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
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonDepthwiseConvolutionWorkload.hpp"
#include "NeonWorkloadUtils.hpp"
#include <DataLayoutIndexed.hpp>
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <neon/NeonLayerSupport.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <backendsCommon/WorkloadUtils.hpp>
#include <arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h>
using namespace armnnUtils;
namespace armnn
{
using namespace armcomputetensorutils;
arm_compute::Status NeonDepthwiseConvolutionWorkloadValidate(const TensorInfo& input,
const TensorInfo& output,
const DepthwiseConvolution2dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases)
{
const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
// ArmNN's weight format is [ M, I, H, W ]
const unsigned int aclDepthMultiplier = weights.GetShape()[0];
// Convert the weight format from ArmNN's [ M, I, H, W ] (does NOT depend on the data layout) to either
// [ 1, H, W, I * M ] (if NHWC) or [ 1, I * M, H, W ] (if NCHW), as required by the compute library
TensorInfo weightsPermuted = ConvertWeightTensorInfoFromArmnnToAcl(weights, descriptor.m_DataLayout);
// Convert the weights into the compute library format
const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weightsPermuted, descriptor.m_DataLayout);
arm_compute::TensorInfo aclBiasesInfo;
arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr;
if (descriptor.m_BiasEnabled)
{
BOOST_ASSERT(biases.has_value());
aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
optionalAclBiasesInfo = &aclBiasesInfo;
}
arm_compute::PadStrideInfo aclPadStrideInfo = BuildArmComputePadStrideInfo(descriptor);
const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(
descriptor.m_DilationX,descriptor.m_DilationY);
return arm_compute::NEDepthwiseConvolutionLayer::validate(&aclInputInfo,
&aclWeightsInfo,
optionalAclBiasesInfo,
&aclOutputInfo,
aclPadStrideInfo,
aclDepthMultiplier,
arm_compute::ActivationLayerInfo(),
aclDilationInfo);
}
NeonDepthwiseConvolutionWorkload::NeonDepthwiseConvolutionWorkload(
const DepthwiseConvolution2dQueueDescriptor& descriptor,
const WorkloadInfo& info)
: BaseWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info)
{
// ArmNN's weight format is [ M, I, H, W ]
auto& weightInfo = m_Data.m_Weight->GetTensorInfo();
// Allocate a buffer for the swizzling of the weight tensor
std::unique_ptr<unsigned char[]> permuteBuffer(new unsigned char[m_Data.m_Weight->GetTensorInfo().GetNumBytes()]);
// Convert the weight format from ArmNN's [ M, I, H, W ] (does NOT depend on the data layout) to either
// [ 1, H, W, I * M ] (if NHWC) or [ 1, I * M, H, W ] (if NCHW), as required by the compute library
ConstTensor weightPermuted = ConvertWeightTensorFromArmnnToAcl(m_Data.m_Weight,
m_Data.m_Parameters.m_DataLayout,
permuteBuffer.get());
// Convert the weights into the compute library format
m_KernelTensor = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_KernelTensor, weightPermuted.GetInfo(), m_Data.m_Parameters.m_DataLayout);
if (m_Data.m_Parameters.m_BiasEnabled)
{
m_BiasTensor = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout);
}
const arm_compute::Size2D aclDilationInfo = BuildArmComputeSize2D(
m_Data.m_Parameters.m_DilationX, m_Data.m_Parameters.m_DilationY);
m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionWorkload", 1, 1);
IAclTensorHandle* inputTensorHandle = static_cast<IAclTensorHandle*>(m_Data.m_Inputs[0]);
IAclTensorHandle* outputTensorHandle = static_cast<IAclTensorHandle*>(m_Data.m_Outputs[0]);
arm_compute::ITensor& input = inputTensorHandle->GetTensor();
arm_compute::ITensor& output = outputTensorHandle->GetTensor();
arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
input.info()->set_data_layout(aclDataLayout);
output.info()->set_data_layout(aclDataLayout);
// Get the depth multiplier
const unsigned int depthMultiplier = weightInfo.GetShape()[0];
arm_compute::PadStrideInfo padStrideInfo = BuildArmComputePadStrideInfo(m_Data.m_Parameters);
m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer>();
static_cast<arm_compute::NEDepthwiseConvolutionLayer*>(
m_pDepthwiseConvolutionLayer.get())->configure(&input,
m_KernelTensor.get(),
m_BiasTensor.get(),
&output,
padStrideInfo,
depthMultiplier,
arm_compute::ActivationLayerInfo(),
aclDilationInfo);
BOOST_ASSERT(m_pDepthwiseConvolutionLayer);
ScopedCpuTensorHandle weightsPermutedHandle(weightPermuted);
InitializeArmComputeTensorData(*m_KernelTensor, &weightsPermutedHandle);
if (m_Data.m_Parameters.m_BiasEnabled)
{
InitializeArmComputeTensorData(*m_BiasTensor, m_Data.m_Bias);
}
m_pDepthwiseConvolutionLayer->prepare();
FreeUnusedTensors();
}
void NeonDepthwiseConvolutionWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonDepthwiseConvolutionWorkload_Execute");
BOOST_ASSERT(m_pDepthwiseConvolutionLayer);
m_pDepthwiseConvolutionLayer->run();
}
void NeonDepthwiseConvolutionWorkload::FreeUnusedTensors()
{
FreeTensorIfUnused(m_KernelTensor);
FreeTensorIfUnused(m_BiasTensor);
}
} //namespace armnn
|
