IVGCVSW-1946: Remove armnn/src from the include paths

Change-Id: I663a0a0fccb43ee960ec070121a59df9db0bb04e

1 files changed, 0 insertions, 559 deletions

diff --git a/src/backends/test/ActivationTestImpl.hpp b/src/backends/test/ActivationTestImpl.hpp
deleted file mode 100644
index 63716453cd..0000000000
--- a/src/backends/test/ActivationTestImpl.hpp
+++ /dev/null
@@ -1,559 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-
-#include <test/TensorHelpers.hpp>
-#include "QuantizeHelper.hpp"
-
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/WorkloadFactory.hpp>
-#include "ActivationFixture.hpp"
-
-#include <algorithm>
-
-template<typename T>
-LayerTestResult<T, 4> BoundedReLuTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                        float upperBound, float lowerBound,
-                                        float inputScale, int32_t inputOffset, float outputScale, int32_t outputOffset,
-                                        const std::vector<T>& inputData, const std::vector<T>& outputExpectedData,
-                                        unsigned int inputWidth, unsigned int inputHeight,
-                                        unsigned int inputChannels, unsigned int inputBatchSize)
-{
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputHeight = inputHeight;
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::GetDataType<T>());
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::GetDataType<T>());
-
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(inputScale);
-        inputTensorInfo.SetQuantizationOffset(inputOffset);
-
-        outputTensorInfo.SetQuantizationScale(outputScale);
-        outputTensorInfo.SetQuantizationOffset(outputOffset);
-    }
-
-    LayerTestResult<T, 4> result(inputTensorInfo);
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Setup bounded ReLu.
-    armnn::ActivationQueueDescriptor descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu;
-    descriptor.m_Parameters.m_A = upperBound;
-    descriptor.m_Parameters.m_B = lowerBound;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData);
-
-    return result;
-}
-
-LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth = 4u;
-    unsigned int inputHeight = 5u;
-    unsigned int inputChannels = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<float> input = std::vector<float>{
-      -2.0f,       0.1f,     0.5f,     1.25f,
-     0.786f,    0.9875f,    -1.5f,    0.384f,
-    1.0001f,       3.5f,     7.5f,    0.896f,
-     2.126f,       2.0f,     0.3f,     0.15f,
-     0.999f,       1.2f,    0.89f,      6.1f,
-    };
-
-    // Calculated manually.
-    std::vector<float> output = std::vector<float>{
-      -1.0f,       0.1f,     0.5f,      1.0f,
-     0.786f,    0.9875f,    -1.0f,    0.384f,
-       1.0f,       1.0f,     1.0f,    0.896f,
-       1.0f,       1.0f,     0.3f,     0.15f,
-     0.999f,       1.0f,    0.89f,      1.0f,
-    };
-
-    return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth = 4u;
-    unsigned int inputHeight = 5u;
-    unsigned int inputChannels = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<float> input = std::vector<float>{
-      -1.0f,       0.1f,     0.5f,      6.25f,
-     0.786f,    5.9875f,    -0.5f,     0.384f,
-    6.0001f,       3.5f,     7.5f,     0.896f,
-     2.126f,      12.0f,     0.3f,      0.15f,
-     0.999f,       1.2f,    0.89f,       6.1f,
-    };
-
-    // Calculated manually.
-    std::vector<float> output = std::vector<float>{
-       0.0f,       0.1f,     0.5f,       6.0f,
-     0.786f,    5.9875f,     0.0f,     0.384f,
-       6.0f,       3.5f,     6.0f,     0.896f,
-     2.126f,       6.0f,     0.3f,      0.15f,
-     0.999f,       1.2f,    0.89f,       6.0f,
-    };
-
-    return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth     = 3u;
-    unsigned int inputHeight    = 2u;
-    unsigned int inputChannels  = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<uint8_t> input = std::vector<uint8_t>{
-         51, 124, 28,
-        251,   8, 92
-    };
-
-    // Calculated manually.
-    std::vector<uint8_t> output = std::vector<uint8_t>{
-          0, 122,  0,
-        255,   0, 58
-    };
-
-    float inputScale     = 12.0f / 255.0f;
-    int32_t inputOffset  = 63;
-    float outputScale    = 6.0f / 255.0f;
-    int32_t outputOffset = 0;
-
-    return BoundedReLuTestCommon(workloadFactory, 6.0f, 0.0f,
-                                 inputScale, inputOffset, outputScale, outputOffset,
-                                 input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    unsigned int inputWidth     = 3u;
-    unsigned int inputHeight    = 2u;
-    unsigned int inputChannels  = 1u;
-    unsigned int inputBatchSize = 1;
-
-    std::vector<uint8_t> input = std::vector<uint8_t>{
-         51, 230, 28,
-        251,   8, 92
-    };
-
-    // Calculated manually.
-    std::vector<uint8_t> output = std::vector<uint8_t>{
-         51, 192, 32,
-        192,  32, 92
-    };
-
-    int32_t inputOffset = 112;
-    float inputScale    = 0.0125f;
-
-    return BoundedReLuTestCommon(workloadFactory, 1.0f, -1.0f,
-                                 inputScale, inputOffset, inputScale, inputOffset, // Input/output scale & offset same.
-                                 input, output,
-                                 inputWidth, inputHeight, inputChannels, inputBatchSize);
-}
-
-namespace
-{
-
-struct BoundedReLuRandomInputTestTraits
-{
-    constexpr static unsigned int inputHeight = 31u;
-    constexpr static unsigned int inputWidth = 19u;
-    constexpr static unsigned int inputChannels = 4u;
-    constexpr static unsigned int inputBatchSize = 2;
-
-    constexpr static unsigned int outputHeight = inputHeight;
-    constexpr static unsigned int outputWidth = inputWidth;
-    constexpr static unsigned int outputChannels = inputChannels;
-    constexpr static unsigned int outputBatchSize = inputBatchSize;
-
-    static armnn::TensorInfo GetInputTensorInfo()
-    {
-        return armnn::TensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-            armnn::DataType::Float32);
-    }
-
-    static armnn::TensorInfo GetOutputTensorInfo()
-    {
-        return armnn::TensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-            armnn::DataType::Float32);
-    }
-};
-
-boost::multi_array<float, 4> BoundedReLuRandomInputTest(armnn::IWorkloadFactory& workloadFactory,
-                                                        float lowerBound,
-                                                        float upperBound,
-                                                        const armnn::ActivationDescriptor& activationDescriptor)
-{
-    const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo();
-    const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo();
-
-    boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo));
-
-    // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu
-    // range [lowerBound, upperBound].
-    auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Set up bounded ReLu.
-    armnn::ActivationQueueDescriptor descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-    descriptor.m_Parameters = activationDescriptor;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get());
-
-    return output;
-}
-
-} // namespace
-
-LayerTestResult<float, 4> CompareBoundedReLuTest(armnn::IWorkloadFactory& workloadFactory,
-                                          armnn::IWorkloadFactory& otherWorkloadFactory,
-                                          float upperBound,
-                                          float lowerBound)
-{
-    LayerTestResult<float, 4> result(BoundedReLuRandomInputTestTraits::GetOutputTensorInfo());
-
-    armnn::ActivationDescriptor activationDescriptor;
-    activationDescriptor.m_Function = armnn::ActivationFunction::BoundedReLu;
-    activationDescriptor.m_A = upperBound;
-    activationDescriptor.m_B = lowerBound;
-
-    result.output = BoundedReLuRandomInputTest(workloadFactory, 0.0f, upperBound, activationDescriptor);
-    result.outputExpected = BoundedReLuRandomInputTest(otherWorkloadFactory, 0.0f, upperBound, activationDescriptor);
-
-    return result;
-}
-
-template<typename T>
-LayerTestResult<T,4> ConstantLinearActivationTestCommon(armnn::IWorkloadFactory& workloadFactory,
-                                                        float qScale = 0.0f,
-                                                        int32_t qOffset = 0)
-{
-    unsigned int inputHeight    = 20;
-    unsigned int inputWidth     = 17;
-    unsigned int inputChannels  = 3;
-    unsigned int batchSize      = 5;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[]  = {batchSize, inputChannels, inputHeight, inputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Do linear activation that should leave the tensor unchanged.
-    armnn::ActivationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_A = 1.0f;
-    data.m_Parameters.m_B = 0.0f;
-    data.m_Parameters.m_Function = armnn::ActivationFunction::Linear;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561);
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    // Ensure output equals input.
-    ret.outputExpected = input;
-
-    return ret;
-}
-
-LayerTestResult<float, 4> ConstantLinearActivationTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantLinearActivationTestCommon<float>(workloadFactory);
-}
-
-LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return ConstantLinearActivationTestCommon<uint8_t>(workloadFactory, 4.0f, 3);
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleActivationTest(armnn::IWorkloadFactory& workloadFactory,
-                                           armnn::ActivationFunction activationFunction,
-                                           float activationParameterA,
-                                           float activationParameterB,
-                                           float qScale,
-                                           int32_t qOffset,
-                                           const std::vector<float>& inputData,
-                                           const std::vector<float>& outputExpectedData)
-{
-    constexpr static unsigned int inputWidth = 16u;
-    constexpr static unsigned int inputHeight = 1u;
-    constexpr static unsigned int inputChannels = 1u;
-    constexpr static unsigned int inputBatchSize = 1u;
-
-    constexpr static unsigned int outputWidth = inputWidth;
-    constexpr static unsigned int outputHeight = inputHeight;
-    constexpr static unsigned int outputChannels = inputChannels;
-    constexpr static unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-                                      armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-                                       armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    LayerTestResult<T, 4> result(inputTensorInfo);
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData));
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    // Setup bounded ReLu.
-    armnn::ActivationQueueDescriptor descriptor;
-    armnn::WorkloadInfo workloadInfo;
-    AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
-
-    descriptor.m_Parameters.m_Function = activationFunction;
-    descriptor.m_Parameters.m_A = activationParameterA;
-    descriptor.m_Parameters.m_B = activationParameterB;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get());
-
-    // Calculated manually.
-    result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, outputExpectedData));
-
-    return result;
-}
-
-template<typename T>
-LayerTestResult<T, 4> SimpleSigmoidTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset)
-{
-    std::vector<float> inputData = {
-        -0.1f, -0.2f, -0.3f, -0.4f,
-        0.1f,  0.2f,  0.3f,  0.4f,
-        -1.0f, -2.0f, -3.0f, -4.0f,
-        1.0f,  2.0f,  3.0f,  4.0f
-    };
-
-    // Calculate output values for input.
-    auto f = [](float value)
-    {
-        return 1.0f / (1.0f + std::exp(-value));
-    };
-    std::vector<float> outputExpectedData(inputData.size());
-    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
-
-    return SimpleActivationTest<T>(workloadFactory,
-                                   armnn::ActivationFunction::Sigmoid,
-                                   0.f,
-                                   0.f,
-                                   qScale,
-                                   qOffset,
-                                   inputData,
-                                   outputExpectedData);
-}
-
-LayerTestResult<float, 4> SimpleSigmoidTest(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SimpleSigmoidTestCommon<float>(workloadFactory, 0.0f, 0);
-}
-
-LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(armnn::IWorkloadFactory& workloadFactory)
-{
-    return SimpleSigmoidTestCommon<uint8_t>(workloadFactory, 0.1f, 50);
-}
-
-template<typename T>
-LayerTestResult<T,4> CompareActivationTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                               armnn::IWorkloadFactory& refWorkloadFactory,
-                                               armnn::ActivationFunction f,
-                                               unsigned int batchSize = 5,
-                                               float qScale = 0.0f,
-                                               int32_t qOffset = 0)
-{
-    unsigned int width     = 17;
-    unsigned int height    = 29;
-    unsigned int channels  = 2;
-
-    float a = 0.234f;
-    float b = -12.345f;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[] = {batchSize, channels, height, width};
-
-    inputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::GetDataType<T>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputTensorInfo.SetQuantizationScale(qScale);
-        inputTensorInfo.SetQuantizationOffset(qOffset);
-        outputTensorInfo.SetQuantizationScale(qScale);
-        outputTensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    float minVal = -10.f;
-    if (f == armnn::ActivationFunction::Sqrt)
-    {
-        minVal = 0.f;
-    }
-
-    boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f);
-
-
-    LayerTestResult<T,4> ret(outputTensorInfo);
-    auto boostArrayExtents = boost::extents
-        [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)]
-    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)]
-    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(height)]
-    [boost::numeric_cast<boost::multi_array_types::extent_gen::index>(width)];
-    ret.output.resize(boostArrayExtents);
-    ret.outputExpected.resize(boostArrayExtents);
-
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ActivationQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Parameters.m_A        = a;
-    data.m_Parameters.m_B        = b;
-    data.m_Parameters.m_Function = f;
-
-    armnn::ActivationQueueDescriptor refData = data;
-    armnn::WorkloadInfo refInfo = info;
-    SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get());
-    SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(data, info);
-    BOOST_ASSERT(workload != nullptr);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateActivation(refData, refInfo);
-    BOOST_ASSERT(workloadRef != nullptr);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-    inputHandleRef->Allocate();
-    outputHandleRef->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workload->Execute();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
-LayerTestResult<float,4> CompareActivationTest(armnn::IWorkloadFactory& workloadFactory,
-                                               armnn::IWorkloadFactory& refWorkloadFactory,
-                                               armnn::ActivationFunction f,
-                                               unsigned int batchSize)
-{
-    return CompareActivationTestImpl<float>(workloadFactory, refWorkloadFactory, f, batchSize);
-}
-
-LayerTestResult<uint8_t,4> CompareActivationUint8Test(armnn::IWorkloadFactory& workloadFactory,
-                                                      armnn::IWorkloadFactory& refWorkloadFactory,
-                                                      armnn::ActivationFunction f)
-{
-    return CompareActivationTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, f, 5, 0.1f, 50);
-}