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review.mlplatform.org / ml / ComputeLibrary / c9e519d2ea4780297d71e68cccc5de9c7bb7c0b4 / . / tests / validation / NEON / DirectConvolutionLayer.cpp
blob: 0f4c6bb2799f41dc5c51deffb2f89c3d220eabdc [file] [log] [blame]
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]1/*
Adnan AlSinan0ef2c212022-01-24 10:20:40 +0000[diff] [blame]2 * Copyright (c) 2017-2022 Arm Limited.
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[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 */
Manuel Bottini87350f42020-09-15 13:03:34 +0100[diff] [blame]24#include "arm_compute/core/Helpers.h"
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]25#include "arm_compute/core/Types.h"
26#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
27#include "arm_compute/runtime/Tensor.h"
28#include "arm_compute/runtime/TensorAllocator.h"
alerah01c9e519d2022-01-31 19:04:10 +0200[diff] [blame^]29#include "src/common/cpuinfo/CpuIsaInfo.h"
30#include "src/cpu/kernels/CpuDirectConv2dKernel.h"
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]31#include "tests/NEON/Accessor.h"
32#include "tests/PaddingCalculator.h"
Moritz Pflanzera09de0c2017-09-01 20:41:12 +0100[diff] [blame]33#include "tests/datasets/ShapeDatasets.h"
34#include "tests/framework/Asserts.h"
35#include "tests/framework/Macros.h"
36#include "tests/framework/datasets/Datasets.h"
37#include "tests/validation/Validation.h"
38#include "tests/validation/fixtures/DirectConvolutionLayerFixture.h"
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]39
40namespace arm_compute
41{
42namespace test
43{
44namespace validation
45{
46namespace
47{
Ioan-Cristian Szabo5edbd1c2017-11-13 13:34:08 +0000[diff] [blame]48#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
Gian Marco Iodice41acb762018-08-23 10:25:06 +0100[diff] [blame]49const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */
50const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */
51constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */
52#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
53constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]54
Gian Marco Iodice41acb762018-08-23 10:25:06 +0100[diff] [blame]55/** Direct convolution data set.for FP32 */
Giorgio Arenac0f54432018-03-16 14:02:34 +0000[diff] [blame]56const auto data_pad_f32 = concat(concat(combine(framework::dataset::make("PadX", { 0, 1 }),
57 combine(framework::dataset::make("PadY", { 0, 1 }),
58 framework::dataset::make("KernelSize", 3))),
59 combine(framework::dataset::make("PadX", { 0, 2 }),
60 combine(framework::dataset::make("PadY", { 0, 2 }),
Pablo Tello06da39d2017-08-10 15:10:40 +0100[diff] [blame]61 framework::dataset::make("KernelSize", 3)))),
Giorgio Arenac0f54432018-03-16 14:02:34 +0000[diff] [blame]62 combine(framework::dataset::make("PadX", { 0, 3 }),
63 combine(framework::dataset::make("PadY", { 0, 3 }),
Pablo Tello06da39d2017-08-10 15:10:40 +0100[diff] [blame]64 framework::dataset::make("KernelSize", 5))));
65
Gian Marco Iodice41acb762018-08-23 10:25:06 +0100[diff] [blame]66/** Direct convolution data set.for FP16 */
67const auto data_pad_f16 = concat(combine(framework::dataset::make("PadX", { 0, 1 }),
68 combine(framework::dataset::make("PadY", { 0, 1 }),
69 framework::dataset::make("KernelSize", 3))),
70 combine(framework::dataset::make("PadX", { 0 }),
71 combine(framework::dataset::make("PadY", { 0 }),
72 framework::dataset::make("KernelSize", 1))));
73
Pablo Tello06da39d2017-08-10 15:10:40 +0100[diff] [blame]74const auto data_f32 = combine(datasets::SmallDirectConvolutionShapes(),
Adnan AlSinan0ef2c212022-01-24 10:20:40 +0000[diff] [blame]75 combine(framework::dataset::make("StrideX", { 1, 2, 3, 4 }),
76 combine(framework::dataset::make("StrideY", { 1, 2, 3, 4 }),
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]77 data_pad_f32)));
Pablo Tello06da39d2017-08-10 15:10:40 +0100[diff] [blame]78
Gian Marco Iodice41acb762018-08-23 10:25:06 +0100[diff] [blame]79const auto data_f16 = combine(datasets::SmallDirectConvolutionShapes(),
80 combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
81 combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]82 data_pad_f16)));
83
Manuel Bottini87350f42020-09-15 13:03:34 +0100[diff] [blame]84const auto data_prec = combine(datasets::SmallDirectConvolutionShapes(),
85 combine(framework::dataset::make("StrideX", { 1 }),
86 combine(framework::dataset::make("StrideY", { 1 }),
87 combine(framework::dataset::make("PadX", { 1 }),
88 combine(framework::dataset::make("PadY", { 1 }),
89 framework::dataset::make("KernelSize", 3))))));
Michalis Spyrouaeebe4a2019-01-09 14:21:03 +0000[diff] [blame]90
Gian Marco Iodice95f93612019-06-13 15:58:32 +0100[diff] [blame]91const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(),
Adnan AlSinan0ef2c212022-01-24 10:20:40 +0000[diff] [blame]92 combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
93 combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
Gian Marco Iodice95f93612019-06-13 15:58:32 +0100[diff] [blame]94 combine(framework::dataset::make("PadX", { 0, 2 }),
95 combine(framework::dataset::make("PadY", { 0, 3 }),
96 framework::dataset::make("KernelSize", 9))))));
97
Pablo Marquez Tello4d44ac82021-12-08 15:56:01 +0000[diff] [blame]98const auto data8x8 = combine(datasets::SmallDirectConvolutionShapes(),
Adnan AlSinan0ef2c212022-01-24 10:20:40 +0000[diff] [blame]99 combine(framework::dataset::make("StrideX", { 1, 2, 3 }),
100 combine(framework::dataset::make("StrideY", { 1, 2, 3 }),
Pablo Marquez Tello4d44ac82021-12-08 15:56:01 +0000[diff] [blame]101 combine(framework::dataset::make("PadX", { 0 }),
102 combine(framework::dataset::make("PadY", { 0 }),
103 framework::dataset::make("KernelSize", 8))))));
104
Adnan AlSinan0ef2c212022-01-24 10:20:40 +0000[diff] [blame]105const auto data_f32_nightly = combine(data_f32, framework::dataset::make("NumKernels", { 1, 4, 5 }));
106const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKernels", { 1, 4, 5 }));
Michalis Spyrouaeebe4a2019-01-09 14:21:03 +0000[diff] [blame]107
Manuel Bottini87350f42020-09-15 13:03:34 +0100[diff] [blame]108const auto data_precommit = combine(data_prec, framework::dataset::make("NumKernels", { 1 }));
Gian Marco Iodice95f93612019-06-13 15:58:32 +0100[diff] [blame]109const auto data_precommit9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 4 }));
Pablo Marquez Tello4d44ac82021-12-08 15:56:01 +0000[diff] [blame]110const auto data_precommit8x8 = combine(data8x8, framework::dataset::make("NumKernels", { 4 }));
111
Sang-Hoon Park38515422020-07-08 11:06:30 +0100[diff] [blame]112/* The following tests is from real use-case that made DirectConvolution
113 * overflows in terms of its tensor indexing. This test case is using
114 * a separate tolerance due to the following reason.
115 * - It has shown that it requires generally larger absolute tolerance
116 * for large numbers or larger relative tolerance for small numbers.
117 * - With the first reason, since it is mainly testing index overflow,
118 * a value with a margin is used to avoid uninteded test failures
119 * during nightly.
120 */
121constexpr AbsoluteTolerance<float> usecase_tolerance_fp32(0.05f);
122
123const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }),
124 combine(framework::dataset::make("StrideX", { 1 }),
125 combine(framework::dataset::make("StrideY", { 1 }),
126 combine(framework::dataset::make("PadX", { 4 }),
127 combine(framework::dataset::make("PadY", { 4 }),
128 combine(framework::dataset::make("KernelSize", 9),
129 framework::dataset::make("NumKernels", { 16 })))))));
130
Isabella Gottardi3f217ec2018-02-12 14:59:19 +0000[diff] [blame]131/** Activation function Dataset*/
132const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
Gian Marco Iodice95f93612019-06-13 15:58:32 +0100[diff] [blame]133{
134 ActivationLayerInfo(),
135 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f)
136});
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]137} // namespace
138
139TEST_SUITE(NEON)
140TEST_SUITE(DirectConvolutionLayer)
141
Michele Di Giorgio97e25802021-03-25 12:37:45 +0000[diff] [blame]142/** Check whether the configuration of a Direct Convolution layer with no
143 * bias leads to a successful execution.
144 */
145TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT)
146{
147 const auto src_shape = TensorShape(27U, 13U, 2U);
148 const auto weights_shape = TensorShape(3U, 3U, 2U, 4U);
149 const auto bias_shape = TensorShape(4U);
150 const auto dst_shape = TensorShape(25U, 11U, 4U);
151 constexpr auto dt = DataType::F32;
152
153 auto src = create_tensor<Tensor>(src_shape, dt);
154 auto weights = create_tensor<Tensor>(weights_shape, dt);
155 auto dst = create_tensor<Tensor>(dst_shape, dt);
156
157 const auto conv_info = PadStrideInfo(1, 1, 0, 0);
158
159 // Create Direct Convolution function
160 NEDirectConvolutionLayer conv{};
161 conv.configure(&src, &weights, nullptr, &dst, conv_info);
162
163 src.allocator()->allocate();
164 weights.allocator()->allocate();
165 dst.allocator()->allocate();
166
167 library->fill_tensor_value(Accessor(src), 1.f);
168 library->fill_tensor_value(Accessor(weights), 1.f);
169
170 conv.run();
171
172 // Compute reference to compare
173 SimpleTensor<float> ref_src{ src_shape, dt };
174 SimpleTensor<float> ref_weights{ weights_shape, dt };
175 SimpleTensor<float> ref_bias{ bias_shape, dt };
176 library->fill_tensor_value(ref_src, 1.f);
177 library->fill_tensor_value(ref_weights, 1.f);
178 // No bias
179 library->fill_tensor_value(ref_bias, 0.f);
180 auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info);
181
182 validate(Accessor(dst), ref_dst);
183}
184
alerah01c9e519d2022-01-31 19:04:10 +0200[diff] [blame^]185DATA_TEST_CASE(KernelSelection, framework::DatasetMode::ALL,
186 concat(combine(combine(framework::dataset::make("CpuExt", std::string("NEON")),
187 framework::dataset::make("DataType", { DataType::F32 })),
188 framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
189 combine(combine(framework::dataset::make("CpuExt", std::string("NEON")),
190 framework::dataset::make("DataType", { DataType::F16 })),
191 framework::dataset::make("DataLayout", { DataLayout::NCHW }))),
192 cpu_ext, data_type, data_layout)
193{
194 using namespace cpu::kernels;
195
196 cpuinfo::CpuIsaInfo cpu_isa{};
197 cpu_isa.neon = (cpu_ext == "NEON");
198 cpu_isa.fp16 = (data_type == DataType::F16);
199
200 const auto *selected_impl = CpuDirectConv2dKernel::get_implementation(DataTypeDataLayoutISASelectorData{ data_type, data_layout, cpu_isa }, cpu::KernelSelectionType::Preferred);
201
202 ARM_COMPUTE_ERROR_ON_NULLPTR(selected_impl);
203
204 std::string data_layout_str;
205 if(data_layout == DataLayout::NCHW)
206 {
207 data_layout_str = "nchw";
208 }
209 else
210 {
211 data_layout_str = "nhwc";
212 }
213
214 std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_" + data_layout_str + "_directconv2d";
215 std::string actual = selected_impl->name;
216
217 ARM_COMPUTE_EXPECT_EQUAL(expected, actual, framework::LogLevel::ERRORS);
218}
219
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]220// *INDENT-OFF*
221// clang-format off
Isabella Gottardi3f217ec2018-02-12 14:59:19 +0000[diff] [blame]222DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
Vidhya Sudhan Loganathan7485d5a2018-07-04 09:34:00 +0100[diff] [blame]223 framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights
224 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps
225 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width
226 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions
227 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions
228 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride
229 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size
230 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions
231 TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]232 }),
Vidhya Sudhan Loganathan7485d5a2018-07-04 09:34:00 +0100[diff] [blame]233 framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
234 TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
235 TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
236 TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
237 TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
238 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
239 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
240 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
241 TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]242 })),
Vidhya Sudhan Loganathan7485d5a2018-07-04 09:34:00 +0100[diff] [blame]243 framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32),
244 TensorInfo(TensorShape(4U), 1, DataType::F32),
245 TensorInfo(TensorShape(4U), 1, DataType::F32),
246 TensorInfo(TensorShape(4U), 1, DataType::F32),
247 TensorInfo(TensorShape(4U), 1, DataType::F32),
248 TensorInfo(TensorShape(4U), 1, DataType::F32),
249 TensorInfo(TensorShape(3U), 1, DataType::F32),
250 TensorInfo(TensorShape(4U, 2U), 1, DataType::F32),
251 TensorInfo(TensorShape(4U), 1, DataType::F32),
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]252 })),
Vidhya Sudhan Loganathan7485d5a2018-07-04 09:34:00 +0100[diff] [blame]253 framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
254 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
255 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
256 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
257 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
258 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
259 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
260 TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32),
261 TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32),
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]262 })),
263 framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0),
264 PadStrideInfo(1, 1, 0, 0),
265 PadStrideInfo(1, 1, 0, 0),
266 PadStrideInfo(1, 1, 0, 0),
267 PadStrideInfo(1, 1, 0, 0),
268 PadStrideInfo(3, 3, 0, 0),
269 PadStrideInfo(1, 1, 0, 0),
270 PadStrideInfo(1, 1, 0, 0),
271 PadStrideInfo(1, 1, 0, 0),
272 })),
Isabella Gottardi3f217ec2018-02-12 14:59:19 +0000[diff] [blame]273 framework::dataset::make("ActivationInfo",
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]274{
Isabella Gottardi3f217ec2018-02-12 14:59:19 +0000[diff] [blame]275 ActivationLayerInfo(),
Pablo Marquez Tello90805b82021-06-15 11:51:46 +0100[diff] [blame]276 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
277 ActivationLayerInfo(),
278 ActivationLayerInfo(),
279 ActivationLayerInfo(),
280 ActivationLayerInfo(),
281 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
282 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
283 ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
Isabella Gottardi3f217ec2018-02-12 14:59:19 +0000[diff] [blame]284})),
285 framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })),
286 input_info, weights_info, biases_info, output_info, conv_info, act_info, expected)
287{
288 bool is_valid = bool(NEDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info));
Michalis Spyrouafa5d812017-11-30 14:25:57 +0000[diff] [blame]289 ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
290}
291// clang-format on
292// *INDENT-ON*
293
Manuel Bottini87350f42020-09-15 13:03:34 +0100[diff] [blame]294DATA_TEST_CASE(NoPaddingNHWCKernel, framework::DatasetMode::ALL, combine(combine(combine(data_precommit,
295 framework::dataset::make("DataType", DataType::F32)),
296 ActivationFunctionsDataset),
297 framework::dataset::make("DataLayout", { DataLayout::NHWC })),
298
299 shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, act_info, data_layout)
300{
301 TensorShape input_shape = TensorShape(shape);
302 TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels);
303 const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR);
304
305 TensorInfo input_info = TensorInfo(input_shape, 1, data_type);
306 TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type);
307
308 TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info);
309
310 if(data_layout == DataLayout::NHWC)
311 {
312 permute(input_shape, PermutationVector(2U, 0U, 1U));
313 permute(weights_shape, PermutationVector(2U, 0U, 1U));
314 permute(output_shape, PermutationVector(2U, 0U, 1U));
315 }
316
317 // Create tensors
318 Tensor src = create_tensor<Tensor>(input_shape, data_type, 1, QuantizationInfo(), data_layout);
319 Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1, QuantizationInfo(), data_layout);
320 Tensor dst = create_tensor<Tensor>(output_shape, data_type, 1, QuantizationInfo(), data_layout);
321
322 // Create and configure function
323 NEDirectConvolutionLayer conv;
324 conv.configure(&src, &weights, nullptr, &dst, info, act_info);
325
326 validate(src.info()->padding(), PaddingSize(0, 0, 0, 0));
327 validate(weights.info()->padding(), PaddingSize(0, 0, 0, 0));
328 validate(dst.info()->padding(), PaddingSize(0, 0, 0, 0));
329}
330
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]331template <typename T>
332using NEDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>;
Manuel Bottinica62c6f2021-03-23 11:50:34 +0000[diff] [blame]333template <typename T>
334using NEDirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T, true>;
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]335
336TEST_SUITE(Float)
Ioan-Cristian Szabo5edbd1c2017-11-13 13:34:08 +0000[diff] [blame]337#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]338TEST_SUITE(FP16)
Michalis Spyrouaeebe4a2019-01-09 14:21:03 +0000[diff] [blame]339FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]340 DataType::F16)),
341 ActivationFunctionsDataset),
342 framework::dataset::make("DataLayout", DataLayout::NCHW)))
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]343{
344 // Validate output
Gian Marco Iodice41acb762018-08-23 10:25:06 +0100[diff] [blame]345 validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]346}
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]347FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f16_nightly, framework::dataset::make("DataType", DataType::F16)),
348 ActivationFunctionsDataset),
349 framework::dataset::make("DataLayout", DataLayout::NCHW)))
350{
351 // Validate output
352 validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16);
353}
354TEST_SUITE_END() // FP16
355#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]356
357TEST_SUITE(FP32)
Michalis Spyrouaeebe4a2019-01-09 14:21:03 +0000[diff] [blame]358FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]359 DataType::F32)),
360 ActivationFunctionsDataset),
361 framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]362{
363 // Validate output
364 validate(Accessor(_target), _reference, tolerance_fp32);
365}
Manuel Bottinica62c6f2021-03-23 11:50:34 +0000[diff] [blame]366FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit,
Adnan AlSinan0ef2c212022-01-24 10:20:40 +0000[diff] [blame]367 framework::dataset::make("DataType", DataType::F32)),
368 ActivationFunctionsDataset),
369 framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
Manuel Bottinica62c6f2021-03-23 11:50:34 +0000[diff] [blame]370{
371 // Validate output
372 validate(Accessor(_target), _reference, tolerance_fp32);
373}
Pablo Marquez Tello4d44ac82021-12-08 15:56:01 +0000[diff] [blame]374
375FIXTURE_DATA_TEST_CASE(RunSmall8x8, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit8x8, framework::dataset::make("DataType",
376 DataType::F32)),
377 ActivationFunctionsDataset),
378 framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
379{
380 // Validate output
381 validate(Accessor(_target), _reference, tolerance_fp32);
382}
383
Gian Marco Iodice95f93612019-06-13 15:58:32 +0100[diff] [blame]384FIXTURE_DATA_TEST_CASE(RunSmall9x9, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit9x9, framework::dataset::make("DataType",
385 DataType::F32)),
386 ActivationFunctionsDataset),
387 framework::dataset::make("DataLayout", { DataLayout::NHWC })))
388{
389 // Validate output
390 validate(Accessor(_target), _reference, tolerance_fp32);
391}
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]392FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f32_nightly, framework::dataset::make("DataType",
393 DataType::F32)),
394 ActivationFunctionsDataset),
395 framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
396{
397 // Validate output
398 validate(Accessor(_target), _reference, tolerance_fp32);
399}
Sang-Hoon Park38515422020-07-08 11:06:30 +0100[diff] [blame]400FIXTURE_DATA_TEST_CASE(RunLargeUsecase, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_usecase, framework::dataset::make("DataType",
401 DataType::F32)),
402 framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })),
403 framework::dataset::make("DataLayout", { DataLayout::NHWC })))
404{
405 // Validate output
406 validate(Accessor(_target), _reference, usecase_tolerance_fp32);
407}
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]408TEST_SUITE_END() // FP32
409TEST_SUITE_END() // Float
Michalis Spyrou064add62018-11-01 18:14:27 +0000[diff] [blame]410TEST_SUITE_END() // DirectConvolutionLayer
Sheri Zhangac6499a2021-02-10 15:32:38 +0000[diff] [blame]411TEST_SUITE_END() // Neon
Moritz Pflanzerb3d25792017-07-26 11:49:37 +0100[diff] [blame]412} // namespace validation
413} // namespace test
414} // namespace arm_compute