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
|
/*
* Copyright (c) 2017-2019 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/CL/CLTensor.h"
#include "arm_compute/runtime/CL/CLTensorAllocator.h"
#include "arm_compute/runtime/CL/functions/CLActivationLayer.h"
#include "arm_compute/runtime/RuntimeContext.h"
#include "tests/CL/CLAccessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/ActivationFunctionsDataset.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"
#include "tests/validation/fixtures/ActivationLayerFixture.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
constexpr AbsoluteTolerance<float> tolerance_qsymm16(1.f);
/** Define tolerance of the activation layer.
*
* @param[in] activation The activation function used.
* @param[in] data_type Data type.
*
* @return Tolerance depending on the activation function.
*/
AbsoluteTolerance<float> tolerance(ActivationLayerInfo::ActivationFunction activation, DataType data_type)
{
constexpr float epsilon = 1e-6f;
switch(activation)
{
case ActivationLayerInfo::ActivationFunction::LINEAR:
return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.2f : epsilon);
case ActivationLayerInfo::ActivationFunction::SQUARE:
return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.1f : epsilon);
case ActivationLayerInfo::ActivationFunction::LOGISTIC:
return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.001f : epsilon);
case ActivationLayerInfo::ActivationFunction::LEAKY_RELU:
return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.00001f : epsilon);
case ActivationLayerInfo::ActivationFunction::SOFT_RELU:
case ActivationLayerInfo::ActivationFunction::ELU:
case ActivationLayerInfo::ActivationFunction::SQRT:
return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.01f : 0.00001f);
case ActivationLayerInfo::ActivationFunction::TANH:
return AbsoluteTolerance<float>(data_type == DataType::F16 ? 0.001f : 0.00001f);
default:
return AbsoluteTolerance<float>(epsilon);
}
}
/** CNN data types */
const auto CNNDataTypes = framework::dataset::make("DataType",
{
DataType::F16,
DataType::F32
});
/** Input data sets. */
const auto ActivationDataset = combine(combine(framework::dataset::make("InPlace", { false, true }), datasets::ActivationFunctions()), framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
} // namespace
TEST_SUITE(CL)
TEST_SUITE(ActivationLayer)
DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(datasets::SmallShapes(), CNNDataTypes), framework::dataset::make("InPlace", { false, true })),
shape, data_type, in_place)
{
// Create context
auto ctx = parameters->get_ctx<CLTensor>();
// Create tensors
CLTensor src = create_tensor<CLTensor>(shape, data_type, 1, QuantizationInfo(), DataLayout::NCHW, ctx);
CLTensor dst = create_tensor<CLTensor>(shape, data_type, 1, QuantizationInfo(), DataLayout::NCHW, ctx);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Create and configure function
CLActivationLayer act_layer(ctx);
if(in_place)
{
act_layer.configure(&src, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS));
}
else
{
act_layer.configure(&src, &dst, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS));
}
// Validate valid region
const ValidRegion valid_region = shape_to_valid_region(shape);
validate(src.info()->valid_region(), valid_region);
if(!in_place)
{
validate(dst.info()->valid_region(), valid_region);
}
// Validate padding
const int step = 16 / arm_compute::data_size_from_type(data_type);
const PaddingSize padding = PaddingCalculator(shape.x(), step).required_padding();
validate(src.info()->padding(), padding);
if(!in_place)
{
validate(dst.info()->padding(), padding);
}
}
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data types
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8), // Invalid quantization info
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), // Invalid activation function for QSYMM16
}),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::QASYMM8),
TensorInfo(TensorShape(30U, 11U, 2U), 1, DataType::F32),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f / 32768.f, 0)),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f / 32768.f, 0)),
TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16, QuantizationInfo(1.f / 32768.f, 0)),
})),
framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC),
ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SQRT),
})),
framework::dataset::make("Expected", { false, false, true, true, false, false, true, true, false })),
input_info, output_info, act_info, expected)
{
ARM_COMPUTE_EXPECT(bool(CLActivationLayer::validate(&input_info.clone()->set_is_resizable(false), (output_info.total_size() == 0) ? nullptr : &output_info.clone()->set_is_resizable(false), act_info)) == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
/** [CLActivationLayerFixture snippet] **/
template <typename T>
using CLActivationLayerFixture = ActivationValidationFixture<CLTensor, CLAccessor, CLActivationLayer, T>;
/** [CLActivationLayerFixture snippet] **/
TEST_SUITE(Float)
TEST_SUITE(FP16)
/** [CLActivationLayer Test snippet] **/
FIXTURE_DATA_TEST_CASE(RunSmall, CLActivationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), ActivationDataset),
framework::dataset::make("DataType",
DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance(_function, _data_type));
}
/** [CLActivationLayer Test snippet] **/
FIXTURE_DATA_TEST_CASE(RunLarge, CLActivationLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), ActivationDataset),
framework::dataset::make("DataType",
DataType::F16)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance(_function, _data_type));
}
TEST_SUITE_END() // FP16
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, CLActivationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), ActivationDataset), framework::dataset::make("DataType",
DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance(_function, _data_type));
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLActivationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), ActivationDataset), framework::dataset::make("DataType",
DataType::F32)))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance(_function, _data_type));
}
TEST_SUITE_END() // FP32
TEST_SUITE_END() // Float
template <typename T>
using CLActivationLayerQuantizedFixture = ActivationValidationQuantizedFixture<CLTensor, CLAccessor, CLActivationLayer, T>;
const auto QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }), datasets::ActivationFunctionsQuantized()),
framework::dataset::make("AlphaBeta", { 0.5f, 1.f }));
TEST_SUITE(Quantized)
TEST_SUITE(QASYMM8)
FIXTURE_DATA_TEST_CASE(RunSmall, CLActivationLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallShapes(), QuantizedActivationDataset),
framework::dataset::make("DataType",
DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.1f, 128.0f) })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance(_function, _data_type));
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLActivationLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeShapes(), QuantizedActivationDataset),
framework::dataset::make("DataType",
DataType::QASYMM8)),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.1f, 128.0f) })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance(_function, _data_type));
}
TEST_SUITE_END() // QASYMM8
TEST_SUITE(QSYMM16)
FIXTURE_DATA_TEST_CASE(RunSmall, CLActivationLayerQuantizedFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallShapes(), QuantizedActivationDataset),
framework::dataset::make("DataType",
DataType::QSYMM16)),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 32768.f, 0) })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qsymm16);
}
FIXTURE_DATA_TEST_CASE(RunLarge, CLActivationLayerQuantizedFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeShapes(), QuantizedActivationDataset),
framework::dataset::make("DataType",
DataType::QSYMM16)),
framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 32768.f, 0) })))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_qsymm16);
}
TEST_SUITE_END() // QSYMM16
TEST_SUITE_END() // Quantized
TEST_SUITE_END() // ActivationLayer
TEST_SUITE_END() // CL
} // namespace validation
} // namespace test
} // namespace arm_compute
|
