IVGCVSW-3381 Break up LayerTests.hpp into more manageable files

Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Icf39434f09fd340ad664cb3b97b8bee6d9da4838

1 files changed, 1084 insertions, 0 deletions

diff --git a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp
new file mode 100644
index 0000000000..c05dfd6170
--- /dev/null
+++ b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp
@@ -0,0 +1,1084 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ActivationTestImpl.hpp"
+
+#include <ResolveType.hpp>
+
+#include <armnn/ArmNN.hpp>
+
+#include <backendsCommon/test/ActivationFixture.hpp>
+#include <backendsCommon/test/QuantizeHelper.hpp>
+#include <backendsCommon/test/TensorCopyUtils.hpp>
+#include <backendsCommon/test/WorkloadTestUtils.hpp>
+
+#include <test/TensorHelpers.hpp>
+
+#include <boost/multi_array.hpp>
+
+#include <algorithm>
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> BoundedReLuTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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 }, ArmnnType);
+
+    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType);
+
+    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,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    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<armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 1.0f, -1.0f, 1.0f, 0, 1.0f, 0, input, output,
+        inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<float, 4> BoundedReLuUpperBoundOnlyTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    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<armnn::DataType::Float32>(
+        workloadFactory, memoryManager, 6.0f, 0.0f, 1.0f, 0, 1.0f, 0, input, output,
+        inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperBoundOnlyTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    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<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory, memoryManager, 6.0f, 0.0f,
+        inputScale, inputOffset, outputScale, outputOffset,
+        input, output, inputWidth, inputHeight, inputChannels, inputBatchSize);
+}
+
+LayerTestResult<uint8_t, 4> BoundedReLuUint8UpperAndLowerBoundTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    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<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory, memoryManager, 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,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    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, memoryManager, 0.0f, upperBound, activationDescriptor);
+    result.outputExpected = BoundedReLuRandomInputTest(
+        refWorkloadFactory, nullptr, 0.0f, upperBound, activationDescriptor);
+
+    return result;
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T,4> ConstantLinearActivationTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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, ArmnnType);
+    outputTensorInfo = armnn::TensorInfo(4, shape, ArmnnType);
+
+    // 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,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ConstantLinearActivationTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager);
+}
+
+LayerTestResult<uint8_t, 4> ConstantLinearActivationUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ConstantLinearActivationTestCommon<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory, memoryManager, 4.0f, 3);
+}
+
+LayerTestResult<int16_t, 4> ConstantLinearActivationInt16Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ConstantLinearActivationTestCommon<armnn::DataType::QuantisedSymm16>(
+            workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleActivationTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::ActivationFunction activationFunction,
+    float activationParameterA,
+    float activationParameterB,
+    float scale,
+    int32_t offset,
+    const std::vector<float>& inputData,
+    float outScale,
+    int32_t outOffset,
+    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 }, ArmnnType);
+    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, ArmnnType);
+
+    // Set quantization parameters if the requested type is a quantized type.
+    if(armnn::IsQuantizedType<T>())
+    {
+        inputTensorInfo.SetQuantizationScale(scale);
+        inputTensorInfo.SetQuantizationOffset(offset);
+        outputTensorInfo.SetQuantizationScale(outScale);
+        outputTensorInfo.SetQuantizationOffset(outOffset);
+    }
+
+    LayerTestResult<T, 4> result(inputTensorInfo);
+
+    auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(scale, offset, 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>(outScale, outOffset,
+                                                                                  outputExpectedData));
+
+    return result;
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SimpleSigmoidTestCommon(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::Sigmoid,
+                                           0.f,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           1.f / 256.f,
+                                           0,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> SimpleSigmoidTest(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SimpleSigmoidTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.0f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SimpleSigmoidUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SimpleSigmoidTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.1f, 50);
+}
+
+LayerTestResult<int16_t, 4> SimpleSigmoidInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SimpleSigmoidTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> ReLuTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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 std::fmax(0.0f, value);
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::ReLu,
+                                           0.f,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<int16_t, 4> ReLuInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+
+LayerTestResult<uint8_t, 4> ReLuUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ReLuTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<float, 4> ReLuTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ReLuTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> BoundedReLuTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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
+    };
+    const float a = 1.0f;
+    const float b = -1.0f;
+    // Calculate output values for input.
+    auto f = [a, b](float value)
+    {
+        return std::min(a, std::max(b, value));
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::BoundedReLu,
+                                           a,
+                                           b,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<int16_t, 4> BoundedReLuInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return ReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SoftReLuTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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 std::log(1.0f + std::exp(value));
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::SoftReLu,
+                                           0.f,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> SoftReLuTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SoftReLuTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SoftReLuUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SoftReLuTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.0625f, 64);
+}
+
+LayerTestResult<int16_t, 4> SoftReLuInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SoftReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> LeakyReLuTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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
+    };
+
+    const float a = 0.01f;
+    // Calculate output values for input.
+    auto f = [a](float value)
+    {
+        return value > 0.0f ? value : (value * a);
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::LeakyReLu,
+                                           a,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> LeakyReLuTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return LeakyReLuTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<uint8_t, 4> LeakyReLuUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return LeakyReLuTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.0625f, 64);
+}
+
+LayerTestResult<int16_t, 4> LeakyReLuInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return LeakyReLuTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> AbsTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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 std::abs(value);
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::Abs,
+                                           0.f,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> AbsTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return AbsTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<uint8_t, 4> AbsUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return AbsTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.0625f, 64);
+}
+
+LayerTestResult<int16_t, 4> AbsInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return AbsTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SqrtTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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 std::sqrt(value);
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::Sqrt,
+                                           0.f,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> SqrtTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SqrtTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SqrtUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SqrtTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.0625f, 64);
+}
+
+LayerTestResult<int16_t, 4> SqrtInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SqrtTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> SquareTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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 std::pow(value,2);
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::Square,
+                                           0.f,
+                                           0.f,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> SquareTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SquareTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<uint8_t, 4> SquareUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SquareTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.0625f, 64);
+}
+
+LayerTestResult<int16_t, 4> SquareInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return SquareTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T, 4> TanhTestCommon(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        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
+    };
+
+    const float a = 2.0f;
+    const float b = 3.0f;
+    // Calculate output values for input.
+    auto f = [a, b](float value)
+    {
+        return a * tanhf(b * value);
+    };
+    std::vector<float> outputExpectedData(inputData.size());
+    std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f);
+
+    return SimpleActivationTest<ArmnnType>(workloadFactory,
+                                           memoryManager,
+                                           armnn::ActivationFunction::TanH,
+                                           a,
+                                           b,
+                                           qScale,
+                                           qOffset,
+                                           inputData,
+                                           qScale,
+                                           qOffset,
+                                           outputExpectedData);
+}
+
+LayerTestResult<float, 4> TanhTest(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return TanhTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+LayerTestResult<uint8_t, 4> TanhUint8Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return TanhTestCommon<armnn::DataType::QuantisedAsymm8>(workloadFactory, memoryManager, 0.1f, 64);
+}
+
+LayerTestResult<int16_t, 4> TanhInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager)
+{
+    return TanhTestCommon<armnn::DataType::QuantisedSymm16>(workloadFactory, memoryManager, 0.1f, 0);
+}
+
+
+
+template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
+LayerTestResult<T,4> CompareActivationTestImpl(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    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, ArmnnType);
+    outputTensorInfo = armnn::TensorInfo(4, shape, ArmnnType);
+
+    // 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,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    armnn::ActivationFunction f,
+    unsigned int batchSize)
+{
+    return CompareActivationTestImpl<armnn::DataType::Float32>(
+        workloadFactory, memoryManager, refWorkloadFactory, f, batchSize);
+}
+
+LayerTestResult<uint8_t,4> CompareActivationUint8Test(
+    armnn::IWorkloadFactory& workloadFactory,
+    const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+    armnn::IWorkloadFactory& refWorkloadFactory,
+    armnn::ActivationFunction f)
+{
+    return CompareActivationTestImpl<armnn::DataType::QuantisedAsymm8>(
+        workloadFactory, memoryManager, refWorkloadFactory, f, 5, 0.1f, 50);
+}
+
+LayerTestResult<int16_t,4> CompareActivationInt16Test(
+        armnn::IWorkloadFactory& workloadFactory,
+        const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
+        armnn::IWorkloadFactory& refWorkloadFactory,
+        armnn::ActivationFunction f)
+{
+    return CompareActivationTestImpl<armnn::DataType::QuantisedSymm16>(
+            workloadFactory, memoryManager, refWorkloadFactory, f, 5, 0.1f, 0);
+}