1 //
2 // Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5
6 #include "TransposeConvolution2dLayer.hpp"
7 #include "LayerCloneBase.hpp"
8
9 #include <armnnUtils/DataLayoutIndexed.hpp>
10
11 #include <backendsCommon/CpuTensorHandle.hpp>
12 #include <backendsCommon/WorkloadFactory.hpp>
13
14 using namespace armnnUtils;
15
16 namespace armnn
17 {
18
TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor & param,const char * name)19 TransposeConvolution2dLayer::TransposeConvolution2dLayer(const TransposeConvolution2dDescriptor& param,
20 const char* name)
21 : LayerWithParameters(1, 1, LayerType::TransposeConvolution2d, param, name)
22 {
23 }
24
CreateWorkload(const IWorkloadFactory & factory) const25 std::unique_ptr<IWorkload> TransposeConvolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
26 {
27 ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weights data should not be null.");
28
29 TransposeConvolution2dQueueDescriptor descriptor;
30 descriptor.m_Weight = m_Weight.get();
31
32 if (m_Param.m_BiasEnabled)
33 {
34 ARMNN_ASSERT_MSG(m_Bias != nullptr, "TransposeConvolution2dLayer: Bias data should not be null.");
35 descriptor.m_Bias = m_Bias.get();
36 }
37
38 SetAdditionalInfo(descriptor);
39
40 return factory.CreateTransposeConvolution2d(descriptor, PrepInfoAndDesc(descriptor));
41 }
42
Clone(Graph & graph) const43 TransposeConvolution2dLayer* TransposeConvolution2dLayer::Clone(Graph& graph) const
44 {
45 auto layer = CloneBase<TransposeConvolution2dLayer>(graph, m_Param, GetName());
46
47 layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
48
49 if (layer->m_Param.m_BiasEnabled)
50 {
51 layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
52 }
53
54 return std::move(layer);
55 }
56
InferOutputShapes(const std::vector<TensorShape> & inputShapes) const57 std::vector<TensorShape> TransposeConvolution2dLayer::InferOutputShapes(
58 const std::vector<TensorShape>& inputShapes) const
59 {
60 ARMNN_ASSERT(inputShapes.size() == 2);
61 const TensorShape& inputShape = inputShapes[0];
62 const TensorShape& kernelShape = inputShapes[1];
63
64 ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Transpose convolutions will always have 4D input");
65
66 DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
67
68 const unsigned int batches = inputShape[0];
69
70 const unsigned int wInput = inputShape[dataLayoutIndex.GetWidthIndex()];
71 const unsigned int hInput = inputShape[dataLayoutIndex.GetHeightIndex()];
72
73 const unsigned int wKernel = kernelShape[dataLayoutIndex.GetWidthIndex()];
74 const unsigned int hKernel = kernelShape[dataLayoutIndex.GetHeightIndex()];
75
76 unsigned int wPadding = m_Param.m_PadLeft + m_Param.m_PadRight;
77 unsigned int hPadding = m_Param.m_PadTop + m_Param.m_PadBottom;
78
79 unsigned int wOutput = (wInput - 1) * m_Param.m_StrideX + wKernel - wPadding;
80 unsigned int hOutput = (hInput - 1) * m_Param.m_StrideY + hKernel - hPadding;
81
82 unsigned int kernelElements = kernelShape[0] * kernelShape[dataLayoutIndex.GetChannelsIndex()];
83 unsigned int inputElements = batches * inputShape[dataLayoutIndex.GetChannelsIndex()];
84
85 ARMNN_ASSERT_MSG(inputElements != 0, "Invalid number of input elements");
86
87 unsigned int channels;
88 if (kernelElements >= inputElements)
89 {
90 ARMNN_ASSERT_MSG(kernelElements % inputElements == 0 , "Invalid number of elements");
91 channels = kernelElements / inputElements;
92 }
93 else
94 {
95 ARMNN_ASSERT_MSG(inputElements % kernelElements == 0 , "Invalid number of elements");
96 channels = kernelShape[0];
97 }
98
99 TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
100 TensorShape( { batches, hOutput, wOutput, channels } ) :
101 TensorShape( { batches, channels, hOutput, wOutput });
102
103 return std::vector<TensorShape>({ tensorShape });
104 }
105
ValidateTensorShapesFromInputs()106 void TransposeConvolution2dLayer::ValidateTensorShapesFromInputs()
107 {
108 VerifyLayerConnections(1, CHECK_LOCATION());
109
110 const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
111
112 VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
113
114 ARMNN_ASSERT_MSG(m_Weight != nullptr, "TransposeConvolution2dLayer: Weight data cannot be null.");
115
116 std::vector<TensorShape> expectedOutputShape;
117 // If output_shape was specified then use it rather than calculate an inferred output shape.
118 if (m_Param.m_OutputShapeEnabled)
119 {
120 TensorShape shapeAsTensorShape(static_cast<unsigned int>(m_Param.m_OutputShape.size()),
121 m_Param.m_OutputShape.data());
122 expectedOutputShape.push_back(shapeAsTensorShape);
123 }
124 else
125 {
126 expectedOutputShape = InferOutputShapes({GetInputSlot(0).GetConnection()->GetTensorInfo().GetShape(),
127 m_Weight->GetTensorInfo().GetShape() });
128 }
129
130 ARMNN_ASSERT(expectedOutputShape.size() == 1);
131
132 ValidateAndCopyShape(outputShape, expectedOutputShape[0], m_ShapeInferenceMethod, "TransposeConvolution2dLayer");
133 }
134
GetConstantTensorsByRef()135 Layer::ConstantTensors TransposeConvolution2dLayer::GetConstantTensorsByRef()
136 {
137 return {m_Weight, m_Bias};
138 }
139
Accept(ILayerVisitor & visitor) const140 void TransposeConvolution2dLayer::Accept(ILayerVisitor& visitor) const
141 {
142 ConstTensor weightsTensor(m_Weight->GetTensorInfo(), m_Weight->Map(true)) ;
143 Optional<ConstTensor> optionalBiasTensor = EmptyOptional();
144
145 if (GetParameters().m_BiasEnabled)
146 {
147 ConstTensor biasTensor(m_Bias->GetTensorInfo(), m_Bias->Map(true));
148 optionalBiasTensor = Optional<ConstTensor>(biasTensor);
149 }
150
151 visitor.VisitTransposeConvolution2dLayer(this, GetParameters(), weightsTensor, optionalBiasTensor, GetName());
152 }
153
154 } // namespace armnn
155