1 /*
2 * Copyright (c) 2019-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "arm_compute/runtime/NEON/NEScheduler.h"
25 #include "arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h"
26 #include "arm_compute/runtime/NEON/functions/NEPermute.h"
27 #include "arm_compute/runtime/NEON/functions/NESlice.h"
28 #include "src/core/NEON/kernels/NEGenerateProposalsLayerKernel.h"
29 #include "tests/Globals.h"
30 #include "tests/NEON/Accessor.h"
31 #include "tests/NEON/ArrayAccessor.h"
32 #include "tests/NEON/Helper.h"
33 #include "tests/framework/Macros.h"
34 #include "tests/framework/datasets/Datasets.h"
35 #include "tests/validation/Validation.h"
36 #include "tests/validation/fixtures/ComputeAllAnchorsFixture.h"
37 #include "utils/TypePrinter.h"
38
39 namespace arm_compute
40 {
41 namespace test
42 {
43 namespace validation
44 {
45 namespace
46 {
47 using NEComputeAllAnchors = NESynthetizeFunction<NEComputeAllAnchorsKernel>;
48
49 template <typename U, typename T>
fill_tensor(U && tensor,const std::vector<T> & v)50 inline void fill_tensor(U &&tensor, const std::vector<T> &v)
51 {
52 std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size());
53 }
54
55 template <typename T>
fill_tensor(Accessor && tensor,const std::vector<T> & v)56 inline void fill_tensor(Accessor &&tensor, const std::vector<T> &v)
57 {
58 if(tensor.data_layout() == DataLayout::NCHW)
59 {
60 std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size());
61 }
62 else
63 {
64 const int channels = tensor.shape()[0];
65 const int width = tensor.shape()[1];
66 const int height = tensor.shape()[2];
67 for(int x = 0; x < width; ++x)
68 {
69 for(int y = 0; y < height; ++y)
70 {
71 for(int c = 0; c < channels; ++c)
72 {
73 *(reinterpret_cast<T *>(tensor(Coordinates(c, x, y)))) = *(reinterpret_cast<const T *>(v.data() + x + y * width + c * height * width));
74 }
75 }
76 }
77 }
78 }
79
80 const auto ComputeAllInfoDataset = framework::dataset::make("ComputeAllInfo",
81 {
82 ComputeAnchorsInfo(10U, 10U, 1. / 16.f),
83 ComputeAnchorsInfo(100U, 1U, 1. / 2.f),
84 ComputeAnchorsInfo(100U, 1U, 1. / 4.f),
85 ComputeAnchorsInfo(100U, 100U, 1. / 4.f),
86
87 });
88
89 constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1);
90 } // namespace
91
92 TEST_SUITE(NEON)
TEST_SUITE(GenerateProposals)93 TEST_SUITE(GenerateProposals)
94
95 // *INDENT-OFF*
96 // clang-format off
97 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
98 framework::dataset::make("scores", { TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F32),
99 TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Mismatching types
100 TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong deltas (number of transformation non multiple of 4)
101 TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Wrong anchors (number of values per roi != 5)
102 TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16), // Output tensor num_valid_proposals not scalar
103 TensorInfo(TensorShape(100U, 100U, 9U), 1, DataType::F16)}), // num_valid_proposals not U32
104 framework::dataset::make("deltas",{ TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32),
105 TensorInfo(TensorShape(100U, 100U, 36U), 1, DataType::F32),
106 TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
107 TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
108 TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32),
109 TensorInfo(TensorShape(100U, 100U, 38U), 1, DataType::F32)})),
110 framework::dataset::make("anchors", { TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
111 TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
112 TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
113 TensorInfo(TensorShape(5U, 9U), 1, DataType::F32),
114 TensorInfo(TensorShape(4U, 9U), 1, DataType::F32),
115 TensorInfo(TensorShape(4U, 9U), 1, DataType::F32)})),
116 framework::dataset::make("proposals", { TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
117 TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
118 TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
119 TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
120 TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32),
121 TensorInfo(TensorShape(5U, 100U*100U*9U), 1, DataType::F32)})),
122 framework::dataset::make("scores_out", { TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
123 TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
124 TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
125 TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
126 TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32),
127 TensorInfo(TensorShape(100U*100U*9U), 1, DataType::F32)})),
128 framework::dataset::make("num_valid_proposals", { TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
129 TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
130 TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
131 TensorInfo(TensorShape(1U, 1U), 1, DataType::U32),
132 TensorInfo(TensorShape(1U, 10U), 1, DataType::U32),
133 TensorInfo(TensorShape(1U, 1U), 1, DataType::F16)})),
134 framework::dataset::make("generate_proposals_info", { GenerateProposalsInfo(10.f, 10.f, 1.f),
135 GenerateProposalsInfo(10.f, 10.f, 1.f),
136 GenerateProposalsInfo(10.f, 10.f, 1.f),
137 GenerateProposalsInfo(10.f, 10.f, 1.f),
138 GenerateProposalsInfo(10.f, 10.f, 1.f),
139 GenerateProposalsInfo(10.f, 10.f, 1.f)})),
140 framework::dataset::make("Expected", { true, false, false, false, false, false })),
141 scores, deltas, anchors, proposals, scores_out, num_valid_proposals, generate_proposals_info, expected)
142 {
143 ARM_COMPUTE_EXPECT(bool(NEGenerateProposalsLayer::validate(&scores.clone()->set_is_resizable(true),
144 &deltas.clone()->set_is_resizable(true),
145 &anchors.clone()->set_is_resizable(true),
146 &proposals.clone()->set_is_resizable(true),
147 &scores_out.clone()->set_is_resizable(true),
148 &num_valid_proposals.clone()->set_is_resizable(true),
149 generate_proposals_info)) == expected, framework::LogLevel::ERRORS);
150 }
151 // clang-format on
152 // *INDENT-ON*
153
154 template <typename T>
155 using NEComputeAllAnchorsFixture = ComputeAllAnchorsFixture<Tensor, Accessor, NEComputeAllAnchors, T>;
156
157 TEST_SUITE(Float)
TEST_SUITE(FP32)158 TEST_SUITE(FP32)
159 DATA_TEST_CASE(IntegrationTestCaseAllAnchors, framework::DatasetMode::ALL, framework::dataset::make("DataType", { DataType::F32 }),
160 data_type)
161 {
162 const int values_per_roi = 4;
163 const int num_anchors = 3;
164 const int feature_height = 4;
165 const int feature_width = 3;
166
167 SimpleTensor<float> anchors_expected(TensorShape(values_per_roi, feature_width * feature_height * num_anchors), DataType::F32);
168 fill_tensor(anchors_expected, std::vector<float> { -26, -19, 87, 86,
169 -81, -27, 58, 63,
170 -44, -15, 55, 36,
171 -10, -19, 103, 86,
172 -65, -27, 74, 63,
173 -28, -15, 71, 36,
174 6, -19, 119, 86,
175 -49, -27, 90, 63,
176 -12, -15, 87, 36,
177 -26, -3, 87, 102,
178 -81, -11, 58, 79,
179 -44, 1, 55, 52,
180 -10, -3, 103, 102,
181 -65, -11, 74, 79,
182 -28, 1, 71, 52,
183 6, -3, 119, 102,
184 -49, -11, 90, 79,
185 -12, 1, 87, 52,
186 -26, 13, 87, 118,
187 -81, 5, 58, 95,
188 -44, 17, 55, 68,
189 -10, 13, 103, 118,
190 -65, 5, 74, 95,
191 -28, 17, 71, 68,
192 6, 13, 119, 118,
193 -49, 5, 90, 95,
194 -12, 17, 87, 68,
195 -26, 29, 87, 134,
196 -81, 21, 58, 111,
197 -44, 33, 55, 84,
198 -10, 29, 103, 134,
199 -65, 21, 74, 111,
200 -28, 33, 71, 84,
201 6, 29, 119, 134,
202 -49, 21, 90, 111,
203 -12, 33, 87, 84
204 });
205
206 Tensor all_anchors;
207 Tensor anchors = create_tensor<Tensor>(TensorShape(4, num_anchors), data_type);
208
209 // Create and configure function
210 NEComputeAllAnchors compute_anchors;
211 compute_anchors.configure(&anchors, &all_anchors, ComputeAnchorsInfo(feature_width, feature_height, 1. / 16.0));
212 anchors.allocator()->allocate();
213 all_anchors.allocator()->allocate();
214
215 fill_tensor(Accessor(anchors), std::vector<float> { -26, -19, 87, 86,
216 -81, -27, 58, 63,
217 -44, -15, 55, 36
218 });
219 // Compute function
220 compute_anchors.run();
221 validate(Accessor(all_anchors), anchors_expected);
222 }
223
224 DATA_TEST_CASE(IntegrationTestCaseGenerateProposals, framework::DatasetMode::ALL, combine(framework::dataset::make("DataType", { DataType::F32 }),
225 framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
226 data_type, data_layout)
227 {
228 const int values_per_roi = 4;
229 const int num_anchors = 2;
230 const int feature_height = 4;
231 const int feature_width = 5;
232
233 std::vector<float> scores_vector
234 {
235 5.055894435664012e-04f, 1.270304909820112e-03f, 2.492271113912067e-03f, 5.951663827809190e-03f,
236 7.846917156877404e-03f, 6.776275276294789e-03f, 6.761571012891965e-03f, 4.898292096237725e-03f,
237 6.044472332578605e-04f, 3.203334118759474e-03f, 2.947527908919908e-03f, 6.313238560015770e-03f,
238 7.931767757095738e-03f, 8.764345805102866e-03f, 7.325012199914913e-03f, 4.317069470446271e-03f,
239 2.372537409795522e-03f, 1.589227460352735e-03f, 7.419477503600818e-03f, 3.157690354133824e-05f,
240 1.125915135986472e-03f, 9.865363483872330e-03f, 2.429454743386769e-03f, 2.724460564167563e-03f,
241 7.670409838207963e-03f, 5.558891552328172e-03f, 7.876904873099614e-03f, 6.824746047239291e-03f,
242 7.023817548067892e-03f, 3.651314909238673e-04f, 6.720443709032501e-03f, 5.935615511606155e-03f,
243 2.837349642759774e-03f, 1.787235113610299e-03f, 4.538568889918262e-03f, 3.391510678188818e-03f,
244 7.328474239481874e-03f, 6.306967923936016e-03f, 8.102218904895860e-04f, 3.366646521610209e-03f
245 };
246
247 std::vector<float> bbx_vector
248 {
249 5.066650471856862e-03, -7.638671742936328e-03, 2.549596503988635e-03, -8.316416756423296e-03,
250 -2.397471917924575e-04, 7.370595187754891e-03, -2.771880178185262e-03, 3.958364873973579e-03,
251 4.493661094712284e-03, 2.016487051533088e-03, -5.893883038142033e-03, 7.570636080807809e-03,
252 -1.395511229386785e-03, 3.686686052704696e-03, -7.738166245767079e-03, -1.947306329828059e-03,
253 -9.299719716045681e-03, -3.476410493413708e-03, -2.390761190919604e-03, 4.359281254364210e-03,
254 -2.135251160164030e-04, 9.203299843371962e-03, 4.042322775006053e-03, -9.464271243910754e-03,
255 2.566239543229305e-03, -9.691093900220627e-03, -4.019283034310979e-03, 8.145470429508792e-03,
256 7.345087308315662e-04, 7.049642787384043e-03, -2.768492313674294e-03, 6.997160053405803e-03,
257 6.675346697112969e-03, 2.353293365652274e-03, -3.612002585241749e-04, 1.592076522068768e-03,
258 -8.354188900818149e-04, -5.232515333564140e-04, 6.946683728847089e-03, -8.469757407935994e-03,
259 -8.985324496496555e-03, 4.885832859017961e-03, -7.662967577576512e-03, 7.284124004335807e-03,
260 -5.812167510299458e-03, -5.760336800482398e-03, 6.040416930336549e-03, 5.861508595443691e-03,
261 -5.509243096133549e-04, -2.006142470055888e-03, -7.205925340416066e-03, -1.117459082969758e-03,
262 4.233247017623154e-03, 8.079257498201178e-03, 2.962639022639513e-03, 7.069474943472751e-03,
263 -8.562946284971293e-03, -8.228634642768271e-03, -6.116245322799971e-04, -7.213122000180859e-03,
264 1.693094399433209e-03, -4.287504459132290e-03, 8.740365683925144e-03, 3.751788160720638e-03,
265 7.006764222862830e-03, 9.676754678358187e-03, -6.458757235812945e-03, -4.486506575589758e-03,
266 -4.371087196816259e-03, 3.542166755953152e-03, -2.504808998699504e-03, 5.666601724512010e-03,
267 -3.691862724546129e-03, 3.689809719085287e-03, 9.079930264704458e-03, 6.365127787359476e-03,
268 2.881681788246101e-06, 9.991866069315165e-03, -1.104757466496565e-03, -2.668455405633477e-03,
269 -1.225748887087659e-03, 6.530536159094015e-03, 3.629468917975644e-03, 1.374426066950348e-03,
270 -2.404098881570632e-03, -4.791365049441602e-03, -2.970654027009094e-03, 7.807553690294366e-03,
271 -1.198321129505323e-03, -3.574885336949881e-03, -5.380848303732298e-03, 9.705151282165116e-03,
272 -1.005217683242201e-03, 9.178094036278405e-03, -5.615977269541644e-03, 5.333533158509859e-03,
273 -2.817116206168516e-03, 6.672609782000503e-03, 6.575769501651313e-03, 8.987596634989362e-03,
274 -1.283530791296188e-03, 1.687717120057778e-03, 3.242391851439037e-03, -7.312060454341677e-03,
275 4.735335326324270e-03, -6.832367028817463e-03, -5.414854835884652e-03, -9.352380213755996e-03,
276 -3.682662043703889e-03, -6.127508590419776e-04, -7.682256596819467e-03, 9.569532628790246e-03,
277 -1.572157284518933e-03, -6.023034366859191e-03, -5.110873282582924e-03, -8.697072236660256e-03,
278 -3.235150419663566e-03, -8.286320236471386e-03, -5.229472409112913e-03, 9.920785896115053e-03,
279 -2.478413362126123e-03, -9.261324796935007e-03, 1.718512310840434e-04, 3.015875488208480e-03,
280 -6.172932549255669e-03, -4.031715551985103e-03, -9.263878005853677e-03, -2.815310738453385e-03,
281 7.075307462133643e-03, 1.404611747938669e-03, -1.518548732533266e-03, -9.293430941655778e-03,
282 6.382186966633246e-03, 8.256835789169248e-03, 3.196907843506736e-03, 8.821615689753433e-03,
283 -7.661543424832439e-03, 1.636273081822326e-03, -8.792373335756125e-03, 2.958775812049877e-03,
284 -6.269300278071262e-03, 6.248285790856450e-03, -3.675414624536002e-03, -1.692616700318762e-03,
285 4.126007647815893e-03, -9.155291689759584e-03, -8.432616039924004e-03, 4.899980636213323e-03,
286 3.511535019681671e-03, -1.582745757177339e-03, -2.703657774917963e-03, 6.738168990840388e-03,
287 4.300455303937919e-03, 9.618312854781494e-03, 2.762142918402472e-03, -6.590025003382154e-03,
288 -2.071168373801788e-03, 8.613893943683627e-03, 9.411190295341036e-03, -6.129018930548372e-03
289 };
290
291 const std::vector<float> anchors_vector{ -26, -19, 87, 86, -81, -27, 58, 63 };
292 SimpleTensor<float> proposals_expected(TensorShape(5, 9), DataType::F32);
293 fill_tensor(proposals_expected, std::vector<float>
294 {
295 0, 0, 0, 75.269, 64.4388,
296 0, 21.9579, 13.0535, 119, 99,
297 0, 38.303, 0, 119, 87.6447,
298 0, 0, 0, 119, 64.619,
299 0, 0, 20.7997, 74.0714, 99,
300 0, 0, 0, 91.8963, 79.3724,
301 0, 0, 4.42377, 58.1405, 95.1781,
302 0, 0, 13.4405, 104.799, 99,
303 0, 38.9066, 28.2434, 119, 99,
304
305 });
306
307 SimpleTensor<float> scores_expected(TensorShape(9), DataType::F32);
308 fill_tensor(scores_expected, std::vector<float>
309 {
310 0.00986536,
311 0.00876435,
312 0.00784692,
313 0.00767041,
314 0.00732847,
315 0.00682475,
316 0.00672044,
317 0.00631324,
318 3.15769e-05
319 });
320
321 TensorShape scores_shape = TensorShape(feature_width, feature_height, num_anchors);
322 TensorShape deltas_shape = TensorShape(feature_width, feature_height, values_per_roi * num_anchors);
323 if(data_layout == DataLayout::NHWC)
324 {
325 permute(scores_shape, PermutationVector(2U, 0U, 1U));
326 permute(deltas_shape, PermutationVector(2U, 0U, 1U));
327 }
328 // Inputs
329 Tensor scores = create_tensor<Tensor>(scores_shape, data_type, 1, QuantizationInfo(), data_layout);
330 Tensor bbox_deltas = create_tensor<Tensor>(deltas_shape, data_type, 1, QuantizationInfo(), data_layout);
331 Tensor anchors = create_tensor<Tensor>(TensorShape(values_per_roi, num_anchors), data_type);
332
333 // Outputs
334 Tensor proposals;
335 Tensor num_valid_proposals;
336 Tensor scores_out;
337 num_valid_proposals.allocator()->init(TensorInfo(TensorShape(1), 1, DataType::U32));
338
339 NEGenerateProposalsLayer generate_proposals;
340 generate_proposals.configure(&scores, &bbox_deltas, &anchors, &proposals, &scores_out, &num_valid_proposals,
341 GenerateProposalsInfo(120, 100, 0.166667f, 1 / 16.0, 6000, 300, 0.7f, 16.0f));
342
343 // Allocate memory for input/output tensors
344 scores.allocator()->allocate();
345 bbox_deltas.allocator()->allocate();
346 anchors.allocator()->allocate();
347 proposals.allocator()->allocate();
348 num_valid_proposals.allocator()->allocate();
349 scores_out.allocator()->allocate();
350 // Fill inputs
351 fill_tensor(Accessor(scores), scores_vector);
352 fill_tensor(Accessor(bbox_deltas), bbx_vector);
353 fill_tensor(Accessor(anchors), anchors_vector);
354
355 // Run operator
356 generate_proposals.run();
357 // Gather num_valid_proposals
358 const uint32_t N = *reinterpret_cast<uint32_t *>(num_valid_proposals.ptr_to_element(Coordinates(0, 0)));
359
360 // Select the first N entries of the proposals
361 Tensor proposals_final;
362 NESlice select_proposals;
363 select_proposals.configure(&proposals, &proposals_final, Coordinates(0, 0), Coordinates(values_per_roi + 1, N));
364
365 proposals_final.allocator()->allocate();
366 select_proposals.run();
367
368 // Select the first N entries of the proposals
369 Tensor scores_final;
370 NESlice select_scores;
371 select_scores.configure(&scores_out, &scores_final, Coordinates(0), Coordinates(N));
372 scores_final.allocator()->allocate();
373 select_scores.run();
374
375 const RelativeTolerance<float> tolerance_f32(1e-5f);
376 // Validate the output
377 validate(Accessor(proposals_final), proposals_expected, tolerance_f32);
378 validate(Accessor(scores_final), scores_expected, tolerance_f32);
379 }
380
381 FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsFixture<float>, framework::DatasetMode::ALL,
382 combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F32 })))
383 {
384 // Validate output
385 validate(Accessor(_target), _reference);
386 }
387 TEST_SUITE_END() // FP32
388 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)389 TEST_SUITE(FP16)
390 FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsFixture<half>, framework::DatasetMode::ALL,
391 combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F16 })))
392 {
393 // Validate output
394 validate(Accessor(_target), _reference);
395 }
396 TEST_SUITE_END() // FP16
397 #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
398
399 TEST_SUITE_END() // Float
400
401 template <typename T>
402 using NEComputeAllAnchorsQuantizedFixture = ComputeAllAnchorsQuantizedFixture<Tensor, Accessor, NEComputeAllAnchors, T>;
403
404 TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)405 TEST_SUITE(QASYMM8)
406 FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, NEComputeAllAnchorsQuantizedFixture<int16_t>, framework::DatasetMode::ALL,
407 combine(combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset),
408 framework::dataset::make("DataType", { DataType::QSYMM16 })),
409 framework::dataset::make("QuantInfo", { QuantizationInfo(0.125f, 0) })))
410 {
411 // Validate output
412 validate(Accessor(_target), _reference, tolerance_qsymm16);
413 }
414 TEST_SUITE_END() // QASYMM8
415 TEST_SUITE_END() // Quantized
416
417 TEST_SUITE_END() // GenerateProposals
418 TEST_SUITE_END() // Neon
419 } // namespace validation
420 } // namespace test
421 } // namespace arm_compute
422