IVGCVSW-5386 TfLiteDelegate: Add Strided Slice operator

Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Icd87b1c54e1a5de84893882da30840a9097f6d84

4 files changed, 605 insertions, 6 deletions

diff --git a/delegate/CMakeLists.txt b/delegate/CMakeLists.txt
index f792821d1c..7de168fa97 100644
--- a/delegate/CMakeLists.txt
+++ b/delegate/CMakeLists.txt
@@ -157,6 +157,8 @@ if(BUILD_UNIT_TESTS)
         src/test/SoftmaxTestHelper.hpp
         src/test/SpaceDepthTest.cpp
         src/test/SpaceDepthTestHelper.hpp
+        src/test/SliceTest.cpp
+        src/test/SliceTestHelper.hpp
         src/test/SplitTest.cpp
         src/test/SplitTestHelper.hpp
         src/test/TestUtils.hpp
diff --git a/delegate/src/Slice.hpp b/delegate/src/Slice.hpp
index 0311abf41c..a237034bb6 100644
--- a/delegate/src/Slice.hpp
+++ b/delegate/src/Slice.hpp
@@ -21,13 +21,126 @@ TfLiteStatus VisitSliceOperator(DelegateData& delegateData,
                                 int nodeIndex,
                                 int32_t sliceOperatorCode)
 {
-    armnn::IgnoreUnused(delegateData,
-                        tfLiteContext,
-                        tfLiteNode,
-                        nodeIndex,
-                        sliceOperatorCode);
+    TF_LITE_ENSURE_STATUS(ValidateNumInputs(tfLiteContext, tfLiteNode, 4, nodeIndex));
+    TF_LITE_ENSURE_STATUS(ValidateNumOutputs(tfLiteContext, tfLiteNode, 1, nodeIndex));
 
-    return kTfLiteError;
+    // Read inputs [input, begin, end, strides]
+    int numInputs = tfLiteNode->inputs->size;
+    std::vector<const TfLiteTensor*> tfLiteInputs;
+    tfLiteInputs.reserve(numInputs);
+    const TfLiteTensor* tfLiteTensors = tfLiteContext->tensors;
+    for (int i = 0; i < numInputs; i++)
+    {
+        const TfLiteTensor* inputTensor = &tfLiteTensors[tfLiteNode->inputs->data[i]];
+        tfLiteInputs.push_back(inputTensor);
+        if (!IsValid(tfLiteContext, *inputTensor, sliceOperatorCode, nodeIndex))
+        {
+            return kTfLiteError;
+        }
+    }
+
+    // We save the begin, end and strides tensors in our descriptor. Therefore we have to read those values from inputs
+    int inputRank = tfLiteInputs[0]->dims->size;
+    auto ReadInt32Input = [&](int inputIndex, std::vector<int32_t>& outputData) ->  TfLiteStatus
+    {
+        if (tfLiteInputs[inputIndex]->type != kTfLiteInt32)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to "
+                    "be of type int32. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        int rank = tfLiteInputs[inputIndex]->dims->size;
+        if (rank != 1)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The Begin-, End- and Stride-Tensors of the StridedSlice operation need to "
+                    "be a 1D-Tensor. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        int numValues = tfLiteInputs[inputIndex]->dims->data[0];
+        if (numValues != inputRank)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                    tfLiteContext,
+                    "TfLiteArmnnDelegate: The number of values in the Begin-, End- and Stride-Tensors of the "
+                    "StridedSlice operation need to be equal to the rank of the Input-Tensor. Operator: #%d node #%d: ",
+                    sliceOperatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+        // return tensor data
+        auto* tensorDataPtr = tflite::GetTensorData<int32_t>(tfLiteInputs[inputIndex]);
+        outputData.assign(tensorDataPtr, tensorDataPtr+numValues);
+        return kTfLiteOk;
+    };
+
+    std::vector<int32_t> beginData;
+    if (ReadInt32Input(1, beginData) != kTfLiteOk)
+        return kTfLiteError;
+    std::vector<int32_t> endData;
+    if (ReadInt32Input(2, endData) != kTfLiteOk)
+        return kTfLiteError;
+    std::vector<int32_t> strideData;
+    if (ReadInt32Input(3, strideData) != kTfLiteOk)
+        return kTfLiteError;
+
+    // parse built in options
+    auto* stridedSliceParams = reinterpret_cast<TfLiteStridedSliceParams*>(tfLiteNode->builtin_data);
+
+    // Write all data to the descriptor
+    armnn::StridedSliceDescriptor descriptor;
+    descriptor.m_Begin          = std::move(beginData);
+    descriptor.m_End            = std::move(endData);
+    descriptor.m_Stride         = std::move(strideData);
+    descriptor.m_BeginMask      = stridedSliceParams->begin_mask;
+    descriptor.m_EllipsisMask   = stridedSliceParams->ellipsis_mask;
+    descriptor.m_EndMask        = stridedSliceParams->end_mask;
+    descriptor.m_NewAxisMask    = stridedSliceParams->new_axis_mask;
+    descriptor.m_ShrinkAxisMask = stridedSliceParams->shrink_axis_mask;
+    descriptor.m_DataLayout     = armnn::DataLayout::NHWC;
+
+    // Validate output
+    const TfLiteTensor& tfLiteOutputTensor = tfLiteTensors[tfLiteNode->outputs->data[0]];
+    if (!IsValid(tfLiteContext, tfLiteOutputTensor, sliceOperatorCode, nodeIndex))
+    {
+        return kTfLiteError;
+    }
+
+    const armnn::TensorInfo& inputTensorInfo  = GetTensorInfoForTfLiteTensor(*tfLiteInputs[0]);
+    const armnn::TensorInfo& outputTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteOutputTensor);
+
+    bool isSupported = false;
+    auto validateFunc = [&](const armnn::TensorInfo& outInfo, bool& isSupported)
+    {
+        FORWARD_LAYER_SUPPORT_FUNC(__func__,
+                                   tfLiteContext,
+                                   IsStridedSliceSupported,
+                                   delegateData.m_Backends,
+                                   isSupported,
+                                   inputTensorInfo,
+                                   outInfo,
+                                   descriptor);
+    };
+
+    if (!delegateData.m_Network)
+    {
+        validateFunc(outputTensorInfo, isSupported);
+        return isSupported ? kTfLiteOk : kTfLiteError;
+    }
+
+    // Add a StridedSlice layer
+    armnn::IConnectableLayer* layer = delegateData.m_Network->AddStridedSliceLayer(descriptor);
+    ARMNN_ASSERT(layer != nullptr);
+
+    armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
+    outputSlot.SetTensorInfo(outputTensorInfo);
+
+    // Connect
+    return Connect(layer, tfLiteNode, delegateData);
 }
 
 } // namespace armnnDelegate
diff --git a/delegate/src/test/SliceTest.cpp b/delegate/src/test/SliceTest.cpp
new file mode 100644
index 0000000000..bd0584936e
--- /dev/null
+++ b/delegate/src/test/SliceTest.cpp
@@ -0,0 +1,243 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "SliceTestHelper.hpp"
+
+#include <armnn_delegate.hpp>
+
+#include <flatbuffers/flatbuffers.h>
+#include <tensorflow/lite/schema/schema_generated.h>
+
+#include <doctest/doctest.h>
+
+namespace armnnDelegate
+{
+
+void StridedSlice4DTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> inputShape  { 3, 2, 3, 1 };
+    std::vector<int32_t> outputShape { 1, 2, 3, 1 };
+    std::vector<int32_t> beginShape  { 4 };
+    std::vector<int32_t> endShape    { 4 };
+    std::vector<int32_t> strideShape { 4 };
+
+    std::vector<int32_t> beginData  { 1, 0, 0, 0 };
+    std::vector<int32_t> endData    { 2, 2, 3, 1 };
+    std::vector<int32_t> strideData { 1, 1, 1, 1 };
+    std::vector<float> inputData  { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+                                    3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+                                    5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+    std::vector<float> outputData { 3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f };
+
+    StridedSliceTestImpl<float>(
+            backends,
+            inputData,
+            outputData,
+            beginData,
+            endData,
+            strideData,
+            inputShape,
+            beginShape,
+            endShape,
+            strideShape,
+            outputShape
+            );
+}
+
+void StridedSlice4DReverseTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> inputShape  { 3, 2, 3, 1 };
+    std::vector<int32_t> outputShape { 1, 2, 3, 1 };
+    std::vector<int32_t> beginShape  { 4 };
+    std::vector<int32_t> endShape    { 4 };
+    std::vector<int32_t> strideShape { 4 };
+
+    std::vector<int32_t> beginData  { 1, -1, 0, 0 };
+    std::vector<int32_t> endData    { 2, -3, 3, 1 };
+    std::vector<int32_t> strideData { 1, -1, 1, 1 };
+    std::vector<float>   inputData  { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+                                      3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+                                      5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+    std::vector<float>   outputData { 4.0f, 4.0f, 4.0f, 3.0f, 3.0f, 3.0f };
+
+    StridedSliceTestImpl<float>(
+            backends,
+            inputData,
+            outputData,
+            beginData,
+            endData,
+            strideData,
+            inputShape,
+            beginShape,
+            endShape,
+            strideShape,
+            outputShape
+    );
+}
+
+void StridedSliceSimpleStrideTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> inputShape  { 3, 2, 3, 1 };
+    std::vector<int32_t> outputShape { 2, 1, 2, 1 };
+    std::vector<int32_t> beginShape  { 4 };
+    std::vector<int32_t> endShape    { 4 };
+    std::vector<int32_t> strideShape { 4 };
+
+    std::vector<int32_t> beginData  { 0, 0, 0, 0 };
+    std::vector<int32_t> endData    { 3, 2, 3, 1 };
+    std::vector<int32_t> strideData { 2, 2, 2, 1 };
+    std::vector<float>   inputData  { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+                                      3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+                                      5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+    std::vector<float>   outputData { 1.0f, 1.0f,
+                                      5.0f, 5.0f };
+
+    StridedSliceTestImpl<float>(
+            backends,
+            inputData,
+            outputData,
+            beginData,
+            endData,
+            strideData,
+            inputShape,
+            beginShape,
+            endShape,
+            strideShape,
+            outputShape
+    );
+}
+
+void StridedSliceSimpleRangeMaskTest(std::vector<armnn::BackendId>& backends)
+{
+    std::vector<int32_t> inputShape  { 3, 2, 3, 1 };
+    std::vector<int32_t> outputShape { 3, 2, 3, 1 };
+    std::vector<int32_t> beginShape  { 4 };
+    std::vector<int32_t> endShape    { 4 };
+    std::vector<int32_t> strideShape { 4 };
+
+    std::vector<int32_t> beginData  { 1, 1, 1, 1 };
+    std::vector<int32_t> endData    { 1, 1, 1, 1 };
+    std::vector<int32_t> strideData { 1, 1, 1, 1 };
+
+    int beginMask = -1;
+    int endMask   = -1;
+
+    std::vector<float>   inputData  { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+                                      3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+                                      5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+    std::vector<float>   outputData { 1.0f, 1.0f, 1.0f, 2.0f, 2.0f, 2.0f,
+                                      3.0f, 3.0f, 3.0f, 4.0f, 4.0f, 4.0f,
+                                      5.0f, 5.0f, 5.0f, 6.0f, 6.0f, 6.0f };
+
+    StridedSliceTestImpl<float>(
+            backends,
+            inputData,
+            outputData,
+            beginData,
+            endData,
+            strideData,
+            inputShape,
+            beginShape,
+            endShape,
+            strideShape,
+            outputShape,
+            beginMask,
+            endMask
+    );
+}
+
+
+TEST_SUITE("StridedSlice_CpuRefTests")
+{
+
+TEST_CASE ("StridedSlice_4D_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+    StridedSlice4DTest(backends);
+}
+
+TEST_CASE ("StridedSlice_4D_Reverse_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+    StridedSlice4DReverseTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleStride_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+    StridedSliceSimpleStrideTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleRange_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+    StridedSliceSimpleRangeMaskTest(backends);
+}
+
+} // StridedSlice_CpuRefTests TestSuite
+
+
+
+TEST_SUITE("StridedSlice_CpuAccTests")
+{
+
+TEST_CASE ("StridedSlice_4D_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+    StridedSlice4DTest(backends);
+}
+
+TEST_CASE ("StridedSlice_4D_Reverse_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+    StridedSlice4DReverseTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleStride_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+    StridedSliceSimpleStrideTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleRange_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::CpuAcc};
+    StridedSliceSimpleRangeMaskTest(backends);
+}
+
+} // StridedSlice_CpuAccTests TestSuite
+
+
+
+TEST_SUITE("StridedSlice_GpuAccTests")
+{
+
+TEST_CASE ("StridedSlice_4D_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+    StridedSlice4DTest(backends);
+}
+
+TEST_CASE ("StridedSlice_4D_Reverse_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+    StridedSlice4DReverseTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleStride_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+    StridedSliceSimpleStrideTest(backends);
+}
+
+TEST_CASE ("StridedSlice_SimpleRange_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
+    StridedSliceSimpleRangeMaskTest(backends);
+}
+
+} // StridedSlice_GpuAccTests TestSuite
+
+} // namespace armnnDelegate
\ No newline at end of file
diff --git a/delegate/src/test/SliceTestHelper.hpp b/delegate/src/test/SliceTestHelper.hpp
new file mode 100644
index 0000000000..abaa807aed
--- /dev/null
+++ b/delegate/src/test/SliceTestHelper.hpp
@@ -0,0 +1,241 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "TestUtils.hpp"
+
+#include <armnn_delegate.hpp>
+#include <armnn/DescriptorsFwd.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
+{
+
+struct StridedSliceParams
+{
+    StridedSliceParams(std::vector<int32_t>& inputTensorShape,
+                       std::vector<int32_t>& beginTensorData,
+                       std::vector<int32_t>& endTensorData,
+                       std::vector<int32_t>& strideTensorData,
+                       std::vector<int32_t>& outputTensorShape,
+                       armnn::StridedSliceDescriptor& descriptor)
+        : m_InputTensorShape(inputTensorShape),
+          m_BeginTensorData(beginTensorData),
+          m_EndTensorData(endTensorData),
+          m_StrideTensorData(strideTensorData),
+          m_OutputTensorShape(outputTensorShape),
+          m_Descriptor (descriptor) {}
+
+    std::vector<int32_t> m_InputTensorShape;
+    std::vector<int32_t> m_BeginTensorData;
+    std::vector<int32_t> m_EndTensorData;
+    std::vector<int32_t> m_StrideTensorData;
+    std::vector<int32_t> m_OutputTensorShape;
+    armnn::StridedSliceDescriptor m_Descriptor;
+};
+
+std::vector<char> CreateSliceTfLiteModel(tflite::TensorType tensorType,
+                                         const std::vector<int32_t>& inputTensorShape,
+                                         const std::vector<int32_t>& beginTensorData,
+                                         const std::vector<int32_t>& endTensorData,
+                                         const std::vector<int32_t>& strideTensorData,
+                                         const std::vector<int32_t>& beginTensorShape,
+                                         const std::vector<int32_t>& endTensorShape,
+                                         const std::vector<int32_t>& strideTensorShape,
+                                         const std::vector<int32_t>& outputTensorShape,
+                                         const int32_t beginMask,
+                                         const int32_t endMask,
+                                         const int32_t ellipsisMask,
+                                         const int32_t newAxisMask,
+                                         const int32_t ShrinkAxisMask,
+                                         const armnn::DataLayout& dataLayout)
+{
+    using namespace tflite;
+    flatbuffers::FlatBufferBuilder flatBufferBuilder;
+
+    std::array<flatbuffers::Offset<tflite::Buffer>, 4> buffers;
+    buffers[0] = CreateBuffer(flatBufferBuilder, flatBufferBuilder.CreateVector({}));
+    buffers[1] = CreateBuffer(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(beginTensorData.data()),
+                                                             sizeof(int32_t) * beginTensorData.size()));
+    buffers[2] = CreateBuffer(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(endTensorData.data()),
+                                                             sizeof(int32_t) * endTensorData.size()));
+    buffers[3] = CreateBuffer(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector(reinterpret_cast<const uint8_t*>(strideTensorData.data()),
+                                                             sizeof(int32_t) * strideTensorData.size()));
+
+    std::array<flatbuffers::Offset<Tensor>, 5> tensors;
+    tensors[0] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(inputTensorShape.data(),
+                                                                      inputTensorShape.size()),
+                              tensorType,
+                              0,
+                              flatBufferBuilder.CreateString("input"));
+    tensors[1] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(beginTensorShape.data(),
+                                                                      beginTensorShape.size()),
+                              ::tflite::TensorType_INT32,
+                              1,
+                              flatBufferBuilder.CreateString("begin_tensor"));
+    tensors[2] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(endTensorShape.data(),
+                                                                      endTensorShape.size()),
+                              ::tflite::TensorType_INT32,
+                              2,
+                              flatBufferBuilder.CreateString("end_tensor"));
+    tensors[3] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(strideTensorShape.data(),
+                                                                      strideTensorShape.size()),
+                              ::tflite::TensorType_INT32,
+                              3,
+                              flatBufferBuilder.CreateString("stride_tensor"));
+    tensors[4] = CreateTensor(flatBufferBuilder,
+                              flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
+                                                                      outputTensorShape.size()),
+                              tensorType,
+                              0,
+                              flatBufferBuilder.CreateString("output"));
+
+
+    // create operator
+    tflite::BuiltinOptions operatorBuiltinOptionsType = tflite::BuiltinOptions_StridedSliceOptions;
+    flatbuffers::Offset<void> operatorBuiltinOptions = CreateStridedSliceOptions(flatBufferBuilder,
+                                                                                 beginMask,
+                                                                                 endMask,
+                                                                                 ellipsisMask,
+                                                                                 newAxisMask,
+                                                                                 ShrinkAxisMask).Union();
+
+    const std::vector<int> operatorInputs{ 0, 1, 2, 3 };
+    const std::vector<int> operatorOutputs{ 4 };
+    flatbuffers::Offset <Operator> sliceOperator =
+            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, 2, 3 };
+    const std::vector<int> subgraphOutputs{ 4 };
+    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(&sliceOperator, 1));
+
+    flatbuffers::Offset <flatbuffers::String> modelDescription =
+            flatBufferBuilder.CreateString("ArmnnDelegate: StridedSlice Operator Model");
+    flatbuffers::Offset <OperatorCode> operatorCode = CreateOperatorCode(flatBufferBuilder,
+                                                                         BuiltinOperator_STRIDED_SLICE);
+
+    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 StridedSliceTestImpl(std::vector<armnn::BackendId>& backends,
+                          std::vector<T>& inputValues,
+                          std::vector<T>& expectedOutputValues,
+                          std::vector<int32_t>& beginTensorData,
+                          std::vector<int32_t>& endTensorData,
+                          std::vector<int32_t>& strideTensorData,
+                          std::vector<int32_t>& inputTensorShape,
+                          std::vector<int32_t>& beginTensorShape,
+                          std::vector<int32_t>& endTensorShape,
+                          std::vector<int32_t>& strideTensorShape,
+                          std::vector<int32_t>& outputTensorShape,
+                          const int32_t beginMask = 0,
+                          const int32_t endMask = 0,
+                          const int32_t ellipsisMask = 0,
+                          const int32_t newAxisMask = 0,
+                          const int32_t ShrinkAxisMask = 0,
+                          const armnn::DataLayout& dataLayout = armnn::DataLayout::NHWC)
+{
+    using namespace tflite;
+    std::vector<char> modelBuffer = CreateSliceTfLiteModel(
+            ::tflite::TensorType_FLOAT32,
+            inputTensorShape,
+            beginTensorData,
+            endTensorData,
+            strideTensorData,
+            beginTensorShape,
+            endTensorShape,
+            strideTensorShape,
+            outputTensorShape,
+            beginMask,
+            endMask,
+            ellipsisMask,
+            newAxisMask,
+            ShrinkAxisMask,
+            dataLayout);
+
+    auto tfLiteModel = GetModel(modelBuffer.data());
+
+    // Create TfLite Interpreters
+    std::unique_ptr<Interpreter> armnnDelegate;
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+              (&armnnDelegate) == kTfLiteOk);
+    CHECK(armnnDelegate != nullptr);
+    CHECK(armnnDelegate->AllocateTensors() == kTfLiteOk);
+
+    std::unique_ptr<Interpreter> tfLiteDelegate;
+    CHECK(InterpreterBuilder(tfLiteModel, ::tflite::ops::builtin::BuiltinOpResolver())
+              (&tfLiteDelegate) == kTfLiteOk);
+    CHECK(tfLiteDelegate != nullptr);
+    CHECK(tfLiteDelegate->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(armnnDelegate->ModifyGraphWithDelegate(theArmnnDelegate.get()) == kTfLiteOk);
+
+    // Set input data
+    armnnDelegate::FillInput<T>(tfLiteDelegate, 0, inputValues);
+    armnnDelegate::FillInput<T>(armnnDelegate, 0, inputValues);
+
+    // Run EnqueWorkload
+    CHECK(tfLiteDelegate->Invoke() == kTfLiteOk);
+    CHECK(armnnDelegate->Invoke() == kTfLiteOk);
+
+    // Compare output data
+    armnnDelegate::CompareOutputData<T>(tfLiteDelegate,
+                                        armnnDelegate,
+                                        outputTensorShape,
+                                        expectedOutputValues);
+
+    tfLiteDelegate.reset(nullptr);
+    armnnDelegate.reset(nullptr);
+} // End of StridedSlice Test
+
+} // anonymous namespace
\ No newline at end of file