openJpgs = indexedDB.open('jpgs', 1)
+
+openJsons.onupgradeneeded = function() {
+  jsonsDb = openJsons.result
+  jsonsDb.createObjectStore('jsons', { keyPath: 'id' })
+}
+openJpgs.onupgradeneeded = function() {
+  jpgsDb = openJpgs.result
+  jpgsDb.createObjectStore('jpgs', { keyPath: 'id' })
+}
+openJsons.onsuccess = function() {
+  console.log('connected to jsons')
+  jsonsDb = openJsons.result
+}
+openJpgs.onsuccess = function() {
+  console.log('connected to jpgs')
+  jpgsDb = openJpgs.result
+}
+
+function putReq(store, obj) {
+  return new Promise((res, rej) => {
+    const req = store.put(obj)
+    req.onsuccess = res
+    req.onerror = rej
+  })
+}
+
+function getReq(store, id, throwIfNoResult = true) {
+  return new Promise((res, rej) => {
+    const req = store.get(id)
+    req.onsuccess = () => {
+      if (!req.result && throwIfNoResult) {
+        return rej(`no result for id: ${id}`)
+      }
+      res(req.result)
+    }
+    req.onerror = rej
+  })
+}
+
+function existsReq(store, id) {
+  return getReq(store, id, false)
+}
+
+async function getNotFound(store, ids) {
+  return (await Promise.all(ids.map(async id => ({ id, exists: await existsReq(store, id) }))))
+    .filter(({ exists }) => !exists)
+    .map(({ id }) => id)
+}
+
+async function getNotFoundPts(ids) {
+  const store = jsonsDb.transaction('jsons', 'readonly').objectStore('jsons')
+  return getNotFound(store, ids)
+}
+
+async function getNotFoundJpegs(ids) {
+  const store = jpgsDb.transaction('jpgs', 'readonly').objectStore('jpgs')
+  return getNotFound(store, ids)
+}
+
+async function persistPts(ptsById, overwrite = false) {
+  const store = jsonsDb.transaction('jsons', 'readwrite').objectStore('jsons')
+  for (let i = 0; i < ptsById.length; i++) {
+    const { id, pts } = ptsById[i]
+    if (!await existsReq(store, id)) {
+      console.log('persistPts - inserting %s', id)
+      await putReq(store, { id, pts })
+    }
+  }
+}
+
+function getPts(ids) {
+  const store = jsonsDb.transaction('jsons', 'readonly').objectStore('jsons')
+  return Promise.all(ids.map(id => getReq(store, id)))
+}
+
+async function persistJpgs(jpgsById, overwrite = false) {
+  const store = jpgsDb.transaction('jpgs', 'readwrite').objectStore('jpgs')
+  for (let i = 0; i < jpgsById.length; i++) {
+    const { id, blob } = jpgsById[i]
+    if (!await existsReq(store, id)) {
+      console.log('persistJpgs - inserting %s', id)
+      await putReq(store, { id, blob })
+    }
+  }
+}
+
+function getJpgs(ids) {
+  const store = jpgsDb.transaction('jpgs', 'readonly').objectStore('jpgs')
+  return Promise.all(ids.map(id => getReq(store, id)))
+}
+

tools/train/faceLandmarks/js/printIndexDbSizes.js

+// https://gist.github.com/tralves/9e5de2bd9f582007a52708d7d4209865
+
+var getTableSize = function(db, dbName){
+  return new Promise((resolve,reject) => {
+    if (db == null) {
+      return reject();
+    }
+    var size = 0;
+    db = event.target.result;
+    var transaction = db.transaction([dbName])
+      .objectStore(dbName)
+      .openCursor();
+
+    transaction.onsuccess = function(event){
+        var cursor = event.target.result;
+        if(cursor){
+            var storedObject = cursor.value;
+            var json = JSON.stringify(storedObject);
+            size += json.length;
+            cursor.continue();
+        }
+        else{
+          resolve(size);
+        }
+    }.bind(this);
+    transaction.onerror = function(err){
+        reject("error in " + dbName + ": " + err);
+    }
+  });
+};
+
+var getDatabaseSize = function (dbName) {
+  var request = indexedDB.open(dbName);
+  var db;
+  var dbSize = 0;
+  request.onerror = function(event) {
+    alert("Why didn't you allow my web app to use IndexedDB?!");
+  };
+  request.onsuccess = function(event) {
+    db = event.target.result;
+    var tableNames = [ ...db.objectStoreNames ];
+    (function(tableNames, db) {
+      var tableSizeGetters = tableNames
+        .reduce( (acc, tableName) => {
+          acc.push( getTableSize(db, tableName) );
+          return acc;
+        }, []);
+
+      Promise.all(tableSizeGetters)
+        .then(sizes => {
+          console.log('--------- ' + db.name + ' -------------');
+          tableNames.forEach( (tableName,i) => {
+            console.log(" - " + tableName + "\t: " + humanReadableSize(sizes[i]));
+          });
+          var total = sizes.reduce(function(acc, val) {
+            return acc + val;
+          }, 0);
+
+          console.log("TOTAL: " + humanReadableSize(total))
+        });
+      })(tableNames, db);
+  };
+};
+
+var humanReadableSize = function (bytes) {
+  var thresh = 1024;
+  if(Math.abs(bytes) < thresh) {
+    return bytes + ' B';
+  }
+  var units = ['KB','MB','GB','TB','PB','EB','ZB','YB'];
+  var u = -1;
+  do {
+    bytes /= thresh;
+    ++u;
+  } while(Math.abs(bytes) >= thresh && u < units.length - 1);
+  return bytes.toFixed(1)+' '+units[u];
+}
+
+getDatabaseSize('jsons')
+getDatabaseSize('jpgs')
\ No newline at end of file

tools/train/faceLandmarks/js/randomCrop.js

+function randomCrop(img, pts) {
+  const xCoords = pts.map(pt => pt.x)
+  const yCoords = pts.map(pt => pt.y)
+  const minX = xCoords.reduce((x, min) => x < min ? x : min, img.width)
+  const minY = yCoords.reduce((y, min) => y < min ? y : min, img.height)
+  const maxX = xCoords.reduce((x, max) => x < max ? max : x, 0)
+  const maxY = yCoords.reduce((y, max) => y < max ? max : y, 0)
+
+  const x0 = Math.random() * minX
+  const y0 = Math.random() * minY
+
+  const x1 = (Math.random() * Math.abs(img.width - maxX)) + maxX
+  const y1 = (Math.random() * Math.abs(img.height - maxY)) + maxY
+
+  const targetCanvas = faceapi.createCanvas({ width: x1 - x0, height: y1 - y0 })
+  const inputCanvas = img instanceof HTMLCanvasElement ? img : faceapi.createCanvasFromMedia(img)
+  const region = faceapi.getContext2dOrThrow(inputCanvas)
+    .getImageData(x0, y0, targetCanvas.width, targetCanvas.height)
+  faceapi.getContext2dOrThrow(targetCanvas).putImageData(region, 0, 0)
+
+  return {
+    croppedImage: targetCanvas,
+    shiftedPts: pts.map(pt => new faceapi.Point(pt.x - x0, pt.y - y0))
+  }
+}
\ No newline at end of file

tools/train/faceLandmarks/js/synchronizeDb.js

+async function fillPtsDb(ids) {
+  const numFetchPerIteration = 4000
+
+  for (let i = 0; i < Math.floor(ids.length / numFetchPerIteration) + 1; i++) {
+    const ptsById = await Promise.all(
+      ids.slice(i * numFetchPerIteration, (i + 1) * numFetchPerIteration)
+        .map(async id => ({
+          id,
+          pts: await (await fetch(`${id}.json`)).json()
+        }))
+    )
+
+    console.log('persistPts')
+    console.time('persistPts')
+    await persistPts(ptsById)
+    console.timeEnd('persistPts')
+  }
+}
+
+async function fillJpgsDb(ids) {
+  const numFetchPerIteration = 4000
+
+  for (let i = 0; i < Math.floor(ids.length / numFetchPerIteration) + 1; i++) {
+    const jpgsById = await Promise.all(
+      ids.slice(i * numFetchPerIteration, (i + 1) * numFetchPerIteration)
+        .map(async id => ({
+          id,
+          blob: await fetchImage(`${id}.jpg`)
+        }))
+    )
+    console.log('persistJpgs')
+    console.time('persistJpgs')
+    await persistJpgs(jpgsById)
+    console.timeEnd('persistJpgs')
+  }
+}
\ No newline at end of file

tools/train/faceLandmarks/test.html

+<!DOCTYPE html>
+<html>
+<head>
+  <script src="face-api.js"></script>
+  <script src="commons.js"></script>
+  <script src="js/randomCrop.js"></script>
+  <script src="js/indexedDb.js"></script>
+  <script src="js/synchronizeDb.js"></script>
+  <link rel="stylesheet" href="styles.css">
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
+  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
+</head>
+<body>
+  <div id="result" style="position: relative" class="margin">
+    <img id="inputImg" src="" style="max-width: 400px;" />
+    <canvas id="overlay" />
+  </div>
+  <script>
+
+    const net = new faceapi.FaceLandmark68TinyNet()
+    const modelCheckpoint = 'tmp/densenet3_conv0/checkpoints/landmarks_epoch59.weights'
+    const crops = 4
+
+    async function loadNetWeights(uri) {
+      return new Float32Array(await (await fetch(uri)).arrayBuffer())
+    }
+
+    async function load() {
+      await net.load('./')
+      //const weights = await loadNetWeights(modelCheckpoint)
+      //await net.load(weights)
+      console.log('model loaded')
+    }
+
+    load()
+
+    function pointsToFlatArray(pts) {
+      return pts.map(pt => [pt.x, pt.y]).reduce((flat, arr) => flat.concat(arr))
+    }
+
+    async function showResults(landmarks) {
+      const inputImgEl = $('#inputImg').get(0)
+      const { width, height } = inputImgEl
+      const canvas = $('#overlay').get(0)
+      canvas.width = width
+      canvas.height = height
+
+      faceapi.drawLandmarks('overlay', landmarks, { drawLines: true })
+    }
+
+    async function testAll() {
+      window.testIds = (await fetch('/face_landmarks_test_ids').then(res => res.json()))
+      test()
+    }
+
+    async function testSidewaysX(useTrainSet = false) {
+      window.testIds = (await fetch(useTrainSet ? '/sideways_x_train' : '/sideways_x_test').then(res => res.json()))
+      test()
+    }
+
+    async function testStrongSidewaysX(useTrainSet = false) {
+      window.testIds = (await fetch(useTrainSet ? '/strong_sideways_x_train' : '/strong_sideways_x_test').then(res => res.json()))
+      test()
+    }
+
+    async function testStrongSidewaysY(useTrainSet = false) {
+      window.testIds = (await fetch(useTrainSet ? '/strong_sideways_y_train' : '/strong_sideways_y_test').then(res => res.json()))
+      test()
+    }
+
+    async function test() {
+      let totalError = 0
+
+      for (let i = 0; i < window.testIds.length; i++) {
+
+        const dataId = window.testIds[i]
+        let originalImage = (await getJpgs([dataId]))[0]
+        originalImage = await faceapi.bufferToImage(originalImage.blob)
+        const groundTruthPts = (await getPts([dataId])).map(res => res.pts)[0]
+
+        for (let c = 0; c < (crops || 1); c++) {
+
+          console.log(i, c, i / window.testIds.length)
+
+          const { croppedImage, shiftedPts } = crops > 0
+            ? randomCrop(originalImage, groundTruthPts)
+            : { croppedImage: originalImage, shiftedPts: groundTruthPts }
+
+          const squaredImg = faceapi.imageToSquare(croppedImage, 112, true)
+
+          const landmarks = (await net.detectLandmarks(squaredImg)).forSize(croppedImage.width, croppedImage.height)
+
+          const dist = faceapi.euclideanDistance(
+            pointsToFlatArray(landmarks.getPositions()),
+            pointsToFlatArray(shiftedPts)
+          )
+
+          const distByCoordinate = dist / 136
+          totalError += distByCoordinate
+
+          if (window.debug) {
+            const inputImgEl = $('#inputImg').get(0)
+            inputImgEl.src = croppedImage instanceof HTMLImageElement ? croppedImage.src : croppedImage.toDataURL()
+            inputImgEl.onload = () => showResults(landmarks)
+
+            for (let i = 0; i < 30; i++)
+              await faceapi.tf.nextFrame()
+          }
+        }
+      }
+
+      console.log('done...')
+      console.log('test set size:', window.testIds.length)
+      console.log('total error:', totalError / (crops || 1))
+      console.log('average error:', totalError / ((crops || 1) * window.testIds.length))
+    }
+
+
+
+  </script>
+</body>
+</html>
\ No newline at end of file

tools/train/faceLandmarks/train.html

 <!DOCTYPE html>
 <html>
 <head>
-  <link rel="stylesheet" href="styles.css">
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
-  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
   <script src="face-api.js"></script>
   <script src="commons.js"></script>
-  <script src="faceLandmarksUi.js"></script>
-  <script src="faceLandmarksTrain.js"></script>
+  <script src="js/randomCrop.js"></script>
+  <script src="js/indexedDb.js"></script>
+  <script src="js/synchronizeDb.js"></script>
   <script src="FileSaver.js"></script>
-  <script src="trainUtils.js"></script>
 </head>
 <body>
-  <div id="navbar"></div>
-  <div class="center-content page-container">
-    <div>
-      <div class="progress" id="loader">
-        <div class="indeterminate"></div>
-      </div>
-      <div class="row side-by-side">
-        <div class="center-content">
-          <div id="faceContainer"></div>
-          <div id="selectList"></div>
-        </div>
-      </div>
-      <p>
-        <input type="checkbox" id="drawLinesCheckbox" checked="checked" onchange="onChangeDrawLines(event)" />
-        <label for="drawLinesCheckbox">Draw Lines</label>
-      </p>
-      <div class="row">
-        <label for="imgByNr">Enter image NR: </label>
-        <input id="imgByNr" type="text" class="bold">
-      </div>
-    </div>
-  </div>
+  <div id="container"></div>
 
   <script>
     tf = faceapi.tf
-    window.saveEveryNthIteration = 2
-    window.drawLines = true
-    window.nets = []
-    window.landmarks = []
-
-    window.trainSteps = 100
-    window.learningRate = 0.01
-    //window.optimizer = tf.train.sgd(learningRate)
-    window.optimizer = tf.train.adam(0.001, 0.9, 0.999, 1e-8)
-
-    function lossFunction(labels, out) {
-      return tf.losses.meanSquaredError(labels, out)
+
+    // uri to weights file of last checkpoint
+    window.net = new faceapi.FaceLandmark68TinyNet()
+    const modelCheckpoint = '/tmp/initial_glorot.weights.weights'
+    const startEpoch = 0
+
+    const learningRate = 0.001 // 0.001
+    window.optimizer = tf.train.adam(learningRate, 0.9, 0.999, 1e-8)
+
+    window.saveEveryNthSample = Infinity
+
+    window.withRandomCrop = true
+
+    window.batchSize = 12
+    //window.batchSize = 32
+
+    window.lossValues = []
+
+    window.iterDelay = 0
+    window.withLogging = true
+
+    const log = (str, ...args) => console.log(`[${[(new Date()).toTimeString().substr(0, 8)]}] ${str || ''}`, ...args)
+
+    function saveWeights(net, filename = 'train_tmp') {
+      saveAs(new Blob([net.serializeParams()]), filename)
+    }
+
+    async function loadNetWeights(uri) {
+      return new Float32Array(await (await fetch(uri)).arrayBuffer())
+    }
+
+    async function delay(ms) {
+      return new Promise(res => setTimeout(res, ms))
     }
 
-    async function run() {
-      window.trainData = await getTrainData()
-      window.trainNet.variable()
+    async function fetchTrainDataIds() {
+      return fetch('/face_landmarks_train_ids').then(res => res.json())
+    }
+
+    async function load() {
+      window.trainIds = (await fetchTrainDataIds())
+      const weights = await loadNetWeights(modelCheckpoint)
+      await window.net.load(weights)
+      window.net.variable()
+    }
+
+    async function onEpochDone(epoch) {
+      saveWeights(window.net, `landmarks_epoch59_sideways71_epoch8_sideways35_epoch${epoch}.weights`)
 
-      return
+      const loss = window.lossValues[epoch]
+      saveAs(new Blob([JSON.stringify({ loss, avgLoss: loss / window.trainIds.length })]), `landmarks_epoch59_sideways71_epoch8_sideways35_epoch${epoch}.json`)
 
-      await train()
     }
 
-    async function train(batchSize = 1) {
-      for (let i = 0; i < trainSteps; i++) {
-        console.log('step', i)
-        const batchCreators = createBatchCreators(shuffle(window.trainData), batchSize)
-        let ts = Date.now()
-        await trainStep(batchCreators)
-        ts = Date.now() - ts
-        console.log('step %s done (%s ms)', i, ts)
-        if (((i + 1) % saveEveryNthIteration) === 0) {
-          //saveWeights(window.trainNet, 'landmark_trained_weights_' + idx + '.weights')
+    async function train() {
+      await load()
+
+      for (let epoch = startEpoch; epoch < Infinity; epoch++) {
+
+        if (epoch !== startEpoch) {
+          // ugly hack to wait for loss datas for that epoch to be resolved
+          setTimeout(() => onEpochDone(epoch - 1), 10000)
+        }
+        window.lossValues[epoch] = 0
+
+        const shuffledInputs = faceapi.shuffleArray(window.trainIds)
+
+        for (let dataIdx = 0; dataIdx < shuffledInputs.length; dataIdx += window.batchSize) {
+          const tsIter = Date.now()
+
+          const ids = shuffledInputs.slice(dataIdx, dataIdx + window.batchSize)
+
+          // fetch image and ground truth bounding boxes
+          let tsFetch = Date.now()
+          let tsFetchPts = Date.now()
+
+          const allPts = (await getPts(ids)).map(res => res.pts)
+
+          tsFetchPts = Date.now() - tsFetchPts
+
+          let tsFetchJpgs = Date.now()
+
+          const allJpgBlobs = (await getJpgs(ids)).map(res => res.blob)
+
+          tsFetchJpgs = Date.now() - tsFetchJpgs
+
+          let tsBufferToImage = Date.now()
+
+          const allJpgs = await Promise.all(
+            allJpgBlobs.map(blob => faceapi.bufferToImage(blob))
+          )
+
+          tsBufferToImage = Date.now() - tsBufferToImage
+
+          tsFetch = Date.now() - tsFetch
+
+          const bImages = []
+          const bPts = []
+
+          for (let batchIdx = 0; batchIdx < ids.length; batchIdx += 1) {
+
+            const pts = allPts[batchIdx]
+            const img = allJpgs[batchIdx]
+
+            const { croppedImage, shiftedPts } = window.withRandomCrop
+              ? randomCrop(img, pts)
+              : { croppedImage: img, shiftedPts: pts }
+
+            const squaredImg = faceapi.imageToSquare(croppedImage, 112, true)
+
+            const reshapedDimensions = faceapi.computeReshapedDimensions(croppedImage, 112)
+
+            const pad = Math.abs(reshapedDimensions.width - reshapedDimensions.height) /  (2 * 112)
+            const padX = reshapedDimensions.width < reshapedDimensions.height ? pad : 0
+            const padY = reshapedDimensions.height < reshapedDimensions.width ? pad : 0
+
+            const groundTruthLandmarks = shiftedPts.map(pt => new faceapi.Point(
+              padX + (pt.x / croppedImage.width) * (reshapedDimensions.width / 112),
+              padY + (pt.y / croppedImage.height)* (reshapedDimensions.height / 112)
+            ))
+
+            bImages.push(squaredImg)
+            bPts.push(groundTruthLandmarks)
+          }
+
+          const netInput = await faceapi.toNetInput(bImages)
+
+          let tsBackward = Date.now()
+          let tsForward = Date.now()
+
+          const loss = optimizer.minimize(() => {
+            const out = window.net.runNet(netInput)
+            tsForward = Date.now() - tsForward
+            tsBackward = Date.now()
+            const landmarksTensor = tf.tensor2d(
+              bPts
+                .reduce((flat, arr) => flat.concat(arr))
+                .map(pt => [pt.x, pt.y])
+                .reduce((flat, arr) => flat.concat(arr)),
+              [ids.length, 136]
+            )
+
+            const loss = tf.losses.meanSquaredError(
+              landmarksTensor,
+              out,
+              tf.Reduction.MEAN
+            )
+
+            return loss
+          }, true)
+          tsBackward = Date.now() - tsBackward
+
+          // start next iteration without waiting for loss data
+
+          loss.data().then(data => {
+            const lossValue = data[0]
+            window.lossValues[epoch] += lossValue
+            window.withLogging && log(`epoch ${epoch}, dataIdx ${dataIdx} - loss: ${lossValue}, ( ${window.lossValues[epoch]})`)
+            loss.dispose()
+          })
+
+          window.withLogging && log(`epoch ${epoch}, dataIdx ${dataIdx} - forward: ${tsForward} ms, backprop: ${tsBackward} ms, iter: ${Date.now() - tsIter} ms`)
+          if (window.logsilly)  {
+            log(`fetch: ${tsFetch} ms, pts: ${tsFetchPts} ms, jpgs: ${tsFetchJpgs} ms, bufferToImage: ${tsBufferToImage} ms`)
+          }
+          if (window.iterDelay) {
+            await delay(window.iterDelay)
+          } else {
+            await tf.nextFrame()
+          }
         }
       }
     }
 
   </script>
-
 </body>
 </html>
\ No newline at end of file

tools/train/faceLandmarks/train_debug.html

+<!DOCTYPE html>
+<html>
+<head>
+  <script src="face-api.js"></script>
+  <script src="commons.js"></script>
+  <script src="js/randomCrop.js"></script>
+  <script src="js/indexedDb.js"></script>
+  <script src="FileSaver.js"></script>
+</head>
+<body>
+  <div id="container"></div>
+
+  <script>
+    tf = faceapi.tf
+
+    // uri to weights file of last checkpoint
+    window.net = new faceapi.FaceLandmark68TinyNet()
+    const modelCheckpoint = '/tmp/initial_glorot.weights'
+    const startEpoch = 0
+
+    const learningRate = 0.001 // 0.001
+    window.optimizer = tf.train.adam(learningRate, 0.9, 0.999, 1e-8)
+
+    window.saveEveryNthSample = 50000000
+
+    window.withRandomCrop = false
+
+    window.batchSize = 18
+
+    window.lossValue = 0
+
+    window.drawResults = true
+
+    const log = (str, ...args) => console.log(`[${[(new Date()).toTimeString().substr(0, 8)]}] ${str || ''}`, ...args)
+
+    function saveWeights(net, filename = 'train_tmp') {
+      saveAs(new Blob([net.serializeParams()]), filename)
+    }
+
+    async function loadNetWeights(uri) {
+      return new Float32Array(await (await fetch(uri)).arrayBuffer())
+    }
+
+    async function fetchTrainDataFilenames() {
+      return fetch('/face_landmarks_train_ids').then(res => res.json())
+    }
+
+    async function load() {
+      window.ptsFiles = await fetchTrainDataFilenames()
+
+      window.ptsFiles = window.ptsFiles.slice(1000, 1000 + window.batchSize)
+      //window.ptsFiles = window.ptsFiles.slice(7, 8)
+
+      const weights = await loadNetWeights(modelCheckpoint)
+      await window.net.load(weights)
+      window.net.variable()
+    }
+
+    function randomCrop(img, pts) {
+      const xCoords = pts.map(pt => pt.x)
+      const yCoords = pts.map(pt => pt.y)
+      const minX = xCoords.reduce((x, min) => x < min ? x : min, img.width)
+      const minY = yCoords.reduce((y, min) => y < min ? y : min, img.height)
+      const maxX = xCoords.reduce((x, max) => x < max ? max : x, 0)
+      const maxY = yCoords.reduce((y, max) => y < max ? max : y, 0)
+
+      const x0 = Math.random() * minX
+      const y0 = Math.random() * minY
+
+      const x1 = (Math.random() * Math.abs(img.width - maxX)) + maxX
+      const y1 = (Math.random() * Math.abs(img.height - maxY)) + maxY
+
+      const targetCanvas = faceapi.createCanvas({ width: x1 - x0, height: y1 - y0 })
+      const inputCanvas = img instanceof HTMLCanvasElement ? img : faceapi.createCanvasFromMedia(img)
+      const region = faceapi.getContext2dOrThrow(inputCanvas)
+        .getImageData(x0, y0, targetCanvas.width, targetCanvas.height)
+      faceapi.getContext2dOrThrow(targetCanvas).putImageData(region, 0, 0)
+
+      return {
+        croppedImage: targetCanvas,
+        shiftedPts: pts.map(pt => new faceapi.Point(pt.x - x0, pt.y - y0))
+      }
+    }
+
+    async function train() {
+      await load()
+
+      for (let epoch = startEpoch; epoch < Infinity; epoch++) {
+
+        if (epoch !== startEpoch) {
+          //saveWeights(window.net, `landmarks_epoch${epoch - 1}.weights`)
+          //saveAs(new Blob([JSON.stringify({ loss: window.lossValue, avgLoss: window.lossValue / 20000 })]), `landmarks_epoch${epoch - 1}.json`)
+          window.lossValue = 0
+        }
+
+        const shuffledInputs = window.drawResults ? window.ptsFiles : faceapi.shuffleArray(window.ptsFiles)
+
+        for (let dataIdx = 0; dataIdx < shuffledInputs.length; dataIdx += window.batchSize) {
+
+          const ids = shuffledInputs.slice(dataIdx, dataIdx + window.batchSize)
+
+          // fetch image and ground truth bounding boxes
+          console.time('getPts')
+          const allPts = (await getPts(ids)).map(res => res.pts)
+          console.timeEnd('getPts')
+          console.time('getJpgs')
+          const allJpgs = await Promise.all(
+            (await getJpgs(ids)).map(res => res.blob).map(blob => faceapi.bufferToImage(blob))
+          )
+          console.timeEnd('getJpgs')
+
+          const bSquaredImages = []
+          const bCroppedImages = []
+          const bImages = []
+          const bPts = []
+
+          for (let batchIdx = 0; batchIdx < ids.length; batchIdx += 1) {
+
+            const pts = allPts[batchIdx]
+            const img = allJpgs[batchIdx]
+
+            const { croppedImage, shiftedPts } = window.withRandomCrop
+              ? randomCrop(img, pts)
+              : { croppedImage: img, shiftedPts: pts }
+
+            const squaredImg = faceapi.imageToSquare(croppedImage, 112, true)
+
+            const reshapedDimensions = faceapi.computeReshapedDimensions(croppedImage, 112)
+
+            const pad = Math.abs(reshapedDimensions.width - reshapedDimensions.height) /  (2 * 112)
+            const padX = reshapedDimensions.width < reshapedDimensions.height ? pad : 0
+            const padY = reshapedDimensions.height < reshapedDimensions.width ? pad : 0
+
+            const groundTruthLandmarks = shiftedPts.map(pt => new faceapi.Point(
+              padX + (pt.x / croppedImage.width) * (reshapedDimensions.width / 112),
+              padY + (pt.y / croppedImage.height)* (reshapedDimensions.height / 112)
+            ))
+
+            bSquaredImages.push(squaredImg)
+            bCroppedImages.push(croppedImage)
+            bImages.push(squaredImg)
+            bPts.push(groundTruthLandmarks)
+          }
+
+          const netInput = await faceapi.toNetInput(bImages)
+
+          const ts = Date.now()
+
+          const loss = optimizer.minimize(() => {
+            console.time('runNet')
+            const out = window.net.runNet(netInput)
+            console.timeEnd('runNet')
+
+            if (window.drawResults) {
+              // debug
+              const landmarkTensors = tf.unstack(out)
+              landmarkTensors.forEach((t, i) => {
+                const croppedImage = bSquaredImages[i]
+
+
+                const landmarksArray = Array.from(t.dataSync())
+                const xCoords = landmarksArray.filter((_, i) => faceapi.isEven(i))
+                const yCoords = landmarksArray.filter((_, i) => !faceapi.isEven(i))
+ /*
+                const landmarksArray = bPts[i]
+                const xCoords = landmarksArray.map(pt => pt.x)
+                const yCoords = landmarksArray.map(pt => pt.y)
+*/
+                const marks = new faceapi.FaceLandmarks68(
+                  Array(68).fill(0).map((_, i) => new faceapi.Point(xCoords[i], yCoords[i])),
+                  croppedImage
+                )
+
+                const canvas = croppedImage instanceof HTMLCanvasElement ? croppedImage : faceapi.createCanvasFromMedia(croppedImage)
+                faceapi.drawLandmarks(canvas, marks, { drawLines: true })
+                const container = document.getElementById('container')
+                if (container.children[i]) {
+                  const oldChild = container.children[i]
+                  container.insertBefore(canvas, oldChild)
+                  container.removeChild(oldChild)
+                } else {
+                  container.appendChild(canvas)
+                }
+              })
+              // debug
+            }
+
+            const landmarksTensor = tf.tensor2d(
+              bPts
+                .reduce((flat, arr) => flat.concat(arr))
+                .map(pt => [pt.x, pt.y])
+                .reduce((flat, arr) => flat.concat(arr)),
+              [window.batchSize, 136]
+            )
+
+            const loss = tf.losses.meanSquaredError(
+              landmarksTensor,
+              out,
+              tf.Reduction.MEAN
+            )
+
+            //return tf.sum(tf.abs(tf.sub(landmarksTensor, out)))
+            return loss
+          }, true)
+
+          // start next iteration without waiting for loss data
+          loss.data().then(data => {
+            const lossValue = data[0]
+            window.lossValue += lossValue
+            log(`epoch ${epoch}, dataIdx ${dataIdx} - loss: ${lossValue}`)
+            loss.dispose()
+          })
+
+          if (window.iterDelay) {
+            await delay(window.iterDelay)
+          } else {
+            await tf.nextFrame()
+          }
+        }
+      }
+    }
+
+  </script>
+</body>
+</html>
\ No newline at end of file

tools/train/faceLandmarks/verify.html

+<!DOCTYPE html>
+<html>
+<head>
+  <script src="face-api.js"></script>
+  <script src="commons.js"></script>
+  <script src="js/randomCrop.js"></script>
+  <link rel="stylesheet" href="styles.css">
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
+  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
+</head>
+<body>
+  <div id="navbar"></div>
+  <div class="center-content page-container">
+    <div class="progress" id="loader">
+      <div class="indeterminate"></div>
+    </div>
+    <div id="results" class="side-by-side">
+      <div id="result0" style="position: relative" class="margin">
+        <img id="inputImg0" src="" style="max-width: 400px;" />
+        <canvas class="overlay" id="overlay0" />
+      </div>
+      <div id="result1" style="position: relative" class="margin">
+        <img id="inputImg1" src="" style="max-width: 400px;" />
+        <canvas class="overlay" id="overlay1" />
+      </div>
+      <div id="result2" style="position: relative" class="margin">
+        <img id="inputImg2" src="" style="max-width: 400px;" />
+        <canvas class="overlay" id="overlay2" />
+      </div>
+    </div>
+    <div class="row">
+      <label for="imgByNr">Enter image NR: </label>
+      <input id="imgByNr" type="text" class="bold" value="0"/>
+    </div>
+  </div>
+
+  <script>
+
+    const modelCheckpoints = [
+      //'tmp/densenet3_conv0/checkpoints/landmarks_epoch59.weights'
+    ]
+    let originalImage = null
+
+    function cloneImage(img) {
+      var image = document.createElement("img")
+      image.src = img.src
+      return image
+    }
+
+    function getInputValue(input) {
+      input = input || $('#imgByNr').get(0)
+      return input.value || 0
+    }
+
+    function onKeyDown(e) {
+      e.target.value = (
+        parseInt(e.target.value) + (e.keyCode === 38 ? 1 : (e.keyCode === 40 ? -1 : 0))
+      ) || getInputValue(e.target)
+
+
+      const imgUri = `${window.testIds[e.target.value]}.jpg`
+
+      console.log(imgUri)
+      onSelectionChanged(imgUri)
+    }
+
+    async function updateResults(_landmarks) {
+      const inputImgEl = $('#inputImg0').get(0)
+      const { width, height } = inputImgEl
+
+      for (let i = 0; i < window.nets.length; i++) {
+        const net = window.nets[i]
+
+        const canvas = $(`#overlay${i}`).get(0)
+        canvas.width = width
+        canvas.height = height
+        console.time('detectLandmarks')
+        const landmarks = await net.detectLandmarks(inputImgEl)
+        console.timeEnd('detectLandmarks')
+        faceapi.drawLandmarks(`overlay${i}`, landmarks.forSize(width, height), { drawLines: true })
+      }
+
+    }
+
+    async function onSelectionChanged(imgUri) {
+      const img = await faceapi.bufferToImage(await fetchImage(imgUri))
+
+      window.nets.forEach(async (_, i) => {
+        $(`#inputImg${i}`).get(0).src = img.src
+      })
+      originalImage = cloneImage(img)
+      updateResults()
+    }
+
+    async function applyRandomCrop() {
+      const dataId = window.testIds[getInputValue()]
+      const pts = await (await fetch(`${dataId}.json`)).json()
+
+      const img = cloneImage(originalImage)
+
+      const { croppedImage, shiftedPts } = randomCrop(img, pts)
+
+      img.src = croppedImage.toDataURL()
+      $(`#result${0}`).get(0).removeChild($(`#inputImg${0}`).get(0))
+      $(`#result${0}`).get(0).appendChild(img)
+      window.nets.slice(1).forEach((_, i) => {
+        const im = cloneImage(img)
+        im.id = `#inputImg${i + 1}`
+        $(`#result${i + 1}`).get(0).removeChild($(`#inputImg${i + 1}`).get(0))
+        $(`#result${i + 1}`).get(0).appendChild(im)
+      })
+      const { width, height } = croppedImage
+      img.onload = () => updateResults(
+        new faceapi.FaceLandmarks68(
+          shiftedPts.map(pt =>
+            new faceapi.Point(
+              pt.x / width,
+              pt.y / height
+            )
+          ),
+          { width, height }
+        )
+      )
+    }
+
+    async function loadNetWeights(uri) {
+      return new Float32Array(await (await fetch(uri)).arrayBuffer())
+    }
+
+    async function run() {
+      $('#imgByNr').keydown(onKeyDown)
+
+      faceapi.loadFaceLandmarkModel('./')
+
+      //window.trainIds = (await fetch('/face_landmarks_train_ids').then(res => res.json()))
+      //window.testIds = (await fetch('/face_landmarks_test_ids').then(res => res.json()))
+      window.testIds = (await fetch('/strong_sideways_test').then(res => res.json()))
+
+      window.nets = []
+      window.nets.push(faceapi)
+      const net = new faceapi.FaceLandmark68TinyNet()
+      await net.load('./')
+      window.nets.push(net)
+      modelCheckpoints.forEach(async checkpoint => {
+        const net = new faceapi.FaceLandmark68TinyNet()
+        const weights = await loadNetWeights(checkpoint)
+        await net.load(weights)
+        window.nets.push(net)
+      })
+
+      $('#loader').hide()
+    }
+
+    $(document).ready(() => run())
+
+  </script>
+</body>
+</html>
\ No newline at end of file

tools/train/serveFaceLandmarks.js

@@ -2,6 +2,7 @@ require('./faceLandmarks/.env')
 
 const express = require('express')
 const path = require('path')
+const fs = require('fs')
 
 const app = express()
 
@@ -14,9 +15,72 @@ app.use(express.static(path.join(__dirname, '../../weights')))
 app.use(express.static(path.join(__dirname, '../../dist')))
 
 const trainDataPath = path.resolve(process.env.TRAIN_DATA_PATH)
-app.use(express.static(trainDataPath))
 
-app.get('/', (req, res) => res.redirect('/face_landmarks'))
-app.get('/face_landmarks', (req, res) => res.sendFile(path.join(publicDir, 'train.html')))
+const pngPath = path.join(trainDataPath, 'png')
+const jpgPath = path.join(trainDataPath, 'jpg')
+const groundTruthPath = path.join(trainDataPath, 'pts')
 
-app.listen(3000, () => console.log('Listening on port 3000!'))
+app.use(express.static(jpgPath))
\ No newline at end of file
+app.use(express.static(groundTruthPath))
+
+const trainIds = JSON.parse(fs.readFileSync(path.join(publicDir, './trainData.json')))
+const trainIdsSet = new Set(trainIds)
+
+const testFileIds = fs.readdirSync(groundTruthPath)
+  .map(file => file.replace('.json', ''))
+  .filter(file => !trainIdsSet.has(file))
+
+app.get('/face_landmarks_train_ids', (req, res) => res.status(202).send(trainIds))
+app.get('/face_landmarks_test_ids', (req, res) => res.status(202).send(testFileIds))
+
+const poseAnchors = JSON.parse(fs.readFileSync(path.join(trainDataPath, './pose-anchors.json')))
+
+const sidewaysXTrain = []
+const strongSidewaysXTrain = []
+const strongSidewaysYTrain = []
+const strongSidewaysTrain = []
+const sidewaysXTest = []
+const strongSidewaysXTest = []
+const strongSidewaysYTest = []
+const strongSidewaysTest = []
+
+poseAnchors.forEach((pt, idx) => {
+  const id = `${idx}`
+  if (Math.abs(0.5 - pt.x) > 0.15) {
+    (trainIdsSet.has(id) ? sidewaysXTrain : sidewaysXTest).push(id)
+  }
+  if (Math.abs(0.5 - pt.x) > 0.2) {
+    (trainIdsSet.has(id) ? strongSidewaysXTrain : strongSidewaysXTest).push(id)
+  }
+  if (Math.abs(0.44 - pt.y) > 0.1) {
+    (trainIdsSet.has(id) ? strongSidewaysYTrain : strongSidewaysYTest).push(id)
+  }
+  if (Math.abs(0.5 - pt.x) > 0.2 || Math.abs(0.44 - pt.y) > 0.1) {
+    (trainIdsSet.has(id) ? strongSidewaysTrain : strongSidewaysTest).push(id)
+  }
+})
+
+console.log(sidewaysXTrain.length)
+console.log(strongSidewaysXTrain.length)
+console.log(strongSidewaysYTrain.length)
+console.log(strongSidewaysTrain.length)
+console.log(sidewaysXTest.length)
+console.log(strongSidewaysXTest.length)
+console.log(strongSidewaysYTest.length)
+console.log(strongSidewaysTrain.length)
+
+app.get('/sideways_x_train', (req, res) => res.status(202).send(sidewaysXTrain))
+app.get('/strong_sideways_x_train', (req, res) => res.status(202).send(strongSidewaysXTrain))
+app.get('/strong_sideways_y_train', (req, res) => res.status(202).send(strongSidewaysYTrain))
+app.get('/strong_sideways_train', (req, res) => res.status(202).send(strongSidewaysTrain))
+app.get('/sideways_x_test', (req, res) => res.status(202).send(sidewaysXTest))
+app.get('/strong_sideways_x_test', (req, res) => res.status(202).send(strongSidewaysXTest))
+app.get('/strong_sideways_y_test', (req, res) => res.status(202).send(strongSidewaysYTest))
+app.get('/strong_sideways_test', (req, res) => res.status(202).send(strongSidewaysTest))
+
+app.get('/', (req, res) => res.redirect('/train'))
+app.get('/train', (req, res) => res.sendFile(path.join(publicDir, 'train.html')))
+app.get('/verify', (req, res) => res.sendFile(path.join(publicDir, 'verify.html')))
+app.get('/test', (req, res) => res.sendFile(path.join(publicDir, 'test.html')))
+
+app.listen(8000, () => console.log('Listening on port 8000!'))
\ No newline at end of file

tools/train/serveTinyYolov2.js

-require('./tinyYolov2/.env')
-
-const express = require('express')
-const path = require('path')
-const fs = require('fs')
-
-const app = express()
-
-const publicDir = path.join(__dirname, './tinyYolov2')
-app.use(express.static(publicDir))
-app.use(express.static(path.join(__dirname, './shared')))
-app.use(express.static(path.join(__dirname, './node_modules/file-saver')))
-app.use(express.static(path.join(__dirname, '../../examples/public')))
-app.use(express.static(path.join(__dirname, '../../weights')))
-app.use(express.static(path.join(__dirname, '../../dist')))
-
-const trainDataPath = path.resolve(process.env.TRAIN_DATA_PATH)
-const testDataPath = path.resolve(process.env.TEST_DATA_PATH)
-const imagesPath = path.join(trainDataPath, './final_images')
-const detectionsPath = path.join(trainDataPath, './final_detections')
-app.use(express.static(imagesPath))
-app.use(express.static(detectionsPath))
-app.use(express.static(testDataPath))
-
-const detectionFilenames = fs.readdirSync(detectionsPath)
-const detectionFilenamesMultibox = JSON.parse(fs.readFileSync(path.join(__dirname, './tinyYolov2/multibox.json')))
-
-app.use(express.static(trainDataPath))
-
-app.get('/detection_filenames', (req, res) => res.status(202).send(detectionFilenames))
-app.get('/detection_filenames_multibox', (req, res) => res.status(202).send(detectionFilenamesMultibox))
-app.get('/', (req, res) => res.sendFile(path.join(publicDir, 'train.html')))
-app.get('/verify', (req, res) => res.sendFile(path.join(publicDir, 'verify.html')))
-app.get('/test', (req, res) => res.sendFile(path.join(publicDir, 'test.html')))
-
-app.listen(3000, () => console.log('Listening on port 3000!'))
\ No newline at end of file

tools/train/shared/trainUtils.js

-const log = (str, ...args) => console.log(`[${[(new Date()).toTimeString().substr(0, 8)]}] ${str || ''}`, ...args)
-
-async function promiseSequential(promises) {
-  const curr = promises[0]
-  if (!curr) {
-    return
-  }
-
-  await curr()
-  return promiseSequential(promises.slice(1))
-}
-
-// https://stackoverflow.com/questions/6274339/how-can-i-shuffle-an-array
-function shuffle(a) {
-  var j, x, i;
-  for (i = a.length - 1; i > 0; i--) {
-      j = Math.floor(Math.random() * (i + 1));
-      x = a[i];
-      a[i] = a[j];
-      a[j] = x;
-  }
-  return a;
-}
-
-function saveWeights(net, filename = 'train_tmp') {
-  const binaryWeights = new Float32Array(
-    net.getParamList()
-      .map(({ tensor }) => Array.from(tensor.dataSync()))
-      .reduce((flat, arr) => flat.concat(arr))
-  )
-  saveAs(new Blob([binaryWeights]), filename)
-}
-
-function imageToSquare(img) {
-  const scale = 608 / Math.max(img.height, img.width)
-  const width = scale * img.width
-  const height = scale * img.height
-
-  const canvas1 = faceapi.createCanvasFromMedia(img)
-  const targetCanvas = faceapi.createCanvas({ width: 608, height: 608 })
-  targetCanvas.getContext('2d').putImageData(canvas1.getContext('2d').getImageData(0, 0, width, height), 0, 0)
-  return targetCanvas
-}
-
-function getPaddingsAndReshapedSize(img, inputSize) {
-  const [h, w] = [img.height, img.width]
-  const maxDim = Math.max(h, w)
-
-  const f = inputSize / maxDim
-  const reshapedImgDims = {
-    height: Math.floor(h * f),
-    width: Math.floor(w * f)
-  }
-
-  const paddings = new faceapi.Point(
-    maxDim / img.width,
-    maxDim / img.height
-  )
-
-  return { paddings, reshapedImgDims }
-}
\ No newline at end of file

tools/train/tinyYolov2/initWeights.html

-<!DOCTYPE html>
-<html>
-<head>
-  <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.12.0"> </script>
-  <script src="FileSaver.js"></script>
-</head>
-<body>
-  <script>
-
-    const glorotNormal = tf.initializers.glorotNormal()
-
-    saveWeights()
-
-    function initSeparableConvWeights(inChannels, outChannels) {
-      return {
-        depthwiseFilter: glorotNormal.apply([3, 3, inChannels, 1]),
-        pointwiseFilter: glorotNormal.apply([1, 1, inChannels, outChannels]),
-        bias: tf.zeros([outChannels])
-      }
-    }
-
-    function initTinyYolov2SeparableWeights() {
-
-      const conv0 = initSeparableConvWeights(3, 16)
-      const conv1 = initSeparableConvWeights(16, 32)
-      const conv2 = initSeparableConvWeights(32, 64)
-      const conv3 = initSeparableConvWeights(64, 128)
-      const conv4 = initSeparableConvWeights(128, 256)
-      const conv5 = initSeparableConvWeights(256, 512)
-      const conv6 = initSeparableConvWeights(512, 1024)
-      const conv7 = initSeparableConvWeights(1024, 1024)
-      const conv8 = {
-        filters: glorotNormal.apply([1, 1, 1024, 25]),
-        bias: tf.zeros([25])
-      }
-
-      return {
-        conv0,
-        conv1,
-        conv2,
-        conv3,
-        conv4,
-        conv5,
-        conv6,
-        conv7,
-        conv8
-      }
-    }
-
-    function saveWeights() {
-      const w = initTinyYolov2SeparableWeights()
-      const binaryWeights = new Float32Array(
-        Array(8).fill(0)
-          .map((_, i) => w[`conv${i}`])
-          .map(ps => [ps.depthwiseFilter, ps.pointwiseFilter, ps.bias])
-          .reduce((flat, arr) => flat.concat(arr))
-          .concat([w.conv8.filters, w.conv8.bias])
-          .map(tensor => console.log(tensor) || Array.from(tensor.dataSync()))
-          .reduce((flat, arr) => flat.concat(arr))
-      )
-      saveAs(new Blob([binaryWeights]), 'foo.weights')
-    }
-  </script>
-</body>
-</html>
\ No newline at end of file

tools/train/tinyYolov2/loss.js

-const CELL_SIZE = 32
-
-const getNumCells = inputSize => inputSize / CELL_SIZE
-
-const inverseSigmoid = x => Math.log(x / (1 - x))
-
-function getAnchors() {
-  return window.net.anchors
-}
-
-function squaredSumOverMask(lossTensors, mask) {
-  return tf.tidy(() => tf.sum(tf.square(tf.mul(mask, lossTensors))))
-}
-
-function assignBoxesToAnchors(groundTruthBoxes, reshapedImgDims) {
-
-  const inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height)
-  const numCells = getNumCells(inputSize)
-
-  return groundTruthBoxes.map(box => {
-    const { left, top, width, height } = box.rescale(reshapedImgDims)
-
-    const ctX = left + (width / 2)
-    const ctY = top + (height / 2)
-
-    const col = Math.floor((ctX / inputSize) * numCells)
-    const row = Math.floor((ctY / inputSize) * numCells)
-
-    const anchorsByIou = getAnchors().map((anchor, idx) => ({
-      idx,
-      iou: faceapi.iou(
-        new faceapi.BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE),
-        new faceapi.BoundingBox(0, 0, width, height)
-      )
-    })).sort((a1, a2) => a2.iou - a1.iou)
-
-    const anchor = anchorsByIou[0].idx
-
-    return { row, col, anchor, box }
-  })
-}
-
-function getGroundTruthMask(groundTruthBoxes, inputSize) {
-
-  const numCells = getNumCells(inputSize)
-
-  const mask = tf.zeros([numCells, numCells, 25])
-  const buf = mask.buffer()
-
-  groundTruthBoxes.forEach(({ row, col, anchor }) => {
-    const anchorOffset = anchor * 5
-    for (let i = 0; i < 5; i++) {
-      buf.set(1, row, col, anchorOffset + i)
-    }
-  })
-
-   return mask
-}
-
-function getCoordAndScoreMasks(inputSize) {
-
-  const numCells = getNumCells(inputSize)
-
-  const coordMask = tf.zeros([numCells, numCells, 25])
-  const scoreMask = tf.zeros([numCells, numCells, 25])
-  const coordBuf = coordMask.buffer()
-  const scoreBuf = scoreMask.buffer()
-
-  for (let row = 0; row < numCells; row++) {
-    for (let col = 0; col < numCells; col++) {
-      for (let anchor = 0; anchor < 5; anchor++) {
-        const anchorOffset = 5 * anchor
-        for (let i = 0; i < 4; i++) {
-          coordBuf.set(1, row, col, anchorOffset + i)
-        }
-        scoreBuf.set(1, row, col, anchorOffset + 4)
-      }
-    }
-  }
-
-  return { coordMask, scoreMask }
-}
-
-function computeBoxAdjustments(groundTruthBoxes, reshapedImgDims) {
-
-  const inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height)
-  const numCells = getNumCells(inputSize)
-
-  const adjustments = tf.zeros([numCells, numCells, 25])
-  const buf = adjustments.buffer()
-
-  groundTruthBoxes.forEach(({ row, col, anchor, box }) => {
-    const { left, top, right, bottom, width, height } = box.rescale(reshapedImgDims)
-
-    const centerX = (left + right) / 2
-    const centerY = (top + bottom) / 2
-
-    //const dCenterX = centerX - (col * CELL_SIZE + (CELL_SIZE / 2))
-    //const dCenterY = centerY - (row * CELL_SIZE + (CELL_SIZE / 2))
-    const dCenterX = centerX - (col * CELL_SIZE)
-    const dCenterY = centerY - (row * CELL_SIZE)
-
-    const dx = inverseSigmoid(dCenterX / CELL_SIZE)
-    const dy = inverseSigmoid(dCenterY / CELL_SIZE)
-    //const dx = dCenterX / CELL_SIZE
-    //const dy = dCenterY / CELL_SIZE
-    const dw = Math.log((width / CELL_SIZE) / getAnchors()[anchor].x)
-    const dh = Math.log((height / CELL_SIZE) / getAnchors()[anchor].y)
-
-    const anchorOffset = anchor * 5
-    buf.set(dx, row, col, anchorOffset + 0)
-    buf.set(dy, row, col, anchorOffset + 1)
-    buf.set(dw, row, col, anchorOffset + 2)
-    buf.set(dh, row, col, anchorOffset + 3)
-  })
-
-  return adjustments
-}
-
-function computeIous(predBoxes, groundTruthBoxes, reshapedImgDims) {
-
-  const inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height)
-  const numCells = getNumCells(inputSize)
-
-  const isSameAnchor = p1 => p2 =>
-    p1.row === p2.row
-      && p1.col === p2.col
-      && p1.anchor === p2.anchor
-
-  const ious = tf.zeros([numCells, numCells, 25])
-  const buf = ious.buffer()
-
-  groundTruthBoxes.forEach(({ row, col, anchor, box }) => {
-    const predBox = predBoxes.find(isSameAnchor({ row, col, anchor }))
-
-    if (!predBox) {
-      console.log(groundTruthBoxes)
-      console.log(predBoxes)
-      throw new Error(`no output box found for: row ${row}, col ${col}, anchor ${anchor}`)
-    }
-
-    const iou = faceapi.iou(
-      box.rescale(reshapedImgDims),
-      predBox.box.rescale(reshapedImgDims)
-    )
-
-    if (window.debug) {
-      console.log('ground thruth box:', box.rescale(reshapedImgDims).toRect())
-      console.log('predicted box:', predBox.box.rescale(reshapedImgDims).toRect())
-      console.log('predicted score:', predBox.score)
-      console.log('iou:', iou)
-    }
-
-    const anchorOffset = anchor * 5
-    buf.set(iou, row, col, anchorOffset + 4)
-  })
-
-  return ious
-}
-
-window.computeNoObjectLoss = function(outTensor, mask) {
-  return tf.tidy(() => {
-    const lossTensor = tf.sigmoid(outTensor)
-    return squaredSumOverMask(lossTensor, mask)
-  })
-}
-
-function computeObjectLoss(outTensor, groundTruthBoxes, reshapedImgDims, paddings, mask) {
-  return tf.tidy(() => {
-    const predBoxes = window.net.postProcess(
-      outTensor,
-      { paddings }
-    )
-
-    const ious = computeIous(
-      predBoxes,
-      groundTruthBoxes,
-      reshapedImgDims
-    )
-
-    const lossTensor = tf.sub(ious, tf.sigmoid(outTensor))
-
-    return squaredSumOverMask(lossTensor, mask)
-  })
-}
-
-function computeCoordLoss(groundTruthBoxes, outTensor, reshapedImgDims, mask, paddings) {
-  return tf.tidy(() => {
-    const boxAdjustments = computeBoxAdjustments(
-      groundTruthBoxes,
-      reshapedImgDims
-    )
-
-    if (window.debug) {
-      const indToPos = []
-      const numCells = outTensor.shape[1]
-      for (let row = 0; row < numCells; row++) {
-        for (let col = 0; col < numCells; col++) {
-          for (let anchor = 0; anchor < 25; anchor++) {
-            indToPos.push({ row, col, anchor: parseInt(anchor / 5) })
-          }
-        }
-      }
-
-      const indices = Array.from(mask.dataSync()).map((val, ind) => ({ val, ind })).filter(v => v.val !== 0).map(v => v.ind)
-      const gt = Array.from(boxAdjustments.dataSync())
-      const out = Array.from(outTensor.dataSync())
-
-      const comp = indices.map(i => (
-        {
-          pos: indToPos[i],
-          gt: gt[i],
-          out: out[i]
-        }
-      ))
-      console.log(comp.map(c => `gt: ${c.gt}, out: ${c.out}`))
-
-      const getBbox = (which) => {
-        const { row, col, anchor } = comp[0].pos
-
-        const ctX = ((col + faceapi.sigmoid(comp[0][which])) / numCells) * paddings.x
-        const ctY = ((row + faceapi.sigmoid(comp[1][which])) / numCells) * paddings.y
-        const width = ((Math.exp(comp[2][which]) * getAnchors()[anchor].x) / numCells) * paddings.x
-        const height = ((Math.exp(comp[3][which]) * getAnchors()[anchor].y) / numCells) * paddings.y
-
-        const x = (ctX - (width / 2))
-        const y = (ctY - (height / 2))
-
-        return new faceapi.BoundingBox(x, y, x + width, y + height)
-      }
-
-      const outRect = getBbox('out').rescale(reshapedImgDims).toRect()
-      const gtRect = getBbox('gt').rescale(reshapedImgDims).toRect()
-      console.log('out', outRect)
-      console.log('gtRect', gtRect)
-    }
-
-
-    const lossTensor = tf.sub(boxAdjustments, outTensor)
-
-    return squaredSumOverMask(lossTensor, mask)
-  })
-}
-
-function computeLoss(outTensor, groundTruth, reshapedImgDims, paddings) {
-
-  const inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height)
-
-  if (!inputSize) {
-    throw new Error(`invalid inputSize: ${inputSize}`)
-  }
-
-  let groundTruthBoxes = assignBoxesToAnchors(
-    groundTruth
-      .map(({ x, y, width, height }) => new faceapi.Rect(x, y, width, height))
-      .map(rect => rect.toBoundingBox()),
-    reshapedImgDims
-  )
-
-  const groundTruthMask = getGroundTruthMask(groundTruthBoxes, inputSize)
-  const { coordMask, scoreMask } = getCoordAndScoreMasks(inputSize)
-
-  const noObjectLossMask = tf.tidy(() => tf.mul(scoreMask, tf.sub(tf.scalar(1), groundTruthMask)))
-  const objectLossMask = tf.tidy(() => tf.mul(scoreMask, groundTruthMask))
-  const coordLossMask = tf.tidy(() => tf.mul(coordMask, groundTruthMask))
-
-  const noObjectLoss = tf.tidy(() =>
-    tf.mul(
-      tf.scalar(noObjectScale),
-      computeNoObjectLoss(outTensor, noObjectLossMask)
-    )
-  )
-  const objectLoss = tf.tidy(() =>
-    tf.mul(
-      tf.scalar(objectScale),
-      computeObjectLoss(outTensor, groundTruthBoxes, reshapedImgDims, paddings, objectLossMask)
-    )
-  )
-
-  const coordLoss = tf.tidy(() =>
-    tf.mul(
-      tf.scalar(coordScale),
-      computeCoordLoss(groundTruthBoxes, outTensor, reshapedImgDims, coordLossMask, paddings)
-    )
-  )
-
-  const totalLoss = tf.tidy(() => noObjectLoss.add(objectLoss).add(coordLoss))
-
-  return {
-    noObjectLoss,
-    objectLoss,
-    coordLoss,
-    totalLoss
-  }
-}
\ No newline at end of file

tools/train/tinyYolov2/loss.test.ts

-import * as _tf from '@tensorflow/tfjs-core';
-
-const faceapi = require('../../../dist/face-api.js')
-const tf: typeof _tf = faceapi.tf
-require('./loss')
-
-window['faceapi'] = faceapi
-window['tf'] = tf
-
-const anchors = [
-  new faceapi.Point(1.603231, 2.094468),
-  new faceapi.Point(6.041143, 7.080126),
-  new faceapi.Point(2.882459, 3.518061),
-  new faceapi.Point(4.266906, 5.178857),
-  new faceapi.Point(9.041765, 10.66308)
-]
-
-window['net'] = {
-  getAnchors() {
-    return anchors
-  }
-}
-
-describe('loss', () => {
-
-  describe('computeNoObjectLoss', () => {
-
-    const computeNoObjectLoss = window['computeNoObjectLoss']
-
-    it('should only compute loss over scores, 1x1 grid', () => tf.tidy(() => {
-      const outTensor = tf.zeros([1, 1, 1, 25])
-      const loss = tf.sum(computeNoObjectLoss(outTensor)).dataSync()[0]
-
-      expect(loss).toEqual(0.5 * 0.5 * 5)
-    }))
-
-    it('should only compute loss over scores, 13x13 grid', () => tf.tidy(() => {
-      const outTensor = tf.zeros([1, 13, 13, 25])
-      const loss = tf.sum(computeNoObjectLoss(outTensor)).dataSync()[0]
-
-      expect(loss).toEqual(0.5 * 0.5 * 5 * 13 * 13)
-    }))
-
-    it('should only compute loss over scores, 13x13 grid, batchSize: 10', () => tf.tidy(() => {
-      const outTensor = tf.zeros([10, 13, 13, 25])
-      const loss = tf.sum(computeNoObjectLoss(outTensor)).dataSync()[0]
-
-      expect(loss).toEqual(0.5 * 0.5 * 5 * 13 * 13 * 10)
-    }))
-
-  })
-})
\ No newline at end of file

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\ No newline at end of file

tools/train/tinyYolov2/test.html

-<!DOCTYPE html>
-<html>
-<head>
-  <script src="face-api.js"></script>
-  <script src="commons.js"></script>
-  <script src="trainUtils.js"></script>
-  <script src="loss.js"></script>
-  <script src="FileSaver.js"></script>
-  <link rel="stylesheet" href="styles.css">
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
-  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
-</head>
-<body>
-  <script>
-    window.tf = faceapi.tf
-
-    // hyper parameters
-    window.objectScale = 5
-    window.noObjectScale = 1
-    window.coordScale = 1
-
-    const weightsUrl = `/tmp/tmp__224_35060__320_41188__416_31050__608_16520.weights`
-    //const inputSizes = [160, 224, 320, 416]
-    const inputSizes = [512, 608]
-
-    async function loadNetWeights(uri) {
-      return new Float32Array(await (await fetch(uri)).arrayBuffer())
-    }
-
-    async function fetchFddbJson() {
-      return fetch('/fddb-detections.json').then(res => res.json())
-    }
-
-    async function run() {
-      window.fddbJson = await fetchFddbJson()
-
-      const weights = await loadNetWeights(weightsUrl)
-      window.net = new faceapi.TinyYolov2(true)
-      await window.net.load(weights)
-
-      await promiseSequential(inputSizes.map(inputSize => async () => {
-        await promiseSequential(window.fddbJson.map(({ filePath, rects }) => () => {
-          return test(filePath, rects, inputSize)
-        }))
-
-        const losses = Object.keys(window.lossMap[inputSize]).map(k => window.lossMap[inputSize][k])
-
-        const totalLoss = losses
-          .map(l => l.totalLoss)
-          .reduce((sum, l) => sum + l)
-        const avgLoss = totalLoss / losses.length
-
-        log(`totalLoss (${inputSize}): ${totalLoss}`)
-        log(`avgLoss (${inputSize}): ${avgLoss}`)
-
-        window.losses = window.losses || {}
-        window.losses[inputSize] = { totalLoss, avgLoss }
-      }))
-
-      console.log(window.losses)
-    }
-
-    async function test(fileUri, rects, inputSize) {
-      const img = await faceapi.bufferToImage(await fetchImage(fileUri))
-
-      const groundTruthBoxes = rects
-        .map(({ x, y, width, height }) => new faceapi.Rect(x, y, width, height))
-        .map(rect => rect.clipAtImageBorders(img.width, img.height))
-        .map(({ x, y, width, height }) => ({
-          x: x / img.width,
-          y: y / img.height,
-          width: width / img.width,
-          height: height / img.height,
-        }))
-
-      const { reshapedImgDims, paddings } = getPaddingsAndReshapedSize(img, inputSize)
-      const squareImg = imageToSquare(img)
-
-      const netInput = (await faceapi.toNetInput(squareImg)).managed()
-
-      const losses = tf.tidy(() => {
-        const outTensor = window.net.forwardInput(netInput, inputSize)
-
-        const {
-          noObjectLoss,
-          objectLoss,
-          coordLoss,
-          totalLoss
-        } = computeLoss(
-          outTensor,
-          groundTruthBoxes,
-          reshapedImgDims,
-          paddings
-        )
-
-        const losses = {
-          totalLoss: totalLoss.dataSync()[0],
-          noObjectLoss: noObjectLoss.dataSync()[0],
-          objectLoss: objectLoss.dataSync()[0],
-          coordLoss: coordLoss.dataSync()[0]
-        }
-
-        return losses
-      })
-
-      log(`${fileUri}:`)
-      log(`ground truth boxes: ${groundTruthBoxes.length}`)
-      log(`noObjectLoss: ${losses.noObjectLoss}`)
-      log(`objectLoss: ${losses.objectLoss}`)
-      log(`coordLoss: ${losses.coordLoss}`)
-      log(`totalLoss: ${losses.totalLoss}`)
-
-      if (Object.keys(losses).map(k => losses[k]).some(loss => isNaN(loss) || loss === Infinity)) {
-        console.log(groundTruthBoxes)
-        console.log(img)
-        console.log(losses)
-        throw new Error('corrupted loss value')
-      }
-      window.lossMap = window.lossMap || {}
-      window.lossMap[inputSize] = window.lossMap[inputSize] || {}
-      window.lossMap[inputSize][fileUri] = losses
-    }
-
-    $(document).ready(function() {
-      run()
-    })
-  </script>
-</body>
-</html>
\ No newline at end of file

tools/train/tinyYolov2/train.html

-<!DOCTYPE html>
-<html>
-<head>
-  <link rel="stylesheet" href="styles.css">
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
-  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
-  <script src="face-api.js"></script>
-  <script src="commons.js"></script>
-  <script src="FileSaver.js"></script>
-  <script src="trainUtils.js"></script>
-  <script src="train.js"></script>
-  <script src="loss.js"></script>
-</head>
-<body>
-
-  <script>
-    tf = faceapi.tf
-
-    const startIdx160 = 26600
-    const startIdx224 = 61660
-    const startIdx320 = 67788
-    const startIdx416 = 57650
-    const startIdx608 = 16520
-
-    //const weightsUrl = `/tmp/tmp__160_${startIdx160}__224_${startIdx224}__320_${startIdx320}__416_${startIdx416}__608_${startIdx608}.weights`
-    const weightsUrl = `/tmp/tmp_multiscale_count_8700.weights`
-
-    const fromEpoch = 0
-
-    const trainOnlyMultibox = false
-
-    const trainSizes = [416, 512, 608]
-    //const trainSizes = [608]
-
-    window.debug = false
-    window.logTrainSteps = true
-    window.count = 0
-
-
-    // hyper parameters
-    window.objectScale = 5
-    window.noObjectScale = 1
-    window.coordScale = 1
-
-    const rescaleEveryNthBatch = 100
-    window.saveEveryNthDataIdx = trainSizes.length * rescaleEveryNthBatch
-    window.trainSteps = 4000
-    //window.optimizer = tf.train.sgd(0.001)
-    window.optimizer = tf.train.adam(0.001, 0.9, 0.999, 1e-8)
-
-    // all samples
-    //const dataStartIdx = 8000
-    const dataStartIdx = 0
-    const numTrainSamples = Infinity
-
-    async function loadNetWeights(uri) {
-      return new Float32Array(await (await fetch(uri)).arrayBuffer())
-    }
-
-    async function fetchDetectionFilenames() {
-      return fetch('/detection_filenames').then(res => res.json())
-    }
-
-    async function fetchDetectionFilenamesMultibox() {
-      return fetch('/detection_filenames_multibox').then(res => res.json())
-    }
-
-    async function run() {
-      const weights = await loadNetWeights(weightsUrl)
-      window.net = new faceapi.TinyYolov2(true)
-      window.net.load(weights)
-      window.net.variable()
-
-      const fetchDetectionsFn = trainOnlyMultibox
-        ? fetchDetectionFilenamesMultibox
-        : fetchDetectionFilenames
-
-      window.detectionFilenames = (await fetchDetectionsFn()).slice(dataStartIdx, dataStartIdx + numTrainSamples)
-
-      window.lossMap = {}
-
-      console.log('ready')
-    }
-
-    function logLossChange(lossType) {
-      const { currentLoss, prevLoss, detectionFilenames } = window
-      log(`${lossType} : ${faceapi.round(currentLoss[lossType])} (avg: ${faceapi.round(currentLoss[lossType] / detectionFilenames.length)}) (delta: ${currentLoss[lossType] - prevLoss[lossType]})`)
-    }
-
-
-    function onBatchProcessed(dataIdx, inputSize) {
-      window.count++
-      const idx = (dataIdx + 1) + (window.epoch * window.detectionFilenames.length)
-      console.log('dataIdx', dataIdx)
-      if ((window.count % saveEveryNthDataIdx) === 0) {
-        saveWeights(window.net, `tmp_multiscale_count_${window.count}.weights`)
-      }
-    }
-
-    function _onBatchProcessed(dataIdx, inputSize) {
-      const idx = (dataIdx + 1) + (window.epoch * window.detectionFilenames.length)
-      console.log('idx', idx)
-      if ((idx % saveEveryNthDataIdx) === 0) {
-        saveWeights(window.net, `tmp__224_${startIdx224 + (inputSize === 224 ? idx : 0)}__320_${startIdx320 + (inputSize === 320 ? idx : 0)}__416_${startIdx416 + (inputSize === 416 ? idx : 0)}__608_${startIdx608 + (inputSize === 608 ? idx : 0)}.weights`)
-      }
-    }
-
-    async function train() {
-
-      const batchSize = 1
-
-      for (let i = fromEpoch; i < trainSteps; i++) {
-        window.epoch = i
-        log('step', i)
-        let ts2 = Date.now()
-
-        const batchCreators = createBatchCreators(shuffle(window.detectionFilenames), batchSize)
-
-        await trainStep(batchCreators, trainSizes, rescaleEveryNthBatch, onBatchProcessed)
-
-        ts2 = Date.now() - ts2
-      }
-
-      ts = Date.now() - ts
-
-      log()
-      log('--------------------')
-      log()
-      log('step %s done (%s ms)', i, ts)
-
-      window.prevLoss = window.currentLoss
-      window.currentLoss = Object.keys(lossMap)
-        .map(filename => lossMap[filename])
-        .reduce((accumulatedLosses, losses) =>
-          Object.keys(losses)
-            .map(key => ({
-              [key]: (accumulatedLosses[key] || 0) + losses[key]
-            }))
-            .reduce((map, curr) => ({ ...map, ...curr }), {}),
-          {}
-        )
-
-      if (window.prevLoss) {
-        logLossChange('noObjectLoss')
-        logLossChange('objectLoss')
-        logLossChange('coordLoss')
-        logLossChange('totalLoss')
-      }
-      log()
-      log('--------------------')
-      log()
-    }
-
-    run()
-
-  </script>
-
-</body>
-</html>
\ No newline at end of file

tools/train/tinyYolov2/train.js

-const batchIdx = 0
-
-function minimize(groundTruthBoxes, batchInput, inputSize, batch, { reshapedImgDims, paddings }) {
-  const filename = batch.filenames[batchIdx]
-  const { dataIdx } = batch
-
-  return optimizer.minimize(() => {
-
-    const outTensor = window.net.forwardInput(batchInput, inputSize)
-
-    const {
-      noObjectLoss,
-      objectLoss,
-      coordLoss,
-      totalLoss
-    } = computeLoss(
-      outTensor,
-      groundTruthBoxes,
-      reshapedImgDims,
-      paddings
-    )
-
-    const losses = {
-      totalLoss: totalLoss.dataSync()[0],
-      noObjectLoss: noObjectLoss.dataSync()[0],
-      objectLoss: objectLoss.dataSync()[0],
-      coordLoss: coordLoss.dataSync()[0]
-    }
-
-    const lossKey = `${filename}_${inputSize}`
-
-    if (window.logTrainSteps) {
-      log(`ground truth boxes: ${groundTruthBoxes.length}`)
-      log(`noObjectLoss[${dataIdx}]: ${losses.noObjectLoss}`)
-      log(`objectLoss[${dataIdx}]: ${losses.objectLoss}`)
-      log(`coordLoss[${dataIdx}]: ${losses.coordLoss}`)
-      log(`totalLoss[${dataIdx}]: ${losses.totalLoss}`)
-
-      if (window.lossMap[lossKey]) {
-        log(`loss change: ${losses.totalLoss - window.lossMap[lossKey].totalLoss}`)
-      }
-    }
-
-    window.lossMap[lossKey] = losses
-
-    return totalLoss
-  }, true)
-}
-
-async function trainStep(batchCreators, inputSizes, rescaleEveryNthBatch, onBatchProcessed = () => {}) {
-
-  async function step(currentBatchCreators) {
-    if (!currentBatchCreators.batchCreators.length) {
-      return
-    }
-
-    await promiseSequential(inputSizes.map(inputSize => async () => {
-
-      await promiseSequential(currentBatchCreators.batchCreators.map(batchCreator => async () => {
-
-        const batch = await batchCreator()
-        const { imgs, groundTruthBoxes, filenames, dataIdx } = batch
-
-        const img = imgs[0]
-        const { reshapedImgDims, paddings } = getPaddingsAndReshapedSize(img, inputSize)
-        const squareImg = imageToSquare(img)
-
-        const batchInput = await faceapi.toNetInput(squareImg)
-
-        const [imgHeight, imgWidth] = batchInput.inputs[batchIdx].shape
-
-        // skip groundTruthBoxes, which are too tiny
-        const scaleFactor = inputSize / Math.max(imgHeight, imgWidth)
-        const filteredGroundTruthBoxes = groundTruthBoxes[batchIdx].filter(({ x, y, width, height }) => {
-          const box = (new faceapi.Rect(x, y, width, height))
-            .toBoundingBox()
-            .rescale({ height: imgHeight, width: imgWidth })
-            .rescale(scaleFactor)
-
-          const isTooTiny = box.width < 40 || box.height < 40
-          if (isTooTiny && window.debug) {
-            log(`skipping box for input size ${inputSize}: (${Math.floor(box.width)} x ${Math.floor(box.height)})`)
-          }
-          return !isTooTiny
-        })
-
-        if (!filteredGroundTruthBoxes.length) {
-          if (window.debug) {
-            log(`no boxes for input size ${inputSize}, ${groundTruthBoxes[batchIdx].length} boxes were too small`)
-          }
-          batchInput.dispose()
-          onBatchProcessed(dataIdx, inputSize)
-          return
-        }
-
-        let ts = Date.now()
-        const loss = minimize(filteredGroundTruthBoxes, batchInput, inputSize, batch, { reshapedImgDims, paddings })
-
-        ts = Date.now() - ts
-        if (window.logTrainSteps) {
-          log(`trainStep time for dataIdx ${dataIdx} (${inputSize}): ${ts} ms`)
-        }
-        loss.dispose()
-        batchInput.dispose()
-        onBatchProcessed(dataIdx, inputSize)
-
-        await tf.nextFrame()
-      }))
-
-    }))
-
-    await step(currentBatchCreators.next(rescaleEveryNthBatch))
-  }
-
-  await step(batchCreators.next(rescaleEveryNthBatch))
-}
-
-async function fetchGroundTruthBoxesForFile(file) {
-  const boxes = await fetch(file).then(res => res.json())
-  return {
-    file,
-    boxes
-  }
-}
-
-function createBatchCreators(detectionFilenames, batchSize) {
-  if (batchSize < 1) {
-    throw new Error('invalid batch size: ' + batchSize)
-  }
-
-  const batches = []
-  const pushToBatch = (remaining) => {
-    if (remaining.length) {
-      batches.push(remaining.slice(0, batchSize))
-      pushToBatch(remaining.slice(batchSize))
-    }
-    return batches
-  }
-
-  pushToBatch(detectionFilenames)
-
-  const batchCreators = batches.map((filenamesForBatch, dataIdx) => async () => {
-    const groundTruthBoxes = (await Promise.all(filenamesForBatch.map(fetchGroundTruthBoxesForFile)))
-      .map(({ boxes }) => boxes)
-
-    const imgs = await Promise.all(filenamesForBatch.map(
-      async file => await faceapi.bufferToImage(await fetchImage(file.replace('.json', '')))
-    ))
-
-    return {
-      imgs,
-      groundTruthBoxes,
-      filenames: filenamesForBatch,
-      dataIdx
-    }
-  })
-
-  let idx = 0
-  function next(n) {
-    const nextBatchCreators = batchCreators.slice(idx, idx + n)
-    idx += n
-
-    return {
-      batchCreators: nextBatchCreators,
-      next
-    }
-  }
-
-  return {
-    data: batchCreators,
-    next
-  }
-}
\ No newline at end of file

tools/train/tinyYolov2/verify.html

-<!DOCTYPE html>
-<html>
-<head>
-  <script src="face-api.js"></script>
-  <script src="commons.js"></script>
-  <link rel="stylesheet" href="styles.css">
-  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/css/materialize.css">
-  <script type="text/javascript" src="https://code.jquery.com/jquery-2.1.1.min.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/materialize/0.100.2/js/materialize.min.js"></script>
-</head>
-<body>
-  <div id="navbar"></div>
-  <div class="center-content page-container">
-    <div class="progress" id="loader">
-      <div class="indeterminate"></div>
-    </div>
-    <div style="position: relative" class="margin">
-      <img id="inputImg" src="" style="max-width: 800px;" />
-      <canvas id="overlay" />
-    </div>
-    <div class="row side-by-side">
-      <div id="selectList"></div>
-      <div class="row">
-        <label for="imgUrlInput">Get image from URL:</label>
-        <input id="imgUrlInput" type="text" class="bold">
-      </div>
-      <button
-        class="waves-effect waves-light btn"
-        onclick="loadImageFromUrl()"
-      >
-        Ok
-      </button>
-    </div>
-    <div class="row side-by-side">
-      <div class="row input-field" style="margin-right: 20px;">
-        <select id="sizeType">
-          <option value="128">128 x 128</option>
-          <option value="160">160 x 160</option>
-          <option value="224">224 x 224</option>
-          <option value="320">320 x 320</option>
-          <option value="416">416 x 416</option>
-          <option value="608">608 x 608</option>
-        </select>
-        <label>Input Size</label>
-      </div>
-      <div class="row">
-        <label for="scoreThreshold">Score Threshold:</label>
-        <input disabled value="0.5" id="scoreThreshold" type="text" class="bold">
-      </div>
-      <button
-        class="waves-effect waves-light btn"
-        onclick="onDecreaseThreshold()"
-      >
-        <i class="material-icons left">-</i>
-      </button>
-      <button
-        class="waves-effect waves-light btn"
-        onclick="onIncreaseThreshold()"
-      >
-        <i class="material-icons left">+</i>
-      </button>
-    </div>
-    <div class="row">
-      <label for="imgByNr">Enter image NR: </label>
-      <input id="imgByNr" type="text" class="bold">
-    </div>
-  </div>
-
-  <script>
-    let scoreThreshold = 0.5
-    let sizeType = 608
-
-    function onKeyDown(e) {
-      e.target.value = (
-        parseInt(e.target.value) + (e.keyCode === 38 ? 1 : (e.keyCode === 40 ? -1 : 0))
-      ) || e.target.value || 0
-
-
-      const imgUri = window.imgs[e.target.value]
-
-      console.log(imgUri)
-      onSelectionChanged(imgUri)
-    }
-
-    function onIncreaseThreshold() {
-      scoreThreshold = Math.min(faceapi.round(scoreThreshold + 0.1), 1.0)
-      $('#scoreThreshold').val(scoreThreshold)
-      updateResults()
-    }
-
-    function onDecreaseThreshold() {
-      scoreThreshold = Math.max(faceapi.round(scoreThreshold - 0.1), 0.1)
-      $('#scoreThreshold').val(scoreThreshold)
-      updateResults()
-    }
-
-    function onSizeTypeChanged(e, c) {
-      sizeType = e.target.value
-      $('#sizeType').val(sizeType)
-      updateResults()
-    }
-
-    async function loadImageFromUrl(url) {
-      const img = await requestExternalImage($('#imgUrlInput').val())
-      $('#inputImg').get(0).src = img.src
-      updateResults()
-    }
-
-    async function updateResults() {
-      const inputImgEl = $('#inputImg').get(0)
-      const { width, height } = inputImgEl
-      const canvas = $('#overlay').get(0)
-      canvas.width = width
-      canvas.height = height
-
-      const forwardParams = {
-        inputSize: parseInt(sizeType),
-        scoreThreshold
-      }
-
-      const detections = await window.net.locateFaces(inputImgEl, forwardParams)
-
-      faceapi.drawDetection('overlay', detections.map(det => det.forSize(width, height)))
-    }
-
-    async function onSelectionChanged(uri) {
-      const imgBuf = await fetchImage(uri)
-      $(`#inputImg`).get(0).src = (await faceapi.bufferToImage(imgBuf)).src
-      updateResults()
-    }
-
-    async function loadNetWeights(uri) {
-      return new Float32Array(await (await fetch(uri)).arrayBuffer())
-    }
-
-    async function fetchDetectionFilenames() {
-      return fetch('/detection_filenames').then(res => res.json())
-    }
-
-    async function run() {
-      $('#imgByNr').keydown(onKeyDown)
-
-      const startIdx224 = 35060
-      const startIdx320 = 41188
-      const startIdx416 = 31050
-      const startIdx608 = 16520
-
-      //const weightsUrl = `/tmp/tmp__224_${startIdx224}__320_${startIdx320}__416_${startIdx416}__608_${startIdx608}.weights`
-      const weightsUrl = `/tmp/tmp_multiscale_count_4200.weights`
-
-      const weights = await loadNetWeights(weightsUrl)
-      window.net = new faceapi.TinyYolov2(true)
-      await window.net.load(weights)
-
-      window.imgs = (await fetchDetectionFilenames()).map(f => f.replace('.json', ''))
-
-      $('#loader').hide()
-      onSelectionChanged($('#selectList select').val())
-    }
-
-    $(document).ready(function() {
-      renderNavBar('#navbar', 'tiny_yolov2_face_detection')
-      renderImageSelectList(
-        '#selectList',
-        async (uri) => {
-          await onSelectionChanged(uri)
-        },
-        'bbt1.jpg'
-      )
-
-      const sizeTypeSelect = $('#sizeType')
-      sizeTypeSelect.val(sizeType)
-      sizeTypeSelect.on('change', onSizeTypeChanged)
-      sizeTypeSelect.material_select()
-      run()
-    })
-  </script>
-</body>
-</html>
\ No newline at end of file

weights/face_landmark_68_tiny_model-weights_manifest.json

+[{"weights":[{"name":"dense0/conv0/filters","shape":[3,3,3,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007822331026488659,"min":-0.8761010749667297}},{"name":"dense0/conv0/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0061304877786075365,"min":-0.8460073134478401}},{"name":"dense0/conv1/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006931193669637044,"min":-0.928779951731364}},{"name":"dense0/conv1/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.01667951135074391,"min":-2.201695498298196}},{"name":"dense0/conv1/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00381035027550716,"min":-0.7658804053769392}},{"name":"dense0/conv2/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006376917455710617,"min":-0.9437837834451713}},{"name":"dense0/conv2/pointwise_filter","shape":[1,1,32,32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013428742745343376,"min":-1.2757305608076208}},{"name":"dense0/conv2/bias","shape":[32],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003760007666606529,"min":-0.6091212419902577}},{"name":"dense1/conv0/depthwise_filter","shape":[3,3,32,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003171338637669881,"min":-0.38373197515805557}},{"name":"dense1/conv0/pointwise_filter","shape":[1,1,32,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014757957645491059,"min":-1.8447447056863824}},{"name":"dense1/conv0/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0032720065584369734,"min":-0.369736741103378}},{"name":"dense1/conv1/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.006221360318800983,"min":-0.6096933112424963}},{"name":"dense1/conv1/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.011515324723486807,"min":-1.6236607860116399}},{"name":"dense1/conv1/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002638959300284292,"min":-0.316675116034115}},{"name":"dense1/conv2/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.005790043110940971,"min":-0.7237553888676214}},{"name":"dense1/conv2/pointwise_filter","shape":[1,1,64,64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013147306442260743,"min":-1.6960025310516358}},{"name":"dense1/conv2/bias","shape":[64],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0036364357845455994,"min":-0.5163738814054751}},{"name":"dense2/conv0/depthwise_filter","shape":[3,3,64,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0025823556909374164,"min":-0.3279591727490519}},{"name":"dense2/conv0/pointwise_filter","shape":[1,1,64,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.014951597475538066,"min":-1.8689496844422582}},{"name":"dense2/conv0/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0034451217043633556,"min":-0.561554837811227}},{"name":"dense2/conv1/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.00580287587408926,"min":-0.7775853671279609}},{"name":"dense2/conv1/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.020558691024780272,"min":-2.2203386306762694}},{"name":"dense2/conv1/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.003580525459027758,"min":-0.47262936059166405}},{"name":"dense2/conv2/depthwise_filter","shape":[3,3,128,1],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.007787894267661899,"min":-0.996850466260723}},{"name":"dense2/conv2/pointwise_filter","shape":[1,1,128,128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0294371997608858,"min":-3.6207755705889535}},{"name":"dense2/conv2/bias","shape":[128],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.0037439118413364184,"min":-0.40434247886433317}},{"name":"fc/weights","shape":[128,136],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.013749843485215131,"min":-1.8424790270188276}},{"name":"fc/bias","shape":[136],"dtype":"float32","quantization":{"dtype":"uint8","scale":0.002984718394045736,"min":0.0885973796248436}}],"paths":["face_landmark_68_tiny_model-shard1"]}]
\ No newline at end of file