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480 lines
16 KiB
C++
480 lines
16 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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//
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// Copyright (C) 2019-2021 Intel Corporation
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#ifndef OPENCV_GAPI_INFER_IE_HPP
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#define OPENCV_GAPI_INFER_IE_HPP
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#include <unordered_map>
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#include <unordered_set>
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#include <string>
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#include <array>
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#include <tuple> // tuple, tuple_size
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#include <map>
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#include <opencv2/gapi/opencv_includes.hpp>
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#include <opencv2/gapi/util/any.hpp>
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#include <opencv2/core/cvdef.h> // GAPI_EXPORTS
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#include <opencv2/gapi/gkernel.hpp> // GKernelPackage
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#include <opencv2/gapi/infer.hpp> // Generic
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namespace cv {
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namespace gapi {
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// FIXME: introduce a new sub-namespace for NN?
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/**
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* @brief This namespace contains G-API OpenVINO backend functions,
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* structures, and symbols.
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*/
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namespace ie {
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GAPI_EXPORTS cv::gapi::GBackend backend();
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/**
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* Specifies how G-API and IE should trait input data
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*
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* In OpenCV, the same cv::Mat is used to represent both
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* image and tensor data. Sometimes those are hardly distinguishable,
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* so this extra parameter is used to give G-API a hint.
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*
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* This hint controls how G-API reinterprets the data when converting
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* it to IE Blob format (and which layout/etc is assigned to this data).
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*/
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enum class TraitAs: int
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{
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TENSOR, //!< G-API traits an associated cv::Mat as a raw tensor and passes dimensions as-is
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IMAGE //!< G-API traits an associated cv::Mat as an image so creates an "image" blob (NCHW/NHWC, etc)
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};
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using IEConfig = std::map<std::string, std::string>;
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namespace detail {
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struct ParamDesc {
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std::string model_path;
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std::string weights_path;
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std::string device_id;
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std::vector<std::string> input_names;
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std::vector<std::string> output_names;
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using ConstInput = std::pair<cv::Mat, TraitAs>;
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std::unordered_map<std::string, ConstInput> const_inputs;
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std::size_t num_in;
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std::size_t num_out;
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enum class Kind {Load, Import};
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Kind kind;
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bool is_generic;
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IEConfig config;
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std::map<std::string, std::vector<std::size_t>> reshape_table;
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std::unordered_set<std::string> layer_names_to_reshape;
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// NB: Number of asyncrhonious infer requests
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size_t nireq;
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// NB: An optional config to setup RemoteContext for IE
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cv::util::any context_config;
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// NB: batch_size can't be equal to 1 by default, because some of models
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// have 2D (Layout::NC) input and if the first dimension not equal to 1
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// net.setBatchSize(1) will overwrite it.
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cv::optional<size_t> batch_size;
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};
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} // namespace detail
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// FIXME: this is probably a shared (reusable) thing
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template<typename Net>
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struct PortCfg {
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using In = std::array
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< std::string
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, std::tuple_size<typename Net::InArgs>::value >;
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using Out = std::array
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< std::string
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, std::tuple_size<typename Net::OutArgs>::value >;
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};
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/**
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* @brief This structure provides functions
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* that fill inference parameters for "OpenVINO Toolkit" model.
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*/
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template<typename Net> class Params {
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public:
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/** @brief Class constructor.
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Constructs Params based on model information and specifies default values for other
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inference description parameters. Model is loaded and compiled using "OpenVINO Toolkit".
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@param model Path to topology IR (.xml file).
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@param weights Path to weights (.bin file).
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@param device target device to use.
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*/
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Params(const std::string &model,
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const std::string &weights,
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const std::string &device)
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: desc{ model, weights, device, {}, {}, {}
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, std::tuple_size<typename Net::InArgs>::value // num_in
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, std::tuple_size<typename Net::OutArgs>::value // num_out
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, detail::ParamDesc::Kind::Load
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, false
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, {}
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, {}
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, {}
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, 1u
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, {}
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, {}} {
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};
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/** @overload
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Use this constructor to work with pre-compiled network.
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Model is imported from a pre-compiled blob.
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@param model Path to model.
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@param device target device to use.
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*/
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Params(const std::string &model,
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const std::string &device)
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: desc{ model, {}, device, {}, {}, {}
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, std::tuple_size<typename Net::InArgs>::value // num_in
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, std::tuple_size<typename Net::OutArgs>::value // num_out
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, detail::ParamDesc::Kind::Import
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, false
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, {}
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, {}
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, {}
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, 1u
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, {}
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, {}} {
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};
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/** @brief Specifies sequence of network input layers names for inference.
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The function is used to associate cv::gapi::infer<> inputs with the model inputs.
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Number of names has to match the number of network inputs as defined in G_API_NET().
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In case a network has only single input layer, there is no need to specify name manually.
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@param layer_names std::array<std::string, N> where N is the number of inputs
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as defined in the @ref G_API_NET. Contains names of input layers.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgInputLayers(const typename PortCfg<Net>::In &layer_names) {
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desc.input_names.clear();
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desc.input_names.reserve(layer_names.size());
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std::copy(layer_names.begin(), layer_names.end(),
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std::back_inserter(desc.input_names));
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return *this;
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}
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/** @brief Specifies sequence of network output layers names for inference.
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The function is used to associate cv::gapi::infer<> outputs with the model outputs.
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Number of names has to match the number of network outputs as defined in G_API_NET().
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In case a network has only single output layer, there is no need to specify name manually.
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@param layer_names std::array<std::string, N> where N is the number of outputs
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as defined in the @ref G_API_NET. Contains names of output layers.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgOutputLayers(const typename PortCfg<Net>::Out &layer_names) {
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desc.output_names.clear();
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desc.output_names.reserve(layer_names.size());
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std::copy(layer_names.begin(), layer_names.end(),
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std::back_inserter(desc.output_names));
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return *this;
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}
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/** @brief Specifies a constant input.
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The function is used to set a constant input. This input has to be
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a preprocessed tensor if its type is TENSOR. Need to provide name of the
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network layer which will receive provided data.
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@param layer_name Name of network layer.
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@param data cv::Mat that contains data which will be associated with network layer.
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@param hint Input type @sa cv::gapi::ie::TraitAs.
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@return reference to this parameter structure.
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*/
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Params<Net>& constInput(const std::string &layer_name,
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const cv::Mat &data,
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TraitAs hint = TraitAs::TENSOR) {
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desc.const_inputs[layer_name] = {data, hint};
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return *this;
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}
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/** @brief Specifies OpenVINO plugin configuration.
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The function is used to set configuration for OpenVINO plugin. Some parameters
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can be different for each plugin. Please follow https://docs.openvinotoolkit.org/latest/index.html
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to check information about specific plugin.
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@param cfg Map of pairs: (config parameter name, config parameter value).
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@return reference to this parameter structure.
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*/
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Params& pluginConfig(const IEConfig& cfg) {
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desc.config = cfg;
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return *this;
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}
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/** @overload
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Function with a rvalue parameter.
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@param cfg rvalue map of pairs: (config parameter name, config parameter value).
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@return reference to this parameter structure.
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*/
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Params& pluginConfig(IEConfig&& cfg) {
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desc.config = std::move(cfg);
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return *this;
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}
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/** @brief Specifies configuration for RemoteContext in InferenceEngine.
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When RemoteContext is configured the backend imports the networks using the context.
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It also expects cv::MediaFrames to be actually remote, to operate with blobs via the context.
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@param ctx_cfg cv::util::any value which holds InferenceEngine::ParamMap.
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@return reference to this parameter structure.
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*/
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Params& cfgContextParams(const cv::util::any& ctx_cfg) {
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desc.context_config = ctx_cfg;
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return *this;
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}
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/** @overload
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Function with an rvalue parameter.
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@param ctx_cfg cv::util::any value which holds InferenceEngine::ParamMap.
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@return reference to this parameter structure.
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*/
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Params& cfgContextParams(cv::util::any&& ctx_cfg) {
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desc.context_config = std::move(ctx_cfg);
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return *this;
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}
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/** @brief Specifies number of asynchronous inference requests.
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@param nireq Number of inference asynchronous requests.
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@return reference to this parameter structure.
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*/
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Params& cfgNumRequests(size_t nireq) {
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GAPI_Assert(nireq > 0 && "Number of infer requests must be greater than zero!");
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desc.nireq = nireq;
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return *this;
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}
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/** @brief Specifies new input shapes for the network inputs.
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The function is used to specify new input shapes for the network inputs.
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Follow https://docs.openvinotoolkit.org/latest/classInferenceEngine_1_1networkNetwork.html
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for additional information.
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@param reshape_table Map of pairs: name of corresponding data and its dimension.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgInputReshape(const std::map<std::string, std::vector<std::size_t>>& reshape_table) {
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desc.reshape_table = reshape_table;
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return *this;
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}
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/** @overload */
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Params<Net>& cfgInputReshape(std::map<std::string, std::vector<std::size_t>>&& reshape_table) {
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desc.reshape_table = std::move(reshape_table);
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return *this;
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}
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/** @overload
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@param layer_name Name of layer.
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@param layer_dims New dimensions for this layer.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgInputReshape(const std::string& layer_name, const std::vector<size_t>& layer_dims) {
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desc.reshape_table.emplace(layer_name, layer_dims);
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return *this;
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}
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/** @overload */
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Params<Net>& cfgInputReshape(std::string&& layer_name, std::vector<size_t>&& layer_dims) {
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desc.reshape_table.emplace(layer_name, layer_dims);
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return *this;
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}
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/** @overload
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@param layer_names set of names of network layers that will be used for network reshape.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgInputReshape(const std::unordered_set<std::string>& layer_names) {
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desc.layer_names_to_reshape = layer_names;
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return *this;
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}
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/** @overload
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@param layer_names rvalue set of the selected layers will be reshaped automatically
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its input image size.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgInputReshape(std::unordered_set<std::string>&& layer_names) {
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desc.layer_names_to_reshape = std::move(layer_names);
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return *this;
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}
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/** @brief Specifies the inference batch size.
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The function is used to specify inference batch size.
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Follow https://docs.openvinotoolkit.org/latest/classInferenceEngine_1_1CNNNetwork.html#a8e9d19270a48aab50cb5b1c43eecb8e9 for additional information
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@param size batch size which will be used.
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@return reference to this parameter structure.
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*/
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Params<Net>& cfgBatchSize(const size_t size) {
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desc.batch_size = cv::util::make_optional(size);
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return *this;
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}
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// BEGIN(G-API's network parametrization API)
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GBackend backend() const { return cv::gapi::ie::backend(); }
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std::string tag() const { return Net::tag(); }
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cv::util::any params() const { return { desc }; }
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// END(G-API's network parametrization API)
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protected:
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detail::ParamDesc desc;
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};
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/*
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* @brief This structure provides functions for generic network type that
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* fill inference parameters.
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* @see struct Generic
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*/
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template<>
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class Params<cv::gapi::Generic> {
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public:
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/** @brief Class constructor.
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Constructs Params based on model information and sets default values for other
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inference description parameters. Model is loaded and compiled using OpenVINO Toolkit.
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@param tag string tag of the network for which these parameters are intended.
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@param model path to topology IR (.xml file).
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@param weights path to weights (.bin file).
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@param device target device to use.
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*/
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Params(const std::string &tag,
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const std::string &model,
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const std::string &weights,
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const std::string &device)
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: desc{ model, weights, device, {}, {}, {}, 0u, 0u,
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detail::ParamDesc::Kind::Load, true, {}, {}, {}, 1u,
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{}, {}},
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m_tag(tag) {
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};
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/** @overload
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This constructor for pre-compiled networks. Model is imported from pre-compiled
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blob.
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@param tag string tag of the network for which these parameters are intended.
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@param model path to model.
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@param device target device to use.
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*/
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Params(const std::string &tag,
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const std::string &model,
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const std::string &device)
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: desc{ model, {}, device, {}, {}, {}, 0u, 0u,
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detail::ParamDesc::Kind::Import, true, {}, {}, {}, 1u,
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{}, {}},
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m_tag(tag) {
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};
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/** @see ie::Params::pluginConfig. */
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Params& pluginConfig(const IEConfig& cfg) {
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desc.config = cfg;
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return *this;
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}
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/** @overload */
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Params& pluginConfig(IEConfig&& cfg) {
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desc.config = std::move(cfg);
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return *this;
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}
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/** @see ie::Params::constInput. */
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Params& constInput(const std::string &layer_name,
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const cv::Mat &data,
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TraitAs hint = TraitAs::TENSOR) {
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desc.const_inputs[layer_name] = {data, hint};
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return *this;
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}
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/** @see ie::Params::cfgNumRequests. */
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Params& cfgNumRequests(size_t nireq) {
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GAPI_Assert(nireq > 0 && "Number of infer requests must be greater than zero!");
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desc.nireq = nireq;
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return *this;
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}
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/** @see ie::Params::cfgInputReshape */
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Params& cfgInputReshape(const std::map<std::string, std::vector<std::size_t>>&reshape_table) {
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desc.reshape_table = reshape_table;
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return *this;
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}
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/** @overload */
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Params& cfgInputReshape(std::map<std::string, std::vector<std::size_t>> && reshape_table) {
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desc.reshape_table = std::move(reshape_table);
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return *this;
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}
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/** @overload */
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Params& cfgInputReshape(std::string && layer_name, std::vector<size_t> && layer_dims) {
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desc.reshape_table.emplace(layer_name, layer_dims);
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return *this;
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}
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/** @overload */
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Params& cfgInputReshape(const std::string & layer_name, const std::vector<size_t>&layer_dims) {
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desc.reshape_table.emplace(layer_name, layer_dims);
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return *this;
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}
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/** @overload */
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Params& cfgInputReshape(std::unordered_set<std::string> && layer_names) {
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desc.layer_names_to_reshape = std::move(layer_names);
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return *this;
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}
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/** @overload */
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Params& cfgInputReshape(const std::unordered_set<std::string>&layer_names) {
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desc.layer_names_to_reshape = layer_names;
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return *this;
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}
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/** @see ie::Params::cfgBatchSize */
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Params& cfgBatchSize(const size_t size) {
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desc.batch_size = cv::util::make_optional(size);
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return *this;
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}
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// BEGIN(G-API's network parametrization API)
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GBackend backend() const { return cv::gapi::ie::backend(); }
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std::string tag() const { return m_tag; }
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cv::util::any params() const { return { desc }; }
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// END(G-API's network parametrization API)
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protected:
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detail::ParamDesc desc;
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std::string m_tag;
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};
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} // namespace ie
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} // namespace gapi
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} // namespace cv
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#endif // OPENCV_GAPI_INFER_IE_HPP
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