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Diffstat (limited to 'delegate/src/test/Pooling3dTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/Pooling3dTestHelper.hpp | 298 |
1 files changed, 0 insertions, 298 deletions
diff --git a/delegate/src/test/Pooling3dTestHelper.hpp b/delegate/src/test/Pooling3dTestHelper.hpp deleted file mode 100644 index 47e00f7b7f..0000000000 --- a/delegate/src/test/Pooling3dTestHelper.hpp +++ /dev/null @@ -1,298 +0,0 @@ -// -// Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#pragma once - -#include "TestUtils.hpp" - -#include <armnn_delegate.hpp> - -#include <flatbuffers/flatbuffers.h> -#include <flatbuffers/flexbuffers.h> -#include <tensorflow/lite/interpreter.h> -#include <tensorflow/lite/kernels/custom_ops_register.h> -#include <tensorflow/lite/kernels/register.h> -#include <tensorflow/lite/model.h> -#include <tensorflow/lite/schema/schema_generated.h> -#include <tensorflow/lite/version.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); - - 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 tflite; - // 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); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - - // Custom ops need to be added to the BuiltinOp resolver before the interpreter is created - // Based on the poolType from the test case add the custom operator using the name and the tflite - // registration function - tflite::ops::builtin::BuiltinOpResolver armnn_op_resolver; - if (poolType == "kMax") - { - armnn_op_resolver.AddCustom("MaxPool3D", tflite::ops::custom::Register_MAX_POOL_3D()); - } - else - { - armnn_op_resolver.AddCustom("AveragePool3D", tflite::ops::custom::Register_AVG_POOL_3D()); - } - - CHECK(InterpreterBuilder(tfLiteModel, armnn_op_resolver) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - - // Custom ops need to be added to the BuiltinOp resolver before the interpreter is created - // Based on the poolType from the test case add the custom operator using the name and the tflite - // registration function - tflite::ops::builtin::BuiltinOpResolver tflite_op_resolver; - if (poolType == "kMax") - { - tflite_op_resolver.AddCustom("MaxPool3D", tflite::ops::custom::Register_MAX_POOL_3D()); - } - else - { - tflite_op_resolver.AddCustom("AveragePool3D", tflite::ops::custom::Register_AVG_POOL_3D()); - } - - CHECK(InterpreterBuilder(tfLiteModel, tflite_op_resolver) - (&tfLiteInterpreter) == kTfLiteOk); - CHECK(tfLiteInterpreter != nullptr); - CHECK(tfLiteInterpreter->AllocateTensors() == kTfLiteOk); - - // Create the ArmNN Delegate - armnnDelegate::DelegateOptions delegateOptions(backends); - std::unique_ptr<TfLiteDelegate, decltype(&armnnDelegate::TfLiteArmnnDelegateDelete)> - theArmnnDelegate(armnnDelegate::TfLiteArmnnDelegateCreate(delegateOptions), - armnnDelegate::TfLiteArmnnDelegateDelete); - CHECK(theArmnnDelegate != nullptr); - - // Modify armnnDelegateInterpreter to use armnnDelegate - CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk); - - // Set input data - auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0]; - auto tfLiteDelegateInputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - tfLiteDelegateInputData[i] = inputValues[i]; - } - - auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0]; - auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateInputId); - for (unsigned int i = 0; i < inputValues.size(); ++i) - { - armnnDelegateInputData[i] = inputValues[i]; - } - - // Run EnqueueWorkload - CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk); - CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk); - - armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues); -} - -// 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 - - - - |