113 lines
2.9 KiB
JavaScript
113 lines
2.9 KiB
JavaScript
"use strict";
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import path from 'path';
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import fs from 'fs';
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import tf from '@tensorflow/tfjs-node';
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// import tf from '@tensorflow/tfjs-node-gpu';
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// import tf from '@tensorflow/tfjs';
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import make_model from './model.mjs';
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class FilmPredictor {
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constructor(settings, in_categories) {
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this.settings = settings;
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this.cats = in_categories;
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this.image_size = 256;
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this.use_crossentropy = false;
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this.batch_size = 32;
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this.prefetch = 64;
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}
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async init(dirpath = null) {
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if(dirpath !== null)
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await this.load_model(dirpath);
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else {
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if(!fs.existsSync(this.settings.output))
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await fs.promises.mkdir(this.settings.output, { recursive: true, mode: 0o755 });
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this.dir_checkpoints = path.join(this.settings.output, "checkpoints");
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if(!fs.existsSync(this.dir_checkpoints))
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await fs.promises.mkdir(this.dir_checkpoints, { recursive: true, mode: 0o755 });
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await fs.promises.copyFile(
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this.cats.filename,
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path.join(this.settings.output, "categories.txt")
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);
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this.model = make_model(
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this.cats.count,
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this.image_size,
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this.use_crossentropy
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);
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this.model.summary();
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}
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}
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async load_model(dirpath) {
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if(!fs.existsSync(dirpath))
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throw new Error(`Error: The directory ${dirpath} doesn't exist.`);
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console.error(`>>> Loading model from '${dirpath}'`);
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this.model = await tf.loadLayersModel(`file://${path.resolve(dirpath, "model.json")}`);
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console.error(`>>> Model loading complete`);
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}
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async train(dataset_train, dataset_validate) {
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dataset_train = dataset_train.batch(this.batch_size).prefetch(this.prefetch);
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dataset_validate = dataset_validate.batch(this.batch_size).prefetch(this.prefetch);
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await this.model.fitDataset(dataset_train, {
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epochs: 50,
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verbose: 1,
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validationData: dataset_validate,
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yieldEvery: "batch",
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shuffle: false,
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callbacks: {
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onEpochEnd: async (epoch, metrics) => {
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console.error(`>>> Epoch ${epoch} complete, metrics:`, metrics);
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let dir_output_checkpoint = `file://${path.join(this.dir_checkpoints, `${epoch.toString()}`)}`;
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await Promise.all([
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this.model.save(dir_output_checkpoint),
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fs.promises.appendFile(
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path.join(this.settings.output, `metrics.stream.json`), JSON.stringify(metrics) + "\n"
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)
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]);
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}
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}
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})
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}
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async predict(imagefilepath) {
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if(!fs.existsSync(imagefilepath))
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throw new Error(`Error: No file exists at '${imagefilepath}'`);
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let image_data = await fs.promises.readFile(imagefilepath);
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let imagetensor = tf.tidy(() => {
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let image = tf.node.decodeImage(
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image_data,
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3 // channels
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);
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return image.reshape([1, ...image.shape ]);
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})
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let result_array = (await this.model.predict(imagetensor).array())[0];
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return this.array2genres(result_array);
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}
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array2genres(arr) {
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let result = [];
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for(let i = 0; i < arr.length; i++) {
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if(arr[i] < 0.5)
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continue;
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result.push(this.cats.values[i]);
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}
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return result;
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}
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}
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export default FilmPredictor;
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