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
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "ClMeanWorkload.hpp"
#include <cl/ClTensorHandle.hpp>
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include "ClWorkloadUtils.hpp"
namespace
{
void ConvertArmnnAxesToAclCoordinates(size_t inputDimensions,
unsigned int originalInputRank,
const std::vector<unsigned int>& armnnAxes,
arm_compute::Coordinates& outAclCoords)
{
if (armnnAxes.empty())
{
// If no reduction axes were provided, then the input must be reduced along all dimensions.
// Since arm_compute::CLReduceMean does not accept an empty vector as the reduction dimensions, we then
// manually create a vector including all the input dimensions (in reversed order) as:
//
// { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 }
//
outAclCoords.set_num_dimensions(inputDimensions);
std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () mutable { return d--; });
}
else
{
// Create a vector of reduction dimensions (in reversed order) with the given reduction axes.
//
// Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any
// dimension correction).
// For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the
// new value for that reduction axis should be 1.
//
// Example:
// ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 }
// ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 }
// ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 }
//
// The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1
//
outAclCoords.set_num_dimensions(armnnAxes.size());
std::transform(armnnAxes.begin(), armnnAxes.end(),
outAclCoords.begin(),
[originalInputRank](unsigned int i){ return originalInputRank - i - 1; });
}
}
} // anonymous namespace
namespace armnn
{
using namespace armcomputetensorutils;
arm_compute::Status ClMeanValidate(const TensorInfo& input,
const TensorInfo& output,
const MeanDescriptor& desc)
{
const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input);
const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output);
arm_compute::Coordinates coords;
ConvertArmnnAxesToAclCoordinates(aclInputInfo.num_dimensions(),
input.GetNumDimensions(),
desc.m_Axis,
coords);
return arm_compute::CLReduceMean::validate(&aclInputInfo, coords, desc.m_KeepDims, &aclOutputInfo);
}
ClMeanWorkload::ClMeanWorkload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info)
: BaseWorkload<MeanQueueDescriptor>(descriptor, info)
{
m_Data.ValidateInputsOutputs("ClMeanWorkload", 1, 1);
arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
arm_compute::Coordinates coords;
ConvertArmnnAxesToAclCoordinates(input.info()->num_dimensions(),
info.m_InputTensorInfos[0].GetNumDimensions(),
m_Data.m_Parameters.m_Axis,
coords);
m_Layer.configure(&input, coords, m_Data.m_Parameters.m_KeepDims, &output);
}
void ClMeanWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_CL("ClMeanWorkload_Execute");
m_Layer.run();
}
} //namespace armnn
|
