/**
 * @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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-converter/dist/executor/graph_model" />
import { InferenceModel, io, ModelPredictConfig, NamedTensorMap, Tensor } from '@tensorflow/tfjs-core';
import { NamedTensorsMap, TensorInfo } from '../data/types';
export declare const TFHUB_SEARCH_PARAM = "?tfjs-format=file";
export declare const DEFAULT_MODEL_NAME = "model.json";
/**
 * A `tf.GraphModel` is a directed, acyclic graph built from a
 * SavedModel GraphDef and allows inference execution.
 *
 * A `tf.GraphModel` can only be created by loading from a model converted from
 * a [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) using
 * the command line converter tool and loaded via `tf.loadGraphModel`.
 *
 * @doc {heading: 'Models', subheading: 'Classes'}
 */
export declare class GraphModel implements InferenceModel {
    private modelUrl;
    private loadOptions;
    private executor;
    private version;
    private handler;
    private artifacts;
    private initializer;
    private resourceManager;
    private signature;
    readonly modelVersion: string;
    readonly inputNodes: string[];
    readonly outputNodes: string[];
    readonly inputs: TensorInfo[];
    readonly outputs: TensorInfo[];
    readonly weights: NamedTensorsMap;
    readonly metadata: {};
    readonly modelSignature: {};
    /**
     * @param modelUrl url for the model, or an `io.IOHandler`.
     * @param weightManifestUrl url for the weight file generated by
     * scripts/convert.py script.
     * @param requestOption options for Request, which allows to send credentials
     * and custom headers.
     * @param onProgress Optional, progress callback function, fired periodically
     * before the load is completed.
     */
    constructor(modelUrl: string | io.IOHandler, loadOptions?: io.LoadOptions);
    private findIOHandler;
    /**
     * Loads the model and weight files, construct the in memory weight map and
     * compile the inference graph.
     */
    load(): Promise<boolean>;
    /**
     * Synchronously construct the in memory weight map and
     * compile the inference graph. Also initialize hashtable if any.
     *
     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
     */
    loadSync(artifacts: io.ModelArtifacts): boolean;
    /**
     * Save the configuration and/or weights of the GraphModel.
     *
     * An `IOHandler` is an object that has a `save` method of the proper
     * signature defined. The `save` method manages the storing or
     * transmission of serialized data ("artifacts") that represent the
     * model's topology and weights onto or via a specific medium, such as
     * file downloads, local storage, IndexedDB in the web browser and HTTP
     * requests to a server. TensorFlow.js provides `IOHandler`
     * implementations for a number of frequently used saving mediums, such as
     * `tf.io.browserDownloads` and `tf.io.browserLocalStorage`. See `tf.io`
     * for more details.
     *
     * This method also allows you to refer to certain types of `IOHandler`s
     * as URL-like string shortcuts, such as 'localstorage://' and
     * 'indexeddb://'.
     *
     * Example 1: Save `model`'s topology and weights to browser [local
     * storage](https://developer.mozilla.org/en-US/docs/Web/API/Window/localStorage);
     * then load it back.
     *
     * ```js
     * const modelUrl =
     *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
     * const model = await tf.loadGraphModel(modelUrl);
     * const zeros = tf.zeros([1, 224, 224, 3]);
     * model.predict(zeros).print();
     *
     * const saveResults = await model.save('localstorage://my-model-1');
     *
     * const loadedModel = await tf.loadGraphModel('localstorage://my-model-1');
     * console.log('Prediction from loaded model:');
     * model.predict(zeros).print();
     * ```
     *
     * @param handlerOrURL An instance of `IOHandler` or a URL-like,
     * scheme-based string shortcut for `IOHandler`.
     * @param config Options for saving the model.
     * @returns A `Promise` of `SaveResult`, which summarizes the result of
     * the saving, such as byte sizes of the saved artifacts for the model's
     *   topology and weight values.
     *
     * @doc {heading: 'Models', subheading: 'Classes', ignoreCI: true}
     */
    save(handlerOrURL: io.IOHandler | string, config?: io.SaveConfig): Promise<io.SaveResult>;
    /**
     * 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 `tf.Tensor`. For models with mutliple inputs,
     * inputs params should be in either `tf.Tensor`[] if the input order is
     * fixed, or otherwise NamedTensorMap format.
     *
     * For model with multiple inputs, we recommend you use NamedTensorMap as the
     * input type, if you use `tf.Tensor`[], the order of the array needs to
     * follow the
     * order of inputNodes array. @see {@link GraphModel.inputNodes}
     *
     * You can also feed any intermediate nodes using the NamedTensorMap as the
     * input type. For example, given the graph
     *    InputNode => Intermediate => OutputNode,
     * you can execute the subgraph Intermediate => OutputNode by calling
     *    model.execute('IntermediateNode' : tf.tensor(...));
     *
     * This is useful for models that uses tf.dynamic_rnn, where the intermediate
     * state needs to be fed manually.
     *
     * 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 and
     * output node names. Currently the batch size option is ignored for graph
     * model.
     *
     * @returns Inference result tensors. The output would be single `tf.Tensor`
     * if model has single output node, otherwise Tensor[] or NamedTensorMap[]
     * will be returned for model with multiple outputs.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
    private normalizeInputs;
    private normalizeOutputs;
    /**
     * Executes inference for the model for given input tensors.
     * @param inputs tensor, tensor array or tensor map of the inputs for the
     * model, keyed by the input node names.
     * @param outputs output node name from the Tensorflow model, if no
     * outputs are specified, the default outputs of the model would be used.
     * You can inspect intermediate nodes of the model by adding them to the
     * outputs array.
     *
     * @returns A single tensor if provided with a single output or no outputs
     * are provided and there is only one default output, otherwise return a
     * tensor array. The order of the tensor array is the same as the outputs
     * if provided, otherwise the order of outputNodes attribute of the model.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Tensor | Tensor[];
    /**
     * Executes inference for the model for given input tensors in async
     * fashion, use this method when your model contains control flow ops.
     * @param inputs tensor, tensor array or tensor map of the inputs for the
     * model, keyed by the input node names.
     * @param outputs output node name from the Tensorflow model, if no outputs
     * are specified, the default outputs of the model would be used. You can
     * inspect intermediate nodes of the model by adding them to the outputs
     * array.
     *
     * @returns A Promise of single tensor if provided with a single output or
     * no outputs are provided and there is only one default output, otherwise
     * return a tensor map.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    executeAsync(inputs: Tensor | Tensor[] | NamedTensorMap, outputs?: string | string[]): Promise<Tensor | Tensor[]>;
    private convertTensorMapToTensorsMap;
    /**
     * Releases the memory used by the weight tensors and resourceManager.
     *
     * @doc {heading: 'Models', subheading: 'Classes'}
     */
    dispose(): void;
}
/**
 * Load a graph model given a URL to the model definition.
 *
 * Example of loading MobileNetV2 from a URL and making a prediction with a
 * zeros input:
 *
 * ```js
 * const modelUrl =
 *    'https://storage.googleapis.com/tfjs-models/savedmodel/mobilenet_v2_1.0_224/model.json';
 * const model = await tf.loadGraphModel(modelUrl);
 * const zeros = tf.zeros([1, 224, 224, 3]);
 * model.predict(zeros).print();
 * ```
 *
 * Example of loading MobileNetV2 from a TF Hub URL and making a prediction with
 * a zeros input:
 *
 * ```js
 * const modelUrl =
 *    'https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/2';
 * const model = await tf.loadGraphModel(modelUrl, {fromTFHub: true});
 * const zeros = tf.zeros([1, 224, 224, 3]);
 * model.predict(zeros).print();
 * ```
 * @param modelUrl The url or an `io.IOHandler` that loads the model.
 * @param options Options for the HTTP request, which allows to send credentials
 *    and custom headers.
 *
 * @doc {heading: 'Models', subheading: 'Loading'}
 */
export declare function loadGraphModel(modelUrl: string | io.IOHandler, options?: io.LoadOptions): Promise<GraphModel>;