@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.
* =============================================================================
*/
/**
* IOHandler implementations based on HTTP requests in the web browser.
*
* Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).
*/
import { env } from '../environment';
import { assert } from '../util';
import { concatenateArrayBuffers, getModelArtifactsInfoForJSON } from './io_utils';
import { IORouterRegistry } from './router_registry';
import { loadWeightsAsArrayBuffer } from './weights_loader';
const OCTET_STREAM_MIME_TYPE = 'application/octet-stream';
const JSON_TYPE = 'application/json';
export class HTTPRequest {
constructor(path, loadOptions) {
this.DEFAULT_METHOD = 'POST';
if (loadOptions == null) {
loadOptions = {};
}
this.weightPathPrefix = loadOptions.weightPathPrefix;
this.onProgress = loadOptions.onProgress;
this.weightUrlConverter = loadOptions.weightUrlConverter;
if (loadOptions.fetchFunc != null) {
assert(typeof loadOptions.fetchFunc === 'function', () => 'Must pass a function that matches the signature of ' +
'`fetch` (see ' +
'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');
this.fetch = loadOptions.fetchFunc;
}
else {
this.fetch = env().platform.fetch;
}
assert(path != null && path.length > 0, () => 'URL path for http must not be null, undefined or ' +
'empty.');
if (Array.isArray(path)) {
assert(path.length === 2, () => 'URL paths for http must have a length of 2, ' +
`(actual length is ${path.length}).`);
}
this.path = path;
if (loadOptions.requestInit != null &&
loadOptions.requestInit.body != null) {
throw new Error('requestInit is expected to have no pre-existing body, but has one.');
}
this.requestInit = loadOptions.requestInit || {};
}
async save(modelArtifacts) {
if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +
'in binary formats yet.');
}
const init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
init.body = new FormData();
const weightsManifest = [{
paths: ['./model.weights.bin'],
weights: modelArtifacts.weightSpecs,
}];
const modelTopologyAndWeightManifest = {
modelTopology: modelArtifacts.modelTopology,
format: modelArtifacts.format,
generatedBy: modelArtifacts.generatedBy,
convertedBy: modelArtifacts.convertedBy,
weightsManifest
};
if (modelArtifacts.signature != null) {
modelTopologyAndWeightManifest.signature = modelArtifacts.signature;
}
if (modelArtifacts.userDefinedMetadata != null) {
modelTopologyAndWeightManifest.userDefinedMetadata =
modelArtifacts.userDefinedMetadata;
}
if (modelArtifacts.modelInitializer != null) {
modelTopologyAndWeightManifest.modelInitializer =
modelArtifacts.modelInitializer;
}
init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: JSON_TYPE }), 'model.json');
if (modelArtifacts.weightData != null) {
init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: OCTET_STREAM_MIME_TYPE }), 'model.weights.bin');
}
const response = await this.fetch(this.path, init);
if (response.ok) {
return {
modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
responses: [response],
};
}
else {
throw new Error(`BrowserHTTPRequest.save() failed due to HTTP response status ` +
`${response.status}.`);
}
}
/**
* Load model artifacts via HTTP request(s).
*
* See the documentation to `tf.io.http` for details on the saved
* artifacts.
*
* @returns The loaded model artifacts (if loading succeeds).
*/
async load() {
const modelConfigRequest = await this.fetch(this.path, this.requestInit);
if (!modelConfigRequest.ok) {
throw new Error(`Request to ${this.path} failed with status code ` +
`${modelConfigRequest.status}. Please verify this URL points to ` +
`the model JSON of the model to load.`);
}
let modelConfig;
try {
modelConfig = await modelConfigRequest.json();
}
catch (e) {
let message = `Failed to parse model JSON of response from ${this.path}.`;
// TODO(nsthorat): Remove this after some time when we're comfortable that
// .pb files are mostly gone.
if (this.path.endsWith('.pb')) {
message += ' Your path contains a .pb file extension. ' +
'Support for .pb models have been removed in TensorFlow.js 1.0 ' +
'in favor of .json models. You can re-convert your Python ' +
'TensorFlow model using the TensorFlow.js 1.0 conversion scripts ' +
'or you can convert your.pb models with the \'pb2json\'' +
'NPM script in the tensorflow/tfjs-converter repository.';
}
else {
message += ' Please make sure the server is serving valid ' +
'JSON for this request.';
}
throw new Error(message);
}
const modelTopology = modelConfig.modelTopology;
const weightsManifest = modelConfig.weightsManifest;
const generatedBy = modelConfig.generatedBy;
const convertedBy = modelConfig.convertedBy;
const format = modelConfig.format;
const signature = modelConfig.signature;
const userDefinedMetadata = modelConfig.userDefinedMetadata;
// We do not allow both modelTopology and weightsManifest to be missing.
if (modelTopology == null && weightsManifest == null) {
throw new Error(`The JSON from HTTP path ${this.path} contains neither model ` +
`topology or manifest for weights.`);
}
let weightSpecs;
let weightData;
if (weightsManifest != null) {
const results = await this.loadWeights(weightsManifest);
[weightSpecs, weightData] = results;
}
const artifacts = {
modelTopology,
weightSpecs,
weightData,
generatedBy,
convertedBy,
format
};
if (signature != null) {
artifacts.signature = signature;
}
if (userDefinedMetadata != null) {
artifacts.userDefinedMetadata = userDefinedMetadata;
}
const initializer = modelConfig.modelInitializer;
if (initializer) {
artifacts.modelInitializer = initializer;
}
return artifacts;
}
async loadWeights(weightsManifest) {
const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
const [prefix, suffix] = parseUrl(weightPath);
const pathPrefix = this.weightPathPrefix || prefix;
const weightSpecs = [];
for (const entry of weightsManifest) {
weightSpecs.push(...entry.weights);
}
const fetchURLs = [];
const urlPromises = [];
for (const weightsGroup of weightsManifest) {
for (const path of weightsGroup.paths) {
if (this.weightUrlConverter != null) {
urlPromises.push(this.weightUrlConverter(path));
}
else {
fetchURLs.push(pathPrefix + path + suffix);
}
}
}
if (this.weightUrlConverter) {
fetchURLs.push(...await Promise.all(urlPromises));
}
const buffers = await loadWeightsAsArrayBuffer(fetchURLs, {
requestInit: this.requestInit,
fetchFunc: this.fetch,
onProgress: this.onProgress
});
return [weightSpecs, concatenateArrayBuffers(buffers)];
}
}
HTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
/**
* Extract the prefix and suffix of the url, where the prefix is the path before
* the last file, and suffix is the search params after the last file.
* ```
* const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'
* [prefix, suffix] = parseUrl(url)
* // prefix = 'http://tfhub.dev/model/1/'
* // suffix = '?tfjs-format=file'
* ```
* @param url the model url to be parsed.
*/
export function parseUrl(url) {
const lastSlash = url.lastIndexOf('/');
const lastSearchParam = url.lastIndexOf('?');
const prefix = url.substring(0, lastSlash);
const suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';
return [prefix + '/', suffix];
}
export function isHTTPScheme(url) {
return url.match(HTTPRequest.URL_SCHEME_REGEX) != null;
}
export const httpRouter = (url, loadOptions) => {
if (typeof fetch === 'undefined' &&
(loadOptions == null || loadOptions.fetchFunc == null)) {
// `http` uses `fetch` or `node-fetch`, if one wants to use it in
// an environment that is not the browser or node they have to setup a
// global fetch polyfill.
return null;
}
else {
let isHTTP = true;
if (Array.isArray(url)) {
isHTTP = url.every(urlItem => isHTTPScheme(urlItem));
}
else {
isHTTP = isHTTPScheme(url);
}
if (isHTTP) {
return http(url, loadOptions);
}
}
return null;
};
IORouterRegistry.registerSaveRouter(httpRouter);
IORouterRegistry.registerLoadRouter(httpRouter);
/**
* Creates an IOHandler subtype that sends model artifacts to HTTP server.
*
* An HTTP request of the `multipart/form-data` mime type will be sent to the
* `path` URL. The form data includes artifacts that represent the topology
* and/or weights of the model. In the case of Keras-style `tf.Model`, two
* blobs (files) exist in form-data:
* - A JSON file consisting of `modelTopology` and `weightsManifest`.
* - A binary weights file consisting of the concatenated weight values.
* These files are in the same format as the one generated by
* [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).
*
* The following code snippet exemplifies the client-side code that uses this
* function:
*
* ```js
* const model = tf.sequential();
* model.add(
* tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));
*
* const saveResult = await model.save(tf.io.http(
* 'http://model-server:5000/upload', {requestInit: {method: 'PUT'}}));
* console.log(saveResult);
* ```
*
* If the default `POST` method is to be used, without any custom parameters
* such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:
*
* ```js
* const saveResult = await model.save('http://model-server:5000/upload');
* ```
*
* The following GitHub Gist
* https://gist.github.com/dsmilkov/1b6046fd6132d7408d5257b0976f7864
* implements a server based on [flask](https://github.com/pallets/flask) that
* can receive the request. Upon receiving the model artifacts via the requst,
* this particular server reconsistutes instances of [Keras
* Models](https://keras.io/models/model/) in memory.
*
*
* @param path A URL path to the model.
* Can be an absolute HTTP path (e.g.,
* 'http://localhost:8000/model-upload)') or a relative path (e.g.,
* './model-upload').
* @param requestInit Request configurations to be used when sending
* HTTP request to server using `fetch`. It can contain fields such as
* `method`, `credentials`, `headers`, `mode`, etc. See
* https://developer.mozilla.org/en-US/docs/Web/API/Request/Request
* for more information. `requestInit` must not have a body, because the
* body will be set by TensorFlow.js. File blobs representing the model
* topology (filename: 'model.json') and the weights of the model (filename:
* 'model.weights.bin') will be appended to the body. If `requestInit` has a
* `body`, an Error will be thrown.
* @param loadOptions Optional configuration for the loading. It includes the
* following fields:
* - weightPathPrefix Optional, this specifies the path prefix for weight
* files, by default this is calculated from the path param.
* - fetchFunc Optional, custom `fetch` function. E.g., in Node.js,
* the `fetch` from node-fetch can be used here.
* - onProgress Optional, progress callback function, fired periodically
* before the load is completed.
* @returns An instance of `IOHandler`.
*
* @doc {
* heading: 'Models',
* subheading: 'Loading',
* namespace: 'io',
* ignoreCI: true
* }
*/
export function http(path, loadOptions) {
return new HTTPRequest(path, loadOptions);
}
/**
* Deprecated. Use `tf.io.http`.
* @param path
* @param loadOptions
*/
export function browserHTTPRequest(path, loadOptions) {
return http(path, loadOptions);
}
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