207 lines
7.9 KiB
C++
207 lines
7.9 KiB
C++
// Copyright (C) 2018-2019 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 is a header file for the ICNNNetwork class
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* @file ie_icnn_network.hpp
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*/
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#pragma once
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#include "ie_common.h"
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#include "ie_layers.h"
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#include "ie_data.h"
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#include "ie_device.hpp"
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#include "ie_blob.h"
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#include "details/ie_irelease.hpp"
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#include "ie_preprocess.hpp"
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#include "ie_input_info.hpp"
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#include "ie_icnn_network_stats.hpp"
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#include "ie_iextension.h"
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#include <memory>
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#include <map>
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#include <string>
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namespace InferenceEngine {
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/**
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* @brief A collection that contains string as key, and Data smart pointer as value
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*/
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using OutputsDataMap = std::map<std::string, DataPtr>;
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/**
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* @brief This is the main interface to describe the NN topology
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*/
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class ICNNNetwork : public details::IRelease {
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public:
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using Ptr = std::shared_ptr<ICNNNetwork>;
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/**
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* @brief Returns the main network operating precision.
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* This may be MIXED if not homogeneous.
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* @return A precision type
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*/
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virtual Precision getPrecision() const noexcept = 0;
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/**
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* @brief Gets the network output Data node information. The received info is stored in the given Data node.
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* For single and multiple outputs networks.
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* @param out Reference to the OutputsDataMap object
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*/
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virtual void getOutputsInfo(OutputsDataMap& out) const noexcept = 0;
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/**
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* @brief Gets the network input Data node information. The received info is stored in the given InputsDataMap object.
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* For single and multiple inputs networks.
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* This method must be called to find out input names for using them later during filling of a map
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* of blobs passed later to InferenceEngine::IInferencePlugin::Infer()
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* @param inputs Reference to InputsDataMap object.
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*/
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virtual void getInputsInfo(InputsDataMap& inputs) const noexcept = 0;
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/**
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* @brief Returns information on certain input pointed by inputName
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* @param inputName Name of input layer to get info on
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* @return A smart pointer to the input information
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*/
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virtual InputInfo::Ptr getInput(const std::string& inputName) const noexcept = 0;
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/**
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* @brief Gets the network name. The name is stored in the given pName string.
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* @param pName - will receive actual network name, specified in IR file,
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* pName should point to valid memory address before invoking this function
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* @param len - size in bytes of pName buffer, actual name is trimmed by this size
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*/
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virtual void getName(char* pName, size_t len) const noexcept = 0;
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/**
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* @brief Returns the network name.
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* @return Network name
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*/
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virtual const std::string& getName() const noexcept = 0;
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/**
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* @brief Returns the number of layers in the network as an integer value
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* @return The number of layers as an integer value
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*/
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virtual size_t layerCount() const noexcept = 0;
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/**
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* @brief Returns a smart pointer reference to a Data node given its name.
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* If the Data node is missing, returns reference to a default initialized new empty data pointer with given name.
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* @param dname Name of the Data node
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* @return Data node smart pointer
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*/
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virtual DataPtr& getData(const char* dname) noexcept = 0;
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/**
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* @brief Insert a layer into the network. A user is responsible to connect it to other data elements.
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* @param layer Const reference to a layer smart pointer
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*/
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virtual void addLayer(const CNNLayerPtr& layer) noexcept = 0;
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/**
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* @brief Adds output to the layer
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* @param layerName Name of the layer
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* @param outputIndex Index of the output
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* @param resp Response message
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* @return Status code of the operation
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*/
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virtual StatusCode
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addOutput(const std::string& layerName, size_t outputIndex = 0, ResponseDesc* resp = nullptr) noexcept = 0;
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/**
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* @brief Gets network layer with the given name
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* @param layerName Given name of the layer
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* @param out Pointer to the found CNNLayer object with the given name
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* @param resp Pointer to the response message that holds a description of an error if any occurred
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* @return Status code of the operation. OK if succeeded
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*/
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virtual StatusCode getLayerByName(const char* layerName, CNNLayerPtr& out, ResponseDesc* resp) const noexcept = 0;
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/**
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* @deprecated Deprecated since TargetDevice is deprecated. Specify target device in InferenceEngine::Core directly.
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* @brief Sets a desirable device to perform all work on.
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* Some plug-ins might not support some target devices and may abort execution with an appropriate error message.
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* @param device Device to set as a target
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*/
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#ifndef _WIN32
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INFERENCE_ENGINE_DEPRECATED
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#endif
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virtual void setTargetDevice(TargetDevice device) noexcept = 0;
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/**
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* @deprecated Deprecated since TargetDevice is deprecated
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* @brief Gets the target device.
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* If setTargetDevice() was not called before, returns eDefault
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* @return A TargetDevice instance
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*/
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#ifndef _WIN32
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INFERENCE_ENGINE_DEPRECATED
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#endif
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virtual TargetDevice getTargetDevice() const noexcept = 0;
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/**
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* @deprecated Use ICNNNetwork::setBatchSize(size_t, ResponseDesc*)
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* @brief Changes the inference batch size
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*/
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INFERENCE_ENGINE_DEPRECATED
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virtual StatusCode setBatchSize(const size_t size) noexcept {
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ResponseDesc resp;
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return setBatchSize(size, &resp);
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}
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/**
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* @brief Changes the inference batch size.
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* @note There are several limitations and it's not recommended to use it. Set batch to the input shape and call ICNNNetwork::reshape.
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* @param size Size of batch to set
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* @return Status code of the operation
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* @note: Current implementation of the function sets batch size to the first dimension of all layers in the networks.
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* Before calling it make sure that all your layers have batch in the first dimension, otherwise the method works incorrectly.
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* This limitation is resolved via shape inference feature
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* by using InferenceEngine::ICNNNetwork::reshape method.
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* To read more refer to the Shape Inference section in documentation
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*/
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virtual StatusCode setBatchSize(size_t size, ResponseDesc* responseDesc) noexcept = 0;
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/**
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* @brief Gets the inference batch size
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* @return The size of batch as a size_t value
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*/
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virtual size_t getBatchSize() const noexcept = 0;
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/**
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* @brief Map of pairs: name of corresponding data and its dimension.
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*/
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using InputShapes = std::map<std::string, SizeVector>;
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/**
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* @brief Run shape inference with new input shapes for the network
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* @param inputShapes - map of pairs: name of corresponding data and its dimension.
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* @param resp Pointer to the response message that holds a description of an error if any occurred
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* @return Status code of the operation
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*/
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virtual StatusCode reshape(const InputShapes& /*inputShapes*/, ResponseDesc* /*resp*/) noexcept { return NOT_IMPLEMENTED; };
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/**
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* @brief Registers extension within the plugin
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* @param extension Pointer to already loaded reader extension with shape propagation implementations
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* @param resp Pointer to the response message that holds a description of an error if any occurred
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* @return Status code of the operation. OK if succeeded
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*/
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virtual StatusCode
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AddExtension(const IShapeInferExtensionPtr& /*extension*/, ResponseDesc* /*resp*/) noexcept { return NOT_IMPLEMENTED; };
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virtual StatusCode getStats(ICNNNetworkStats** /*stats*/, ResponseDesc* /*resp*/) const noexcept { return NOT_IMPLEMENTED; };
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/**
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* @brief Serialize network to IR and weights files.
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* @param xmlPath Path to output IR file.
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* @param binPath Path to output weights file.
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* @return Status code of the operation
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*/
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virtual StatusCode serialize(const std::string &xmlPath, const std::string &binPath, ResponseDesc* resp) const noexcept = 0;
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};
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} // namespace InferenceEngine
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