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Unverified Commit 123df714 by Vincent Mühler Committed by GitHub
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Merge pull request #246 from javyxx/master

Update to tensorflowjs 1.0.1
parents 21ce7136 4c7fb27f
Showing with 233 additions and 346 deletions
examples/examples-browser/package-lock.json
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package-lock.json
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......@@ -148,25 +66,25 @@
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"resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.1.2.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/node-fetch/-/node-fetch-2.1.2.tgz",
"integrity": "sha1-q4hOjn5X44qUR1POxwb3iNF2i7U=",
"dev": true
}
}
},
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"dev": true
},
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"resolved": "https://registry.npmjs.org/@tensorflow/tfjs-node/-/tfjs-node-0.2.3.tgz",
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"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/@tensorflow/tfjs-node/-/tfjs-node-1.0.1.tgz",
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"dev": true,
"requires": {
"@tensorflow/tfjs": "~0.14.2",
"@tensorflow/tfjs": "~1.0.1",
"adm-zip": "^0.4.11",
"bindings": "~1.3.0",
"https-proxy-agent": "^2.2.1",
......@@ -188,12 +106,6 @@
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"dev": true
},
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"resolved": "https://registry.npmjs.org/@types/long/-/long-3.0.32.tgz",
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"dev": true
},
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"version": "10.12.18",
"resolved": "https://registry.npmjs.org/@types/node/-/node-10.12.18.tgz",
......@@ -201,9 +113,9 @@
"dev": true
},
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"dev": true,
"requires": {
"@types/node": "*"
......@@ -248,7 +160,7 @@
},
"adm-zip": {
"version": "0.4.13",
"resolved": "https://registry.npmjs.org/adm-zip/-/adm-zip-0.4.13.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/adm-zip/-/adm-zip-0.4.13.tgz",
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"dev": true
},
......@@ -260,7 +172,7 @@
},
"agent-base": {
"version": "4.2.1",
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"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/agent-base/-/agent-base-4.2.1.tgz",
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"dev": true,
"requires": {
......@@ -567,7 +479,7 @@
},
"bindings": {
"version": "1.3.1",
"resolved": "https://registry.npmjs.org/bindings/-/bindings-1.3.1.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/bindings/-/bindings-1.3.1.tgz",
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"dev": true
},
......@@ -1473,14 +1385,14 @@
}
},
"es6-promise": {
"version": "4.2.5",
"resolved": "https://registry.npmjs.org/es6-promise/-/es6-promise-4.2.5.tgz",
"integrity": "sha512-n6wvpdE43VFtJq+lUDYDBFUwV8TZbuGXLV4D6wKafg13ldznKsyEvatubnmUe31zcvelSzOHF+XbaT+Bl9ObDg==",
"version": "4.2.6",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/es6-promise/-/es6-promise-4.2.6.tgz",
"integrity": "sha512-aRVgGdnmW2OiySVPUC9e6m+plolMAJKjZnQlCwNSuK5yQ0JN61DZSO1X1Ufd1foqWRAlig0rhduTCHe7sVtK5Q==",
"dev": true
},
"es6-promisify": {
"version": "5.0.0",
"resolved": "https://registry.npmjs.org/es6-promisify/-/es6-promisify-5.0.0.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/es6-promisify/-/es6-promisify-5.0.0.tgz",
"integrity": "sha1-UQnWLz5W6pZ8S2NQWu8IKRyKUgM=",
"dev": true,
"requires": {
......@@ -2683,7 +2595,7 @@
},
"https-proxy-agent": {
"version": "2.2.1",
"resolved": "https://registry.npmjs.org/https-proxy-agent/-/https-proxy-agent-2.2.1.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/https-proxy-agent/-/https-proxy-agent-2.2.1.tgz",
"integrity": "sha512-HPCTS1LW51bcyMYbxUIOO4HEOlQ1/1qRaFWcyxvwaqUS9TY88aoEuHUY33kuAh1YhVVaDQhLZsnPd+XNARWZlQ==",
"dev": true,
"requires": {
......@@ -2693,7 +2605,7 @@
"dependencies": {
"debug": {
"version": "3.2.6",
"resolved": "https://registry.npmjs.org/debug/-/debug-3.2.6.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/debug/-/debug-3.2.6.tgz",
"integrity": "sha512-mel+jf7nrtEl5Pn1Qx46zARXKDpBbvzezse7p7LqINmdoIk8PYP5SySaxEmYv6TZ0JyEKA1hsCId6DIhgITtWQ==",
"dev": true,
"requires": {
......@@ -2702,7 +2614,7 @@
},
"ms": {
"version": "2.1.1",
"resolved": "https://registry.npmjs.org/ms/-/ms-2.1.1.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/ms/-/ms-2.1.1.tgz",
"integrity": "sha512-tgp+dl5cGk28utYktBsrFqA7HKgrhgPsg6Z/EfhWI4gl1Hwq8B/GmY/0oXZ6nF8hDVesS/FpnYaD/kOWhYQvyg==",
"dev": true
}
......@@ -3429,12 +3341,6 @@
}
}
},
"long": {
"version": "4.0.0",
"resolved": "https://registry.npmjs.org/long/-/long-4.0.0.tgz",
"integrity": "sha512-XsP+KhQif4bjX1kbuSiySJFNAehNxgLb6hPRGJ9QsUr8ajHkuXGdrHmFUTUUXhDwVX2R5bY4JNZEwbUiMhV+MA==",
"dev": true
},
"loud-rejection": {
"version": "1.6.0",
"resolved": "https://registry.npmjs.org/loud-rejection/-/loud-rejection-1.6.0.tgz",
......@@ -3735,7 +3641,7 @@
},
"node-fetch": {
"version": "2.3.0",
"resolved": "https://registry.npmjs.org/node-fetch/-/node-fetch-2.3.0.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/node-fetch/-/node-fetch-2.3.0.tgz",
"integrity": "sha512-MOd8pV3fxENbryESLgVIeaGKrdl+uaYhCSSVkjeOb/31/njTpcis5aWfdqgNlHIrKOLRbMnfPINPOML2CIFeXA==",
"dev": true
},
......@@ -4241,39 +4147,10 @@
},
"progress": {
"version": "2.0.3",
"resolved": "https://registry.npmjs.org/progress/-/progress-2.0.3.tgz",
"resolved": "https://tclar30.es.telefonica/artifacts/repo/npm/progress/-/progress-2.0.3.tgz",
"integrity": "sha512-7PiHtLll5LdnKIMw100I+8xJXR5gW2QwWYkT6iJva0bXitZKa/XMrSbdmg3r2Xnaidz9Qumd0VPaMrZlF9V9sA==",
"dev": true
},
"protobufjs": {
"version": "6.8.8",
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"integrity": "sha512-AAmHtD5pXgZfi7GMpllpO3q1Xw1OYldr+dMUlAnffGTAhqkg72WdmSY71uKBF/JuyiKs8psYbtKrhi0ASCD8qw==",
"dev": true,
"requires": {
"@protobufjs/aspromise": "^1.1.2",
"@protobufjs/base64": "^1.1.2",
"@protobufjs/codegen": "^2.0.4",
"@protobufjs/eventemitter": "^1.1.0",
"@protobufjs/fetch": "^1.1.0",
"@protobufjs/float": "^1.0.2",
"@protobufjs/inquire": "^1.1.0",
"@protobufjs/path": "^1.1.2",
"@protobufjs/pool": "^1.1.0",
"@protobufjs/utf8": "^1.1.0",
"@types/long": "^4.0.0",
"@types/node": "^10.1.0",
"long": "^4.0.0"
},
"dependencies": {
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"dev": true
}
}
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......@@ -5363,11 +5240,11 @@
}
},
"tfjs-image-recognition-base": {
"version": "0.4.1",
"resolved": "https://registry.npmjs.org/tfjs-image-recognition-base/-/tfjs-image-recognition-base-0.4.1.tgz",
"integrity": "sha512-oJOLByZa0OgPaLB17ZlL2qO8dIQwgV1ma4/iEFbzVbF8yd0qOomIuEGvyPzXDfD0dmyPwzj8Ysa3Jq+O375Fpw==",
"version": "0.5.0",
"resolved": "https://registry.npmjs.org/tfjs-image-recognition-base/-/tfjs-image-recognition-base-0.5.0.tgz",
"integrity": "sha512-u/BPhsoieBm8bE3QXd5WdKmo/CKXoeab0x4x11yyg7O24esUM9RqdX9dI5Byw8jcjjrb705izSSznqXr61GTHg==",
"requires": {
"@tensorflow/tfjs-core": "0.14.2",
"@tensorflow/tfjs-core": "1.0.1",
"tslib": "^1.9.3"
}
},
......
package.json
......@@ -35,12 +35,12 @@
"author": "justadudewhohacks",
"license": "MIT",
"dependencies": {
"@tensorflow/tfjs-core": "0.14.2",
"tfjs-image-recognition-base": "^0.4.1",
"@tensorflow/tfjs-core": "1.0.1",
"tfjs-image-recognition-base": "^0.5.0",
"tslib": "^1.9.3"
},
"devDependencies": {
"@tensorflow/tfjs-node": "^0.2.3",
"@tensorflow/tfjs-node": "^1.0.1",
"@types/jasmine": "^3.3.8",
"@types/node": "^10.12.18",
"canvas": "2.0.1",
......
src/mtcnn/Mtcnn.ts
......@@ -45,7 +45,7 @@ export class Mtcnn extends NeuralNetwork<NetParams> {
const imgTensor = tf.tidy(() =>
bgrToRgbTensor(
tf.expandDims(tf.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
tf.expandDims(tf.browser.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
)
)
......
src/mtcnn/stage1.ts
......@@ -28,9 +28,10 @@ function extractBoundingBoxes(
// TODO: fix this!, maybe better to use tf.gather here
const indices: Point[] = []
const scoresData = scoresTensor.arraySync();
for (let y = 0; y < scoresTensor.shape[0]; y++) {
for (let x = 0; x < scoresTensor.shape[1]; x++) {
if (scoresTensor.get(y, x) >= scoreThreshold) {
if (scoresData[y][x] >= scoreThreshold) {
indices.push(new Point(x, y))
}
}
......@@ -44,13 +45,14 @@ function extractBoundingBoxes(
Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)
)
const score = scoresTensor.get(idx.y, idx.x)
const score = scoresData[idx.y][idx.x]
const regionsData = regionsTensor.arraySync()
const region = new MtcnnBox(
regionsTensor.get(idx.y, idx.x, 0),
regionsTensor.get(idx.y, idx.x, 1),
regionsTensor.get(idx.y, idx.x, 2),
regionsTensor.get(idx.y, idx.x, 3)
regionsData[idx.y][idx.x][0],
regionsData[idx.y][idx.x][1],
regionsData[idx.y][idx.x][2],
regionsData[idx.y][idx.x][3]
)
return {
......
src/mtcnn/stage2.ts
......@@ -54,13 +54,15 @@ export async function stage2(
)
stats.stage2_nms = Date.now() - ts
const regions = indicesNms.map(idx =>
new MtcnnBox(
rnetOuts[indices[idx]].regions.get(0, 0),
rnetOuts[indices[idx]].regions.get(0, 1),
rnetOuts[indices[idx]].regions.get(0, 2),
rnetOuts[indices[idx]].regions.get(0, 3)
)
const regions = indicesNms.map(idx =>{
const regionsData = rnetOuts[indices[idx]].regions.arraySync()
return new MtcnnBox(
regionsData[0][0],
regionsData[0][1],
regionsData[0][2],
regionsData[0][3]
)
}
)
finalScores = indicesNms.map(idx => filteredScores[idx])
......
src/mtcnn/stage3.ts
......@@ -39,12 +39,14 @@ export async function stage3(
.filter(c => c.score > scoreThreshold)
.map(({ idx }) => idx)
const filteredRegions = indices.map(idx => new MtcnnBox(
onetOuts[idx].regions.get(0, 0),
onetOuts[idx].regions.get(0, 1),
onetOuts[idx].regions.get(0, 2),
onetOuts[idx].regions.get(0, 3)
))
const filteredRegions = indices.map(idx => {
const regionsData = onetOuts[idx].regions.arraySync();
return new MtcnnBox(
regionsData[0][0],
regionsData[0][1],
regionsData[0][2],
regionsData[0][3]
)})
const filteredBoxes = indices
.map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))
const filteredScores = indices.map(idx => scores[idx])
......@@ -67,11 +69,13 @@ export async function stage3(
finalBoxes = indicesNms.map(idx => filteredBoxes[idx])
finalScores = indicesNms.map(idx => filteredScores[idx])
points = indicesNms.map((idx, i) =>
Array(5).fill(0).map((_, ptIdx) =>
new Point(
((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,
((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top)
)
Array(5).fill(0).map((_, ptIdx) =>{
const pointsData = onetOuts[idx].points.arraySync()
return new Point(
((pointsData[0][ptIdx] * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,
((pointsData[0][ptIdx+5] * (finalBoxes[i].height + 1)) + finalBoxes[i].top)
)
}
)
)
}
......
src/ssdMobilenetv1/SsdMobilenetv1.ts
......@@ -85,15 +85,16 @@ export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
const padX = inputSize / reshapedDims.width
const padY = inputSize / reshapedDims.height
const boxesData = boxes.arraySync()
const results = indices
.map(idx => {
const [top, bottom] = [
Math.max(0, boxes.get(idx, 0)),
Math.min(1.0, boxes.get(idx, 2))
Math.max(0, boxesData[idx][0]),
Math.min(1.0, boxesData[idx][2])
].map(val => val * padY)
const [left, right] = [
Math.max(0, boxes.get(idx, 1)),
Math.min(1.0, boxes.get(idx, 3))
Math.max(0, boxesData[idx][1]),
Math.min(1.0, boxesData[idx][3])
].map(val => val * padX)
return new FaceDetection(
scoresData[idx],
......
src/ssdMobilenetv1/mobileNetV1.ts
......@@ -13,13 +13,13 @@ function depthwiseConvLayer(
return tf.tidy(() => {
let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')
out = tf.batchNormalization<tf.Rank.R4>(
out = tf.batchNorm<tf.Rank.R4>(
out,
params.batch_norm_mean,
params.batch_norm_variance,
epsilon,
params.batch_norm_offset,
params.batch_norm_scale,
params.batch_norm_offset
epsilon
)
return tf.clipByValue(out, 0, 6)
......
src/ssdMobilenetv1/nonMaxSuppression.ts
......@@ -49,14 +49,15 @@ export function nonMaxSuppression(
}
function IOU(boxes: tf.Tensor2D, i: number, j: number) {
const yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2))
const xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3))
const ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2))
const xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3))
const yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2))
const xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3))
const ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2))
const xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3))
const boxesData = boxes.arraySync()
const yminI = Math.min(boxesData[i][0], boxesData[i][2])
const xminI = Math.min(boxesData[i][1], boxesData[i][3])
const ymaxI = Math.max(boxesData[i][0], boxesData[i][2])
const xmaxI = Math.max(boxesData[i][1], boxesData[i][3])
const yminJ = Math.min(boxesData[j][0], boxesData[j][2])
const xminJ = Math.min(boxesData[j][1], boxesData[j][3])
const ymaxJ = Math.max(boxesData[j][0], boxesData[j][2])
const xmaxJ = Math.max(boxesData[j][1], boxesData[j][3])
const areaI = (ymaxI - yminI) * (xmaxI - xminI)
const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)
if (areaI <= 0 || areaJ <= 0) {
......
test/tests/dom/extractFaceTensors.test.ts
......@@ -6,7 +6,7 @@ describe('extractFaceTensors', () => {
let imgTensor: tf.Tensor3D
beforeAll(async () => {
imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png')))
imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/face1.png')))
})
describe('extracts tensors', () => {
......
test/tests/faceExpressionNet/faceExpressionNet.test.ts
......@@ -57,7 +57,7 @@ describe('faceExpressionNet', () => {
})
it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgElAngry, imgElSurprised].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const inputs = [imgElAngry, imgElSurprised].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][]
expect(Array.isArray(results)).toBe(true)
......@@ -80,7 +80,7 @@ describe('faceExpressionNet', () => {
})
it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgElAngry, tf.fromPixels(createCanvasFromMedia(imgElSurprised))]
const inputs = [imgElAngry, tf.browser.fromPixels(createCanvasFromMedia(imgElSurprised))]
const results = await faceExpressionNet.predictExpressions(inputs) as FaceExpressionPrediction[][]
expect(Array.isArray(results)).toBe(true)
......@@ -125,7 +125,7 @@ describe('faceExpressionNet', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry))
const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgElAngry))
await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor))
......@@ -136,7 +136,7 @@ describe('faceExpressionNet', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors))
......@@ -147,7 +147,7 @@ describe('faceExpressionNet', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensor))
......@@ -159,7 +159,7 @@ describe('faceExpressionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const outTensor = await faceExpressionNet.forwardInput(await toNetInput(tensors))
......@@ -186,7 +186,7 @@ describe('faceExpressionNet', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgElAngry))
const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgElAngry))
await expectAllTensorsReleased(async () => {
await faceExpressionNet.predictExpressions(tensor)
......@@ -196,7 +196,7 @@ describe('faceExpressionNet', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const tensors = [imgElAngry, imgElAngry, imgElAngry].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => {
......@@ -207,7 +207,7 @@ describe('faceExpressionNet', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgElAngry)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await faceExpressionNet.predictExpressions(tensor)
......@@ -218,7 +218,7 @@ describe('faceExpressionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgElAngry, imgElAngry, imgElAngry]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
await faceExpressionNet.predictExpressions(tensors)
......
test/tests/faceLandmarkNet/faceLandmark68Net.test.ts
......@@ -92,7 +92,7 @@ describe('faceLandmark68Net', () => {
})
it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
const faceLandmarkPositions = [
faceLandmarkPositions1,
......@@ -117,7 +117,7 @@ describe('faceLandmark68Net', () => {
})
it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))]
const inputs = [imgEl1, tf.browser.fromPixels(createCanvasFromMedia(imgEl2)), tf.browser.fromPixels(createCanvasFromMedia(imgElRect))]
const faceLandmarkPositions = [
faceLandmarkPositions1,
......@@ -164,7 +164,7 @@ describe('faceLandmark68Net', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor])
......@@ -176,7 +176,7 @@ describe('faceLandmark68Net', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors)
......@@ -188,7 +188,7 @@ describe('faceLandmark68Net', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensor))
......@@ -200,7 +200,7 @@ describe('faceLandmark68Net', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensors))
......@@ -227,7 +227,7 @@ describe('faceLandmark68Net', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => {
await faceLandmark68Net.detectLandmarks(tensor)
......@@ -237,7 +237,7 @@ describe('faceLandmark68Net', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => {
......@@ -248,7 +248,7 @@ describe('faceLandmark68Net', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await faceLandmark68Net.detectLandmarks(tensor)
......@@ -259,7 +259,7 @@ describe('faceLandmark68Net', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
await faceLandmark68Net.detectLandmarks(tensors)
......
test/tests/faceLandmarkNet/faceLandmark68TinyNet.test.ts
......@@ -91,7 +91,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
const faceLandmarkPositions = [
faceLandmarkPositions1,
......@@ -116,7 +116,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(createCanvasFromMedia(imgEl2)), tf.fromPixels(createCanvasFromMedia(imgElRect))]
const inputs = [imgEl1, tf.browser.fromPixels(createCanvasFromMedia(imgEl2)), tf.browser.fromPixels(createCanvasFromMedia(imgElRect))]
const faceLandmarkPositions = [
faceLandmarkPositions1,
......@@ -164,7 +164,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor])
......@@ -176,7 +176,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors)
......@@ -188,7 +188,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensor))
......@@ -200,7 +200,7 @@ describe('faceLandmark68TinyNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensors))
......@@ -227,7 +227,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(createCanvasFromMedia(imgEl1))
const tensor = tf.browser.fromPixels(createCanvasFromMedia(imgEl1))
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensor)
......@@ -237,7 +237,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(createCanvasFromMedia(el)))
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(createCanvasFromMedia(el)))
await expectAllTensorsReleased(async () => {
......@@ -248,7 +248,7 @@ describe('faceLandmark68TinyNet', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(imgEl1)).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensor)
......@@ -259,7 +259,7 @@ describe('faceLandmark68TinyNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(createCanvasFromMedia(el)).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensors)
......
test/tests/faceRecognitionNet/faceRecognitionNet.test.ts
......@@ -60,7 +60,7 @@ describe('faceRecognitionNet', () => {
})
it('computes face descriptors for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el))
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.browser.fromPixels(el))
const faceDescriptors = [
faceDescriptor1,
......@@ -77,7 +77,7 @@ describe('faceRecognitionNet', () => {
})
it('computes face descriptors for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)]
const inputs = [imgEl1, tf.browser.fromPixels(imgEl2), tf.browser.fromPixels(imgElRect)]
const faceDescriptors = [
faceDescriptor1,
......@@ -116,7 +116,7 @@ describe('faceRecognitionNet', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1)
const tensor = tf.browser.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor])
......@@ -128,7 +128,7 @@ describe('faceRecognitionNet', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(el))
await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors)
......@@ -140,7 +140,7 @@ describe('faceRecognitionNet', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensor))
......@@ -152,7 +152,7 @@ describe('faceRecognitionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const outTensor = await faceRecognitionNet.forwardInput(await toNetInput(tensors))
......@@ -179,7 +179,7 @@ describe('faceRecognitionNet', () => {
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1)
const tensor = tf.browser.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
await faceRecognitionNet.computeFaceDescriptor(tensor)
......@@ -189,7 +189,7 @@ describe('faceRecognitionNet', () => {
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.browser.fromPixels(el))
await expectAllTensorsReleased(async () => {
......@@ -200,7 +200,7 @@ describe('faceRecognitionNet', () => {
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
const tensor = tf.tidy(() => tf.browser.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await faceRecognitionNet.computeFaceDescriptor(tensor)
......@@ -211,7 +211,7 @@ describe('faceRecognitionNet', () => {
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
.map(el => tf.tidy(() => tf.browser.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
await faceRecognitionNet.computeFaceDescriptor(tensors)
......
test/tests/ssdMobilenetv1/ssdMobilenetv1.node.test.ts
......@@ -15,7 +15,7 @@ describe('ssdMobilenetv1 - node', () => {
const expectedScores = [0.54, 0.81, 0.97, 0.88, 0.84, 0.61]
beforeAll(async () => {
imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedSsdBoxes)
})
......
test/tests/tinyFaceDetector/tinyFaceDetector.node.test.ts
......@@ -15,7 +15,7 @@ describe('tinyFaceDetector - node', () => {
const expectedScores = [0.7, 0.82, 0.93, 0.86, 0.79, 0.84]
beforeAll(async () => {
imgTensor = tf.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
imgTensor = tf.browser.fromPixels(createCanvasFromMedia(await loadImage('test/images/faces.jpg')))
expectedFullFaceDescriptions = await assembleExpectedFullFaceDescriptions(expectedTinyFaceDetectorBoxes)
})
......
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