1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
|
//
// Copyright © 2021-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Convolution3dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnn/backends/TensorHandle.hpp>
using namespace armnnUtils;
namespace armnn
{
Convolution3dLayer::Convolution3dLayer(const Convolution3dDescriptor& param, const char* name)
: LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution3d, param, name)
{
}
void Convolution3dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const
{
const std::vector<TensorShape>& inputShapes =
{
GetInputSlot(0).GetTensorInfo().GetShape(),
GetInputSlot(1).GetTensorInfo().GetShape(),
};
// Conv3d Filter Layout: [D,H,W,I,O]
const TensorShape filterShape = inputShapes[1];
unsigned int filterDepth = filterShape[0];
unsigned int filterHeight = filterShape[1];
unsigned int filterWidth = filterShape[2];
unsigned int inChannels = filterShape[3];
unsigned int outChannels = filterShape[4];
fn("FilterDepth",std::to_string(filterDepth));
fn("FilterHeight",std::to_string(filterHeight));
fn("FilterWidth",std::to_string(filterWidth));
fn("InputChannels",std::to_string(inChannels));
fn("OutputChannels",std::to_string(outChannels));
LayerWithParameters<Convolution3dDescriptor>::SerializeLayerParameters(fn);
}
std::unique_ptr<IWorkload> Convolution3dLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
Convolution3dQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::Convolution3d, descriptor, PrepInfoAndDesc(descriptor));
}
Convolution3dLayer* Convolution3dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<Convolution3dLayer>(graph, m_Param, GetName());
return std::move(layer);
}
std::vector<TensorShape> Convolution3dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
if (inputShapes.size() != 2)
{
throw armnn::Exception("inputShapes' size is \"" + std::to_string(inputShapes.size()) +
"\" - should be \"2\".");
}
const TensorShape& inputShape = inputShapes[0];
const TensorShape& filterShape = inputShapes[1];
if (inputShape.GetNumDimensions() != 5)
{
throw armnn::Exception("Convolutions will always have 5D input.");
}
if (m_Param.m_StrideX == 0)
{
throw armnn::Exception("m_StrideX cannot be 0.");
}
if (m_Param.m_StrideY == 0)
{
throw armnn::Exception("m_StrideY cannot be 0.");
}
if (m_Param.m_StrideZ == 0)
{
throw armnn::Exception("m_StrideZ cannot be 0.");
}
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
unsigned int inDepth = inputShape[dataLayoutIndex.GetDepthIndex()];
unsigned int inBatchSize = inputShape[0];
// Conv3d Filter Layout: [D,H,W,I,O]
unsigned int filterDepth = filterShape[0];
unsigned int dilatedFilterDepth = filterDepth + (m_Param.m_DilationZ - 1) * (filterDepth - 1);
unsigned int readDepth = (inDepth + m_Param.m_PadFront + m_Param.m_PadBack) - dilatedFilterDepth;
unsigned int outDepth = 1 + (readDepth / m_Param.m_StrideZ);
unsigned int filterHeight = filterShape[1];
unsigned int dilatedFilterHeight = filterHeight + (m_Param.m_DilationY - 1) * (filterHeight - 1);
unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - dilatedFilterHeight;
unsigned int outHeight = 1 + (readHeight / m_Param.m_StrideY);
unsigned int filterWidth = filterShape[2];
unsigned int dilatedFilterWidth = filterWidth + (m_Param.m_DilationX - 1) * (filterWidth - 1);
unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - dilatedFilterWidth;
unsigned int outWidth = 1 + (readWidth / m_Param.m_StrideX);
unsigned int outChannels = filterShape[4];
unsigned int outBatchSize = inBatchSize;
TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NDHWC ?
TensorShape( { outBatchSize, outDepth, outHeight, outWidth, outChannels } ) :
TensorShape( { outBatchSize, outChannels, outDepth, outHeight, outWidth });
return std::vector<TensorShape>({ tensorShape });
}
void Convolution3dLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
if (!GetInputSlot(1).GetConnection())
{
throw armnn::LayerValidationException("Convolution3dLayer: Weights should be connected to input slot 1.");
}
auto inferredShapes = InferOutputShapes({
GetInputSlot(0).GetTensorInfo().GetShape(),
GetInputSlot(1).GetTensorInfo().GetShape() });
if (inferredShapes.size() != 1)
{
throw armnn::LayerValidationException("inferredShapes has "
+ std::to_string(inferredShapes.size()) +
" elements - should only have 1.");
}
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution3dLayer");
}
void Convolution3dLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn
|
