@tensorflow/tfjs-node/dist/saved_model.js

"use strict";
/**
 * @license
 * Copyright 2019 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); } }
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        step((generator = generator.apply(thisArg, _arguments || [])).next());
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};
var __generator = (this && this.__generator) || function (thisArg, body) {
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    }
};
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_1 = require("@tensorflow/tfjs");
var fs = require("fs");
var util_1 = require("util");
var nodejs_kernel_backend_1 = require("./nodejs_kernel_backend");
var readFile = util_1.promisify(fs.readFile);
// tslint:disable-next-line:no-require-imports
var messages = require('./proto/api_pb');
var SAVED_MODEL_FILE_NAME = '/saved_model.pb';
var SAVED_MODEL_INIT_OP_KEY = '__saved_model_init_op';
// This map is used to keep track of loaded SavedModel metagraph mapping
// information. The map key is TFSavedModel id in JavaScript, value is
// an object of path to the SavedModel, metagraph tags, and loaded Session ID in
// the c++ bindings. When user loads a SavedModel signature, it will go through
// entries in this map to find if the corresponding SavedModel session has
// already been loaded in C++ addon and will reuse it if existing.
var loadedSavedModelPathMap = new Map();
// The ID of loaded TFSavedModel. This ID is used to keep track of loaded
// TFSavedModel, so the loaded session in c++ bindings for the corresponding
// TFSavedModel can be properly reused/disposed.
var nextTFSavedModelId = 0;
/**
 * Get a key in an object by its value. This is used to get protobuf enum value
 * from index.
 *
 * @param object
 * @param value
 */
// tslint:disable-next-line:no-any
function getEnumKeyFromValue(object, value) {
    return Object.keys(object).find(function (key) { return object[key] === value; });
}
exports.getEnumKeyFromValue = getEnumKeyFromValue;
/**
 * Read SavedModel proto message from path.
 *
 * @param path Path to SavedModel folder.
 */
function readSavedModelProto(path) {
    return __awaiter(this, void 0, void 0, function () {
        var modelFile, array;
        return __generator(this, function (_a) {
            switch (_a.label) {
                case 0:
                    // Load the SavedModel pb file and deserialize it into message.
                    try {
                        fs.accessSync(path + SAVED_MODEL_FILE_NAME, fs.constants.R_OK);
                    }
                    catch (error) {
                        throw new Error('There is no saved_model.pb file in the directory: ' + path);
                    }
                    return [4 /*yield*/, readFile(path + SAVED_MODEL_FILE_NAME)];
                case 1:
                    modelFile = _a.sent();
                    array = new Uint8Array(modelFile);
                    return [2 /*return*/, messages.SavedModel.deserializeBinary(array)];
            }
        });
    });
}
exports.readSavedModelProto = readSavedModelProto;
/**
 * Inspect the MetaGraphs of the SavedModel from the provided path. This
 * function will return an array of `MetaGraphInfo` objects.
 *
 * @param path Path to SavedModel folder.
 *
 * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
 */
function getMetaGraphsFromSavedModel(path) {
    return __awaiter(this, void 0, void 0, function () {
        var result, modelMessage, metaGraphList, i, metaGraph, tags, signatureDef, signatureDefMap, signatureDefKeys, key, signatureDefEntry, inputsMapMessage, inputsMapKeys, inputs, inputsMapKey, inputTensor, inputTensorInfo, outputsMapMessage, outputsMapKeys, outputs, outputsMapKey, outputTensor, outputTensorInfo;
        return __generator(this, function (_a) {
            switch (_a.label) {
                case 0:
                    result = [];
                    return [4 /*yield*/, readSavedModelProto(path)];
                case 1:
                    modelMessage = _a.sent();
                    metaGraphList = modelMessage.getMetaGraphsList();
                    for (i = 0; i < metaGraphList.length; i++) {
                        metaGraph = {};
                        tags = metaGraphList[i].getMetaInfoDef().getTagsList();
                        metaGraph.tags = tags;
                        signatureDef = {};
                        signatureDefMap = metaGraphList[i].getSignatureDefMap();
                        signatureDefKeys = signatureDefMap.keys();
                        // Go through all signatureDefs
                        while (true) {
                            key = signatureDefKeys.next();
                            if (key.done) {
                                break;
                            }
                            // Skip TensorFlow internal Signature '__saved_model_init_op'.
                            if (key.value === SAVED_MODEL_INIT_OP_KEY) {
                                continue;
                            }
                            signatureDefEntry = signatureDefMap.get(key.value);
                            inputsMapMessage = signatureDefEntry.getInputsMap();
                            inputsMapKeys = inputsMapMessage.keys();
                            inputs = {};
                            while (true) {
                                inputsMapKey = inputsMapKeys.next();
                                if (inputsMapKey.done) {
                                    break;
                                }
                                inputTensor = inputsMapMessage.get(inputsMapKey.value);
                                inputTensorInfo = {};
                                inputTensorInfo.dtype = mapTFDtypeToJSDtype(getEnumKeyFromValue(messages.DataType, inputTensor.getDtype()));
                                inputTensorInfo.name = inputTensor.getName();
                                inputTensorInfo.shape = inputTensor.getTensorShape().getDimList();
                                inputs[inputsMapKey.value] = inputTensorInfo;
                            }
                            outputsMapMessage = signatureDefEntry.getOutputsMap();
                            outputsMapKeys = outputsMapMessage.keys();
                            outputs = {};
                            while (true) {
                                outputsMapKey = outputsMapKeys.next();
                                if (outputsMapKey.done) {
                                    break;
                                }
                                outputTensor = outputsMapMessage.get(outputsMapKey.value);
                                outputTensorInfo = {};
                                outputTensorInfo.dtype = mapTFDtypeToJSDtype(getEnumKeyFromValue(messages.DataType, outputTensor.getDtype()));
                                outputTensorInfo.name = outputTensor.getName();
                                outputTensorInfo.shape = outputTensor.getTensorShape().getDimList();
                                outputs[outputsMapKey.value] = outputTensorInfo;
                            }
                            signatureDef[key.value] = { inputs: inputs, outputs: outputs };
                        }
                        metaGraph.signatureDefs = signatureDef;
                        result.push(metaGraph);
                    }
                    return [2 /*return*/, result];
            }
        });
    });
}
exports.getMetaGraphsFromSavedModel = getMetaGraphsFromSavedModel;
/**
 * Get input and output node names from SavedModel metagraphs info. The
 * input.output node names will be used when executing a SavedModel signature.
 *
 * @param savedModelInfo The MetaGraphInfo array loaded through
 *     getMetaGraphsFromSavedModel().
 * @param tags The tags of the MetaGraph to get input/output node names from.
 * @param signature The signature to get input/output node names from.
 */
function getInputAndOutputNodeNameFromMetaGraphInfo(savedModelInfo, tags, signature) {
    for (var i = 0; i < savedModelInfo.length; i++) {
        var metaGraphInfo = savedModelInfo[i];
        if (stringArraysHaveSameElements(tags, metaGraphInfo.tags)) {
            if (metaGraphInfo.signatureDefs[signature] == null) {
                throw new Error('The SavedModel does not have signature: ' + signature);
            }
            var inputNodeNames = {};
            var outputNodeNames = {};
            for (var _i = 0, _a = Object.keys(metaGraphInfo.signatureDefs); _i < _a.length; _i++) {
                var signatureDef = _a[_i];
                if (signatureDef === signature) {
                    for (var _b = 0, _c = Object.keys(metaGraphInfo.signatureDefs[signature].inputs); _b < _c.length; _b++) {
                        var tensorName = _c[_b];
                        inputNodeNames[tensorName] =
                            metaGraphInfo.signatureDefs[signature].inputs[tensorName].name;
                    }
                    for (var _d = 0, _e = Object.keys(metaGraphInfo.signatureDefs[signature].outputs); _d < _e.length; _d++) {
                        var tensorName = _e[_d];
                        outputNodeNames[tensorName] =
                            metaGraphInfo.signatureDefs[signature].outputs[tensorName].name;
                    }
                }
            }
            return [inputNodeNames, outputNodeNames];
        }
    }
    throw new Error("The SavedModel does not have tags: " + tags);
}
exports.getInputAndOutputNodeNameFromMetaGraphInfo = getInputAndOutputNodeNameFromMetaGraphInfo;
/**
 * A `tf.TFSavedModel` is a signature loaded from a SavedModel
 * metagraph, and allows inference execution.
 *
 * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
 */
var TFSavedModel = /** @class */ (function () {
    function TFSavedModel(sessionId, jsid, inputNodeNames, outputNodeNames, backend) {
        this.sessionId = sessionId;
        this.jsid = jsid;
        this.inputNodeNames = inputNodeNames;
        this.outputNodeNames = outputNodeNames;
        this.backend = backend;
        this.disposed = false;
    }
    Object.defineProperty(TFSavedModel.prototype, "inputs", {
        /**
         * Return the array of input tensor info.
         *
         * @doc {heading: 'Models', subheading: 'SavedModel'}
         */
        get: function () {
            throw new Error('SavedModel inputs information is not available yet.');
        },
        enumerable: true,
        configurable: true
    });
    Object.defineProperty(TFSavedModel.prototype, "outputs", {
        /**
         * Return the array of output tensor info.
         *
         * @doc {heading: 'Models', subheading: 'SavedModel'}
         */
        get: function () {
            throw new Error('SavedModel outputs information is not available yet.');
        },
        enumerable: true,
        configurable: true
    });
    /**
     * Delete the SavedModel from nodeBackend and delete corresponding session in
     * the C++ backend if the session is only used by this TFSavedModel.
     *
     * @doc {heading: 'Models', subheading: 'SavedModel'}
     */
    TFSavedModel.prototype.dispose = function () {
        if (!this.disposed) {
            this.disposed = true;
            loadedSavedModelPathMap.delete(this.jsid);
            for (var _i = 0, _a = Array.from(loadedSavedModelPathMap.keys()); _i < _a.length; _i++) {
                var id = _a[_i];
                var value = loadedSavedModelPathMap.get(id);
                if (value.sessionId === this.sessionId) {
                    return;
                }
            }
            this.backend.deleteSavedModel(this.sessionId);
        }
        else {
            throw new Error('This SavedModel has already been deleted.');
        }
    };
    /**
     * Execute the inference for the input tensors.
     *
     * @param input The input tensors, when there is single input for the model,
     * inputs param should be a Tensor. For models with multiple inputs, inputs
     * params should be in either Tensor[] if the input order is fixed, or
     * otherwise NamedTensorMap format. The keys in the NamedTensorMap are the
     * name of input tensors in SavedModel signatureDef. It can be found through
     * `tf.node.getMetaGraphsFromSavedModel()`.
     *
     * For batch inference execution, the tensors for each input need to be
     * concatenated together. For example with mobilenet, the required input shape
     * is [1, 244, 244, 3], which represents the [batch, height, width, channel].
     * If we are provide a batched data of 100 images, the input tensor should be
     * in the shape of [100, 244, 244, 3].
     *
     * @param config Prediction configuration for specifying the batch size.
     *
     * @returns Inference result tensors. The output would be single Tensor if
     * model has single output node, otherwise Tensor[] or NamedTensorMap[] will
     * be returned for model with multiple outputs.
     *
     * @doc {heading: 'Models', subheading: 'SavedModel'}
     */
    TFSavedModel.prototype.predict = function (inputs, config) {
        var _this = this;
        if (this.disposed) {
            throw new Error('The TFSavedModel has already been deleted!');
        }
        else {
            var inputTensors = [];
            if (inputs instanceof tfjs_1.Tensor) {
                inputTensors.push(inputs);
                var result = this.backend.runSavedModel(this.sessionId, inputTensors, Object.values(this.inputNodeNames), Object.values(this.outputNodeNames));
                return result.length > 1 ? result : result[0];
            }
            else if (Array.isArray(inputs)) {
                inputTensors = inputs;
                return this.backend.runSavedModel(this.sessionId, inputTensors, Object.values(this.inputNodeNames), Object.values(this.outputNodeNames));
            }
            else {
                var inputTensorNames = Object.keys(this.inputNodeNames);
                var providedInputNames = Object.keys(inputs);
                if (!stringArraysHaveSameElements(inputTensorNames, providedInputNames)) {
                    throw new Error("The model signatureDef input names are " + inputTensorNames.join() + ", however the provided input names are " + providedInputNames.join() + ".");
                }
                var inputNodeNamesArray = [];
                for (var i = 0; i < inputTensorNames.length; i++) {
                    inputTensors.push(inputs[inputTensorNames[i]]);
                    inputNodeNamesArray.push(this.inputNodeNames[inputTensorNames[i]]);
                }
                var outputTensorNames = Object.keys(this.outputNodeNames);
                var outputNodeNamesArray = [];
                for (var i = 0; i < outputTensorNames.length; i++) {
                    outputNodeNamesArray.push(this.outputNodeNames[outputTensorNames[i]]);
                }
                var outputTensors_1 = this.backend.runSavedModel(this.sessionId, inputTensors, inputNodeNamesArray, outputNodeNamesArray);
                tfjs_1.util.assert(outputTensors_1.length === outputNodeNamesArray.length, function () { return 'Output tensors do not match output node names, ' +
                    ("receive " + outputTensors_1.length + ") output tensors but ") +
                    ("there are " + _this.outputNodeNames.length + " output nodes."); });
                var outputMap = {};
                for (var i = 0; i < outputTensorNames.length; i++) {
                    outputMap[outputTensorNames[i]] = outputTensors_1[i];
                }
                return outputMap;
            }
        }
    };
    /**
     * Execute the inference for the input tensors and return activation
     * values for specified output node names without batching.
     *
     * @param input The input tensors, when there is single input for the model,
     * inputs param should be a Tensor. For models with multiple inputs, inputs
     * params should be in either Tensor[] if the input order is fixed, or
     * otherwise NamedTensorMap format.
     *
     * @param outputs string|string[]. List of output node names to retrieve
     * activation from.
     *
     * @returns Activation values for the output nodes result tensors. The return
     * type matches specified parameter outputs type. The output would be single
     * Tensor if single output is specified, otherwise Tensor[] for multiple
     * outputs.
     *
     * @doc {heading: 'Models', subheading: 'SavedModel'}
     */
    TFSavedModel.prototype.execute = function (inputs, outputs) {
        throw new Error('execute() of TFSavedModel is not supported yet.');
    };
    return TFSavedModel;
}());
exports.TFSavedModel = TFSavedModel;
/**
 * Load a TensorFlow SavedModel from disk. TensorFlow SavedModel is different
 * from TensorFlow.js model format. A SavedModel is a directory containing
 * serialized signatures and the states needed to run them. The directory has a
 * saved_model.pb (or saved_model.pbtxt) file storing the actual TensorFlow
 * program, or model, and a set of named signatures, each identifying a
 * function. The directory also has a variables directory contains a standard
 * training checkpoint. The directory may also has a assets directory contains
 * files used by the TensorFlow graph, for example text files used to initialize
 * vocabulary tables. These are supported datatypes: float32, int32, complex64,
 * string.For more information, see this guide:
 * https://www.tensorflow.org/guide/saved_model.
 *
 * @param path The path to the SavedModel.
 * @param tags The tags of the MetaGraph to load. The available tags of a
 *     SavedModel can be retrieved through tf.node.getMetaGraphsFromSavedModel()
 *     API. Defaults to ['serve'].
 * @param signature The name of the SignatureDef to load. The available
 *     SignatureDefs of a SavedModel can be retrieved through
 *     tf.node.getMetaGraphsFromSavedModel() API. Defaults to 'serving_default'.
 *
 * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
 */
function loadSavedModel(path, tags, signature) {
    if (tags === void 0) { tags = ['serve']; }
    if (signature === void 0) { signature = 'serving_default'; }
    return __awaiter(this, void 0, void 0, function () {
        var backend, savedModelInfo, _a, inputNodeNames, outputNodeNames, sessionId, _i, _b, id_1, modelInfo, tagsString, id, savedModel;
        return __generator(this, function (_c) {
            switch (_c.label) {
                case 0:
                    nodejs_kernel_backend_1.ensureTensorflowBackend();
                    backend = nodejs_kernel_backend_1.nodeBackend();
                    return [4 /*yield*/, getMetaGraphsFromSavedModel(path)];
                case 1:
                    savedModelInfo = _c.sent();
                    _a = getInputAndOutputNodeNameFromMetaGraphInfo(savedModelInfo, tags, signature), inputNodeNames = _a[0], outputNodeNames = _a[1];
                    for (_i = 0, _b = Array.from(loadedSavedModelPathMap.keys()); _i < _b.length; _i++) {
                        id_1 = _b[_i];
                        modelInfo = loadedSavedModelPathMap.get(id_1);
                        if (modelInfo.path === path &&
                            stringArraysHaveSameElements(modelInfo.tags, tags)) {
                            sessionId = modelInfo.sessionId;
                        }
                    }
                    if (sessionId == null) {
                        tagsString = tags.join(',');
                        sessionId = backend.loadSavedModelMetaGraph(path, tagsString);
                    }
                    id = nextTFSavedModelId++;
                    savedModel = new TFSavedModel(sessionId, id, inputNodeNames, outputNodeNames, backend);
                    loadedSavedModelPathMap.set(id, { path: path, tags: tags, sessionId: sessionId });
                    return [2 /*return*/, savedModel];
            }
        });
    });
}
exports.loadSavedModel = loadSavedModel;
/**
 * Compare if two unsorted arrays of string have the same elements.
 * @param arrayA
 * @param arrayB
 */
function stringArraysHaveSameElements(arrayA, arrayB) {
    if (arrayA.length === arrayB.length &&
        arrayA.sort().join() === arrayB.sort().join()) {
        return true;
    }
    return false;
}
function mapTFDtypeToJSDtype(tfDtype) {
    switch (tfDtype) {
        case 'DT_FLOAT':
            return 'float32';
        case 'DT_INT32':
            return 'int32';
        case 'DT_BOOL':
            return 'bool';
        case 'DT_COMPLEX64':
            return 'complex64';
        case 'DT_STRING':
            return 'string';
        default:
            throw new Error('Unsupported tensor DataType: ' + tfDtype +
                ', try to modify the model in python to convert the datatype');
    }
}
function getNumOfSavedModels() {
    nodejs_kernel_backend_1.ensureTensorflowBackend();
    var backend = nodejs_kernel_backend_1.nodeBackend();
    return backend.getNumOfSavedModels();
}
exports.getNumOfSavedModels = getNumOfSavedModels;