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node_http_test.ts
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168 lines (155 loc) · 5.25 KB
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/**
* @license
* Copyright 2018 Google Inc. 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 tfc from '@tensorflow/tfjs-core';
import * as tfl from '@tensorflow/tfjs-layers';
import * as tfn from '../index';
// We still need node-fetch so that we can mock the core
// tfc.env().platform.fetch call and return a valid response.
// tslint:disable-next-line:no-require-imports
const fetch = require('node-fetch');
const OCTET_STREAM_TYPE = 'application/octet-stream';
const JSON_TYPE = 'application/json';
// 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'
};
describe('nodeHTTPRequest-load', () => {
let requestInits: RequestInit[];
const setupFakeWeightFiles = (fileBufferMap: {
[filename: string]: string|Float32Array|Int32Array|ArrayBuffer|Uint8Array|
Uint16Array
}) => {
spyOn(tfc.env().platform, 'fetch')
.and.callFake((path: string, init: RequestInit) => {
return new Promise((resolve, reject) => {
let contentType = '';
if (path.endsWith('model.json')) {
contentType = JSON_TYPE;
} else if (
path.endsWith('weightfile0') || path.endsWith('weightfile1')) {
contentType = OCTET_STREAM_TYPE;
} else {
reject(new Error(`Invalid path: ${path}`));
}
requestInits.push(init);
resolve(new fetch.Response(
fileBufferMap[path],
{'headers': {'Content-Type': contentType}}));
});
});
};
beforeEach(() => {
requestInits = [];
});
it('Constructor', () => {
const handler = tfn.io.nodeHTTPRequest('./foo_model.json');
expect(handler == null).toEqual(false);
expect(typeof handler.load).toEqual('function');
expect(typeof handler.save).toEqual('function');
});
it('Load through NodeHTTPRequest object', async () => {
const weightManifest1: tfc.io.WeightsManifestConfig = [{
paths: ['weightfile0'],
weights: [
{
name: 'dense/kernel',
shape: [3, 1],
dtype: 'float32',
},
{
name: 'dense/bias',
shape: [1],
dtype: 'float32',
}
]
}];
const floatData = new Float32Array([1, 3, 3, 7]);
setupFakeWeightFiles({
'http://localhost/model.json': JSON.stringify(
{modelTopology: modelTopology1, weightsManifest: weightManifest1}),
'http://localhost/weightfile0': floatData,
});
const handler = tfn.io.nodeHTTPRequest(
'http://localhost/model.json',
{credentials: 'include', cache: 'no-cache'});
const modelArtifacts = await handler.load();
expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
expect(requestInits).toEqual([
{credentials: 'include', cache: 'no-cache'},
{credentials: 'include', cache: 'no-cache'}
]);
});
it('Load through registered handler', async () => {
const weightManifest1: tfc.io.WeightsManifestConfig = [{
paths: ['weightfile0'],
weights: [
{
name: 'dense/kernel',
shape: [3, 1],
dtype: 'float32',
},
{
name: 'dense/bias',
shape: [1],
dtype: 'float32',
}
]
}];
const floatData = new Float32Array([1, 3, 3, 7]);
setupFakeWeightFiles({
'https://localhost/model.json': JSON.stringify(
{modelTopology: modelTopology1, weightsManifest: weightManifest1}),
'https://localhost/weightfile0': floatData,
});
const model = await tfl.loadLayersModel('https://localhost/model.json');
expect(model.inputs.length).toEqual(1);
expect(model.inputs[0].shape).toEqual([null, 3]);
expect(model.outputs.length).toEqual(1);
expect(model.outputs[0].shape).toEqual([null, 1]);
});
});