From d905decd256558bbee165e636ce4242ac3b9c917 Mon Sep 17 00:00:00 2001
From: Matthew Sloyan <matthew.sloyan@arm.com>
Date: Mon, 3 May 2021 12:22:03 +0100
Subject: MLCE-418 Reduce layer does not support multiple axes

 * Added backend specific optimization to chain new reduces layers
   for each axis to simulate behaviour of a layer with multiple axes.
 * Added function to calculate reduced output shape.
 * Added unit tests.

Signed-off-by: Matthew Sloyan <matthew.sloyan@arm.com>
Change-Id: I180b0b111b7bcf3d0c283f1db0b82d5f17757682
---
 src/backends/aclCommon/ArmComputeUtils.hpp | 55 ++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)

(limited to 'src/backends/aclCommon/ArmComputeUtils.hpp')

diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp
index d9efab288f..5bc5abcb05 100644
--- a/src/backends/aclCommon/ArmComputeUtils.hpp
+++ b/src/backends/aclCommon/ArmComputeUtils.hpp
@@ -7,6 +7,7 @@
 #include <armnn/Descriptors.hpp>
 #include <armnn/Tensor.hpp>
 #include <armnn/utility/Assert.hpp>
+#include <armnn/utility/NumericCast.hpp>
 #include <backendsCommon/WorkloadData.hpp>
 
 #include <arm_compute/core/Types.h>
@@ -267,4 +268,58 @@ inline arm_compute::ReductionOperation ConvertReductionOperationToAcl(const Redu
     }
 }
 
+/// Function to compute the output tensor shape based on the axes and if keepDims is set.
+inline const TensorShape ComputeReductionTensorShape(const armnn::TensorInfo& input,
+                                                     const std::vector<uint32_t>& vAxis,
+                                                     const bool keepDims)
+{
+    unsigned int rank = input.GetNumDimensions();
+    unsigned int outputRank = 0;
+
+    // Calculate output dimension
+    if (keepDims)
+    {
+        outputRank = rank;
+    }
+    else if (vAxis.empty())
+    {
+        outputRank = 1;
+    }
+    else if (vAxis.size() > input.GetNumDimensions())
+    {
+        throw LayerValidationException("ReduceLayer: Dimensions to reduce can not be bigger than input dimensions");
+    }
+    else
+    {
+        outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(vAxis.size());
+        if (outputRank == 0)
+        {
+            outputRank = 1;
+        }
+    }
+
+    std::vector<unsigned int> dimSizes(outputRank, 1);
+    if (!vAxis.empty())
+    {
+        // Skip the dimension that has been reduced unless keepDims is true.
+        unsigned int outputIndex = 0;
+        for (unsigned int i = 0; i < input.GetNumDimensions(); ++i)
+        {
+            if (std::find(vAxis.begin(), vAxis.end(), i) == vAxis.end())
+            {
+                dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input.GetShape()[i]);
+                ++outputIndex;
+            }
+            else if (keepDims)
+            {
+                dimSizes[outputIndex] = 1;
+                ++outputIndex;
+            }
+        }
+    }
+
+    const TensorShape inferredShape = TensorShape(outputRank, dimSizes.data());
+    return inferredShape;
+}
+
 } // namespace armnn
-- 
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