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//
// Copyright © 2023 Arm Ltd and Contributors. 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>>
std::vector<LayerTestResult<T,4>> AddMulAddTest(armnn::IWorkloadFactory& workloadFactory,
const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager,
const armnn::ITensorHandleFactory& tensorHandleFactory,
bool addOutput)
{
using namespace armnn;
IgnoreUnused(memoryManager);
TensorInfo input0TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
TensorInfo input1TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
TensorInfo mulInput1TensorInfo({ 3 }, ArmnnType);
TensorInfo addInput1TensorInfo({ 3 }, ArmnnType);
TensorInfo output0TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
TensorInfo output1TensorInfo({ 1, 2, 2, 3 }, ArmnnType);
if (IsQuantizedType<T>())
{
input0TensorInfo.SetQuantizationScale(0.25f);
input0TensorInfo.SetQuantizationOffset(10);
input1TensorInfo.SetQuantizationScale(0.25f);
input1TensorInfo.SetQuantizationOffset(11);
mulInput1TensorInfo.SetQuantizationScale(0.25f);
mulInput1TensorInfo.SetQuantizationOffset(12);
addInput1TensorInfo.SetQuantizationScale(0.25f);
addInput1TensorInfo.SetQuantizationOffset(13);
output0TensorInfo.SetQuantizationScale(0.5f);
output0TensorInfo.SetQuantizationOffset(14);
output1TensorInfo.SetQuantizationScale(0.5f);
output1TensorInfo.SetQuantizationOffset(15);
}
std::vector<float> input0Data
{
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> input1Data
{
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> mulInput1Data
{
2.0f, 1.0f, 1.0f
};
std::vector<float> addInput1Data
{
3.0f, 0.0f, 0.0f
};
std::vector<float> output0ExpectedData =
{
0.0f, 0.0f, 0.0f,
2.0f, 2.0f, 2.0f,
-2.0f, -2.0f, -2.0f,
-4.0f, -4.0f, -4.0f
};
std::vector<float> output1ExpectedData =
{
3.0f, 0.0f, 0.0f,
7.0f, 2.0f, 2.0f,
-1.0f, -2.0f, -2.0f,
-5.0f, -4.0f, -4.0f
};
std::vector<T> input0 = armnnUtils::QuantizedVector<T>(input0Data,
input0TensorInfo.GetQuantizationScale(),
input0TensorInfo.GetQuantizationOffset());
std::vector<T> input1 = armnnUtils::QuantizedVector<T>(input1Data,
input1TensorInfo.GetQuantizationScale(),
input1TensorInfo.GetQuantizationOffset());
std::vector<T> mulInput1 = armnnUtils::QuantizedVector<T>(mulInput1Data,
mulInput1TensorInfo.GetQuantizationScale(),
mulInput1TensorInfo.GetQuantizationOffset());
std::vector<T> addInput1 = armnnUtils::QuantizedVector<T>(addInput1Data,
addInput1TensorInfo.GetQuantizationScale(),
addInput1TensorInfo.GetQuantizationOffset());
std::vector<T> output0Expected = armnnUtils::QuantizedVector<T>(output0ExpectedData,
output0TensorInfo.GetQuantizationScale(),
output0TensorInfo.GetQuantizationOffset());
std::vector<T> output1Expected = armnnUtils::QuantizedVector<T>(output1ExpectedData,
output1TensorInfo.GetQuantizationScale(),
output1TensorInfo.GetQuantizationOffset());
std::vector<T> output0Actual(output0TensorInfo.GetNumElements());
std::vector<T> output1Actual(output1TensorInfo.GetNumElements());
std::unique_ptr<ITensorHandle> input0Handle = tensorHandleFactory.CreateTensorHandle(input0TensorInfo);
std::unique_ptr<ITensorHandle> input1Handle = tensorHandleFactory.CreateTensorHandle(input1TensorInfo);
std::unique_ptr<ITensorHandle> mulInput1Handle = tensorHandleFactory.CreateTensorHandle(mulInput1TensorInfo);
std::unique_ptr<ITensorHandle> addInput1Handle = tensorHandleFactory.CreateTensorHandle(addInput1TensorInfo);
std::unique_ptr<ITensorHandle> output0Handle = tensorHandleFactory.CreateTensorHandle(output0TensorInfo);
std::unique_ptr<ITensorHandle> output1Handle = tensorHandleFactory.CreateTensorHandle(output1TensorInfo);
uint32_t numOutputs = addOutput ? 2 : 1;
FusedDescriptor descriptor(4, numOutputs, FusedKernelType::AddMulAdd);
FusedQueueDescriptor fusedQueueDescriptor;
fusedQueueDescriptor.m_Parameters = descriptor;
WorkloadInfo info;
AddInputToWorkload (fusedQueueDescriptor, info, input0TensorInfo, input0Handle.get());
AddInputToWorkload (fusedQueueDescriptor, info, input1TensorInfo, input1Handle.get());
AddInputToWorkload (fusedQueueDescriptor, info, mulInput1TensorInfo, mulInput1Handle.get());
AddInputToWorkload (fusedQueueDescriptor, info, addInput1TensorInfo, addInput1Handle.get());
if (addOutput)
{
AddOutputToWorkload(fusedQueueDescriptor, info, output0TensorInfo, output0Handle.get());
}
AddOutputToWorkload(fusedQueueDescriptor, info, output1TensorInfo, output1Handle.get());
if (addOutput)
{
AddOutputToWorkload(fusedQueueDescriptor, info, output0TensorInfo, output0Handle.get());
}
AddOutputToWorkload(fusedQueueDescriptor, info, output1TensorInfo, output1Handle.get());
std::unique_ptr<IWorkload> workload = workloadFactory.CreateWorkload(LayerType::Fused,
fusedQueueDescriptor,
info);
input0Handle->Allocate();
input1Handle->Allocate();
mulInput1Handle->Allocate();
addInput1Handle->Allocate();
if (addOutput)
{
output0Handle->Allocate();
}
output1Handle->Allocate();
CopyDataToITensorHandle(input0Handle.get(), input0.data());
CopyDataToITensorHandle(input1Handle.get(), input1.data());
CopyDataToITensorHandle(mulInput1Handle.get(), mulInput1.data());
CopyDataToITensorHandle(addInput1Handle.get(), addInput1.data());
workload->Execute();
CopyDataFromITensorHandle(output1Actual.data(), output1Handle.get());
LayerTestResult<T,4> ret1(output1Actual,
output1Expected,
output1Handle->GetShape(),
output1TensorInfo.GetShape());
std::vector<LayerTestResult<T,4>> ret = {ret1};
if (addOutput)
{
CopyDataFromITensorHandle(output0Actual.data(), output0Handle.get());
LayerTestResult<T,4> ret0(output0Actual,
output0Expected,
output0Handle->GetShape(),
output0TensorInfo.GetShape());
ret = {ret0, ret1};
}
return ret;
}
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