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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnnTestUtils/LayerTestResult.hpp>
#include <armnnUtils/QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <armnn/backends/IBackendInternal.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
#include <armnnTestUtils/TensorCopyUtils.hpp>
#include <backendsCommon/test/WorkloadFactoryHelper.hpp>
#include <armnnTestUtils/WorkloadTestUtils.hpp>
#include <armnnTestUtils/TensorHelpers.hpp>
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
LayerTestResult<T, 4> PreluTest(
armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory)
{
IgnoreUnused(memoryManager);
armnn::TensorInfo inputTensorInfo ({ 1, 2, 2, 3 }, ArmnnType);
armnn::TensorInfo alphaTensorInfo ({ 1, 1, 1, 3 }, ArmnnType);
armnn::TensorInfo outputTensorInfo({ 1, 2, 2, 3 }, ArmnnType);
if (armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(0.25f);
inputTensorInfo.SetQuantizationOffset(128);
alphaTensorInfo.SetQuantizationScale(0.25f);
alphaTensorInfo.SetQuantizationOffset(50);
outputTensorInfo.SetQuantizationScale(0.5f);
outputTensorInfo.SetQuantizationOffset(120);
}
std::vector<float> inputData
{
// Expected quantized values:
// 128, 128, 128, 132, 132, 132, 124, 124, 124, 120, 120, 120
0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, -1.0f, -1.0f, -1.0f, -2.0f, -2.0f, -2.0f
};
std::vector<float> alphaData
{
// Expected quantized values:
// 50, 54, 58
0.0f, 1.0f, 2.0f
};
std::vector<float> outputExpectedData =
{
// Expected quantized values:
// 20, 120, 120, 122, 122, 122, 120, 118, 116, 120, 116, 112
0.0f, 0.0f, 0.0f, 1.0f, 1.0f, 1.0f, 0.0f, -1.0f, -2.0f, 0.0f, -2.0f, -4.0f
};
std::vector<T> input = armnnUtils::QuantizedVector<T>(inputData,
inputTensorInfo.GetQuantizationScale(),
inputTensorInfo.GetQuantizationOffset());
std::vector<T> alpha = armnnUtils::QuantizedVector<T>(alphaData,
alphaTensorInfo.GetQuantizationScale(),
alphaTensorInfo.GetQuantizationOffset());
std::vector<T> actualOutput(outputTensorInfo.GetNumElements());
std::vector<T> expectedOutput = armnnUtils::QuantizedVector<T>(outputExpectedData,
outputTensorInfo.GetQuantizationScale(),
outputTensorInfo.GetQuantizationOffset());
std::unique_ptr <armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr <armnn::ITensorHandle> alphaHandle = tensorHandleFactory.CreateTensorHandle(alphaTensorInfo);
std::unique_ptr <armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo);
armnn::PreluQueueDescriptor descriptor;
armnn::WorkloadInfo info;
AddInputToWorkload (descriptor, info, inputTensorInfo, inputHandle.get());
AddInputToWorkload (descriptor, info, alphaTensorInfo, alphaHandle.get());
AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get());
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::Prelu,
descriptor,
info);
inputHandle->Allocate();
alphaHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), input.data());
CopyDataToITensorHandle(alphaHandle.get(), alpha.data());
workload->Execute();
CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get());
return LayerTestResult<T, 4>(actualOutput,
expectedOutput,
outputHandle->GetShape(),
outputTensorInfo.GetShape());
}
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