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Diffstat (limited to 'delegate/src/test/Pooling2dTestHelper.hpp')
| -rw-r--r-- | delegate/src/test/Pooling2dTestHelper.hpp | 196 |
1 files changed, 0 insertions, 196 deletions
diff --git a/delegate/src/test/Pooling2dTestHelper.hpp b/delegate/src/test/Pooling2dTestHelper.hpp deleted file mode 100644 index c7457dbb22..0000000000 --- a/delegate/src/test/Pooling2dTestHelper.hpp +++ /dev/null @@ -1,196 +0,0 @@ -// -// Copyright © 2020, 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 <tensorflow/lite/interpreter.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 -{ - -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); - - 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 tflite; - std::vector<char> modelBuffer = CreatePooling2dTfLiteModel(poolingOperatorCode, - tensorType, - inputShape, - outputShape, - padding, - strideWidth, - strideHeight, - filterWidth, - filterHeight, - fusedActivation, - quantScale, - quantOffset); - - const Model* tfLiteModel = GetModel(modelBuffer.data()); - CHECK(tfLiteModel != nullptr); - // Create TfLite Interpreters - std::unique_ptr<Interpreter> armnnDelegateInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&armnnDelegateInterpreter) == kTfLiteOk); - CHECK(armnnDelegateInterpreter != nullptr); - CHECK(armnnDelegateInterpreter->AllocateTensors() == kTfLiteOk); - - std::unique_ptr<Interpreter> tfLiteInterpreter; - CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver()) - (&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); -} - -} // anonymous namespace - - - - |