| // |
| // Copyright © 2022-2023 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 <flatbuffers/flexbuffers.h> |
| #include <tensorflow/lite/kernels/register.h> |
| #include <tensorflow/lite/kernels/custom_ops_register.h> |
| #include <tensorflow/lite/version.h> |
| |
| #include <schema_generated.h> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| #if defined(ARMNN_POST_TFLITE_2_5) |
| |
| std::vector<uint8_t> CreateCustomOptions(int, int, int, int, int, int, TfLitePadding); |
| |
| std::vector<char> CreatePooling3dTfLiteModel( |
| std::string poolType, |
| tflite::TensorType tensorType, |
| const std::vector<int32_t>& inputTensorShape, |
| const std::vector<int32_t>& outputTensorShape, |
| TfLitePadding padding = kTfLitePaddingSame, |
| int32_t strideWidth = 0, |
| int32_t strideHeight = 0, |
| int32_t strideDepth = 0, |
| int32_t filterWidth = 0, |
| int32_t filterHeight = 0, |
| int32_t filterDepth = 0, |
| tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, |
| 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)); |
| |
| |
| auto quantizationParameters = |
| CreateQuantizationParameters(flatBufferBuilder, |
| 0, |
| 0, |
| flatBufferBuilder.CreateVector<float>({ quantScale }), |
| flatBufferBuilder.CreateVector<int64_t>({ quantOffset })); |
| |
| // Create the input and output tensors |
| std::array<flatbuffers::Offset<Tensor>, 2> 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>(outputTensorShape.data(), |
| outputTensorShape.size()), |
| tensorType, |
| 0, |
| flatBufferBuilder.CreateString("output"), |
| quantizationParameters); |
| |
| // Create the custom options from the function below |
| std::vector<uint8_t> customOperatorOptions = CreateCustomOptions(strideHeight, strideWidth, strideDepth, |
| filterHeight, filterWidth, filterDepth, padding); |
| // opCodeIndex is created as a uint8_t to avoid map lookup |
| uint8_t opCodeIndex = 0; |
| // Set the operator name based on the PoolType passed in from the test case |
| std::string opName = ""; |
| if (poolType == "kMax") |
| { |
| opName = "MaxPool3D"; |
| } |
| else |
| { |
| opName = "AveragePool3D"; |
| } |
| // To create a custom operator code you pass in the builtin code for custom operators and the name of the custom op |
| flatbuffers::Offset<OperatorCode> operatorCode = CreateOperatorCodeDirect(flatBufferBuilder, |
| tflite::BuiltinOperator_CUSTOM, |
| opName.c_str()); |
| |
| // Create the Operator using the opCodeIndex and custom options. Also sets builtin options to none. |
| const std::vector<int32_t> operatorInputs{ 0 }; |
| const std::vector<int32_t> operatorOutputs{ 1 }; |
| flatbuffers::Offset<Operator> poolingOperator = |
| CreateOperator(flatBufferBuilder, |
| opCodeIndex, |
| flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()), |
| flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()), |
| tflite::BuiltinOptions_NONE, |
| 0, |
| flatBufferBuilder.CreateVector<uint8_t>(customOperatorOptions), |
| tflite::CustomOptionsFormat_FLEXBUFFERS); |
| |
| // Create the subgraph using the operator created above. |
| const std::vector<int> subgraphInputs{ 0 }; |
| 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(&poolingOperator, 1)); |
| |
| flatbuffers::Offset<flatbuffers::String> modelDescription = |
| flatBufferBuilder.CreateString("ArmnnDelegate: Pooling3d Operator Model"); |
| |
| // Create the model using operatorCode and the subgraph. |
| 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 Pooling3dTest(std::string poolType, |
| 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, |
| TfLitePadding padding = kTfLitePaddingSame, |
| int32_t strideWidth = 0, |
| int32_t strideHeight = 0, |
| int32_t strideDepth = 0, |
| int32_t filterWidth = 0, |
| int32_t filterHeight = 0, |
| int32_t filterDepth = 0, |
| tflite::ActivationFunctionType fusedActivation = tflite::ActivationFunctionType_NONE, |
| float quantScale = 1.0f, |
| int quantOffset = 0) |
| { |
| using namespace delegateTestInterpreter; |
| // Create the single op model buffer |
| std::vector<char> modelBuffer = CreatePooling3dTfLiteModel(poolType, |
| tensorType, |
| inputShape, |
| outputShape, |
| padding, |
| strideWidth, |
| strideHeight, |
| strideDepth, |
| filterWidth, |
| filterHeight, |
| filterDepth, |
| fusedActivation, |
| quantScale, |
| quantOffset); |
| |
| std::string opType = ""; |
| if (poolType == "kMax") |
| { |
| opType = "MaxPool3D"; |
| } |
| else |
| { |
| opType = "AveragePool3D"; |
| } |
| |
| // Setup interpreter with just TFLite Runtime. |
| auto tfLiteInterpreter = DelegateTestInterpreter(modelBuffer, opType); |
| 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, opType); |
| 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(); |
| } |
| |
| // Function to create the flexbuffer custom options for the custom pooling3d operator. |
| std::vector<uint8_t> CreateCustomOptions(int strideHeight, int strideWidth, int strideDepth, |
| int filterHeight, int filterWidth, int filterDepth, TfLitePadding padding) |
| { |
| auto flex_builder = std::make_unique<flexbuffers::Builder>(); |
| size_t map_start = flex_builder->StartMap(); |
| flex_builder->String("data_format", "NDHWC"); |
| // Padding is created as a key and padding type. Only VALID and SAME supported |
| if (padding == kTfLitePaddingValid) |
| { |
| flex_builder->String("padding", "VALID"); |
| } |
| else |
| { |
| flex_builder->String("padding", "SAME"); |
| } |
| |
| // Vector of filter dimensions in order ( 1, Depth, Height, Width, 1 ) |
| auto start = flex_builder->StartVector("ksize"); |
| flex_builder->Add(1); |
| flex_builder->Add(filterDepth); |
| flex_builder->Add(filterHeight); |
| flex_builder->Add(filterWidth); |
| flex_builder->Add(1); |
| // EndVector( start, bool typed, bool fixed) |
| flex_builder->EndVector(start, true, false); |
| |
| // Vector of stride dimensions in order ( 1, Depth, Height, Width, 1 ) |
| auto stridesStart = flex_builder->StartVector("strides"); |
| flex_builder->Add(1); |
| flex_builder->Add(strideDepth); |
| flex_builder->Add(strideHeight); |
| flex_builder->Add(strideWidth); |
| flex_builder->Add(1); |
| // EndVector( stridesStart, bool typed, bool fixed) |
| flex_builder->EndVector(stridesStart, true, false); |
| |
| flex_builder->EndMap(map_start); |
| flex_builder->Finish(); |
| |
| return flex_builder->GetBuffer(); |
| } |
| #endif |
| } // anonymous namespace |
| |
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