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, 75 insertions, 0 deletions

diff --git a/src/backends/reference/workloads/RefLogicalBinaryWorkload.cpp b/src/backends/reference/workloads/RefLogicalBinaryWorkload.cpp
new file mode 100644
index 0000000000..1b4e8f9aa0
--- /dev/null
+++ b/src/backends/reference/workloads/RefLogicalBinaryWorkload.cpp
@@ -0,0 +1,75 @@
+//
+// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "RefLogicalBinaryWorkload.hpp"
+
+#include "Decoders.hpp"
+#include "ElementwiseFunction.hpp"
+#include "Encoders.hpp"
+#include "RefWorkloadUtils.hpp"
+
+#include <Profiling.hpp>
+
+#include <armnn/TypesUtils.hpp>
+
+namespace armnn
+{
+
+RefLogicalBinaryWorkload::RefLogicalBinaryWorkload(const LogicalBinaryQueueDescriptor& desc,
+                                                   const WorkloadInfo& info)
+    : BaseWorkload<LogicalBinaryQueueDescriptor>(desc, info)
+{}
+
+void RefLogicalBinaryWorkload::PostAllocationConfigure()
+{
+    const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]);
+    const TensorInfo& inputInfo1 = GetTensorInfo(m_Data.m_Inputs[1]);
+    const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
+
+    m_Input0 = MakeDecoder<InType>(inputInfo0);
+    m_Input1 = MakeDecoder<InType>(inputInfo1);
+    m_Output = MakeEncoder<OutType>(outputInfo);
+}
+
+void RefLogicalBinaryWorkload::Execute() const
+{
+    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefLogicalBinaryWorkload_Execute");
+
+    const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]);
+    const TensorInfo& inputInfo1 = GetTensorInfo(m_Data.m_Inputs[1]);
+    const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
+
+    const TensorShape& inShape0 = inputInfo0.GetShape();
+    const TensorShape& inShape1 = inputInfo1.GetShape();
+    const TensorShape& outShape = outputInfo.GetShape();
+
+    m_Input0->Reset(m_Data.m_Inputs[0]->Map());
+    m_Input1->Reset(m_Data.m_Inputs[1]->Map());
+    m_Output->Reset(m_Data.m_Outputs[0]->Map());
+
+    using AndFunction = LogicalBinaryFunction<std::logical_and<bool>>;
+    using OrFunction  = LogicalBinaryFunction<std::logical_or<bool>>;
+
+    switch (m_Data.m_Parameters.m_Operation)
+    {
+        case LogicalBinaryOperation::LogicalAnd:
+        {
+            AndFunction(inShape0, inShape1, outShape, *m_Input0, *m_Input1, *m_Output);
+            break;
+        }
+        case LogicalBinaryOperation::LogicalOr:
+        {
+            OrFunction(inShape0, inShape1, outShape, *m_Input0, *m_Input1, *m_Output);
+            break;
+        }
+        default:
+        {
+            throw InvalidArgumentException(std::string("Unsupported Logical Binary operation") +
+                GetLogicalBinaryOperationAsCString(m_Data.m_Parameters.m_Operation), CHECK_LOCATION());
+        }
+    }
+}
+
+} // namespace armnn