| // |
| // Copyright © 2021, 2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <flatbuffers/flatbuffers.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #include <tensorflow/lite/version.h> |
| |
| #include <schema_generated.h> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| |
| struct StreamRedirector |
| { |
| public: |
| StreamRedirector(std::ostream &stream, std::streambuf *newStreamBuffer) |
| : m_Stream(stream), m_BackupBuffer(m_Stream.rdbuf(newStreamBuffer)) {} |
| |
| ~StreamRedirector() { m_Stream.rdbuf(m_BackupBuffer); } |
| |
| private: |
| std::ostream &m_Stream; |
| std::streambuf *m_BackupBuffer; |
| }; |
| |
| std::vector<char> CreateAddDivTfLiteModel(tflite::TensorType tensorType, |
| const std::vector<int32_t>& tensorShape, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| |
| std::array<flatbuffers::Offset<Tensor>, 5> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input_0"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("input_1"), |
| quantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 3, |
| flatBufferBuilder.CreateString("input_2"), |
| quantizationParameters); |
| tensors[3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 4, |
| flatBufferBuilder.CreateString("add"), |
| quantizationParameters); |
| tensors[4] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 5, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| // create operator |
| tflite::BuiltinOptions addBuiltinOptionsType = tflite::BuiltinOptions_AddOptions; |
| flatbuffers::Offset<void> addBuiltinOptions = |
| CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union(); |
| |
| tflite::BuiltinOptions divBuiltinOptionsType = tflite::BuiltinOptions_DivOptions; |
| flatbuffers::Offset<void> divBuiltinOptions = |
| CreateAddOptions(flatBufferBuilder, ActivationFunctionType_NONE).Union(); |
| |
| std::array<flatbuffers::Offset<Operator>, 2> operators; |
| const std::vector<int32_t> addInputs{0, 1}; |
| const std::vector<int32_t> addOutputs{3}; |
| operators[0] = CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(addInputs.data(), addInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(addOutputs.data(), addOutputs.size()), |
| addBuiltinOptionsType, |
| addBuiltinOptions); |
| const std::vector<int32_t> divInputs{3, 2}; |
| const std::vector<int32_t> divOutputs{4}; |
| operators[1] = CreateOperator(flatBufferBuilder, |
| 1, |
| flatBufferBuilder.CreateVector<int32_t>(divInputs.data(), divInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(divOutputs.data(), divOutputs.size()), |
| divBuiltinOptionsType, |
| divBuiltinOptions); |
| |
| const std::vector<int> subgraphInputs{0, 1, 2}; |
| const std::vector<int> subgraphOutputs{4}; |
| flatbuffers::Offset<SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| flatBufferBuilder.CreateVector(operators.data(), operators.size())); |
| |
| flatbuffers::Offset<flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Add and Div Operator Model"); |
| |
| std::array<flatbuffers::Offset<OperatorCode>, 2> codes; |
| codes[0] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_ADD); |
| codes[1] = CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_DIV); |
| |
| flatbuffers::Offset<Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(codes.data(), codes.size()), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| std::vector<char> CreateCosTfLiteModel(tflite::TensorType tensorType, |
| const std::vector <int32_t>& tensorShape, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::vector<flatbuffers::Offset<tflite::Buffer>> buffers; |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({quantScale}), |
| flatBufferBuilder.CreateVector<int64_t>({quantOffset})); |
| |
| std::array<flatbuffers::Offset<Tensor>, 2> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(tensorShape.data(), |
| tensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| const std::vector<int32_t> operatorInputs({0}); |
| const std::vector<int32_t> operatorOutputs({1}); |
| |
| flatbuffers::Offset<Operator> ceilOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| BuiltinOptions_NONE); |
| |
| flatbuffers::Offset<flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: CEIL Operator Model"); |
| flatbuffers::Offset<OperatorCode> operatorCode = |
| CreateOperatorCode(flatBufferBuilder, tflite::BuiltinOperator_COS); |
| |
| const std::vector<int32_t> subgraphInputs({0}); |
| const std::vector<int32_t> subgraphOutputs({1}); |
| flatbuffers::Offset<SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors.data(), tensors.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs.data(), subgraphInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs.data(), subgraphOutputs.size()), |
| flatBufferBuilder.CreateVector(&ceilOperator, 1)); |
| |
| flatbuffers::Offset<Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers.data(), buffers.size())); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void DelegateOptionTest(tflite::TensorType tensorType, |
| std::vector<int32_t>& tensorShape, |
| std::vector<T>& input0Values, |
| std::vector<T>& input1Values, |
| std::vector<T>& input2Values, |
| std::vector<T>& expectedOutputValues, |
| const armnnDelegate::DelegateOptions& delegateOptions, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateAddDivTfLiteModel(tensorType, |
| tensorShape, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(input1Values, 1) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(input2Values, 2) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, delegateOptions); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(input0Values, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(input1Values, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(input2Values, 2) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| template <typename T> |
| void DelegateOptionNoFallbackTest(tflite::TensorType tensorType, |
| std::vector<int32_t>& tensorShape, |
| std::vector<T>& inputValues, |
| std::vector<T>& expectedOutputValues, |
| const armnnDelegate::DelegateOptions& delegateOptions, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateCosTfLiteModel(tensorType, |
| tensorShape, |
| quantScale, |
| quantOffset); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| tfLiteInterpreter.Cleanup(); |
| |
| try |
| { |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, delegateOptions); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| armnnInterpreter.Cleanup(); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, tensorShape); |
| } |
| catch (const armnn::Exception& e) |
| { |
| // Forward the exception message to std::cout |
| std::cout << e.what() << std::endl; |
| } |
| } |
| |
| } // anonymous namespace |