From 377fb21e956ea68ffd234be47481002a0e46ee46 Mon Sep 17 00:00:00 2001
From: Mike Kelly <mike.kelly@arm.com>
Date: Tue, 10 Jan 2023 15:55:28 +0000
Subject: IVGCVSW-7244 ConcatLayer overlapping views on TFLiteParser

 * Added ability to calculate dynamic tensors and propagate
   them through the model so that when those tensors are later
   used as inputs they have the right shapes.
 * Added InferOutputShapes to DetectionPostProcessLayer.
 * Added InferOutputShapes to MeanLayer.
 * Added InferOutputShapes to RankLayer.
 * Added InferOutputShapes to ReduceLayer.
 * Fixed typos in TfLiteParser.

Signed-off-by: Mike Kelly <mike.kelly@arm.com>
Change-Id: I880c0716938ef278f5dbf01a8a73a5cc99ce5ded
---
 src/armnn/layers/MeanLayer.cpp | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

(limited to 'src/armnn/layers/MeanLayer.cpp')

diff --git a/src/armnn/layers/MeanLayer.cpp b/src/armnn/layers/MeanLayer.cpp
index 49eac04a8e..a6f721b076 100644
--- a/src/armnn/layers/MeanLayer.cpp
+++ b/src/armnn/layers/MeanLayer.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -49,7 +49,19 @@ void MeanLayer::ValidateTensorShapesFromInputs()
 
     VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
 
-    const TensorInfo& input = GetInputSlot(0).GetConnection()->GetTensorInfo();
+    std::vector<TensorShape> inferredShapes = InferOutputShapes(
+            { GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
+
+    ARMNN_ASSERT(inferredShapes.size() == 1);
+    ARMNN_ASSERT(inferredShapes[0].GetDimensionality() == Dimensionality::Specified);
+
+    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "MeanLayer");
+}
+
+std::vector<TensorShape> MeanLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
+{
+    ARMNN_ASSERT(inputShapes.size() == 1);
+    const TensorShape& input = inputShapes[0];
 
     ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
                      "MeanLayer: Mean supports up to 4D input.");
@@ -88,7 +100,7 @@ void MeanLayer::ValidateTensorShapesFromInputs()
         {
             if (std::find(m_Param.m_Axis.begin(), m_Param.m_Axis.end(), i) == m_Param.m_Axis.end())
             {
-                dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input.GetShape()[i]);
+                dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input[i]);
                 ++outputIndex;
             }
             else if (m_Param.m_KeepDims)
@@ -98,9 +110,7 @@ void MeanLayer::ValidateTensorShapesFromInputs()
             }
         }
     }
-    const TensorShape& inferredShape = TensorShape(outputRank, dimSizes.data());
-
-    ValidateAndCopyShape(outputShape, inferredShape, m_ShapeInferenceMethod, "MeanLayer");
+    return std::vector<TensorShape>({ TensorShape(outputRank, dimSizes.data()) });
 }
 
 void MeanLayer::ExecuteStrategy(IStrategy& strategy) const
-- 
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