ArmNN
 20.02
NeonTimerTest.cpp
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1 //
2 // Copyright © 2017 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
7 
8 
9 #include <test/TensorHelpers.hpp>
10 
13 
14 #include <neon/NeonTimer.hpp>
16 
20 
21 #include <boost/test/unit_test.hpp>
22 
23 #include <cstdlib>
24 #include <algorithm>
25 
26 using namespace armnn;
27 
28 BOOST_AUTO_TEST_SUITE(NeonTimerInstrument)
29 
30 
31 BOOST_AUTO_TEST_CASE(NeonTimerGetName)
32 {
33  NeonTimer neonTimer;
34  BOOST_CHECK_EQUAL(neonTimer.GetName(), "NeonKernelTimer");
35 }
36 
37 BOOST_AUTO_TEST_CASE(NeonTimerMeasure)
38 {
39  NeonWorkloadFactory workloadFactory =
40  NeonWorkloadFactoryHelper::GetFactory(NeonWorkloadFactoryHelper::GetMemoryManager());
41 
42  unsigned int inputWidth = 2000u;
43  unsigned int inputHeight = 2000u;
44  unsigned int inputChannels = 1u;
45  unsigned int inputBatchSize = 1u;
46 
47  float upperBound = 1.0f;
48  float lowerBound = -1.0f;
49 
50  size_t inputSize = inputWidth * inputHeight * inputChannels * inputBatchSize;
51  std::vector<float> inputData(inputSize, 0.f);
52  std::generate(inputData.begin(), inputData.end(), [](){
53  return (static_cast<float>(rand()) / static_cast<float>(RAND_MAX / 3)) + 1.f; });
54 
55  unsigned int outputWidth = inputWidth;
56  unsigned int outputHeight = inputHeight;
57  unsigned int outputChannels = inputChannels;
58  unsigned int outputBatchSize = inputBatchSize;
59 
60  armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth },
62 
63  armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth },
65 
66  LayerTestResult<float, 4> result(inputTensorInfo);
67 
68  auto input = MakeTensor<float, 4>(inputTensorInfo, inputData);
69 
70  std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo);
71  std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo);
72 
73  // Setup bounded ReLu
75  armnn::WorkloadInfo workloadInfo;
76  AddInputToWorkload(descriptor, workloadInfo, inputTensorInfo, inputHandle.get());
77  AddOutputToWorkload(descriptor, workloadInfo, outputTensorInfo, outputHandle.get());
78 
80  descriptor.m_Parameters.m_A = upperBound;
81  descriptor.m_Parameters.m_B = lowerBound;
82 
83  std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateActivation(descriptor, workloadInfo);
84 
85  inputHandle->Allocate();
86  outputHandle->Allocate();
87 
88  CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]);
89 
90  NeonTimer neonTimer;
91  // Start the timer.
92  neonTimer.Start();
93  // Execute the workload.
94  workload->Execute();
95  // Stop the timer.
96  neonTimer.Stop();
97 
98  std::vector<Measurement> measurements = neonTimer.GetMeasurements();
99 
100  BOOST_CHECK(measurements.size() <= 2);
101  if (measurements.size() > 1)
102  {
103  BOOST_CHECK_EQUAL(measurements[0].m_Name, "NeonKernelTimer/0: NEFillBorderKernel");
104  BOOST_CHECK(measurements[0].m_Value > 0.0);
105  }
106  std::ostringstream oss;
107  oss << "NeonKernelTimer/" << measurements.size()-1 << ": NEActivationLayerKernel";
108  BOOST_CHECK_EQUAL(measurements[measurements.size()-1].m_Name, oss.str());
109  BOOST_CHECK(measurements[measurements.size()-1].m_Value > 0.0);
110 }
111 
BOOST_AUTO_TEST_SUITE(TensorflowLiteParser)
void Start() override
Definition: NeonTimer.cpp:21
Copyright (c) 2020 ARM Limited.
BOOST_CHECK(profilingService.GetCurrentState()==ProfilingState::WaitingForAck)
BOOST_AUTO_TEST_CASE(CheckConvolution2dLayer)
min(a, max(b, input)) ReLu1 & ReLu6.
std::unique_ptr< IWorkload > CreateActivation(const ActivationQueueDescriptor &descriptor, const WorkloadInfo &info) const override
float m_A
Alpha upper bound value used by the activation functions. (BoundedReLu, Linear, TanH).
Definition: Descriptors.hpp:37
BOOST_AUTO_TEST_SUITE_END()
const char * GetName() const override
Definition: NeonTimer.cpp:58
Contains information about inputs and outputs to a layer.
float m_B
Beta lower bound value used by the activation functions. (BoundedReLu, Linear, TanH).
Definition: Descriptors.hpp:39
ActivationFunction m_Function
The activation function to use (Sigmoid, TanH, Linear, ReLu, BoundedReLu, SoftReLu, LeakyReLu, Abs, Sqrt, Square).
Definition: Descriptors.hpp:35
std::unique_ptr< ITensorHandle > CreateTensorHandle(const TensorInfo &tensorInfo, const bool IsMemoryManaged=true) const override
void CopyDataToITensorHandle(armnn::ITensorHandle *tensorHandle, const void *memory)