IVGCVSW-1897 : build infrastructure for the src/backends folder

Change-Id: I7ebafb675ccc77ad54d1deb01412a8379a5356bb

1 files changed, 0 insertions, 921 deletions

diff --git a/src/armnn/backends/test/Conv2dTestImpl.hpp b/src/armnn/backends/test/Conv2dTestImpl.hpp
deleted file mode 100644
index eb7165bf09..0000000000
--- a/src/armnn/backends/test/Conv2dTestImpl.hpp
+++ /dev/null
@@ -1,921 +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 <backends/WorkloadInfo.hpp>
-
-#include "test/TensorHelpers.hpp"
-#include "QuantizeHelper.hpp"
-
-#include "backends/CpuTensorHandle.hpp"
-#include "backends/WorkloadFactory.hpp"
-
-// Mapping from input type to bias type for fully connected layers.
-// float => float, uint8_t => int32_t
-template<typename T>
-struct FullyConnectedBiasTypeForInputType;
-
-template<>
-struct FullyConnectedBiasTypeForInputType<float>
-{
-    using Type = float;
-};
-
-template<>
-struct FullyConnectedBiasTypeForInputType<uint8_t>
-{
-    using Type = int32_t;
-};
-
-// Modifies a std::vector in-place using a specified bias.
-template<typename T, typename B>
-void ApplyBias(std::vector<T>& v, float vScale, int32_t vOffset,
-    const std::vector<B>& bias, float bScale, int32_t bOffset, uint32_t w, uint32_t h)
-{
-    BOOST_ASSERT_MSG((armnn::IsQuantizedType<T>() && vScale != 0.0f) || (!armnn::IsQuantizedType<T>()),
-                     "Invalid type and parameter combination.");
-    BOOST_ASSERT_MSG((armnn::IsQuantizedType<B>() && bScale != 0.0f) || (!armnn::IsQuantizedType<B>()),
-                     "Invalid type and parameter combination.");
-
-    // Note we need to dequantize and re-quantize the image value and the bias.
-    for (uint32_t i = 0; i < bias.size(); ++i)
-    {
-        float dBias = SelectiveDequantize(bias[i], bScale, bOffset);
-        for (uint32_t y = 0; y < h; ++y)
-        {
-            for (uint32_t x = 0; x < w; ++x)
-            {
-                uint32_t offset = (i * h + y) * w + x;
-                BOOST_ASSERT(offset < v.size());
-                T& outRef = v[offset];
-                float dOutput = SelectiveDequantize(outRef, vScale, vOffset);
-                outRef = SelectiveQuantize<T>(dOutput + dBias, vScale, vOffset);
-            }
-        }
-    }
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> SimpleConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                  const boost::multi_array<T, 4>& input,
-                                                  const boost::multi_array<T, 4>& kernel,
-                                                  const boost::multi_array<B, 1>& bias,
-                                                  const boost::multi_array<T, 4>& outputExpected,
-                                                  float qScale,
-                                                  int32_t qOffset,
-                                                  uint32_t padLeft = 0,
-                                                  uint32_t padTop = 0,
-                                                  uint32_t padRight = 0,
-                                                  uint32_t padBottom = 0)
-{
-    unsigned int inputHeight   = boost::numeric_cast<unsigned int>(input.shape()[2]);
-    unsigned int inputWidth    = boost::numeric_cast<unsigned int>(input.shape()[3]);
-    unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[1]);
-    unsigned int inputNum      = boost::numeric_cast<unsigned int>(input.shape()[0]);
-
-    unsigned int outputHeight   = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
-    unsigned int outputWidth    = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
-    unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-    unsigned int outputNum      = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-
-    unsigned int kernelHeight = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
-    unsigned int kernelWidth = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
-    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
-    unsigned int kernelDepthMul = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
-
-    bool biasEnabled = bias.size() > 0;
-
-    // This function currently assumes 1 batch of input/output (and duplicates this into 2 batches).
-    BOOST_ASSERT(inputNum == 1);
-    BOOST_ASSERT(outputNum == 1);
-
-    // If a bias is used, its size must equal the number of output channels.
-    BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
-
-
-    // Note these tensors will use two (identical) batches.
-    armnn::TensorInfo inputTensorInfo({2*inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({2*outputNum, outputChannels, outputHeight, outputWidth},
-        armnn::GetDataType<T>());
-    armnn::TensorInfo kernelDesc({kernelDepthMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
-
-    // 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);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    // Construct input data - two batches of the same input image.
-    std::vector<T> inputImage;
-    inputImage.assign(input.data(), input.data() + 1*inputChannels*inputHeight*inputWidth);
-    std::vector<T> inputData;
-    inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
-    inputData.insert(inputData.end(), inputImage.begin(), inputImage.end());
-    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    std::vector<T> outputImage;
-    outputImage.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
-
-    // Apply bias to output image if it is enabled.
-    if(biasEnabled)
-    {
-        std::vector<T> biasV;
-        biasV.assign(bias.data(), bias.data() + outputChannels);
-        ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-            biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-            outputWidth, outputHeight);
-    }
-
-    // Construct expected output data - two identical images.
-    std::vector<T> outputData;
-    outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
-    outputData.insert(outputData.end(), outputImage.begin(), outputImage.end());
-
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    // Todo: nontrivial padding and strides.
-    uint32_t                    strideX  = 1;
-    uint32_t                    strideY  = 1;
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::Convolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-
-    if(biasEnabled)
-    {
-        AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-    }
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - can be a source of bugs.
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padLeft;
-    data.m_Parameters.m_PadRight = padRight;
-    data.m_Parameters.m_PadTop = padTop;
-    data.m_Parameters.m_PadBottom = padBottom;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                               const boost::multi_array<T, 4>& input,
-                                                               const boost::multi_array<T, 4>& kernel,
-                                                               const boost::multi_array<B, 1>& bias,
-                                                               const boost::multi_array<T, 4>& outputExpected,
-                                                               float qScale,
-                                                               int32_t qOffset,
-                                                               uint32_t padLeft = 0,
-                                                               uint32_t padTop = 0,
-                                                               uint32_t padRight = 0,
-                                                               uint32_t padBottom = 0,
-                                                               uint32_t strideX = 1,
-                                                               uint32_t strideY = 1)
-{
-    unsigned int inputNum       = boost::numeric_cast<unsigned int>(input.shape()[0]);
-    unsigned int inputChannels  = boost::numeric_cast<unsigned int>(input.shape()[1]);
-    unsigned int inputHeight    = boost::numeric_cast<unsigned int>(input.shape()[2]);
-    unsigned int inputWidth     = boost::numeric_cast<unsigned int>(input.shape()[3]);
-    unsigned int kernelChanMul  = boost::numeric_cast<unsigned int>(kernel.shape()[0]);
-    unsigned int kernelChannels = boost::numeric_cast<unsigned int>(kernel.shape()[1]);
-    unsigned int kernelHeight   = boost::numeric_cast<unsigned int>(kernel.shape()[2]);
-    unsigned int kernelWidth    = boost::numeric_cast<unsigned int>(kernel.shape()[3]);
-    unsigned int outputNum      = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]);
-    unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[1]);
-    unsigned int outputHeight   = boost::numeric_cast<unsigned int>(outputExpected.shape()[2]);
-    unsigned int outputWidth    = boost::numeric_cast<unsigned int>(outputExpected.shape()[3]);
-
-    // If a bias is used, its size must equal the number of output channels.
-    bool biasEnabled = bias.size() > 0;
-    BOOST_ASSERT(!biasEnabled || bias.size() == outputChannels);
-
-    // Creates the tensors.
-    armnn::TensorInfo inputTensorInfo({inputNum, inputChannels, inputHeight, inputWidth}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({outputNum, outputChannels, outputHeight, outputWidth},
-                                       armnn::GetDataType<T>());
-    armnn::TensorInfo kernelDesc({kernelChanMul, kernelChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasDesc({static_cast<unsigned int>(bias.size())}, armnn::GetDataType<B>());
-
-    // 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);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    // Construct the input data.
-    std::vector<T> inputData;
-    inputData.assign(input.data(), input.data() + inputChannels*inputHeight*inputWidth);
-    auto batchedInput = MakeTensor<T, 4>(inputTensorInfo, inputData);
-
-    // Construct the output data, with bias applied, as appropriate.
-    std::vector<T> outputData;
-    outputData.assign(outputExpected.data(), outputExpected.data() + outputChannels*outputHeight*outputWidth);
-    if (biasEnabled)
-    {
-        std::vector<T> biasV;
-        biasV.assign(bias.data(), bias.data() + outputChannels);
-        ApplyBias(outputData, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-            biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-            outputWidth, outputHeight);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-    if (biasEnabled)
-    {
-        AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-    }
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled - it can be a source of bugs.
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padLeft;
-    data.m_Parameters.m_PadRight = padRight;
-    data.m_Parameters.m_PadTop = padTop;
-    data.m_Parameters.m_PadBottom = padBottom;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-
-    armnn::WorkloadInfo info;
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &batchedInput[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dDepthMul1TestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                              float qScale,
-                                                              int32_t qOffset,
-                                                              bool biasEnabled)
-{
-    unsigned int inputHeight = 3;
-    unsigned int inputWidth = 3;
-    unsigned int inputChannels = 2;
-    unsigned int inputNum = 1;
-
-    unsigned int kernelHeight = 3;
-    unsigned int kernelWidth = 3;
-    unsigned int kernelChannels = inputChannels;
-
-    unsigned int outputHeight = 1;
-    unsigned int outputWidth = 1;
-    unsigned int outputChannels = kernelChannels;
-    unsigned int outputNum = inputNum;
-
-    armnn::TensorInfo inputTensorInfo({ inputNum, inputChannels, inputHeight, inputWidth }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({ outputNum, outputChannels, outputHeight, outputWidth },
-        armnn::GetDataType<T>());
-    armnn::TensorInfo kernelDesc({ 1, outputChannels, kernelHeight, kernelWidth }, armnn::GetDataType<T>());
-    armnn::TensorInfo biasDesc({ outputChannels }, armnn::GetDataType<B>());
-
-    // 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);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
-            1.f, 2.f, 1.f,
-            2.f, 1.f, 2.f,
-            1.f, 2.f, 1.f,
-
-            1.f, 2.f, 1.f,
-            2.f, 1.f, 2.f,
-            1.f, 2.f, 1.f,
-        })));
-
-    std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-                                            {0, 2}));
-    auto bias = MakeTensor<B, 1>(biasDesc, biasV);
-
-    auto kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
-        QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
-            1.f, 0.f,  1.f,
-            0.f, 0.f,  0.f,
-           -1.f, 0.f, -1.f,
-
-            1.f, 0.f,  1.f,
-            0.f, 0.f,  0.f,
-           -1.f, 0.f, -1.f,
-        })));
-
-    // Manually calculated.
-    std::vector<T> outputImage(
-        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(),
-                           outputTensorInfo.GetQuantizationOffset(),
-                           {0.f, 0.f})
-    );
-
-    // Optionally apply bias to output image.
-    if(biasEnabled)
-    {
-        ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-                  biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-                  outputWidth, outputHeight);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
-    data.m_Parameters.m_StrideX = 1;
-    data.m_Parameters.m_StrideY = 1;
-    data.m_Parameters.m_PadLeft = 0;
-    data.m_Parameters.m_PadRight = 0;
-    data.m_Parameters.m_PadTop = 0;
-    data.m_Parameters.m_PadBottom = 0;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T, typename B>
-LayerTestResult<T, 4> DepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                     float qScale,
-                                                     int32_t qOffset,
-                                                     bool biasEnabled)
-{
-    unsigned int depthMultiplier = 2;
-
-    unsigned int inputHeight    = 8;
-    unsigned int inputWidth     = 16;
-    unsigned int inputChannels  = 2;
-    unsigned int inputBatchSize = 1;
-
-    unsigned int kernelHeight = 5;
-    unsigned int kernelWidth  = 3;
-
-    unsigned int outputHeight    = inputHeight - kernelHeight + 1 + 2;
-    unsigned int outputWidth     = (inputWidth - kernelWidth + 1)/2;
-    unsigned int outputChannels  = inputChannels * depthMultiplier;
-    unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({inputBatchSize, inputChannels, inputHeight, inputWidth},
-                                      armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo({outputBatchSize, outputChannels, outputHeight, outputWidth},
-        armnn::GetDataType<T>());
-    armnn::TensorInfo kernelDesc({depthMultiplier, inputChannels, kernelHeight, kernelWidth}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasDesc({outputChannels}, armnn::GetDataType<B>());
-
-    // 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);
-        kernelDesc.SetQuantizationScale(qScale);
-        kernelDesc.SetQuantizationOffset(qOffset);
-        biasDesc.SetQuantizationScale(qScale*qScale);
-        biasDesc.SetQuantizationOffset(0);
-    }
-
-    auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(
-        QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), {
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f,
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-            0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
-        })));
-
-    std::vector<B> biasV(QuantizedVector<B>(biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-        {0, 2, 1, -1}));
-    auto bias = MakeTensor<B, 1>(biasDesc, biasV);
-
-    auto kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>(
-        QuantizedVector<T>(kernelDesc.GetQuantizationScale(), kernelDesc.GetQuantizationOffset(), {
-            1, 1, 1,
-            1, -1, 1,
-            1, 1, 1,
-            1, 1, 1,
-            1, 1, 1,
-
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-            2, 2, 2,
-
-            0, 0, 0,
-            0, -1, 0,
-            0, 0, 0,
-            0, 0, 0,
-            0, 0, 0,
-
-            0, 0, 0,
-            0, 0, 0,
-            0, 1, 0,
-            0, 0, 0,
-            0, 0, 0
-        })));
-
-    // Manually calculated.
-    std::vector<T> outputImage = std::vector<T>(
-        QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), {
-            3.5f,  3.5f,  3.5f,  3.5f,  3.5f,  3.5f,  3.5f,
-            6.0f,  6.0f,  6.0f,  6.0f,  6.0f,  6.0f,  6.0f,
-            5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,
-            6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,
-            6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,  6.5f,
-            5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,  5.0f,
-
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-            -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f, -0.5f,
-
-            8.0f,  8.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            10.0f, 10.0f, 0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            8.0f,  8.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,
-            0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f,  0.0f
-        }));
-
-    // Optionally apply bias to output image.
-    if(biasEnabled)
-    {
-        ApplyBias(outputImage, outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(),
-            biasV, biasDesc.GetQuantizationScale(), biasDesc.GetQuantizationOffset(),
-            outputWidth, outputHeight);
-    }
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-    ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputImage);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor; // Still set this whether or not bias is enabled.
-    data.m_Parameters.m_StrideX = 2;
-    data.m_Parameters.m_StrideY = 1;
-    data.m_Parameters.m_PadLeft = 0;
-    data.m_Parameters.m_PadRight = 0;
-    data.m_Parameters.m_PadTop = 1;
-    data.m_Parameters.m_PadBottom = 1;
-    data.m_Parameters.m_BiasEnabled = biasEnabled;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDepthwiseConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-
-    return ret;
-}
-
-template<typename T>
-LayerTestResult<T,4> Convolution1dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                           float qScale,
-                                           int32_t qOffset,
-                                           bool biasEnabled)
-{
-    using B = typename FullyConnectedBiasTypeForInputType<T>::Type;
-
-    // Until we have a specialist 1D convolution layer, we can fake one using
-    // 2D convolution with the final dimension set to 1.
-    // I don't anticipate this being particularly slow, given that convolution is implemented
-    // as a matrix multiplication, at which point dimension doesn't matter.
-
-    unsigned int batchSize      = 1;
-    unsigned int inputChannels  = 2;
-    unsigned int outputChannels = 3;
-    unsigned int inputSize      = 5; // The 1D size (could view as 'width' or 'height').
-    unsigned int kernelSize     = 3;
-    unsigned int padSize        = 2;
-    unsigned int stride         = 1;
-    unsigned int outputSize     = 7; // (inputSize + 2 * padSize - kernelSize + 1) / stride.
-
-    armnn::TensorInfo inputInfo({batchSize, inputChannels, inputSize, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo outputInfo({batchSize, outputChannels, outputSize, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo kernelInfo({outputChannels, inputChannels, kernelSize, 1}, armnn::GetDataType<T>());
-    armnn::TensorInfo biasInfo({outputChannels}, armnn::GetDataType<B>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(armnn::IsQuantizedType<T>())
-    {
-        inputInfo.SetQuantizationScale(qScale);
-        inputInfo.SetQuantizationOffset(qOffset);
-        outputInfo.SetQuantizationScale(qScale);
-        outputInfo.SetQuantizationOffset(qOffset);
-        kernelInfo.SetQuantizationScale(qScale);
-        kernelInfo.SetQuantizationOffset(qOffset);
-        biasInfo.SetQuantizationScale(inputInfo.GetQuantizationScale()*kernelInfo.GetQuantizationScale());
-        biasInfo.SetQuantizationOffset(0);
-    }
-
-    std::vector<T> inputData(
-        QuantizedVector<T>(inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset(), {
-            5.0f, -2.0f, 2.5f, 0.0f, 1.0f,
-            -3.0f, 3.2f, 5.0f, 2.0f, 3.0f,
-        }));
-
-    std::vector<T> kernelData(
-        QuantizedVector<T>(kernelInfo.GetQuantizationScale(), kernelInfo.GetQuantizationOffset(), {
-            1.0f, 0.0f, 0.0f,
-            0.0f, 2.0f, -1.5f,
-
-            0.0f, 0.0f, 0.0f,
-            0.2f, 0.2f, 0.2f,
-
-            0.5f, 0.0f, 0.5f,
-            0.0f, -1.0f, 0.0f
-        }));
-
-    std::vector<B> biasData(
-        QuantizedVector<B>(biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(), {
-            1.0f, 0.0f, 0.0f
-        }));
-
-    std::vector<T> outputData(
-        QuantizedVector<T>(outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(), {
-            4.5f, -10.8f, 5.0f + 6.4f - 7.5f, -2.0f + 10.0f -3.0f, 2.5f + 4.0f - 4.5f, 6.0f, 1.0f,
-            -0.6f, -0.6f + 0.64f, -0.6f + 0.64f + 1.0f, 0.64f + 1.0f + 0.4f, 1.0f + 0.4f + 0.6f, 0.4f + 0.6f, 0.6f,
-            2.5f, -1.0f + 3.0f, 1.25f - 3.2f + 2.5f, -1.0f - 5.0f, 1.25f + 0.5f - 2.0f, -3.0f, 0.5f
-        }));
-
-    // Optionally apply bias to output image.
-    if(biasEnabled)
-    {
-        ApplyBias(outputData, outputInfo.GetQuantizationScale(), outputInfo.GetQuantizationOffset(),
-            biasData, biasInfo.GetQuantizationScale(), biasInfo.GetQuantizationOffset(),
-            1, outputSize);
-    }
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle  = workloadFactory.CreateTensorHandle(inputInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo);
-
-    armnn::Convolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle         weightsTensor(kernelInfo);
-    armnn::ScopedCpuTensorHandle         biasTensor(biasInfo);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, kernelData.data());
-    AllocateAndCopyDataToITensorHandle(&biasTensor, biasData.data());
-
-    AddInputToWorkload(data, info, inputInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputInfo, outputHandle.get());
-
-    data.m_Weight         = &weightsTensor;
-    data.m_Bias           = &biasTensor;
-    data.m_Parameters.m_StrideX        = 1;
-    data.m_Parameters.m_StrideY        = stride;
-    data.m_Parameters.m_PadLeft        = 0;
-    data.m_Parameters.m_PadRight       = 0;
-    data.m_Parameters.m_PadTop         = padSize;
-    data.m_Parameters.m_PadBottom      = padSize;
-    data.m_Parameters.m_BiasEnabled    = biasEnabled;
-
-    std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConvolution2d(data, info);
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), inputData.data());
-
-    workloadFactory.Finalize();
-    workload->Execute();
-
-    // Output
-    LayerTestResult<T,4> ret(outputInfo);
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    ret.outputExpected = MakeTensor<T, 4>(outputInfo, outputData);
-    return ret;
-}
-
-
-
-template<typename T>
-LayerTestResult<T,4> CompareConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-                                                armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    unsigned int inputHeight   = 8;
-    unsigned int inputWidth    = 16;
-    unsigned int inputChannels = 3;
-    unsigned int inputNum      = 5;
-
-    unsigned int kernelHeight = 3;
-    unsigned int kernelWidth  = 3;
-
-    unsigned int strideX = 2;
-    unsigned int strideY = 3;
-    unsigned int padX    = 1;
-    unsigned int padY    = 1;
-
-    unsigned int outputNum      = inputNum;
-    unsigned int outputChannels = 2;
-    unsigned int outputHeight   = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;
-    unsigned int outputWidth    = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo kernelDesc;
-    armnn::TensorInfo biasDesc;
-
-    unsigned int inputShape[]    = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[]   = {outputNum, outputChannels, outputHeight, outputWidth};
-    unsigned int kernelShape[]   = {outputChannels, inputChannels, kernelHeight, kernelWidth};
-    unsigned int biasShape[]     = {outputChannels};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>());
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>());
-    kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType<T>());
-    biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetDataType<T>());
-
-    LayerTestResult<T,4> ret(outputTensorInfo);
-
-    auto input  = MakeRandomTensor<T, 4>(inputTensorInfo, 124908);
-    auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234);
-    auto bias   = MakeRandomTensor<T, 1>(biasDesc, 1028);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::Convolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padX;
-    data.m_Parameters.m_PadRight = padX;
-    data.m_Parameters.m_PadTop = padY;
-    data.m_Parameters.m_PadBottom = padY;
-    data.m_Parameters.m_BiasEnabled = true;
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    armnn::Convolution2dQueueDescriptor 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.CreateConvolution2d(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateConvolution2d(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}
-
-template<typename T>
-LayerTestResult<T, 4> CompareDepthwiseConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory,
-    armnn::IWorkloadFactory& refWorkloadFactory)
-{
-    unsigned int inputHeight = 8;
-    unsigned int inputWidth = 16;
-    unsigned int inputChannels = 3;
-    unsigned int inputNum = 5;
-
-    unsigned int kernelHeight = 3;
-    unsigned int kernelWidth = 3;
-    unsigned int channelMultiplier = 1;
-
-    unsigned int strideX = 2;
-    unsigned int strideY = 3;
-    unsigned int padX = 1;
-    unsigned int padY = 1;
-
-    unsigned int outputNum = inputNum;
-    unsigned int outputChannels = inputChannels * channelMultiplier;
-    unsigned int outputHeight = (inputHeight + 2 * padY - kernelHeight + strideY) / strideY;
-    unsigned int outputWidth = (inputWidth + 2 * padX - kernelWidth + strideX) / strideX;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo kernelDesc;
-    armnn::TensorInfo biasDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
-    unsigned int kernelShape[] = { channelMultiplier, inputChannels, kernelHeight, kernelWidth };
-    unsigned int biasShape[] = { outputChannels };
-
-    float inputsQScale = armnn::IsQuantizedType<T>() ? 1.0f : 0;
-    float outputQScale = armnn::IsQuantizedType<T>() ? 2.0f : 0;
-    int32_t qOffset = 0;
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>(), inputsQScale, qOffset);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>(), outputQScale, qOffset);
-    kernelDesc = armnn::TensorInfo(4, kernelShape, armnn::GetDataType<T>(), inputsQScale, qOffset);
-    biasDesc = armnn::TensorInfo(1, biasShape, armnn::GetBiasDataType(armnn::GetDataType<T>()), inputsQScale, qOffset);
-
-    LayerTestResult<T, 4> ret(outputTensorInfo);
-
-    auto input = MakeRandomTensor<T, 4>(inputTensorInfo, 124908, 0.0f, 255.0f);
-    auto kernel = MakeRandomTensor<T, 4>(kernelDesc, 891234, 0.0f, 255.0f);
-    auto bias = MakeRandomTensor<typename FullyConnectedBiasTypeForInputType<T>::Type, 1>(biasDesc, 1028, 0.0f, 255.0f);
-
-    std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor data;
-    armnn::WorkloadInfo info;
-    armnn::ScopedCpuTensorHandle weightsTensor(kernelDesc);
-    armnn::ScopedCpuTensorHandle biasTensor(biasDesc);
-
-    AllocateAndCopyDataToITensorHandle(&weightsTensor, &kernel[0][0][0][0]);
-    AllocateAndCopyDataToITensorHandle(&biasTensor, &bias[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Weight = &weightsTensor;
-    data.m_Bias = &biasTensor;
-    data.m_Parameters.m_StrideX = strideX;
-    data.m_Parameters.m_StrideY = strideY;
-    data.m_Parameters.m_PadLeft = padX;
-    data.m_Parameters.m_PadRight = padX;
-    data.m_Parameters.m_PadTop = padY;
-    data.m_Parameters.m_PadBottom = padY;
-    data.m_Parameters.m_BiasEnabled = true;
-
-    std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo);
-    std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo);
-
-    armnn::DepthwiseConvolution2dQueueDescriptor 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.CreateDepthwiseConvolution2d(data, info);
-    std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateDepthwiseConvolution2d(refData, refInfo);
-
-    outputHandleRef->Allocate();
-    inputHandleRef->Allocate();
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-    CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]);
-
-    workloadFactory.Finalize();
-    workload->Execute();
-    refWorkloadFactory.Finalize();
-    workloadRef->Execute();
-
-    CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
-    CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get());
-
-    return ret;
-}