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, 186 deletions

diff --git a/delegate/src/test/PackTestHelper.hpp b/delegate/src/test/PackTestHelper.hpp
deleted file mode 100644
index a9e2ee17bc..0000000000
--- a/delegate/src/test/PackTestHelper.hpp
+++ /dev/null
@@ -1,186 +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>
-
-#include <string>
-
-namespace
-{
-
-std::vector<char> CreatePackTfLiteModel(tflite::BuiltinOperator packOperatorCode,
-                                        tflite::TensorType tensorType,
-                                        std::vector<int32_t>& inputTensorShape,
-                                        const std::vector <int32_t>& outputTensorShape,
-                                        const int32_t inputTensorNum,
-                                        unsigned int axis = 0,
-                                        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));
-
-    auto quantizationParameters =
-            CreateQuantizationParameters(flatBufferBuilder,
-                                         0,
-                                         0,
-                                         flatBufferBuilder.CreateVector<float>({ quantScale }),
-                                         flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
-
-    std::vector<int32_t> operatorInputs{};
-    const std::vector<int32_t> operatorOutputs{inputTensorNum};
-    std::vector<int> subgraphInputs{};
-    const std::vector<int> subgraphOutputs{inputTensorNum};
-
-    std::vector<flatbuffers::Offset<Tensor>> tensors(inputTensorNum + 1);
-    for (int i = 0; i < inputTensorNum; ++i)
-    {
-        tensors[i] = CreateTensor(flatBufferBuilder,
-                                  flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
-                                                                          inputTensorShape.size()),
-                                  tensorType,
-                                  1,
-                                  flatBufferBuilder.CreateString("input" + std::to_string(i)),
-                                  quantizationParameters);
-
-        // Add number of inputs to vector.
-        operatorInputs.push_back(i);
-        subgraphInputs.push_back(i);
-    }
-
-    // Create output tensor
-    tensors[inputTensorNum] = CreateTensor(flatBufferBuilder,
-                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
-                                                                      outputTensorShape.size()),
-                              tensorType,
-                              0,
-                              flatBufferBuilder.CreateString("output"),
-                              quantizationParameters);
-
-    // create operator
-    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_PackOptions;
-    flatbuffers::Offset<void> operatorBuiltinOptions =
-            CreatePackOptions(flatBufferBuilder, inputTensorNum, axis).Union();
-
-    flatbuffers::Offset <Operator> packOperator =
-            CreateOperator(flatBufferBuilder,
-                           0,
-                           flatBufferBuilder.CreateVector<int32_t>(operatorInputs.data(), operatorInputs.size()),
-                           flatBufferBuilder.CreateVector<int32_t>(operatorOutputs.data(), operatorOutputs.size()),
-                           operatorBuiltinOptionsType,
-                           operatorBuiltinOptions);
-
-    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(&packOperator, 1));
-
-    flatbuffers::Offset <flatbuffers::String> modelDescription =
-            flatBufferBuilder.CreateString("ArmnnDelegate: Pack Operator Model");
-    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, packOperatorCode);
-
-    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 PackTest(tflite::BuiltinOperator packOperatorCode,
-              tflite::TensorType tensorType,
-              std::vector<armnn::BackendId>& backends,
-              std::vector<int32_t>& inputShape,
-              std::vector<int32_t>& expectedOutputShape,
-              std::vector<std::vector<T>>& inputValues,
-              std::vector<T>& expectedOutputValues,
-              unsigned int axis = 0,
-              float quantScale = 1.0f,
-              int quantOffset  = 0)
-{
-    using namespace tflite;
-    std::vector<char> modelBuffer = CreatePackTfLiteModel(packOperatorCode,
-                                                          tensorType,
-                                                          inputShape,
-                                                          expectedOutputShape,
-                                                          inputValues.size(),
-                                                          axis,
-                                                          quantScale,
-                                                          quantOffset);
-
-    const Model* tfLiteModel = GetModel(modelBuffer.data());
-
-    // 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 for all input tensors.
-    for (unsigned int i = 0; i < inputValues.size(); ++i)
-    {
-        // Get single input tensor and assign to interpreters.
-        auto inputTensorValues = inputValues[i];
-        armnnDelegate::FillInput<T>(tfLiteInterpreter, i, inputTensorValues);
-        armnnDelegate::FillInput<T>(armnnDelegateInterpreter, i, inputTensorValues);
-    }
-
-    // Run EnqueWorkload
-    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
-    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
-
-    // Compare output data
-    armnnDelegate::CompareOutputData<T>(tfLiteInterpreter,
-                                        armnnDelegateInterpreter,
-                                        expectedOutputShape,
-                                        expectedOutputValues);
-
-    armnnDelegateInterpreter.reset(nullptr);
-}
-
-} // anonymous namespace
\ No newline at end of file