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human
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enhanced automated tests

pull/193/head
Vladimir Mandic 2021-09-13 13:30:46 -04:00
parent c5dc4ecaff
commit 7e706f0db3
1 changed files with 30 additions and 7 deletions
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37
test/test-main.js
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@ -2,7 +2,7 @@ const process = require('process');
const canvasJS = require('canvas');
let fetch; // fetch is dynamically imported later
let tensors = 0;
let config;
const log = (status, ...data) => {
@ -52,7 +52,7 @@ async function getImage(human, input) {
}
function printResults(detect) {
const person = (detect.face && detect.face[0]) ? { confidence: detect.face[0].confidence, age: detect.face[0].age, gender: detect.face[0].gender } : {};
const person = (detect.face && detect.face[0]) ? { score: detect.face[0].score, age: detect.face[0].age, gender: detect.face[0].gender } : {};
const object = (detect.object && detect.object[0]) ? { score: detect.object[0].score, class: detect.object[0].label } : {};
const body = (detect.body && detect.body[0]) ? { score: detect.body[0].score, keypoints: detect.body[0].keypoints.length } : {};
const persons = detect.persons;
@ -70,8 +70,10 @@ async function testInstance(human) {
log('info', 'tfjs version:', human.tf.version.tfjs);
await human.load();
tensors = human.tf.engine().state.numTensors;
if (config.backend === human.tf.getBackend()) log('state', 'passed: set backend:', config.backend);
else log('error', 'failed: set backend:', config.backend);
log('state', 'tensors', tensors);
if (human.models) {
log('state', 'passed: load models');
@ -93,6 +95,8 @@ async function testWarmup(human, title) {
}
if (warmup) {
log('state', 'passed: warmup:', config.warmup, title);
// const count = human.tf.engine().state.numTensors;
// if (count - tensors > 0) log('warn', 'failed: memory', config.warmup, title, 'tensors:', count - tensors);
printResults(warmup);
return true;
}
@ -101,6 +105,8 @@ async function testWarmup(human, title) {
}
async function testDetect(human, input, title) {
await human.load(config);
tensors = human.tf.engine().state.numTensors;
const image = input ? await getImage(human, input) : human.tf.randomNormal([1, 1024, 1024, 3]);
if (!image) {
log('error', 'failed: detect: input is null');
@ -115,12 +121,19 @@ async function testDetect(human, input, title) {
if (image instanceof human.tf.Tensor) human.tf.dispose(image);
if (detect) {
log('state', 'passed: detect:', input || 'random', title);
// const count = human.tf.engine().state.numTensors;
// if (count - tensors > 0) log('warn', 'failed: memory', config.warmup, title, 'tensors:', count - tensors);
printResults(detect);
return true;
}
log('error', 'failed: detect', input || 'random', title);
return false;
}
const evt = { image: 0, detect: 0, warmup: 0 };
async function events(event) {
log('state', 'event:', event);
evt[event]++;
}
async function test(Human, inputConfig) {
config = inputConfig;
@ -132,6 +145,10 @@ async function test(Human, inputConfig) {
}
const t0 = process.hrtime.bigint();
const human = new Human(config);
human.events.addEventListener('warmup', () => events('warmup'));
human.events.addEventListener('image', () => events('image'));
human.events.addEventListener('detect', () => events('detect'));
// await human.tf.ready();
await testInstance(human);
config.warmup = 'none';
@ -141,12 +158,14 @@ async function test(Human, inputConfig) {
config.warmup = 'body';
await testWarmup(human, 'default');
log('info', 'test body variants');
config.body = { modelPath: 'posenet.json', enabled: true };
await testDetect(human, 'samples/ai-body.jpg', 'posenet');
config.body = { modelPath: 'movenet-lightning.json', enabled: true };
await testDetect(human, 'samples/ai-body.jpg', 'movenet');
log('info', 'test default');
await testDetect(human, 'samples/ai-body.jpg', 'default');
log('info', 'test body variants');
config.body = { modelPath: 'posenet.json' };
await testDetect(human, 'samples/ai-body.jpg', 'posenet');
config.body = { modelPath: 'movenet-lightning.json' };
await testDetect(human, 'samples/ai-body.jpg', 'movenet');
await testDetect(human, null, 'default');
log('info', 'test: first instance');
await testDetect(human, 'samples/ai-upper.jpg', 'default');
@ -163,6 +182,10 @@ async function test(Human, inputConfig) {
testDetect(second, 'samples/ai-upper.jpg', 'default'),
]);
const t1 = process.hrtime.bigint();
const leak = human.tf.engine().state.numTensors - tensors;
if (leak === 0) log('state', 'passeed: no memory leak');
else log('error', 'failed: memory leak', leak);
log('info', 'events:', evt);
log('info', 'test complete:', Math.trunc(Number(t1 - t0) / 1000 / 1000), 'ms');
}