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review.mlplatform.org / ml / ComputeLibrary / 35981ca1cf8fab278831902664b7a103bcb216e3 / . / tests / validation / CL / GenerateProposalsLayer.cpp
blob: 646a0e0530642349ac5a762f840f74af5d74aab5 [file] [log] [blame]
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]1/*
Michele Di Giorgiod9eaf612020-07-08 11:12:57 +0100[diff] [blame]2 * Copyright (c) 2019-2020 Arm Limited.
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]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/CL/CLScheduler.h"
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]25#include "arm_compute/runtime/CL/functions/CLGenerateProposalsLayer.h"
26#include "arm_compute/runtime/CL/functions/CLPermute.h"
27#include "arm_compute/runtime/CL/functions/CLSlice.h"
Georgios Pinitas8c3c0e72020-12-03 20:11:53 +0000[diff] [blame]28#include "src/core/CL/kernels/CLGenerateProposalsLayerKernel.h"
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]29#include "tests/CL/CLAccessor.h"
30#include "tests/CL/CLArrayAccessor.h"
Georgios Pinitas8c3c0e72020-12-03 20:11:53 +0000[diff] [blame]31#include "tests/CL/Helper.h"
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]32#include "tests/Globals.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
39namespace arm_compute
40{
41namespace test
42{
43namespace validation
44{
45namespace
46{
Georgios Pinitas8c3c0e72020-12-03 20:11:53 +0000[diff] [blame]47using CLComputeAllAnchors = CLSynthetizeFunction<CLComputeAllAnchorsKernel>;
48
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]49template <typename U, typename T>
50inline void fill_tensor(U &&tensor, const std::vector<T> &v)
51{
52 std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size());
53}
54
55template <typename T>
56inline void fill_tensor(CLAccessor &&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
80const 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});
Michele Di Giorgio6b612f52019-09-05 12:30:22 +0100[diff] [blame]88
89constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1);
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]90} // namespace
91
92TEST_SUITE(CL)
93TEST_SUITE(GenerateProposals)
94
95// *INDENT-OFF*
96// clang-format off
97DATA_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(CLGenerateProposalsLayer::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
154template <typename T>
155using CLComputeAllAnchorsFixture = ComputeAllAnchorsFixture<CLTensor, CLAccessor, CLComputeAllAnchors, T>;
156
157TEST_SUITE(Float)
158TEST_SUITE(FP32)
159DATA_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 CLTensor all_anchors;
207 CLTensor anchors = create_tensor<CLTensor>(TensorShape(4, num_anchors), data_type);
208
209 // Create and configure function
210 CLComputeAllAnchors 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(CLAccessor(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(CLAccessor(all_anchors), anchors_expected);
222}
223
224DATA_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
Giorgio Arena6e9d0e02020-01-03 15:02:04 +0000[diff] [blame]291 std::vector<float> anchors_vector{ -26, -19, 87, 86, -81, -27, 58, 63 };
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]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
329 // Inputs
330 CLTensor scores = create_tensor<CLTensor>(scores_shape, data_type, 1, QuantizationInfo(), data_layout);
331 CLTensor bbox_deltas = create_tensor<CLTensor>(deltas_shape, data_type, 1, QuantizationInfo(), data_layout);
332 CLTensor anchors = create_tensor<CLTensor>(TensorShape(values_per_roi, num_anchors), data_type);
333
334 // Outputs
335 CLTensor proposals;
336 CLTensor num_valid_proposals;
337 CLTensor scores_out;
338 num_valid_proposals.allocator()->init(TensorInfo(TensorShape(1), 1, DataType::U32));
339
340 CLGenerateProposalsLayer generate_proposals;
341 generate_proposals.configure(&scores, &bbox_deltas, &anchors, &proposals, &scores_out, &num_valid_proposals,
342 GenerateProposalsInfo(120, 100, 0.166667f, 1 / 16.0, 6000, 300, 0.7f, 16.0f));
343
344 // Allocate memory for input/output tensors
345 scores.allocator()->allocate();
346 bbox_deltas.allocator()->allocate();
347 anchors.allocator()->allocate();
348 proposals.allocator()->allocate();
349 num_valid_proposals.allocator()->allocate();
350 scores_out.allocator()->allocate();
351 // Fill inputs
352 fill_tensor(CLAccessor(scores), scores_vector);
353 fill_tensor(CLAccessor(bbox_deltas), bbx_vector);
354 fill_tensor(CLAccessor(anchors), anchors_vector);
355
356 // Run operator
357 generate_proposals.run();
358 // Gather num_valid_proposals
359 num_valid_proposals.map();
360 const uint32_t N = *reinterpret_cast<uint32_t *>(num_valid_proposals.ptr_to_element(Coordinates(0, 0)));
361 num_valid_proposals.unmap();
362
363 // Select the first N entries of the proposals
364 CLTensor proposals_final;
365 CLSlice select_proposals;
366 select_proposals.configure(&proposals, &proposals_final, Coordinates(0, 0), Coordinates(values_per_roi + 1, N));
367 proposals_final.allocator()->allocate();
368 select_proposals.run();
369
Michele Di Giorgio6b612f52019-09-05 12:30:22 +0100[diff] [blame]370 // Select the first N entries of the scores
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]371 CLTensor scores_final;
372 CLSlice select_scores;
373 select_scores.configure(&scores_out, &scores_final, Coordinates(0), Coordinates(N));
374 scores_final.allocator()->allocate();
375 select_scores.run();
376
377 const RelativeTolerance<float> tolerance_f32(1e-5f);
378 // Validate the output
379 validate(CLAccessor(proposals_final), proposals_expected, tolerance_f32);
380 validate(CLAccessor(scores_final), scores_expected, tolerance_f32);
381}
382
383FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, CLComputeAllAnchorsFixture<float>, framework::DatasetMode::ALL,
384 combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F32 })))
385{
386 // Validate output
387 validate(CLAccessor(_target), _reference);
388}
389TEST_SUITE_END() // FP32
390
391TEST_SUITE(FP16)
392FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, CLComputeAllAnchorsFixture<half>, framework::DatasetMode::ALL,
393 combine(combine(framework::dataset::make("NumAnchors", { 2, 4, 8 }), ComputeAllInfoDataset), framework::dataset::make("DataType", { DataType::F16 })))
394{
395 // Validate output
396 validate(CLAccessor(_target), _reference);
397}
398TEST_SUITE_END() // FP16
399TEST_SUITE_END() // Float
400
Michele Di Giorgio6b612f52019-09-05 12:30:22 +0100[diff] [blame]401template <typename T>
402using CLComputeAllAnchorsQuantizedFixture = ComputeAllAnchorsQuantizedFixture<CLTensor, CLAccessor, CLComputeAllAnchors, T>;
403
404TEST_SUITE(Quantized)
405TEST_SUITE(QASYMM8)
406FIXTURE_DATA_TEST_CASE(ComputeAllAnchors, CLComputeAllAnchorsQuantizedFixture<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(CLAccessor(_target), _reference, tolerance_qsymm16);
413}
414TEST_SUITE_END() // QASYMM8
415TEST_SUITE_END() // Quantized
416
Manuel Bottini5209be52019-02-13 16:34:56 +0000[diff] [blame]417TEST_SUITE_END() // GenerateProposals
418TEST_SUITE_END() // CL
419
420} // namespace validation
421} // namespace test
422} // namespace arm_compute