«Как правильно утилизировать тензоры в утечке Face-API.JS + TFJS, чтобы предотвратить утечку памяти?» ⇐ Javascript

[Anonymous]

Я использую Face-api.js (в частности, в среде сервера @vladmandic/face-api ) в среде сервера Node.js, и хотя обнаружение лица и сравнение работают, я сталкиваюсь с серьезной проблемой управления памятью. Я подозреваю, что некоторые тензоры не выпускаются правильно, но я не могу определить, где и почему. Я завернул создание тензора в Try ... наконец -то блоки и даже память журнала с использованием tf.memory () , но количество активных тензоров не снижается, как и ожидалось.
// face-api/src/server.js
const express = require('express');
const multer = require('multer');
const tf = require('@tensorflow/tfjs-node');
const faceapi = require('@vladmandic/face-api');
const { Canvas, Image, ImageData } = require('canvas');
const path = require('path');
const { logger } = require('./logger');

const app = express();
const upload = multer({
limits: {
fileSize: 10 * 1024 * 1024,
fieldSize: 25 * 1024 * 1024
}
});
const PORT = 3002;

class FaceApiService {
constructor() {
this.modelPath = path.join(__dirname, '../models');
this.isModelLoaded = false;
this.initializeFaceApi();
}

async initializeFaceApi() {
if (this.isModelLoaded) return;

faceapi.env.monkeyPatch({ Canvas, Image, ImageData });

try {
await Promise.all([
faceapi.nets.faceRecognitionNet.loadFromDisk(this.modelPath),
faceapi.nets.faceLandmark68Net.loadFromDisk(this.modelPath),
faceapi.nets.ssdMobilenetv1.loadFromDisk(this.modelPath)
]);

this.isModelLoaded = true;
console.log("load model success")
logger.info('Face recognition models loaded successfully');
} catch (error) {
logger.error('Error loading face recognition models: ' + error);
throw new Error("Error initializing face recognition system");
}
}

disposeTensor(tensor) {
if (tensor) {
if (Array.isArray(tensor)) {
tensor.forEach(t => this.disposeTensor(t));
} else if (tensor.dispose) {
try {
tensor.dispose();
} catch (err) {
logger.warn('Error disposing tensor: ' + err);
}
}
}
}

async verifyFace(uploadedImage, referenceImages) {
let mem = tf.memory();
console.log(`Tensor count: ${mem.numTensors}, Bytes: ${mem.numBytes}`);
let inputTensor = null;
const referenceDescriptors = [];
try {
console.log("uploadedImage size: ", uploadedImage.length / 1024, "KB");
inputTensor = tf.node.decodeImage(uploadedImage);
console.log("decoded imgae uploadedImage success")
const detection = await faceapi
.detectSingleFace(inputTensor)
.withFaceLandmarks()
.withFaceDescriptor();
console.log("detection imgae uploadedImage success")

if (!detection) {
throw new Error("No face detected in the uploaded photo");
}

for (const refImage of referenceImages) {
let refTensor = null;
try {
console.log("referenceImage size: ", refImage.length / 1024, "KB");
refTensor = tf.node.decodeImage(refImage);
console.log("decoded imgae refTensor success")
const refDetection = await faceapi
.detectSingleFace(refTensor)
.withFaceLandmarks()
.withFaceDescriptor();
console.log("detection imgae refTensor success")

if (refDetection) {
referenceDescriptors.push(refDetection.descriptor);
}
} catch (refErr) {
logger.warn('Error processing reference image: ' + refErr);
} finally {
this.disposeTensor(refTensor);
}
}

if (referenceDescriptors.length === 0) {
throw new Error("No valid reference photos available for comparison");
}

const faceMatcher = new faceapi.FaceMatcher(
[new faceapi.LabeledFaceDescriptors('user', referenceDescriptors)],
0.6
);
console.log("faceMatcher success")

const match = faceMatcher.findBestMatch(detection.descriptor);
console.log("match success")

return {
verified: match.label !== 'unknown',
confidence: 1 - match.distance
};

} catch (err) {
logger.error('Error in face verification: ' + err);
throw err;
} finally {
console.log("dispose tensor")
this.disposeTensor(inputTensor);
// Dispose semua descriptor tensor reference
for (const desc of referenceDescriptors) {
this.disposeTensor(desc); // penting!
}

console.log("dispose success");
let mem = tf.memory();
console.log(`Tensor count: ${mem.numTensors}, Bytes: ${mem.numBytes}`);
}
}
}

const faceApiService = new FaceApiService();

app.use((req, res, next) => {
const waktuAkses = new Date().toISOString();
logger.info(`${req.method} url:: ${req.url} - accessed at ${waktuAkses}`);
next();
});

app.get('/', (req, res) => {
res.json({ status: true });
});

app.post('/verify', upload.single('image'), async (req, res) => {
try {
if (!req.file || !req.body.referenceImages) {
return res.status(400).json({ error: 'Missing required files' });
}

const referenceImages = JSON.parse(req.body.referenceImages);
const referenceBuffers = referenceImages.map(base64 =>
Buffer.from(base64, 'base64')
);

const result = await faceApiService.verifyFace(
req.file.buffer,
referenceBuffers
);

res.json(result);
} catch (error) {
logger.error('Verification error: ' + error);
res.status(500).json({
error: error instanceof Error ? error.message : 'Internal server error'
});
}
});

app.listen(PORT, '0.0.0.0', () => {
logger.info(`Face API service running on http://localhost:${PORT}`);
});

module.exports = app;


Подробнее здесь: https://stackoverflow.com/questions/796 ... -memory-le