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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonSoftmaxUint8Workload.hpp"
#include "NeonWorkloadUtils.hpp"
#include <aclCommon/ArmComputeUtils.hpp>
#include <arm_compute/runtime/NEON/functions/NESoftmaxLayer.h>
namespace armnn
{
NeonSoftmaxUint8Workload::NeonSoftmaxUint8Workload(const SoftmaxQueueDescriptor& descriptor,
const WorkloadInfo& info,
std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
: Uint8Workload<SoftmaxQueueDescriptor>(descriptor, info)
{
m_Data.ValidateInputsOutputs("NeonSoftmaxUint8Workload", 1, 1);
arm_compute::ITensor& input = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
const auto outputQuantization = output.info()->quantization_info();
if ((!outputQuantization.scale().empty() && outputQuantization.scale()[0] != (1.0f / 256.0f)) ||
(!outputQuantization.offset().empty() && outputQuantization.offset()[0] != 0) ||
outputQuantization.scale().empty() || outputQuantization.offset().empty())
{
throw InvalidArgumentException(
"Invalid quantization for output. Only scale = 1.0f / 256.0f and offset = 0 supported");
}
auto layer = std::make_unique<arm_compute::NESoftmaxLayer>(memoryManager);
unsigned int aclAxis = ComputeSoftmaxAclAxis(m_Data.m_Parameters, info.m_InputTensorInfos[0]);
layer->configure(&input, &output, descriptor.m_Parameters.m_Beta, aclAxis);
m_SoftmaxLayer.reset(layer.release());
}
void NeonSoftmaxUint8Workload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonSoftmaxUint8Workload_Execute");
m_SoftmaxLayer->run();
}
} //namespace armnn
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