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
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonWorkloadFactoryHelper.hpp"
#include <TensorHelpers.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <armnn/backends/WorkloadFactory.hpp>
#include <neon/NeonTimer.hpp>
#include <neon/NeonWorkloadFactory.hpp>
#include <backendsCommon/test/LayerTests.hpp>
#include <armnnTestUtils/TensorCopyUtils.hpp>
#include <armnnTestUtils/WorkloadTestUtils.hpp>
#include <doctest/doctest.h>
#include <cstdlib>
#include <algorithm>
using namespace armnn;
TEST_SUITE("NeonTimerInstrument")
{
TEST_CASE("NeonTimerGetName")
{
NeonTimer neonTimer;
CHECK_EQ(std::string(neonTimer.GetName()), "NeonKernelTimer");
}
TEST_CASE("NeonTimerMeasure")
{
NeonWorkloadFactory workloadFactory =
NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager());
unsigned int inputWidth = 2000u;
unsigned int inputHeight = 2000u;
unsigned int inputChannels = 1u;
unsigned int inputBatchSize = 1u;
float upperBound = 1.0f;
float lowerBound = -1.0f;
size_t inputSize = inputWidth * inputHeight * inputChannels * inputBatchSize;
std::vector<float> inputData(inputSize, 0.f);
std::generate(inputData.begin(), inputData.end(), [](){
return (static_cast<float>(rand()) / static_cast<float>(RAND_MAX / 3)) + 1.f; });
unsigned int outputWidth = inputWidth;
unsigned int outputHeight = inputHeight;
unsigned int outputChannels = inputChannels;
unsigned int outputBatchSize = inputBatchSize;
armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
armnn::DataType::Float32);
armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
armnn::DataType::Float32);
ARMNN_NO_DEPRECATE_WARN_BEGIN
std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
ARMNN_NO_DEPRECATE_WARN_END
// Setup bounded ReLu
armnn::ActivationQueueDescriptor descriptor;
armnn::WorkloadInfo workloadInfo;
AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
descriptor.m_Parameters.m_Function = armnn::ActivationFunction::BoundedReLu;
descriptor.m_Parameters.m_A = upperBound;
descriptor.m_Parameters.m_B = lowerBound;
std::unique_ptr<armnn::IWorkload> workload
= workloadFactory.CreateWorkload(LayerType::Activation, descriptor, workloadInfo);
inputHandle->Allocate();
outputHandle->Allocate();
CopyDataToITensorHandle(inputHandle.get(), inputData.data());
NeonTimer neonTimer;
// Start the timer.
neonTimer.Start();
// Execute the workload.
workload->Execute();
// Stop the timer.
neonTimer.Stop();
std::vector<Measurement> measurements = neonTimer.GetMeasurements();
CHECK(measurements.size() <= 2);
if (measurements.size() > 1)
{
CHECK_EQ(measurements[0].m_Name, "NeonKernelTimer/0: NEFillBorderKernel");
CHECK(measurements[0].m_Value > 0.0);
}
std::ostringstream oss_neon;
std::ostringstream oss_cpu;
oss_neon << "NeonKernelTimer/" << measurements.size()-1 << ": NEActivationLayerKernel";
oss_cpu << "NeonKernelTimer/" << measurements.size()-1 << ": CpuActivationKernel";
bool kernelCheck = ((measurements[measurements.size()-1].m_Name.find(oss_neon.str()) != std::string::npos)
|| (measurements[measurements.size()-1].m_Name.find(oss_cpu.str()) != std::string::npos));
CHECK(kernelCheck);
CHECK(measurements[measurements.size()-1].m_Value > 0.0);
}
}
|
