@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
252 lines • 12.3 kB
JavaScript
;
/**
* @license
* Copyright 2018 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
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;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
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;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
/**
* Unit tests for passthrough IOHandlers.
*/
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var modelTopology1 = {
'class_name': 'Sequential',
'keras_version': '2.1.4',
'config': [{
'class_name': 'Dense',
'config': {
'kernel_initializer': {
'class_name': 'VarianceScaling',
'config': {
'distribution': 'uniform',
'scale': 1.0,
'seed': null,
'mode': 'fan_avg'
}
},
'name': 'dense',
'kernel_constraint': null,
'bias_regularizer': null,
'bias_constraint': null,
'dtype': 'float32',
'activation': 'linear',
'trainable': true,
'kernel_regularizer': null,
'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
'units': 1,
'batch_input_shape': [null, 3],
'use_bias': true,
'activity_regularizer': null
}
}],
'backend': 'tensorflow'
};
var weightSpecs1 = [
{
name: 'dense/kernel',
shape: [3, 1],
dtype: 'float32',
},
{
name: 'dense/bias',
shape: [1],
dtype: 'float32',
}
];
var weightData1 = new ArrayBuffer(16);
var artifacts1 = {
modelTopology: modelTopology1,
weightSpecs: weightSpecs1,
weightData: weightData1,
};
jasmine_util_1.describeWithFlags('Passthrough Saver', jasmine_util_1.BROWSER_ENVS, function () {
it('passes provided arguments through on save', function () { return __awaiter(_this, void 0, void 0, function () {
function saveHandler(artifacts) {
return __awaiter(this, void 0, void 0, function () {
return __generator(this, function (_a) {
savedArtifacts = artifacts;
return [2 /*return*/, {
modelArtifactsInfo: {
dateSaved: testStartDate,
modelTopologyType: 'JSON',
modelTopologyBytes: JSON.stringify(modelTopology1).length,
weightSpecsBytes: JSON.stringify(weightSpecs1).length,
weightDataBytes: weightData1.byteLength,
}
}];
});
});
}
var testStartDate, savedArtifacts, saveTrigger, saveResult, artifactsInfo;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
testStartDate = new Date();
savedArtifacts = null;
saveTrigger = tf.io.withSaveHandler(saveHandler);
return [4 /*yield*/, saveTrigger.save(artifacts1)];
case 1:
saveResult = _a.sent();
expect(saveResult.errors).toEqual(undefined);
artifactsInfo = saveResult.modelArtifactsInfo;
expect(artifactsInfo.dateSaved.getTime())
.toBeGreaterThanOrEqual(testStartDate.getTime());
expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
.toEqual(JSON.stringify(modelTopology1).length);
expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
.toEqual(JSON.stringify(weightSpecs1).length);
expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
expect(savedArtifacts.modelTopology).toEqual(modelTopology1);
expect(savedArtifacts.weightSpecs).toEqual(weightSpecs1);
expect(savedArtifacts.weightData).toEqual(weightData1);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('Passthrough Loader', jasmine_util_1.BROWSER_ENVS, function () {
it('load topology and weights: legacy signature', function () { return __awaiter(_this, void 0, void 0, function () {
var passthroughHandler, modelArtifacts;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
passthroughHandler = tf.io.fromMemory(modelTopology1, weightSpecs1, weightData1);
return [4 /*yield*/, passthroughHandler.load()];
case 1:
modelArtifacts = _a.sent();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
expect(modelArtifacts.weightData).toEqual(weightData1);
expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
return [2 /*return*/];
}
});
}); });
it('load topology and weights', function () { return __awaiter(_this, void 0, void 0, function () {
var passthroughHandler, modelArtifacts;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
passthroughHandler = tf.io.fromMemory({
modelTopology: modelTopology1,
weightSpecs: weightSpecs1,
weightData: weightData1
});
return [4 /*yield*/, passthroughHandler.load()];
case 1:
modelArtifacts = _a.sent();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
expect(modelArtifacts.weightData).toEqual(weightData1);
expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
return [2 /*return*/];
}
});
}); });
it('load model topology only: legacy signature', function () { return __awaiter(_this, void 0, void 0, function () {
var passthroughHandler, modelArtifacts;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
passthroughHandler = tf.io.fromMemory(modelTopology1);
return [4 /*yield*/, passthroughHandler.load()];
case 1:
modelArtifacts = _a.sent();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(undefined);
expect(modelArtifacts.weightData).toEqual(undefined);
expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
return [2 /*return*/];
}
});
}); });
it('load model topology only', function () { return __awaiter(_this, void 0, void 0, function () {
var passthroughHandler, modelArtifacts;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
passthroughHandler = tf.io.fromMemory({ modelTopology: modelTopology1 });
return [4 /*yield*/, passthroughHandler.load()];
case 1:
modelArtifacts = _a.sent();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(undefined);
expect(modelArtifacts.weightData).toEqual(undefined);
expect(modelArtifacts.userDefinedMetadata).toEqual(undefined);
return [2 /*return*/];
}
});
}); });
it('load topology, weights, and user-defined metadata', function () { return __awaiter(_this, void 0, void 0, function () {
var userDefinedMetadata, passthroughHandler, modelArtifacts;
return __generator(this, function (_a) {
switch (_a.label) {
case 0:
userDefinedMetadata = { 'fooField': 'fooValue' };
passthroughHandler = tf.io.fromMemory({
modelTopology: modelTopology1,
weightSpecs: weightSpecs1,
weightData: weightData1,
userDefinedMetadata: userDefinedMetadata
});
return [4 /*yield*/, passthroughHandler.load()];
case 1:
modelArtifacts = _a.sent();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
expect(modelArtifacts.weightData).toEqual(weightData1);
expect(modelArtifacts.userDefinedMetadata).toEqual(userDefinedMetadata);
return [2 /*return*/];
}
});
}); });
});
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