| // |
| // 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 |
| { |
| |
| std::vector<char> CreatePreluTfLiteModel(tflite::BuiltinOperator preluOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputShape, |
| const std::vector<int32_t>& alphaShape, |
| const std::vector<int32_t>& outputShape, |
| std::vector<float>& alphaData, |
| bool alphaIsConstant) |
| { |
| 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 *>(alphaData.data()), sizeof(float) * alphaData.size()))); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ 1.0f }), |
| flatBufferBuilder.CreateVector<int64_t>({ 0 })); |
| |
| auto inputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputShape.data(), |
| inputShape.size()), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters); |
| |
| auto alphaTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(alphaShape.data(), |
| alphaShape.size()), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("alpha"), |
| quantizationParameters); |
| |
| auto outputTensor = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputShape.data(), |
| outputShape.size()), |
| tensorType, |
| 3, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, alphaTensor, outputTensor }; |
| |
| const std::vector<int> operatorInputs{0, 1}; |
| const std::vector<int> operatorOutputs{2}; |
| flatbuffers::Offset <Operator> preluOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size())); |
| |
| std::vector<int> subgraphInputs{0}; |
| if (!alphaIsConstant) |
| { |
| subgraphInputs.push_back(1); |
| } |
| |
| const std::vector<int> subgraphOutputs{2}; |
| 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(&preluOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Prelu Operator Model"); |
| flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, preluOperatorCode); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&opCode, 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()); |
| } |
| |
| void PreluTest(tflite::BuiltinOperator preluOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector<armnn::BackendId>& backends, |
| const std::vector<int32_t>& inputShape, |
| const std::vector<int32_t>& alphaShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<float>& inputData, |
| std::vector<float>& alphaData, |
| std::vector<float>& expectedOutput, |
| bool alphaIsConstant) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer = CreatePreluTfLiteModel(preluOperatorCode, |
| tensorType, |
| inputShape, |
| alphaShape, |
| outputShape, |
| alphaData, |
| alphaIsConstant); |
| |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| |
| CHECK(armnnInterpreter.FillInputTensor<float>(inputData, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<float>(inputData, 0) == kTfLiteOk); |
| |
| // Set alpha data if not constant |
| if (!alphaIsConstant) |
| { |
| CHECK(tfLiteInterpreter.FillInputTensor<float>(alphaData, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<float>(alphaData, 1) == kTfLiteOk); |
| } |
| |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0); |
| |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0); |
| |
| armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutput); |
| |
| // Don't compare shapes on dynamic output tests, as output shape gets cleared. |
| if(!outputShape.empty()) |
| { |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, outputShape); |
| } |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| } // anonymous namespace |