Revert "IVGCVSW-6699 Add End-To-End tests to CL to check re-use of allocated data"

This reverts commit ae91a5e058da31e912c0768f516b2ef013c3b39e.

Reason for revert: There is an intermittent failure which may affect the review jobs. If it becomes a problem merge this and ping Dave :)

Change-Id: Ie0d56e4184a525e55cd2ae59042d060bd5609017

1 files changed, 0 insertions, 442 deletions

diff --git a/src/backends/cl/test/ClImportTensorHandleTests.cpp b/src/backends/cl/test/ClImportTensorHandleTests.cpp
index d8ac3e799b..27cd1365fd 100644
--- a/src/backends/cl/test/ClImportTensorHandleTests.cpp
+++ b/src/backends/cl/test/ClImportTensorHandleTests.cpp
@@ -427,446 +427,4 @@ TEST_CASE_FIXTURE(ClContextControlFixture, "ClForceImportConv2dEndToEnd")
     CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end()));
 }
 
-TEST_CASE_FIXTURE(ClContextControlFixture, "ClForceImportRepeatedInferencesEndToEndTest")
-{
-/*
- * This is a test to check the functionality of the Forced Import functionality when using repeated inferences that
- * require switching from importing to copy. For the first inference we create aligned Pointers and check they are
- * imported correctly. For the second we use similar pointers but don't use PreImporting to force fall back to copy.
- */
-    // Create runtime in which test will run
-    IRuntime::CreationOptions options;
-    IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    // build up the structure of the network
-    INetworkPtr network(INetwork::Create());
-
-    armnn::TensorInfo inputInfo({ 1, 3, 4, 1 }, DataType::Float32);
-    armnn::TensorInfo kernelInfo({ 1, 3, 3, 1 }, DataType::Float32);
-    armnn::TensorInfo outputInfo({ 1, 3, 4, 1 }, DataType::Float32);
-
-    kernelInfo.SetConstant(true);
-
-    std::vector<float> kernel =
-    {
-        4, 5, 6,
-        0, 0, 0,
-        3, 2, 1
-    };
-
-    const std::vector<float> expectedOutput =
-    {
-        23, 41, 33, 21,
-        44, 65, 76, 52,
-        82, 85, 79, 42
-    };
-
-    unsigned int numElements = inputInfo.GetNumElements();
-    size_t totalBytes = numElements * sizeof(float);
-
-    IConnectableLayer* const inputLayer = network->AddInputLayer(0, "input");
-    ARMNN_ASSERT(inputLayer);
-
-    armnn::ConstTensor weights(kernelInfo, kernel);
-
-    armnn::Convolution2dDescriptor convDesc2d;
-    convDesc2d.m_StrideX = 1;
-    convDesc2d.m_StrideY = 1;
-    convDesc2d.m_PadLeft = 1;
-    convDesc2d.m_PadRight = 1;
-    convDesc2d.m_PadTop = 1;
-    convDesc2d.m_PadBottom = 1;
-    convDesc2d.m_DataLayout = DataLayout::NHWC;
-    armnn::IConnectableLayer* const convLayer = network->AddConvolution2dLayer(convDesc2d,
-                                                                          weights,
-                                                                          armnn::EmptyOptional(),
-                                                                          "conv");
-    ARMNN_ASSERT(convLayer);
-
-    inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
-    IConnectableLayer* output = network->AddOutputLayer(0, "output");
-    convLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-    convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
-    // Optimize the network
-    OptimizerOptions optOptions;
-    optOptions.m_ImportEnabled = false;
-    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
-    IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optOptions);
-    CHECK(optNet);
-
-    // Loads it into the runtime.
-    NetworkId netId;
-    std::string ignoredErrorMessage;
-    // Enable Importing
-    INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined);
-    runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);
-
-    // Creates structures for input & output
-    const size_t alignment =
-        arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
-    size_t space = totalBytes + alignment + alignment;
-    auto inputData = std::make_unique<uint8_t[]>(space);
-    void* alignedInputPtr = inputData.get();
-    CHECK(std::align(alignment, totalBytes, alignedInputPtr, space));
-
-    // Fill input with values
-    auto* inputPtr = reinterpret_cast<float*>(alignedInputPtr);
-    inputPtr[0] = 1;
-    inputPtr[1] = 5;
-    inputPtr[2] = 2;
-    inputPtr[3] = 3;
-    inputPtr[4] = 8;
-    inputPtr[5] = 7;
-    inputPtr[6] = 3;
-    inputPtr[7] = 6;
-    inputPtr[8] = 3;
-    inputPtr[9] = 3;
-    inputPtr[10] = 9;
-    inputPtr[11] = 1;
-
-
-    auto outputData = std::make_unique<uint8_t[]>(space);
-    void* alignedOutputPtr = outputData.get();
-    CHECK(std::align(alignment, totalBytes, alignedOutputPtr, space));
-    auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr);
-    std::fill_n(outputPtr, numElements, -10.0f);
-
-    TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(netId, 0);
-    inputTensorInfo.SetConstant(true);
-    InputTensors inputTensors
-    {
-        {0,armnn::ConstTensor(inputTensorInfo, alignedInputPtr)},
-    };
-    OutputTensors outputTensors
-    {
-        {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), alignedOutputPtr)}
-    };
-
-    runtime->GetProfiler(netId)->EnableProfiling(true);
-
-    INFO("Run ImportInputs");
-    std::vector<ImportedInputId> importedInputIds =
-        runtime->ImportInputs(netId, inputTensors, MemorySource::Malloc);
-    std::vector<ImportedOutputId> importedOutputIds =
-        runtime->ImportOutputs(netId, outputTensors, MemorySource::Malloc);
-
-    // Do the inference
-    runtime->EnqueueWorkload(netId, inputTensors, outputTensors, importedInputIds, importedOutputIds);
-
-    // Retrieve the Profiler.AnalyzeEventsAndWriteResults() output to get the workload execution
-    ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance();
-    std::stringstream ss;
-    profilerManager.GetProfiler()->AnalyzeEventsAndWriteResults(ss);
-    std::string dump = ss.str();
-
-    // Contains Convolution2dWorkload
-    std::size_t found = dump.find("Convolution2dWorkload");
-    CHECK(found != std::string::npos);
-
-    // Contains SyncMemGeneric
-    found = dump.find("SyncMemGeneric");
-    CHECK(found != std::string::npos);
-
-    // Does not contain CopyMemGeneric
-    found = dump.find("CopyMemGeneric");
-    CHECK(found == std::string::npos);
-
-    // Sync the outputs so we can read the data
-    arm_compute::CLScheduler::get().sync();
-
-    // Check output is as expected
-    auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr);
-    CHECK(outputResult);
-    CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end()));
-
-    // Repeat the inference, with new tensors and without using PreImporting to force it to fall back to copying
-
-    // Creates structures for input & output
-    auto inputDataCopy = std::make_unique<uint8_t[]>(space);
-    void* alignedInputCopyPtr = inputDataCopy.get();
-    CHECK(std::align(alignment, totalBytes, alignedInputCopyPtr, space));
-
-    // Fill input with values
-    auto* inputCopyPtr = reinterpret_cast<float*>(alignedInputCopyPtr);
-    inputCopyPtr[0] = 1;
-    inputCopyPtr[1] = 5;
-    inputCopyPtr[2] = 2;
-    inputCopyPtr[3] = 3;
-    inputCopyPtr[4] = 8;
-    inputCopyPtr[5] = 7;
-    inputCopyPtr[6] = 3;
-    inputCopyPtr[7] = 6;
-    inputCopyPtr[8] = 3;
-    inputCopyPtr[9] = 3;
-    inputCopyPtr[10] = 9;
-    inputCopyPtr[11] = 1;
-
-    // Output pre-filled with -10.0f
-    auto outputDataCopy = std::make_unique<uint8_t[]>(space);
-    void* alignedOutputCopyPtr = outputDataCopy.get();
-    CHECK(std::align(alignment, totalBytes, alignedOutputCopyPtr, space));
-    auto* outputCopyPtr = reinterpret_cast<float*>(alignedOutputCopyPtr);
-    std::fill_n(outputCopyPtr, numElements, -10.0f);
-
-    InputTensors inputTensorsCopy
-    {
-        {0,armnn::ConstTensor(inputTensorInfo, alignedInputCopyPtr)},
-    };
-    OutputTensors outputTensorsCopy
-    {
-        {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), alignedOutputCopyPtr)}
-    };
-
-    // Do the inference without any pre-imported input/output ids
-    runtime->EnqueueWorkload(netId, inputTensorsCopy, outputTensorsCopy);
-    // Sync the outputs so we can read the data
-    arm_compute::CLScheduler::get().sync();
-
-    // Check the output is correct
-    outputResult = reinterpret_cast<float*>(alignedOutputCopyPtr);
-    CHECK(outputResult);
-    CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end()));
-
-    // Query the profiler again, this will contain the results of both inferences
-    profilerManager.GetProfiler()->AnalyzeEventsAndWriteResults(ss);
-    dump = ss.str();
-
-    // Contains Convolution2dWorkload
-    found = dump.find("Convolution2dWorkload");
-    CHECK(found != std::string::npos);
-
-    // Should still contain the SyncMemGeneric
-    found = dump.find("SyncMemGeneric");
-    CHECK(found != std::string::npos);
-
-    // Should now also contain a CopyMemGeneric
-    found = dump.find("CopyMemGeneric");
-    CHECK(found != std::string::npos);
-    runtime->UnloadNetwork(netId);
-}
-
-TEST_CASE_FIXTURE(ClContextControlFixture, "ClForceImportRepeatedInferencesInvertedEndToEndTest")
-{
-/*
- * This test is similar to the test above but instead of importing and then copying, we start by copying and then do
- * the import.
- */
-    // Create runtime in which test will run
-    IRuntime::CreationOptions options;
-    IRuntimePtr runtime(armnn::IRuntime::Create(options));
-
-    // build up the structure of the network
-    INetworkPtr network(INetwork::Create());
-
-    armnn::TensorInfo inputInfo({ 1, 3, 4, 1 }, DataType::Float32);
-    armnn::TensorInfo kernelInfo({ 1, 3, 3, 1 }, DataType::Float32);
-    armnn::TensorInfo outputInfo({ 1, 3, 4, 1 }, DataType::Float32);
-
-    kernelInfo.SetConstant(true);
-
-    std::vector<float> kernel =
-    {
-        4, 5, 6,
-        0, 0, 0,
-        3, 2, 1
-    };
-
-    const std::vector<float> expectedOutput =
-    {
-        23, 41, 33, 21,
-        44, 65, 76, 52,
-        82, 85, 79, 42
-    };
-
-    unsigned int numElements = inputInfo.GetNumElements();
-    size_t totalBytes = numElements * sizeof(float);
-
-    IConnectableLayer* const inputLayer = network->AddInputLayer(0, "input");
-    ARMNN_ASSERT(inputLayer);
-
-    armnn::ConstTensor weights(kernelInfo, kernel);
-
-    armnn::Convolution2dDescriptor convDesc2d;
-    convDesc2d.m_StrideX = 1;
-    convDesc2d.m_StrideY = 1;
-    convDesc2d.m_PadLeft = 1;
-    convDesc2d.m_PadRight = 1;
-    convDesc2d.m_PadTop = 1;
-    convDesc2d.m_PadBottom = 1;
-    convDesc2d.m_DataLayout = DataLayout::NHWC;
-    armnn::IConnectableLayer* const convLayer = network->AddConvolution2dLayer(convDesc2d,
-                                                                          weights,
-                                                                          armnn::EmptyOptional(),
-                                                                          "conv");
-    ARMNN_ASSERT(convLayer);
-
-    inputLayer->GetOutputSlot(0).Connect(convLayer->GetInputSlot(0));
-    inputLayer->GetOutputSlot(0).SetTensorInfo(inputInfo);
-
-    IConnectableLayer* output = network->AddOutputLayer(0, "output");
-    convLayer->GetOutputSlot(0).Connect(output->GetInputSlot(0));
-    convLayer->GetOutputSlot(0).SetTensorInfo(outputInfo);
-
-    // Optimize the network
-    OptimizerOptions optOptions;
-    optOptions.m_ImportEnabled = false;
-    std::vector<armnn::BackendId> backends = {armnn::Compute::GpuAcc};
-    IOptimizedNetworkPtr optNet = Optimize(*network, backends, runtime->GetDeviceSpec(), optOptions);
-    CHECK(optNet);
-
-    // Loads it into the runtime.
-    NetworkId netId;
-    std::string ignoredErrorMessage;
-    // Enable Importing
-    INetworkProperties networkProperties(false, MemorySource::Undefined, MemorySource::Undefined);
-    runtime->LoadNetwork(netId, std::move(optNet), ignoredErrorMessage, networkProperties);
-
-    // Creates structures for input & output
-    const size_t alignment =
-        arm_compute::CLKernelLibrary::get().get_device().getInfo<CL_DEVICE_GLOBAL_MEM_CACHELINE_SIZE>();
-    size_t space = totalBytes + alignment + alignment;
-    auto inputData = std::make_unique<uint8_t[]>(space);
-    void* alignedInputPtr = inputData.get();
-    CHECK(std::align(alignment, totalBytes, alignedInputPtr, space));
-
-    // Fill input with values
-    auto* inputPtr = reinterpret_cast<float*>(alignedInputPtr);
-    inputPtr[0] = 1;
-    inputPtr[1] = 5;
-    inputPtr[2] = 2;
-    inputPtr[3] = 3;
-    inputPtr[4] = 8;
-    inputPtr[5] = 7;
-    inputPtr[6] = 3;
-    inputPtr[7] = 6;
-    inputPtr[8] = 3;
-    inputPtr[9] = 3;
-    inputPtr[10] = 9;
-    inputPtr[11] = 1;
-
-    // Create output buffer and fill it with -10.0f
-    auto outputData = std::make_unique<uint8_t[]>(space);
-    void* alignedOutputPtr = outputData.get();
-    CHECK(std::align(alignment, totalBytes, alignedOutputPtr, space));
-    auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr);
-    std::fill_n(outputPtr, numElements, -10.0f);
-
-    TensorInfo inputTensorInfo = runtime->GetInputTensorInfo(netId, 0);
-    inputTensorInfo.SetConstant(true);
-    InputTensors inputTensors
-    {
-        {0,armnn::ConstTensor(inputTensorInfo, alignedInputPtr)},
-    };
-    OutputTensors outputTensors
-    {
-        {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), alignedOutputPtr)}
-    };
-
-    runtime->GetProfiler(netId)->EnableProfiling(true);
-
-    // Do the inference without any pre-imported inputs/outputs
-    runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
-
-    // Retrieve the Profiler.AnalyzeEventsAndWriteResults() output to get the workload execution
-    ProfilerManager& profilerManager = armnn::ProfilerManager::GetInstance();
-    std::stringstream ss;
-    profilerManager.GetProfiler()->AnalyzeEventsAndWriteResults(ss);
-    std::string dump = ss.str();
-
-    // Contains Convolution2dWorkload
-    std::size_t found = dump.find("Convolution2dWorkload");
-    CHECK(found != std::string::npos);
-
-    // Does not contain SyncMemGeneric
-    found = dump.find("SyncMemGeneric");
-    CHECK(found == std::string::npos);
-
-    // Does contain CopyMemGeneric
-    found = dump.find("CopyMemGeneric");
-    CHECK(found != std::string::npos);
-
-    // Sync the outputs so we can read the data
-    arm_compute::CLScheduler::get().sync();
-
-    // Check output is as expected
-    auto* outputResult = reinterpret_cast<float*>(alignedOutputPtr);
-    CHECK(outputResult);
-    CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end()));
-
-    // Repeat the inference, with new tensors and while using pre-importing to force it to import
-
-    // Creates structures for input & output
-    auto inputDataImport = std::make_unique<uint8_t[]>(space);
-    void* alignedInputImportPtr = inputDataImport.get();
-    CHECK(std::align(alignment, totalBytes, alignedInputImportPtr, space));
-
-    // Fill input with values
-    auto* inputImportPtr = reinterpret_cast<float*>(alignedInputImportPtr);
-    inputImportPtr[0] = 1;
-    inputImportPtr[1] = 5;
-    inputImportPtr[2] = 2;
-    inputImportPtr[3] = 3;
-    inputImportPtr[4] = 8;
-    inputImportPtr[5] = 7;
-    inputImportPtr[6] = 3;
-    inputImportPtr[7] = 6;
-    inputImportPtr[8] = 3;
-    inputImportPtr[9] = 3;
-    inputImportPtr[10] = 9;
-    inputImportPtr[11] = 1;
-
-    // Output pre-filled with -10.0f
-    auto outputDataImport = std::make_unique<uint8_t[]>(space);
-    void* alignedOutputImportPtr = outputDataImport.get();
-    CHECK(std::align(alignment, totalBytes, alignedOutputImportPtr, space));
-    auto* outputImportPtr = reinterpret_cast<float*>(alignedOutputImportPtr);
-    std::fill_n(outputImportPtr, numElements, -10.0f);
-
-    InputTensors inputTensorsImport
-    {
-        {0,armnn::ConstTensor(inputTensorInfo, alignedInputImportPtr)},
-    };
-    OutputTensors outputTensorsImport
-    {
-        {0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), alignedOutputImportPtr)}
-    };
-
-    INFO("Run ImportInputs");
-    std::vector<ImportedInputId> importedInputIds =
-        runtime->ImportInputs(netId, inputTensorsImport, MemorySource::Malloc);
-    std::vector<ImportedOutputId> importedOutputIds =
-        runtime->ImportOutputs(netId, outputTensorsImport, MemorySource::Malloc);
-
-    // Do the inference with pre-imported inputs/outputs
-    runtime->EnqueueWorkload(netId, inputTensorsImport, outputTensorsImport, importedInputIds, importedOutputIds);
-    // Sync the outputs so we can read the data
-    arm_compute::CLScheduler::get().sync();
-
-    // Check the output is correct
-    outputResult = reinterpret_cast<float*>(alignedOutputImportPtr);
-    CHECK(outputResult);
-    CHECK(std::equal(outputResult, outputResult + numElements, expectedOutput.begin(), expectedOutput.end()));
-
-
-    // Query the profiler again, this will contain the results of both inferences
-    profilerManager.GetProfiler()->AnalyzeEventsAndWriteResults(ss);
-    dump = ss.str();
-
-    // Contains Convolution2dWorkload
-    found = dump.find("Convolution2dWorkload");
-    CHECK(found != std::string::npos);
-
-    // Should now contain the SyncMemGeneric
-    found = dump.find("SyncMemGeneric");
-    CHECK(found != std::string::npos);
-
-    // Should still contain a CopyMemGeneric from the first inference
-    found = dump.find("CopyMemGeneric");
-    CHECK(found != std::string::npos);
-    runtime->UnloadNetwork(netId);
-}
-
 }