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, 152 deletions

diff --git a/delegate/src/test/TestUtils.cpp b/delegate/src/test/TestUtils.cpp
deleted file mode 100644
index 9dce4461da..0000000000
--- a/delegate/src/test/TestUtils.cpp
+++ /dev/null
@@ -1,152 +0,0 @@
-//
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "TestUtils.hpp"
-
-namespace armnnDelegate
-{
-
-void CompareData(bool tensor1[], bool tensor2[], size_t tensorSize)
-{
-    auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));};
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(compareBool(tensor1[i], tensor2[i]));
-    }
-}
-
-void CompareData(std::vector<bool>& tensor1, bool tensor2[], size_t tensorSize)
-{
-    auto compareBool = [](auto a, auto b) {return (((a == 0) && (b == 0)) || ((a != 0) && (b != 0)));};
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(compareBool(tensor1[i], tensor2[i]));
-    }
-}
-
-void CompareData(float tensor1[], float tensor2[], size_t tensorSize)
-{
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(tensor1[i] == doctest::Approx( tensor2[i] ));
-    }
-}
-
-void CompareData(float tensor1[], float tensor2[], size_t tensorSize, float percentTolerance)
-{
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <=
-              std::abs(tensor1[i]*percentTolerance/100));
-    }
-}
-
-void CompareData(uint8_t tensor1[], uint8_t tensor2[], size_t tensorSize)
-{
-    uint8_t tolerance = 1;
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
-    }
-}
-
-void CompareData(int16_t tensor1[], int16_t tensor2[], size_t tensorSize)
-{
-    int16_t tolerance = 1;
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
-    }
-}
-
-void CompareData(int32_t tensor1[], int32_t tensor2[], size_t tensorSize)
-{
-    int32_t tolerance = 1;
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
-    }
-}
-
-void CompareData(int8_t tensor1[], int8_t tensor2[], size_t tensorSize)
-{
-    int8_t tolerance = 1;
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(std::max(tensor1[i], tensor2[i]) - std::min(tensor1[i], tensor2[i]) <= tolerance);
-    }
-}
-
-void CompareData(Half tensor1[], Half tensor2[], size_t tensorSize)
-{
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        CHECK(tensor1[i] == doctest::Approx( tensor2[i] ));
-    }
-}
-
-void CompareData(TfLiteFloat16 tensor1[], TfLiteFloat16 tensor2[], size_t tensorSize)
-{
-    uint16_t tolerance = 1;
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        uint16_t tensor1Data = tensor1[i].data;
-        uint16_t tensor2Data = tensor2[i].data;
-        CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance);
-    }
-}
-
-void CompareData(TfLiteFloat16 tensor1[], Half tensor2[], size_t tensorSize) {
-    uint16_t tolerance = 1;
-    for (size_t i = 0; i < tensorSize; i++)
-    {
-        uint16_t tensor1Data = tensor1[i].data;
-        uint16_t tensor2Data = half_float::detail::float2half<std::round_indeterminate, float>(tensor2[i]);
-        CHECK(std::max(tensor1Data, tensor2Data) - std::min(tensor1Data, tensor2Data) <= tolerance);
-    }
-}
-
-template <>
-void CompareOutputData(std::unique_ptr<tflite::Interpreter>& tfLiteInterpreter,
-                       std::unique_ptr<tflite::Interpreter>& armnnDelegateInterpreter,
-                       std::vector<int32_t>& expectedOutputShape,
-                       std::vector<Half>& expectedOutputValues,
-                       unsigned int outputIndex)
-{
-    auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[outputIndex];
-    auto tfLiteDelegateOutputTensor = tfLiteInterpreter->tensor(tfLiteDelegateOutputId);
-    auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateOutputId);
-    auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[outputIndex];
-    auto armnnDelegateOutputTensor = armnnDelegateInterpreter->tensor(armnnDelegateOutputId);
-    auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<TfLiteFloat16>(armnnDelegateOutputId);
-
-        CHECK(expectedOutputShape.size() == tfLiteDelegateOutputTensor->dims->size);
-        CHECK(expectedOutputShape.size() == armnnDelegateOutputTensor->dims->size);
-
-    for (size_t i = 0; i < expectedOutputShape.size(); i++)
-    {
-        CHECK(armnnDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]);
-        CHECK(tfLiteDelegateOutputTensor->dims->data[i] == expectedOutputShape[i]);
-        CHECK(tfLiteDelegateOutputTensor->dims->data[i] == armnnDelegateOutputTensor->dims->data[i]);
-    }
-
-    armnnDelegate::CompareData(armnnDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size());
-    armnnDelegate::CompareData(tfLiteDelegateOutputData, expectedOutputValues.data(), expectedOutputValues.size());
-    armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
-}
-
-template <>
-void FillInput<Half>(std::unique_ptr<tflite::Interpreter>& interpreter, int inputIndex, std::vector<Half>& inputValues)
-{
-    auto tfLiteDelegateInputId = interpreter->inputs()[inputIndex];
-    auto tfLiteDelageInputData = interpreter->typed_tensor<TfLiteFloat16>(tfLiteDelegateInputId);
-    for (unsigned int i = 0; i < inputValues.size(); ++i)
-    {
-        tfLiteDelageInputData[i].data = half_float::detail::float2half<std::round_indeterminate, float>(inputValues[i]);
-
-    }
-}
-
-} // namespace armnnDelegate
\ No newline at end of file