IVGCVSW-1898 : Ref backend folder structure

* Reference backend is renamed to backends/reference as per
  https://confluence.arm.com/display/MLENG/Pluggable+backends

Change-Id: I27a13c274eb60995dfb459e3c49c0e2f60bcd32c

1 files changed, 49 insertions, 0 deletions

diff --git a/src/backends/reference/workloads/RefBaseConstantWorkload.cpp b/src/backends/reference/workloads/RefBaseConstantWorkload.cpp
new file mode 100644
index 0000000000..647677b4fb
--- /dev/null
+++ b/src/backends/reference/workloads/RefBaseConstantWorkload.cpp
@@ -0,0 +1,49 @@
+//
+// 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