IVGCVSW-1938: Move backend-specific source files to the corresponding backend

Change-Id: I558a9a007604afc55e536d877f8da7d0215cc9c3

4 files changed, 0 insertions, 463 deletions

diff --git a/src/armnn/test/CreateWorkloadClNeon.hpp b/src/armnn/test/CreateWorkloadClNeon.hpp
deleted file mode 100644
index 56de085f8e..0000000000
--- a/src/armnn/test/CreateWorkloadClNeon.hpp
+++ /dev/null
@@ -1,108 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-#pragma once
-
-#include "CreateWorkload.hpp"
-
-#include <backends/MemCopyWorkload.hpp>
-#include <backends/reference/RefWorkloadFactory.hpp>
-
-#if ARMCOMPUTECL_ENABLED
-#include <backends/cl/ClTensorHandle.hpp>
-#endif
-
-#if ARMCOMPUTENEON_ENABLED
-#include <backends/neon/NeonTensorHandle.hpp>
-#endif
-
-
-using namespace armnn;
-
-namespace
-{
-
-using namespace std;
-
-template<typename IComputeTensorHandle>
-boost::test_tools::predicate_result CompareTensorHandleShape(IComputeTensorHandle*               tensorHandle,
-                                                             std::initializer_list<unsigned int> expectedDimensions)
-{
-    arm_compute::ITensorInfo* info = tensorHandle->GetTensor().info();
-
-    auto infoNumDims = info->num_dimensions();
-    auto numExpectedDims = expectedDimensions.size();
-    if (infoNumDims != numExpectedDims)
-    {
-        boost::test_tools::predicate_result res(false);
-        res.message() << "Different number of dimensions [" << info->num_dimensions()
-                      << "!=" << expectedDimensions.size() << "]";
-        return res;
-    }
-
-    size_t i = info->num_dimensions() - 1;
-
-    for (unsigned int expectedDimension : expectedDimensions)
-    {
-        if (info->dimension(i) != expectedDimension)
-        {
-            boost::test_tools::predicate_result res(false);
-            res.message() << "Different dimension [" << info->dimension(i) << "!=" << expectedDimension << "]";
-            return res;
-        }
-
-        i--;
-    }
-
-    return true;
-}
-
-template<typename IComputeTensorHandle>
-void CreateMemCopyWorkloads(IWorkloadFactory& factory)
-{
-    Graph graph;
-    RefWorkloadFactory refFactory;
-
-    // Creates the layers we're testing.
-    Layer* const layer1 = graph.AddLayer<MemCopyLayer>("layer1");
-    Layer* const layer2 = graph.AddLayer<MemCopyLayer>("layer2");
-
-    // Creates extra layers.
-    Layer* const input = graph.AddLayer<InputLayer>(0, "input");
-    Layer* const output = graph.AddLayer<OutputLayer>(0, "output");
-
-    // Connects up.
-    TensorInfo tensorInfo({2, 3}, DataType::Float32);
-    Connect(input, layer1, tensorInfo);
-    Connect(layer1, layer2, tensorInfo);
-    Connect(layer2, output, tensorInfo);
-
-    input->CreateTensorHandles(graph, refFactory);
-    layer1->CreateTensorHandles(graph, factory);
-    layer2->CreateTensorHandles(graph, refFactory);
-    output->CreateTensorHandles(graph, refFactory);
-
-    // make the workloads and check them
-    auto workload1 = MakeAndCheckWorkload<CopyMemGenericWorkload>(*layer1, graph, factory);
-    auto workload2 = MakeAndCheckWorkload<CopyMemGenericWorkload>(*layer2, graph, refFactory);
-
-    MemCopyQueueDescriptor queueDescriptor1 = workload1->GetData();
-    BOOST_TEST(queueDescriptor1.m_Inputs.size() == 1);
-    BOOST_TEST(queueDescriptor1.m_Outputs.size() == 1);
-    auto inputHandle1  = boost::polymorphic_downcast<ConstCpuTensorHandle*>(queueDescriptor1.m_Inputs[0]);
-    auto outputHandle1 = boost::polymorphic_downcast<IComputeTensorHandle*>(queueDescriptor1.m_Outputs[0]);
-    BOOST_TEST((inputHandle1->GetTensorInfo() == TensorInfo({2, 3}, DataType::Float32)));
-    BOOST_TEST(CompareTensorHandleShape<IComputeTensorHandle>(outputHandle1, {2, 3}));
-
-
-    MemCopyQueueDescriptor queueDescriptor2 = workload2->GetData();
-    BOOST_TEST(queueDescriptor2.m_Inputs.size() == 1);
-    BOOST_TEST(queueDescriptor2.m_Outputs.size() == 1);
-    auto inputHandle2  = boost::polymorphic_downcast<IComputeTensorHandle*>(queueDescriptor2.m_Inputs[0]);
-    auto outputHandle2 = boost::polymorphic_downcast<CpuTensorHandle*>(queueDescriptor2.m_Outputs[0]);
-    BOOST_TEST(CompareTensorHandleShape<IComputeTensorHandle>(inputHandle2, {2, 3}));
-    BOOST_TEST((outputHandle2->GetTensorInfo() == TensorInfo({2, 3}, DataType::Float32)));
-}
-
-} //namespace
\ No newline at end of file
diff --git a/src/armnn/test/FP16SupportTest.cpp b/src/armnn/test/FP16SupportTest.cpp
deleted file mode 100644
index 2706d1f363..0000000000
--- a/src/armnn/test/FP16SupportTest.cpp
+++ /dev/null
@@ -1,115 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Descriptors.hpp>
-#include <armnn/IRuntime.hpp>
-#include <armnn/INetwork.hpp>
-#include <armnnUtils/Half.hpp>
-
-#include <Graph.hpp>
-#include <Optimizer.hpp>
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/test/QuantizeHelper.hpp>
-
-#include <boost/core/ignore_unused.hpp>
-#include <boost/test/unit_test.hpp>
-
-#include <set>
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(Fp16Support)
-
-BOOST_AUTO_TEST_CASE(Fp16DataTypeSupport)
-{
-    Graph graph;
-
-    Layer* const inputLayer1 = graph.AddLayer<InputLayer>(1, "input1");
-    Layer* const inputLayer2 = graph.AddLayer<InputLayer>(2, "input2");
-
-    Layer* const additionLayer = graph.AddLayer<AdditionLayer>("addition");
-    Layer* const outputLayer = graph.AddLayer<armnn::OutputLayer>(0, "output");
-
-    TensorInfo fp16TensorInfo({1, 2, 3, 5}, armnn::DataType::Float16);
-    inputLayer1->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(0));
-    inputLayer2->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(1));
-    additionLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
-    inputLayer1->GetOutputSlot().SetTensorInfo(fp16TensorInfo);
-    inputLayer2->GetOutputSlot().SetTensorInfo(fp16TensorInfo);
-    additionLayer->GetOutputSlot().SetTensorInfo(fp16TensorInfo);
-
-    BOOST_CHECK(inputLayer1->GetOutputSlot(0).GetTensorInfo().GetDataType() == armnn::DataType::Float16);
-    BOOST_CHECK(inputLayer2->GetOutputSlot(0).GetTensorInfo().GetDataType() == armnn::DataType::Float16);
-    BOOST_CHECK(additionLayer->GetOutputSlot(0).GetTensorInfo().GetDataType() == armnn::DataType::Float16);
-
-}
-
-BOOST_AUTO_TEST_CASE(Fp16AdditionTest)
-{
-   using namespace half_float::literal;
-   // Create runtime in which test will run
-   IRuntime::CreationOptions options;
-   IRuntimePtr  runtime(IRuntime::Create(options));
-
-   // Builds up the structure of the network.
-   INetworkPtr net(INetwork::Create());
-
-
-   IConnectableLayer* inputLayer1 = net->AddInputLayer(0);
-   IConnectableLayer* inputLayer2 = net->AddInputLayer(1);
-   IConnectableLayer* additionLayer = net->AddAdditionLayer();
-   IConnectableLayer* outputLayer = net->AddOutputLayer(0);
-
-   inputLayer1->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(0));
-   inputLayer2->GetOutputSlot(0).Connect(additionLayer->GetInputSlot(1));
-   additionLayer->GetOutputSlot(0).Connect(outputLayer->GetInputSlot(0));
-
-   //change to float16
-   TensorInfo fp16TensorInfo(TensorShape({4}), DataType::Float16);
-   inputLayer1->GetOutputSlot(0).SetTensorInfo(fp16TensorInfo);
-   inputLayer2->GetOutputSlot(0).SetTensorInfo(fp16TensorInfo);
-   additionLayer->GetOutputSlot(0).SetTensorInfo(fp16TensorInfo);
-
-   // optimize the network
-   std::vector<Compute> backends = {Compute::GpuAcc};
-   IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
-
-   // Loads it into the runtime.
-
-   NetworkId netId;
-   runtime->LoadNetwork(netId, std::move(optNet));
-
-   std::vector<Half> input1Data
-   {
-       1.0_h, 2.0_h, 3.0_h, 4.0_h
-   };
-
-   std::vector<Half> input2Data
-   {
-       100.0_h, 200.0_h, 300.0_h, 400.0_h
-   };
-
-   InputTensors inputTensors
-   {
-       {0,ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())},
-       {1,ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())}
-   };
-
-   std::vector<Half> outputData(input1Data.size());
-   OutputTensors outputTensors
-   {
-       {0,Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
-   };
-
-   // Does the inference.
-   runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
-   // Checks the results.
-   BOOST_TEST(outputData == std::vector<Half>({ 101.0_h, 202.0_h, 303.0_h, 404.0_h})); // Add
-}
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnn/test/NeonTimerTest.cpp b/src/armnn/test/NeonTimerTest.cpp
deleted file mode 100644
index 6d0429c8b9..0000000000
--- a/src/armnn/test/NeonTimerTest.cpp
+++ /dev/null
@@ -1,103 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include <NeonTimer.hpp>
-#include "TensorHelpers.hpp"
-
-#include <armnn/ArmNN.hpp>
-#include <armnn/Tensor.hpp>
-#include <armnn/TypesUtils.hpp>
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/neon/NeonWorkloadFactory.hpp>
-#include <backends/WorkloadFactory.hpp>
-#include <backends/test/LayerTests.hpp>
-#include <backends/test/TensorCopyUtils.hpp>
-#include <backends/test/WorkloadTestUtils.hpp>
-
-#include <boost/test/unit_test.hpp>
-#include <cstdlib>
-#include <algorithm>
-
-using namespace armnn;
-
-BOOST_AUTO_TEST_SUITE(NeonTimerInstrument)
-
-
-BOOST_AUTO_TEST_CASE(NeonTimerGetName)
-{
-    NeonTimer neonTimer;
-    BOOST_CHECK_EQUAL(neonTimer.GetName(), "NeonKernelTimer");
-}
-
-BOOST_AUTO_TEST_CASE(NeonTimerMeasure)
-{
-    NeonWorkloadFactory workloadFactory;
-
-    unsigned int inputWidth = 4000u;
-    unsigned int inputHeight = 5000u;
-    unsigned int inputChannels = 1u;
-    unsigned int inputBatchSize = 1u;
-
-    float upperBound = 1.0f;
-    float lowerBound = -1.0f;
-
-    size_t inputSize = inputWidth * inputHeight * inputChannels * inputBatchSize;
-    std::vector<float> inputData(inputSize, 0.f);
-    std::generate(inputData.begin(), inputData.end(), [](){
-        return (static_cast<float>(rand()) / static_cast<float>(RAND_MAX / 3)) + 1.f; });
-
-    unsigned int outputWidth = inputWidth;
-    unsigned int outputHeight = inputHeight;
-    unsigned int outputChannels = inputChannels;
-    unsigned int outputBatchSize = inputBatchSize;
-
-    armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
-        armnn::GetDataType<float>());
-
-    armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
-        armnn::GetDataType<float>());
-
-    LayerTestResult<float, 4> result(inputTensorInfo);
-
-    auto input = MakeTensor<float, 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]);
-
-    NeonTimer neonTimer;
-    // Start the timer.
-    neonTimer.Start();
-    // Execute the workload.
-    workload->Execute();
-    // Stop the timer.
-    neonTimer.Stop();
-
-    std::vector<Measurement> measurements = neonTimer.GetMeasurements();
-
-    BOOST_CHECK_EQUAL(measurements.size(), 2);
-    BOOST_CHECK_EQUAL(measurements[0].m_Name, "NeonKernelTimer/0: NEFillBorderKernel");
-    BOOST_CHECK(measurements[0].m_Value > 0.0);
-    BOOST_CHECK_EQUAL(measurements[1].m_Name, "NeonKernelTimer/1: NEActivationLayerKernel");
-    BOOST_CHECK(measurements[1].m_Value > 0.0);
-}
-
-BOOST_AUTO_TEST_SUITE_END()
diff --git a/src/armnn/test/OpenClTimerTest.cpp b/src/armnn/test/OpenClTimerTest.cpp
deleted file mode 100644
index 76cffec4f3..0000000000
--- a/src/armnn/test/OpenClTimerTest.cpp
+++ /dev/null
@@ -1,137 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#if (defined(__aarch64__)) || (defined(__x86_64__)) // disable test failing on FireFly/Armv7
-
-#include <arm_compute/runtime/CL/CLScheduler.h>
-#include <backends/cl/ClContextControl.hpp>
-#include <backends/cl/ClWorkloadFactory.hpp>
-#include <backends/CpuTensorHandle.hpp>
-#include <boost/format.hpp>
-#include <iostream>
-#include <OpenClTimer.hpp>
-#include <backends/test/TensorCopyUtils.hpp>
-#include "TensorHelpers.hpp"
-#include <boost/test/unit_test.hpp>
-#include <backends/WorkloadFactory.hpp>
-#include <backends/test/WorkloadTestUtils.hpp>
-
-using namespace armnn;
-
-struct OpenClFixture
-{
-    // Initialising ClContextControl to ensure OpenCL is loaded correctly for each test case.
-    // NOTE: Profiling needs to be enabled in ClContextControl to be able to obtain execution
-    // times from OpenClTimer.
-    OpenClFixture() : m_ClContextControl(nullptr, true) {}
-    ~OpenClFixture() {}
-
-    ClContextControl m_ClContextControl;
-};
-
-BOOST_FIXTURE_TEST_SUITE(OpenClTimerBatchNorm, OpenClFixture)
-using FactoryType = ClWorkloadFactory;
-
-BOOST_AUTO_TEST_CASE(OpenClTimerBatchNorm)
-{
-    ClWorkloadFactory  workloadFactory;
-
-    const unsigned int width    = 2;
-    const unsigned int height   = 3;
-    const unsigned int channels = 2;
-    const unsigned int num      = 1;
-    int32_t qOffset = 0;
-    float qScale = 0.f;
-
-    TensorInfo inputTensorInfo({num, channels, height, width}, GetDataType<float>());
-    TensorInfo outputTensorInfo({num, channels, height, width}, GetDataType<float>());
-    TensorInfo tensorInfo({channels}, GetDataType<float>());
-
-    // Set quantization parameters if the requested type is a quantized type.
-    if(IsQuantizedType<float>())
-    {
-         inputTensorInfo.SetQuantizationScale(qScale);
-         inputTensorInfo.SetQuantizationOffset(qOffset);
-         outputTensorInfo.SetQuantizationScale(qScale);
-         outputTensorInfo.SetQuantizationOffset(qOffset);
-         tensorInfo.SetQuantizationScale(qScale);
-         tensorInfo.SetQuantizationOffset(qOffset);
-    }
-
-    auto input = MakeTensor<float, 4>(inputTensorInfo,
-    QuantizedVector<float>(qScale, qOffset,
-    {
-        1.f, 4.f,
-        4.f, 2.f,
-        1.f, 6.f,
-
-        1.f, 1.f,
-        4.f, 1.f,
-        -2.f, 4.f
-    }));
-    // these values are per-channel of the input
-    auto mean     = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {3, -2}));
-    auto variance = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {4, 9}));
-    auto beta     = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {3, 2}));
-    auto gamma    = MakeTensor<float, 1>(tensorInfo, QuantizedVector<float>(qScale, qOffset, {2, 1}));
-
-    std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
-    std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
-
-    BatchNormalizationQueueDescriptor data;
-    WorkloadInfo info;
-    ScopedCpuTensorHandle meanTensor(tensorInfo);
-    ScopedCpuTensorHandle varianceTensor(tensorInfo);
-    ScopedCpuTensorHandle betaTensor(tensorInfo);
-    ScopedCpuTensorHandle gammaTensor(tensorInfo);
-
-    AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]);
-    AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]);
-    AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]);
-    AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]);
-
-    AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
-    AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get());
-    data.m_Mean             = &meanTensor;
-    data.m_Variance         = &varianceTensor;
-    data.m_Beta             = &betaTensor;
-    data.m_Gamma            = &gammaTensor;
-    data.m_Parameters.m_Eps = 0.0f;
-
-    // for each channel:
-    // substract mean, divide by standard deviation (with an epsilon to avoid div by 0)
-    // multiply by gamma and add beta
-    std::unique_ptr<IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
-
-    inputHandle->Allocate();
-    outputHandle->Allocate();
-
-    CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
-
-    OpenClTimer openClTimer;
-
-    BOOST_CHECK_EQUAL(openClTimer.GetName(), "OpenClKernelTimer");
-
-    //Start the timer
-    openClTimer.Start();
-
-    //Execute the workload
-    workload->Execute();
-
-    //Stop the timer
-    openClTimer.Stop();
-
-    BOOST_CHECK_EQUAL(openClTimer.GetMeasurements().size(), 1);
-
-    BOOST_CHECK_EQUAL(openClTimer.GetMeasurements().front().m_Name,
-                      "OpenClKernelTimer/0: batchnormalization_layer_nchw GWS[1,3,2]");
-
-    BOOST_CHECK(openClTimer.GetMeasurements().front().m_Value > 0);
-
-}
-
-BOOST_AUTO_TEST_SUITE_END()
-
-#endif //aarch64 or x86_64
\ No newline at end of file