/**
 * @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.
 * =============================================================================
 */
import { InferenceModel, MetaGraph, ModelPredictConfig, ModelTensorInfo, NamedTensorMap, Tensor } from '@tensorflow/tfjs';
import { NodeJSKernelBackend } from './nodejs_kernel_backend';
/**
 * Get a key in an object by its value. This is used to get protobuf enum value
 * from index.
 *
 * @param object
 * @param value
 */
export declare function getEnumKeyFromValue(object: any, value: number): string;
/**
 * Read SavedModel proto message from path.
 *
 * @param path Path to SavedModel folder.
 */
export declare function readSavedModelProto(path: string): Promise<any>;
/**
 * 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'}
 */
export declare function getMetaGraphsFromSavedModel(path: string): Promise<MetaGraph[]>;
/**
 * 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.
 */
export declare function getInputAndOutputNodeNameFromMetaGraphInfo(savedModelInfo: MetaGraph[], tags: string[], signature: string): {
    [key: string]: string;
}[];
/**
 * A `tf.TFSavedModel` is a signature loaded from a SavedModel
 * metagraph, and allows inference execution.
 *
 * @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
 */
export declare class TFSavedModel implements InferenceModel {
    private sessionId;
    private jsid;
    private inputNodeNames;
    private outputNodeNames;
    private backend;
    private disposed;
    constructor(sessionId: number, jsid: number, inputNodeNames: {
        [key: string]: string;
    }, outputNodeNames: {
        [key: string]: string;
    }, backend: NodeJSKernelBackend);
    /**
     * Return the array of input tensor info.
     *
     * @doc {heading: 'Models', subheading: 'SavedModel'}
     */
    readonly inputs: ModelTensorInfo[];
    /**
     * Return the array of output tensor info.
     *
     * @doc {heading: 'Models', subheading: 'SavedModel'}
     */
    readonly outputs: ModelTensorInfo[];
    /**
     * 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'}
     */
    dispose(): void;
    /**
     * 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'}
     */
    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
    /**
     * 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'}
     */
    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
}
/**
 * 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'}
 */
export declare function loadSavedModel(path: string, tags?: string[], signature?: string): Promise<TFSavedModel>;
export declare function getNumOfSavedModels(): number;