IVGCVSW-1783 refactor add/mul/div layers

Change-Id: Ic043030455b3cf8ad7f92fd0a75175c1827a95f4

10 files changed, 112 insertions, 166 deletions

diff --git a/Android.mk b/Android.mk
index c712ced385..eae457375c 100644
--- a/Android.mk
+++ b/Android.mk
@@ -172,6 +172,7 @@ LOCAL_SRC_FILES := \
         src/armnn/backends/WorkloadFactory.cpp \
         src/armnn/layers/ActivationLayer.cpp \
         src/armnn/layers/AdditionLayer.cpp \
+        src/armnn/layers/ArithmeticBaseLayer.cpp \
         src/armnn/layers/BatchNormalizationLayer.cpp \
         src/armnn/layers/ConstantLayer.cpp \
         src/armnn/layers/Convolution2dLayer.cpp \
diff --git a/CMakeLists.txt b/CMakeLists.txt
index 09761af4b1..802ca50cfd 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -291,6 +291,8 @@ list(APPEND armnn_sources
     src/armnn/layers/ActivationLayer.cpp
     src/armnn/layers/AdditionLayer.hpp
     src/armnn/layers/AdditionLayer.cpp
+    src/armnn/layers/ArithmeticBaseLayer.hpp
+    src/armnn/layers/ArithmeticBaseLayer.cpp
     src/armnn/layers/BatchNormalizationLayer.hpp
     src/armnn/layers/BatchNormalizationLayer.cpp
     src/armnn/layers/ConstantLayer.hpp
diff --git a/src/armnn/layers/AdditionLayer.cpp b/src/armnn/layers/AdditionLayer.cpp
index ab73a918db..c1413102b8 100644
--- a/src/armnn/layers/AdditionLayer.cpp
+++ b/src/armnn/layers/AdditionLayer.cpp
@@ -3,6 +3,7 @@
 // See LICENSE file in the project root for full license information.
 //
 #include "AdditionLayer.hpp"
+
 #include "LayerCloneBase.hpp"
 
 #include <armnn/TypesUtils.hpp>
@@ -13,11 +14,12 @@ namespace armnn
 {
 
 AdditionLayer::AdditionLayer(const char* name)
-    : Layer(2, 1, LayerType::Addition, name)
+    : ArithmeticBaseLayer(2, 1, LayerType::Addition, name)
 {
 }
 
-std::unique_ptr<IWorkload> AdditionLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const
+std::unique_ptr<IWorkload> AdditionLayer::CreateWorkload(const Graph& graph,
+                                                         const IWorkloadFactory& factory) const
 {
     AdditionQueueDescriptor descriptor;
     return factory.CreateAddition(descriptor, PrepInfoAndDesc(descriptor, graph));
@@ -28,51 +30,4 @@ AdditionLayer* AdditionLayer::Clone(Graph& graph) const
     return CloneBase<AdditionLayer>(graph, GetName());
 }
 
-std::vector<TensorShape> AdditionLayer::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 AdditionLayer::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>(
-        "AdditionLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
-        GetOutputSlot(0).GetTensorInfo().GetShape(),
-        inferredShapes[0]);
-}
-
 } // namespace armnn
diff --git a/src/armnn/layers/AdditionLayer.hpp b/src/armnn/layers/AdditionLayer.hpp
index 37f0b5c259..d34536d114 100644
--- a/src/armnn/layers/AdditionLayer.hpp
+++ b/src/armnn/layers/AdditionLayer.hpp
@@ -4,23 +4,19 @@
 //
 #pragma once
 
-#include <Layer.hpp>
+#include "ArithmeticBaseLayer.hpp"
 
 namespace armnn
 {
 
-class AdditionLayer : public Layer
+class AdditionLayer : public ArithmeticBaseLayer
 {
 public:
-    virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph&            graph,
+    virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph& graph,
                                                       const IWorkloadFactory& factory) const override;
 
     AdditionLayer* Clone(Graph& graph) const override;
 
-    void ValidateTensorShapesFromInputs() override;
-
-    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
-
 protected:
     AdditionLayer(const char* name);
     ~AdditionLayer() = default;
diff --git a/src/armnn/layers/ArithmeticBaseLayer.cpp b/src/armnn/layers/ArithmeticBaseLayer.cpp
new file mode 100644
index 0000000000..63937fba6c
--- /dev/null
+++ b/src/armnn/layers/ArithmeticBaseLayer.cpp
@@ -0,0 +1,68 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#include "ArithmeticBaseLayer.hpp"
+
+#include "InternalTypes.hpp"
+#include "armnn/Exceptions.hpp"
+#include <armnn/TypesUtils.hpp>
+
+#include <boost/assert.hpp>
+
+namespace armnn
+{
+
+ArithmeticBaseLayer::ArithmeticBaseLayer(unsigned int numInputSlots, unsigned int numOutputSlots,
+                                         LayerType type, const char* name)
+    : Layer(numInputSlots, numOutputSlots, type, name)
+{
+}
+
+std::vector<TensorShape> ArithmeticBaseLayer::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];
+
+#if !NDEBUG
+        // Validate inputs are broadcast compatible.
+        BOOST_ASSERT_MSG(dim0 == dim1 || 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 ArithmeticBaseLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(2, CHECK_LOCATION());
+
+    auto inferredShapes = InferOutputShapes({
+        GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
+        GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
+    });
+
+    BOOST_ASSERT(inferredShapes.size() == 1);
+
+    std::string msg = GetLayerTypeAsCString(GetType());
+    msg += "Layer: TensorShape set on OutputSlot[0] does not match the inferred shape.";
+    ConditionalThrowIfNotEqual<LayerValidationException>(msg,
+                                                         GetOutputSlot(0).GetTensorInfo().GetShape(),
+                                                         inferredShapes[0]);
+}
+
+} // namespace armnn
diff --git a/src/armnn/layers/ArithmeticBaseLayer.hpp b/src/armnn/layers/ArithmeticBaseLayer.hpp
new file mode 100644
index 0000000000..eee612add4
--- /dev/null
+++ b/src/armnn/layers/ArithmeticBaseLayer.hpp
@@ -0,0 +1,26 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// See LICENSE file in the project root for full license information.
+//
+#pragma once
+
+#include <Layer.hpp>
+
+namespace armnn
+{
+
+/// NOTE: this is an abstract class, it does not implement:
+///  std::unique_ptr<IWorkload> Layer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const = 0;
+///  Layer* Clone(Graph& graph) const = 0;
+class ArithmeticBaseLayer : public Layer
+{
+public:
+    void ValidateTensorShapesFromInputs() override;
+    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
+protected:
+    ArithmeticBaseLayer(unsigned int numInputSlots, unsigned int numOutputSlots, LayerType type, const char* name);
+    ~ArithmeticBaseLayer() = default;
+};
+
+} // namespace
diff --git a/src/armnn/layers/DivisionLayer.cpp b/src/armnn/layers/DivisionLayer.cpp
index bf09e14229..c0f958929e 100644
--- a/src/armnn/layers/DivisionLayer.cpp
+++ b/src/armnn/layers/DivisionLayer.cpp
@@ -14,7 +14,7 @@ namespace armnn
 {
 
 DivisionLayer::DivisionLayer(const char* name)
-        : Layer(2, 1, LayerType::Division, name)
+    : ArithmeticBaseLayer(2, 1, LayerType::Division, name)
 {
 }
 
@@ -22,7 +22,6 @@ std::unique_ptr<IWorkload> DivisionLayer::CreateWorkload(const Graph& graph,
                                                          const IWorkloadFactory& factory) const
 {
     DivisionQueueDescriptor descriptor;
-
     return factory.CreateDivision(descriptor, PrepInfoAndDesc(descriptor, graph));
 }
 
@@ -31,51 +30,4 @@ 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
diff --git a/src/armnn/layers/DivisionLayer.hpp b/src/armnn/layers/DivisionLayer.hpp
index 1bd69c4446..c6a642e318 100644
--- a/src/armnn/layers/DivisionLayer.hpp
+++ b/src/armnn/layers/DivisionLayer.hpp
@@ -4,12 +4,12 @@
 //
 #pragma once
 
-#include <Layer.hpp>
+#include "ArithmeticBaseLayer.hpp"
 
 namespace armnn
 {
 
-class DivisionLayer : public Layer
+class DivisionLayer : public ArithmeticBaseLayer
 {
 public:
     virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph& graph,
@@ -17,9 +17,6 @@ public:
 
     DivisionLayer* Clone(Graph& graph) const override;
 
-    void ValidateTensorShapesFromInputs() override;
-    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
-
 protected:
     DivisionLayer(const char* name);
     ~DivisionLayer() = default;
diff --git a/src/armnn/layers/MultiplicationLayer.cpp b/src/armnn/layers/MultiplicationLayer.cpp
index ed7683da5f..6970b3f7f4 100644
--- a/src/armnn/layers/MultiplicationLayer.cpp
+++ b/src/armnn/layers/MultiplicationLayer.cpp
@@ -14,15 +14,14 @@ namespace armnn
 {
 
 MultiplicationLayer::MultiplicationLayer(const char* name)
-    : Layer(2, 1, LayerType::Multiplication, name)
+    : ArithmeticBaseLayer(2, 1, LayerType::Multiplication, name)
 {
 }
 
-std::unique_ptr<IWorkload> MultiplicationLayer::CreateWorkload(const Graph&            graph,
+std::unique_ptr<IWorkload> MultiplicationLayer::CreateWorkload(const Graph& graph,
                                                                const IWorkloadFactory& factory) const
 {
     MultiplicationQueueDescriptor descriptor;
-
     return factory.CreateMultiplication(descriptor, PrepInfoAndDesc(descriptor, graph));
 }
 
@@ -31,51 +30,4 @@ MultiplicationLayer* MultiplicationLayer::Clone(Graph& graph) const
     return CloneBase<MultiplicationLayer>(graph, GetName());
 }
 
-std::vector<TensorShape> MultiplicationLayer::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 MultiplicationLayer::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>(
-        "MultiplicationLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
-        GetOutputSlot(0).GetTensorInfo().GetShape(),
-        inferredShapes[0]);
-}
-
 } // namespace armnn
diff --git a/src/armnn/layers/MultiplicationLayer.hpp b/src/armnn/layers/MultiplicationLayer.hpp
index bbfd1ee694..264d12feeb 100644
--- a/src/armnn/layers/MultiplicationLayer.hpp
+++ b/src/armnn/layers/MultiplicationLayer.hpp
@@ -4,22 +4,19 @@
 //
 #pragma once
 
-#include <Layer.hpp>
+#include "ArithmeticBaseLayer.hpp"
 
 namespace armnn
 {
 
-class MultiplicationLayer : public Layer
+class MultiplicationLayer : public ArithmeticBaseLayer
 {
 public:
-    virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph&            graph,
+    virtual std::unique_ptr<IWorkload> CreateWorkload(const Graph& graph,
                                                       const IWorkloadFactory& factory) const override;
 
     MultiplicationLayer* Clone(Graph& graph) const override;
 
-    void ValidateTensorShapesFromInputs() override;
-    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
-
 protected:
     MultiplicationLayer(const char* name);
     ~MultiplicationLayer() = default;