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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "RefQuantizeWorkload.hpp"
#include <armnn/TypesUtils.hpp>
namespace armnn
{
namespace
{
template<typename T>
void QuantizeImpl(const void *input, void *output, size_t numValues, float scale, int offset)
{
auto in = static_cast<const float *>(input);
auto out = static_cast<T *>(output);
for (size_t i = 0; i < numValues; i++, in++, out++)
{
*out = armnn::Quantize<T>(*in, scale, offset);
}
}
} //namespace
RefQuantizeWorkload::RefQuantizeWorkload(const QuantizeQueueDescriptor& descriptor, const WorkloadInfo &info)
: BaseWorkload(descriptor, info)
, m_NumElements(info.m_InputTensorInfos[0].GetNumElements())
, m_TargetType(info.m_OutputTensorInfos[0].GetDataType())
, m_Scale(info.m_OutputTensorInfos[0].GetQuantizationScale())
, m_Offset(info.m_OutputTensorInfos[0].GetQuantizationOffset())
{
}
void RefQuantizeWorkload::Execute() const
{
const void* input = m_Data.m_Inputs[0]->Map(true);
void* output = m_Data.m_Outputs[0]->Map(true);
switch(m_TargetType)
{
case DataType::QAsymmU8:
{
QuantizeImpl<uint8_t>(input, output, m_NumElements, m_Scale, m_Offset);
break;
}
case DataType::QSymmS8:
{
QuantizeImpl<int8_t>(input, output, m_NumElements, m_Scale, 0);
break;
}
case DataType::QSymmS16:
{
QuantizeImpl<int16_t>(input, output, m_NumElements, m_Scale, 0);
break;
}
default:
{
BOOST_ASSERT_MSG(false, "RefQuantizeWorkload: Non quantized output type encountered");
}
}
m_Data.m_Inputs[0]->Unmap();
m_Data.m_Outputs[0]->Unmap();
}
} //namespace armnn
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