initial commit
This commit is contained in:
336
server/node_modules/firebase-admin/lib/machine-learning/machine-learning.js
generated
vendored
Normal file
336
server/node_modules/firebase-admin/lib/machine-learning/machine-learning.js
generated
vendored
Normal file
@@ -0,0 +1,336 @@
|
||||
/*! firebase-admin v13.5.0 */
|
||||
"use strict";
|
||||
/*!
|
||||
* Copyright 2020 Google Inc.
|
||||
*
|
||||
* 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.
|
||||
*/
|
||||
Object.defineProperty(exports, "__esModule", { value: true });
|
||||
exports.Model = exports.MachineLearning = void 0;
|
||||
const index_1 = require("../storage/index");
|
||||
const error_1 = require("../utils/error");
|
||||
const validator = require("../utils/validator");
|
||||
const deep_copy_1 = require("../utils/deep-copy");
|
||||
const utils = require("../utils");
|
||||
const machine_learning_api_client_1 = require("./machine-learning-api-client");
|
||||
const machine_learning_utils_1 = require("./machine-learning-utils");
|
||||
/**
|
||||
* The Firebase `MachineLearning` service interface.
|
||||
*/
|
||||
class MachineLearning {
|
||||
/**
|
||||
* @param app - The app for this ML service.
|
||||
* @constructor
|
||||
* @internal
|
||||
*/
|
||||
constructor(app) {
|
||||
if (!validator.isNonNullObject(app) || !('options' in app)) {
|
||||
throw new error_1.FirebaseError({
|
||||
code: 'machine-learning/invalid-argument',
|
||||
message: 'First argument passed to admin.machineLearning() must be a ' +
|
||||
'valid Firebase app instance.',
|
||||
});
|
||||
}
|
||||
this.appInternal = app;
|
||||
this.client = new machine_learning_api_client_1.MachineLearningApiClient(app);
|
||||
}
|
||||
/**
|
||||
* The {@link firebase-admin.app#App} associated with the current `MachineLearning`
|
||||
* service instance.
|
||||
*/
|
||||
get app() {
|
||||
return this.appInternal;
|
||||
}
|
||||
/**
|
||||
* Creates a model in the current Firebase project.
|
||||
*
|
||||
* @param model - The model to create.
|
||||
*
|
||||
* @returns A Promise fulfilled with the created model.
|
||||
*/
|
||||
createModel(model) {
|
||||
return this.signUrlIfPresent(model)
|
||||
.then((modelContent) => this.client.createModel(modelContent))
|
||||
.then((operation) => this.client.handleOperation(operation))
|
||||
.then((modelResponse) => new Model(modelResponse, this.client));
|
||||
}
|
||||
/**
|
||||
* Updates a model's metadata or model file.
|
||||
*
|
||||
* @param modelId - The ID of the model to update.
|
||||
* @param model - The model fields to update.
|
||||
*
|
||||
* @returns A Promise fulfilled with the updated model.
|
||||
*/
|
||||
updateModel(modelId, model) {
|
||||
const updateMask = utils.generateUpdateMask(model);
|
||||
return this.signUrlIfPresent(model)
|
||||
.then((modelContent) => this.client.updateModel(modelId, modelContent, updateMask))
|
||||
.then((operation) => this.client.handleOperation(operation))
|
||||
.then((modelResponse) => new Model(modelResponse, this.client));
|
||||
}
|
||||
/**
|
||||
* Publishes a Firebase ML model.
|
||||
*
|
||||
* A published model can be downloaded to client apps.
|
||||
*
|
||||
* @param modelId - The ID of the model to publish.
|
||||
*
|
||||
* @returns A Promise fulfilled with the published model.
|
||||
*/
|
||||
publishModel(modelId) {
|
||||
return this.setPublishStatus(modelId, true);
|
||||
}
|
||||
/**
|
||||
* Unpublishes a Firebase ML model.
|
||||
*
|
||||
* @param modelId - The ID of the model to unpublish.
|
||||
*
|
||||
* @returns A Promise fulfilled with the unpublished model.
|
||||
*/
|
||||
unpublishModel(modelId) {
|
||||
return this.setPublishStatus(modelId, false);
|
||||
}
|
||||
/**
|
||||
* Gets the model specified by the given ID.
|
||||
*
|
||||
* @param modelId - The ID of the model to get.
|
||||
*
|
||||
* @returns A Promise fulfilled with the model object.
|
||||
*/
|
||||
getModel(modelId) {
|
||||
return this.client.getModel(modelId)
|
||||
.then((modelResponse) => new Model(modelResponse, this.client));
|
||||
}
|
||||
/**
|
||||
* Lists the current project's models.
|
||||
*
|
||||
* @param options - The listing options.
|
||||
*
|
||||
* @returns A promise that
|
||||
* resolves with the current (filtered) list of models and the next page
|
||||
* token. For the last page, an empty list of models and no page token
|
||||
* are returned.
|
||||
*/
|
||||
listModels(options = {}) {
|
||||
return this.client.listModels(options)
|
||||
.then((resp) => {
|
||||
if (!validator.isNonNullObject(resp)) {
|
||||
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', `Invalid ListModels response: ${JSON.stringify(resp)}`);
|
||||
}
|
||||
let models = [];
|
||||
if (resp.models) {
|
||||
models = resp.models.map((rs) => new Model(rs, this.client));
|
||||
}
|
||||
const result = { models };
|
||||
if (resp.nextPageToken) {
|
||||
result.pageToken = resp.nextPageToken;
|
||||
}
|
||||
return result;
|
||||
});
|
||||
}
|
||||
/**
|
||||
* Deletes a model from the current project.
|
||||
*
|
||||
* @param modelId - The ID of the model to delete.
|
||||
*/
|
||||
deleteModel(modelId) {
|
||||
return this.client.deleteModel(modelId);
|
||||
}
|
||||
setPublishStatus(modelId, publish) {
|
||||
const updateMask = ['state.published'];
|
||||
const options = { state: { published: publish } };
|
||||
return this.client.updateModel(modelId, options, updateMask)
|
||||
.then((operation) => this.client.handleOperation(operation))
|
||||
.then((modelResponse) => new Model(modelResponse, this.client));
|
||||
}
|
||||
signUrlIfPresent(options) {
|
||||
const modelOptions = (0, deep_copy_1.deepCopy)(options);
|
||||
if ((0, machine_learning_api_client_1.isGcsTfliteModelOptions)(modelOptions)) {
|
||||
return this.signUrl(modelOptions.tfliteModel.gcsTfliteUri)
|
||||
.then((uri) => {
|
||||
modelOptions.tfliteModel.gcsTfliteUri = uri;
|
||||
return modelOptions;
|
||||
})
|
||||
.catch((err) => {
|
||||
throw new machine_learning_utils_1.FirebaseMachineLearningError('internal-error', `Error during signing upload url: ${err.message}`);
|
||||
});
|
||||
}
|
||||
return Promise.resolve(modelOptions);
|
||||
}
|
||||
signUrl(unsignedUrl) {
|
||||
const MINUTES_IN_MILLIS = 60 * 1000;
|
||||
const URL_VALID_DURATION = 10 * MINUTES_IN_MILLIS;
|
||||
const gcsRegex = /^gs:\/\/([a-z0-9_.-]{3,63})\/(.+)$/;
|
||||
const matches = gcsRegex.exec(unsignedUrl);
|
||||
if (!matches) {
|
||||
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-argument', `Invalid unsigned url: ${unsignedUrl}`);
|
||||
}
|
||||
const bucketName = matches[1];
|
||||
const blobName = matches[2];
|
||||
const bucket = (0, index_1.getStorage)(this.app).bucket(bucketName);
|
||||
const blob = bucket.file(blobName);
|
||||
return blob.getSignedUrl({
|
||||
action: 'read',
|
||||
expires: Date.now() + URL_VALID_DURATION,
|
||||
}).then((signUrl) => signUrl[0]);
|
||||
}
|
||||
}
|
||||
exports.MachineLearning = MachineLearning;
|
||||
/**
|
||||
* A Firebase ML Model output object.
|
||||
*/
|
||||
class Model {
|
||||
/**
|
||||
* @internal
|
||||
*/
|
||||
constructor(model, client) {
|
||||
this.model = Model.validateAndClone(model);
|
||||
this.client = client;
|
||||
}
|
||||
/** The ID of the model. */
|
||||
get modelId() {
|
||||
return extractModelId(this.model.name);
|
||||
}
|
||||
/**
|
||||
* The model's name. This is the name you use from your app to load the
|
||||
* model.
|
||||
*/
|
||||
get displayName() {
|
||||
return this.model.displayName;
|
||||
}
|
||||
/**
|
||||
* The model's tags, which can be used to group or filter models in list
|
||||
* operations.
|
||||
*/
|
||||
get tags() {
|
||||
return this.model.tags || [];
|
||||
}
|
||||
/** The timestamp of the model's creation. */
|
||||
get createTime() {
|
||||
return new Date(this.model.createTime).toUTCString();
|
||||
}
|
||||
/** The timestamp of the model's most recent update. */
|
||||
get updateTime() {
|
||||
return new Date(this.model.updateTime).toUTCString();
|
||||
}
|
||||
/** Error message when model validation fails. */
|
||||
get validationError() {
|
||||
return this.model.state?.validationError?.message;
|
||||
}
|
||||
/** True if the model is published. */
|
||||
get published() {
|
||||
return this.model.state?.published || false;
|
||||
}
|
||||
/**
|
||||
* The ETag identifier of the current version of the model. This value
|
||||
* changes whenever you update any of the model's properties.
|
||||
*/
|
||||
get etag() {
|
||||
return this.model.etag;
|
||||
}
|
||||
/**
|
||||
* The hash of the model's `tflite` file. This value changes only when
|
||||
* you upload a new TensorFlow Lite model.
|
||||
*/
|
||||
get modelHash() {
|
||||
return this.model.modelHash;
|
||||
}
|
||||
/** Metadata about the model's TensorFlow Lite model file. */
|
||||
get tfliteModel() {
|
||||
// Make a copy so people can't directly modify the private this.model object.
|
||||
return (0, deep_copy_1.deepCopy)(this.model.tfliteModel);
|
||||
}
|
||||
/**
|
||||
* True if the model is locked by a server-side operation. You can't make
|
||||
* changes to a locked model. See {@link Model.waitForUnlocked}.
|
||||
*/
|
||||
get locked() {
|
||||
return (this.model.activeOperations?.length ?? 0) > 0;
|
||||
}
|
||||
/**
|
||||
* Return the model as a JSON object.
|
||||
*/
|
||||
toJSON() {
|
||||
// We can't just return this.model because it has extra fields and
|
||||
// different formats etc. So we build the expected model object.
|
||||
const jsonModel = {
|
||||
modelId: this.modelId,
|
||||
displayName: this.displayName,
|
||||
tags: this.tags,
|
||||
createTime: this.createTime,
|
||||
updateTime: this.updateTime,
|
||||
published: this.published,
|
||||
etag: this.etag,
|
||||
locked: this.locked,
|
||||
};
|
||||
// Also add possibly undefined fields if they exist.
|
||||
if (this.validationError) {
|
||||
jsonModel['validationError'] = this.validationError;
|
||||
}
|
||||
if (this.modelHash) {
|
||||
jsonModel['modelHash'] = this.modelHash;
|
||||
}
|
||||
if (this.tfliteModel) {
|
||||
jsonModel['tfliteModel'] = this.tfliteModel;
|
||||
}
|
||||
return jsonModel;
|
||||
}
|
||||
/**
|
||||
* Wait for the model to be unlocked.
|
||||
*
|
||||
* @param maxTimeMillis - The maximum time in milliseconds to wait.
|
||||
* If not specified, a default maximum of 2 minutes is used.
|
||||
*
|
||||
* @returns A promise that resolves when the model is unlocked
|
||||
* or the maximum wait time has passed.
|
||||
*/
|
||||
waitForUnlocked(maxTimeMillis) {
|
||||
if ((this.model.activeOperations?.length ?? 0) > 0) {
|
||||
// The client will always be defined on Models that have activeOperations
|
||||
// because models with active operations came back from the server and
|
||||
// were constructed with a non-empty client.
|
||||
return this.client.handleOperation(this.model.activeOperations[0], { wait: true, maxTimeMillis })
|
||||
.then((modelResponse) => {
|
||||
this.model = Model.validateAndClone(modelResponse);
|
||||
});
|
||||
}
|
||||
return Promise.resolve();
|
||||
}
|
||||
static validateAndClone(model) {
|
||||
if (!validator.isNonNullObject(model) ||
|
||||
!validator.isNonEmptyString(model.name) ||
|
||||
!validator.isNonEmptyString(model.createTime) ||
|
||||
!validator.isNonEmptyString(model.updateTime) ||
|
||||
!validator.isNonEmptyString(model.displayName) ||
|
||||
!validator.isNonEmptyString(model.etag)) {
|
||||
throw new machine_learning_utils_1.FirebaseMachineLearningError('invalid-server-response', `Invalid Model response: ${JSON.stringify(model)}`);
|
||||
}
|
||||
const tmpModel = (0, deep_copy_1.deepCopy)(model);
|
||||
// If tflite Model is specified, it must have a source of {gcsTfliteUri}
|
||||
if (model.tfliteModel &&
|
||||
!validator.isNonEmptyString(model.tfliteModel.gcsTfliteUri)) {
|
||||
// If we have some other source, ignore the whole tfliteModel.
|
||||
delete tmpModel.tfliteModel;
|
||||
}
|
||||
// Remove '@type' field. We don't need it.
|
||||
if (tmpModel['@type']) {
|
||||
delete tmpModel['@type'];
|
||||
}
|
||||
return tmpModel;
|
||||
}
|
||||
}
|
||||
exports.Model = Model;
|
||||
function extractModelId(resourceName) {
|
||||
return resourceName.split('/').pop();
|
||||
}
|
||||
Reference in New Issue
Block a user