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
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <backendsCommon/test/EndToEndTestImpl.hpp>
#include <backendsCommon/test/MergerTestImpl.hpp>
#include <backendsCommon/test/ArithmeticTestImpl.hpp>
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(NeonEndToEnd)
std::vector<armnn::BackendId> defaultBackends = {armnn::Compute::CpuAcc};
BOOST_AUTO_TEST_CASE(ConstantUsage_Neon_Float32)
{
BOOST_TEST(ConstantUsageFloat32Test(defaultBackends));
}
BOOST_AUTO_TEST_CASE(FallbackToCpuRef)
{
using namespace armnn;
// Create runtime in which test will run and allow fallback to CpuRef.
IRuntime::CreationOptions options;
IRuntimePtr runtime(IRuntime::Create(options));
// Builds up the structure of the network.
INetworkPtr net(INetwork::Create());
IConnectableLayer* input = net->AddInputLayer(0);
// This layer configuration isn't supported by CpuAcc but we allow fallback to CpuRef so it shoud pass.
NormalizationDescriptor descriptor;
IConnectableLayer* pooling = net->AddNormalizationLayer(descriptor);
IConnectableLayer* output = net->AddOutputLayer(0);
input->GetOutputSlot(0).Connect(pooling->GetInputSlot(0));
pooling->GetOutputSlot(0).Connect(output->GetInputSlot(0));
input->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
pooling->GetOutputSlot(0).SetTensorInfo(TensorInfo({ 1, 1, 4, 4 }, DataType::Float32));
// optimize the network
std::vector<BackendId> backends = {Compute::CpuAcc, Compute::CpuRef};
IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
// Load it into the runtime. It should pass.
NetworkId netId;
BOOST_TEST(runtime->LoadNetwork(netId, std::move(optNet)) == Status::Success);
}
BOOST_AUTO_TEST_CASE(NeonGreaterSimpleEndToEndTest)
{
const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0 });
ArithmeticSimpleEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends,
LayerType::Greater,
expectedOutput);
}
BOOST_AUTO_TEST_CASE(NeonGreaterSimpleEndToEndUint8Test)
{
const std::vector<uint8_t> expectedOutput({ 0, 0, 0, 0, 1, 1, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0 });
ArithmeticSimpleEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends,
LayerType::Greater,
expectedOutput);
}
BOOST_AUTO_TEST_CASE(NeonGreaterBroadcastEndToEndTest)
{
const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1 });
ArithmeticBroadcastEndToEnd<armnn::DataType::Float32, armnn::DataType::Boolean>(defaultBackends,
LayerType::Greater,
expectedOutput);
}
BOOST_AUTO_TEST_CASE(NeonGreaterBroadcastEndToEndUint8Test)
{
const std::vector<uint8_t> expectedOutput({ 0, 1, 0, 0, 0, 1,
1, 1, 1, 1, 1, 1 });
ArithmeticBroadcastEndToEnd<armnn::DataType::QuantisedAsymm8, armnn::DataType::Boolean>(defaultBackends,
LayerType::Greater,
expectedOutput);
}
BOOST_AUTO_TEST_CASE(NeonMergerEndToEndDim0Test)
{
MergerDim0EndToEnd<armnn::DataType::Float32>(defaultBackends);
}
BOOST_AUTO_TEST_CASE(NeonMergerEndToEndDim0Uint8Test)
{
MergerDim0EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends);
}
BOOST_AUTO_TEST_CASE(NeonMergerEndToEndDim1Test)
{
MergerDim1EndToEnd<armnn::DataType::Float32>(defaultBackends);
}
BOOST_AUTO_TEST_CASE(NeonMergerEndToEndDim1Uint8Test)
{
MergerDim1EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends);
}
BOOST_AUTO_TEST_CASE(NeonMergerEndToEndDim3Test)
{
MergerDim3EndToEnd<armnn::DataType::Float32>(defaultBackends);
}
BOOST_AUTO_TEST_CASE(NeonMergerEndToEndDim3Uint8Test)
{
MergerDim3EndToEnd<armnn::DataType::QuantisedAsymm8>(defaultBackends);
}
BOOST_AUTO_TEST_SUITE_END()
|
