IVGCVSW-5399 'TfLiteDelegate: Implement the ArgMinMax operators'

* Added ARG_MIN and ARG_MAX support to armnn_delegate

Signed-off-by: Sadik Armagan <sadik.armagan@arm.com>
Change-Id: Ia000c4b64378e28320164edd4df2902ca13dcda6

5 files changed, 490 insertions, 8 deletions

diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index 9f64353d9a..8383722628 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -108,6 +108,8 @@ if(BUILD_UNIT_TESTS)
     list(APPEND armnnDelegate_unittest_sources
         src/test/ActivationTest.cpp
         src/test/ActivationTestHelper.hpp
+        src/test/ArgMinMaxTest.cpp
+        src/test/ArgMinMaxTestHelper.hpp
         src/test/ArmnnDelegateTest.cpp
         src/test/ComparisonTest.cpp
         src/test/ComparisonTestHelper.hpp
diff --git a/delegate/TensorFlowLiteDelegateSupport.md b/delegate/TensorFlowLiteDelegateSupport.md
index e506b85f23..dd4cbace7b 100644
--- a/delegate/TensorFlowLiteDelegateSupport.md
+++ b/delegate/TensorFlowLiteDelegateSupport.md
@@ -10,6 +10,10 @@ The Arm NN SDK TensorFlow Lite delegate currently supports the following operato
 
 * ADD
 
+* ARGMAX
+
+* ARGMIN
+
 * AVERAGE_POOL_2D, Supported Fused Activation: RELU , RELU6 , TANH, NONE
 
 * CONCATENATION, Supported Fused Activation: RELU , RELU6 , TANH, NONE
diff --git a/delegate/src/ArgMinMax.hpp b/delegate/src/ArgMinMax.hpp
index 367ef2ed14..090d18ef65 100644
--- a/delegate/src/ArgMinMax.hpp
+++ b/delegate/src/ArgMinMax.hpp
@@ -5,11 +5,10 @@
 
 #pragma once
 
-#include <armnn/utility/IgnoreUnused.hpp>
-
 #include <tensorflow/lite/builtin_ops.h>
 #include <tensorflow/lite/c/builtin_op_data.h>
 #include <tensorflow/lite/c/common.h>
+#include <tensorflow/lite/kernels/internal/tensor_ctypes.h>
 #include <tensorflow/lite/minimal_logging.h>
 
 namespace armnnDelegate
@@ -21,13 +20,118 @@ TfLiteStatus VisitArgMinMaxOperator(DelegateData& delegateData,
                                     int nodeIndex,
                                     int32_t argMinMaxOperatorCode)
 {
-    armnn::IgnoreUnused(delegateData,
-                        tfLiteContext,
-                        tfLiteNode,
-                        nodeIndex,
-                        argMinMaxOperatorCode);
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 2, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
+
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    const TfLiteTensor& tfLiteInputTensor = tfLiteTensors[tfLiteNode->inputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteInputTensor, argMinMaxOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, argMinMaxOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+    // Get const axis value from model and set it to descriptor.
+    const TfLiteTensor& tfLiteAxisTensor = tfLiteTensors[tfLiteNode->inputs->data[1]];
+    if (!IsValid(tfLiteContext, tfLiteAxisTensor, argMinMaxOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    armnn::ArgMinMaxDescriptor desc;
+    // Get the axis value from the input tensor
+    switch (tfLiteAxisTensor.type)
+    {
+        case kTfLiteInt32:
+        case kTfLiteInt64:
+            desc.m_Axis = tflite::GetTensorData<int>(&tfLiteAxisTensor)[0];
+            break;
+        default:
+            TF_LITE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnDelegate: Axis value data type is not supported in operator #%d node #%d: ",
+                argMinMaxOperatorCode, nodeIndex);
+            return kTfLiteError;
+    }
+
+    // If output_type is int32 then set Signed32 else Signed64. Default type is Signed64.
+    if (argMinMaxOperatorCode == kTfLiteBuiltinArgMax)
+    {
+        desc.m_Function = armnn::ArgMinMaxFunction::Max;
+        auto* argMaxParameters = reinterpret_cast<TfLiteArgMaxParams*>(tfLiteNode->builtin_data);
+        switch (argMaxParameters->output_type)
+        {
+            case kTfLiteInt32:
+                desc.m_Output_Type = armnn::DataType::Signed32;
+                break;
+            case kTfLiteInt64:
+                desc.m_Output_Type = armnn::DataType::Signed64;
+                break;
+            default:
+                TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ",
+                    argMinMaxOperatorCode, nodeIndex);
+                return kTfLiteError;
+        }
+    }
+    else
+    {
+        desc.m_Function = armnn::ArgMinMaxFunction::Min;
+        auto* argMinParameters = reinterpret_cast<TfLiteArgMinParams*>(tfLiteNode->builtin_data);
+        switch (argMinParameters->output_type)
+        {
+            case kTfLiteInt32:
+                desc.m_Output_Type = armnn::DataType::Signed32;
+                break;
+            case kTfLiteInt64:
+                desc.m_Output_Type = armnn::DataType::Signed64;
+                break;
+            default:
+                TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: output_type data type is not supported in operator #%d node #%d: ",
+                    argMinMaxOperatorCode, nodeIndex);
+                return kTfLiteError;
+        }
+    }
+
+    bool isSupported = false;
+    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC(__func__,
+                                   tfLiteContext,
+                                   IsArgMinMaxSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   inputTensorInfo,
+                                   outInfo,
+                                   desc);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Add an ArgMinMax layer
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddArgMinMaxLayer(desc);
+    ARMNN_ASSERT(layer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
 
-    return kTfLiteError;
+    // Connect
+    return Connect(layer, tfLiteNode, delegateData);
 }
 
 } // namespace armnnDelegate
diff --git a/delegate/src/test/ArgMinMaxTest.cpp b/delegate/src/test/ArgMinMaxTest.cpp
new file mode 100644
index 0000000000..bf60a77cb2
--- /dev/null
+++ b/delegate/src/test/ArgMinMaxTest.cpp
@@ -0,0 +1,174 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ArgMinMaxTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+void ArgMaxFP32Test(std::vector<armnn::BackendId>& backends, int axisValue)
+{
+    // Set input data
+    std::vector<int32_t> inputShape { 1, 3, 2, 4 };
+    std::vector<int32_t> outputShape { 1, 3, 4 };
+    std::vector<int32_t> axisShape { 1 };
+
+    std::vector<float> inputValues = { 1.0f,   2.0f,   3.0f,   4.0f,
+                                       5.0f,   6.0f,   7.0f,   8.0f,
+
+                                       10.0f,  20.0f,  30.0f,  40.0f,
+                                       50.0f,  60.0f,  70.0f,  80.0f,
+
+                                       100.0f, 200.0f, 300.0f, 400.0f,
+                                       500.0f, 600.0f, 700.0f, 800.0f };
+
+    std::vector<int32_t> expectedOutputValues = { 1, 1, 1, 1,
+                                                  1, 1, 1, 1,
+                                                  1, 1, 1, 1 };
+
+    ArgMinMaxTest<float, int32_t>(tflite::BuiltinOperator_ARG_MAX,
+                                  ::tflite::TensorType_FLOAT32,
+                                  backends,
+                                  inputShape,
+                                  axisShape,
+                                  outputShape,
+                                  inputValues,
+                                  expectedOutputValues,
+                                  axisValue,
+                                  ::tflite::TensorType_INT32);
+}
+
+void ArgMinFP32Test(std::vector<armnn::BackendId>& backends, int axisValue)
+{
+    // Set input data
+    std::vector<int32_t> inputShape { 1, 3, 2, 4 };
+    std::vector<int32_t> outputShape { 1, 3, 2 };
+    std::vector<int32_t> axisShape { 1 };
+
+    std::vector<float> inputValues = { 1.0f,   2.0f,   3.0f,   4.0f,
+                                       5.0f,   6.0f,   7.0f,   8.0f,
+
+                                       10.0f,  20.0f,  30.0f,  40.0f,
+                                       50.0f,  60.0f,  70.0f,  80.0f,
+
+                                       100.0f, 200.0f, 300.0f, 400.0f,
+                                       500.0f, 600.0f, 700.0f, 800.0f };
+
+    std::vector<int32_t> expectedOutputValues = { 0, 0,
+                                                  0, 0,
+                                                  0, 0 };
+
+    ArgMinMaxTest<float, int32_t>(tflite::BuiltinOperator_ARG_MIN,
+                                  ::tflite::TensorType_FLOAT32,
+                                  backends,
+                                  inputShape,
+                                  axisShape,
+                                  outputShape,
+                                  inputValues,
+                                  expectedOutputValues,
+                                  axisValue,
+                                  ::tflite::TensorType_INT32);
+}
+
+void ArgMaxUint8Test(std::vector<armnn::BackendId>& backends, int axisValue)
+{
+    // Set input data
+    std::vector<int32_t> inputShape { 1, 1, 1, 5 };
+    std::vector<int32_t> outputShape { 1, 1, 1 };
+    std::vector<int32_t> axisShape { 1 };
+
+    std::vector<uint8_t> inputValues = { 5, 2, 8, 10, 9 };
+
+    std::vector<int32_t> expectedOutputValues = { 3 };
+
+    ArgMinMaxTest<uint8_t, int32_t>(tflite::BuiltinOperator_ARG_MAX,
+                                    ::tflite::TensorType_UINT8,
+                                    backends,
+                                    inputShape,
+                                    axisShape,
+                                    outputShape,
+                                    inputValues,
+                                    expectedOutputValues,
+                                    axisValue,
+                                    ::tflite::TensorType_INT32);
+}
+
+TEST_SUITE("ArgMinMax_CpuRefTests")
+{
+
+TEST_CASE ("ArgMaxFP32Test_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    ArgMaxFP32Test(backends, 2);
+}
+
+TEST_CASE ("ArgMinFP32Test_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    ArgMinFP32Test(backends, 3);
+}
+
+TEST_CASE ("ArgMaxUint8Test_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    ArgMaxUint8Test(backends, -1);
+}
+
+} // TEST_SUITE("ArgMinMax_CpuRefTests")
+
+TEST_SUITE("ArgMinMax_CpuAccTests")
+{
+
+TEST_CASE ("ArgMaxFP32Test_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    ArgMaxFP32Test(backends, 2);
+}
+
+TEST_CASE ("ArgMinFP32Test_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    ArgMinFP32Test(backends, 3);
+}
+
+TEST_CASE ("ArgMaxUint8Test_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    ArgMaxUint8Test(backends, -1);
+}
+
+} // TEST_SUITE("ArgMinMax_CpuAccTests")
+
+TEST_SUITE("ArgMinMax_GpuAccTests")
+{
+
+TEST_CASE ("ArgMaxFP32Test_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    ArgMaxFP32Test(backends, 2);
+}
+
+TEST_CASE ("ArgMinFP32Test_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    ArgMinFP32Test(backends, 3);
+}
+
+TEST_CASE ("ArgMaxUint8Test_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    ArgMaxUint8Test(backends, -1);
+}
+
+} // TEST_SUITE("ArgMinMax_GpuAccTests")
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/ArgMinMaxTestHelper.hpp b/delegate/src/test/ArgMinMaxTestHelper.hpp
new file mode 100644
index 0000000000..d071653ac7
--- /dev/null
+++ b/delegate/src/test/ArgMinMaxTestHelper.hpp
@@ -0,0 +1,198 @@
+//
+// Copyright © 2021 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
+{
+
+template <typename InputT, typename OutputT>
+std::vector<char> CreateArgMinMaxTfLiteModel(tflite::BuiltinOperator argMinMaxOperatorCode,
+                                             tflite::TensorType tensorType,
+                                             const std::vector<int32_t>& inputTensorShape,
+                                             const std::vector<int32_t>& axisTensorShape,
+                                             const std::vector<int32_t>& outputTensorShape,
+                                             const std::vector<OutputT> axisValue,
+                                             tflite::TensorType outputType,
+                                             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,
+                                    0,
+                                    flatBufferBuilder.CreateString("input"),
+                                    quantizationParameters);
+
+    auto axisTensor = CreateTensor(flatBufferBuilder,
+                                   flatBufferBuilder.CreateVector<int32_t>(axisTensorShape.data(),
+                                                                           axisTensorShape.size()),
+                                   tflite::TensorType_INT32,
+                                   1,
+                                   flatBufferBuilder.CreateString("axis"));
+
+    auto outputTensor = CreateTensor(flatBufferBuilder,
+                                     flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                             outputTensorShape.size()),
+                                     outputType,
+                                     2,
+                                     flatBufferBuilder.CreateString("output"),
+                                     quantizationParameters);
+
+    std::vector<flatbuffers::Offset<Tensor>> tensors = { inputTensor, axisTensor, outputTensor };
+
+    std::vector<flatbuffers::Offset<tflite::Buffer>> buffers;
+    buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+    buffers.push_back(
+        CreateBuffer(flatBufferBuilder,
+                     flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(axisValue.data()),
+                                                    sizeof(OutputT))));
+    buffers.push_back(CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({})));
+
+    std::vector<int32_t> operatorInputs = {{ 0, 1 }};
+    std::vector<int> subgraphInputs = {{ 0, 1 }};
+
+    tflite::BuiltinOptions operatorBuiltinOptionsType = BuiltinOptions_ArgMaxOptions;
+    flatbuffers::Offset<void> operatorBuiltinOptions = CreateArgMaxOptions(flatBufferBuilder, outputType).Union();
+
+    if (argMinMaxOperatorCode == tflite::BuiltinOperator_ARG_MIN)
+    {
+        operatorBuiltinOptionsType = BuiltinOptions_ArgMinOptions;
+        operatorBuiltinOptions = CreateArgMinOptions(flatBufferBuilder, outputType).Union();
+    }
+
+    // create operator
+    const std::vector<int32_t> operatorOutputs{{ 2 }};
+    flatbuffers::Offset <Operator> argMinMaxOperator =
+        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{{ 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(&argMinMaxOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+        flatBufferBuilder.CreateString("ArmnnDelegate: ArgMinMax Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+                                                                         argMinMaxOperatorCode);
+
+    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 InputT, typename OutputT>
+void ArgMinMaxTest(tflite::BuiltinOperator argMinMaxOperatorCode,
+                   tflite::TensorType tensorType,
+                   const std::vector<armnn::BackendId>& backends,
+                   const std::vector<int32_t>& inputShape,
+                   const std::vector<int32_t>& axisShape,
+                   std::vector<int32_t>& outputShape,
+                   std::vector<InputT>& inputValues,
+                   std::vector<OutputT>& expectedOutputValues,
+                   OutputT axisValue,
+                   tflite::TensorType outputType,
+                   float quantScale = 1.0f,
+                   int quantOffset  = 0)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateArgMinMaxTfLiteModel<InputT, OutputT>(argMinMaxOperatorCode,
+                                                                                tensorType,
+                                                                                inputShape,
+                                                                                axisShape,
+                                                                                outputShape,
+                                                                                {axisValue},
+                                                                                outputType,
+                                                                                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<InputT>(tfLiteInterpreter, 0, inputValues);
+    armnnDelegate::FillInput<InputT>(armnnDelegateInterpreter, 0, inputValues);
+
+    // Run EnqueueWorkload
+    CHECK(tfLiteInterpreter->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegateInterpreter->Invoke() == kTfLiteOk);
+
+    // Compare output data
+    auto tfLiteDelegateOutputId = tfLiteInterpreter->outputs()[0];
+    auto tfLiteDelageOutputData = tfLiteInterpreter->typed_tensor<OutputT>(tfLiteDelegateOutputId);
+    auto armnnDelegateOutputId = armnnDelegateInterpreter->outputs()[0];
+    auto armnnDelegateOutputData = armnnDelegateInterpreter->typed_tensor<OutputT>(armnnDelegateOutputId);
+
+    for (size_t i = 0; i < expectedOutputValues.size(); i++)
+    {
+        CHECK(expectedOutputValues[i] == armnnDelegateOutputData[i]);
+        CHECK(tfLiteDelageOutputData[i] == expectedOutputValues[i]);
+        CHECK(tfLiteDelageOutputData[i] == armnnDelegateOutputData[i]);
+    }
+}
+
+} // anonymous namespace
\ No newline at end of file