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Commit d5de3f5b by vincent
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test cases for FaceLandmark68Net and FaceLandmark68TinyNet

parent 4d3706fd
Showing with 421 additions and 83 deletions
test/data/faceLandmarkPositions1.json
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\ No newline at end of file
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\ No newline at end of file
test/data/faceLandmarkPositions1Tiny.json 0 → 100644
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\ No newline at end of file
test/data/faceLandmarkPositions2.json
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\ No newline at end of file
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\ No newline at end of file
test/data/faceLandmarkPositions2Tiny.json 0 → 100644
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test/data/faceLandmarkPositionsRect.json
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test/data/faceLandmarkPositionsRectTiny.json 0 → 100644
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test/data/facesFaceDescriptors.json
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\ No newline at end of file
test/tests/e2e/faceLandmarkNet.test.ts → test/tests/e2e/faceLandmark68Net.test.ts
import * as tf from '@tensorflow/tfjs-core';
import {
bufferToImage,
createFaceLandmarkNet,
Dimensions,
isTensor3D,
NetInput,
Point,
TMediaElement,
toNetInput,
} from '../../../src';
import { bufferToImage, Dimensions, isTensor3D, NetInput, Point, TMediaElement, toNetInput } from '../../../src';
import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
import { createFaceLandmarkNet } from '../../../src/faceLandmarkNet';
import { FaceLandmark68Net } from '../../../src/faceLandmarkNet/FaceLandmark68Net';
import { describeWithNets, expectAllTensorsReleased, expectMaxDelta } from '../../utils';
import { describeWithNets, expectAllTensorsReleased, expectMaxDelta, expectPointClose } from '../../utils';
function getInputDims (input: tf.Tensor | TMediaElement): Dimensions {
if (input instanceof tf.Tensor) {
......@@ -22,7 +14,7 @@ function getInputDims (input: tf.Tensor | TMediaElement): Dimensions {
return input
}
describe('faceLandmarkNet', () => {
describe('faceLandmark68Net', () => {
let imgEl1: HTMLImageElement
let imgEl2: HTMLImageElement
......@@ -43,71 +35,71 @@ describe('faceLandmarkNet', () => {
faceLandmarkPositionsRect = await (await fetch('base/test/data/faceLandmarkPositionsRect.json')).json()
})
describeWithNets('uncompressed weights', { withFaceLandmarkNet: { quantized: false } }, ({ faceLandmarkNet }) => {
describeWithNets('uncompressed weights', { withFaceLandmark68Net: { quantized: false } }, ({ faceLandmark68Net }) => {
it('computes face landmarks for squared input', async () => {
const { width, height } = imgEl1
const result = await faceLandmarkNet.detectLandmarks(imgEl1) as FaceLandmarks68
const result = await faceLandmark68Net.detectLandmarks(imgEl1) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositions1[i].x, 1)
expectMaxDelta(y, faceLandmarkPositions1[i].y, 1)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions1[i]
expectPointClose(pt, { x, y }, 1)
})
})
it('computes face landmarks for rectangular input', async () => {
const { width, height } = imgElRect
const result = await faceLandmarkNet.detectLandmarks(imgElRect) as FaceLandmarks68
const result = await faceLandmark68Net.detectLandmarks(imgElRect) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositionsRect[i].x, 2)
expectMaxDelta(y, faceLandmarkPositionsRect[i].y, 2)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositionsRect[i]
expectPointClose(pt, { x, y }, 2)
})
})
})
describeWithNets('quantized weights', { withFaceLandmarkNet: { quantized: true } }, ({ faceLandmarkNet }) => {
describeWithNets('quantized weights', { withFaceLandmark68Net: { quantized: true } }, ({ faceLandmark68Net }) => {
it('computes face landmarks for squared input', async () => {
const { width, height } = imgEl1
const result = await faceLandmarkNet.detectLandmarks(imgEl1) as FaceLandmarks68
const result = await faceLandmark68Net.detectLandmarks(imgEl1) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositions1[i].x, 2)
expectMaxDelta(y, faceLandmarkPositions1[i].y, 2)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions1[i]
expectPointClose(pt, { x, y }, 2)
})
})
it('computes face landmarks for rectangular input', async () => {
const { width, height } = imgElRect
const result = await faceLandmarkNet.detectLandmarks(imgElRect) as FaceLandmarks68
const result = await faceLandmark68Net.detectLandmarks(imgElRect) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositionsRect[i].x, 6)
expectMaxDelta(y, faceLandmarkPositionsRect[i].y, 6)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositionsRect[i]
expectPointClose(pt, { x, y }, 6)
})
})
})
describeWithNets('batch inputs', { withFaceLandmarkNet: { quantized: false } }, ({ faceLandmarkNet }) => {
describeWithNets('batch inputs', { withFaceLandmark68Net: { quantized: false } }, ({ faceLandmark68Net }) => {
it('computes face landmarks for batch of image elements', async () => {
const inputs = [imgEl1, imgEl2, imgElRect]
......@@ -118,7 +110,7 @@ describe('faceLandmarkNet', () => {
faceLandmarkPositionsRect
]
const results = await faceLandmarkNet.detectLandmarks(inputs) as FaceLandmarks68[]
const results = await faceLandmark68Net.detectLandmarks(inputs) as FaceLandmarks68[]
expect(Array.isArray(results)).toBe(true)
expect(results.length).toEqual(3)
results.forEach((result, batchIdx) => {
......@@ -143,7 +135,7 @@ describe('faceLandmarkNet', () => {
faceLandmarkPositionsRect
]
const results = await faceLandmarkNet.detectLandmarks(inputs) as FaceLandmarks68[]
const results = await faceLandmark68Net.detectLandmarks(inputs) as FaceLandmarks68[]
expect(Array.isArray(results)).toBe(true)
expect(results.length).toEqual(3)
results.forEach((result, batchIdx) => {
......@@ -153,8 +145,8 @@ describe('faceLandmarkNet', () => {
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 1)
expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 1)
expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 3)
expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 3)
})
})
})
......@@ -168,7 +160,7 @@ describe('faceLandmarkNet', () => {
faceLandmarkPositionsRect
]
const results = await faceLandmarkNet.detectLandmarks(inputs) as FaceLandmarks68[]
const results = await faceLandmark68Net.detectLandmarks(inputs) as FaceLandmarks68[]
expect(Array.isArray(results)).toBe(true)
expect(results.length).toEqual(3)
results.forEach((result, batchIdx) => {
......@@ -178,15 +170,15 @@ describe('faceLandmarkNet', () => {
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach(({ x, y }, i) => {
expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 1)
expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 1)
expectMaxDelta(x, faceLandmarkPositions[batchIdx][i].x, 3)
expectMaxDelta(y, faceLandmarkPositions[batchIdx][i].y, 3)
})
})
})
})
describeWithNets('no memory leaks', { withFaceLandmarkNet: { quantized: true } }, ({ faceLandmarkNet }) => {
describeWithNets('no memory leaks', { withFaceLandmark68Net: { quantized: true } }, ({ faceLandmark68Net }) => {
describe('NeuralNetwork, uncompressed model', () => {
......@@ -218,7 +210,7 @@ describe('faceLandmarkNet', () => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([imgEl1])
const outTensor = await faceLandmarkNet.forwardInput(netInput)
const outTensor = await faceLandmark68Net.forwardInput(netInput)
outTensor.dispose()
})
})
......@@ -226,7 +218,7 @@ describe('faceLandmarkNet', () => {
it('multiple image elements', async () => {
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([imgEl1, imgEl1, imgEl1])
const outTensor = await faceLandmarkNet.forwardInput(netInput)
const outTensor = await faceLandmark68Net.forwardInput(netInput)
outTensor.dispose()
})
})
......@@ -236,7 +228,7 @@ describe('faceLandmarkNet', () => {
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor])
const outTensor = await faceLandmarkNet.forwardInput(netInput)
const outTensor = await faceLandmark68Net.forwardInput(netInput)
outTensor.dispose()
})
......@@ -248,7 +240,7 @@ describe('faceLandmarkNet', () => {
await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors)
const outTensor = await faceLandmarkNet.forwardInput(netInput)
const outTensor = await faceLandmark68Net.forwardInput(netInput)
outTensor.dispose()
})
......@@ -259,7 +251,7 @@ describe('faceLandmarkNet', () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmarkNet.forwardInput(await toNetInput(tensor))
const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensor))
outTensor.dispose()
})
......@@ -271,7 +263,7 @@ describe('faceLandmarkNet', () => {
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmarkNet.forwardInput(await toNetInput(tensors))
const outTensor = await faceLandmark68Net.forwardInput(await toNetInput(tensors))
outTensor.dispose()
})
......@@ -284,13 +276,13 @@ describe('faceLandmarkNet', () => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks(imgEl1)
await faceLandmark68Net.detectLandmarks(imgEl1)
})
})
it('multiple image elements', async () => {
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks([imgEl1, imgEl1, imgEl1])
await faceLandmark68Net.detectLandmarks([imgEl1, imgEl1, imgEl1])
})
})
......@@ -298,7 +290,7 @@ describe('faceLandmarkNet', () => {
const tensor = tf.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks(tensor)
await faceLandmark68Net.detectLandmarks(tensor)
})
tensor.dispose()
......@@ -309,7 +301,7 @@ describe('faceLandmarkNet', () => {
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks(tensors)
await faceLandmark68Net.detectLandmarks(tensors)
})
tensors.forEach(t => t.dispose())
......@@ -319,7 +311,7 @@ describe('faceLandmarkNet', () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks(tensor)
await faceLandmark68Net.detectLandmarks(tensor)
})
tensor.dispose()
......@@ -330,7 +322,7 @@ describe('faceLandmarkNet', () => {
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks(tensors)
await faceLandmark68Net.detectLandmarks(tensors)
})
tensors.forEach(t => t.dispose())
......
test/tests/e2e/faceLandmark68TinyNet.test.ts 0 → 100644
import * as tf from '@tensorflow/tfjs-core';
import { bufferToImage, Dimensions, isTensor3D, NetInput, Point, TMediaElement, toNetInput } from '../../../src';
import { FaceLandmarks68 } from '../../../src/classes/FaceLandmarks68';
import { createFaceLandmarkNet } from '../../../src/faceLandmarkNet';
import { FaceLandmark68TinyNet } from '../../../src/faceLandmarkNet/FaceLandmark68TinyNet';
import { describeWithNets, expectAllTensorsReleased, expectMaxDelta, expectPointClose } from '../../utils';
function getInputDims (input: tf.Tensor | TMediaElement): Dimensions {
if (input instanceof tf.Tensor) {
const [height, width] = input.shape.slice(isTensor3D(input) ? 0 : 1)
return { width, height }
}
return input
}
describe('faceLandmark68TinyNet', () => {
let imgEl1: HTMLImageElement
let imgEl2: HTMLImageElement
let imgElRect: HTMLImageElement
let faceLandmarkPositions1: Point[]
let faceLandmarkPositions2: Point[]
let faceLandmarkPositionsRect: Point[]
beforeAll(async () => {
const img1 = await (await fetch('base/test/images/face1.png')).blob()
imgEl1 = await bufferToImage(img1)
const img2 = await (await fetch('base/test/images/face2.png')).blob()
imgEl2 = await bufferToImage(img2)
const imgRect = await (await fetch('base/test/images/face_rectangular.png')).blob()
imgElRect = await bufferToImage(imgRect)
faceLandmarkPositions1 = await (await fetch('base/test/data/faceLandmarkPositions1Tiny.json')).json()
faceLandmarkPositions2 = await (await fetch('base/test/data/faceLandmarkPositions2Tiny.json')).json()
faceLandmarkPositionsRect = await (await fetch('base/test/data/faceLandmarkPositionsRectTiny.json')).json()
})
describeWithNets('uncompressed weights', { withFaceLandmark68TinyNet: { quantized: false } }, ({ faceLandmark68TinyNet }) => {
it('computes face landmarks for squared input', async () => {
const { width, height } = imgEl1
const result = await faceLandmark68TinyNet.detectLandmarks(imgEl1) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions1[i]
expectPointClose(pt, { x, y }, 5)
})
})
it('computes face landmarks for rectangular input', async () => {
const { width, height } = imgElRect
const result = await faceLandmark68TinyNet.detectLandmarks(imgElRect) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositionsRect[i]
expectPointClose(pt, { x, y }, 5)
})
})
})
describeWithNets('quantized weights', { withFaceLandmark68TinyNet: { quantized: true } }, ({ faceLandmark68TinyNet }) => {
it('computes face landmarks for squared input', async () => {
const { width, height } = imgEl1
const result = await faceLandmark68TinyNet.detectLandmarks(imgEl1) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions1[i]
expectPointClose(pt, { x, y }, 5)
})
})
it('computes face landmarks for rectangular input', async () => {
const { width, height } = imgElRect
const result = await faceLandmark68TinyNet.detectLandmarks(imgElRect) as FaceLandmarks68
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositionsRect[i]
expectPointClose(pt, { x, y }, 5)
})
})
})
describeWithNets('batch inputs', { withFaceLandmark68TinyNet: { quantized: false } }, ({ faceLandmark68TinyNet }) => {
it('computes face landmarks for batch of image elements', async () => {
const inputs = [imgEl1, imgEl2, imgElRect]
const faceLandmarkPositions = [
faceLandmarkPositions1,
faceLandmarkPositions2,
faceLandmarkPositionsRect
]
const results = await faceLandmark68TinyNet.detectLandmarks(inputs) as FaceLandmarks68[]
expect(Array.isArray(results)).toBe(true)
expect(results.length).toEqual(3)
results.forEach((result, batchIdx) => {
const { width, height } = getInputDims(inputs[batchIdx])
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions[batchIdx][i]
expectPointClose(pt, { x, y }, 5)
})
})
})
it('computes face landmarks for batch of tf.Tensor3D', async () => {
const inputs = [imgEl1, imgEl2, imgElRect].map(el => tf.fromPixels(el))
const faceLandmarkPositions = [
faceLandmarkPositions1,
faceLandmarkPositions2,
faceLandmarkPositionsRect
]
const results = await faceLandmark68TinyNet.detectLandmarks(inputs) as FaceLandmarks68[]
expect(Array.isArray(results)).toBe(true)
expect(results.length).toEqual(3)
results.forEach((result, batchIdx) => {
const { width, height } = getInputDims(inputs[batchIdx])
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions[batchIdx][i]
expectPointClose(pt, { x, y }, 3)
})
})
})
it('computes face landmarks for batch of mixed inputs', async () => {
const inputs = [imgEl1, tf.fromPixels(imgEl2), tf.fromPixels(imgElRect)]
const faceLandmarkPositions = [
faceLandmarkPositions1,
faceLandmarkPositions2,
faceLandmarkPositionsRect
]
const results = await faceLandmark68TinyNet.detectLandmarks(inputs) as FaceLandmarks68[]
expect(Array.isArray(results)).toBe(true)
expect(results.length).toEqual(3)
results.forEach((result, batchIdx) => {
const { width, height } = getInputDims(inputs[batchIdx])
expect(result.getImageWidth()).toEqual(width)
expect(result.getImageHeight()).toEqual(height)
expect(result.getShift().x).toEqual(0)
expect(result.getShift().y).toEqual(0)
result.getPositions().forEach((pt, i) => {
const { x, y } = faceLandmarkPositions[batchIdx][i]
expectPointClose(pt, { x, y }, 3)
})
})
})
})
describeWithNets('no memory leaks', { withFaceLandmark68TinyNet: { quantized: true } }, ({ faceLandmark68TinyNet }) => {
describe('NeuralNetwork, uncompressed model', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const res = await fetch('base/weights_uncompressed/face_landmark_68_model.weights')
const weights = new Float32Array(await res.arrayBuffer())
const net = createFaceLandmarkNet(weights)
net.dispose()
})
})
})
describe('NeuralNetwork, quantized model', () => {
it('disposes all param tensors', async () => {
await expectAllTensorsReleased(async () => {
const net = new FaceLandmark68TinyNet()
await net.load('base/weights')
net.dispose()
})
})
})
describe('forwardInput', () => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([imgEl1])
const outTensor = await faceLandmark68TinyNet.forwardInput(netInput)
outTensor.dispose()
})
})
it('multiple image elements', async () => {
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([imgEl1, imgEl1, imgEl1])
const outTensor = await faceLandmark68TinyNet.forwardInput(netInput)
outTensor.dispose()
})
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
const netInput = new NetInput([tensor])
const outTensor = await faceLandmark68TinyNet.forwardInput(netInput)
outTensor.dispose()
})
tensor.dispose()
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
await expectAllTensorsReleased(async () => {
const netInput = new NetInput(tensors)
const outTensor = await faceLandmark68TinyNet.forwardInput(netInput)
outTensor.dispose()
})
tensors.forEach(t => t.dispose())
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensor))
outTensor.dispose()
})
tensor.dispose()
})
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const outTensor = await faceLandmark68TinyNet.forwardInput(await toNetInput(tensors))
outTensor.dispose()
})
tensors.forEach(t => t.dispose())
})
})
describe('detectLandmarks', () => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(imgEl1)
})
})
it('multiple image elements', async () => {
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks([imgEl1, imgEl1, imgEl1])
})
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensor)
})
tensor.dispose()
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensors)
})
tensors.forEach(t => t.dispose())
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl1).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensor)
})
tensor.dispose()
})
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
await faceLandmark68TinyNet.detectLandmarks(tensors)
})
tensors.forEach(t => t.dispose())
})
})
})
})
test/utils.ts
import * as tf from '@tensorflow/tfjs-core';
import {
FaceDetectionNet,
FaceLandmarkNet,
FaceRecognitionNet,
IPoint,
IRect,
Mtcnn,
NeuralNetwork,
TinyYolov2,
} from '../src/';
import { FaceDetectionNet, FaceRecognitionNet, IPoint, IRect, Mtcnn, NeuralNetwork, TinyYolov2 } from '../src/';
import { allFacesMtcnnFactory, allFacesSsdMobilenetv1Factory, allFacesTinyYolov2Factory } from '../src/allFacesFactory';
import { FaceDetection } from '../src/classes/FaceDetection';
import { FaceLandmarks } from '../src/classes/FaceLandmarks';
import { FullFaceDescription } from '../src/classes/FullFaceDescription';
import { FaceLandmark68Net } from '../src/faceLandmarkNet/FaceLandmark68Net';
import { FaceLandmark68TinyNet } from '../src/faceLandmarkNet/FaceLandmark68TinyNet';
import { allFacesMtcnnFunction, allFacesSsdMobilenetv1Function, allFacesTinyYolov2Function } from '../src/globalApi';
jasmine.DEFAULT_TIMEOUT_INTERVAL = 60000
......@@ -106,7 +99,8 @@ export type InjectNetArgs = {
allFacesTinyYolov2: allFacesTinyYolov2Function
allFacesMtcnn: allFacesMtcnnFunction
faceDetectionNet: FaceDetectionNet
faceLandmarkNet: FaceLandmarkNet
faceLandmark68Net: FaceLandmark68Net
faceLandmark68TinyNet: FaceLandmark68TinyNet
faceRecognitionNet: FaceRecognitionNet
mtcnn: Mtcnn
tinyYolov2: TinyYolov2
......@@ -118,7 +112,8 @@ export type DescribeWithNetsOptions = {
withAllFacesTinyYolov2?: boolean
withAllFacesMtcnn?: boolean
withFaceDetectionNet?: WithNetOptions
withFaceLandmarkNet?: WithNetOptions
withFaceLandmark68Net?: WithNetOptions
withFaceLandmark68TinyNet?: WithNetOptions
withFaceRecognitionNet?: WithNetOptions
withMtcnn?: WithNetOptions
withTinyYolov2?: WithTinyYolov2Options
......@@ -148,12 +143,13 @@ export function describeWithNets(
describe(description, () => {
let faceDetectionNet: FaceDetectionNet = new FaceDetectionNet()
let faceLandmarkNet: FaceLandmarkNet = new FaceLandmarkNet()
let faceLandmark68Net: FaceLandmark68Net = new FaceLandmark68Net()
let faceLandmark68TinyNet: FaceLandmark68TinyNet = new FaceLandmark68TinyNet()
let faceRecognitionNet: FaceRecognitionNet = new FaceRecognitionNet()
let mtcnn: Mtcnn = new Mtcnn()
let tinyYolov2: TinyYolov2 = new TinyYolov2(options.withTinyYolov2 && options.withTinyYolov2.withSeparableConv)
let allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(faceDetectionNet, faceLandmarkNet, faceRecognitionNet)
let allFacesTinyYolov2 = allFacesTinyYolov2Factory(tinyYolov2, faceLandmarkNet, faceRecognitionNet)
let allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1Factory(faceDetectionNet, faceLandmark68Net, faceRecognitionNet)
let allFacesTinyYolov2 = allFacesTinyYolov2Factory(tinyYolov2, faceLandmark68Net, faceRecognitionNet)
let allFacesMtcnn = allFacesMtcnnFactory(mtcnn, faceRecognitionNet)
beforeAll(async () => {
......@@ -162,7 +158,8 @@ export function describeWithNets(
withAllFacesTinyYolov2,
withAllFacesMtcnn,
withFaceDetectionNet,
withFaceLandmarkNet,
withFaceLandmark68Net,
withFaceLandmark68TinyNet,
withFaceRecognitionNet,
withMtcnn,
withTinyYolov2
......@@ -175,10 +172,17 @@ export function describeWithNets(
)
}
if (withFaceLandmarkNet || withAllFacesSsdMobilenetv1 || withAllFacesTinyYolov2) {
await initNet<FaceLandmarkNet>(
faceLandmarkNet,
!!withFaceLandmarkNet && !withFaceLandmarkNet.quantized && 'face_landmark_68_model.weights'
if (withFaceLandmark68Net || withAllFacesSsdMobilenetv1 || withAllFacesTinyYolov2) {
await initNet<FaceLandmark68Net>(
faceLandmark68Net,
!!withFaceLandmark68Net && !withFaceLandmark68Net.quantized && 'face_landmark_68_model.weights'
)
}
if (withFaceLandmark68TinyNet) {
await initNet<FaceLandmark68TinyNet>(
faceLandmark68TinyNet,
!!withFaceLandmark68TinyNet && !withFaceLandmark68TinyNet.quantized && 'face_landmark_68_tiny_model.weights'
)
}
......@@ -208,7 +212,7 @@ export function describeWithNets(
afterAll(() => {
faceDetectionNet && faceDetectionNet.dispose()
faceLandmarkNet && faceLandmarkNet.dispose()
faceLandmark68Net && faceLandmark68Net.dispose()
faceRecognitionNet && faceRecognitionNet.dispose()
mtcnn && mtcnn.dispose(),
tinyYolov2 && tinyYolov2.dispose()
......@@ -219,7 +223,8 @@ export function describeWithNets(
allFacesTinyYolov2,
allFacesMtcnn,
faceDetectionNet,
faceLandmarkNet,
faceLandmark68Net,
faceLandmark68TinyNet,
faceRecognitionNet,
mtcnn,
tinyYolov2
......
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