| // |
| // Copyright © 2020, 2023-2024 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "TestUtils.hpp" |
| |
| #include <armnn_delegate.hpp> |
| #include <DelegateTestInterpreter.hpp> |
| |
| #include <tensorflow/lite/version.h> |
| |
| namespace |
| { |
| |
| std::vector<char> CreateReshapeTfLiteModel( |
| tflite::BuiltinOperator redefineOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| const std::vector<int32_t>& targetShape, |
| bool useOption = true, |
| 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)); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| auto inputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| |
| std::vector<flatbuffers::Offset<Tensor>> tensors; |
| std::vector<int32_t> operatorInputs; |
| std::vector<int> subgraphInputs; |
| flatbuffers::Offset<void> operatorBuiltinOptions; |
| |
| if (useOption) |
| { |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| tensors = { inputTensor, outputTensor}; |
| operatorInputs = {0}; |
| subgraphInputs = {0}; |
| operatorBuiltinOptions = CreateReshapeOptions( |
| flatBufferBuilder, |
| flatBufferBuilder.CreateVector(targetShape.data(), targetShape.size())).Union(); |
| } |
| else |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(targetShape.data()), |
| sizeof(int32_t) * targetShape.size()))); |
| int32_t size = static_cast<int32_t>(targetShape.size()); |
| auto shapeTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>( { size } ), |
| tflite::TensorType_INT32, |
| 2, |
| flatBufferBuilder.CreateString("shape")); |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 3, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| tensors = { inputTensor, outputTensor, shapeTensor }; |
| operatorInputs = {0, 2}; |
| subgraphInputs = {0, 2}; |
| operatorBuiltinOptions = CreateReshapeOptions(flatBufferBuilder).Union(); |
| } |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ReshapeOptions; |
| |
| const std::vector<int32_t> operatorOutputs{1}; |
| flatbuffers::Offset <Operator> redefineOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const std::vector<int> 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(&redefineOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Reshape Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| redefineOperatorCode); |
| |
| 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()); |
| } |
| |
| std::vector<char> CreateRedefineTfLiteModel( |
| tflite::BuiltinOperator redefineOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| const std::vector<int32_t>& squeezeOrAxisData, |
| 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)); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| auto inputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| |
| std::vector<flatbuffers::Offset<Tensor>> tensors; |
| std::vector<int32_t> operatorInputs; |
| std::vector<int> subgraphInputs; |
| flatbuffers::Offset<void> operatorBuiltinOptions; |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_SqueezeOptions; |
| |
| if (redefineOperatorCode == tflite::BuiltinOperator_SQUEEZE) |
| { |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| tensors = { inputTensor, outputTensor}; |
| operatorInputs = {0}; |
| subgraphInputs = {0}; |
| operatorBuiltinOptions = |
| CreateSqueezeOptions(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(squeezeOrAxisData.data(), |
| squeezeOrAxisData.size())).Union(); |
| |
| operatorBuiltinOptionsType = BuiltinOptions_SqueezeOptions; |
| } |
| else if (redefineOperatorCode == tflite::BuiltinOperator_EXPAND_DIMS) |
| { |
| buffers.push_back( |
| CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(squeezeOrAxisData.data()), |
| sizeof(int32_t) * squeezeOrAxisData.size()))); |
| auto shapeTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>( { 1 } ), |
| tflite::TensorType_INT32, |
| 2, |
| flatBufferBuilder.CreateString("axis")); |
| |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 3, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| tensors = { inputTensor, outputTensor, shapeTensor }; |
| operatorInputs = {0, 2}; |
| subgraphInputs = {0, 2}; |
| operatorBuiltinOptions = CreateExpandDimsOptions(flatBufferBuilder).Union(); |
| |
| operatorBuiltinOptionsType = BuiltinOptions_ExpandDimsOptions; |
| } |
| |
| const std::vector<int32_t> operatorOutputs{1}; |
| flatbuffers::Offset <Operator> redefineOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const std::vector<int> 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(&redefineOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Redefine Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, |
| redefineOperatorCode); |
| |
| 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 RedefineTest(tflite::BuiltinOperator redefineOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& inputValues, |
| std::vector<T>& expectedOutputValues, |
| std::vector<int32_t>& additionalData, |
| bool useOption = true, |
| const std::vector<armnn::BackendId>& backends = {}, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer; |
| if (redefineOperatorCode == tflite::BuiltinOperator_EXPAND_DIMS) |
| { |
| modelBuffer = CreateRedefineTfLiteModel(redefineOperatorCode, |
| tensorType, |
| inputShape, |
| outputShape, |
| additionalData, |
| quantScale, |
| quantOffset); |
| } |
| else if (redefineOperatorCode == tflite::BuiltinOperator_RESHAPE) |
| { |
| modelBuffer = CreateReshapeTfLiteModel(redefineOperatorCode, |
| tensorType, |
| inputShape, |
| outputShape, |
| additionalData, |
| useOption, |
| quantScale, |
| quantOffset); |
| } |
| else if (redefineOperatorCode == tflite::BuiltinOperator_SQUEEZE) |
| { |
| modelBuffer = CreateRedefineTfLiteModel(redefineOperatorCode, |
| tensorType, |
| inputShape, |
| outputShape, |
| additionalData, |
| 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); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, CaptureAvailableBackends(backends)); |
| 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); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |