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
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
|
/*
* Copyright (c) 2019-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NEDetectionPostProcessLayer.h"
#include "arm_compute/runtime/Tensor.h"
#include "arm_compute/runtime/TensorAllocator.h"
#include "tests/NEON/Accessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
template <typename U, typename T>
inline void fill_tensor(U &&tensor, const std::vector<T> &v)
{
std::memcpy(tensor.data(), v.data(), sizeof(T) * v.size());
}
template <typename U, typename T>
inline void quantize_and_fill_tensor(U &&tensor, const std::vector<T> &v)
{
QuantizationInfo qi = tensor.quantization_info();
std::vector<uint8_t> quantized;
quantized.reserve(v.size());
for(auto elem : v)
{
quantized.emplace_back(quantize_qasymm8(elem, qi));
}
std::memcpy(tensor.data(), quantized.data(), sizeof(uint8_t) * quantized.size());
}
inline QuantizationInfo qinfo_scaleoffset_from_minmax(const float min, const float max)
{
int offset = 0;
float scale = 0;
const uint8_t qmin = std::numeric_limits<uint8_t>::min();
const uint8_t qmax = std::numeric_limits<uint8_t>::max();
const float f_qmin = qmin;
const float f_qmax = qmax;
// Continue only if [min,max] is a valid range and not a point
if(min != max)
{
scale = (max - min) / (f_qmax - f_qmin);
const float offset_from_min = f_qmin - min / scale;
const float offset_from_max = f_qmax - max / scale;
const float offset_from_min_error = std::abs(f_qmin) + std::abs(min / scale);
const float offset_from_max_error = std::abs(f_qmax) + std::abs(max / scale);
const float f_offset = offset_from_min_error < offset_from_max_error ? offset_from_min : offset_from_max;
uint8_t uint8_offset = 0;
if(f_offset < f_qmin)
{
uint8_offset = qmin;
}
else if(f_offset > f_qmax)
{
uint8_offset = qmax;
}
else
{
uint8_offset = static_cast<uint8_t>(support::cpp11::round(f_offset));
}
offset = uint8_offset;
}
return QuantizationInfo(scale, offset);
}
inline void base_test_case(DetectionPostProcessLayerInfo info, DataType data_type, const SimpleTensor<float> &expected_output_boxes,
const SimpleTensor<float> &expected_output_classes, const SimpleTensor<float> &expected_output_scores, const SimpleTensor<float> &expected_num_detection,
AbsoluteTolerance<float> tolerance_boxes = AbsoluteTolerance<float>(0.1f), AbsoluteTolerance<float> tolerance_others = AbsoluteTolerance<float>(0.1f))
{
Tensor box_encoding = create_tensor<Tensor>(TensorShape(4U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(-1.0f, 1.0f));
Tensor class_prediction = create_tensor<Tensor>(TensorShape(3U, 6U, 1U), data_type, 1, qinfo_scaleoffset_from_minmax(0.0f, 1.0f));
Tensor anchors = create_tensor<Tensor>(TensorShape(4U, 6U), data_type, 1, qinfo_scaleoffset_from_minmax(0.0f, 100.5f));
box_encoding.allocator()->allocate();
class_prediction.allocator()->allocate();
anchors.allocator()->allocate();
std::vector<float> box_encoding_vector =
{
0.0f, 1.0f, 0.0f, 0.0f,
0.0f, -1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f,
0.0f, 1.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f, 0.0f
};
std::vector<float> class_prediction_vector =
{
0.0f, 0.7f, 0.68f,
0.0f, 0.6f, 0.5f,
0.0f, 0.9f, 0.83f,
0.0f, 0.91f, 0.97f,
0.0f, 0.5f, 0.4f,
0.0f, 0.31f, 0.22f
};
std::vector<float> anchors_vector =
{
0.4f, 0.4f, 1.1f, 1.1f,
0.4f, 0.4f, 1.1f, 1.1f,
0.4f, 0.4f, 1.1f, 1.1f,
0.4f, 10.4f, 1.1f, 1.1f,
0.4f, 10.4f, 1.1f, 1.1f,
0.4f, 100.4f, 1.1f, 1.1f
};
// Fill the tensors with random pre-generated values
if(data_type == DataType::F32)
{
fill_tensor(Accessor(box_encoding), box_encoding_vector);
fill_tensor(Accessor(class_prediction), class_prediction_vector);
fill_tensor(Accessor(anchors), anchors_vector);
}
else
{
quantize_and_fill_tensor(Accessor(box_encoding), box_encoding_vector);
quantize_and_fill_tensor(Accessor(class_prediction), class_prediction_vector);
quantize_and_fill_tensor(Accessor(anchors), anchors_vector);
}
// Determine the output through the Neon kernel
Tensor output_boxes;
Tensor output_classes;
Tensor output_scores;
Tensor num_detection;
NEDetectionPostProcessLayer detection;
detection.configure(&box_encoding, &class_prediction, &anchors, &output_boxes, &output_classes, &output_scores, &num_detection, info);
output_boxes.allocator()->allocate();
output_classes.allocator()->allocate();
output_scores.allocator()->allocate();
num_detection.allocator()->allocate();
// Run the kernel
detection.run();
// Validate against the expected output
// Validate output boxes
validate(Accessor(output_boxes), expected_output_boxes, tolerance_boxes);
// Validate detection classes
validate(Accessor(output_classes), expected_output_classes, tolerance_others);
// Validate detection scores
validate(Accessor(output_scores), expected_output_scores, tolerance_others);
// Validate num detections
validate(Accessor(num_detection), expected_num_detection, tolerance_others);
}
} // namespace
TEST_SUITE(NEON)
TEST_SUITE(DetectionPostProcessLayer)
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip(
framework::dataset::make("BoxEncodingsInfo", { TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 10U, 3U), 1, DataType::F32), // Mismatching batch_size
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::S8), // Unsupported data type
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), // Wrong Detection Info
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32), // Wrong boxes dimensions
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8)}), // Wrong score dimension
framework::dataset::make("ClassPredsInfo",{ TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
TensorInfo(TensorShape(3U ,10U), 1, DataType::F32),
TensorInfo(TensorShape(3U ,10U), 1, DataType::QASYMM8)})),
framework::dataset::make("AnchorsInfo",{ TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 10U, 1U), 1, DataType::QASYMM8)})),
framework::dataset::make("OutputBoxInfo", { TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::S8),
TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(1U, 5U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(4U, 3U, 1U), 1, DataType::F32)})),
framework::dataset::make("OuputClassesInfo",{ TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(6U, 1U), 1, DataType::F32)})),
framework::dataset::make("OutputScoresInfo",{ TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(3U, 1U), 1, DataType::F32),
TensorInfo(TensorShape(6U, 1U), 1, DataType::F32)})),
framework::dataset::make("NumDetectionsInfo",{ TensorInfo(TensorShape(1U), 1, DataType::F32),
TensorInfo(TensorShape(1U), 1, DataType::F32),
TensorInfo(TensorShape(1U), 1, DataType::F32),
TensorInfo(TensorShape(1U), 1, DataType::F32),
TensorInfo(TensorShape(1U), 1, DataType::F32),
TensorInfo(TensorShape(1U), 1, DataType::F32)})),
framework::dataset::make("DetectionPostProcessLayerInfo",{ DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
DetectionPostProcessLayerInfo(3, 1, 0.0f, 1.5f, 2, {0.0f,0.1f,0.1f,0.1f}),
DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f}),
DetectionPostProcessLayerInfo(3, 1, 0.0f, 0.5f, 2, {0.1f,0.1f,0.1f,0.1f})})),
framework::dataset::make("Expected", {true, false, false, false, false, false })),
box_encodings_info, classes_info, anchors_info, output_boxes_info, output_classes_info,output_scores_info, num_detection_info, detect_info, expected)
{
const Status status = NEDetectionPostProcessLayer::validate(&box_encodings_info.clone()->set_is_resizable(false),
&classes_info.clone()->set_is_resizable(false),
&anchors_info.clone()->set_is_resizable(false),
&output_boxes_info.clone()->set_is_resizable(false),
&output_classes_info.clone()->set_is_resizable(false),
&output_scores_info.clone()->set_is_resizable(false), &num_detection_info.clone()->set_is_resizable(false), detect_info);
ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
TEST_SUITE(F32)
TEST_CASE(Float_general, framework::DatasetMode::ALL)
{
DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/);
// Fill expected detection boxes
SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
// Fill expected detection classes
SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
// Fill expected detection scores
SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
// Fill expected num detections
SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
fill_tensor(expected_num_detection, std::vector<float> { 3.f });
// Run base test
base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection);
}
TEST_CASE(Float_fast, framework::DatasetMode::ALL)
{
DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
false /*use_regular_nms*/, 1 /*detections_per_class*/);
// Fill expected detection boxes
SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
// Fill expected detection classes
SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
// Fill expected detection scores
SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
// Fill expected num detections
SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
fill_tensor(expected_num_detection, std::vector<float> { 3.f });
// Run base test
base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection);
}
TEST_CASE(Float_regular, framework::DatasetMode::ALL)
{
DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
true /*use_regular_nms*/, 1 /*detections_per_class*/);
// Fill expected detection boxes
SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f });
// Fill expected detection classes
SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
// Fill expected detection scores
SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.91f, 0.0f });
// Fill expected num detections
SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
fill_tensor(expected_num_detection, std::vector<float> { 2.f });
// Run test
base_test_case(info, DataType::F32, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection);
}
TEST_SUITE_END() // F32
TEST_SUITE(QASYMM8)
TEST_CASE(Quantized_general, framework::DatasetMode::ALL)
{
DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/);
// Fill expected detection boxes
SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
// Fill expected detection classes
SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
// Fill expected detection scores
SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
// Fill expected num detections
SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
fill_tensor(expected_num_detection, std::vector<float> { 3.f });
// Run test
base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
}
TEST_CASE(Quantized_fast, framework::DatasetMode::ALL)
{
DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
false /*use_regular_nms*/, 1 /*detections_per_class*/);
// Fill expected detection boxes
SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, -0.15, 0.95, 0.95, -0.15, 99.85, 0.95, 100.95 });
// Fill expected detection classes
SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
// Fill expected detection scores
SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_scores, std::vector<float> { 0.97f, 0.95f, 0.31f });
// Fill expected num detections
SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
fill_tensor(expected_num_detection, std::vector<float> { 3.f });
// Run base test
base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
}
TEST_CASE(Quantized_regular, framework::DatasetMode::ALL)
{
DetectionPostProcessLayerInfo info = DetectionPostProcessLayerInfo(3 /*max_detections*/, 1 /*max_classes_per_detection*/, 0.0 /*nms_score_threshold*/,
0.5 /*nms_iou_threshold*/, 2 /*num_classes*/, { 11.0, 11.0, 6.0, 6.0 } /*scale*/,
true /*use_regular_nms*/, 1 /*detections_per_class*/);
// Fill expected detection boxes
SimpleTensor<float> expected_output_boxes(TensorShape(4U, 3U), DataType::F32);
fill_tensor(expected_output_boxes, std::vector<float> { -0.15, 9.85, 0.95, 10.95, -0.15, 9.85, 0.95, 10.95, 0.0f, 0.0f, 0.0f, 0.0f });
// Fill expected detection classes
SimpleTensor<float> expected_output_classes(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_classes, std::vector<float> { 1.0f, 0.0f, 0.0f });
// Fill expected detection scores
SimpleTensor<float> expected_output_scores(TensorShape(3U), DataType::F32);
fill_tensor(expected_output_scores, std::vector<float> { 0.95f, 0.91f, 0.0f });
// Fill expected num detections
SimpleTensor<float> expected_num_detection(TensorShape(1U), DataType::F32);
fill_tensor(expected_num_detection, std::vector<float> { 2.f });
// Run test
base_test_case(info, DataType::QASYMM8, expected_output_boxes, expected_output_classes, expected_output_scores, expected_num_detection, AbsoluteTolerance<float>(0.3f));
}
TEST_SUITE_END() // QASYMM8
TEST_SUITE_END() // DetectionPostProcessLayer
TEST_SUITE_END() // Neon
} // namespace validation
} // namespace test
} // namespace arm_compute
|
