| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "SchemaSerialize.hpp" |
| #include <armnnTestUtils/TensorHelpers.hpp> |
| |
| #include "flatbuffers/idl.h" |
| #include "flatbuffers/util.h" |
| |
| #include <ArmnnSchema_generated.h> |
| #include <armnn/IRuntime.hpp> |
| #include <armnnDeserializer/IDeserializer.hpp> |
| #include <armnn/utility/Assert.hpp> |
| #include <armnn/utility/IgnoreUnused.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <fmt/format.h> |
| #include <doctest/doctest.h> |
| |
| #include <vector> |
| |
| using armnnDeserializer::IDeserializer; |
| using TensorRawPtr = armnnSerializer::TensorInfo*; |
| |
| struct ParserFlatbuffersSerializeFixture |
| { |
| ParserFlatbuffersSerializeFixture() : |
| m_Parser(IDeserializer::Create()), |
| m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())), |
| m_NetworkIdentifier(-1) |
| { |
| } |
| |
| std::vector<uint8_t> m_GraphBinary; |
| std::string m_JsonString; |
| std::unique_ptr<IDeserializer, void (*)(IDeserializer* 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; |
| |
| void Setup() |
| { |
| bool ok = ReadStringToBinary(); |
| if (!ok) |
| { |
| throw armnn::Exception("LoadNetwork failed while reading binary input"); |
| } |
| |
| armnn::INetworkPtr network = |
| m_Parser->CreateNetworkFromBinary(m_GraphBinary); |
| |
| if (!network) |
| { |
| throw armnn::Exception("The parser failed to create an ArmNN network"); |
| } |
| |
| auto optimized = Optimize(*network, {armnn::Compute::CpuRef}, |
| m_Runtime->GetDeviceSpec()); |
| |
| std::string errorMessage; |
| armnn::Status ret = m_Runtime->LoadNetwork(m_NetworkIdentifier, move(optimized), errorMessage); |
| |
| if (ret != armnn::Status::Success) |
| { |
| throw armnn::Exception(fmt::format("The runtime failed to load the network. " |
| "Error was: {0}. in {1} [{2}:{3}]", |
| errorMessage, |
| __func__, |
| __FILE__, |
| __LINE__)); |
| } |
| |
| } |
| |
| void SetupSingleInputSingleOutput(const std::string& inputName, const std::string& outputName) |
| { |
| // Store 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(); |
| } |
| |
| bool ReadStringToBinary() |
| { |
| std::string schemafile(&deserialize_schema_start, &deserialize_schema_end); |
| |
| // parse schema first, so we can use it to parse the data after |
| flatbuffers::Parser parser; |
| |
| bool ok = parser.Parse(schemafile.c_str()); |
| CHECK_MESSAGE(ok, std::string("Failed to parse schema file. Error was: " + parser.error_).c_str()); |
| |
| ok &= parser.Parse(m_JsonString.c_str()); |
| CHECK_MESSAGE(ok, std::string("Failed to parse json input. Error was: " + parser.error_).c_str()); |
| |
| if (!ok) |
| { |
| return false; |
| } |
| |
| { |
| const uint8_t* bufferPtr = parser.builder_.GetBufferPointer(); |
| size_t size = static_cast<size_t>(parser.builder_.GetSize()); |
| m_GraphBinary.assign(bufferPtr, bufferPtr+size); |
| } |
| return ok; |
| } |
| |
| /// Executes the network with the given input tensor and checks the result against the given output tensor. |
| /// This overload assumes the network has a single input and a single output. |
| template<std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnType, |
| typename DataType = armnn::ResolveType<ArmnnType>> |
| void RunTest(unsigned int layersId, |
| const std::vector<DataType>& inputData, |
| const std::vector<DataType>& expectedOutputData); |
| |
| template<std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnInputType, |
| armnn::DataType ArmnnOutputType, |
| typename InputDataType = armnn::ResolveType<ArmnnInputType>, |
| typename OutputDataType = armnn::ResolveType<ArmnnOutputType>> |
| void RunTest(unsigned int layersId, |
| const std::vector<InputDataType>& inputData, |
| const std::vector<OutputDataType>& 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, |
| armnn::DataType ArmnnType, |
| typename DataType = armnn::ResolveType<ArmnnType>> |
| void RunTest(unsigned int layersId, |
| const std::map<std::string, std::vector<DataType>>& inputData, |
| const std::map<std::string, std::vector<DataType>>& expectedOutputData); |
| |
| template<std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnInputType, |
| armnn::DataType ArmnnOutputType, |
| typename InputDataType = armnn::ResolveType<ArmnnInputType>, |
| typename OutputDataType = armnn::ResolveType<ArmnnOutputType>> |
| void RunTest(unsigned int layersId, |
| const std::map<std::string, std::vector<InputDataType>>& inputData, |
| const std::map<std::string, std::vector<OutputDataType>>& expectedOutputData); |
| |
| void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape, |
| armnnSerializer::TensorInfo tensorType, const std::string& name, |
| const float scale, const int64_t zeroPoint) |
| { |
| armnn::IgnoreUnused(name); |
| CHECK_EQ(shapeSize, tensors->dimensions()->size()); |
| CHECK(std::equal(shape.begin(), shape.end(), |
| tensors->dimensions()->begin(), tensors->dimensions()->end())); |
| CHECK_EQ(tensorType.dataType(), tensors->dataType()); |
| CHECK_EQ(scale, tensors->quantizationScale()); |
| CHECK_EQ(zeroPoint, tensors->quantizationOffset()); |
| } |
| }; |
| |
| template<std::size_t NumOutputDimensions, armnn::DataType ArmnnType, typename DataType> |
| void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId, |
| const std::vector<DataType>& inputData, |
| const std::vector<DataType>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions, ArmnnType, ArmnnType, DataType, DataType>(layersId, inputData, expectedOutputData); |
| } |
| |
| template<std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnInputType, |
| armnn::DataType ArmnnOutputType, |
| typename InputDataType, |
| typename OutputDataType> |
| void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId, |
| const std::vector<InputDataType>& inputData, |
| const std::vector<OutputDataType>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions, ArmnnInputType, ArmnnOutputType>(layersId, |
| { { m_SingleInputName, inputData } }, |
| { { m_SingleOutputName, expectedOutputData } }); |
| } |
| |
| template<std::size_t NumOutputDimensions, armnn::DataType ArmnnType, typename DataType> |
| void ParserFlatbuffersSerializeFixture::RunTest(unsigned int layersId, |
| const std::map<std::string, std::vector<DataType>>& inputData, |
| const std::map<std::string, std::vector<DataType>>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions, ArmnnType, ArmnnType, DataType, DataType>(layersId, inputData, expectedOutputData); |
| } |
| |
| template<std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnInputType, |
| armnn::DataType ArmnnOutputType, |
| typename InputDataType, |
| typename OutputDataType> |
| void ParserFlatbuffersSerializeFixture::RunTest( |
| unsigned int layersId, |
| const std::map<std::string, std::vector<InputDataType>>& inputData, |
| const std::map<std::string, std::vector<OutputDataType>>& expectedOutputData) |
| { |
| auto ConvertBindingInfo = [](const armnnDeserializer::BindingPointInfo& bindingInfo) |
| { |
| return std::make_pair(bindingInfo.m_BindingId, bindingInfo.m_TensorInfo); |
| }; |
| |
| // Setup the armnn input tensors from the given vectors. |
| armnn::InputTensors inputTensors; |
| for (auto&& it : inputData) |
| { |
| armnn::BindingPointInfo bindingInfo = ConvertBindingInfo( |
| m_Parser->GetNetworkInputBindingInfo(layersId, it.first)); |
| bindingInfo.second.SetConstant(true); |
| armnn::VerifyTensorInfoDataType(bindingInfo.second, ArmnnInputType); |
| inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) }); |
| } |
| |
| // Allocate storage for the output tensors to be written to and setup the armnn output tensors. |
| std::map<std::string, std::vector<OutputDataType>> outputStorage; |
| armnn::OutputTensors outputTensors; |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::BindingPointInfo bindingInfo = ConvertBindingInfo( |
| m_Parser->GetNetworkOutputBindingInfo(layersId, it.first)); |
| armnn::VerifyTensorInfoDataType(bindingInfo.second, ArmnnOutputType); |
| outputStorage.emplace(it.first, std::vector<OutputDataType>(bindingInfo.second.GetNumElements())); |
| outputTensors.push_back( |
| { bindingInfo.first, armnn::Tensor(bindingInfo.second, outputStorage.at(it.first).data()) }); |
| } |
| |
| m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors); |
| |
| // Compare each output tensor to the expected values |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::BindingPointInfo bindingInfo = ConvertBindingInfo( |
| m_Parser->GetNetworkOutputBindingInfo(layersId, it.first)); |
| auto outputExpected = it.second; |
| auto result = CompareTensors(outputExpected, outputStorage[it.first], |
| bindingInfo.second.GetShape(), bindingInfo.second.GetShape()); |
| CHECK_MESSAGE(result.m_Result, result.m_Message.str()); |
| } |
| } |