Release 18.05

1 files changed, 50 insertions, 0 deletions

diff --git a/src/armnn/layers/L2NormalizationLayer.cpp b/src/armnn/layers/L2NormalizationLayer.cpp
new file mode 100644
index 0000000000..07020bfdca
--- /dev/null
+++ b/src/armnn/layers/L2NormalizationLayer.cpp
@@ -0,0 +1,50 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "L2NormalizationLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+#include <armnn/TypesUtils.hpp>
+#include <backends/WorkloadData.hpp>
+#include <backends/WorkloadFactory.hpp>
+
+namespace armnn
+{
+
+L2NormalizationLayer::L2NormalizationLayer(const char* name)
+    : Layer(1, 1, LayerType::L2Normalization, name)
+{
+}
+
+std::unique_ptr<IWorkload> L2NormalizationLayer::CreateWorkload(const Graph& graph,
+    const IWorkloadFactory& factory) const
+{
+    L2NormalizationQueueDescriptor descriptor;
+    return factory.CreateL2Normalization(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+L2NormalizationLayer* L2NormalizationLayer::Clone(Graph& graph) const
+{
+    return CloneBase<L2NormalizationLayer>(graph, GetName());
+}
+
+void L2NormalizationLayer::ValidateTensorShapesFromInputs()
+{
+    ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr,
+                     "L2NormalizationLayer: InputSlot must be connected to an OutputSlot");
+    ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(),
+                     "L2NormalizationLayer: TensorInfo must be set on connected OutputSlot.");
+
+    IOutputSlot* input = GetInputSlot(0).GetConnection();
+
+    // input and output shapes are the same
+    TensorShape const& outShape = input->GetTensorInfo().GetShape();
+    ConditionalThrowIfNotEqual<LayerValidationException>(
+        "L2NormalizationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+        GetOutputSlot(0).GetTensorInfo().GetShape(),
+        outShape);
+}
+
+} // namespace armnn