ad997410c8
* add single_layer_test for Interpolate-1 (#6133) * Fixed InferenceEngineConfig.cmake usage in include() (#6136) * Deprecated API updates (#6252) * Fixed links to OMZ / DL Streamer (#6257) * Added doxygen deprecation for LowLatency Co-authored-by: Elena Gvozdeva <elena.gvozdeva@intel.com>
92 lines
4.2 KiB
C++
92 lines
4.2 KiB
C++
// Copyright (C) 2018-2021 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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/**
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* @brief This header file defines the list of public transformations.
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*
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* @file ie_transformations.hpp
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*/
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#pragma once
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#include <ie_api.h>
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#include <cpp/ie_cnn_network.h>
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namespace InferenceEngine {
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/**
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* @deprecated Use InferenceEngine::lowLatency2 instead
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* @brief The transformation finds all TensorIterator layers in the network, processes all back
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* edges that describe a connection between Result and Parameter of the TensorIterator body,
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* and inserts ReadValue layer between Parameter and the next layers after this Parameter,
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* and Assign layer after the layers before the Result layer.
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* Supported platforms: CPU, GNA.
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*
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* The example below describes the changes to the inner part (body, back edges) of the TensorIterator layer.
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* [] - TensorIterator body
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* () - new layer
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*
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* before applying the transformation:
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* back_edge_1 -> [Parameter -> some layers ... -> Result ] -> back_edge_1
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*
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* after applying the transformation:
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* back_edge_1 -> [Parameter -> (ReadValue layer) -> some layers ... -> (Assign layer) ]
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* \
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* -> Result ] -> back_edge_1
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*
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* It is recommended to use this transformation in conjunction with the Reshape feature to set sequence
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* dimension to 1 and with the UnrollTensorIterator transformation.
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* For convenience, we have already enabled the unconditional execution of the UnrollTensorIterator
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* transformation when using the LowLatency transformation for CPU, GNA plugins, no action is required here.
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* After applying both of these transformations, the resulting network can be inferred step by
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* step, the states will store between inferences.
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*
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* An illustrative example, not real API:
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*
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* network->reshape(...) // Set sequence dimension to 1, recalculating shapes. Optional, depends on the network.
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* LowLatency(network) // Applying LowLatency and UnrollTensorIterator transformations.
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* network->infer (...) // Calculating new values for states.
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* // All states are stored between inferences via Assign, ReadValue layers.
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* network->infer (...) // Using stored states, calculating new values for states.
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*
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* @param network A network to apply LowLatency transformation
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*/
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INFERENCE_ENGINE_DEPRECATED("This transformation will be removed in 2023.1. "
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"Use InferenceEngine::lowLatency2 instead.")
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INFERENCE_ENGINE_API_CPP(void) LowLatency(InferenceEngine::CNNNetwork& network);
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/**
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* @brief The transformation finds all TensorIterator/Loop layers in the network,
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* processes all back edges that describe a connection between Result and Parameter
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* of the TensorIterator/Loop bodies,and inserts ReadValue and Assign layers at the
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* input and output corresponding to this back edge.
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* Supported platforms: CPU, GNA.
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*
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* The example below describes the changes made by the transformation
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* [] - TensorIterator body
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* () - new layer
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* BE - back-edge
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*
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* before applying the transformation:
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* -> input1[BE_1 -> Parameter -> Layers ... -> Result -> BE_1 ]output1->
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*
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* after applying the transformation:
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* ->(ReadValue)-> input1[BE_1 ->Parameter->Layers ...->Result->BE_1]output1 ->(Assign)
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* \
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* ->...
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* After applying the transformation, the resulting network can be inferred
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* step by step, the states will store between inferences.
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* @param network A network to apply LowLatency transformation
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* @param use_const_initializer Changes the type of the initializing subgraph for ReadValue operations.
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If "true", then the transformation inserts Constant before ReadValue operation.
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If "false, then the transformation leaves existed initializing subgraph for ReadValue operation.
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* Loop operation by a given number. Does not affect TensorIterators.
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*/
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INFERENCE_ENGINE_API_CPP(void) lowLatency2(InferenceEngine::CNNNetwork& network,
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bool use_const_initializer = true);
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} // namespace InferenceEngine
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