| // |
| // Copyright © 2018-2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "Schema.hpp" |
| |
| #include <armnn/Descriptors.hpp> |
| #include <armnn/IRuntime.hpp> |
| #include <armnn/TypesUtils.hpp> |
| #include <armnn/BackendRegistry.hpp> |
| |
| #include "../TfLiteParser.hpp" |
| |
| #include <ResolveType.hpp> |
| |
| #include <armnnTestUtils/TensorHelpers.hpp> |
| |
| #include <fmt/format.h> |
| #include <doctest/doctest.h> |
| |
| #include "flatbuffers/idl.h" |
| #include "flatbuffers/util.h" |
| #include "flatbuffers/flexbuffers.h" |
| |
| #include <schema_generated.h> |
| |
| |
| using armnnTfLiteParser::ITfLiteParser; |
| using armnnTfLiteParser::ITfLiteParserPtr; |
| |
| using TensorRawPtr = const tflite::TensorT *; |
| struct ParserFlatbuffersFixture |
| { |
| ParserFlatbuffersFixture() : |
| m_Runtime(armnn::IRuntime::Create(armnn::IRuntime::CreationOptions())), |
| m_NetworkIdentifier(0), |
| m_DynamicNetworkIdentifier(1) |
| { |
| ITfLiteParser::TfLiteParserOptions options; |
| options.m_StandInLayerForUnsupported = true; |
| options.m_InferAndValidate = true; |
| |
| m_Parser = std::make_unique<armnnTfLiteParser::TfLiteParserImpl>( |
| armnn::Optional<ITfLiteParser::TfLiteParserOptions>(options)); |
| } |
| |
| std::vector<uint8_t> m_GraphBinary; |
| std::string m_JsonString; |
| armnn::IRuntimePtr m_Runtime; |
| armnn::NetworkId m_NetworkIdentifier; |
| armnn::NetworkId m_DynamicNetworkIdentifier; |
| bool m_TestDynamic; |
| std::unique_ptr<armnnTfLiteParser::TfLiteParserImpl> m_Parser; |
| |
| /// 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 testDynamic = true) |
| { |
| m_TestDynamic = testDynamic; |
| loadNetwork(m_NetworkIdentifier, false); |
| |
| if (m_TestDynamic) |
| { |
| loadNetwork(m_DynamicNetworkIdentifier, true); |
| } |
| } |
| |
| std::unique_ptr<tflite::ModelT> MakeModelDynamic(std::vector<uint8_t> graphBinary) |
| { |
| const uint8_t* binaryContent = graphBinary.data(); |
| const size_t len = graphBinary.size(); |
| if (binaryContent == nullptr) |
| { |
| throw armnn::InvalidArgumentException(fmt::format("Invalid (null) binary content {}", |
| CHECK_LOCATION().AsString())); |
| } |
| flatbuffers::Verifier verifier(binaryContent, len); |
| if (verifier.VerifyBuffer<tflite::Model>() == false) |
| { |
| throw armnn::ParseException(fmt::format("Buffer doesn't conform to the expected Tensorflow Lite " |
| "flatbuffers format. size:{} {}", |
| len, |
| CHECK_LOCATION().AsString())); |
| } |
| auto model = tflite::UnPackModel(binaryContent); |
| |
| for (auto const& subgraph : model->subgraphs) |
| { |
| std::vector<int32_t> inputIds = subgraph->inputs; |
| for (unsigned int tensorIndex = 0; tensorIndex < subgraph->tensors.size(); ++tensorIndex) |
| { |
| if (std::find(inputIds.begin(), inputIds.end(), tensorIndex) != inputIds.end()) |
| { |
| continue; |
| } |
| for (auto const& tensor : subgraph->tensors) |
| { |
| if (tensor->shape_signature.size() != 0) |
| { |
| continue; |
| } |
| |
| for (unsigned int i = 0; i < tensor->shape.size(); ++i) |
| { |
| tensor->shape_signature.push_back(-1); |
| } |
| } |
| } |
| } |
| |
| return model; |
| } |
| |
| void loadNetwork(armnn::NetworkId networkId, bool loadDynamic) |
| { |
| if (!ReadStringToBinary()) |
| { |
| throw armnn::Exception("LoadNetwork failed while reading binary input"); |
| } |
| |
| armnn::INetworkPtr network = loadDynamic ? m_Parser->LoadModel(MakeModelDynamic(m_GraphBinary)) |
| : 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(networkId, std::move(optimized), errorMessage); |
| |
| if (ret != armnn::Status::Success) |
| { |
| throw armnn::Exception( |
| fmt::format("The runtime failed to load the network. " |
| "Error was: {}. in {} [{}:{}]", |
| 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(g_TfLiteSchemaText, g_TfLiteSchemaText + g_TfLiteSchemaText_len); |
| |
| // 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()); |
| |
| { |
| 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 assumes the network has a single input and a single output. |
| template <std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnType> |
| void RunTest(size_t subgraphId, |
| const std::vector<armnn::ResolveType<ArmnnType>>& inputData, |
| const std::vector<armnn::ResolveType<ArmnnType>>& 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> |
| void RunTest(size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType>>>& inputData, |
| const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType>>>& expectedOutputData); |
| |
| /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes. |
| /// 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 along with the allowance for |
| /// the input datatype to be different to the output |
| template <std::size_t NumOutputDimensions, |
| armnn::DataType ArmnnType1, |
| armnn::DataType ArmnnType2> |
| void RunTest(size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData, |
| const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData, |
| bool isDynamic = false); |
| |
| /// Multiple Inputs with different DataTypes, Multiple Outputs w/ Variable DataTypes |
| /// 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 along with the allowance for |
| /// the input datatype to be different to the output |
| template <std::size_t NumOutputDimensions, |
| armnn::DataType inputType1, |
| armnn::DataType inputType2, |
| armnn::DataType outputType> |
| void RunTest(size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data, |
| const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data, |
| const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& expectedOutputData); |
| |
| /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes. |
| /// 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 along with the allowance for |
| /// the input datatype to be different to the output |
| template<armnn::DataType ArmnnType1, |
| armnn::DataType ArmnnType2> |
| void RunTest(std::size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType1>>>& inputData, |
| const std::map<std::string, std::vector<armnn::ResolveType<ArmnnType2>>>& expectedOutputData); |
| |
| static inline std::string GenerateDetectionPostProcessJsonString( |
| const armnn::DetectionPostProcessDescriptor& descriptor) |
| { |
| flexbuffers::Builder detectPostProcess; |
| detectPostProcess.Map([&]() { |
| detectPostProcess.Bool("use_regular_nms", descriptor.m_UseRegularNms); |
| detectPostProcess.Int("max_detections", descriptor.m_MaxDetections); |
| detectPostProcess.Int("max_classes_per_detection", descriptor.m_MaxClassesPerDetection); |
| detectPostProcess.Int("detections_per_class", descriptor.m_DetectionsPerClass); |
| detectPostProcess.Int("num_classes", descriptor.m_NumClasses); |
| detectPostProcess.Float("nms_score_threshold", descriptor.m_NmsScoreThreshold); |
| detectPostProcess.Float("nms_iou_threshold", descriptor.m_NmsIouThreshold); |
| detectPostProcess.Float("h_scale", descriptor.m_ScaleH); |
| detectPostProcess.Float("w_scale", descriptor.m_ScaleW); |
| detectPostProcess.Float("x_scale", descriptor.m_ScaleX); |
| detectPostProcess.Float("y_scale", descriptor.m_ScaleY); |
| }); |
| detectPostProcess.Finish(); |
| |
| // Create JSON string |
| std::stringstream strStream; |
| std::vector<uint8_t> buffer = detectPostProcess.GetBuffer(); |
| std::copy(buffer.begin(), buffer.end(),std::ostream_iterator<int>(strStream,",")); |
| |
| return strStream.str(); |
| } |
| |
| void CheckTensors(const TensorRawPtr& tensors, size_t shapeSize, const std::vector<int32_t>& shape, |
| tflite::TensorType tensorType, uint32_t buffer, const std::string& name, |
| const std::vector<float>& min, const std::vector<float>& max, |
| const std::vector<float>& scale, const std::vector<int64_t>& zeroPoint) |
| { |
| CHECK(tensors); |
| CHECK_EQ(shapeSize, tensors->shape.size()); |
| CHECK(std::equal(shape.begin(), shape.end(), tensors->shape.begin(), tensors->shape.end())); |
| CHECK_EQ(tensorType, tensors->type); |
| CHECK_EQ(buffer, tensors->buffer); |
| CHECK_EQ(name, tensors->name); |
| CHECK(tensors->quantization); |
| CHECK(std::equal(min.begin(), min.end(), tensors->quantization.get()->min.begin(), |
| tensors->quantization.get()->min.end())); |
| CHECK(std::equal(max.begin(), max.end(), tensors->quantization.get()->max.begin(), |
| tensors->quantization.get()->max.end())); |
| CHECK(std::equal(scale.begin(), scale.end(), tensors->quantization.get()->scale.begin(), |
| tensors->quantization.get()->scale.end())); |
| CHECK(std::equal(zeroPoint.begin(), zeroPoint.end(), |
| tensors->quantization.get()->zero_point.begin(), |
| tensors->quantization.get()->zero_point.end())); |
| } |
| |
| private: |
| /// Fills the InputTensors with given input data |
| template <armnn::DataType dataType> |
| void FillInputTensors(armnn::InputTensors& inputTensors, |
| const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData, |
| size_t subgraphId); |
| }; |
| |
| /// Fills the InputTensors with given input data |
| template <armnn::DataType dataType> |
| void ParserFlatbuffersFixture::FillInputTensors( |
| armnn::InputTensors& inputTensors, |
| const std::map<std::string, std::vector<armnn::ResolveType<dataType>>>& inputData, |
| size_t subgraphId) |
| { |
| for (auto&& it : inputData) |
| { |
| armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkInputBindingInfo(subgraphId, it.first); |
| bindingInfo.second.SetConstant(true); |
| armnn::VerifyTensorInfoDataType(bindingInfo.second, dataType); |
| inputTensors.push_back({ bindingInfo.first, armnn::ConstTensor(bindingInfo.second, it.second.data()) }); |
| } |
| } |
| |
| /// Single Input, Single Output |
| /// 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> |
| void ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| const std::vector<armnn::ResolveType<armnnType>>& inputData, |
| const std::vector<armnn::ResolveType<armnnType>>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions, armnnType>(subgraphId, |
| { { m_SingleInputName, inputData } }, |
| { { m_SingleOutputName, expectedOutputData } }); |
| } |
| |
| /// Multiple Inputs, Multiple Outputs |
| /// 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> |
| void ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<armnnType>>>& inputData, |
| const std::map<std::string, std::vector<armnn::ResolveType<armnnType>>>& expectedOutputData) |
| { |
| RunTest<NumOutputDimensions, armnnType, armnnType>(subgraphId, inputData, expectedOutputData); |
| } |
| |
| /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes |
| /// 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 along with the allowance for |
| /// the input datatype to be different to the output |
| template <std::size_t NumOutputDimensions, |
| armnn::DataType armnnType1, |
| armnn::DataType armnnType2> |
| void ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData, |
| const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData, |
| bool isDynamic) |
| { |
| using DataType2 = armnn::ResolveType<armnnType2>; |
| |
| // Setup the armnn input tensors from the given vectors. |
| armnn::InputTensors inputTensors; |
| FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId); |
| |
| // Allocate storage for the output tensors to be written to and setup the armnn output tensors. |
| std::map<std::string, std::vector<DataType2>> outputStorage; |
| armnn::OutputTensors outputTensors; |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::LayerBindingId outputBindingId = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first).first; |
| armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkIdentifier, outputBindingId); |
| |
| // Check that output tensors have correct number of dimensions (NumOutputDimensions specified in test) |
| auto outputNumDimensions = outputTensorInfo.GetNumDimensions(); |
| CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions), |
| fmt::format("Number of dimensions expected {}, but got {} for output layer {}", |
| NumOutputDimensions, |
| outputNumDimensions, |
| it.first)); |
| |
| armnn::VerifyTensorInfoDataType(outputTensorInfo, armnnType2); |
| outputStorage.emplace(it.first, std::vector<DataType2>(outputTensorInfo.GetNumElements())); |
| outputTensors.push_back( |
| { outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) }); |
| } |
| |
| m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors); |
| |
| // Set flag so that the correct comparison function is called if the output is boolean. |
| bool isBoolean = armnnType2 == armnn::DataType::Boolean ? true : false; |
| |
| // Compare each output tensor to the expected values |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first); |
| auto outputExpected = it.second; |
| auto result = CompareTensors(outputExpected, outputStorage[it.first], |
| bindingInfo.second.GetShape(), bindingInfo.second.GetShape(), |
| isBoolean, isDynamic); |
| CHECK_MESSAGE(result.m_Result, result.m_Message.str()); |
| } |
| |
| if (isDynamic) |
| { |
| m_Runtime->EnqueueWorkload(m_DynamicNetworkIdentifier, inputTensors, outputTensors); |
| |
| // Compare each output tensor to the expected values |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first); |
| auto outputExpected = it.second; |
| auto result = CompareTensors(outputExpected, outputStorage[it.first], |
| bindingInfo.second.GetShape(), bindingInfo.second.GetShape(), |
| false, isDynamic); |
| CHECK_MESSAGE(result.m_Result, result.m_Message.str()); |
| } |
| } |
| } |
| |
| /// Multiple Inputs, Multiple Outputs w/ Variable Datatypes and different dimension sizes. |
| /// 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 along with the allowance for |
| /// the input datatype to be different to the output. |
| template <armnn::DataType armnnType1, |
| armnn::DataType armnnType2> |
| void ParserFlatbuffersFixture::RunTest(std::size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<armnnType1>>>& inputData, |
| const std::map<std::string, std::vector<armnn::ResolveType<armnnType2>>>& expectedOutputData) |
| { |
| using DataType2 = armnn::ResolveType<armnnType2>; |
| |
| // Setup the armnn input tensors from the given vectors. |
| armnn::InputTensors inputTensors; |
| FillInputTensors<armnnType1>(inputTensors, inputData, subgraphId); |
| |
| armnn::OutputTensors outputTensors; |
| outputTensors.reserve(expectedOutputData.size()); |
| std::map<std::string, std::vector<DataType2>> outputStorage; |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first); |
| armnn::VerifyTensorInfoDataType(bindingInfo.second, armnnType2); |
| |
| std::vector<DataType2> out(it.second.size()); |
| outputStorage.emplace(it.first, out); |
| outputTensors.push_back({ bindingInfo.first, |
| armnn::Tensor(bindingInfo.second, |
| outputStorage.at(it.first).data()) }); |
| } |
| |
| m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors); |
| |
| // Checks the results. |
| for (auto&& it : expectedOutputData) |
| { |
| std::vector<armnn::ResolveType<armnnType2>> out = outputStorage.at(it.first); |
| { |
| for (unsigned int i = 0; i < out.size(); ++i) |
| { |
| CHECK(doctest::Approx(it.second[i]).epsilon(0.000001f) == out[i]); |
| } |
| } |
| } |
| } |
| |
| /// Multiple Inputs with different DataTypes, Multiple Outputs w/ Variable DataTypes |
| /// 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 along with the allowance for |
| /// the input datatype to be different to the output |
| template <std::size_t NumOutputDimensions, |
| armnn::DataType inputType1, |
| armnn::DataType inputType2, |
| armnn::DataType outputType> |
| void ParserFlatbuffersFixture::RunTest(size_t subgraphId, |
| const std::map<std::string, std::vector<armnn::ResolveType<inputType1>>>& input1Data, |
| const std::map<std::string, std::vector<armnn::ResolveType<inputType2>>>& input2Data, |
| const std::map<std::string, std::vector<armnn::ResolveType<outputType>>>& expectedOutputData) |
| { |
| using DataType2 = armnn::ResolveType<outputType>; |
| |
| // Setup the armnn input tensors from the given vectors. |
| armnn::InputTensors inputTensors; |
| FillInputTensors<inputType1>(inputTensors, input1Data, subgraphId); |
| FillInputTensors<inputType2>(inputTensors, input2Data, subgraphId); |
| |
| // Allocate storage for the output tensors to be written to and setup the armnn output tensors. |
| std::map<std::string, std::vector<DataType2>> outputStorage; |
| armnn::OutputTensors outputTensors; |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::LayerBindingId outputBindingId = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first).first; |
| armnn::TensorInfo outputTensorInfo = m_Runtime->GetOutputTensorInfo(m_NetworkIdentifier, outputBindingId); |
| |
| // Check that output tensors have correct number of dimensions (NumOutputDimensions specified in test) |
| auto outputNumDimensions = outputTensorInfo.GetNumDimensions(); |
| CHECK_MESSAGE((outputNumDimensions == NumOutputDimensions), |
| fmt::format("Number of dimensions expected {}, but got {} for output layer {}", |
| NumOutputDimensions, |
| outputNumDimensions, |
| it.first)); |
| |
| armnn::VerifyTensorInfoDataType(outputTensorInfo, outputType); |
| outputStorage.emplace(it.first, std::vector<DataType2>(outputTensorInfo.GetNumElements())); |
| outputTensors.push_back( |
| { outputBindingId, armnn::Tensor(outputTensorInfo, outputStorage.at(it.first).data()) }); |
| } |
| |
| m_Runtime->EnqueueWorkload(m_NetworkIdentifier, inputTensors, outputTensors); |
| |
| // Set flag so that the correct comparison function is called if the output is boolean. |
| bool isBoolean = outputType == armnn::DataType::Boolean ? true : false; |
| |
| // Compare each output tensor to the expected values |
| for (auto&& it : expectedOutputData) |
| { |
| armnn::BindingPointInfo bindingInfo = m_Parser->GetNetworkOutputBindingInfo(subgraphId, it.first); |
| auto outputExpected = it.second; |
| auto result = CompareTensors(outputExpected, outputStorage[it.first], |
| bindingInfo.second.GetShape(), bindingInfo.second.GetShape(), |
| isBoolean); |
| CHECK_MESSAGE(result.m_Result, result.m_Message.str()); |
| } |
| } |