check in latest training script

tools/train/serveTinyYolov2.js

@@ -21,10 +21,12 @@ app.use(express.static(imagesPath))
 app.use(express.static(detectionsPath))
 
 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')))
 

tools/train/tinyYolov2/train.html

@@ -17,14 +17,17 @@
   <script>
     tf = faceapi.tf
 
-    const startIdx224 = 3220
-    const startIdx320 = 20688
-    const startIdx416 = 950
-    const startIdx608 = 15220
+    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_2_count_41000.weights'
     const fromEpoch = 0
 
+    const trainOnlyMultibox = false
 
     window.debug = false
     window.logTrainSteps = true
@@ -42,6 +45,8 @@
     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) {
@@ -52,18 +57,28 @@
       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()
-      window.detectionFilenames = (await fetchDetectionFilenames()).slice(0, numTrainSamples)
+
+      const fetchDetectionsFn = trainOnlyMultibox
+        ? fetchDetectionFilenamesMultibox
+        : fetchDetectionFilenames
+
+      window.detectionFilenames = (await fetchDetectionsFn()).slice(dataStartIdx, dataStartIdx + numTrainSamples)
+
       window.lossMap = {}
 
       console.log('ready')
     }
 
-    //const trainSizes = [224, 320, 416, 608]
+    //const trainSizes = [224, 320, 416]
     const trainSizes = [608]
 
     function logLossChange(lossType) {
@@ -71,9 +86,22 @@
       log(`${lossType} : ${faceapi.round(currentLoss[lossType])} (avg: ${faceapi.round(currentLoss[lossType] / detectionFilenames.length)}) (delta: ${currentLoss[lossType] - prevLoss[lossType]})`)
     }
 
+    window.count = 0
+
+    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_2_count_${window.count}.weights`)
+      }
+    }
+
     function onBatchProcessed(dataIdx, inputSize) {
-      if (((dataIdx + 1) % saveEveryNthDataIdx) === 0) {
-        saveWeights(window.net, `tmp__224_${startIdx224}__320_${startIdx320}__416_${startIdx416}__608_${startIdx608 + dataIdx + 1}.weights`)
+      const idx = (dataIdx + 1) + (window.epoch * window.detectionFilenames.length)
+      console.log('idx', idx)
+      if ((idx % saveEveryNthDataIdx) === 0) {
+        saveWeights(window.net, `tmp__224_${startIdx224 + (inputSize === 224 ? idx : 0)}__320_${startIdx320 + (inputSize === 320 ? idx : 0)}__416_${startIdx416 + (inputSize === 416 ? idx : 0)}__608_${startIdx608 + (inputSize === 608 ? idx : 0)}.weights`)
       }
     }
 
@@ -82,6 +110,7 @@
       const batchSize = 1
 
       for (let i = fromEpoch; i < trainSteps; i++) {
+        window.epoch = i
         log('step', i)
         let ts2 = Date.now()
 

tools/train/tinyYolov2/train.js

@@ -73,15 +73,17 @@ async function trainStep(batchCreators, inputSizes, rescaleEveryNthBatch, onBatc
             .rescale({ height: imgHeight, width: imgWidth })
             .rescale(scaleFactor)
 
-          const isTooTiny = box.width < 50 || box.height < 50
-          if (isTooTiny) {
+          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) {
-          log(`no boxes for input size ${inputSize}, ${groundTruthBoxes[batchIdx].length} boxes were too small`)
+          if (window.debug) {
+            log(`no boxes for input size ${inputSize}, ${groundTruthBoxes[batchIdx].length} boxes were too small`)
+          }
           batchInput.dispose()
           onBatchProcessed(dataIdx, inputSize)
           return
@@ -89,7 +91,6 @@ async function trainStep(batchCreators, inputSizes, rescaleEveryNthBatch, onBatc
 
         let ts = Date.now()
         const loss = minimize(filteredGroundTruthBoxes, batchInput, inputSize, batch)
-
         ts = Date.now() - ts
         if (window.logTrainSteps) {
           log(`trainStep time for dataIdx ${dataIdx} (${inputSize}): ${ts} ms`)
@@ -109,7 +110,15 @@ async function trainStep(batchCreators, inputSizes, rescaleEveryNthBatch, onBatc
   await step(batchCreators.next(rescaleEveryNthBatch))
 }
 
-function createBatchCreators(detectionFilenames, batchSize, ) {
+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)
   }
@@ -126,9 +135,8 @@ function createBatchCreators(detectionFilenames, batchSize, ) {
   pushToBatch(detectionFilenames)
 
   const batchCreators = batches.map((filenamesForBatch, dataIdx) => async () => {
-    const groundTruthBoxes = await Promise.all(filenamesForBatch.map(
-      file => fetch(file).then(res => res.json())
-    ))
+    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', '')))

tools/train/tinyYolov2/verify.html

@@ -138,10 +138,11 @@
 
     async function run() {
       $('#imgByNr').keydown(onKeyDown)
-      const startIdx224 = 3220
-      const startIdx320 = 20688
-      const startIdx416 = 950
-      const startIdx608 = 15220
+
+      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`
 
@@ -149,7 +150,7 @@
       window.net = new faceapi.TinyYolov2(true)
       await window.net.load(weights)
 
-      window.imgs = (await fetchDetectionFilenames()).slice(0, 100).map(f => f.replace('.json', ''))
+      window.imgs = (await fetchDetectionFilenames()).map(f => f.replace('.json', ''))
 
       $('#loader').hide()
       onSelectionChanged($('#selectList select').val())