149 lines
6.9 KiB
C++
149 lines
6.9 KiB
C++
//
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// Copyright © 2020 Arm Ltd. All rights reserved.
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// SPDX-License-Identifier: MIT
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//
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#pragma once
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#include "ArmnnDriver.hpp"
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#include "ArmnnDriverImpl.hpp"
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#include "RequestThread_1_3.hpp"
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#include "ModelToINetworkConverter.hpp"
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#include <NeuralNetworks.h>
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#include <armnn/ArmNN.hpp>
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#include <string>
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#include <vector>
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namespace armnn_driver
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{
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using CallbackAsync_1_3 = std::function<
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void(V1_3::ErrorStatus errorStatus,
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std::vector<::android::hardware::neuralnetworks::V1_2::OutputShape> outputShapes,
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const ::android::hardware::neuralnetworks::V1_2::Timing& timing,
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std::string callingFunction)>;
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struct ExecutionContext_1_3
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{
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::android::hardware::neuralnetworks::V1_2::MeasureTiming measureTimings =
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::android::hardware::neuralnetworks::V1_2::MeasureTiming::NO;
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TimePoint driverStart;
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TimePoint driverEnd;
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TimePoint deviceStart;
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TimePoint deviceEnd;
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};
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using CallbackContext_1_3 = CallbackContext<CallbackAsync_1_3, ExecutionContext_1_3>;
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using executeFenced_cb = std::function<void(::android::hardware::neuralnetworks::V1_3::ErrorStatus status,
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const ::android::hardware::hidl_handle& syncFence,
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const ::android::sp<::android::hardware::neuralnetworks::V1_3::IFencedExecutionCallback>& callback)>;
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template <typename HalVersion>
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class ArmnnPreparedModel_1_3 : public V1_3::IPreparedModel
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{
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public:
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using HalModel = typename V1_3::Model;
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ArmnnPreparedModel_1_3(armnn::NetworkId networkId,
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armnn::IRuntime* runtime,
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const HalModel& model,
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const std::string& requestInputsAndOutputsDumpDir,
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const bool gpuProfilingEnabled,
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V1_3::Priority priority = V1_3::Priority::MEDIUM);
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virtual ~ArmnnPreparedModel_1_3();
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Return<V1_0::ErrorStatus> execute(const V1_0::Request& request,
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const sp<V1_0::IExecutionCallback>& callback) override;
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Return<V1_0::ErrorStatus> execute_1_2(const V1_0::Request& request, V1_2::MeasureTiming measure,
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const sp<V1_2::IExecutionCallback>& callback) override;
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Return<V1_3::ErrorStatus> execute_1_3(const V1_3::Request& request,
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V1_2::MeasureTiming measure,
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const V1_3::OptionalTimePoint&,
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const V1_3::OptionalTimeoutDuration&,
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const sp<V1_3::IExecutionCallback>& callback) override;
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Return<void> executeSynchronously(const V1_0::Request &request,
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V1_2::MeasureTiming measure,
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V1_3::IPreparedModel::executeSynchronously_cb cb) override;
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Return<void> executeSynchronously_1_3(const V1_3::Request &request,
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V1_2::MeasureTiming measure,
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const V1_3::OptionalTimePoint& deadline,
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const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
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V1_3::IPreparedModel::executeSynchronously_1_3_cb cb) override;
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Return<void> executeFenced(const V1_3::Request& request,
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const android::hardware::hidl_vec<android::hardware::hidl_handle>& fenceWaitFor,
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V1_2::MeasureTiming measure,
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const V1_3::OptionalTimePoint& deadline,
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const V1_3::OptionalTimeoutDuration& loopTimeoutDuration,
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const V1_3::OptionalTimeoutDuration& duration,
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executeFenced_cb callback) override;
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Return<void> configureExecutionBurst(
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const sp<V1_2::IBurstCallback>& callback,
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const android::hardware::MQDescriptorSync<V1_2::FmqRequestDatum>& requestChannel,
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const android::hardware::MQDescriptorSync<V1_2::FmqResultDatum>& resultChannel,
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configureExecutionBurst_cb cb) override;
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template<typename CallbackContext>
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Return<void> ExecuteSynchronously(const V1_3::Request& request, CallbackContext cbCtx);
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/// execute the graph prepared from the request
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template<typename CallbackContext>
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Return <V1_3::ErrorStatus> ExecuteGraph(
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std::shared_ptr<std::vector<::android::nn::RunTimePoolInfo>>& pMemPools,
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armnn::InputTensors& inputTensors,
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armnn::OutputTensors& outputTensors,
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CallbackContext callback);
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/// Executes this model with dummy inputs (e.g. all zeroes).
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/// \return false on failure, otherwise true
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bool ExecuteWithDummyInputs();
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V1_3::Priority GetModelPriority();
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private:
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Return <V1_3::ErrorStatus> Execute(const V1_3::Request& request,
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V1_2::MeasureTiming measureTiming,
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CallbackAsync_1_3 callback);
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Return<V1_3::ErrorStatus> PrepareMemoryForInputs(
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armnn::InputTensors& inputs,
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const V1_3::Request& request,
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const std::vector<android::nn::RunTimePoolInfo>& memPools);
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Return<V1_3::ErrorStatus> PrepareMemoryForOutputs(
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armnn::OutputTensors& outputs,
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std::vector<V1_2::OutputShape> &outputShapes,
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const V1_3::Request& request,
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const std::vector<android::nn::RunTimePoolInfo>& memPools);
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std::tuple<V1_3::ErrorStatus, android::hardware::hidl_vec<V1_2::OutputShape>, V1_2::Timing, std::string> PrepareMemoryForIO(
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armnn::InputTensors& inputs,
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armnn::OutputTensors& outputs,
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std::vector<android::nn::RunTimePoolInfo>& memPools,
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const V1_3::Request& request);
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template <typename TensorBindingCollection>
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void DumpTensorsIfRequired(char const* tensorNamePrefix, const TensorBindingCollection& tensorBindings);
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armnn::NetworkId m_NetworkId;
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armnn::IRuntime* m_Runtime;
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V1_3::Model m_Model;
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// There must be a single RequestThread for all ArmnnPreparedModel objects to ensure serial execution of workloads
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// It is specific to this class, so it is declared as static here
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static RequestThread_1_3<ArmnnPreparedModel_1_3, HalVersion, CallbackContext_1_3> m_RequestThread;
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uint32_t m_RequestCount;
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const std::string& m_RequestInputsAndOutputsDumpDir;
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const bool m_GpuProfilingEnabled;
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V1_3::Priority m_ModelPriority;
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};
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}
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