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

10 files changed, 113 insertions, 39 deletions

diff --git a/src/armnn/layers/ChannelShuffleLayer.hpp b/src/armnn/layers/ChannelShuffleLayer.hpp
index 79ab426a44..45cd5e5d82 100644
--- a/src/armnn/layers/ChannelShuffleLayer.hpp
+++ b/src/armnn/layers/ChannelShuffleLayer.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2021-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 #pragma once
@@ -26,7 +26,6 @@ public:
     /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated.
     void ValidateTensorShapesFromInputs() override;
 
-    // TODO Do you need to create an InferOutputShapes function for ChannelShuffle?
 protected:
     ChannelShuffleLayer(const ChannelShuffleDescriptor& param, const char* name);
 
diff --git a/src/armnn/layers/DetectionPostProcessLayer.cpp b/src/armnn/layers/DetectionPostProcessLayer.cpp
index 28c6d50659..33f894414a 100644
--- a/src/armnn/layers/DetectionPostProcessLayer.cpp
+++ b/src/armnn/layers/DetectionPostProcessLayer.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
 //
 
@@ -49,30 +49,46 @@ void DetectionPostProcessLayer::ValidateTensorShapesFromInputs()
 
     ARMNN_ASSERT_MSG(GetNumOutputSlots() == 4, "DetectionPostProcessLayer: The layer should return 4 outputs.");
 
-    unsigned int detectedBoxes = m_Param.m_MaxDetections * m_Param.m_MaxClassesPerDetection;
+    std::vector<TensorShape> inferredShapes = InferOutputShapes(
+            { GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
+              GetInputSlot(1).GetConnection()->GetTensorInfo().GetShape() });
 
-    const TensorShape& inferredDetectionBoxes = TensorShape({ 1, detectedBoxes, 4 });
-    const TensorShape& inferredDetectionScores = TensorShape({ 1, detectedBoxes });
-    const TensorShape& inferredNumberDetections = TensorShape({ 1 });
+    ARMNN_ASSERT(inferredShapes.size() == 4);
+    ARMNN_ASSERT(inferredShapes[0].GetDimensionality() == Dimensionality::Specified);
+    ARMNN_ASSERT(inferredShapes[1].GetDimensionality() == Dimensionality::Specified);
+    ARMNN_ASSERT(inferredShapes[2].GetDimensionality() == Dimensionality::Specified);
+    ARMNN_ASSERT(inferredShapes[3].GetDimensionality() == Dimensionality::Specified);
 
-    ValidateAndCopyShape(outputShape, inferredDetectionBoxes, m_ShapeInferenceMethod, "DetectionPostProcessLayer");
+    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "DetectionPostProcessLayer");
 
     ValidateAndCopyShape(GetOutputSlot(1).GetTensorInfo().GetShape(),
-                         inferredDetectionScores,
+                         inferredShapes[1],
                          m_ShapeInferenceMethod,
                          "DetectionPostProcessLayer", 1);
 
     ValidateAndCopyShape(GetOutputSlot(2).GetTensorInfo().GetShape(),
-                         inferredDetectionScores,
+                         inferredShapes[2],
                          m_ShapeInferenceMethod,
                          "DetectionPostProcessLayer", 2);
 
     ValidateAndCopyShape(GetOutputSlot(3).GetTensorInfo().GetShape(),
-                         inferredNumberDetections,
+                         inferredShapes[3],
                          m_ShapeInferenceMethod,
                          "DetectionPostProcessLayer", 3);
 }
 
+std::vector<TensorShape> DetectionPostProcessLayer::InferOutputShapes(const std::vector<TensorShape>&) const
+{
+    unsigned int detectedBoxes = m_Param.m_MaxDetections * m_Param.m_MaxClassesPerDetection;
+
+    std::vector<TensorShape> results;
+    results.push_back({ 1, detectedBoxes, 4 });
+    results.push_back({ 1, detectedBoxes });
+    results.push_back({ 1, detectedBoxes });
+    results.push_back({ 1 });
+    return results;
+}
+
 Layer::ConstantTensors DetectionPostProcessLayer::GetConstantTensorsByRef()
 {
     // For API stability DO NOT ALTER order and add new members to the end of vector
diff --git a/src/armnn/layers/DetectionPostProcessLayer.hpp b/src/armnn/layers/DetectionPostProcessLayer.hpp
index 07eb270f1f..e203032db0 100644
--- a/src/armnn/layers/DetectionPostProcessLayer.hpp
+++ b/src/armnn/layers/DetectionPostProcessLayer.hpp
@@ -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
 //
 
@@ -34,6 +34,12 @@ public:
     /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated.
     void ValidateTensorShapesFromInputs() override;
 
+    /// The model does not specify the output shapes. The output shapes are calculated from the max_detection and
+    /// max_classes_per_detection parameters in the DetectionPostProcessDescriptor.
+    /// @param [in] inputShapes The input shapes layer has. These are ignored for DetectionPostProcessLayer.
+    /// @return A vector to the inferred output shape.
+    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
     void ExecuteStrategy(IStrategy& strategy) const override;
 
 protected:
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
diff --git a/src/armnn/layers/MeanLayer.hpp b/src/armnn/layers/MeanLayer.hpp
index 87998bfc08..3bde7c6fcc 100644
--- a/src/armnn/layers/MeanLayer.hpp
+++ b/src/armnn/layers/MeanLayer.hpp
@@ -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
 //
 
@@ -29,6 +29,12 @@ public:
     /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated.
     void ValidateTensorShapesFromInputs() override;
 
+    /// By default returns inputShapes if the number of inputs are equal to number of outputs,
+    /// otherwise infers the output shapes from given input shapes and layer properties.
+    /// @param [in] inputShapes The input shapes layer has.
+    /// @return A vector to the inferred output shape.
+    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
     void ExecuteStrategy(IStrategy& strategy) const override;
 
 protected:
diff --git a/src/armnn/layers/RankLayer.cpp b/src/armnn/layers/RankLayer.cpp
index 0f9327b948..6ea271442f 100644
--- a/src/armnn/layers/RankLayer.cpp
+++ b/src/armnn/layers/RankLayer.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -31,6 +31,11 @@ Layer* RankLayer::Clone(Graph& graph) const
     return clone;
 }
 
+std::vector<TensorShape> RankLayer::InferOutputShapes(const std::vector<TensorShape>&) const
+{
+    return std::vector<TensorShape>({ TensorShape(Dimensionality::Scalar) });
+}
+
 void RankLayer::ValidateTensorShapesFromInputs()
 {
     VerifyLayerConnections(1, CHECK_LOCATION());
diff --git a/src/armnn/layers/RankLayer.hpp b/src/armnn/layers/RankLayer.hpp
index 52d14c446e..a789917de0 100644
--- a/src/armnn/layers/RankLayer.hpp
+++ b/src/armnn/layers/RankLayer.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -12,21 +12,36 @@ namespace armnn
 
 class RankLayer : public Layer
 {
-    public:
-        /// Makes a workload for the Rank type.
-        /// @param [in] factory The workload factory which will create the workload.
-        /// @return A pointer to the created workload, or nullptr if not created.
-        virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory& factory) const override;
+public:
+    /// Makes a workload for the Rank type.
+    /// @param [in] factory The workload factory which will create the workload.
+    /// @return A pointer to the created workload, or nullptr if not created.
+    virtual std::unique_ptr<IWorkload> CreateWorkload(const IWorkloadFactory& factory) const override;
 
-        Layer* Clone(Graph& graph) const override;
+    /// Creates a dynamically-allocated copy of this layer.
+    /// @param [in] graph The graph into which this layer is being cloned.
+    Layer* Clone(Graph& graph) const override;
 
-        void ValidateTensorShapesFromInputs() override;
+    /// Check if the input tensor shape(s)
+    /// will lead to a valid configuration of @ref RankLayer.
+    /// @param [in] shapeInferenceMethod Indicates if output shape shall be overwritten or just validated.
+    void ValidateTensorShapesFromInputs() override;
 
-        void ExecuteStrategy(IStrategy& strategy) const override;
+    /// Rank returns a scalar specifying the rank of the input tensor. The rank of a tensor is the number
+    /// of dimensions it has.
+    /// @param [in] inputShapes The input shapes layer has. This is ignored for Rank.
+    /// @return A vector to the inferred output shape.
+    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
+    void ExecuteStrategy(IStrategy& strategy) const override;
 
 protected:
-        RankLayer(const char* name);
-        ~RankLayer() = default;
+    /// Constructor to create a RankLayer.
+    /// @param [in] name Optional name for the layer.
+    RankLayer(const char* name);
+
+    /// Default destructor
+    ~RankLayer() = default;
 };
 
 } //namespace armnn
diff --git a/src/armnn/layers/ReduceLayer.cpp b/src/armnn/layers/ReduceLayer.cpp
index aa54bc8f0c..e411996ced 100644
--- a/src/armnn/layers/ReduceLayer.cpp
+++ b/src/armnn/layers/ReduceLayer.cpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Samsung Electronics Co Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 
@@ -53,6 +53,19 @@ void ReduceLayer::ValidateTensorShapesFromInputs()
     ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
                      "ReduceLayer: Reduce supports up to 4D input.");
 
+    std::vector<TensorShape> inferredShapes = InferOutputShapes( {input.GetShape() });
+
+    ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "ReduceLayer");
+}
+
+std::vector<TensorShape> ReduceLayer::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,
+                     "ReduceLayer: Reduce supports up to 4D input.");
+
     unsigned int rank = input.GetNumDimensions();
     unsigned int outputRank = 0;
 
@@ -87,7 +100,7 @@ void ReduceLayer::ValidateTensorShapesFromInputs()
         {
             if (std::find(m_Param.m_vAxis.begin(), m_Param.m_vAxis.end(), i) == m_Param.m_vAxis.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)
@@ -97,9 +110,7 @@ void ReduceLayer::ValidateTensorShapesFromInputs()
             }
         }
     }
-    const TensorShape& inferredShape = TensorShape(outputRank, dimSizes.data());
-
-    ValidateAndCopyShape(outputShape, inferredShape, m_ShapeInferenceMethod, "ReduceLayer");
+    return std::vector<TensorShape>({ TensorShape(outputRank, dimSizes.data()) });
 }
 
 void ReduceLayer::ExecuteStrategy(IStrategy& strategy) const
diff --git a/src/armnn/layers/ReduceLayer.hpp b/src/armnn/layers/ReduceLayer.hpp
index e9ea5d8e3f..24f19f812f 100644
--- a/src/armnn/layers/ReduceLayer.hpp
+++ b/src/armnn/layers/ReduceLayer.hpp
@@ -1,5 +1,5 @@
 //
-// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved.
+// Copyright © 2020-2023 Samsung Electronics Co Ltd and Contributors. All rights reserved.
 // SPDX-License-Identifier: MIT
 //
 #pragma once
@@ -23,13 +23,18 @@ public:
     /// @param [in] graph The graph into which this layer is being cloned.
     ReduceLayer* Clone(Graph& graph) const override;
 
+    /// By default returns inputShapes if the number of inputs are equal to number of outputs,
+    /// otherwise infers the output shapes from given input shapes and layer properties.
+    /// @param [in] inputShapes The input shapes layer has.
+    /// @return A vector to the inferred output shape.
+    std::vector<TensorShape> InferOutputShapes(const std::vector<TensorShape>& inputShapes) const override;
+
     /// Check if the input tensor shape(s)
     /// will lead to a valid configuration of @ref ReduceLayer.
     void ValidateTensorShapesFromInputs() override;
 
     void ExecuteStrategy(IStrategy& strategy) const override;
 
-
 protected:
     /// Constructor to create a ReduceLayer.
     /// @param [in] param ReduceDescriptor to configure the reduction operation.
diff --git a/src/armnn/layers/ReshapeLayer.cpp b/src/armnn/layers/ReshapeLayer.cpp
index c5ec45f211..e68460bf32 100644
--- a/src/armnn/layers/ReshapeLayer.cpp
+++ b/src/armnn/layers/ReshapeLayer.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
 //
 #include "ReshapeLayer.hpp"
@@ -49,6 +49,7 @@ void ReshapeLayer::ValidateTensorShapesFromInputs()
     auto 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, "ReshapeLayer");
 }