| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "RefBaseConstantWorkload.hpp" |
| |
| #include "RefWorkloadUtils.hpp" |
| |
| #include <armnn/Types.hpp> |
| |
| #include <boost/assert.hpp> |
| |
| #include <cstring> |
| |
| namespace armnn |
| { |
| |
| template <armnn::DataType DataType> |
| void RefBaseConstantWorkload<DataType>::Execute() const |
| { |
| // Considering the reference backend independently, it could be possible to initialise the intermediate tensor |
| // created by the layer output handler at workload construction time, rather than at workload execution time. |
| // However, this is not an option for other backends (e.g. CL). For consistency, we prefer to align all |
| // implementations. |
| // A similar argument can be made about performing the memory copy in the first place (the layer output handler |
| // could have a non-owning reference to the layer output tensor managed by the const input layer); again, this is |
| // not an option for other backends, and the extra complexity required to make this work for the reference backend |
| // may not be worth the effort (skipping a memory copy in the first inference). |
| if (!m_RanOnce) |
| { |
| const ConstantQueueDescriptor& data = this->m_Data; |
| |
| BOOST_ASSERT(data.m_LayerOutput != nullptr); |
| |
| const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]); |
| BOOST_ASSERT(data.m_LayerOutput->GetTensorInfo().GetNumBytes() == outputInfo.GetNumBytes()); |
| |
| memcpy(GetOutputTensorData<void>(0, data), data.m_LayerOutput->GetConstTensor<void>(), |
| outputInfo.GetNumBytes()); |
| |
| m_RanOnce = true; |
| } |
| } |
| |
| template class RefBaseConstantWorkload<DataType::Float32>; |
| template class RefBaseConstantWorkload<DataType::QuantisedAsymm8>; |
| |
| } //namespace armnn |