IVGCVSW-7555 Restructure Delegate

* New folders created:
  * common is for common code where TfLite API is not used
  * classic is for existing delegate implementations
  * opaque is for new opaque delegate implementation,
  * tests is for shared between existing Delegate and Opaque Delegate which have test utils to work which delegate to use.
* Existing delegate is built to libarmnnDelegate.so and opaque delegate is built as libarmnnOpaqueDelegate.so
* Opaque structure is introduced but no API is added yet.
* CmakeList.txt and delegate/CMakeList.txt have been modified and 2 new CmakeList.txt added
* Rename BUILD_ARMNN_TFLITE_DELEGATE as BUILD_CLASSIC_DELEGATE
* Rename BUILD_ARMNN_TFLITE_OPAQUE_DELEGATE as BUILD_OPAQUE_DELEGATE

Signed-off-by: Teresa Charlin <teresa.charlinreyes@arm.com>
Change-Id: Ib682b9ad0ac8d8acdc4ec6d9099bb0008a9fe8ed

1 files changed, 0 insertions, 263 deletions

diff --git a/delegate/src/test/NormalizationTestHelper.hpp b/delegate/src/test/NormalizationTestHelper.hpp
deleted file mode 100644
index 510b578c02..0000000000
--- a/delegate/src/test/NormalizationTestHelper.hpp
+++ /dev/null
@@ -1,263 +0,0 @@
-//
-// Copyright © 2021, 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> CreateNormalizationTfLiteModel(tflite::BuiltinOperator normalizationOperatorCode,
-                                                 tflite::TensorType tensorType,
-                                                 const std::vector<int32_t>& inputTensorShape,
-                                                 const std::vector<int32_t>& outputTensorShape,
-                                                 int32_t radius,
-                                                 float bias,
-                                                 float alpha,
-                                                 float beta,
-                                                 float quantScale = 1.0f,
-                                                 int quantOffset  = 0)
-{
-    using namespace tflite;
-    flatbuffers::FlatBufferBuilder flatBufferBuilder;
-
-    auto quantizationParameters =
-        CreateQuantizationParameters(flatBufferBuilder,
-                                     0,
-                                     0,
-                                     flatBufferBuilder.CreateVector<float>({ quantScale }),
-                                     flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
-
-    auto inputTensor = CreateTensor(flatBufferBuilder,
-                                    flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
-                                                                            inputTensorShape.size()),
-                                    tensorType,
-                                    1,
-                                    flatBufferBuilder.CreateString("input"),
-                                    quantizationParameters);
-
-    auto outputTensor = CreateTensor(flatBufferBuilder,
-                                     flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
-                                                                             outputTensorShape.size()),
-                                     tensorType,
-                                     2,
-                                     flatBufferBuilder.CreateString("output"),
-                                     quantizationParameters);
-
-    std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, outputTensor };
-
-    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
-    buffers.push_back(CreateBuffer(flatBufferBuilder));
-    buffers.push_back(CreateBuffer(flatBufferBuilder));
-    buffers.push_back(CreateBuffer(flatBufferBuilder));
-
-    std::vector<int32_t> operatorInputs = { 0 };
-    std::vector<int> subgraphInputs = { 0 };
-
-    tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_L2NormOptions;
-    flatbuffers::Offset<void> operatorBuiltinOptions = CreateL2NormOptions(flatBufferBuilder,
-                                                                           tflite::ActivationFunctionType_NONE).Union();
-
-    if (normalizationOperatorCode == tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION)
-    {
-        operatorBuiltinOptionsType = BuiltinOptions_LocalResponseNormalizationOptions;
-        operatorBuiltinOptions =
-            CreateLocalResponseNormalizationOptions(flatBufferBuilder, radius, bias, alpha, beta).Union();
-    }
-
-    // create operator
-    const std::vector<int32_t> operatorOutputs{ 1 };
-    flatbuffers::Offset <Operator> normalizationOperator =
-        CreateOperator(flatBufferBuilder,
-                       0,
-                       flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
-                       flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
-                       operatorBuiltinOptionsType,
-                       operatorBuiltinOptions);
-
-    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(&normalizationOperator, 1));
-
-    flatbuffers::Offset <flatbuffers::String> modelDescription =
-        flatBufferBuilder.CreateString("ArmnnDelegate: Normalization Operator Model");
-    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
-                                                                         normalizationOperatorCode);
-
-    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 NormalizationTest(tflite::BuiltinOperator normalizationOperatorCode,
-                       tflite::TensorType tensorType,
-                       const std::vector<armnn::BackendId>& backends,
-                       const std::vector<int32_t>& inputShape,
-                       std::vector<int32_t>& outputShape,
-                       std::vector<T>& inputValues,
-                       std::vector<T>& expectedOutputValues,
-                       int32_t radius = 0,
-                       float bias = 0.f,
-                       float alpha = 0.f,
-                       float beta = 0.f,
-                       float quantScale = 1.0f,
-                       int quantOffset  = 0)
-{
-    using namespace tflite;
-    std::vector<char> modelBuffer = CreateNormalizationTfLiteModel(normalizationOperatorCode,
-                                                                   tensorType,
-                                                                   inputShape,
-                                                                   outputShape,
-                                                                   radius,
-                                                                   bias,
-                                                                   alpha,
-                                                                   beta,
-                                                                   quantScale,
-                                                                   quantOffset);
-
-    const Model* tfLiteModel = GetModel(modelBuffer.data());
-    CHECK(tfLiteModel != nullptr);
-
-    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
-    armnnDelegate::FillInput<T>(tfLiteInterpreter, 0, inputValues);
-    armnnDelegate::FillInput<T>(armnnDelegateInterpreter, 0, inputValues);
-
-    // Run EnqueueWorkload
-    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
-    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
-    // Compare output data
-    armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, outputShape, expectedOutputValues);
-}
-
-void L2NormalizationTest(std::vector<armnn::BackendId>& backends)
-{
-    // Set input data
-    std::vector<int32_t> inputShape  { 1, 1, 1, 10 };
-    std::vector<int32_t> outputShape { 1, 1, 1, 10 };
-
-    std::vector<float> inputValues
-    {
-        1.0f,
-        2.0f,
-        3.0f,
-        4.0f,
-        5.0f,
-        6.0f,
-        7.0f,
-        8.0f,
-        9.0f,
-        10.0f
-    };
-
-    const float approxInvL2Norm = 0.050964719f;
-    std::vector<float> expectedOutputValues
-    {
-        1.0f  * approxInvL2Norm,
-        2.0f  * approxInvL2Norm,
-        3.0f  * approxInvL2Norm,
-        4.0f  * approxInvL2Norm,
-        5.0f  * approxInvL2Norm,
-        6.0f  * approxInvL2Norm,
-        7.0f  * approxInvL2Norm,
-        8.0f  * approxInvL2Norm,
-        9.0f  * approxInvL2Norm,
-        10.0f * approxInvL2Norm
-    };
-
-    NormalizationTest<float>(tflite::BuiltinOperator_L2_NORMALIZATION,
-                             ::tflite::TensorType_FLOAT32,
-                             backends,
-                             inputShape,
-                             outputShape,
-                             inputValues,
-                             expectedOutputValues);
-}
-
-void LocalResponseNormalizationTest(std::vector<armnn::BackendId>& backends,
-                                    int32_t radius,
-                                    float bias,
-                                    float alpha,
-                                    float beta)
-{
-    // Set input data
-    std::vector<int32_t> inputShape  { 2, 2, 2, 1 };
-    std::vector<int32_t> outputShape { 2, 2, 2, 1 };
-
-    std::vector<float> inputValues
-    {
-        1.0f, 2.0f,
-        3.0f, 4.0f,
-        5.0f, 6.0f,
-        7.0f, 8.0f
-    };
-
-    std::vector<float> expectedOutputValues
-    {
-        0.5f, 0.400000006f, 0.300000012f, 0.235294119f,
-        0.192307696f, 0.16216217f, 0.140000001f, 0.123076923f
-    };
-
-    NormalizationTest<float>(tflite::BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION,
-                             ::tflite::TensorType_FLOAT32,
-                             backends,
-                             inputShape,
-                             outputShape,
-                             inputValues,
-                             expectedOutputValues,
-                             radius,
-                             bias,
-                             alpha,
-                             beta);
-}
-
-} // anonymous namespace
\ No newline at end of file