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-rw-r--r--src/armnn/layers/SliceLayer.cpp66
-rw-r--r--src/armnn/layers/SliceLayer.hpp49
2 files changed, 115 insertions, 0 deletions
diff --git a/src/armnn/layers/SliceLayer.cpp b/src/armnn/layers/SliceLayer.cpp
new file mode 100644
index 0000000000..8ea5fd8f25
--- /dev/null
+++ b/src/armnn/layers/SliceLayer.cpp
@@ -0,0 +1,66 @@
+//
+// Copyright © 2019 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "SliceLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+#include <armnn/TypesUtils.hpp>
+
+#include <backendsCommon/WorkloadData.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <boost/assert.hpp>
+#include <boost/numeric/conversion/cast.hpp>
+
+namespace armnn
+{
+
+SliceLayer::SliceLayer(const SliceDescriptor& param, const char* name)
+ : LayerWithParameters(1, 1, LayerType::Slice, param, name)
+{
+}
+
+std::unique_ptr<IWorkload> SliceLayer::CreateWorkload(const Graph& graph,
+ const IWorkloadFactory& factory) const
+{
+ SliceQueueDescriptor descriptor;
+ return factory.CreateSlice(descriptor, PrepInfoAndDesc(descriptor, graph));
+}
+
+SliceLayer* SliceLayer::Clone(Graph& graph) const
+{
+ return CloneBase<SliceLayer>(graph, m_Param, GetName());
+}
+
+void SliceLayer::ValidateTensorShapesFromInputs()
+{
+ VerifyLayerConnections(1, CHECK_LOCATION());
+
+ auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
+
+ BOOST_ASSERT(inferredShapes.size() == 1);
+
+ ConditionalThrowIfNotEqual<LayerValidationException>(
+ "SliceLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
+ GetOutputSlot(0).GetTensorInfo().GetShape(),
+ inferredShapes[0]);
+}
+
+std::vector<TensorShape> SliceLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+ BOOST_ASSERT(inputShapes.size() == 1);
+
+ TensorShape outputShape(boost::numeric_cast<unsigned int>(m_Param.m_Size.size()), m_Param.m_Size.data());
+
+ return std::vector<TensorShape>({ outputShape });
+}
+
+void SliceLayer::Accept(ILayerVisitor& visitor) const
+{
+ visitor.VisitSliceLayer(this, GetParameters(), GetName());
+}
+
+} // namespace armnn
diff --git a/src/armnn/layers/SliceLayer.hpp b/src/armnn/layers/SliceLayer.hpp
new file mode 100644
index 0000000000..38f0747f05
--- /dev/null
+++ b/src/armnn/layers/SliceLayer.hpp
@@ -0,0 +1,49 @@
+//
+// Copyright © 2019 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#pragma once
+
+#include "LayerWithParameters.hpp"
+
+namespace armnn
+{
+
+class SliceLayer : public LayerWithParameters<SliceDescriptor>
+{
+public:
+ /// Makes a workload for the Slice type.
+ /// @param [in] graph The graph where this layer can be found.
+ /// @param [in] factory The workload factory which will create the workload.
+ /// @return A pointer to the created workload, or nullptr if not created.
+ virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph& graph,
+ const IWorkloadFactory& factory) const override;
+
+ /// Creates a dynamically-allocated copy of this layer.
+ /// @param [in] graph The graph into which this layer is being cloned.
+ SliceLayer* Clone(Graph& graph) const override;
+
+ /// Check if the input tensor shape(s)
+ /// will lead to a valid configuration of @ref SliceLayer.
+ void ValidateTensorShapesFromInputs() override;
+
+ /// By default returns inputShapes if the number of inputs are equal to number of outputs,
+ /// otherwise infers the output shapes from given input shapes and layer properties.
+ /// @param [in] inputShapes The input shapes layer has.
+ /// @return A vector to the inferred output shape.
+ std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
+ void Accept(ILayerVisitor& visitor) const override;
+
+protected:
+ /// Constructor to create a SliceLayer.
+ /// @param [in] param SliceDescriptor to configure the resize operation.
+ /// @param [in] name Optional name for the layer.
+ SliceLayer(const SliceDescriptor& param, const char* name);
+
+ /// Default destructor.
+ ~SliceLayer() = default;
+};
+
+} // namespace armnn