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
|
//
// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "Convolution2dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <armnnUtils/DataLayoutIndexed.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
#include <string>
using namespace armnnUtils;
namespace armnn
{
Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name)
: LayerWithParameters(param.GetNumInputs(), 1, LayerType::Convolution2d, param, name)
{
}
void Convolution2dLayer::SerializeLayerParameters(ParameterStringifyFunction& fn) const
{
//using DescriptorType = Parameters;
const std::vector<TensorShape>& inputShapes =
{
GetInputSlot(0).GetTensorInfo().GetShape(),
GetInputSlot(1).GetTensorInfo().GetShape()
};
const TensorShape filterShape = inputShapes[1];
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
unsigned int filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
unsigned int outChannels = filterShape[0];
fn("OutputChannels",std::to_string(outChannels));
fn("FilterWidth",std::to_string(filterWidth));
fn("FilterHeight",std::to_string(filterHeight));
LayerWithParameters<Convolution2dDescriptor>::SerializeLayerParameters(fn);
}
std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const IWorkloadFactory& factory) const
{
ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "Convolution2dLayer_CreateWorkload");
Convolution2dQueueDescriptor descriptor;
SetAdditionalInfo(descriptor);
return factory.CreateWorkload(LayerType::Convolution2d, descriptor, PrepInfoAndDesc(descriptor));
}
Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
return std::move(layer);
}
std::vector<TensorShape> Convolution2dLayer::InferOutputShapes(const std::vector<TensorShape>& inputShapes) const
{
ARMNN_ASSERT(inputShapes.size() == 2);
const TensorShape& inputShape = inputShapes[0];
const TensorShape filterShape = inputShapes[1];
// If we support multiple batch dimensions in the future, then this assert will need to change.
ARMNN_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
ARMNN_ASSERT( m_Param.m_StrideX > 0);
ARMNN_ASSERT( m_Param.m_StrideY > 0);
DataLayoutIndexed dataLayoutIndex(m_Param.m_DataLayout);
unsigned int inWidth = inputShape[dataLayoutIndex.GetWidthIndex()];
unsigned int inHeight = inputShape[dataLayoutIndex.GetHeightIndex()];
unsigned int inBatchSize = inputShape[0];
unsigned int filterWidth = filterShape[dataLayoutIndex.GetWidthIndex()];
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 filterHeight = filterShape[dataLayoutIndex.GetHeightIndex()];
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 outChannels = filterShape[0];
unsigned int outBatchSize = inBatchSize;
TensorShape tensorShape = m_Param.m_DataLayout == armnn::DataLayout::NHWC ?
TensorShape( { outBatchSize, outHeight, outWidth, outChannels } ) :
TensorShape( { outBatchSize, outChannels, outHeight, outWidth });
return std::vector<TensorShape>({ tensorShape });
}
void Convolution2dLayer::ValidateTensorShapesFromInputs()
{
VerifyLayerConnections(m_Param.GetNumInputs(), CHECK_LOCATION());
const TensorShape& outputShape = GetOutputSlot(0).GetTensorInfo().GetShape();
VerifyShapeInferenceType(outputShape, m_ShapeInferenceMethod);
ARMNN_ASSERT_MSG(GetInputSlot(1).GetConnection(),
"Convolution2dLayer: Weights should be connected to input slot 1.");
std::vector<TensorShape> inferredShapes = InferOutputShapes({
GetInputSlot(0).GetTensorInfo().GetShape(),
GetInputSlot(1).GetTensorInfo().GetShape() });
ARMNN_ASSERT(inferredShapes.size() == 1);
ValidateAndCopyShape(outputShape, inferredShapes[0], m_ShapeInferenceMethod, "Convolution2dLayer");
}
Layer::ImmutableConstantTensors Convolution2dLayer::GetConstantTensorsByRef() const
{
Layer::ImmutableConstantTensors tensors = GetConnectedConstantAsInputTensors();
return tensors;
}
void Convolution2dLayer::ExecuteStrategy(IStrategy& strategy) const
{
strategy.ExecuteStrategy(this, GetParameters(), {}, GetName());
}
} // namespace armnn
|
