1 //
2 // Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "MeanLayer.hpp"
7 #include "LayerCloneBase.hpp"
8
9 #include <armnn/utility/NumericCast.hpp>
10
11 #include <armnn/backends/TensorHandle.hpp>
12 #include <armnn/backends/WorkloadData.hpp>
13 #include <armnn/backends/WorkloadFactory.hpp>
14
15 #include <cstring>
16
17 namespace armnn
18 {
19
MeanLayer(const armnn::MeanDescriptor & param,const char * name)20 MeanLayer::MeanLayer(const armnn::MeanDescriptor& param, const char* name)
21 : LayerWithParameters(1, 1, LayerType::Mean, param, name)
22 {}
23
CreateWorkload(const armnn::IWorkloadFactory & factory) const24 std::unique_ptr<IWorkload> MeanLayer::CreateWorkload(const armnn::IWorkloadFactory& factory) const
25 {
26 MeanQueueDescriptor descriptor;
27 descriptor.m_Parameters.m_Axis = m_Param.m_Axis;
28 descriptor.m_Parameters.m_KeepDims = m_Param.m_KeepDims;
29 SetAdditionalInfo(descriptor);
30
31 return factory.CreateWorkload(LayerType::Mean, descriptor, PrepInfoAndDesc(descriptor));
32 }
33
Clone(Graph & graph) const34 MeanLayer* MeanLayer::Clone(Graph& graph) const
35 {
36 auto layer = CloneBase<MeanLayer>(graph, m_Param, GetName());
37
38 layer->m_Param.m_Axis = m_Param.m_Axis;
39 layer->m_Param.m_KeepDims = m_Param.m_KeepDims;
40
41 return std::move(layer);
42 }
43
ValidateTensorShapesFromInputs()44 void MeanLayer::ValidateTensorShapesFromInputs()
45 {
46 VerifyLayerConnections(1, CHECK_LOCATION());
47
48 const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
49
50 VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
51
52 std::vector<TensorShape> inferredShapes = InferOutputShapes(
53 { GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
54
55 ARMNN_ASSERT(inferredShapes.size() == 1);
56 ARMNN_ASSERT(inferredShapes[0].GetDimensionality() == Dimensionality::Specified);
57
58 ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "MeanLayer");
59 }
60
InferOutputShapes(const std::vector<TensorShape> & inputShapes) const61 std::vector<TensorShape> MeanLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
62 {
63 ARMNN_ASSERT(inputShapes.size() == 1);
64 const TensorShape& input = inputShapes[0];
65
66 ARMNN_ASSERT_MSG(input.GetNumDimensions() > 0 && input.GetNumDimensions() <= 4,
67 "MeanLayer: Mean supports up to 4D input.");
68
69 unsigned int rank = input.GetNumDimensions();
70 unsigned int outputRank = 0;
71
72 // Calculate output dimension
73 if (m_Param.m_KeepDims)
74 {
75 outputRank = rank;
76 }
77 else if (m_Param.m_Axis.empty())
78 {
79 outputRank = 1;
80 }
81 else if (m_Param.m_Axis.size() > input.GetNumDimensions())
82 {
83 throw LayerValidationException("MeanLayer: Dimensions to reduce can not be bigger than input dimensions");
84 }
85 else
86 {
87 outputRank = input.GetNumDimensions() - armnn::numeric_cast<unsigned int>(m_Param.m_Axis.size());
88 if (outputRank == 0)
89 {
90 outputRank = 1;
91 }
92 }
93
94 std::vector<unsigned int> dimSizes(outputRank, 1);
95 if (!m_Param.m_Axis.empty())
96 {
97 // Skip the dimension that has been reduced unless keepDims is true.
98 unsigned int outputIndex = 0;
99 for (unsigned int i = 0; i < input.GetNumDimensions(); ++i)
100 {
101 if (std::find(m_Param.m_Axis.begin(), m_Param.m_Axis.end(), i) == m_Param.m_Axis.end())
102 {
103 dimSizes[outputIndex] = armnn::numeric_cast<unsigned int>(input[i]);
104 ++outputIndex;
105 }
106 else if (m_Param.m_KeepDims)
107 {
108 dimSizes[outputIndex] = 1;
109 ++outputIndex;
110 }
111 }
112 }
113 return std::vector<TensorShape>({ TensorShape(outputRank, dimSizes.data()) });
114 }
115
ExecuteStrategy(IStrategy & strategy) const116 void MeanLayer::ExecuteStrategy(IStrategy& strategy) const
117 {
118 strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
119 }
120
121 } // namespace armnn
122