IVGCVSW-1813 - Add MeanLayer

 * add MeanLayer functionalities
 * modify MeanQueueDescriptor to use parameter
 * add IsMeanSupported placeholder for all backends

Change-Id: Ic69a34a61df667849977aad9b38f9a01eef565b5

1 files changed, 105 insertions, 0 deletions

diff --git a/src/armnn/layers/MeanLayer.cpp b/src/armnn/layers/MeanLayer.cpp
new file mode 100644
index 0000000000..6bbb0943b0
--- /dev/null
+++ b/src/armnn/layers/MeanLayer.cpp
@@ -0,0 +1,105 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "MeanLayer.hpp"
+#include "LayerCloneBase.hpp"
+
+#include "backends/CpuTensorHandle.hpp"
+#include "backends/WorkloadData.hpp"
+#include "backends/WorkloadFactory.hpp"
+
+#include <cstring>
+
+namespace armnn
+{
+
+MeanLayer::MeanLayer(const armnn::MeanDescriptor& param, const char* name)
+    : LayerWithParameters(1, 1, LayerType::Mean, param, name)
+{}
+
+std::unique_ptr<IWorkload> MeanLayer::CreateWorkload(const armnn::Graph& graph,
+                                                     const armnn::IWorkloadFactory& factory) const
+{
+    MeanQueueDescriptor descriptor;
+    descriptor.m_Parameters.m_Axis = m_Param.m_Axis;
+    descriptor.m_Parameters.m_KeepDims = m_Param.m_KeepDims;
+
+    return factory.CreateMean(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+MeanLayer* MeanLayer::Clone(Graph& graph) const
+{
+    auto layer = CloneBase<MeanLayer>(graph, m_Param, GetName());
+
+    layer->m_Param.m_Axis = m_Param.m_Axis;
+    layer->m_Param.m_KeepDims = m_Param.m_KeepDims;
+
+    return std::move(layer);
+}
+
+void MeanLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(1, CHECK_LOCATION());
+
+    const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo();
+    
+    BOOST_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= MaxNumOfTensorDimensions,
+                     "MeanLayer: Mean supports up to 4D input.");
+
+    unsigned int rank = input.GetNumDimensions();
+    unsigned int outputRank = 0;
+
+    // Calculate output dimension
+    if (m_Param.m_KeepDims)
+    {
+        outputRank = rank;
+    }
+    else if (m_Param.m_Axis.empty())
+    {
+        outputRank = 1;
+    }
+    else if (m_Param.m_Axis.size() <= input.GetNumDimensions())
+    {
+        throw LayerValidationException("MeanLayer: Dimensions to reduce can not be bigger than input dimensions");
+    }
+    else
+    {
+        outputRank = input.GetNumDimensions() - boost::numeric_cast<unsigned int>(m_Param.m_Axis.size());
+        if (outputRank == 0)
+        {
+            outputRank = 1;
+        }
+    }
+
+    unsigned int dimSizes[outputRank];
+    memset(dimSizes, 1, outputRank * sizeof(unsigned int));
+
+    if (!m_Param.m_Axis.empty())
+    {
+        // Skip the dimension that has been reduced unless keepDims is true.
+        unsigned int outputIndex = 0;
+        for (unsigned int i = 0; i < input.GetNumDimensions(); ++i)
+        {
+            if (std::find(m_Param.m_Axis.begin(), m_Param.m_Axis.end(), i) == m_Param.m_Axis.end())
+            {
+                dimSizes[outputIndex] = boost::numeric_cast<unsigned int>(input.GetShape()[i]);
+                ++outputIndex;
+            }
+            else if (m_Param.m_KeepDims)
+            {
+                dimSizes[outputIndex] = 1;
+                ++outputIndex;
+            }
+        }
+    }
+    const TensorShape& inferredShape = TensorShape(outputRank, dimSizes);
+
+    ConditionalThrowIfNotEqual<LayerValidationException>(
+        "MeanLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+        GetOutputSlot(0).GetTensorInfo().GetShape(),
+        inferredShape);
+}
+
+} // namespace armnn
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