Refactor Resize layer tests

* Move implementations to newly created source file
  ResizeTestImpl.cpp, leave only the declarations in
  the header
* Reduce code duplication by extracting common code
  into a generic implementation template

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Ib8aa395e5fb4470b26cbe943c177f6b64c860a93

1 files changed, 33 insertions, 975 deletions

diff --git a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp
index 56ce51a844..4fe4b73ada 100644
--- a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp
+++ b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp
@@ -7,1017 +7,75 @@
 
 #include "LayerTestResult.hpp"
 
-#include <Permute.hpp>
-#include <QuantizeHelper.hpp>
 #include <ResolveType.hpp>
-#include <TensorUtils.hpp>
 
-#include <armnn/ArmNN.hpp>
+#include <armnn/Types.hpp>
 
 #include <backendsCommon/IBackendInternal.hpp>
 #include <backendsCommon/WorkloadFactory.hpp>
 
-#include <backendsCommon/test/TensorCopyUtils.hpp>
-#include <backendsCommon/test/WorkloadTestUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-
-//
-// ResizeBilinear
-//
-
+// Bilinear
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeBilinearNopTest(
     armnn::IWorkloadFactory& workloadFactory,
     const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(1.5f);
-        inputTensorInfo.SetQuantizationOffset(-3);
-        outputTensorInfo.SetQuantizationScale(1.5f);
-        outputTensorInfo.SetQuantizationOffset(-3);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1, 2, 3, 4,
-                2, 3, 4, 5,
-                3, 4, 5, 6,
-                4, 5, 6, 7
-            }
-        : std::initializer_list<float>
-            {
-                1.0f, 2.0f, 3.0f, 4.0f,
-                2.0f, 3.0f, 4.0f, 5.0f,
-                3.0f, 4.0f, 5.0f, 6.0f,
-                4.0f, 5.0f, 6.0f, 7.0f,
-
-                1.0f, 2.0f, 3.0f, 4.0f,
-                2.0f, 3.0f, 4.0f, 5.0f,
-                3.0f, 4.0f, 5.0f, 6.0f,
-                4.0f, 5.0f, 6.0f, 7.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = input;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::Bilinear;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> SimpleResizeBilinearTest(
     armnn::IWorkloadFactory& workloadFactory,
     const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-    const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 1, 1, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 1, 1, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(0.1567f);
-        inputTensorInfo.SetQuantizationOffset(1);
-        outputTensorInfo.SetQuantizationScale(0.1567f);
-        outputTensorInfo.SetQuantizationOffset(1);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                  1, 255,
-                200, 250
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f, 255.0f,
-                200.0f, 250.0f,
-
-                250.0f, 200.0f,
-                250.0f,   1.0f
-            };
-
-    // The 'resize bilinear' operation projects the top-left corner of output texels into the input image,
-    // then figures out the interpolants and weights. Note this is different to projecting the centre of the
-    // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as
-    // its single element, the value that was at position (0,0) of the input matrix (rather than an average,
-    // which we would expect if projecting the centre).
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,
-
-                250.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::Bilinear;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeBilinearSqMinTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(3.141592f);
-        inputTensorInfo.SetQuantizationOffset(3);
-        outputTensorInfo.SetQuantizationScale(3.141592f);
-        outputTensorInfo.SetQuantizationOffset(3);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1, 2, 3, 4,
-                2, 3, 4, 5,
-                3, 4, 5, 6,
-                4, 5, 6, 7
-            }
-        : std::initializer_list<float>
-            {
-                1.0f, 2.0f, 3.0f, 4.0f,
-                2.0f, 3.0f, 4.0f, 5.0f,
-                3.0f, 4.0f, 5.0f, 6.0f,
-                4.0f, 5.0f, 6.0f, 7.0f,
-
-                7.0f, 6.0f, 5.0f, 4.0f,
-                6.0f, 5.0f, 4.0f, 3.0f,
-                5.0f, 4.0f, 3.0f, 2.0f,
-                4.0f, 3.0f, 2.0f, 1.0f
-            };
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1, 3,
-                3, 5
-            }
-        : std::initializer_list<float>
-            {
-                1.0f, 3.0f,
-                3.0f, 5.0f,
-
-                7.0f, 5.0f,
-                5.0f, 3.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::Bilinear;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeBilinearMinTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 2, 3, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 1, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 2, 3, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(1.5f);
-        inputTensorInfo.SetQuantizationOffset(-1);
-        outputTensorInfo.SetQuantizationScale(1.5f);
-        outputTensorInfo.SetQuantizationOffset(-1);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                3.0f,  4.5f,  6.0f, // 1,  2,  3, : Expected quantised values
-                9.0f, 13.5f, 21.0f  // 5,  8, 13
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,   2.0f,   3.0f,   5.0f,   8.0f,
-                 13.0f,  21.0f,  34.0f,  55.0f,  89.0f,
-                144.0f, 233.0f, 377.0f, 610.0f, 987.0f,
-
-                987.0f, 610.0f, 377.0f, 233.0f, 144.0f,
-                 89.0f,  55.0f,  34.0f,  21.0f,  13.0f,
-                   8.0f,  5.0f,   3.0f,   2.0f,   1.0f
-            };
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                3.0f, 5.25f // 1, 3
-            }
-        : std::initializer_list<float>
-            {
-                 1.0f,   2.6666f,   6.00f,
-                78.5f, 179.3333f, 401.00f,
-
-                987.0f, 454.6670f, 203.33f,
-                 48.5f,  22.3333f,  10.00f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::Bilinear;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeBilinearMagTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 3, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 3, 2, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 3, 5, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(0.010765f);
-        inputTensorInfo.SetQuantizationOffset(7);
-        outputTensorInfo.SetQuantizationScale(0.010132f);
-        outputTensorInfo.SetQuantizationOffset(-18);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                0.183005f, 2.379065f, // 24, 228, : Expected quantised values
-                1.054970f, 1.302565f, // 105, 128,
-                2.400595f, 0.688960f  // 230, 71
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,   2.0f,
-                 13.0f,  21.0f,
-                144.0f, 233.0f,
-
-                233.0f, 144.0f,
-                 21.0f,  13.0f,
-                  2.0f,   1.0f
-            };
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                0.18300501f, 1.06142902f, 1.93985295f, 2.37906504f, 2.37906504f,
-                1.05497003f, 1.15400803f, 1.25304604f, 1.30256498f, 1.30256498f,
-                2.40059495f, 1.71594095f, 1.03128707f, 0.68896002f, 0.68896002f
-                // 0, 87, 173, 217, 217, : Expected quantised values
-                // 86, 96, 106, 111, 111,
-                // 219, 151, 84, 50, 50
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,   1.4f,   1.8f,   2.0f,   2.0f,
-                 13.0f,  16.2f,  19.4f,  21.0f,  21.0f,
-                144.0f, 179.6f, 215.2f, 233.0f, 233.0f,
-
-                233.0f, 197.4f, 161.8f, 144.0f, 144.0f,
-                 21.0f,  17.8f,  14.6f,  13.0f,  13.0f,
-                  2.0f,   1.6f,   1.2f,   1.0f,   1.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::Bilinear;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
-
-//
-// ResizeNearestNeighbor
-//
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
+// NearestNeighbor
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeNearestNeighborNopTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(1.5f);
-        inputTensorInfo.SetQuantizationOffset(-3);
-        outputTensorInfo.SetQuantizationScale(1.5f);
-        outputTensorInfo.SetQuantizationOffset(-3);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1, 2, 3, 4,
-                2, 3, 4, 5,
-                3, 4, 5, 6,
-                4, 5, 6, 7
-            }
-        : std::initializer_list<float>
-            {
-                1.0f, 2.0f, 3.0f, 4.0f,
-                2.0f, 3.0f, 4.0f, 5.0f,
-                3.0f, 4.0f, 5.0f, 6.0f,
-                4.0f, 5.0f, 6.0f, 7.0f,
-
-                1.0f, 2.0f, 3.0f, 4.0f,
-                2.0f, 3.0f, 4.0f, 5.0f,
-                3.0f, 4.0f, 5.0f, 6.0f,
-                4.0f, 5.0f, 6.0f, 7.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = input;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> SimpleResizeNearestNeighborTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 1, 1, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 1, 1, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(0.1567f);
-        inputTensorInfo.SetQuantizationOffset(1);
-        outputTensorInfo.SetQuantizationScale(0.1567f);
-        outputTensorInfo.SetQuantizationOffset(1);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                  1, 255,
-                200, 250
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f, 255.0f,
-                200.0f, 250.0f,
-
-                250.0f, 200.0f,
-                250.0f,   1.0f
-            };
-
-    // The 'resize' operation projects the top-left corner of output texels into the input image,
-    // then figures out the interpolants and weights. Note this is different to projecting the centre of the
-    // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as
-    // its single element, the value that was at position (0,0) of the input matrix (rather than an average,
-    // which we would expect if projecting the centre).
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,
-
-                250.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::NearestNeighbor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeNearestNeighborSqMinTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(3.141592f);
-        inputTensorInfo.SetQuantizationOffset(3);
-        outputTensorInfo.SetQuantizationScale(3.141592f);
-        outputTensorInfo.SetQuantizationOffset(3);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1, 2, 3, 4,
-                2, 3, 4, 5,
-                3, 4, 5, 6,
-                4, 5, 6, 7
-            }
-        : std::initializer_list<float>
-            {
-                1.0f, 2.0f, 3.0f, 4.0f,
-                2.0f, 3.0f, 4.0f, 5.0f,
-                3.0f, 4.0f, 5.0f, 6.0f,
-                4.0f, 5.0f, 6.0f, 7.0f,
-
-                7.0f, 6.0f, 5.0f, 4.0f,
-                6.0f, 5.0f, 4.0f, 3.0f,
-                5.0f, 4.0f, 3.0f, 2.0f,
-                4.0f, 3.0f, 2.0f, 1.0f
-            };
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                1, 3,
-                3, 5
-            }
-        : std::initializer_list<float>
-            {
-                1.0f, 3.0f,
-                3.0f, 5.0f,
-
-                7.0f, 5.0f,
-                5.0f, 3.0f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    descriptor.m_Parameters.m_Method     = armnn::ResizeMethod::NearestNeighbor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeNearestNeighborMinTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 2, 3, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 1, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 2, 3, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(1.5f);
-        inputTensorInfo.SetQuantizationOffset(-1);
-        outputTensorInfo.SetQuantizationScale(1.5f);
-        outputTensorInfo.SetQuantizationOffset(-1);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                3.0f,  4.5f,  6.0f, // 1,  2,  3, : Expected quantised values
-                9.0f, 13.5f, 21.0f  // 5,  8, 13
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,   2.0f,   3.0f,   5.0f,   8.0f,
-                 13.0f,  21.0f,  34.0f,  55.0f,  89.0f,
-                144.0f, 233.0f, 377.0f, 610.0f, 987.0f,
-
-                987.0f, 610.0f, 377.0f, 233.0f, 144.0f,
-                 89.0f,  55.0f,  34.0f,  21.0f,  13.0f,
-                  8.0f,   5.0f,   3.0f,   2.0f,   1.0f
-            };
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                3.0f, 4.5f // 1, 3
-            }
-        : std::initializer_list<float>
-            {
-                  1.f,   2.f,   5.f,
-                 13.f,  21.f,  55.f,
-
-                987.f, 610.f, 233.f,
-                 89.f,  55.f,  21.f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout);
 
 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
 LayerTestResult<T, 4> ResizeNearestNeighborMagTest(
-        armnn::IWorkloadFactory& workloadFactory,
-        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
-        const armnn::DataLayout dataLayout,
-        float inQuantScale,
-        int32_t inQuantOffset,
-        float outQuantScale,
-        int32_t outQuantOffset)
-{
-    armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 3, 2, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 3, 2, dataLayout, ArmnnType);
-
-    armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>()
-        ?  armnnUtils::GetTensorInfo(1, 1, 3, 5, dataLayout, ArmnnType)
-        :  armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType);
-
-    if (armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(inQuantScale);
-        inputTensorInfo.SetQuantizationOffset(inQuantOffset);
-        outputTensorInfo.SetQuantizationScale(outQuantScale);
-        outputTensorInfo.SetQuantizationOffset(outQuantOffset);
-    }
-
-    std::vector<float> inputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                0.183005f, 2.379065f, //  24, 228, : expected quantised values
-                1.054970f, 1.302565f, // 105, 128,
-                2.400595f, 0.688960f  // 230, 71
-            }
-        : std::initializer_list<float>
-            {
-                  1.0f,   2.0f,
-                 13.0f,  21.0f,
-                144.0f, 233.0f,
-
-                233.0f, 144.0f,
-                 21.0f,  13.0f,
-                  2.0f,   1.0f
-            };
-
-    std::vector<float> outputData = armnn::IsQuantizedType<T>()
-        ? std::initializer_list<float>
-            {
-                0.183005f, 0.183005f, 0.183005f, 2.379065f, 2.379065f,
-                1.054970f, 1.054970f, 1.054970f, 1.302565f, 1.302565f,
-                2.400595f, 2.400595f, 2.400595f, 0.688960f, 0.688960f
-            }
-        : std::initializer_list<float>
-            {
-                  1.f,   1.f,   1.f,   2.f,   2.f,
-                 13.f,  13.f,  13.f,  21.f,  21.f,
-                144.f, 144.f, 144.f, 233.f, 233.f,
-
-                233.f, 233.f, 233.f, 144.f, 144.f,
-                 21.f,  21.f,  21.f,  13.f,  13.f,
-                  2.f,   2.f,   2.f,   1.f,   1.f
-            };
-
-    const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 };
-    if (dataLayout == armnn::DataLayout::NHWC)
-    {
-        std::vector<float> tmp(inputData.size());
-        armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float));
-        inputData = tmp;
-
-        std::vector<float> tmp1(outputData.size());
-        armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float));
-        outputData = tmp1;
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo,
-                                  armnnUtils::QuantizedVector<T>(inputData,
-                                                                 inputTensorInfo.GetQuantizationScale(),
-                                                                 inputTensorInfo.GetQuantizationOffset()));
-
-    LayerTestResult<T, 4> result(outputTensorInfo);
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
-                                             armnnUtils::QuantizedVector<T>(outputData,
-                                                                            outputTensorInfo.GetQuantizationScale(),
-                                                                            outputTensorInfo.GetQuantizationOffset()));
-
-    std::unique_ptr <armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ResizeQueueDescriptor descriptor;
-    descriptor.m_Parameters.m_DataLayout = dataLayout;
-    descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->PostAllocationConfigure();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-    return result;
-}
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    const armnn::DataLayout dataLayout,
+    float inQuantScale,
+    int32_t inQuantOffset,
+    float outQuantScale,
+    int32_t outQuantOffset);