tensorflow-helpers/download-tfjs-model.js

Helper functions to use tensorflow in nodejs for transfer learning, image classification, and more

#!/usr/bin/env node
"use strict";
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    Object.defineProperty(o, "default", { enumerable: true, value: v });
}) : function(o, v) {
    o["default"] = v;
});
var __importStar = (this && this.__importStar) || (function () {
    var ownKeys = function(o) {
        ownKeys = Object.getOwnPropertyNames || function (o) {
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        __setModuleDefault(result, mod);
        return result;
    };
})();
Object.defineProperty(exports, "__esModule", { value: true });
const tf = __importStar(require("@tensorflow/tfjs"));
const fs_1 = require("fs");
const path_1 = require("path");
const model_1 = require("./model");
const model_artifacts_1 = require("./model-artifacts");
let helpMessage = `
Usage: download-tfjs-model <source> <output-dir>

Download and save TensorFlow.js models for use in browser or Node.js.
Supports URLs (TensorFlow Hub, Kaggle) and local model files/directories.

Examples:

  # Download MobileNet V3
  download-tfjs-model https://www.kaggle.com/models/google/mobilenet-v3/TfJs/large-100-224-feature-vector/1 ./browser-models/mobilenet-v3-large-100

  # Download MobileNet V2
  download-tfjs-model https://www.kaggle.com/models/google/mobilenet-v2/TfJs/035-128-feature-vector/3 ./browser-models/mobilenet-v2-035

  # Convert local model
  download-tfjs-model ./hub-models/mobilenet-v2-035-128-feature-vector ./browser-models/mobilenet-v2-035
`.trim();
async function main() {
    const args = process.argv.slice(2);
    if (args.includes('--help') || args.includes('-h')) {
        console.log(helpMessage);
        process.exit(0);
    }
    if (args.length != 2) {
        console.error(helpMessage);
        process.exit(1);
    }
    const [source, outputDir] = args;
    try {
        console.log(`Loading model from: ${source}`);
        const model = await loadModel(source);
        let artifacts = (0, model_artifacts_1.getModelArtifacts)(model);
        artifacts.userDefinedMetadata ||= {};
        artifacts.userDefinedMetadata.source = source;
        console.log(`Saving model to: ${outputDir}`);
        (0, fs_1.mkdirSync)(outputDir, { recursive: true });
        await (0, model_1.saveModel)({ model, dir: outputDir });
        console.log('✅ Model downloaded and saved successfully!');
    }
    catch (error) {
        console.error('❌ Error:', error);
        process.exit(1);
    }
}
async function loadModel(source) {
    if (!source.startsWith('http') && !source.endsWith('.json')) {
        source = (0, path_1.join)(source, 'model.json');
    }
    if (!source.includes('://')) {
        source = `file://${source}`;
    }
    try {
        // try layered model
        if (source.startsWith('file://')) {
            return await (0, model_1.loadLayersModel)({ dir: toDir(source) });
        }
        else {
            return await tf.loadLayersModel(source, { fromTFHub: true });
        }
    }
    catch {
        // try graph model
        if (source.startsWith('file://')) {
            return await (0, model_1.loadGraphModel)({ dir: toDir(source) });
        }
        else {
            return await tf.loadGraphModel(source, { fromTFHub: true });
        }
    }
}
function toDir(source) {
    source = source.replace('file://', '');
    return (0, path_1.dirname)(source);
}
main();