IVGCVSW-5091 Add Logical ops frontend and ref impl

* Add frontend and reference implementation for logical
  ops NOT, AND, OR.
* Unary NOT uses existing ElementwiseUnary layer and
  ElementwiseUnary descriptor.
* Binary AND/OR uses new layer LogicalBinary and new
  LogicalBinary descriptor.
* Add serialization/deserializion support and add missing
  ElementwiseUnary deserializer code.
* Add additional Boolean decoder in BaseIterator.hpp.

Signed-off-by: James Conroy <james.conroy@arm.com>
Change-Id: Id343b01174053a166de1b98b6175e04a5065f720

1 files changed, 80 insertions, 0 deletions

diff --git a/src/armnn/layers/LogicalBinaryLayer.cpp b/src/armnn/layers/LogicalBinaryLayer.cpp
new file mode 100644
index 0000000000..0ae5ea5641
--- /dev/null
+++ b/src/armnn/layers/LogicalBinaryLayer.cpp
@@ -0,0 +1,80 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "LogicalBinaryLayer.hpp"
+
+#include "LayerCloneBase.hpp"
+
+#include <backendsCommon/WorkloadData.hpp>
+#include <backendsCommon/WorkloadFactory.hpp>
+
+#include <algorithm>
+
+namespace armnn
+{
+
+LogicalBinaryLayer::LogicalBinaryLayer(const LogicalBinaryDescriptor& param, const char* name)
+    : LayerWithParameters(2, 1, LayerType::LogicalBinary, param, name)
+{
+}
+
+std::unique_ptr<IWorkload> LogicalBinaryLayer::CreateWorkload(const IWorkloadFactory& factory) const
+{
+    LogicalBinaryQueueDescriptor descriptor;
+    return factory.CreateLogicalBinary(descriptor, PrepInfoAndDesc(descriptor));
+}
+
+LogicalBinaryLayer* LogicalBinaryLayer::Clone(Graph& graph) const
+{
+    return CloneBase<LogicalBinaryLayer>(graph, m_Param, GetName());
+}
+
+std::vector<TensorShape> LogicalBinaryLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+    ARMNN_ASSERT(inputShapes.size() == 2);
+    const TensorShape& input0 = inputShapes[0];
+    const TensorShape& input1 = inputShapes[1];
+
+    ARMNN_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];
+
+        ARMNN_ASSERT_MSG(dim0 == dim1 || dim0 == 1 || dim1 == 1,
+                         "Dimensions should either match or one should be of size 1.");
+
+        dims[i] = std::max(dim0, dim1);
+    }
+
+    return std::vector<TensorShape>({ TensorShape(numDims, dims.data()) });
+}
+
+void LogicalBinaryLayer::ValidateTensorShapesFromInputs()
+{
+    VerifyLayerConnections(2, CHECK_LOCATION());
+
+    const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
+
+    VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
+
+    std::vector<TensorShape> inferredShapes = InferOutputShapes({
+        GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
+        GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape()
+    });
+    ARMNN_ASSERT(inferredShapes.size() == 1);
+
+    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "LogicalBinaryLayer");
+}
+
+void LogicalBinaryLayer::Accept(ILayerVisitor& visitor) const
+{
+    visitor.VisitLogicalBinaryLayer(this, GetParameters(), GetName());
+}
+
+} // namespace armnn