1 files changed, 81 insertions, 0 deletions
diff --git a/src/armnn/layers/DivisionLayer.cpp b/src/armnn/layers/DivisionLayer.cpp
new file mode 100644
index 0000000000..bf09e14229
--- /dev/null
+++ b/src/armnn/layers/DivisionLayer.cpp
@@ -0,0 +1,81 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "DivisionLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+#include <armnn/TypesUtils.hpp>
+#include <backends/WorkloadData.hpp>
+#include <backends/WorkloadFactory.hpp>
+
+namespace armnn
+{
+
+DivisionLayer::DivisionLayer(const char* name)
+        : Layer(2, 1, LayerType::Division, name)
+{
+}
+
+std::unique_ptr<IWorkload> DivisionLayer::CreateWorkload(const Graph& graph,
+                                                         const IWorkloadFactory& factory) const
+{
+    DivisionQueueDescriptor descriptor;
+
+    return factory.CreateDivision(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+DivisionLayer* DivisionLayer::Clone(Graph& graph) const
+{
+    return CloneBase<DivisionLayer>(graph, GetName());
+}
+
+std::vector<TensorShape> DivisionLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+    BOOST_ASSERT(inputShapes.size() == 2);
+    auto& input0 = inputShapes[0];
+    auto& input1 = inputShapes[1];
+
+    // Get the max of the inputs.
+    BOOST_ASSERT(input0.GetNumDimensions() == input1.GetNumDimensions());
+    unsigned int numDims = input0.GetNumDimensions();
+    std::vector<unsigned int> dims(numDims);
+
+    for (unsigned int i = 0; i < numDims; i++)
+    {
+        unsigned int dim0 = input0[i];
+        unsigned int dim1 = input1[i];
+
+        // Validates inputs are broadcast compatible.
+#if !NDEBUG
+        if (dim0 != dim1)
+        {
+            BOOST_ASSERT_MSG(dim0 == 1 || dim1 == 1, "Dimensions should either match or one should be of size 1.");
+        }
+#endif
+
+        dims[i] = std::max(dim0, dim1);
+    }
+
+    return std::vector<TensorShape>({ TensorShape(numDims, dims.data()) });
+}
+
+void DivisionLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(2, CHECK_LOCATION());
+
+    auto inferredShapes = InferOutputShapes({
+                                                    GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
+                                                    GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
+                                            });
+
+    BOOST_ASSERT(inferredShapes.size() == 1);
+
+    ConditionalThrowIfNotEqual<LayerValidationException>(
+            "DivisionLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+            GetOutputSlot(0).GetTensorInfo().GetShape(),
+            inferredShapes[0]);
+}
+
+} // namespace armnn