1 //
2 // Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "Pooling3dLayer.hpp"
7
8 #include "LayerCloneBase.hpp"
9
10 #include <armnn/TypesUtils.hpp>
11
12 #include <armnnUtils/DataLayoutIndexed.hpp>
13
14 #include <armnn/backends/WorkloadData.hpp>
15 #include <armnn/backends/WorkloadFactory.hpp>
16
17 using namespace armnnUtils;
18
19 namespace armnn
20 {
21
Pooling3dLayer(const Pooling3dDescriptor & param,const char * name)22 Pooling3dLayer::Pooling3dLayer(const Pooling3dDescriptor& param, const char* name)
23 : LayerWithParameters(1, 1, LayerType::Pooling3d, param, name)
24 {
25 }
26
CreateWorkload(const IWorkloadFactory & factory) const27 std::unique_ptr<IWorkload> Pooling3dLayer::CreateWorkload(const IWorkloadFactory& factory) const
28 {
29 Pooling3dQueueDescriptor descriptor;
30 SetAdditionalInfo(descriptor);
31
32 return factory.CreateWorkload(LayerType::Pooling3d, descriptor, PrepInfoAndDesc(descriptor));
33 }
34
Clone(Graph & graph) const35 Pooling3dLayer* Pooling3dLayer::Clone(Graph& graph) const
36 {
37 return CloneBase<Pooling3dLayer>(graph, m_Param, GetName());
38 }
39
InferOutputShapes(const std::vector<TensorShape> & inputShapes) const40 std::vector<TensorShape> Pooling3dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
41 {
42 ARMNN_ASSERT(inputShapes.size() == 1);
43 const TensorShape& inputShape = inputShapes[0];
44 const DataLayoutIndexed dimensionIndices = m_Param.m_DataLayout;
45
46 // If we support multiple batch dimensions in the future, then this assert will need to change.
47 ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 5, "Pooling3dLayer will always have 5D input.");
48
49 unsigned int inWidth = inputShape[dimensionIndices.GetWidthIndex()];
50 unsigned int inHeight = inputShape[dimensionIndices.GetHeightIndex()];
51 unsigned int inDepth = inputShape[dimensionIndices.GetDepthIndex()];
52 unsigned int inChannels = inputShape[dimensionIndices.GetChannelsIndex()];
53 unsigned int inBatchSize = inputShape[0];
54
55 bool isGlobalPooling = (m_Param.m_StrideX==0 && m_Param.m_StrideY==0 && m_Param.m_StrideZ==0);
56 unsigned int outWidth = 1;
57 unsigned int outHeight = 1;
58 unsigned int outDepth = 1;
59 if (!isGlobalPooling)
60 {
61 ARMNN_ASSERT_MSG(m_Param.m_StrideX!=0 && m_Param.m_StrideY!=0 && m_Param.m_StrideZ!=0,
62 "Stride can only be zero when performing global pooling");
63
64 auto CalcSize = [](auto inSize, auto lowPad, auto highPad, auto poolSize, auto stride, auto outputShapeRounding)
65 {
66 unsigned int readSize = inSize + lowPad + highPad - poolSize;
67 float div = static_cast<float>(readSize) / static_cast<float>(stride);
68
69 unsigned int size = 0;
70 switch (outputShapeRounding)
71 {
72 case OutputShapeRounding::Ceiling:
73 size = static_cast<unsigned int>(ceil(div)) + 1;
74 break;
75 case OutputShapeRounding ::Floor:
76 size = static_cast<unsigned int>(floor(div)) + 1;
77 break;
78 default:
79 ARMNN_ASSERT_MSG(false, "Unsupported Output Shape Rounding");
80 }
81
82 // Makes sure that border operations will start from inside the input and not the padded area.
83 // This is what CL does...
84 if ((size - 1)*stride >= inSize + lowPad)
85 {
86 --size;
87 }
88
89 return size;
90 };
91
92 outWidth = CalcSize(inWidth, m_Param.m_PadLeft, m_Param.m_PadRight, m_Param.m_PoolWidth, m_Param.m_StrideX,
93 m_Param.m_OutputShapeRounding);
94 outHeight = CalcSize(inHeight, m_Param.m_PadTop, m_Param.m_PadBottom, m_Param.m_PoolHeight, m_Param.m_StrideY,
95 m_Param.m_OutputShapeRounding);
96 outDepth = CalcSize(inDepth, m_Param.m_PadFront, m_Param.m_PadBack, m_Param.m_PoolDepth, m_Param.m_StrideZ,
97 m_Param.m_OutputShapeRounding);
98 }
99 unsigned int outChannels = inChannels;
100 unsigned int outBatchSize = inBatchSize;
101
102 TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NDHWC ?
103 TensorShape( { outBatchSize, outDepth, outHeight, outWidth, outChannels } ) :
104 TensorShape( { outBatchSize, outChannels, outDepth, outHeight, outWidth });
105
106 return std::vector<TensorShape>({ tensorShape });
107 }
108
ValidateTensorShapesFromInputs()109 void Pooling3dLayer::ValidateTensorShapesFromInputs()
110 {
111 VerifyLayerConnections(1, CHECK_LOCATION());
112
113 const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
114
115 VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
116
117 auto inferredShapes = InferOutputShapes({ GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape() });
118
119 ARMNN_ASSERT(inferredShapes.size() == 1);
120
121 ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Pooling3dLayer");
122 }
123
ExecuteStrategy(IStrategy & strategy) const124 void Pooling3dLayer::ExecuteStrategy(IStrategy& strategy) const
125 {
126 strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
127 }
128
129 } // namespace armnn
130