ArmNN
 21.02
ArgMinMaxEndToEndTestImpl.hpp
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1 //
2 // Copyright © 2019 Arm Ltd. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 
6 #pragma once
7 
8 #include "CommonTestUtils.hpp"
9 
10 #include <QuantizeHelper.hpp>
11 #include <ResolveType.hpp>
12 
13 
14 namespace
15 {
16 
17 armnn::INetworkPtr CreateArgMinMaxNetwork(const armnn::TensorInfo& inputTensorInfo,
18  const armnn::TensorInfo& outputTensorInfo,
19  armnn::ArgMinMaxFunction function,
20  int axis)
21 {
22  armnn::INetworkPtr network(armnn::INetwork::Create());
23 
24  armnn::ArgMinMaxDescriptor descriptor;
25  descriptor.m_Function = function;
26  descriptor.m_Axis = axis;
27 
28  armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input");
29  armnn::IConnectableLayer* argMinMaxLayer = network->AddArgMinMaxLayer(descriptor, "ArgMinMax");
30  armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output");
31 
32  Connect(inputLayer, argMinMaxLayer, inputTensorInfo, 0, 0);
33  Connect(argMinMaxLayer, outputLayer, outputTensorInfo, 0, 0);
34 
35  return network;
36 }
37 
38 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
39 void ArgMinMaxEndToEndImpl(const armnn::TensorShape& inputShape,
40  const armnn::TensorShape& outputShape,
41  const std::vector<float>& inputData,
42  const std::vector<int32_t>& expectedOutputData,
43  armnn::ArgMinMaxFunction function,
44  int axis,
45  const std::vector<armnn::BackendId>& backends)
46 {
47  const float qScale = armnn::IsQuantizedType<T>() ? 2.0f : 1.0f;
48  const int32_t qOffset = armnn::IsQuantizedType<T>() ? 2 : 0;
49 
50  armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType, qScale, qOffset);
51  armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Signed32);
52 
53  // quantize data
54  std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
55 
56  armnn::INetworkPtr network = CreateArgMinMaxNetwork(inputTensorInfo,
57  outputTensorInfo,
58  function,
59  axis);
60 
61  EndToEndLayerTestImpl<ArmnnType, armnn::DataType::Signed32>(std::move(network),
62  { { 0, qInputData } },
63  { { 0, expectedOutputData } },
64  backends);
65 }
66 
67 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
68 void ArgMaxEndToEndSimple(const std::vector<armnn::BackendId>& backends)
69 {
70  const armnn::TensorShape inputShape{ 1, 1, 1, 5 };
71  const armnn::TensorShape outputShape{ 1, 1, 1 };
72 
73  std::vector<float> inputData({ 6.0f, 2.0f, 8.0f, 10.0f, 9.0f });
74  std::vector<int32_t> expectedOutputData({ 3 });
75 
76  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
77  outputShape,
78  inputData,
79  expectedOutputData,
80  armnn::ArgMinMaxFunction::Max,
81  -1,
82  backends);
83 }
84 
85 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
86 void ArgMinEndToEndSimple(const std::vector<armnn::BackendId>& backends)
87 {
88  const armnn::TensorShape inputShape{ 1, 1, 1, 5 };
89  const armnn::TensorShape outputShape{ 1, 1, 1 };
90 
91  std::vector<float> inputData({ 6.0f, 2.0f, 8.0f, 10.0f, 9.0f });
92  std::vector<int32_t> expectedOutputData({ 1 });
93 
94  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
95  outputShape,
96  inputData,
97  expectedOutputData,
98  armnn::ArgMinMaxFunction::Min,
99  3,
100  backends);
101 }
102 
103 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
104 void ArgMaxAxis0EndToEnd(const std::vector<armnn::BackendId>& backends)
105 {
106  const armnn::TensorShape inputShape{ 3, 2, 1, 4 };
107  const armnn::TensorShape outputShape{ 2, 1, 4 };
108 
109  std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
110  8.0f, 7.0f, 6.0f, 6.0f,
111  100.0f, 20.0f, 300.0f, 40.0f,
112  500.0f, 476.0f, 450.0f, 426.0f,
113  50.0f, 60.0f, 70.0f, 80.0f,
114  10.0f, 200.0f, 30.0f, 400.0f });
115 
116  std::vector<int32_t> expectedOutputData({ 1, 2, 1, 2,
117  1, 1, 1, 1 });
118 
119  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
120  outputShape,
121  inputData,
122  expectedOutputData,
123  armnn::ArgMinMaxFunction::Max,
124  0,
125  backends);
126 }
127 
128 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
129 void ArgMinAxis0EndToEnd(const std::vector<armnn::BackendId>& backends)
130 {
131  const armnn::TensorShape inputShape{ 3, 2, 1, 4 };
132  const armnn::TensorShape outputShape{ 2, 1, 4 };
133 
134  std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
135  8.0f, 7.0f, 6.0f, 6.0f,
136  100.0f, 20.0f, 300.0f, 40.0f,
137  500.0f, 476.0f, 450.0f, 426.0f,
138  50.0f, 60.0f, 70.0f, 80.0f,
139  10.0f, 200.0f, 30.0f, 400.0f });
140 
141  std::vector<int32_t> expectedOutputData({ 0, 0, 0, 0,
142  0, 0, 0, 0 });
143 
144  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
145  outputShape,
146  inputData,
147  expectedOutputData,
148  armnn::ArgMinMaxFunction::Min,
149  0,
150  backends);
151 }
152 
153 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
154 void ArgMaxAxis1EndToEnd(const std::vector<armnn::BackendId>& backends)
155 {
156  const armnn::TensorShape inputShape{ 1, 3, 2, 4 };
157  const armnn::TensorShape outputShape{ 1, 2, 4 };
158 
159  std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
160  8.0f, 7.0f, 6.0f, 6.0f,
161  100.0f, 20.0f, 300.0f, 40.0f,
162  500.0f, 476.0f, 450.0f, 426.0f,
163  50.0f, 60.0f, 70.0f, 80.0f,
164  10.0f, 200.0f, 30.0f, 400.0f });
165 
166  std::vector<int32_t> expectedOutputData({ 1, 2, 1, 2,
167  1, 1, 1, 1 });
168 
169  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
170  outputShape,
171  inputData,
172  expectedOutputData,
173  armnn::ArgMinMaxFunction::Max,
174  1,
175  backends);
176 }
177 
178 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
179 void ArgMinAxis1EndToEnd(const std::vector<armnn::BackendId>& backends)
180 {
181  const armnn::TensorShape inputShape{ 1, 3, 2, 4 };
182  const armnn::TensorShape outputShape{ 1, 2, 4 };
183 
184  std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
185  8.0f, 7.0f, 6.0f, 6.0f,
186  100.0f, 20.0f, 300.0f, 40.0f,
187  500.0f, 476.0f, 450.0f, 426.0f,
188  50.0f, 60.0f, 70.0f, 80.0f,
189  10.0f, 200.0f, 30.0f, 400.0f });
190 
191  std::vector<int32_t> expectedOutputData({ 0, 0, 0, 0,
192  0, 0, 0, 0 });
193 
194  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
195  outputShape,
196  inputData,
197  expectedOutputData,
198  armnn::ArgMinMaxFunction::Min,
199  1,
200  backends);
201 }
202 
203 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
204 void ArgMaxAxis2EndToEnd(const std::vector<armnn::BackendId>& backends)
205 {
206  const armnn::TensorShape inputShape{ 1, 3, 2, 4 };
207  const armnn::TensorShape outputShape{ 1, 3, 4 };
208 
209  std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
210  8.0f, 7.0f, 6.0f, 6.0f,
211  100.0f, 20.0f, 300.0f, 40.0f,
212  500.0f, 476.0f, 450.0f, 426.0f,
213  10.0f, 200.0f, 30.0f, 400.0f,
214  50.0f, 60.0f, 70.0f, 80.0f });
215 
216  std::vector<int32_t> expectedOutputData({ 1, 1, 1, 1,
217  1, 1, 1, 1,
218  1, 0, 1, 0});
219 
220  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
221  outputShape,
222  inputData,
223  expectedOutputData,
224  armnn::ArgMinMaxFunction::Max,
225  2,
226  backends);
227 }
228 
229 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
230 void ArgMinAxis2EndToEnd(const std::vector<armnn::BackendId>& backends)
231 {
232  const armnn::TensorShape inputShape{ 1, 3, 2, 4 };
233  const armnn::TensorShape outputShape{ 1, 3, 4 };
234 
235  std::vector<float> inputData({ 1.0f, 2.0f, 3.0f, 4.0f,
236  8.0f, 7.0f, 6.0f, 6.0f,
237  100.0f, 20.0f, 300.0f, 40.0f,
238  500.0f, 476.0f, 450.0f, 426.0f,
239  10.0f, 200.0f, 30.0f, 400.0f,
240  50.0f, 60.0f, 70.0f, 80.0f });
241 
242  std::vector<int32_t> expectedOutputData({ 0, 0, 0, 0,
243  0, 0, 0, 0,
244  0, 1, 0, 1 });
245 
246  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
247  outputShape,
248  inputData,
249  expectedOutputData,
250  armnn::ArgMinMaxFunction::Min,
251  2,
252  backends);
253 }
254 
255 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
256 void ArgMaxAxis3EndToEnd(const std::vector<armnn::BackendId>& backends)
257 {
258  const armnn::TensorShape inputShape{ 1, 3, 2, 4 };
259  const armnn::TensorShape outputShape{ 1, 3, 2 };
260 
261  std::vector<float> inputData({ 1.0f, 3.0f, 6.0f, 7.0f,
262  8.0f, 7.0f, 6.0f, 6.0f,
263  100.0f, 20.0f, 300.0f, 40.0f,
264  500.0f, 476.0f, 450.0f, 426.0f,
265  10.0f, 200.0f, 30.0f, 400.0f,
266  50.0f, 60.0f, 70.0f, 80.0f });
267 
268  std::vector<int32_t> expectedOutputData({ 3, 0,
269  2, 0,
270  3, 3});
271 
272  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
273  outputShape,
274  inputData,
275  expectedOutputData,
276  armnn::ArgMinMaxFunction::Max,
277  3,
278  backends);
279 }
280 
281 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
282 void ArgMinAxis3EndToEnd(const std::vector<armnn::BackendId>& backends)
283 {
284  const armnn::TensorShape inputShape{ 1, 3, 2, 4 };
285  const armnn::TensorShape outputShape{ 1, 3, 2 };
286 
287  std::vector<float> inputData({ 1.0f, 3.0f, 6.0f, 7.0f,
288  18.0f, 16.0f, 14.0f, 12.0f,
289  100.0f, 20.0f, 300.0f, 40.0f,
290  500.0f, 476.0f, 450.0f, 426.0f,
291  10.0f, 200.0f, 30.0f, 400.0f,
292  50.0f, 60.0f, 70.0f, 80.0f });
293 
294  std::vector<int32_t> expectedOutputData({ 0, 3,
295  1, 3,
296  0, 0 });
297 
298  ArgMinMaxEndToEndImpl<ArmnnType>(inputShape,
299  outputShape,
300  inputData,
301  expectedOutputData,
302  armnn::ArgMinMaxFunction::Min,
303  3,
304  backends);
305 }
306 
307 } // anonymous namespace
armnn::IConnectableLayer
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
Definition: INetwork.hpp:62
QuantizeHelper.hpp
armnn::ArgMinMaxDescriptor::m_Function
ArgMinMaxFunction m_Function
Specify if the function is to find Min or Max.
Definition: Descriptors.hpp:70
ResolveType.hpp
CommonTestUtils.hpp
armnn::ArgMinMaxDescriptor
An ArgMinMaxDescriptor for ArgMinMaxLayer.
Definition: Descriptors.hpp:56
armnn::ArgMinMaxFunction
ArgMinMaxFunction
Definition: Types.hpp:72
armnn::ArgMinMaxDescriptor::m_Axis
int m_Axis
Axis to reduce across the input tensor.
Definition: Descriptors.hpp:72
Connect
void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)
Definition: TestUtils.cpp:12
armnn::INetworkPtr
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
Definition: INetwork.hpp:173
armnn::INetwork::Create
static INetworkPtr Create(NetworkOptions networkOptions={})
Definition: Network.cpp:510