1c3208ffe0
* initial draft of adding sinks to ngraph::Function * style fixes * code style fixes * code style fixes * code style fix * review fix+build fix * code style fix * fix build * API changed according to latest discussion * review fixes * review fixes + tests * initial draft of adding sinks to ngraph::Function * style fixes * code style fixes * code style fixes * code style fix * review fix+build fix * code style fix * fix build * API changed according to latest discussion * review fixes * review fixes + tests * added 1 more ctor * style fixes * used new api in ir parser * fixed build * update low latency transformation, fix unroll transformation, add unit tests, modify subgraph tests * fix low latency transformation * Update low latency transformation, unit and sub-graph tests * update LowLatency transformation and tests * ngraph codestyle * fix build, update description * resolve review remarks Co-authored-by: Svetlana Dolinina <svetlana.a.dolinina@intel.com>
57 lines
2.4 KiB
C++
57 lines
2.4 KiB
C++
// 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
|