"use strict";
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
|
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
|
if (f) throw new TypeError("Generator is already executing.");
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while (g && (g = 0, op[0] && (_ = 0)), _) try {
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if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
|
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs");
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var fs = require("fs");
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var path = require("path");
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var rimraf = require("rimraf");
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var util_1 = require("util");
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var tfn = require("../index");
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var file_system_1 = require("./file_system");
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describe('File system IOHandler', function () {
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var mkdtemp = (0, util_1.promisify)(fs.mkdtemp);
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var readFile = (0, util_1.promisify)(fs.readFile);
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var writeFile = (0, util_1.promisify)(fs.writeFile);
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var rimrafPromise = (0, util_1.promisify)(rimraf);
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var modelTopology1 = {
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'class_name': 'Sequential',
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'keras_version': '2.1.6',
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'config': [{
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'class_name': 'Dense',
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'config': {
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'kernel_initializer': {
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'class_name': 'VarianceScaling',
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'config': {
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'distribution': 'uniform',
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'scale': 1.0,
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'seed': null,
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'mode': 'fan_avg'
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}
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},
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'name': 'dense',
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'kernel_constraint': null,
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'bias_regularizer': null,
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'bias_constraint': null,
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'dtype': 'float32',
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'activation': 'linear',
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'trainable': true,
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'kernel_regularizer': null,
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'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
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'units': 1,
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'batch_input_shape': [null, 3],
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'use_bias': true,
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'activity_regularizer': null
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}
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}],
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'backend': 'tensorflow'
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};
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var weightSpecs1 = [
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{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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},
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{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}
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];
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var weightData1 = new ArrayBuffer(16);
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var trainingConfig1 = {
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loss: 'categorical_crossentropy',
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metrics: ['accuracy'],
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optimizer_config: { class_name: 'SGD', config: { learningRate: 0.1 } }
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};
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var testDir;
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beforeEach(function () { return __awaiter(void 0, void 0, void 0, function () {
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0: return [4 /*yield*/, mkdtemp('tfjs_node_fs_test')];
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case 1:
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testDir = _a.sent();
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return [2 /*return*/];
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}
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});
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}); });
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afterEach(function () { return __awaiter(void 0, void 0, void 0, function () {
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0: return [4 /*yield*/, rimrafPromise(testDir)];
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case 1:
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_a.sent();
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return [2 /*return*/];
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}
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});
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}); });
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it('save succeeds with newly created directory', function () { return __awaiter(void 0, void 0, void 0, function () {
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var t0, dir, handler, saveResult, modelJSONPath, weightsBinPath, modelJSON, _a, _b, weightData, _c;
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return __generator(this, function (_d) {
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switch (_d.label) {
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case 0:
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t0 = new Date();
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dir = path.join(testDir, 'save-destination');
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handler = tf.io.getSaveHandlers("file://".concat(dir))[0];
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return [4 /*yield*/, handler.save({
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modelTopology: modelTopology1,
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weightSpecs: weightSpecs1,
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weightData: weightData1,
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})];
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case 1:
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saveResult = _d.sent();
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expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
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.toBeGreaterThanOrEqual(t0.getTime());
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expect(saveResult.modelArtifactsInfo.modelTopologyType)
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.toEqual('JSON');
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modelJSONPath = path.join(dir, 'model.json');
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weightsBinPath = path.join(dir, 'weights.bin');
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_b = (_a = JSON).parse;
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return [4 /*yield*/, readFile(modelJSONPath, 'utf8')];
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case 2:
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modelJSON = _b.apply(_a, [_d.sent()]);
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expect(modelJSON.modelTopology).toEqual(modelTopology1);
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expect(modelJSON.weightsManifest.length).toEqual(1);
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expect(modelJSON.weightsManifest[0].paths).toEqual(['weights.bin']);
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expect(modelJSON.weightsManifest[0].weights).toEqual(weightSpecs1);
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_c = Uint8Array.bind;
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return [4 /*yield*/, readFile(weightsBinPath)];
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case 3:
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weightData = new (_c.apply(Uint8Array, [void 0, _d.sent()]))();
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expect(weightData.length).toEqual(16);
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weightData.forEach(function (value) { return expect(value).toEqual(0); });
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return [2 /*return*/];
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}
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});
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}); });
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it('save fails if path exists as a file', function () { return __awaiter(void 0, void 0, void 0, function () {
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var dir, handler;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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dir = path.join(testDir, 'save-destination');
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// Create a file at the locatin.
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return [4 /*yield*/, writeFile(dir, 'foo')];
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case 1:
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// Create a file at the locatin.
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_a.sent();
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handler = tf.io.getSaveHandlers("file://".concat(dir))[0];
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return [4 /*yield*/, expectAsync(handler.save({
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modelTopology: modelTopology1,
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weightSpecs: weightSpecs1,
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weightData: weightData1,
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})).toBeRejectedWithError(/.*exists as a file.*directory.*/)];
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case 2:
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_a.sent();
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return [2 /*return*/];
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}
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});
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}); });
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it('save-load round trip: one weight file', function (done) {
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var handler1 = tf.io.getSaveHandlers("file://".concat(testDir))[0];
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handler1
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.save({
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modelTopology: modelTopology1,
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weightSpecs: weightSpecs1,
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weightData: weightData1,
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trainingConfig: trainingConfig1,
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})
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.then(function (saveResult) {
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var modelJSONPath = path.join(testDir, 'model.json');
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var handler2 = tf.io.getLoadHandlers("file://".concat(modelJSONPath))[0];
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handler2.load()
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.then(function (modelArtifacts) {
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expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
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expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
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expect(modelArtifacts.trainingConfig).toEqual(trainingConfig1);
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expect(new Float32Array(tf.io.CompositeArrayBuffer.join(modelArtifacts.weightData)))
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.toEqual(new Float32Array([0, 0, 0, 0]));
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done();
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})
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.catch(function (err) { return done.fail(err.stack); });
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})
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.catch(function (err) { return done.fail(err.stack); });
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});
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describe('load json model', function () {
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it('load: two weight files', function () { return __awaiter(void 0, void 0, void 0, function () {
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var weightsManifest, modelJSON, modelJSONPath, weightsData1, weightsData2, handler, modelArtifacts;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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weightsManifest = [
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{
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paths: ['weights.1.bin'],
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weights: [{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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}],
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},
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{
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paths: ['weights.2.bin'],
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weights: [{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}]
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}
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];
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modelJSON = {
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modelTopology: modelTopology1,
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weightsManifest: weightsManifest,
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};
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modelJSONPath = path.join(testDir, 'model.json');
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return [4 /*yield*/, writeFile(modelJSONPath, JSON.stringify(modelJSON), 'utf8')];
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case 1:
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_a.sent();
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weightsData1 = Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
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return [4 /*yield*/, writeFile(path.join(testDir, 'weights.1.bin'), weightsData1, 'binary')];
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case 2:
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_a.sent();
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weightsData2 = Buffer.from(new Float32Array([-7.7]).buffer);
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return [4 /*yield*/, writeFile(path.join(testDir, 'weights.2.bin'), weightsData2, 'binary')];
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case 3:
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_a.sent();
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handler = tf.io.getLoadHandlers("file://".concat(modelJSONPath))[0];
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return [4 /*yield*/, handler.load()];
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case 4:
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modelArtifacts = _a.sent();
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expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
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expect(modelArtifacts.weightSpecs).toEqual([
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{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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},
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{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}
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]);
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tf.test_util.expectArraysClose(new Float32Array(tf.io.CompositeArrayBuffer.join(modelArtifacts.weightData)), new Float32Array([-1.1, -3.3, -3.3, -7.7]));
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return [2 /*return*/];
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}
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});
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}); });
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it('loading from nonexistent model.json path fails', function (done) {
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var handler = tf.io.getLoadHandlers("file://".concat(testDir, "/foo/model.json"))[0];
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handler.load()
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.then(function (getModelArtifactsInfoForJSON) {
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done.fail('Loading from nonexisting model.json path succeeded ' +
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'unexpectedly.');
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})
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.catch(function (err) {
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expect(err.message)
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.toMatch(/model\.json.*does not exist.*loading failed/);
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done();
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});
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});
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it('loading from missing weights path fails', function () { return __awaiter(void 0, void 0, void 0, function () {
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var weightsManifest, modelJSON, modelJSONPath, weightsData1, handler;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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weightsManifest = [
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{
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paths: ['weights.1.bin'],
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weights: [{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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}],
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},
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{
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paths: ['weights.2.bin'],
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weights: [{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}]
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}
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];
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modelJSON = {
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modelTopology: modelTopology1,
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weightsManifest: weightsManifest,
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};
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modelJSONPath = path.join(testDir, 'model.json');
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return [4 /*yield*/, writeFile(modelJSONPath, JSON.stringify(modelJSON), 'utf8')];
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case 1:
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_a.sent();
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weightsData1 = Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
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return [4 /*yield*/, writeFile(path.join(testDir, 'weights.1.bin'), weightsData1, 'binary')];
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case 2:
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_a.sent();
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handler = tf.io.getLoadHandlers("file://".concat(modelJSONPath))[0];
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return [4 /*yield*/, expectAsync(handler.load()).toBeRejectedWithError(/Weight file .*weights\.2\.bin does not exist/)];
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case 3:
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_a.sent();
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return [2 /*return*/];
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}
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});
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}); });
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});
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describe('load binary model', function () {
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it('load: two weight files', function () { return __awaiter(void 0, void 0, void 0, function () {
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var weightsManifest, modelPath, modelData, modelManifestJSONPath, weightsData1, weightsData2, handler, modelArtifacts;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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weightsManifest = [
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{
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paths: ['weights.1.bin'],
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weights: [{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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}],
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},
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{
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paths: ['weights.2.bin'],
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weights: [{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}]
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}
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];
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modelPath = path.join(testDir, 'model.pb');
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modelData = Buffer.from(new Uint8Array([1, 2, 3]).buffer);
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return [4 /*yield*/, writeFile(modelPath, modelData, 'binary')];
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case 1:
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_a.sent();
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modelManifestJSONPath = path.join(testDir, 'manifest.json');
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return [4 /*yield*/, writeFile(modelManifestJSONPath, JSON.stringify(weightsManifest), 'utf8')];
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case 2:
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_a.sent();
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weightsData1 = Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
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return [4 /*yield*/, writeFile(path.join(testDir, 'weights.1.bin'), weightsData1, 'binary')];
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case 3:
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_a.sent();
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weightsData2 = Buffer.from(new Float32Array([-7.7]).buffer);
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return [4 /*yield*/, writeFile(path.join(testDir, 'weights.2.bin'), weightsData2, 'binary')];
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case 4:
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_a.sent();
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handler = new file_system_1.NodeFileSystem(["".concat(modelPath), "".concat(modelManifestJSONPath)]);
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return [4 /*yield*/, handler.load()];
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case 5:
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modelArtifacts = _a.sent();
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tf.test_util.expectArraysClose(new Uint8Array(modelArtifacts.modelTopology), new Uint8Array(modelData));
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expect(modelArtifacts.weightSpecs).toEqual([
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{
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name: 'dense/kernel',
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shape: [3, 1],
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dtype: 'float32',
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},
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{
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name: 'dense/bias',
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shape: [1],
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dtype: 'float32',
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}
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]);
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tf.test_util.expectArraysClose(new Float32Array(tf.io.CompositeArrayBuffer.join(modelArtifacts.weightData)), new Float32Array([-1.1, -3.3, -3.3, -7.7]));
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return [2 /*return*/];
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}
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});
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}); });
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it('path length does not equal 2 fails', function () {
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expect(function () { return new file_system_1.NodeFileSystem(["".concat(testDir, "/foo/model.pb")]); })
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.toThrowError(/file paths must have a length of 2.*actual length is 1.*/);
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});
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it('loading from nonexistent model.json path fails', function (done) {
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var handler = new file_system_1.NodeFileSystem(["".concat(testDir, "/foo/model.pb"), "".concat(testDir, "/foo/manifest.json")]);
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handler.load()
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.then(function (getModelArtifactsInfoForJSON) {
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done.fail('Loading from nonexisting model.pb path succeeded ' +
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'unexpectedly.');
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})
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.catch(function (err) {
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expect(err.message)
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.toMatch(/model\.pb.*does not exist.*loading failed/);
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done();
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});
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});
|
it('loading from missing weights path fails', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var weightsManifest, modelPath, modelData, modelManifestJSONPath, weightsData1, handler;
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return __generator(this, function (_a) {
|
switch (_a.label) {
|
case 0:
|
weightsManifest = [
|
{
|
paths: ['weights.1.bin'],
|
weights: [{
|
name: 'dense/kernel',
|
shape: [3, 1],
|
dtype: 'float32',
|
}],
|
},
|
{
|
paths: ['weights.2.bin'],
|
weights: [{
|
name: 'dense/bias',
|
shape: [1],
|
dtype: 'float32',
|
}]
|
}
|
];
|
modelPath = path.join(testDir, 'model.pb');
|
modelData = Buffer.from(new Uint8Array([1, 2, 3]).buffer);
|
return [4 /*yield*/, writeFile(modelPath, modelData, 'binary')];
|
case 1:
|
_a.sent();
|
modelManifestJSONPath = path.join(testDir, 'manifest.json');
|
return [4 /*yield*/, writeFile(modelManifestJSONPath, JSON.stringify(weightsManifest), 'utf8')];
|
case 2:
|
_a.sent();
|
weightsData1 = Buffer.from(new Float32Array([-1.1, -3.3, -3.3]).buffer);
|
return [4 /*yield*/, writeFile(path.join(testDir, 'weights.1.bin'), weightsData1, 'binary')];
|
case 3:
|
_a.sent();
|
handler = new file_system_1.NodeFileSystem(["".concat(modelPath), "".concat(modelManifestJSONPath)]);
|
return [4 /*yield*/, expectAsync(handler.load()).toBeRejectedWithError(/Weight file .*weights\.2\.bin does not exist/)];
|
case 4:
|
_a.sent();
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
});
|
it('Exported file-system handler class exists', function () {
|
var handler = tfn.io.fileSystem(testDir);
|
expect(typeof handler.save).toEqual('function');
|
expect(typeof handler.load).toEqual('function');
|
});
|
it('Save and load model with loss and optimizer', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var model, xs, ys, saveURL, loadURL, model2, optimizerConfig, history2;
|
return __generator(this, function (_a) {
|
switch (_a.label) {
|
case 0:
|
model = tf.sequential();
|
model.add(tf.layers.dense({ units: 1, kernelInitializer: 'zeros', inputShape: [1] }));
|
model.compile({ loss: 'meanSquaredError', optimizer: tf.train.adam(2.5e-2) });
|
xs = tf.tensor2d([1, 2, 3, 4], [4, 1]);
|
ys = tf.tensor2d([-1, -3, -5, -7], [4, 1]);
|
return [4 /*yield*/, model.fit(xs, ys, { epochs: 2, shuffle: false, verbose: 0 })];
|
case 1:
|
_a.sent();
|
saveURL = "file://".concat(testDir);
|
loadURL = "file://".concat(testDir, "/model.json");
|
return [4 /*yield*/, model.save(saveURL, { includeOptimizer: true })];
|
case 2:
|
_a.sent();
|
return [4 /*yield*/, tf.loadLayersModel(loadURL)];
|
case 3:
|
model2 = _a.sent();
|
optimizerConfig = model2.optimizer.getConfig();
|
expect(model2.optimizer.getClassName()).toEqual('Adam');
|
expect(optimizerConfig['learningRate']).toEqual(2.5e-2);
|
return [4 /*yield*/, model2.fit(xs, ys, { epochs: 2, shuffle: false, verbose: 0 })];
|
case 4:
|
history2 = _a.sent();
|
// The final loss value from training the model twice, 2 epochs
|
// at a time, should be equal to the final loss of trainig the
|
// model only once with 4 epochs.
|
expect(history2.history.loss[1]).toBeCloseTo(18.603);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('Save and load model with user-defined metadata', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var model, saveURL, loadURL, model2;
|
return __generator(this, function (_a) {
|
switch (_a.label) {
|
case 0:
|
model = tf.sequential();
|
model.add(tf.layers.dense({ units: 3, inputShape: [4] }));
|
model.setUserDefinedMetadata({ 'outputLabels': ['Label1', 'Label2', 'Label3'] });
|
saveURL = "file://".concat(testDir);
|
loadURL = "file://".concat(testDir, "/model.json");
|
return [4 /*yield*/, model.save(saveURL)];
|
case 1:
|
_a.sent();
|
return [4 /*yield*/, tf.loadLayersModel(loadURL)];
|
case 2:
|
model2 = _a.sent();
|
expect(model2.getUserDefinedMetadata()).toEqual({
|
'outputLabels': ['Label1', 'Label2', 'Label3']
|
});
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('Compile, save with includeOptimizer: true, load and fit model', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var model, saveURL, loadURL, model2;
|
return __generator(this, function (_a) {
|
switch (_a.label) {
|
case 0:
|
model = tf.sequential();
|
model.add(tf.layers.dense({ units: 3, inputShape: [4] }));
|
model.compile({ optimizer: 'sgd', loss: 'meanSquaredError', metrics: ['accuracy'] });
|
saveURL = "file://".concat(testDir);
|
loadURL = "file://".concat(testDir, "/model.json");
|
return [4 /*yield*/, model.save(saveURL, { includeOptimizer: true })];
|
case 1:
|
_a.sent();
|
return [4 /*yield*/, tf.loadLayersModel(loadURL)];
|
case 2:
|
model2 = _a.sent();
|
expect(function () { return model2.fit(tf.zeros([1, 4]), tf.zeros([1, 3])); })
|
.not.toThrow();
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
describe('nodeFileSystemRouter', function () {
|
it('should handle single path', function () {
|
expect((0, file_system_1.nodeFileSystemRouter)('file://model.json')).toBeDefined();
|
});
|
it('should handle multiple paths', function () {
|
expect((0, file_system_1.nodeFileSystemRouter)([
|
'file://model.json', 'file://weights.json'
|
])).toBeDefined();
|
});
|
it('should return null for non file path', function () {
|
expect((0, file_system_1.nodeFileSystemRouter)('http://model.json')).toBeNull();
|
});
|
it('should return null for multiple paths with mismatched scheme', function () {
|
expect((0, file_system_1.nodeFileSystemRouter)([
|
'file://model.json', 'http://weights.json'
|
])).toBeNull();
|
});
|
});
|
});
|
