/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import { InferenceModel, MetaGraph, ModelPredictConfig, ModelTensorInfo, NamedTensorMap, SignatureDefEntry, Tensor } from '@tensorflow/tfjs';
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import { NodeJSKernelBackend } from './nodejs_kernel_backend';
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/**
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* Get a key in an object by its value. This is used to get protobuf enum value
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* from index.
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*
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* @param object
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* @param value
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*/
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export declare function getEnumKeyFromValue(object: any, value: number): string;
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/**
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* Read SavedModel proto message from path.
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*
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* @param path Path to SavedModel folder.
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*/
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export declare function readSavedModelProto(path: string): Promise<any>;
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/**
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* Inspect the MetaGraphs of the SavedModel from the provided path. This
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* function will return an array of `MetaGraphInfo` objects.
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*
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* @param path Path to SavedModel folder.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
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*/
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export declare function getMetaGraphsFromSavedModel(path: string): Promise<MetaGraph[]>;
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/**
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* Get SignatureDefEntry from SavedModel metagraphs info. The SignatureDefEntry
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* will be used when executing a SavedModel signature.
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*
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* @param savedModelInfo The MetaGraphInfo array loaded through
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* getMetaGraphsFromSavedModel().
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* @param tags The tags of the MetaGraph to get input/output node names from.
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* @param signature The signature to get input/output node names from.
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*/
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export declare function getSignatureDefEntryFromMetaGraphInfo(savedModelInfo: MetaGraph[], tags: string[], signature: string): SignatureDefEntry;
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/**
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* A `tf.TFSavedModel` is a signature loaded from a SavedModel
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* metagraph, and allows inference execution.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
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*/
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export declare class TFSavedModel implements InferenceModel {
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private sessionId;
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private jsid;
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private signature;
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private backend;
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private disposed;
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private outputNodeNames_;
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constructor(sessionId: number, jsid: number, signature: SignatureDefEntry, backend: NodeJSKernelBackend);
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/**
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* Return the array of input tensor info.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel'}
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*/
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get inputs(): ModelTensorInfo[];
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/**
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* Return the array of output tensor info.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel'}
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*/
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get outputs(): ModelTensorInfo[];
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/**
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* Delete the SavedModel from nodeBackend and delete corresponding session in
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* the C++ backend if the session is only used by this TFSavedModel.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel'}
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*/
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dispose(): void;
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get outputNodeNames(): {
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[key: string]: string;
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};
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/**
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* Execute the inference for the input tensors.
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*
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* @param input The input tensors, when there is single input for the model,
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* inputs param should be a Tensor. For models with multiple inputs, inputs
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* params should be in either Tensor[] if the input order is fixed, or
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* otherwise NamedTensorMap format. The keys in the NamedTensorMap are the
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* name of input tensors in SavedModel signatureDef. It can be found through
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* `tf.node.getMetaGraphsFromSavedModel()`.
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*
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* For batch inference execution, the tensors for each input need to be
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* concatenated together. For example with mobilenet, the required input shape
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* is [1, 244, 244, 3], which represents the [batch, height, width, channel].
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* If we are provide a batched data of 100 images, the input tensor should be
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* in the shape of [100, 244, 244, 3].
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*
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* @param config Prediction configuration for specifying the batch size.
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*
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* @returns Inference result tensors. The output would be single Tensor if
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* model has single output node, otherwise Tensor[] or NamedTensorMap[] will
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* be returned for model with multiple outputs.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel'}
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*/
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predict(inputs: Tensor | Tensor[] | NamedTensorMap, config?: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
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/**
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* Execute the inference for the input tensors and return activation
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* values for specified output node names without batching.
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*
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* @param input The input tensors, when there is single input for the model,
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* inputs param should be a Tensor. For models with multiple inputs, inputs
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* params should be in either Tensor[] if the input order is fixed, or
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* otherwise NamedTensorMap format.
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*
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* @param outputs string|string[]. List of output node names to retrieve
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* activation from.
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*
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* @returns Activation values for the output nodes result tensors. The return
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* type matches specified parameter outputs type. The output would be single
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* Tensor if single output is specified, otherwise Tensor[] for multiple
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* outputs.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel'}
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*/
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execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
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}
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/**
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* Load a TensorFlow SavedModel from disk. TensorFlow SavedModel is different
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* from TensorFlow.js model format. A SavedModel is a directory containing
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* serialized signatures and the states needed to run them. The directory has a
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* saved_model.pb (or saved_model.pbtxt) file storing the actual TensorFlow
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* program, or model, and a set of named signatures, each identifying a
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* function. The directory also has a variables directory contains a standard
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* training checkpoint. The directory may also has a assets directory contains
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* files used by the TensorFlow graph, for example text files used to initialize
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* vocabulary tables. These are supported datatypes: float32, int32, complex64,
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* string.For more information, see this guide:
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* https://www.tensorflow.org/guide/saved_model.
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*
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* @param path The path to the SavedModel.
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* @param tags The tags of the MetaGraph to load. The available tags of a
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* SavedModel can be retrieved through tf.node.getMetaGraphsFromSavedModel()
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* API. Defaults to ['serve'].
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* @param signature The name of the SignatureDef to load. The available
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* SignatureDefs of a SavedModel can be retrieved through
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* tf.node.getMetaGraphsFromSavedModel() API. Defaults to 'serving_default'.
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*
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* @doc {heading: 'Models', subheading: 'SavedModel', namespace: 'node'}
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*/
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export declare function loadSavedModel(path: string, tags?: string[], signature?: string): Promise<TFSavedModel>;
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export declare function getNumOfSavedModels(): number;
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