125 lines
5.1 KiB
C++
125 lines
5.1 KiB
C++
/**
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* \brief an abstract class for the all the graph based inferencers, extends from AbstractInferencer
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* Subclasses should implement the pure virtual function Inference
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* \author Nan Zhou, nanzhou at kneron dot us
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* \copyright 2019 Kneron Inc. All right reserved.
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*/
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#ifndef PIANO_DYNASTY_INCLUDE_PIANOINFERENCER_H_
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#define PIANO_DYNASTY_INCLUDE_PIANOINFERENCER_H_
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#include <memory>
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#include <string>
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#include <unordered_map>
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#include <vector>
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#include "AbstractInferencer.h"
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class Graph;
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namespace dynasty {
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namespace inferencer {
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template <typename T>
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class PianoInferencer : public AbstractInferencer<T> {
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public:
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class Builder : public AbstractInferencer<T>::Builder {
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private:
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std::string bieFileVersion_;
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protected:
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std::shared_ptr<Graph const> graph_;
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uint32_t optimization_level_ = SOME_MAGIC_INTEGER;
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uint32_t device_id_ = SOME_MAGIC_INTEGER;
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bool dump_enabled_=false;
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static int const SOME_MAGIC_INTEGER = 666;
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int GetDeviceID() { return device_id_; }
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public:
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Builder();
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~Builder() override;
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virtual Builder *WithONNXModel(std::string const &onnx_file);
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virtual Builder *WithBIEModel(std::string const &bie_file);
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virtual Builder *WithUnencryptedGraph(Graph const &graph);
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virtual Builder *WithGraphOptimization(uint32_t level);
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virtual Builder *WithDeviceID(int device_id);
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virtual Builder *WithDumpEnable(bool dump_enabled);
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virtual Builder *WithSettingInfo(const std::unordered_map<std::string, std::string>& inf_config) { return this; }
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virtual std::shared_ptr<Graph const> GetGraph() const;
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InferencerUniquePtr<T> Build() override = 0;
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/**
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* @return {{operation name : bchw}}
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*/
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std::vector<std::pair<std::string, std::vector<int32_t >>> GetInputDimensions();
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std::vector<std::pair<std::string, std::vector<int32_t >>> GetOutputDimensions();
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std::string GetBieFileVersion();
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};
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private:
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std::shared_ptr<Graph const> graph_;
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protected:
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PianoInferencer()=default;
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/**
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* @brief A routine for all inferencers is to load the model. The constructor will construct a graph based on the
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* model
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* @param model_file: a path to the model in ONNX format;
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*/
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explicit PianoInferencer(std::shared_ptr<Graph const> piano_graph);
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/**
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* @brief Check
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* 1. whether preprocess_input provides enough entries to cover graph->input
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* 2. whether entries in preprocess_input have correct dimension
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*/
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void CheckInputCorrectness(std::unordered_map<std::string, std::vector<T>> const &preprocess_input);
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void CheckInputCorrectness(std::unordered_map<std::string, std::shared_ptr<dynasty::common::Tensor>> const &preprocess_input);
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virtual std::unordered_map<std::string, std::vector<T>> GraphBasedInference(
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std::unordered_map<std::string, std::vector<T>> const &preprocess_input, bool only_output_layers) = 0;
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virtual std::unordered_map<std::string, std::vector<T>> GraphBasedInference(
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std::unordered_map<std::string, std::shared_ptr<dynasty::common::Tensor>> const &preprocess_input, bool only_output_layers);
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public:
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virtual ~PianoInferencer();
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public:
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std::shared_ptr<Graph const> GetImmutableGraph();
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/**
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* \param preprocess_input: [{operation_node_name, 1d_vector}]
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* \brief interface need to be implemented, pack output data path names and their float vectors then return
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* \return name_value_pair: {operation node name: corresponding float vector}
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*/
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std::unordered_map<std::string, std::vector<T>> Inference(
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std::unordered_map<std::string, std::vector<T>> const &preprocess_input, bool only_output_layers) override;
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/**
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* \param preprocess_input: [{operation_node_name, 1d_vector}]
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* \brief interface need to be implemented, pack output data path names and their Tensors then return
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* \return name_value_pair: {operation node name: corresponding float vector}
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*/
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std::unordered_map<std::string, std::vector<T>> Inference(
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std::unordered_map<std::string, std::shared_ptr<dynasty::common::Tensor>> const &preprocess_input, bool only_output_layers) override;
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/**
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* \param preprocess_input: [{operation_node_name, path_to_1d_vector}]
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* \brief interface to inference from operation_name and txt pairs
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* \return name_value_pair: {operation node name: corresponding float vector}
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*/
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std::unordered_map<std::string, std::vector<T>> Inference(
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std::unordered_map<std::string, std::string> const &preprocess_input, bool only_output_layers) override;
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//std::unordered_map<std::string, std::vector<int>> ConvertFloatToInt(
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// std::unordered_map<std::string, std::vector<T>> const &float_output, bool only_output_layers) override;
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std::unordered_map<std::string, std::vector<int>> ConvertFloatToInt(std::unordered_map<std::string, std::vector<T>>& float_output, bool only_output_layers) override;
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};
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} // namespace inferencer
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} // namespace dynasty
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#endif // PIANO_ABSTRACTINFERENCER_H
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