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
|
//
// Copyright © 2020 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonSpaceToBatchNdWorkload.hpp"
#include "NeonWorkloadUtils.hpp"
#include <armnn/utility/NumericCast.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
#include <ResolveType.hpp>
namespace armnn
{
using namespace armcomputetensorutils;
arm_compute::Status NeonSpaceToBatchNdWorkloadValidate(const TensorInfo& input,
const TensorInfo& output,
const SpaceToBatchNdDescriptor& descriptor)
{
const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
// ArmNN blockShape is [H, W] Cl asks for W, H
int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]);
int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(
descriptor.m_PadList[1].first, descriptor.m_PadList[0].first);
arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D(
descriptor.m_PadList[1].second, descriptor.m_PadList[0].second);
return arm_compute::NESpaceToBatchLayer::validate(&aclInputInfo,
blockWidth,
blockHeight,
paddingLeftTop,
paddingRightBottom,
&aclOutputInfo);
}
NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueueDescriptor& descriptor,
const WorkloadInfo& info)
: BaseWorkload<SpaceToBatchNdQueueDescriptor>(descriptor, info)
{
// Report Profiling Details
ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonSpaceToBatchNdWorkload_Construct",
descriptor.m_Parameters,
info,
this->GetGuid());
m_Data.ValidateInputsOutputs("NESpaceToBatchNdWorkload", 1, 1);
arm_compute::ITensor& input =
PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output =
PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();
// ArmNN blockShape is [H, W] Cl asks for W, H
int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]);
int32_t blockWidth = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]);
arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(
m_Data.m_Parameters.m_PadList[1].first, m_Data.m_Parameters.m_PadList[0].first);
arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D(
m_Data.m_Parameters.m_PadList[1].second, m_Data.m_Parameters.m_PadList[0].second);
arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
input.info()->set_data_layout(aclDataLayout);
output.info()->set_data_layout(aclDataLayout);
m_Layer.reset(new arm_compute::NESpaceToBatchLayer());
m_Layer->configure(&input,
blockWidth,
blockHeight,
paddingLeftTop,
paddingRightBottom,
&output);
m_Layer->prepare();
}
void NeonSpaceToBatchNdWorkload::Execute() const
{
if (m_Layer)
{
ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid());
m_Layer->run();
}
}
} //namespace armnn
|
