ie_transformations.hpp

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// Copyright (C) 2020 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
 
/**
 * @brief This header file defines the list of public transformations.
 *
 * @file ie_transformations.hpp
 */
 
#pragma once
 
#include <ie_api.h>
#include <cpp/ie_cnn_network.h>
 
namespace InferenceEngine {
 
/**
 * @brief The transformation finds all TensorIterator layers in the network, processes all back
 * edges that describe a connection between Result and Parameter of the TensorIterator body,
 * and inserts ReadValue layer between Parameter and the next layers after this Parameter,
 * and Assign layer after the layers before the Result layer.
 * Supported platforms: CPU, GNA.
 *
 *  The example below describes the changes to the inner part (body, back edges) of the TensorIterator layer.
 *  [] - TensorIterator body
 *  () - new layer
 *
 *  before applying the transformation:
 *  back_edge_1 -> [Parameter -> some layers ... -> Result ] -> back_edge_1
 *
 *  after applying the transformation:
 *  back_edge_1 -> [Parameter -> (ReadValue layer) -> some layers ... -> (Assign layer) ]
 *                                                              \
 *                                                               -> Result ] -> back_edge_1
 *
 *  It is recommended to use this transformation in conjunction with the Reshape feature to set sequence
 *  dimension to 1 and with the UnrollTensorIterator transformation.
 *  For convenience, we have already enabled the unconditional execution of the UnrollTensorIterator
 *  transformation when using the LowLatency transformation for CPU, GNA plugins, no action is required here.
 *  After applying both of these transformations, the resulting network can be inferred step by
 *  step, the states will store between inferences.
 *
 *    An illustrative example, not real API:
 *
 *    network->reshape(...) // Set sequence dimension to 1, recalculating shapes. Optional, depends on the network.
 *    LowLatency(network)   // Applying LowLatency and UnrollTensorIterator transformations.
 *    network->infer (...)  // Calculating new values for states.
 *    // All states are stored between inferences via Assign, ReadValue layers.
 *    network->infer (...)  // Using stored states, calculating new values for states.
 *
 * @param network A network to apply LowLatency transformation
 * *
 */
INFERENCE_ENGINE_API_CPP(void) LowLatency(InferenceEngine::CNNNetwork& network);
} // namespace InferenceEngine