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review.mlplatform.org / ml / armnn / b9546cf1ffde83f63436c4087711dcf098ea4196 / . / src / backends / backendsCommon / test / MergerTestImpl.hpp
blob: e0b823333608914c065ca7952ae02242a225bead [file] [log] [blame]
narpra01b9546cf2018-11-20 15:21:28 +0000[diff] [blame^]1//
2// Copyright © 2017 Arm Ltd. All rights reserved.
3// SPDX-License-Identifier: MIT
4//
5#pragma once
6
7#include <armnn/INetwork.hpp>
8
9#include <backendsCommon/test/CommonTestUtils.hpp>
10
11#include <boost/test/unit_test.hpp>
12
13#include <vector>
14
15namespace
16{
17
18template<typename armnn::DataType DataType>
19INetworkPtr CreateMergerNetwork(const std::vector<TensorShape>& inputShapes,
20 const TensorShape& outputShape,
21 unsigned int concatAxis,
22 const float qScale = 1.0f,
23 const int32_t qOffset = 0)
24{
25 using namespace armnn;
26 // Builds up the structure of the network.
27 INetworkPtr net(INetwork::Create());
28
29 OriginsDescriptor descriptor;
30
31 descriptor = CreateMergerDescriptorForConcatenation(inputShapes.begin(),
32 inputShapes.end(),
33 concatAxis);
34 IConnectableLayer* merger = net->AddMergerLayer(descriptor, "merger");
35
36 for (unsigned int i = 0; i < inputShapes.size(); ++i)
37 {
38 TensorInfo inputTensorInfo(inputShapes[i], DataType, qScale, qOffset);
39 IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i));
40 Connect(input, merger, inputTensorInfo, 0, i);
41 }
42
43 TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset);
44 IConnectableLayer* output = net->AddOutputLayer(0, "output");
45 Connect(merger, output, outputTensorInfo, 0, 0);
46
47 return net;
48}
49
50template<typename T>
51void MergerDim0EndToEnd(const std::vector<BackendId>& backends)
52{
53 using namespace armnn;
54
55 unsigned int concatAxis = 0;
56 const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
57 const TensorShape& outputShape = { 4, 3, 2, 2 };
58
59 // Builds up the structure of the network
60 INetworkPtr net = CreateMergerNetwork<GetDataType<T>()>(inputShapes, outputShape, concatAxis);
61
62 BOOST_TEST_CHECKPOINT("create a network");
63
64 // Creates structures for input & output.
65 std::vector<T> inputData{
66 1, 2,
67 3, 4,
68 5, 6,
69 7, 8,
70 9, 10,
71 11, 12,
72 1, 2,
73 3, 4,
74 5, 6,
75 7, 8,
76 9, 10,
77 11, 12
78 };
79
80 std::vector<T> expectedOutput{
81 1, 2,
82 3, 4,
83 5, 6,
84 7, 8,
85 9, 10,
86 11, 12,
87 1, 2,
88 3, 4,
89 5, 6,
90 7, 8,
91 9, 10,
92 11, 12,
93 1, 2,
94 3, 4,
95 5, 6,
96 7, 8,
97 9, 10,
98 11, 12,
99 1, 2,
100 3, 4,
101 5, 6,
102 7, 8,
103 9, 10,
104 11, 12
105 };
106
107 std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
108 std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
109
110 EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends);
111}
112
113template<typename T>
114void MergerDim1EndToEnd(const std::vector<BackendId>& backends)
115{
116 using namespace armnn;
117
118 unsigned int concatAxis = 1;
119 const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
120 const TensorShape& outputShape = { 2, 6, 2, 2 };
121
122 // Builds up the structure of the network
123 INetworkPtr net = CreateMergerNetwork<GetDataType<T>()>(inputShapes, outputShape, concatAxis);
124
125 BOOST_TEST_CHECKPOINT("create a network");
126
127 // Creates structures for input & output.
128 std::vector<T> inputData{
129 1, 2,
130 3, 4,
131 5, 6,
132 7, 8,
133 9, 10,
134 11, 12,
135 1, 2,
136 3, 4,
137 5, 6,
138 7, 8,
139 9, 10,
140 11, 12
141 };
142
143 std::vector<T> expectedOutput{
144 1, 2,
145 3, 4,
146 5, 6,
147 7, 8,
148 9, 10,
149 11, 12,
150 1, 2,
151 3, 4,
152 5, 6,
153 7, 8,
154 9, 10,
155 11, 12,
156 1, 2,
157 3, 4,
158 5, 6,
159 7, 8,
160 9, 10,
161 11, 12,
162 1, 2,
163 3, 4,
164 5, 6,
165 7, 8,
166 9, 10,
167 11, 12
168 };
169
170 std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
171 std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
172
173 EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends);
174}
175
176template<typename T>
177void MergerDim2EndToEnd(const std::vector<BackendId>& backends)
178{
179 using namespace armnn;
180
181 unsigned int concatAxis = 2;
182 const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
183 const TensorShape& outputShape = { 2, 3, 4, 2 };
184
185 // Builds up the structure of the network
186 INetworkPtr net = CreateMergerNetwork<GetDataType<T>()>(inputShapes, outputShape, concatAxis);
187
188 BOOST_TEST_CHECKPOINT("create a network");
189
190 // Creates structures for input & output.
191 std::vector<T> inputData{
192 1, 2,
193 3, 4,
194 5, 6,
195 7, 8,
196 9, 10,
197 11, 12,
198 1, 2,
199 3, 4,
200 5, 6,
201 7, 8,
202 9, 10,
203 11, 12
204 };
205
206 std::vector<T> expectedOutput{
207 1, 2,
208 3, 4,
209 1, 2,
210 3, 4,
211 5, 6,
212 7, 8,
213 5, 6,
214 7, 8,
215 9, 10,
216 11, 12,
217 9, 10,
218 11, 12,
219 1, 2,
220 3, 4,
221 1, 2,
222 3, 4,
223 5, 6,
224 7, 8,
225 5, 6,
226 7, 8,
227 9, 10,
228 11, 12,
229 9, 10,
230 11, 12
231 };
232
233 std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
234 std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
235
236 EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends);
237}
238
239template<typename T>
240void MergerDim3EndToEnd(const std::vector<BackendId>& backends)
241{
242 using namespace armnn;
243
244 unsigned int concatAxis = 3;
245 const std::vector<TensorShape> inputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }};
246 const TensorShape& outputShape = { 2, 3, 2, 4 };
247
248 // Builds up the structure of the network
249 INetworkPtr net = CreateMergerNetwork<GetDataType<T>()>(inputShapes, outputShape, concatAxis);
250
251 BOOST_TEST_CHECKPOINT("create a network");
252
253 // Creates structures for input & output.
254 std::vector<T> inputData{
255 1, 2,
256 3, 4,
257 5, 6,
258 7, 8,
259 9, 10,
260 11, 12,
261 1, 2,
262 3, 4,
263 5, 6,
264 7, 8,
265 9, 10,
266 11, 12
267 };
268
269 std::vector<T> expectedOutput{
270 1, 2,
271 1, 2,
272 3, 4,
273 3, 4,
274 5, 6,
275 5, 6,
276 7, 8,
277 7, 8,
278 9, 10,
279 9, 10,
280 11, 12,
281 11, 12,
282 1, 2,
283 1, 2,
284 3, 4,
285 3, 4,
286 5, 6,
287 5, 6,
288 7, 8,
289 7, 8,
290 9, 10,
291 9, 10,
292 11, 12,
293 11, 12
294 };
295
296 std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }, { 1,inputData }};
297 std::map<int, std::vector<T>> expectedOutputData = {{ 0,expectedOutput }};
298
299 EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends);
300}
301
302} // anonymous namespace