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Unverified Commit 8ef9b662 by justadudewhohacks Committed by GitHub
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Merge pull request #37 from justadudewhohacks/some-fixes

Some fixes
parents d2e2d367 ae447fae
Showing with 242 additions and 91 deletions
build/NetInput.js
...@@ -22,6 +22,14 @@ var NetInput = /** @class */ (function () { ...@@ -22,6 +22,14 @@ var NetInput = /** @class */ (function () {
// TODO: make sure not to dispose original tensors passed in by the user // TODO: make sure not to dispose original tensors passed in by the user
return tf.clone(input); return tf.clone(input);
} }
if (isTensor_1.isTensor4D(input)) {
var shape = input.shape;
var batchSize = shape[0];
if (batchSize !== 1) {
throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
}
return input.reshape(shape.slice(1));
}
return tf.fromPixels(input instanceof HTMLCanvasElement ? input : utils_1.createCanvasFromMedia(input)); return tf.fromPixels(input instanceof HTMLCanvasElement ? input : utils_1.createCanvasFromMedia(input));
}); });
} }
......
build/NetInput.js.map
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\ No newline at end of file \ No newline at end of file
build/extractFaces.js
...@@ -31,6 +31,9 @@ function extractFaces(input, detections) { ...@@ -31,6 +31,9 @@ function extractFaces(input, detections) {
return [4 /*yield*/, utils_1.imageTensorToCanvas(netInput.inputs[0])]; return [4 /*yield*/, utils_1.imageTensorToCanvas(netInput.inputs[0])];
case 2: case 2:
canvas = _a.sent(); canvas = _a.sent();
if (netInput.isManaged) {
netInput.dispose();
}
_a.label = 3; _a.label = 3;
case 3: case 3:
ctx = utils_1.getContext2dOrThrow(canvas); ctx = utils_1.getContext2dOrThrow(canvas);
......
build/extractFaces.js.map
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\ No newline at end of file \ No newline at end of file
build/toNetInput.js
...@@ -37,26 +37,20 @@ function toNetInput(inputs, manageCreatedInput) { ...@@ -37,26 +37,20 @@ function toNetInput(inputs, manageCreatedInput) {
throw new Error('toNetInput - empty array passed as input'); throw new Error('toNetInput - empty array passed as input');
} }
getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; }; getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
inputArray = inputArgArray inputArray = inputArgArray.map(utils_1.resolveInput);
.map(utils_1.resolveInput) inputArray.forEach(function (input, i) {
.map(function (input, i) { if (!isMediaElement_1.isMediaElement(input) && !isTensor_1.isTensor3D(input) && !isTensor_1.isTensor4D(input)) {
if (typeof inputArgArray[i] === 'string') {
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
}
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
}
if (isTensor_1.isTensor4D(input)) { if (isTensor_1.isTensor4D(input)) {
// if tf.Tensor4D is passed in the input array, the batch size has to be 1 // if tf.Tensor4D is passed in the input array, the batch size has to be 1
var batchSize = input.shape[0]; var batchSize = input.shape[0];
if (batchSize !== 1) { if (batchSize !== 1) {
throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array"); throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
} }
// to tf.Tensor3D
return input.reshape(input.shape.slice(1));
}
return input;
});
inputArray.forEach(function (input, i) {
if (!isMediaElement_1.isMediaElement(input) && !isTensor_1.isTensor3D(input)) {
if (typeof inputArgArray[i] === 'string') {
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
}
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
} }
}); });
// wait for all media elements being loaded // wait for all media elements being loaded
......
build/toNetInput.js.map
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\ No newline at end of file \ No newline at end of file
build/utils.js
...@@ -106,15 +106,17 @@ function bufferToImage(buf) { ...@@ -106,15 +106,17 @@ function bufferToImage(buf) {
exports.bufferToImage = bufferToImage; exports.bufferToImage = bufferToImage;
function imageTensorToCanvas(imgTensor, canvas) { function imageTensorToCanvas(imgTensor, canvas) {
return tslib_1.__awaiter(this, void 0, void 0, function () { return tslib_1.__awaiter(this, void 0, void 0, function () {
var targetCanvas, _a, height, width, numChannels; var targetCanvas, _a, height, width, numChannels, imgTensor3D;
return tslib_1.__generator(this, function (_b) { return tslib_1.__generator(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
targetCanvas = canvas || document.createElement('canvas'); targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape.slice(isTensor_1.isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2]; _a = imgTensor.shape.slice(isTensor_1.isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
return [4 /*yield*/, tf.toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)]; imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
return [4 /*yield*/, tf.toPixels(imgTensor3D, targetCanvas)];
case 1: case 1:
_b.sent(); _b.sent();
imgTensor3D.dispose();
return [2 /*return*/, targetCanvas]; return [2 /*return*/, targetCanvas];
} }
}); });
......
build/utils.js.map
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\ No newline at end of file \ No newline at end of file
dist/face-api.js
...@@ -494,15 +494,17 @@ ...@@ -494,15 +494,17 @@
} }
function imageTensorToCanvas(imgTensor, canvas) { function imageTensorToCanvas(imgTensor, canvas) {
return __awaiter$1(this, void 0, void 0, function () { return __awaiter$1(this, void 0, void 0, function () {
var targetCanvas, _a, height, width, numChannels; var targetCanvas, _a, height, width, numChannels, imgTensor3D;
return __generator$1(this, function (_b) { return __generator$1(this, function (_b) {
switch (_b.label) { switch (_b.label) {
case 0: case 0:
targetCanvas = canvas || document.createElement('canvas'); targetCanvas = canvas || document.createElement('canvas');
_a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2]; _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
return [4 /*yield*/, toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas)]; imgTensor3D = tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
return [4 /*yield*/, toPixels(imgTensor3D, targetCanvas)];
case 1: case 1:
_b.sent(); _b.sent();
imgTensor3D.dispose();
return [2 /*return*/, targetCanvas]; return [2 /*return*/, targetCanvas];
} }
}); });
...@@ -526,6 +528,14 @@ ...@@ -526,6 +528,14 @@
// TODO: make sure not to dispose original tensors passed in by the user // TODO: make sure not to dispose original tensors passed in by the user
return clone(input); return clone(input);
} }
if (isTensor4D(input)) {
var shape = input.shape;
var batchSize = shape[0];
if (batchSize !== 1) {
throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
}
return input.reshape(shape.slice(1));
}
return fromPixels(input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input)); return fromPixels(input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input));
}); });
} }
...@@ -815,26 +825,20 @@ ...@@ -815,26 +825,20 @@
throw new Error('toNetInput - empty array passed as input'); throw new Error('toNetInput - empty array passed as input');
} }
getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; }; getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
inputArray = inputArgArray inputArray = inputArgArray.map(resolveInput);
.map(resolveInput) inputArray.forEach(function (input, i) {
.map(function (input, i) { if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
if (typeof inputArgArray[i] === 'string') {
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
}
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
}
if (isTensor4D(input)) { if (isTensor4D(input)) {
// if tf.Tensor4D is passed in the input array, the batch size has to be 1 // if tf.Tensor4D is passed in the input array, the batch size has to be 1
var batchSize = input.shape[0]; var batchSize = input.shape[0];
if (batchSize !== 1) { if (batchSize !== 1) {
throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array"); throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
} }
// to tf.Tensor3D
return input.reshape(input.shape.slice(1));
}
return input;
});
inputArray.forEach(function (input, i) {
if (!isMediaElement(input) && !isTensor3D(input)) {
if (typeof inputArgArray[i] === 'string') {
throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
}
throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
} }
}); });
// wait for all media elements being loaded // wait for all media elements being loaded
...@@ -875,6 +879,9 @@ ...@@ -875,6 +879,9 @@
return [4 /*yield*/, imageTensorToCanvas(netInput.inputs[0])]; return [4 /*yield*/, imageTensorToCanvas(netInput.inputs[0])];
case 2: case 2:
canvas = _a.sent(); canvas = _a.sent();
if (netInput.isManaged) {
netInput.dispose();
}
_a.label = 3; _a.label = 3;
case 3: case 3:
ctx = getContext2dOrThrow(canvas); ctx = getContext2dOrThrow(canvas);
......
dist/face-api.js.map
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dist/face-api.min.js
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src/NetInput.ts
...@@ -29,6 +29,16 @@ export class NetInput { ...@@ -29,6 +29,16 @@ export class NetInput {
return tf.clone(input as tf.Tensor3D) return tf.clone(input as tf.Tensor3D)
} }
if (isTensor4D(input)) {
const shape = (input as tf.Tensor4D).shape
const batchSize = shape[0]
if (batchSize !== 1) {
throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)
}
return (input as tf.Tensor4D).reshape(shape.slice(1) as [number, number, number]) as tf.Tensor3D
}
return tf.fromPixels( return tf.fromPixels(
input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input as HTMLImageElement | HTMLVideoElement) input instanceof HTMLCanvasElement ? input : createCanvasFromMedia(input as HTMLImageElement | HTMLVideoElement)
) )
......
src/extractFaces.ts
...@@ -3,6 +3,7 @@ import { Rect } from './Rect'; ...@@ -3,6 +3,7 @@ import { Rect } from './Rect';
import { toNetInput } from './toNetInput'; import { toNetInput } from './toNetInput';
import { TNetInput } from './types'; import { TNetInput } from './types';
import { createCanvas, getContext2dOrThrow, imageTensorToCanvas } from './utils'; import { createCanvas, getContext2dOrThrow, imageTensorToCanvas } from './utils';
import * as tf from '@tensorflow/tfjs-core';
/** /**
* Extracts the image regions containing the detected faces. * Extracts the image regions containing the detected faces.
...@@ -29,6 +30,10 @@ export async function extractFaces( ...@@ -29,6 +30,10 @@ export async function extractFaces(
} }
canvas = await imageTensorToCanvas(netInput.inputs[0]) canvas = await imageTensorToCanvas(netInput.inputs[0])
if (netInput.isManaged) {
netInput.dispose()
}
} }
const ctx = getContext2dOrThrow(canvas) const ctx = getContext2dOrThrow(canvas)
......
src/toNetInput.ts
...@@ -41,23 +41,10 @@ export async function toNetInput( ...@@ -41,23 +41,10 @@ export async function toNetInput(
const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : '' const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''
const inputArray = inputArgArray const inputArray = inputArgArray.map(resolveInput)
.map(resolveInput)
.map((input, i) => {
if (isTensor4D(input)) {
// if tf.Tensor4D is passed in the input array, the batch size has to be 1
const batchSize = input.shape[0]
if (batchSize !== 1) {
throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)
}
// to tf.Tensor3D
return input.reshape(input.shape.slice(1))
}
return input
})
inputArray.forEach((input, i) => { inputArray.forEach((input, i) => {
if (!isMediaElement(input) && !isTensor3D(input)) { if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
if (typeof inputArgArray[i] === 'string') { if (typeof inputArgArray[i] === 'string') {
throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`) throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)
...@@ -65,6 +52,14 @@ export async function toNetInput( ...@@ -65,6 +52,14 @@ export async function toNetInput(
throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`) throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)
} }
if (isTensor4D(input)) {
// if tf.Tensor4D is passed in the input array, the batch size has to be 1
const batchSize = input.shape[0]
if (batchSize !== 1) {
throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)
}
}
}) })
// wait for all media elements being loaded // wait for all media elements being loaded
......
src/utils.ts
...@@ -113,7 +113,10 @@ export async function imageTensorToCanvas( ...@@ -113,7 +113,10 @@ export async function imageTensorToCanvas(
const targetCanvas = canvas || document.createElement('canvas') const targetCanvas = canvas || document.createElement('canvas')
const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0) const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)
await tf.toPixels(imgTensor.as3D(height, width, numChannels).toInt(), targetCanvas) const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())
await tf.toPixels(imgTensor3D, targetCanvas)
imgTensor3D.dispose()
return targetCanvas return targetCanvas
} }
\ No newline at end of file
test/tests/NetInput.test.ts
...@@ -7,12 +7,11 @@ import { expectAllTensorsReleased, tensor3D } from '../utils'; ...@@ -7,12 +7,11 @@ import { expectAllTensorsReleased, tensor3D } from '../utils';
describe('NetInput', () => { describe('NetInput', () => {
let imgEl: HTMLImageElement, canvasEl: HTMLCanvasElement let imgEl: HTMLImageElement
beforeAll(async () => { beforeAll(async () => {
const img = await (await fetch('base/test/images/face1.png')).blob() const img = await (await fetch('base/test/images/face1.png')).blob()
imgEl = await bufferToImage(img) imgEl = await bufferToImage(img)
canvasEl = createCanvasFromMedia(imgEl)
}) })
describe('no memory leaks', () => { describe('no memory leaks', () => {
......
test/tests/e2e/faceLandmarkNet.test.ts
...@@ -5,8 +5,9 @@ import { isTensor3D } from '../../../src/commons/isTensor'; ...@@ -5,8 +5,9 @@ import { isTensor3D } from '../../../src/commons/isTensor';
import { FaceLandmarks } from '../../../src/faceLandmarkNet/FaceLandmarks'; import { FaceLandmarks } from '../../../src/faceLandmarkNet/FaceLandmarks';
import { Point } from '../../../src/Point'; import { Point } from '../../../src/Point';
import { Dimensions, TMediaElement } from '../../../src/types'; import { Dimensions, TMediaElement } from '../../../src/types';
import { expectMaxDelta, expectAllTensorsReleased } from '../../utils'; import { expectMaxDelta, expectAllTensorsReleased, tensor3D } from '../../utils';
import { NetInput } from '../../../src/NetInput'; import { NetInput } from '../../../src/NetInput';
import { toNetInput } from '../../../src';
function getInputDims (input: tf.Tensor | TMediaElement): Dimensions { function getInputDims (input: tf.Tensor | TMediaElement): Dimensions {
if (input instanceof tf.Tensor) { if (input instanceof tf.Tensor) {
...@@ -255,6 +256,53 @@ describe('faceLandmarkNet', () => { ...@@ -255,6 +256,53 @@ describe('faceLandmarkNet', () => {
}) })
}) })
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
const netInput = (new NetInput([tensor])).managed()
const outTensor = await faceLandmarkNet.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)).managed()
const outTensor = await faceLandmarkNet.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 faceLandmarkNet.forwardInput(await toNetInput(tensor, true))
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 faceLandmarkNet.forwardInput(await toNetInput(tensors, true))
outTensor.dispose()
})
tensors.forEach(t => t.dispose())
})
}) })
describe('detectLandmarks', () => { describe('detectLandmarks', () => {
...@@ -271,6 +319,48 @@ describe('faceLandmarkNet', () => { ...@@ -271,6 +319,48 @@ describe('faceLandmarkNet', () => {
}) })
}) })
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl1)
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.detectLandmarks(tensor)
})
tensor.dispose()
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl1, imgEl1, imgEl1].map(el => tf.fromPixels(el))
await expectAllTensorsReleased(async () => {
await faceLandmarkNet.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 faceLandmarkNet.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 faceLandmarkNet.detectLandmarks(tensors)
})
tensors.forEach(t => t.dispose())
})
}) })
}) })
......
test/tests/toNetInput.test.ts
import { tf } from '../../src';
import { NetInput } from '../../src/NetInput'; import { NetInput } from '../../src/NetInput';
import { toNetInput } from '../../src/toNetInput'; import { toNetInput } from '../../src/toNetInput';
import { bufferToImage, createCanvasFromMedia } from '../../src/utils'; import { bufferToImage, createCanvasFromMedia } from '../../src/utils';
import { createFakeHTMLVideoElement } from '../utils'; import { expectAllTensorsReleased } from '../utils';
describe('toNetInput', () => { describe('toNetInput', () => {
...@@ -21,13 +22,6 @@ describe('toNetInput', () => { ...@@ -21,13 +22,6 @@ describe('toNetInput', () => {
expect(netInput.batchSize).toEqual(1) expect(netInput.batchSize).toEqual(1)
}) })
it('from HTMLVideoElement', async () => {
const videoEl = await createFakeHTMLVideoElement()
const netInput = await toNetInput(videoEl, true)
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.batchSize).toEqual(1)
})
it('from HTMLCanvasElement', async () => { it('from HTMLCanvasElement', async () => {
const netInput = await toNetInput(canvasEl, true) const netInput = await toNetInput(canvasEl, true)
expect(netInput instanceof NetInput).toBe(true) expect(netInput instanceof NetInput).toBe(true)
...@@ -43,20 +37,6 @@ describe('toNetInput', () => { ...@@ -43,20 +37,6 @@ describe('toNetInput', () => {
expect(netInput.batchSize).toEqual(2) expect(netInput.batchSize).toEqual(2)
}) })
it('from HTMLVideoElement array', async () => {
const videoElements = [
await createFakeHTMLVideoElement(),
await createFakeHTMLVideoElement()
]
videoElements.forEach(videoEl =>
spyOnProperty(videoEl, 'readyState', 'get').and.returnValue(4)
)
const netInput = await toNetInput(videoElements, true)
expect(netInput instanceof NetInput).toBe(true)
expect(netInput.batchSize).toEqual(2)
})
it('from HTMLCanvasElement array', async () => { it('from HTMLCanvasElement array', async () => {
const netInput = await toNetInput([ const netInput = await toNetInput([
canvasEl, canvasEl,
...@@ -70,7 +50,7 @@ describe('toNetInput', () => { ...@@ -70,7 +50,7 @@ describe('toNetInput', () => {
const netInput = await toNetInput([ const netInput = await toNetInput([
imgEl, imgEl,
canvasEl, canvasEl,
await createFakeHTMLVideoElement() canvasEl
], true) ], true)
expect(netInput instanceof NetInput).toBe(true) expect(netInput instanceof NetInput).toBe(true)
expect(netInput.batchSize).toEqual(3) expect(netInput.batchSize).toEqual(3)
...@@ -111,4 +91,67 @@ describe('toNetInput', () => { ...@@ -111,4 +91,67 @@ describe('toNetInput', () => {
}) })
describe('no memory leaks', () => {
it('single image element', async () => {
await expectAllTensorsReleased(async () => {
const netInput = await toNetInput(imgEl)
netInput.dispose()
})
})
it('multiple image elements', async () => {
await expectAllTensorsReleased(async () => {
const netInput = await toNetInput([imgEl, imgEl, imgEl])
netInput.dispose()
})
})
it('single tf.Tensor3D', async () => {
const tensor = tf.fromPixels(imgEl)
await expectAllTensorsReleased(async () => {
const netInput = await toNetInput(tensor)
netInput.dispose()
})
tensor.dispose()
})
it('multiple tf.Tensor3Ds', async () => {
const tensors = [imgEl, imgEl, imgEl].map(el => tf.fromPixels(el))
await expectAllTensorsReleased(async () => {
const netInput = await toNetInput(tensors)
netInput.dispose()
})
tensors.forEach(t => t.dispose())
})
it('single batch size 1 tf.Tensor4Ds', async () => {
const tensor = tf.tidy(() => tf.fromPixels(imgEl).expandDims()) as tf.Tensor4D
await expectAllTensorsReleased(async () => {
const netInput = await toNetInput(tensor)
netInput.dispose()
})
tensor.dispose()
})
it('multiple batch size 1 tf.Tensor4Ds', async () => {
const tensors = [imgEl, imgEl, imgEl]
.map(el => tf.tidy(() => tf.fromPixels(el).expandDims())) as tf.Tensor4D[]
await expectAllTensorsReleased(async () => {
const netInput = await toNetInput(tensors)
netInput.dispose()
})
tensors.forEach(t => t.dispose())
})
})
}) })
test/utils.ts
...@@ -12,15 +12,6 @@ export function expectMaxDelta(val1: number, val2: number, maxDelta: number) { ...@@ -12,15 +12,6 @@ export function expectMaxDelta(val1: number, val2: number, maxDelta: number) {
expect(Math.abs(val1 - val2)).toBeLessThan(maxDelta) expect(Math.abs(val1 - val2)).toBeLessThan(maxDelta)
} }
export async function createFakeHTMLVideoElement() {
const videoEl = document.createElement('video')
videoEl.muted = true
videoEl.src = 'base/test/media/video.mp4'
await videoEl.pause()
await videoEl.play()
return videoEl
}
export async function expectAllTensorsReleased(fn: () => any) { export async function expectAllTensorsReleased(fn: () => any) {
const numTensorsBefore = tf.memory().numTensors const numTensorsBefore = tf.memory().numTensors
await fn() await fn()
......
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