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Commit 4f45297b by vincent
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added test script to validate model accuracy on fddb benchmark

parent a97cd220
Showing with 185 additions and 44 deletions
tools/train/serveTinyYolov2.js
......@@ -15,10 +15,12 @@ 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')))
......@@ -29,5 +31,6 @@ app.get('/detection_filenames', (req, res) => res.status(202).send(detectionFile
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)]}] ${str || ''}`, ...args)
async function promiseSequential(promises) {
const curr = promises[0]
if (!curr) {
......@@ -29,4 +31,31 @@ function saveWeights(net, filename = 'train_tmp') {
saveAs(new Blob([binaryWeights]), filename)
}
const log = (str, ...args) => console.log(`[${[(new Date()).toTimeString().substr(0, 8)]}] ${str || ''}`, ...args)
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/loss.js
......@@ -102,6 +102,8 @@ function computeBoxAdjustments(groundTruthBoxes, reshapedImgDims) {
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)
......
tools/train/tinyYolov2/test.html 0 → 100644
<!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
}
$(document).ready(function() {
run()
})
</script>
</body>
</html>
\ No newline at end of file
tools/train/tinyYolov2/train.html
......@@ -17,22 +17,25 @@
<script>
tf = faceapi.tf
const startIdx224 = 35060
const startIdx320 = 41188
const startIdx416 = 31050
const startIdx160 = 26600
const startIdx224 = 61660
const startIdx320 = 67788
const startIdx416 = 57650
const startIdx608 = 16520
const weightsUrl = `/tmp/tmp__224_${startIdx224}__320_${startIdx320}__416_${startIdx416}__608_${startIdx608}.weights`
//const weightsUrl = `/tmp/tmp__160_${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 = [160, 224, 320, 416]
const trainSizes = [416, 512, 608]
//const trainSizes = [608]
window.debug = false
window.logTrainSteps = true
window.count = 0
// hyper parameters
......@@ -85,7 +88,6 @@
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++
......
tools/train/tinyYolov2/train.js
......@@ -47,35 +47,6 @@ function minimize(groundTruthBoxes, batchInput, inputSize, batch, { reshapedImgD
}, true)
}
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 }
}
async function trainStep(batchCreators, inputSizes, rescaleEveryNthBatch, onBatchProcessed = () => {}) {
async function step(currentBatchCreators) {
......
tools/train/tinyYolov2/verify.html
......@@ -34,11 +34,12 @@
<div class="row side-by-side">
<div class="row input-field" style="margin-right: 20px;">
<select id="sizeType">
<option value="" disabled selected>Input Size:</option>
<option value="xs">XS: 224 x 224</option>
<option value="sm">SM: 320 x 320</option>
<option value="md">MD: 416 x 416</option>
<option value="lg">LG: 608 x 608</option>
<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>
......@@ -67,7 +68,7 @@
<script>
let scoreThreshold = 0.5
let sizeType = 'sm'
let sizeType = 608
function onKeyDown(e) {
e.target.value = (
......@@ -113,7 +114,7 @@
canvas.height = height
const forwardParams = {
inputSize: sizeType,
inputSize: parseInt(sizeType),
scoreThreshold
}
......@@ -145,7 +146,7 @@
const startIdx608 = 16520
//const weightsUrl = `/tmp/tmp__224_${startIdx224}__320_${startIdx320}__416_${startIdx416}__608_${startIdx608}.weights`
const weightsUrl = `/tmp/overfit_count_1500.weights`
const weightsUrl = `/tmp/tmp_multiscale_count_4200.weights`
const weights = await loadNetWeights(weightsUrl)
window.net = new faceapi.TinyYolov2(true)
......
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