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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include <armnn/ArmNN.hpp>
#include <armnn/Tensor.hpp>
#include <test/TensorHelpers.hpp>
#include <backends/CpuTensorHandle.hpp>
#include <backends/WorkloadFactory.hpp>
#include <backends/test/QuantizeHelper.hpp>
template<typename T>
LayerTestResult<T,4> BatchNormTestImpl(armnn::IWorkloadFactory& workloadFactory,
float qScale,
int32_t qOffset)
{
const unsigned int width = 2;
const unsigned int height = 3;
const unsigned int channels = 2;
const unsigned int num = 1;
armnn::TensorInfo inputTensorInfo({num, channels, height, width}, armnn::GetDataType<T>());
armnn::TensorInfo outputTensorInfo({num, channels, height, width}, armnn::GetDataType<T>());
armnn::TensorInfo tensorInfo({channels}, armnn::GetDataType<T>());
// Set quantization parameters if the requested type is a quantized type.
if(armnn::IsQuantizedType<T>())
{
inputTensorInfo.SetQuantizationScale(qScale);
inputTensorInfo.SetQuantizationOffset(qOffset);
outputTensorInfo.SetQuantizationScale(qScale);
outputTensorInfo.SetQuantizationOffset(qOffset);
tensorInfo.SetQuantizationScale(qScale);
tensorInfo.SetQuantizationOffset(qOffset);
}
auto input = MakeTensor<T, 4>(inputTensorInfo,
QuantizedVector<T>(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<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, -2}));
auto variance = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {4, 9}));
auto beta = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {3, 2}));
auto gamma = MakeTensor<T, 1>(tensorInfo, QuantizedVector<T>(qScale, qOffset, {2, 1}));
LayerTestResult<T,4> ret(outputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
armnn::BatchNormalizationQueueDescriptor data;
armnn::WorkloadInfo info;
armnn::ScopedCpuTensorHandle meanTensor(tensorInfo);
armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo);
armnn::ScopedCpuTensorHandle betaTensor(tensorInfo);
armnn::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
ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo,
QuantizedVector<T>(qScale, qOffset,
{
1.f, 4.f,
4.f, 2.f,
1.f, 6.f,
3.f, 3.f,
4.f, 3.f,
2.f, 4.f
}));
std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
workloadFactory.Finalize();
workload->Execute();
CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get());
return ret;
}
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