blob: 20cf3b38f5d72aafcc1c18ced2aa26df3c96612a [file] [log] [blame]
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "RefStackWorkload.hpp"
#include "RefWorkloadUtils.hpp"
#include "Stack.hpp"
#include <Profiling.hpp>
namespace armnn
{
RefStackWorkload::RefStackWorkload(const StackQueueDescriptor& descriptor,
const WorkloadInfo& info)
: BaseWorkload(descriptor, info)
{}
void RefStackWorkload::Execute() const
{
Execute(m_Data.m_Inputs, m_Data.m_Outputs);
}
void RefStackWorkload::ExecuteAsync(WorkingMemDescriptor &workingMemDescriptor)
{
Execute(workingMemDescriptor.m_Inputs, workingMemDescriptor.m_Outputs);
}
void RefStackWorkload::Execute(std::vector<ITensorHandle*> inputs, std::vector<ITensorHandle*> outputs) const
{
ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefStackWorkload_Execute");
// Can perform a simple concatenation when axis == 0
if (!m_Data.m_Parameters.m_Axis)
{
float* output = GetOutputTensorData<float>(0, m_Data);
ARMNN_ASSERT(output != nullptr);
unsigned int numInputs = m_Data.m_Parameters.m_NumInputs;
unsigned int inputLength = GetTensorInfo(inputs[0]).GetNumElements();
for (unsigned int inputIdx=0; inputIdx<numInputs; ++inputIdx)
{
const float* input = GetInputTensorData<float>(inputIdx, m_Data);
for (unsigned int elmt=0; elmt<inputLength; ++elmt)
{
output[(inputIdx * inputLength) + elmt] = input[elmt];
}
}
return;
}
std::vector<std::unique_ptr<Decoder<float>>> inputDecoders;
for (unsigned int i=0; i<inputs.size(); ++i)
{
inputDecoders.push_back(MakeDecoder<float>(GetTensorInfo(inputs[i]),
inputs[i]->Map()));
}
std::unique_ptr<Encoder<float>> outputEncoder = MakeEncoder<float>(GetTensorInfo(outputs[0]),
outputs[0]->Map());
Stack(m_Data, inputDecoders, *outputEncoder);
}
} // namespace armnn