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
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
|
//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include <backends/test/EndToEndTestImpl.hpp>
#include <boost/test/unit_test.hpp>
BOOST_AUTO_TEST_SUITE(RefEndToEnd)
BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Float32)
{
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
BOOST_TEST(ConstantUsageFloat32Test(backends));
}
BOOST_AUTO_TEST_CASE(ConstantUsage_Ref_Uint8)
{
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
BOOST_TEST(ConstantUsageUint8Test(backends));
}
BOOST_AUTO_TEST_CASE(Unsigned8)
{
using namespace armnn;
// Create runtime in which test will run
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
// Builds up the structure of the network.
armnn::INetworkPtr net(INetwork::Create());
IConnectableLayer* input = net->AddInputLayer(0, "input");
IConnectableLayer* softmax = net->AddSoftmaxLayer(SoftmaxDescriptor(), "softmax");
IConnectableLayer* output = net->AddOutputLayer(0, "output");
input->GetOutputSlot(0).Connect(softmax->GetInputSlot(0));
softmax->GetOutputSlot(0).Connect(output->GetInputSlot(0));
// Sets the tensors in the network.
TensorInfo inputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
inputTensorInfo.SetQuantizationOffset(100);
inputTensorInfo.SetQuantizationScale(10000.0f);
input->GetOutputSlot(0).SetTensorInfo(inputTensorInfo);
TensorInfo outputTensorInfo(TensorShape({1, 5}), DataType::QuantisedAsymm8);
outputTensorInfo.SetQuantizationOffset(0);
outputTensorInfo.SetQuantizationScale(1.0f/255.0f);
softmax->GetOutputSlot(0).SetTensorInfo(outputTensorInfo);
// optimize the network
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
// Loads it into the runtime.
NetworkId netId;
auto error = runtime->LoadNetwork(netId, std::move(optNet));
BOOST_TEST(error == Status::Success);
// Creates structures for input & output.
std::vector<uint8_t> inputData
{
1, 10, 3, 200, 5 // Some inputs - one of which is sufficiently larger than the others to saturate softmax.
};
std::vector<uint8_t> outputData(5);
armnn::InputTensors inputTensors
{
{0, armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
};
armnn::OutputTensors outputTensors
{
{0, armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
};
// Does the inference.
runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
// Checks the results.
BOOST_TEST(outputData[0] == 0);
BOOST_TEST(outputData[1] == 0);
BOOST_TEST(outputData[2] == 0);
BOOST_TEST(outputData[3] == 255); // softmax has been saturated.
BOOST_TEST(outputData[4] == 0);
}
BOOST_AUTO_TEST_CASE(TrivialAdd)
{
// This test was designed to match "AddTwo" in android nn/runtime/test/TestTrivialModel.cpp.
using namespace armnn;
// Create runtime in which test will run
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
// Builds up the structure of the network.
armnn::INetworkPtr net(INetwork::Create());
IConnectableLayer* input1 = net->AddInputLayer(0);
IConnectableLayer* input2 = net->AddInputLayer(1);
IConnectableLayer* add = net->AddAdditionLayer();
IConnectableLayer* output = net->AddOutputLayer(0);
input1->GetOutputSlot(0).Connect(add->GetInputSlot(0));
input2->GetOutputSlot(0).Connect(add->GetInputSlot(1));
add->GetOutputSlot(0).Connect(output->GetInputSlot(0));
// Sets the tensors in the network.
TensorInfo tensorInfo(TensorShape({3, 4}), DataType::Float32);
input1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
input2->GetOutputSlot(0).SetTensorInfo(tensorInfo);
add->GetOutputSlot(0).SetTensorInfo(tensorInfo);
// optimize the network
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
// Loads it into the runtime.
NetworkId netId;
runtime->LoadNetwork(netId, std::move(optNet));
// Creates structures for input & output - matching android nn test.
std::vector<float> input1Data
{
1.f, 2.f, 3.f, 4.f, 5.f, 6.f, 7.f, 8.f, 9.f, 10.f, 11.f, 12.f
};
std::vector<float> input2Data
{
100.f, 200.f, 300.f, 400.f, 500.f, 600.f, 700.f, 800.f, 900.f, 1000.f, 1100.f, 1200.f
};
std::vector<float> outputData(12);
InputTensors inputTensors
{
{0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input1Data.data())},
{1,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), input2Data.data())}
};
OutputTensors outputTensors
{
{0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), outputData.data())}
};
// Does the inference.
runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
// Checks the results
BOOST_TEST(outputData[0] == 101);
BOOST_TEST(outputData[1] == 202);
BOOST_TEST(outputData[2] == 303);
BOOST_TEST(outputData[3] == 404);
BOOST_TEST(outputData[4] == 505);
BOOST_TEST(outputData[5] == 606);
BOOST_TEST(outputData[6] == 707);
BOOST_TEST(outputData[7] == 808);
BOOST_TEST(outputData[8] == 909);
BOOST_TEST(outputData[9] == 1010);
BOOST_TEST(outputData[10] == 1111);
BOOST_TEST(outputData[11] == 1212);
}
BOOST_AUTO_TEST_CASE(MultipleOutputs)
{
using namespace armnn;
// Create runtime in which test will run
armnn::IRuntime::CreationOptions options;
armnn::IRuntimePtr runtime(armnn::IRuntime::Create(options));
// Builds up the structure of the network.
INetworkPtr net(INetwork::Create());
IConnectableLayer* input = net->AddInputLayer(0);
// ReLu1
ActivationDescriptor activation1Descriptor;
activation1Descriptor.m_Function = ActivationFunction::BoundedReLu;
activation1Descriptor.m_A = 1.f;
activation1Descriptor.m_B = -1.f;
IConnectableLayer* activation1 = net->AddActivationLayer(activation1Descriptor);
// ReLu6
ActivationDescriptor activation2Descriptor;
activation2Descriptor.m_Function = ActivationFunction::BoundedReLu;
activation2Descriptor.m_A = 6.0f;
IConnectableLayer* activation2 = net->AddActivationLayer(activation2Descriptor);
// BoundedReLu(min=2, max=5)
ActivationDescriptor activation3Descriptor;
activation3Descriptor.m_Function = ActivationFunction::BoundedReLu;
activation3Descriptor.m_A = 5.0f;
activation3Descriptor.m_B = 2.0f;
IConnectableLayer* activation3 = net->AddActivationLayer(activation3Descriptor);
IConnectableLayer* output1 = net->AddOutputLayer(0);
IConnectableLayer* output2 = net->AddOutputLayer(1);
IConnectableLayer* output3 = net->AddOutputLayer(2);
input->GetOutputSlot(0).Connect(activation1->GetInputSlot(0));
input->GetOutputSlot(0).Connect(activation2->GetInputSlot(0));
input->GetOutputSlot(0).Connect(activation3->GetInputSlot(0));
activation1->GetOutputSlot(0).Connect(output1->GetInputSlot(0));
activation2->GetOutputSlot(0).Connect(output2->GetInputSlot(0));
activation3->GetOutputSlot(0).Connect(output3->GetInputSlot(0));
// Sets the tensors in the network.
TensorInfo tensorInfo(TensorShape({ 10 }), DataType::Float32);
input->GetOutputSlot(0).SetTensorInfo(tensorInfo);
activation1->GetOutputSlot(0).SetTensorInfo(tensorInfo);
activation2->GetOutputSlot(0).SetTensorInfo(tensorInfo);
activation3->GetOutputSlot(0).SetTensorInfo(tensorInfo);
// optimize the network
std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
IOptimizedNetworkPtr optNet = Optimize(*net, backends, runtime->GetDeviceSpec());
// Loads it into the runtime.
NetworkId netId;
runtime->LoadNetwork(netId, std::move(optNet));
// Creates structures for input & output.
const std::vector<float> inputData{ 3.f, 5.f, 2.f, 3.f, 7.f, 0.f, -2.f, -1.f, 3.f, 3.f };
std::vector<float> output1Data(inputData.size());
std::vector<float> output2Data(inputData.size());
std::vector<float> output3Data(inputData.size());
InputTensors inputTensors
{
{0,armnn::ConstTensor(runtime->GetInputTensorInfo(netId, 0), inputData.data())}
};
OutputTensors outputTensors
{
{0,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 0), output1Data.data())},
{1,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 1), output2Data.data())},
{2,armnn::Tensor(runtime->GetOutputTensorInfo(netId, 2), output3Data.data())}
};
// Does the inference.
runtime->EnqueueWorkload(netId, inputTensors, outputTensors);
// Checks the results.
BOOST_TEST(output1Data == std::vector<float>({ 1.f, 1.f, 1.f, 1.f, 1.f, 0.f, -1.f, -1.f, 1.f, 1.f })); // ReLu1
BOOST_TEST(output2Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 6.f, 0.f, 0.f, 0.f, 3.f, 3.f })); // ReLu6
BOOST_TEST(output3Data == std::vector<float>({ 3.f, 5.f, 2.f, 3.f, 5.f, 2.f, 2.f, 2.f, 3.f, 3.f })); // [2, 5]
}
BOOST_AUTO_TEST_SUITE_END()
|
