IVGCVSW-5381 TfLiteDelegate: Implement the Logical operators

 * Implemented Logical AND, NOT and OR operators.
 * NOT uses existing ElementwiseUnary VisitLayer function & tests.
 * AND/OR uses new LogicalBinary VisitLayer function & tests.

Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I5e7f1e78b30c36ac7f14c70a712b54f98d664b83

3 files changed, 488 insertions, 13 deletions

diff --git a/delegate/src/test/ElementwiseUnaryTestHelper.hpp b/delegate/src/test/ElementwiseUnaryTestHelper.hpp
index 2683339eb5..dcc7074753 100644
--- a/delegate/src/test/ElementwiseUnaryTestHelper.hpp
+++ b/delegate/src/test/ElementwiseUnaryTestHelper.hpp
@@ -110,25 +110,76 @@ void ElementwiseUnaryFP32Test(tflite::BuiltinOperator unaryOperatorCode,
     CHECK(armnnDelegateInterpreter->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
 
     // Set input data
-    auto tfLiteDelegateInputId = tfLiteInterpreter->inputs()[0];
-    auto tfLiteDelageInputData = tfLiteInterpreter->typed_tensor<float>(tfLiteDelegateInputId);
-    for (unsigned int i = 0; i < inputValues.size(); ++i)
-    {
-        tfLiteDelageInputData[i] = inputValues[i];
-    }
-
-    auto armnnDelegateInputId = armnnDelegateInterpreter->inputs()[0];
-    auto armnnDelegateInputData = armnnDelegateInterpreter->typed_tensor<float>(armnnDelegateInputId);
-    for (unsigned int i = 0; i < inputValues.size(); ++i)
-    {
-        armnnDelegateInputData[i] = inputValues[i];
-    }
+    armnnDelegate::FillInput(armnnDelegateInterpreter, 0, inputValues);
+    armnnDelegate::FillInput(tfLiteInterpreter, 0, inputValues);
+
     // Run EnqueWorkload
     CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
     CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
 
     // Compare output data
     armnnDelegate::CompareOutputData(tfLiteInterpreter, armnnDelegateInterpreter, inputShape, expectedOutputValues);
+
+    armnnDelegateInterpreter.reset(nullptr);
+    tfLiteInterpreter.reset(nullptr);
+}
+
+void ElementwiseUnaryBoolTest(tflite::BuiltinOperator unaryOperatorCode,
+                              std::vector<armnn::BackendId>& backends,
+                              std::vector<int32_t>& inputShape,
+                              std::vector<bool>& inputValues,
+                              std::vector<bool>& expectedOutputValues)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateElementwiseUnaryTfLiteModel(unaryOperatorCode,
+                                                                      ::tflite::TensorType_BOOL,
+                                                                      inputShape);
+
+    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
+    armnnDelegate::FillInput(armnnDelegateInterpreter, 0, inputValues);
+    armnnDelegate::FillInput(tfLiteInterpreter, 0, inputValues);
+
+    // Run EnqueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function
+    // directly instead. This is because Boolean types get converted to a bit representation in a vector.
+    auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+    auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<bool>(tfLiteDelegateOutputId);
+    auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+    auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<bool>(armnnDelegateOutputId);
+
+    armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size());
+    armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size());
+    armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
+
+    armnnDelegateInterpreter.reset(nullptr);
+    tfLiteInterpreter.reset(nullptr);
 }
 
 } // anonymous namespace
diff --git a/delegate/src/test/LogicalTest.cpp b/delegate/src/test/LogicalTest.cpp
new file mode 100644
index 0000000000..9fa2d3dde0
--- /dev/null
+++ b/delegate/src/test/LogicalTest.cpp
@@ -0,0 +1,226 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ElementwiseUnaryTestHelper.hpp"
+#include "LogicalTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+void LogicalBinaryAndBoolTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> input0Shape { 1, 2, 2 };
+    std::vector<int32_t> input1Shape { 1, 2, 2 };
+    std::vector<int32_t> expectedOutputShape { 1, 2, 2 };
+
+    // Set input and output values
+    std::vector<bool> input0Values { 0, 0, 1, 1 };
+    std::vector<bool> input1Values { 0, 1, 0, 1 };
+    std::vector<bool> expectedOutputValues { 0, 0, 0, 1 };
+
+    LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_AND,
+                            ::tflite::TensorType_BOOL,
+                            backends,
+                            input0Shape,
+                            input1Shape,
+                            expectedOutputShape,
+                            input0Values,
+                            input1Values,
+                            expectedOutputValues);
+}
+
+void LogicalBinaryAndBroadcastTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> input0Shape { 1, 2, 2 };
+    std::vector<int32_t> input1Shape { 1, 1, 1 };
+    std::vector<int32_t> expectedOutputShape { 1, 2, 2 };
+
+    std::vector<bool> input0Values { 0, 1, 0, 1 };
+    std::vector<bool> input1Values { 1 };
+    std::vector<bool> expectedOutputValues { 0, 1, 0, 1 };
+
+    LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_AND,
+                            ::tflite::TensorType_BOOL,
+                            backends,
+                            input0Shape,
+                            input1Shape,
+                            expectedOutputShape,
+                            input0Values,
+                            input1Values,
+                            expectedOutputValues);
+}
+
+void LogicalBinaryOrBoolTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> input0Shape { 1, 2, 2 };
+    std::vector<int32_t> input1Shape { 1, 2, 2 };
+    std::vector<int32_t> expectedOutputShape { 1, 2, 2 };
+
+    std::vector<bool> input0Values { 0, 0, 1, 1 };
+    std::vector<bool> input1Values { 0, 1, 0, 1 };
+    std::vector<bool> expectedOutputValues { 0, 1, 1, 1 };
+
+    LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_OR,
+                            ::tflite::TensorType_BOOL,
+                            backends,
+                            input0Shape,
+                            input1Shape,
+                            expectedOutputShape,
+                            input0Values,
+                            input1Values,
+                            expectedOutputValues);
+}
+
+void LogicalBinaryOrBroadcastTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> input0Shape { 1, 2, 2 };
+    std::vector<int32_t> input1Shape { 1, 1, 1 };
+    std::vector<int32_t> expectedOutputShape { 1, 2, 2 };
+
+    std::vector<bool> input0Values { 0, 1, 0, 1 };
+    std::vector<bool> input1Values { 1 };
+    std::vector<bool> expectedOutputValues { 1, 1, 1, 1 };
+
+    LogicalBinaryTest<bool>(tflite::BuiltinOperator_LOGICAL_OR,
+                            ::tflite::TensorType_BOOL,
+                            backends,
+                            input0Shape,
+                            input1Shape,
+                            expectedOutputShape,
+                            input0Values,
+                            input1Values,
+                            expectedOutputValues);
+}
+
+// LogicalNot operator uses ElementwiseUnary unary layer and descriptor but is still classed as logical operator.
+void LogicalNotBoolTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> inputShape { 1, 2, 2 };
+
+    std::vector<bool> inputValues { 0, 1, 0, 1 };
+    std::vector<bool> expectedOutputValues { 1, 0, 1, 0 };
+
+    ElementwiseUnaryBoolTest(tflite::BuiltinOperator_LOGICAL_NOT,
+                             backends,
+                             inputShape,
+                             inputValues,
+                             expectedOutputValues);
+}
+
+TEST_SUITE("LogicalBinaryTests_GpuAccTests")
+{
+
+TEST_CASE ("LogicalBinary_AND_Bool_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    LogicalBinaryAndBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_AND_Broadcast_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    LogicalBinaryAndBroadcastTest(backends);
+}
+
+TEST_CASE ("Logical_NOT_Bool_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    LogicalNotBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_OR_Bool_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    LogicalBinaryOrBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_OR_Broadcast_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    LogicalBinaryOrBroadcastTest(backends);
+}
+
+}
+
+
+TEST_SUITE("LogicalBinaryTests_CpuAccTests")
+{
+
+TEST_CASE ("LogicalBinary_AND_Bool_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    LogicalBinaryAndBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_AND_Broadcast_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    LogicalBinaryAndBroadcastTest(backends);
+}
+
+TEST_CASE ("Logical_NOT_Bool_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    LogicalNotBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_OR_Bool_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    LogicalBinaryOrBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_OR_Broadcast_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    LogicalBinaryOrBroadcastTest(backends);
+}
+
+}
+
+
+TEST_SUITE("LogicalBinaryTests_CpuRefTests")
+{
+
+TEST_CASE ("LogicalBinary_AND_Bool_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    LogicalBinaryAndBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_AND_Broadcast_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    LogicalBinaryAndBroadcastTest(backends);
+}
+
+TEST_CASE ("Logical_NOT_Bool_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    LogicalNotBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_OR_Bool_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    LogicalBinaryOrBoolTest(backends);
+}
+
+TEST_CASE ("LogicalBinary_OR_Broadcast_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    LogicalBinaryOrBroadcastTest(backends);
+}
+
+}
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/LogicalTestHelper.hpp b/delegate/src/test/LogicalTestHelper.hpp
new file mode 100644
index 0000000000..d08a1af388
--- /dev/null
+++ b/delegate/src/test/LogicalTestHelper.hpp
@@ -0,0 +1,198 @@
+//
+// Copyright © 2020 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> CreateLogicalBinaryTfLiteModel(tflite::BuiltinOperator logicalOperatorCode,
+                                                 tflite::TensorType tensorType,
+                                                 const std::vector <int32_t>& input0TensorShape,
+                                                 const std::vector <int32_t>& input1TensorShape,
+                                                 const std::vector <int32_t>& outputTensorShape,
+                                                 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, flatBufferBuilder.CreateVector({})));
+
+    auto quantizationParameters =
+        CreateQuantizationParameters(flatBufferBuilder,
+                                     0,
+                                     0,
+                                     flatBufferBuilder.CreateVector<float>({ quantScale }),
+                                     flatBufferBuilder.CreateVector<int64_t>({ quantOffset }));
+
+
+    std::array<flatbuffers::Offset<Tensor>, 3> tensors;
+    tensors[0] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(input0TensorShape.data(),
+                                                                      input0TensorShape.size()),
+                              tensorType,
+                              0,
+                              flatBufferBuilder.CreateString("input_0"),
+                              quantizationParameters);
+    tensors[1] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(input1TensorShape.data(),
+                                                                      input1TensorShape.size()),
+                              tensorType,
+                              0,
+                              flatBufferBuilder.CreateString("input_1"),
+                              quantizationParameters);
+    tensors[2] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                      outputTensorShape.size()),
+                              tensorType,
+                              0,
+                              flatBufferBuilder.CreateString("output"),
+                              quantizationParameters);
+
+    // create operator
+    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_NONE;
+    flatbuffers::Offset<void> operatorBuiltinOptions = 0;
+    switch (logicalOperatorCode)
+    {
+        case BuiltinOperator_LOGICAL_AND:
+        {
+            operatorBuiltinOptionsType = BuiltinOptions_LogicalAndOptions;
+            operatorBuiltinOptions = CreateLogicalAndOptions(flatBufferBuilder).Union();
+            break;
+        }
+        case BuiltinOperator_LOGICAL_OR:
+        {
+            operatorBuiltinOptionsType = BuiltinOptions_LogicalOrOptions;
+            operatorBuiltinOptions = CreateLogicalOrOptions(flatBufferBuilder).Union();
+            break;
+        }
+        default:
+            break;
+    }
+    const std::vector<int32_t> operatorInputs{ {0, 1} };
+    const std::vector<int32_t> operatorOutputs{ 2 };
+    flatbuffers::Offset <Operator> logicalBinaryOperator =
+        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> subgraphInputs{ {0, 1} };
+    const std::vector<int> subgraphOutputs{ 2 };
+    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(&logicalBinaryOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: Logical Binary Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder, logicalOperatorCode);
+
+    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 LogicalBinaryTest(tflite::BuiltinOperator logicalOperatorCode,
+                       tflite::TensorType tensorType,
+                       std::vector<armnn::BackendId>& backends,
+                       std::vector<int32_t>& input0Shape,
+                       std::vector<int32_t>& input1Shape,
+                       std::vector<int32_t>& expectedOutputShape,
+                       std::vector<T>& input0Values,
+                       std::vector<T>& input1Values,
+                       std::vector<T>& expectedOutputValues,
+                       float quantScale = 1.0f,
+                       int quantOffset  = 0)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateLogicalBinaryTfLiteModel(logicalOperatorCode,
+                                                                   tensorType,
+                                                                   input0Shape,
+                                                                   input1Shape,
+                                                                   expectedOutputShape,
+                                                                   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 the armnn interpreter
+    armnnDelegate::FillInput(armnnDelegateInterpreter, 0, input0Values);
+    armnnDelegate::FillInput(armnnDelegateInterpreter, 1, input1Values);
+
+    // Set input data for the tflite interpreter
+    armnnDelegate::FillInput(tfLiteInterpreter, 0, input0Values);
+    armnnDelegate::FillInput(tfLiteInterpreter, 1, input1Values);
+
+    // Run EnqueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    // Compare output data, comparing Boolean values is handled differently and needs to call the CompareData function
+    // directly. This is because Boolean types get converted to a bit representation in a vector.
+    auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+    auto tfLiteDelegateOutputData = tfLiteInterpreter->typed_tensor<T>(tfLiteDelegateOutputId);
+    auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+    auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<T>(armnnDelegateOutputId);
+
+    armnnDelegate::CompareData(expectedOutputValues, armnnDelegateOutputData, expectedOutputValues.size());
+    armnnDelegate::CompareData(expectedOutputValues, tfLiteDelegateOutputData, expectedOutputValues.size());
+    armnnDelegate::CompareData(tfLiteDelegateOutputData, armnnDelegateOutputData, expectedOutputValues.size());
+
+    armnnDelegateInterpreter.reset(nullptr);
+    tfLiteInterpreter.reset(nullptr);
+}
+
+} // anonymous namespace
\ No newline at end of file