import { log } from '../helpers'; import * as tf from '../../dist/tfjs.esm.js'; import * as profile from '../profile'; /* Prototype implementation for model processing Must implement - load() - predict() Must account for: - image processing, tfjs profiling */ let model; let last = { }; let skipped = Number.MAX_SAFE_INTEGER; export async function load(config) { if (!model) { model = await tf.loadGraphModel(config.prototype.modelPath); if (config.debug) log(`load model: ${config.prototype.modelPath.match(/\/(.*)\./)[1]}`); } return model; } export async function predict(image, config) { if (!model) return null; if ((skipped < config.prototype.skipFrames) && config.videoOptimized && Object.keys(last).length > 0) { skipped++; return last; } if (config.videoOptimized) skipped = 0; else skipped = Number.MAX_SAFE_INTEGER; return new Promise(async (resolve) => { const resize = tf.image.resizeBilinear(image, [model.inputs[0].shape[2], model.inputs[0].shape[1]], false); const enhance = tf.mul(resize, [255.0]); tf.dispose(resize); let resT; if (!config.profile) { if (config.prototype.enabled) resT = await model.predict(enhance); } else { const profileT = config.prototype.enabled ? await tf.profile(() => model.predict(enhance)) : {}; resT = profileT.result.clone(); profileT.result.dispose(); profile.run('prototype', profileT); } enhance.dispose(); let obj = {}; if (resT) { const data = resT.dataSync(); obj = { data }; tf.dispose(resT); } last = obj; resolve(obj); }); }