1 files changed, 100 insertions, 0 deletions
diff --git a/src/armnn/layers/ReduceLayer.cpp b/src/armnn/layers/ReduceLayer.cpp
new file mode 100644
index 0000000000..b68cd2eabc
--- /dev/null
+++ b/src/armnn/layers/ReduceLayer.cpp
@@ -0,0 +1,100 @@
+//
+// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "ReduceLayer.hpp"
+#include "LayerCloneBase.hpp"
+
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/WorkloadData.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+namespace armnn
+{
+
+ReduceLayer::ReduceLayer(const ReduceDescriptor& param, const char* name)
+    : LayerWithParameters(1, 1, LayerType::Reduce, param, name)
+{
+}
+
+std::unique_ptr<IWorkload> ReduceLayer::CreateWorkload(const IWorkloadFactory& factory) const
+{
+    ReduceQueueDescriptor descriptor;
+    return factory.CreateReduce(descriptor, PrepInfoAndDesc(descriptor));
+}
+
+ReduceLayer* ReduceLayer::Clone(Graph& graph) const
+{
+    return CloneBase<ReduceLayer>(graph, m_Param, GetName());
+}
+
+void ReduceLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(1, CHECK_LOCATION());
+
+    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
+
+    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
+
+    const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo();
+
+    ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
+                     "ReduceLayer: Reduce 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_vAxis.empty())
+    {
+        outputRank = 1;
+    }
+    else if (m_Param.m_vAxis.size() > input.GetNumDimensions())
+    {
+        throw LayerValidationException("ReduceLayer: Dimensions to reduce can not be bigger than input dimensions");
+    }
+    else
+    {
+        outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(m_Param.m_vAxis.size());
+        if (outputRank == 0)
+        {
+            outputRank = 1;
+        }
+    }
+
+    std::vector<unsigned int> dimSizes(outputRank, 1);
+    if (!m_Param.m_vAxis.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_vAxis.begin(), m_Param.m_vAxis.end(), i) == m_Param.m_vAxis.end())
+            {
+                dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input.GetShape()[i]);
+                ++outputIndex;
+            }
+            else if (m_Param.m_KeepDims)
+            {
+                dimSizes[outputIndex] = 1;
+                ++outputIndex;
+            }
+        }
+    }
+    const TensorShape& inferredShape = TensorShape(outputRank, dimSizes.data());
+
+    ValidateAndCopyShape(outputShape, inferredShape, m_ShapeInferenceMethod, "ReduceLayer");
+}
+
+void ReduceLayer::Accept(ILayerVisitor& visitor) const
+{
+    visitor.VisitReduceLayer(this, GetParameters(), GetName());
+}
+
+} // namespace armnn