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

Change-Id: I7ebafb675ccc77ad54d1deb01412a8379a5356bb

1 files changed, 0 insertions, 471 deletions

diff --git a/src/armnn/backends/test/WorkloadDataValidation.cpp b/src/armnn/backends/test/WorkloadDataValidation.cpp
deleted file mode 100644
index a5cfbd1270..0000000000
--- a/src/armnn/backends/test/WorkloadDataValidation.cpp
+++ /dev/null
@@ -1,471 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#include <boost/test/unit_test.hpp>
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/Workload.hpp>
-#include <backends/RefWorkloads.hpp>
-#include <backends/RefWorkloadFactory.hpp>
-
-#include <armnn/Exceptions.hpp>
-
-#include "WorkloadTestUtils.hpp"
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(WorkloadInfoValidation)
-
-
-
-BOOST_AUTO_TEST_CASE(QueueDescriptor_Validate_WrongNumOfInputsOutputs)
-{
-    InputQueueDescriptor invalidData;
-    WorkloadInfo invalidInfo;
-    //Invalid argument exception is expected, because no inputs and no outputs were defined.
-    BOOST_CHECK_THROW(RefWorkloadFactory().CreateInput(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(RefPooling2dFloat32Workload_Validate_WrongDimTensor)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {2, 3, 4}; // <- Invalid - input tensor has to be 4D.
-    unsigned int outputShape[] = {2, 3, 4, 5};
-
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-    inputTensorInfo  = armnn::TensorInfo(3, inputShape, armnn::DataType::Float32);
-
-    Pooling2dQueueDescriptor invalidData;
-    WorkloadInfo           invalidInfo;
-
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-
-    // Invalid argument exception is expected, input tensor has to be 4D.
-    BOOST_CHECK_THROW(RefPooling2dFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(SoftmaxQueueDescriptor_Validate_WrongInputHeight)
-{
-    unsigned int inputHeight = 1;
-    unsigned int inputWidth = 1;
-    unsigned int inputChannels = 4;
-    unsigned int inputNum = 2;
-
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputHeight = inputHeight + 1;    //Makes data invalid - Softmax expects height and width to be 1.
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputNum = inputNum;
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    SoftmaxQueueDescriptor invalidData;
-    WorkloadInfo           invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    //Invalid argument exception is expected, because height != 1.
-    BOOST_CHECK_THROW(RefSoftmaxFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing)
-{
-    unsigned int inputWidth = 1;
-    unsigned int inputHeight = 1;
-    unsigned int inputChannels = 5;
-    unsigned int inputNum = 2;
-
-    unsigned int outputWidth = 1;
-    unsigned int outputHeight = 1;
-    unsigned int outputChannels = 3;
-    unsigned int outputNum = 2;
-
-    // Define the tensor descriptors.
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-    armnn::TensorInfo weightsDesc;
-    armnn::TensorInfo biasesDesc;
-
-    unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth };
-    unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth };
-    unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels };
-    unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-    weightsDesc = armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32);
-    biasesDesc = armnn::TensorInfo(4, biasShape, armnn::DataType::Float32);
-
-    FullyConnectedQueueDescriptor invalidData;
-    WorkloadInfo                  invalidInfo;
-
-    ScopedCpuTensorHandle weightTensor(weightsDesc);
-    ScopedCpuTensorHandle biasTensor(biasesDesc);
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    invalidData.m_Weight = &weightTensor;
-    invalidData.m_Bias = &biasTensor;
-    invalidData.m_Parameters.m_BiasEnabled = true;
-    invalidData.m_Parameters.m_TransposeWeightMatrix = false;
-
-
-    //Invalid argument exception is expected, because not all required fields have been provided.
-    //In particular inputsData[0], outputsData[0] and weightsData can not be null.
-    BOOST_CHECK_THROW(RefFullyConnectedFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-
-BOOST_AUTO_TEST_CASE(NormalizationQueueDescriptor_Validate_WrongInputHeight)
-{
-    constexpr unsigned int inputNum = 5;
-    constexpr unsigned int inputHeight   = 32;
-    constexpr unsigned int inputWidth    = 24;
-    constexpr unsigned int inputChannels = 3;
-
-    constexpr unsigned int outputNum = inputNum;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputHeight = inputHeight + 1; //Makes data invalid - normalization requires.
-                                                           //Input and output to have the same dimensions.
-    constexpr unsigned int outputWidth  = inputWidth;
-
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-
-    armnn::NormalizationAlgorithmMethod normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness;
-    armnn::NormalizationAlgorithmChannel normChannel = armnn::NormalizationAlgorithmChannel::Across;
-    float alpha = 1.f;
-    float beta = 1.f;
-    float kappa = 1.f;
-    uint32_t normSize = 5;
-
-    NormalizationQueueDescriptor invalidData;
-    WorkloadInfo                 invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-    invalidData.m_Parameters.m_NormChannelType = normChannel;
-    invalidData.m_Parameters.m_NormMethodType  = normMethod;
-    invalidData.m_Parameters.m_NormSize        = normSize;
-    invalidData.m_Parameters.m_Alpha           = alpha;
-    invalidData.m_Parameters.m_Beta            = beta;
-    invalidData.m_Parameters.m_K               = kappa;
-
-    //Invalid argument exception is expected, because input height != output height.
-    BOOST_CHECK_THROW(RefNormalizationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(SplitterQueueDescriptor_Validate_WrongWindow)
-{
-    constexpr unsigned int inputNum = 1;
-    constexpr unsigned int inputHeight   = 32;
-    constexpr unsigned int inputWidth    = 24;
-    constexpr unsigned int inputChannels = 3;
-
-    constexpr unsigned int outputNum = inputNum;
-    constexpr unsigned int outputChannels = inputChannels;
-    constexpr unsigned int outputHeight = 18;
-    constexpr unsigned int outputWidth  = inputWidth;
-
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    SplitterQueueDescriptor invalidData;
-    WorkloadInfo            invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // Invalid, since it has only 3 dimensions while the input tensor is 4d.
-    std::vector<unsigned int> wOrigin = {0, 0, 0};
-    armnn::SplitterQueueDescriptor::ViewOrigin window(wOrigin);
-    invalidData.m_ViewOrigins.push_back(window);
-
-    BOOST_TEST_INFO("Invalid argument exception is expected, because split window dimensionality does not "
-        "match input.");
-    BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-    // Invalid, since window extends past the boundary of input tensor.
-    std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};
-    armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3);
-    invalidData.m_ViewOrigins[0] = window3;
-    BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight");
-    BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-
-    std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};
-    armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4);
-    invalidData.m_ViewOrigins[0] = window4;
-
-    std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};
-    armnn::SplitterQueueDescriptor::ViewOrigin window5(wOrigin4);
-    invalidData.m_ViewOrigins.push_back(window5);
-
-    BOOST_TEST_INFO("Invalid exception due to number of split windows not matching number of outputs.");
-    BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-
-BOOST_AUTO_TEST_CASE(MergerQueueDescriptor_Validate_WrongWindow)
-{
-    constexpr unsigned int inputNum = 1;
-    constexpr unsigned int inputChannels = 3;
-    constexpr unsigned int inputHeight   = 32;
-    constexpr unsigned int inputWidth    = 24;
-
-    constexpr unsigned int outputNum = 1;
-    constexpr unsigned int outputChannels = 3;
-    constexpr unsigned int outputHeight = 32;
-    constexpr unsigned int outputWidth  = 24;
-
-
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[]  = {inputNum, inputChannels, inputHeight, inputWidth};
-    unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth};
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    MergerQueueDescriptor invalidData;
-    WorkloadInfo          invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // Invalid, since it has only 3 dimensions while the input tensor is 4d.
-    std::vector<unsigned int> wOrigin = {0, 0, 0};
-    armnn::MergerQueueDescriptor::ViewOrigin window(wOrigin);
-    invalidData.m_ViewOrigins.push_back(window);
-
-    BOOST_TEST_INFO("Invalid argument exception is expected, because merge window dimensionality does not "
-        "match input.");
-    BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-    // Invalid, since window extends past the boundary of output tensor.
-    std::vector<unsigned int> wOrigin3 = {0, 0, 15, 0};
-    armnn::MergerQueueDescriptor::ViewOrigin window3(wOrigin3);
-    invalidData.m_ViewOrigins[0] = window3;
-    BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight");
-    BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-
-    std::vector<unsigned int> wOrigin4 = {0, 0, 0, 0};
-    armnn::MergerQueueDescriptor::ViewOrigin window4(wOrigin4);
-    invalidData.m_ViewOrigins[0] = window4;
-
-    std::vector<unsigned int> wOrigin5 = {1, 16, 20, 2};
-    armnn::MergerQueueDescriptor::ViewOrigin window5(wOrigin4);
-    invalidData.m_ViewOrigins.push_back(window5);
-
-    BOOST_TEST_INFO("Invalid exception due to number of merge windows not matching number of inputs.");
-    BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputNumbers)
-{
-    armnn::TensorInfo input1TensorInfo;
-    armnn::TensorInfo input2TensorInfo;
-    armnn::TensorInfo input3TensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape[]  = {1, 1, 1, 1};
-
-    input1TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    input2TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    input3TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32);
-
-    AdditionQueueDescriptor invalidData;
-    WorkloadInfo            invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // Too few inputs.
-    BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-
-    AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
-
-    // Correct.
-    BOOST_CHECK_NO_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo));
-
-    AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr);
-
-    // Too many inputs.
-    BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputShapes)
-{
-    armnn::TensorInfo input1TensorInfo;
-    armnn::TensorInfo input2TensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int shape1[] = {1, 1, 2, 1};
-    unsigned int shape2[] = {1, 1, 3, 2};
-
-    // Incompatible shapes even with broadcasting.
-    {
-        input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-        input2TensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-
-        AdditionQueueDescriptor invalidData;
-        WorkloadInfo            invalidInfo;
-
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-        BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-
-    // Output size not compatible with input sizes.
-    {
-        input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-        input2TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32);
-
-        AdditionQueueDescriptor invalidData;
-        WorkloadInfo            invalidInfo;
-
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-        // Output differs.
-        BOOST_CHECK_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-}
-
-BOOST_AUTO_TEST_CASE(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch)
-{
-    armnn::TensorInfo input0TensorInfo;
-    armnn::TensorInfo input1TensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    constexpr unsigned int input0Shape[] = { 2, 2, 4, 4 };
-    constexpr std::size_t dimensionCount = std::extent<decltype(input0Shape)>::value;
-
-    // Checks dimension consistency for input tensors.
-    for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)
-    {
-        unsigned int input1Shape[dimensionCount];
-        for (unsigned int i = 0; i < dimensionCount; ++i)
-        {
-            input1Shape[i] = input0Shape[i];
-        }
-
-        ++input1Shape[dimIndex];
-
-        input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-        input1TensorInfo = armnn::TensorInfo(dimensionCount, input1Shape, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-
-        MultiplicationQueueDescriptor invalidData;
-        WorkloadInfo                  invalidInfo;
-
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-
-        BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-
-    // Checks dimension consistency for input and output tensors.
-    for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex)
-    {
-        unsigned int outputShape[dimensionCount];
-        for (unsigned int i = 0; i < dimensionCount; ++i)
-        {
-            outputShape[i] = input0Shape[i];
-        }
-
-        ++outputShape[dimIndex];
-
-        input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-        input1TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32);
-        outputTensorInfo = armnn::TensorInfo(dimensionCount, outputShape, armnn::DataType::Float32);
-
-        MultiplicationQueueDescriptor invalidData;
-        WorkloadInfo                  invalidInfo;
-
-        AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr);
-        AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr);
-
-        BOOST_CHECK_THROW(RefMultiplicationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-    }
-}
-
-BOOST_AUTO_TEST_CASE(ReshapeQueueDescriptor_Validate_MismatchingNumElements)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    // The input and output shapes should have the same number of elements, but these don't.
-    unsigned int inputShape[] = { 1, 1, 2, 3 };
-    unsigned int outputShape[] = { 1, 1, 1, 2 };
-
-    inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32);
-
-    ReshapeQueueDescriptor invalidData;
-    WorkloadInfo           invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    // InvalidArgumentException is expected, because the number of elements don't match.
-    BOOST_CHECK_THROW(RefReshapeFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
-}
-
-
-BOOST_AUTO_TEST_CASE(LstmQueueDescriptor_Validate)
-{
-    armnn::TensorInfo inputTensorInfo;
-    armnn::TensorInfo outputTensorInfo;
-
-    unsigned int inputShape[] = { 1, 2 };
-    unsigned int outputShape[] = { 1 };
-
-    inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32);
-    outputTensorInfo = armnn::TensorInfo(1, outputShape, armnn::DataType::Float32);
-
-    LstmQueueDescriptor invalidData;
-    WorkloadInfo        invalidInfo;
-
-    AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr);
-    AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr);
-
-    BOOST_CHECK_THROW(invalidData.Validate(invalidInfo), armnn::InvalidArgumentException);
-}
-
-BOOST_AUTO_TEST_SUITE_END()