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
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#include "Convolution2dLayer.hpp"
#include "LayerCloneBase.hpp"
#include <armnn/TypesUtils.hpp>
#include <backends/CpuTensorHandle.hpp>
#include <backends/WorkloadFactory.hpp>
namespace armnn
{
Convolution2dLayer::Convolution2dLayer(const Convolution2dDescriptor& param, const char* name)
: LayerWithParameters(1, 1, LayerType::Convolution2d, param, name)
{
}
std::unique_ptr<IWorkload> Convolution2dLayer::CreateWorkload(const Graph& graph, const IWorkloadFactory& factory) const
{
Convolution2dQueueDescriptor descriptor;
descriptor.m_Weight = m_Weight.get();
if (m_Param.m_BiasEnabled)
{
descriptor.m_Bias = m_Bias.get();
}
return factory.CreateConvolution2d(descriptor, PrepInfoAndDesc(descriptor, graph));
}
Convolution2dLayer* Convolution2dLayer::Clone(Graph& graph) const
{
auto layer = CloneBase<Convolution2dLayer>(graph, m_Param, GetName());
layer->m_Weight = m_Weight ? std::make_unique<ScopedCpuTensorHandle>(*m_Weight) : nullptr;
if (layer->m_Param.m_BiasEnabled)
{
layer->m_Bias = m_Bias ? std::make_unique<ScopedCpuTensorHandle>(*m_Bias) : nullptr;
}
return std::move(layer);
}
void Convolution2dLayer::ValidateTensorShapesFromInputs()
{
ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection() != nullptr,
"Convolution2dLayer: InputSlot must be connected to an OutputSlot");
ConditionalThrow<LayerValidationException>(GetInputSlot(0).GetConnection()->IsTensorInfoSet(),
"Convolution2dLayer: TensorInfo must be set on connected OutputSlot.");
IOutputSlot* input = GetInputSlot(0).GetConnection();
const TensorShape& inputShape = input->GetTensorInfo().GetShape();
const TensorShape filterShape = m_Weight->GetTensorInfo().GetShape();
// If we support multiple batch dimensions in the future, then this assert will need to change.
BOOST_ASSERT_MSG(inputShape.GetNumDimensions() == 4, "Convolutions will always have 4D input.");
unsigned int inWidth = inputShape[3];
unsigned int inHeight = inputShape[2];
unsigned int inBatchSize = inputShape[0];
unsigned int filterWidth = filterShape[3];
unsigned int readWidth = (inWidth + m_Param.m_PadLeft + m_Param.m_PadRight) - (filterWidth);
unsigned int outWidth = 1+(readWidth / m_Param.m_StrideX);
unsigned int filterHeight = filterShape[2];
unsigned int readHeight = (inHeight + m_Param.m_PadTop + m_Param.m_PadBottom) - (filterHeight);
unsigned int outHeight = 1+(readHeight / m_Param.m_StrideY);
unsigned int outChannels = filterShape[0];
unsigned int outBatchSize = inBatchSize;
TensorShape shapeOut({outBatchSize, outChannels, outHeight, outWidth});
ConditionalThrowIfNotEqual<LayerValidationException>(
"Convolution2dLayer: TensorShape set on OutputSlot[0] does not match the inferred shape.",
GetOutputSlot(0).GetTensorInfo().GetShape(),
shapeOut);
}
} // namespace armnn
|
