| // |
| // 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> CreatePooling2dTfLiteModel( |
| tflite::BuiltinOperator poolingOperatorCode, |
| tflite::TensorType tensorType, |
| const std::vector <int32_t>& inputTensorShape, |
| const std::vector <int32_t>& outputTensorShape, |
| tflite::Padding padding = tflite::Padding_SAME, |
| int32_t strideWidth = 0, |
| int32_t strideHeight = 0, |
| int32_t filterWidth = 0, |
| int32_t filterHeight = 0, |
| tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace tflite; |
| flatbuffers::FlatBufferBuilder flatBufferBuilder; |
| |
| flatbuffers::Offset<tflite::Buffer> buffers[3] = {CreateBuffer(flatBufferBuilder), |
| CreateBuffer(flatBufferBuilder), |
| CreateBuffer(flatBufferBuilder)}; |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| flatbuffers::Offset<Tensor> tensors[2] { |
| CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(inputTensorShape), |
| tensorType, |
| 1, |
| flatBufferBuilder.CreateString("input"), |
| quantizationParameters), |
| |
| CreateTensor(flatBufferBuilder, |
| flatBufferBuilder.CreateVector<int32_t>(outputTensorShape), |
| tensorType, |
| 2, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters) |
| }; |
| |
| // create operator |
| tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_Pool2DOptions; |
| flatbuffers::Offset<void> operatorBuiltinOptions = CreatePool2DOptions(flatBufferBuilder, |
| padding, |
| strideWidth, |
| strideHeight, |
| filterWidth, |
| filterHeight, |
| fusedActivation).Union(); |
| |
| const std::vector<int32_t> operatorInputs{0}; |
| const std::vector<int32_t> operatorOutputs{1}; |
| flatbuffers::Offset <Operator> poolingOperator = |
| CreateOperator(flatBufferBuilder, |
| 0, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs), |
| operatorBuiltinOptionsType, |
| operatorBuiltinOptions); |
| |
| const int subgraphInputs[1] = {0}; |
| const int subgraphOutputs[1] = {1}; |
| flatbuffers::Offset <SubGraph> subgraph = |
| CreateSubGraph(flatBufferBuilder, |
| flatBufferBuilder.CreateVector(tensors, 2), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphInputs, 1), |
| flatBufferBuilder.CreateVector<int32_t>(subgraphOutputs, 1), |
| flatBufferBuilder.CreateVector(&poolingOperator, 1)); |
| |
| flatbuffers::Offset <flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Pooling2d Operator Model"); |
| flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, poolingOperatorCode); |
| |
| flatbuffers::Offset <Model> flatbufferModel = |
| CreateModel(flatBufferBuilder, |
| TFLITE_SCHEMA_VERSION, |
| flatBufferBuilder.CreateVector(&operatorCode, 1), |
| flatBufferBuilder.CreateVector(&subgraph, 1), |
| modelDescription, |
| flatBufferBuilder.CreateVector(buffers, 3)); |
| |
| flatBufferBuilder.Finish(flatbufferModel, armnnDelegate::FILE_IDENTIFIER); |
| |
| return std::vector<char>(flatBufferBuilder.GetBufferPointer(), |
| flatBufferBuilder.GetBufferPointer() + flatBufferBuilder.GetSize()); |
| } |
| |
| template <typename T> |
| void Pooling2dTest(tflite::BuiltinOperator poolingOperatorCode, |
| tflite::TensorType tensorType, |
| std::vector<armnn::BackendId>& backends, |
| std::vector<int32_t>& inputShape, |
| std::vector<int32_t>& outputShape, |
| std::vector<T>& inputValues, |
| std::vector<T>& expectedOutputValues, |
| tflite::Padding padding = tflite::Padding_SAME, |
| int32_t strideWidth = 0, |
| int32_t strideHeight = 0, |
| int32_t filterWidth = 0, |
| int32_t filterHeight = 0, |
| tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| std::vector<char> modelBuffer = CreatePooling2dTfLiteModel(poolingOperatorCode, |
| tensorType, |
| inputShape, |
| outputShape, |
| padding, |
| strideWidth, |
| strideHeight, |
| filterWidth, |
| filterHeight, |
| fusedActivation, |
| 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, 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 |
| |
| |
| |
| |