| // |
| // Copyright © 2020, 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 |
| { |
| |
| std::vector<char> CreateSplitTfLiteModel(tflite::TensorType tensorType, |
| std::vector<int32_t>& axisTensorShape, |
| std::vector<int32_t>& inputTensorShape, |
| const std::vector<std::vector<int32_t>>& outputTensorShapes, |
| std::vector<int32_t>& axisData, |
| const int32_t numSplits, |
| 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, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| sizeof(int32_t) * axisData.size()))); |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| std::array<flatbuffers::Offset<Tensor>, 4> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| axisTensorShape.size()), |
| ::tflite::TensorType_INT32, |
| 2, |
| flatBufferBuilder.CreateString("axis"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| |
| // Create output tensor |
| for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) |
| { |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| tensors[i + 2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), |
| outputTensorShapes[i].size()), |
| tensorType, |
| (i+3), |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| } |
| |
| // create operator. Mean uses ReducerOptions. |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitOptions(flatBufferBuilder, numSplits).Union(); |
| |
| const std::vector<int> operatorInputs{ {0, 1} }; |
| const std::vector<int> operatorOutputs{ {2, 3} }; |
| flatbuffers::Offset <Operator> controlOperator = |
| 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> subgraphInputs{ {0, 1} }; |
| const std::vector<int> subgraphOutputs{ {2, 3} }; |
| 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(&controlOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers)); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void SplitTest(tflite::TensorType tensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& axisTensorShape, |
| std::vector<int32_t>& inputTensorShape, |
| std::vector<std::vector<int32_t>>& outputTensorShapes, |
| std::vector<int32_t>& axisData, |
| std::vector<T>& inputValues, |
| std::vector<std::vector<T>>& expectedOutputValues, |
| const int32_t numSplits, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateSplitTfLiteModel(tensorType, |
| axisTensorShape, |
| inputTensorShape, |
| outputTensorShapes, |
| axisData, |
| numSplits, |
| quantScale, |
| quantOffset); |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) |
| { |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(i); |
| |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(i); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]); |
| } |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| |
| } // End of SPLIT Test |
| |
| std::vector<char> CreateSplitVTfLiteModel(tflite::TensorType tensorType, |
| std::vector<int32_t>& inputTensorShape, |
| std::vector<int32_t>& splitsTensorShape, |
| std::vector<int32_t>& axisTensorShape, |
| const std::vector<std::vector<int32_t>>& outputTensorShapes, |
| std::vector<int32_t>& splitsData, |
| std::vector<int32_t>& axisData, |
| const int32_t numSplits, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| std::array<flatbuffers::Offset<tflite::Buffer>, 3> buffers; |
| buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})); |
| buffers[1] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(splitsData.data()), |
| sizeof(int32_t) * splitsData.size())); |
| buffers[2] = CreateBuffer(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisData.data()), |
| sizeof(int32_t) * axisData.size())); |
| |
| 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>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(splitsTensorShape.data(), |
| splitsTensorShape.size()), |
| ::tflite::TensorType_INT32, |
| 1, |
| flatBufferBuilder.CreateString("splits"), |
| quantizationParameters); |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(), |
| axisTensorShape.size()), |
| ::tflite::TensorType_INT32, |
| 2, |
| flatBufferBuilder.CreateString("axis"), |
| quantizationParameters); |
| |
| // Create output tensor |
| for (unsigned int i = 0; i < outputTensorShapes.size(); ++i) |
| { |
| tensors[i + 3] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShapes[i].data(), |
| outputTensorShapes[i].size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| } |
| |
| // create operator. Mean uses ReducerOptions. |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_SplitVOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreateSplitVOptions(flatBufferBuilder, numSplits).Union(); |
| |
| const std::vector<int> operatorInputs{ {0, 1, 2} }; |
| const std::vector<int> operatorOutputs{ {3, 4} }; |
| flatbuffers::Offset <Operator> controlOperator = |
| 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> subgraphInputs{ {0, 1, 2} }; |
| const std::vector<int> subgraphOutputs{ {3, 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(&controlOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: SPLIT_V Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, BuiltinOperator_SPLIT_V); |
| |
| 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 SplitVTest(tflite::TensorType tensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& inputTensorShape, |
| std::vector<int32_t>& splitsTensorShape, |
| std::vector<int32_t>& axisTensorShape, |
| std::vector<std::vector<int32_t>>& outputTensorShapes, |
| std::vector<T>& inputValues, |
| std::vector<int32_t>& splitsData, |
| std::vector<int32_t>& axisData, |
| std::vector<std::vector<T>>& expectedOutputValues, |
| const int32_t numSplits, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreateSplitVTfLiteModel(tensorType, |
| inputTensorShape, |
| splitsTensorShape, |
| axisTensorShape, |
| outputTensorShapes, |
| splitsData, |
| axisData, |
| numSplits, |
| 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); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<T>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| |
| // Compare output data |
| for (unsigned int i = 0; i < expectedOutputValues.size(); ++i) |
| { |
| std::vector<T> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<T>(i); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(i); |
| |
| std::vector<T> armnnOutputValues = armnnInterpreter.GetOutputResult<T>(i); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(i); |
| |
| armnnDelegate::CompareOutputData<T>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues[i]); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputTensorShapes[i]); |
| } |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } // End of SPLIT_V Test |
| |
| } // anonymous namespace |