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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#if (defined(__aarch64__)) || (defined(__x86_64__)) // disable test failing on FireFly/Armv7
#include "ClWorkloadFactoryHelper.hpp"
#include <TensorHelpers.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
#include <cl/ClContextControl.hpp>
#include <cl/ClWorkloadFactory.hpp>
#include <cl/OpenClTimer.hpp>
#include <armnnTestUtils/TensorCopyUtils.hpp>
#include <armnnTestUtils/WorkloadTestUtils.hpp>
#include <arm_compute/runtime/CL/CLScheduler.h>
#include <doctest/doctest.h>
#include <iostream>
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, nullptr, true) {}
~OpenClFixture() {}
ClContextControl m_ClContextControl;
};
TEST_CASE_FIXTURE(OpenClFixture, "OpenClTimerBatchNorm")
{
//using FactoryType = ClWorkloadFactory;
auto memoryManager = ClWorkloadFactoryHelper::GetMemoryManager();
ClWorkloadFactory workloadFactory = ClWorkloadFactoryHelper::GetFactory(memoryManager);
const unsigned int width = 2;
const unsigned int height = 3;
const unsigned int channels = 2;
const unsigned int num = 1;
TensorInfo inputTensorInfo( {num, channels, height, width}, DataType::Float32);
TensorInfo outputTensorInfo({num, channels, height, width}, DataType::Float32);
TensorInfo tensorInfo({channels}, DataType::Float32);
std::vector<float> input =
{
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
std::vector<float> mean = { 3.f, -2.f };
std::vector<float> variance = { 4.f, 9.f };
std::vector<float> beta = { 3.f, 2.f };
std::vector<float> gamma = { 2.f, 1.f };
ARMNN_NO_DEPRECATE_WARN_BEGIN
std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
ARMNN_NO_DEPRECATE_WARN_END
BatchNormalizationQueueDescriptor data;
WorkloadInfo info;
ScopedTensorHandle meanTensor(tensorInfo);
ScopedTensorHandle varianceTensor(tensorInfo);
ScopedTensorHandle betaTensor(tensorInfo);
ScopedTensorHandle gammaTensor(tensorInfo);
AllocateAndCopyDataToITensorHandle(&meanTensor, mean.data());
AllocateAndCopyDataToITensorHandle(&varianceTensor, variance.data());
AllocateAndCopyDataToITensorHandle(&betaTensor, beta.data());
AllocateAndCopyDataToITensorHandle(&gammaTensor, gamma.data());
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.CreateWorkload(LayerType::BatchNormalization, data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), input.data());
OpenClTimer openClTimer;
CHECK_EQ(openClTimer.GetName(), "OpenClKernelTimer");
//Start the timer
openClTimer.Start();
//Execute the workload
workload->Execute();
//Stop the timer
openClTimer.Stop();
CHECK_EQ(openClTimer.GetMeasurements().size(), 1);
CHECK_EQ(openClTimer.GetMeasurements().front().m_Name,
"OpenClKernelTimer/0: batchnormalization_layer_nchw GWS[1,3,2]");
CHECK(openClTimer.GetMeasurements().front().m_Value > 0);
}
#endif //aarch64 or x86_64
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