@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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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.
* =============================================================================
*/
import * as tf from '../index';
import { BROWSER_ENVS, describeWithFlags } from '../jasmine_util';
import { arrayBufferToBase64String, base64StringToArrayBuffer } from './io_utils';
import { browserLocalStorage, BrowserLocalStorage, BrowserLocalStorageManager, localStorageRouter, purgeLocalStorageArtifacts } from './local_storage';
describeWithFlags('LocalStorage', BROWSER_ENVS, () => {
// Test data.
const 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'
};
const weightSpecs1 = [
{
name: 'dense/kernel',
shape: [3, 1],
dtype: 'float32',
},
{
name: 'dense/bias',
shape: [1],
dtype: 'float32',
}
];
const weightData1 = new ArrayBuffer(16);
const artifacts1 = {
modelTopology: modelTopology1,
weightSpecs: weightSpecs1,
weightData: weightData1,
format: 'layers-model',
generatedBy: 'TensorFlow.js v0.0.0',
convertedBy: '1.13.1',
signature: null,
userDefinedMetadata: {},
modelInitializer: {}
};
const artifactsV0 = {
modelTopology: modelTopology1,
weightSpecs: weightSpecs1,
weightData: weightData1
};
function findOverflowingByteSize() {
const LS = window.localStorage;
const probeKey = `tfjs_test_probe_values_${new Date().getTime()}_${Math.random()}`;
const minKilobytes = 200;
const stepKilobytes = 200;
const maxKilobytes = 40000;
for (let kilobytes = minKilobytes; kilobytes < maxKilobytes; kilobytes += stepKilobytes) {
const bytes = kilobytes * 1024;
const data = new ArrayBuffer(bytes);
try {
const encoded = arrayBufferToBase64String(data);
LS.setItem(probeKey, encoded);
}
catch (err) {
return bytes;
}
LS.removeItem(probeKey);
}
throw new Error(`Unable to determined overflowing byte size up to ${maxKilobytes} kB.`);
}
beforeEach(() => {
purgeLocalStorageArtifacts();
});
afterEach(() => {
purgeLocalStorageArtifacts();
});
it('Save artifacts succeeds', done => {
const testStartDate = new Date();
const handler = tf.io.getSaveHandlers('localstorage://foo/FooModel')[0];
handler.save(artifacts1)
.then(saveResult => {
expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
.toBeGreaterThanOrEqual(testStartDate.getTime());
// Note: The following two assertions work only because there is no
// non-ASCII characters in `modelTopology1` and `weightSpecs1`.
expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
.toEqual(JSON.stringify(modelTopology1).length);
expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
.toEqual(JSON.stringify(weightSpecs1).length);
expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
// Check the content of the saved items in local storage.
const LS = window.localStorage;
const info = JSON.parse(LS.getItem('tensorflowjs_models/foo/FooModel/info'));
expect(Date.parse(info.dateSaved))
.toEqual(saveResult.modelArtifactsInfo.dateSaved.getTime());
expect(info.modelTopologyBytes)
.toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
expect(info.weightSpecsBytes)
.toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
expect(info.weightDataBytes)
.toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
const topologyString = LS.getItem('tensorflowjs_models/foo/FooModel/model_topology');
expect(JSON.stringify(modelTopology1)).toEqual(topologyString);
const weightSpecsString = LS.getItem('tensorflowjs_models/foo/FooModel/weight_specs');
expect(JSON.stringify(weightSpecs1)).toEqual(weightSpecsString);
const weightDataBase64String = LS.getItem('tensorflowjs_models/foo/FooModel/weight_data');
expect(base64StringToArrayBuffer(weightDataBase64String))
.toEqual(weightData1);
done();
})
.catch(err => {
console.error(err.stack);
});
});
it('Save-load round trip succeeds', async () => {
const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
await handler1.save(artifacts1);
const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
const loaded = await handler2.load();
expect(loaded.modelTopology).toEqual(modelTopology1);
expect(loaded.weightSpecs).toEqual(weightSpecs1);
expect(loaded.weightData).toEqual(weightData1);
expect(loaded.format).toEqual('layers-model');
expect(loaded.generatedBy).toEqual('TensorFlow.js v0.0.0');
expect(loaded.convertedBy).toEqual('1.13.1');
expect(loaded.userDefinedMetadata).toEqual({});
expect(loaded.modelInitializer).toEqual({});
});
it('Save-load round trip succeeds: v0 format', async () => {
const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
await handler1.save(artifactsV0);
const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
const loaded = await handler2.load();
expect(loaded.modelTopology).toEqual(modelTopology1);
expect(loaded.weightSpecs).toEqual(weightSpecs1);
expect(loaded.weightData).toEqual(weightData1);
expect(loaded.format).toBeUndefined();
expect(loaded.generatedBy).toBeUndefined();
expect(loaded.convertedBy).toBeUndefined();
expect(loaded.userDefinedMetadata).toBeUndefined();
});
it('Loading nonexistent model fails.', done => {
const handler = tf.io.getSaveHandlers('localstorage://NonexistentModel')[0];
handler.load()
.then(artifacts => {
fail('Loading nonexistent model succeeded unexpectedly.');
})
.catch(err => {
expect(err.message)
.toEqual('In local storage, there is no model with name ' +
'\'NonexistentModel\'');
done();
});
});
it('Loading model with missing topology fails.', done => {
const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
handler1.save(artifacts1)
.then(saveResult => {
// Manually remove the topology item from local storage.
window.localStorage.removeItem('tensorflowjs_models/FooModel/model_topology');
const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
handler2.load()
.then(artifacts => {
fail('Loading of model with missing topology succeeded ' +
'unexpectedly.');
})
.catch(err => {
expect(err.message)
.toEqual('In local storage, the topology of model ' +
'\'FooModel\' is missing.');
done();
});
})
.catch(err => {
console.error(err.stack);
});
});
it('Loading model with missing weight specs fails.', done => {
const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
handler1.save(artifacts1)
.then(saveResult => {
// Manually remove the weight specs item from local storage.
window.localStorage.removeItem('tensorflowjs_models/FooModel/weight_specs');
const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
handler2.load()
.then(artifacts => {
fail('Loading of model with missing weight specs succeeded ' +
'unexpectedly.');
})
.catch(err => {
expect(err.message)
.toEqual('In local storage, the weight specs of model ' +
'\'FooModel\' are missing.');
done();
});
})
.catch(err => {
console.error(err.stack);
});
});
it('Loading model with missing weight data fails.', done => {
const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
handler1.save(artifacts1)
.then(saveResult => {
// Manually remove the weight data item from local storage.
window.localStorage.removeItem('tensorflowjs_models/FooModel/weight_data');
const handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
handler2.load()
.then(artifacts => {
fail('Loading of model with missing weight data succeeded ' +
'unexpectedly.');
})
.catch(err => {
expect(err.message)
.toEqual('In local storage, the binary weight values of model ' +
'\'FooModel\' are missing.');
done();
});
})
.catch(err => {
console.error(err.stack);
});
});
it('Data size too large leads to error thrown', done => {
const overflowByteSize = findOverflowingByteSize();
const overflowArtifacts = {
modelTopology: modelTopology1,
weightSpecs: weightSpecs1,
weightData: new ArrayBuffer(overflowByteSize),
};
const handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
handler1.save(overflowArtifacts)
.then(saveResult => {
fail('Saving of model of overflowing-size weight data succeeded ' +
'unexpectedly.');
})
.catch(err => {
expect(err.message
.indexOf('Failed to save model \'FooModel\' to local storage'))
.toEqual(0);
done();
});
});
it('Null, undefined or empty modelPath throws Error', () => {
expect(() => browserLocalStorage(null))
.toThrowError(/local storage, modelPath must not be null, undefined or empty/);
expect(() => browserLocalStorage(undefined))
.toThrowError(/local storage, modelPath must not be null, undefined or empty/);
expect(() => browserLocalStorage(''))
.toThrowError(/local storage, modelPath must not be null, undefined or empty./);
});
it('router', () => {
expect(localStorageRouter('localstorage://bar') instanceof BrowserLocalStorage)
.toEqual(true);
expect(localStorageRouter('indexeddb://bar')).toBeNull();
expect(localStorageRouter('qux')).toBeNull();
});
it('Manager: List models: 0 result', done => {
// Before any model is saved, listModels should return empty result.
new BrowserLocalStorageManager()
.listModels()
.then(out => {
expect(out).toEqual({});
done();
})
.catch(err => done.fail(err.stack));
});
it('Manager: List models: 1 result', done => {
const handler = tf.io.getSaveHandlers('localstorage://baz/QuxModel')[0];
handler.save(artifacts1)
.then(saveResult => {
// After successful saving, there should be one model.
new BrowserLocalStorageManager()
.listModels()
.then(out => {
expect(Object.keys(out).length).toEqual(1);
expect(out['baz/QuxModel'].modelTopologyType)
.toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
expect(out['baz/QuxModel'].modelTopologyBytes)
.toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
expect(out['baz/QuxModel'].weightSpecsBytes)
.toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
expect(out['baz/QuxModel'].weightDataBytes)
.toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
done();
})
.catch(err => done.fail(err.stack));
})
.catch(err => done.fail(err.stack));
});
it('Manager: List models: 2 results', done => {
// First, save a model.
const handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
handler1.save(artifacts1)
.then(saveResult1 => {
// Then, save the model under another path.
const handler2 = tf.io.getSaveHandlers('localstorage://repeat/QuxModel')[0];
handler2.save(artifacts1)
.then(saveResult2 => {
// After successful saving, there should be two models.
new BrowserLocalStorageManager()
.listModels()
.then(out => {
expect(Object.keys(out).length).toEqual(2);
expect(out['QuxModel'].modelTopologyType)
.toEqual(saveResult1.modelArtifactsInfo.modelTopologyType);
expect(out['QuxModel'].modelTopologyBytes)
.toEqual(saveResult1.modelArtifactsInfo
.modelTopologyBytes);
expect(out['QuxModel'].weightSpecsBytes)
.toEqual(saveResult1.modelArtifactsInfo.weightSpecsBytes);
expect(out['QuxModel'].weightDataBytes)
.toEqual(saveResult1.modelArtifactsInfo.weightDataBytes);
expect(out['repeat/QuxModel'].modelTopologyType)
.toEqual(saveResult2.modelArtifactsInfo.modelTopologyType);
expect(out['repeat/QuxModel'].modelTopologyBytes)
.toEqual(saveResult2.modelArtifactsInfo
.modelTopologyBytes);
expect(out['repeat/QuxModel'].weightSpecsBytes)
.toEqual(saveResult2.modelArtifactsInfo.weightSpecsBytes);
expect(out['repeat/QuxModel'].weightDataBytes)
.toEqual(saveResult2.modelArtifactsInfo.weightDataBytes);
done();
})
.catch(err => done.fail(err.stack));
})
.catch(err => done.fail(err.stack));
})
.catch(err => done.fail(err.stack));
});
it('Manager: Successful deleteModel', done => {
// First, save a model.
const handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
handler1.save(artifacts1)
.then(saveResult1 => {
// Then, save the model under another path.
const handler2 = tf.io.getSaveHandlers('localstorage://repeat/QuxModel')[0];
handler2.save(artifacts1)
.then(saveResult2 => {
// After successful saving, delete the first save, and then
// `listModel` should give only one result.
const manager = new BrowserLocalStorageManager();
manager.removeModel('QuxModel')
.then(deletedInfo => {
manager.listModels().then(out => {
expect(Object.keys(out)).toEqual(['repeat/QuxModel']);
});
done();
})
.catch(err => done.fail(err.stack));
})
.catch(err => done.fail(err.stack));
})
.catch(err => done.fail(err.stack));
});
});
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