| // |
| // Copyright © 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> CreateTileTfLiteModel(tflite::BuiltinOperator operatorCode, |
| tflite::TensorType inputTensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& multiplesTensorData, |
| const std::vector<int32_t>& multiplesTensorShape, |
| const std::vector<int32_t>& outputTensorShape) |
| { |
| 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*>(multiplesTensorData.data()), |
| sizeof(int32_t) * multiplesTensorData.size()))); |
| buffers.push_back(CreateBuffer(flatBufferBuilder)); |
| |
| std::array<flatbuffers::Offset<Tensor>, 3> tensors; |
| tensors[0] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(), |
| inputTensorShape.size()), |
| inputTensorType, |
| 1, |
| flatBufferBuilder.CreateString("input_tensor")); |
| |
| tensors[1] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(multiplesTensorShape.data(), |
| multiplesTensorShape.size()), |
| TensorType_INT32, |
| 2, |
| flatBufferBuilder.CreateString("axis_input_tensor")); |
| |
| tensors[2] = CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| inputTensorType, |
| 3, |
| flatBufferBuilder.CreateString("output_tensor")); |
| |
| // Create Operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE; |
| flatbuffers::Offset<void> operatorBuiltinOption = 0; |
| |
| const std::vector<int> operatorInputs {0, 1}; |
| const std::vector<int> operatorOutputs {2}; |
| |
| flatbuffers::Offset<Operator> tileOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOption); |
| |
| const std::vector<int> subgraphInputs{0, 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(&tileOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Tile Operator Model"); |
| flatbuffers::Offset <OperatorCode> opCode = CreateOperatorCode(flatBufferBuilder, operatorCode); |
| |
| 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 TileFP32TestImpl(tflite::BuiltinOperator operatorCode, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<float>& inputValues, |
| std::vector<int32_t> inputShape, |
| std::vector<int32_t> multiplesValues, |
| std::vector<int32_t> multiplesShapes, |
| std::vector<float>& expectedOutputValues, |
| std::vector<int32_t> expectedOutputShape) |
| { |
| using namespace delegateTestInterpreter; |
| |
| std::vector<char> modelBuffer = CreateTileTfLiteModel(operatorCode, |
| ::tflite::TensorType::TensorType_FLOAT32, |
| inputShape, |
| multiplesValues, |
| multiplesShapes, |
| expectedOutputShape); |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer); |
| CHECK(tfLiteInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.FillInputTensor<int32_t>(multiplesValues, 1) == kTfLiteOk); |
| CHECK(tfLiteInterpreter.Invoke() == kTfLiteOk); |
| std::vector<float> tfLiteOutputValues = tfLiteInterpreter.GetOutputResult<float>(0); |
| std::vector<int32_t> tfLiteOutputShape = tfLiteInterpreter.GetOutputShape(0); |
| |
| // Setup interpreter with Arm NN Delegate applied. |
| auto armnnInterpreter = DelegateTestInterpreter(modelBuffer, backends); |
| CHECK(armnnInterpreter.AllocateTensors() == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<float>(inputValues, 0) == kTfLiteOk); |
| CHECK(armnnInterpreter.FillInputTensor<int32_t>(multiplesValues, 1) == kTfLiteOk); |
| CHECK(armnnInterpreter.Invoke() == kTfLiteOk); |
| std::vector<float> armnnOutputValues = armnnInterpreter.GetOutputResult<float>(0); |
| std::vector<int32_t> armnnOutputShape = armnnInterpreter.GetOutputShape(0); |
| |
| armnnDelegate::CompareOutputData<float>(tfLiteOutputValues, armnnOutputValues, expectedOutputValues); |
| armnnDelegate::CompareOutputShape(tfLiteOutputShape, armnnOutputShape, expectedOutputShape); |
| |
| tfLiteInterpreter.Cleanup(); |
| armnnInterpreter.Cleanup(); |
| } |
| |
| } // anonymous namespace |