| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include <armnn/IRuntime.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| #include <Network.hpp> |
| #include <VerificationHelpers.hpp> |
| |
| #include <boost/format.hpp> |
| |
| #include <iomanip> |
| #include <string> |
| |
| namespace armnnUtils |
| { |
| |
| template<typename TParser> |
| struct ParserPrototxtFixture |
| { |
| ParserPrototxtFixture() |
| : m_Parser(TParser::Create()) |
| , m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())) |
| , m_NetworkIdentifier(-1) |
| { |
| } |
| |
| /// Parses and loads the network defined by the m_Prototext string. |
| /// @{ |
| void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName); |
| void SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape, |
| const std::string& inputName, |
| const std::string& outputName); |
| void SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape, |
| const armnn::TensorShape& outputTensorShape, |
| const std::string& inputName, |
| const std::string& outputName); |
| void Setup(const std::map<std::string, armnn::TensorShape>& inputShapes, |
| const std::vector<std::string>& requestedOutputs); |
| void Setup(); |
| armnn::IOptimizedNetworkPtr SetupOptimizedNetwork( |
| const std::map<std::string,armnn::TensorShape>& inputShapes, |
| const std::vector<std::string>& requestedOutputs); |
| /// @} |
| |
| /// Executes the network with the given input tensor and checks the result against the given output tensor. |
| /// This overload assumes that the network has a single input and a single output. |
| template <std::size_t NumOutputDimensions> |
| void RunTest(const std::vector<float>& inputData, const std::vector<float>& expectedOutputData); |
| |
| /// Executes the network with the given input tensors and checks the results against the given output tensors. |
| /// This overload supports multiple inputs and multiple outputs, identified by name. |
| template <std::size_t NumOutputDimensions> |
| void RunTest(const std::map<std::string, std::vector<float>>& inputData, |
| const std::map<std::string, std::vector<float>>& expectedOutputData); |
| |
| std::string m_Prototext; |
| std::unique_ptr<TParser, void(*)(TParser* parser)> m_Parser; |
| armnn::IRuntimePtr m_Runtime; |
| armnn::NetworkId m_NetworkIdentifier; |
| |
| /// If the single-input-single-output overload of Setup() is called, these will store the input and output name |
| /// so they don't need to be passed to the single-input-single-output overload of RunTest(). |
| /// @{ |
| std::string m_SingleInputName; |
| std::string m_SingleOutputName; |
| /// @} |
| |
| /// This will store the output shape so it don't need to be passed to the single-input-single-output overload |
| /// of RunTest(). |
| armnn::TensorShape m_SingleOutputShape; |
| }; |
| |
| template<typename TParser> |
| void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const std::string& inputName, |
| const std::string& outputName) |
| { |
| // Stores the input and output name so they don't need to be passed to the single-input-single-output RunTest(). |
| m_SingleInputName = inputName; |
| m_SingleOutputName = outputName; |
| Setup({ }, { outputName }); |
| } |
| |
| template<typename TParser> |
| void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape, |
| const std::string& inputName, |
| const std::string& outputName) |
| { |
| // Stores the input and output name so they don't need to be passed to the single-input-single-output RunTest(). |
| m_SingleInputName = inputName; |
| m_SingleOutputName = outputName; |
| Setup({ { inputName, inputTensorShape } }, { outputName }); |
| } |
| |
| template<typename TParser> |
| void ParserPrototxtFixture<TParser>::SetupSingleInputSingleOutput(const armnn::TensorShape& inputTensorShape, |
| const armnn::TensorShape& outputTensorShape, |
| const std::string& inputName, |
| const std::string& outputName) |
| { |
| // Stores the input name, the output name and the output tensor shape |
| // so they don't need to be passed to the single-input-single-output RunTest(). |
| m_SingleInputName = inputName; |
| m_SingleOutputName = outputName; |
| m_SingleOutputShape = outputTensorShape; |
| Setup({ { inputName, inputTensorShape } }, { outputName }); |
| } |
| |
| template<typename TParser> |
| void ParserPrototxtFixture<TParser>::Setup(const std::map<std::string, armnn::TensorShape>& inputShapes, |
| const std::vector<std::string>& requestedOutputs) |
| { |
| std::string errorMessage; |
| |
| armnn::INetworkPtr network = |
| m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes, requestedOutputs); |
| auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec()); |
| armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage); |
| if (ret != armnn::Status::Success) |
| { |
| throw armnn::Exception(boost::str( |
| boost::format("LoadNetwork failed with error: '%1%' %2%") |
| % errorMessage |
| % CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| template<typename TParser> |
| void ParserPrototxtFixture<TParser>::Setup() |
| { |
| std::string errorMessage; |
| |
| armnn::INetworkPtr network = |
| m_Parser->CreateNetworkFromString(m_Prototext.c_str()); |
| auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec()); |
| armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage); |
| if (ret != armnn::Status::Success) |
| { |
| throw armnn::Exception(boost::str( |
| boost::format("LoadNetwork failed with error: '%1%' %2%") |
| % errorMessage |
| % CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| template<typename TParser> |
| armnn::IOptimizedNetworkPtr ParserPrototxtFixture<TParser>::SetupOptimizedNetwork( |
| const std::map<std::string,armnn::TensorShape>& inputShapes, |
| const std::vector<std::string>& requestedOutputs) |
| { |
| armnn::INetworkPtr network = |
| m_Parser->CreateNetworkFromString(m_Prototext.c_str(), inputShapes, requestedOutputs); |
| auto optimized = Optimize(*network, { armnn::Compute::CpuRef }, m_Runtime->GetDeviceSpec()); |
| return optimized; |
| } |
| |
| template<typename TParser> |
| template <std::size_t NumOutputDimensions> |
| void ParserPrototxtFixture<TParser>::RunTest(const std::vector<float>& inputData, |
| const std::vector<float>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions>({ { m_SingleInputName, inputData } }, { { m_SingleOutputName, expectedOutputData } }); |
| } |
| |
| template<typename TParser> |
| template <std::size_t NumOutputDimensions> |
| void ParserPrototxtFixture<TParser>::RunTest(const std::map<std::string, std::vector<float>>& inputData, |
| const std::map<std::string, std::vector<float>>& expectedOutputData) |
| { |
| using BindingPointInfo = std::pair<armnn::LayerBindingId, armnn::TensorInfo>; |
| |
| // Sets up the armnn input tensors from the given vectors. |
| armnn::InputTensors inputTensors; |
| for (auto&& it : inputData) |
| { |
| BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(it.first); |
| inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) }); |
| } |
| |
| // Allocates storage for the output tensors to be written to and sets up the armnn output tensors. |
| std::map<std::string, boost::multi_array<float, NumOutputDimensions>> outputStorage; |
| armnn::OutputTensors outputTensors; |
| for (auto&& it : expectedOutputData) |
| { |
| BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first); |
| outputStorage.emplace(it.first, MakeTensor<float, NumOutputDimensions>(bindingInfo.second)); |
| outputTensors.push_back( |
| { bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) }); |
| } |
| |
| m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors); |
| |
| // Compares each output tensor to the expected values. |
| for (auto&& it : expectedOutputData) |
| { |
| BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(it.first); |
| if (bindingInfo.second.GetNumElements() != it.second.size()) |
| { |
| throw armnn::Exception( |
| boost::str(boost::format("Output tensor %1% is expected to have %2% elements. " |
| "%3% elements supplied. %4%") % |
| it.first % |
| bindingInfo.second.GetNumElements() % |
| it.second.size() % |
| CHECK_LOCATION().AsString())); |
| } |
| |
| // If the expected output shape is set, the output tensor checks will be carried out. |
| if (m_SingleOutputShape.GetNumDimensions() != 0) |
| { |
| |
| if (bindingInfo.second.GetShape().GetNumDimensions() == NumOutputDimensions && |
| bindingInfo.second.GetShape().GetNumDimensions() == m_SingleOutputShape.GetNumDimensions()) |
| { |
| for (unsigned int i = 0; i < m_SingleOutputShape.GetNumDimensions(); ++i) |
| { |
| if (m_SingleOutputShape[i] != bindingInfo.second.GetShape()[i]) |
| { |
| throw armnn::Exception( |
| boost::str(boost::format("Output tensor %1% is expected to have %2% shape. " |
| "%3% shape supplied. %4%") % |
| it.first % |
| bindingInfo.second.GetShape() % |
| m_SingleOutputShape % |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| } |
| else |
| { |
| throw armnn::Exception( |
| boost::str(boost::format("Output tensor %1% is expected to have %2% dimensions. " |
| "%3% dimensions supplied. %4%") % |
| it.first % |
| bindingInfo.second.GetShape().GetNumDimensions() % |
| NumOutputDimensions % |
| CHECK_LOCATION().AsString())); |
| } |
| } |
| |
| auto outputExpected = MakeTensor<float, NumOutputDimensions>(bindingInfo.second, it.second); |
| BOOST_TEST(CompareTensors(outputExpected, outputStorage[it.first])); |
| } |
| } |
| |
| } // namespace armnnUtils |