Compute Library
 22.05
MeanStdDevNormalizationLayer.cpp
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24 #include "arm_compute/core/Types.h"
28 #include "tests/NEON/Accessor.h"
30 #include "tests/datasets/NormalizationTypesDataset.h"
31 #include "tests/datasets/ShapeDatasets.h"
33 #include "tests/framework/Macros.h"
36 #include "tests/validation/fixtures/MeanStdDevNormalizationLayerFixture.h"
37 
38 namespace arm_compute
39 {
40 namespace test
41 {
42 namespace validation
43 {
44 namespace
45 {
46 /** Tolerance for float operations */
47 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
48 RelativeTolerance<half> tolerance_f16(half(0.2f));
49 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
50 RelativeTolerance<float> tolerance_f32(1e-4f);
51 } // namespace
52 
53 TEST_SUITE(NEON)
54 TEST_SUITE(MeanStdDevNormalizationLayer)
55 
56 // *INDENT-OFF*
57 // clang-format off
59  framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Mismatching data type input/output
60  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Mismatching shapes
61  TensorInfo(TensorShape(32U, 13U), 1, DataType::F32),
62  }),
63  framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U), 1, DataType::F16),
64  TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
65  TensorInfo(TensorShape(32U, 13U), 1, DataType::F32),
66  })),
67  framework::dataset::make("Expected", { false, false, true })),
69 {
70  ARM_COMPUTE_EXPECT(bool(NEMeanStdDevNormalizationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false))) == expected, framework::LogLevel::ERRORS);
71 }
72 // clang-format on
73 // *INDENT-ON*
74 
75 template <typename T>
76 using NEMeanStdDevNormalizationLayerFixture = MeanStdDevNormalizationLayerValidationFixture<Tensor, Accessor, NEMeanStdDevNormalizationLayer, T>;
77 
78 TEST_SUITE(Float)
79 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
80 TEST_SUITE(FP16)
83  framework::dataset::make("InPlace", { false, true })),
84  framework::dataset::make("Epsilon", { 1e-3 })))
85 {
86  // Validate output
87  validate(Accessor(_target), _reference, tolerance_f16);
88 }
91  framework::dataset::make("InPlace", { false, true })),
92  framework::dataset::make("Epsilon", { 1e-8 })))
93 {
94  // Validate output
95  validate(Accessor(_target), _reference, tolerance_f16);
96 }
97 TEST_SUITE_END() // FP16
98 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
99 
100 TEST_SUITE(FP32)
101 FIXTURE_DATA_TEST_CASE(RunSmall, NEMeanStdDevNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::Small2DShapes(),
102  framework::dataset::make("DataType", DataType::F32)),
103  framework::dataset::make("InPlace", { false, true })),
104  framework::dataset::make("Epsilon", { 1e-7 })))
105 {
106  // Validate output
107  validate(Accessor(_target), _reference, tolerance_f32);
108 }
111  framework::dataset::make("InPlace", { false, true })),
112  framework::dataset::make("Epsilon", { 1e-8 })))
113 {
114  // Validate output
115  validate(Accessor(_target), _reference, tolerance_f32);
116 }
117 TEST_SUITE_END() // FP32
118 TEST_SUITE_END() // Float
119 
120 TEST_SUITE_END() // MeanStdNormalizationLayer
121 TEST_SUITE_END() // Neon
122 } // namespace validation
123 } // namespace test
124 } // namespace arm_compute
RelativeTolerance< float > tolerance_f32(0.001f)
F32 Tolerance value for comparing reference&#39;s output against implementation&#39;s output for floating poi...
half_float::half half
16-bit floating point type
Definition: Types.h:48
1 channel, 1 F32 per channel
ARM_COMPUTE_EXPECT(has_error==expected, framework::LogLevel::ERRORS)
std::enable_if< is_container< T >::value, ContainerDataset< T > >::type make(std::string name, T &&values)
Helper function to create a ContainerDataset.
Copyright (c) 2017-2022 Arm Limited.
1 channel, 1 F16 per channel
MeanStdDevNormalizationLayerValidationFixture< Tensor, Accessor, NEMeanStdDevNormalizationLayer, T > NEMeanStdDevNormalizationLayerFixture
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), 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(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QSYMM16), TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::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, true, true, true, false, false, true, true, false })), input_info, output_info, act_info, expected)
Accessor implementation for Tensor objects.
Definition: Accessor.h:35
DatasetMode
Possible dataset modes.
Definition: DatasetModes.h:40
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
validate(CLAccessor(output_state), expected_output)
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsLayerFixture< half >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
Definition: AbsLayer.cpp:50
static Status validate(const ITensorInfo *input, const ITensorInfo *output=nullptr, float epsilon=1e-8f)
Static function to check if given info will lead to a valid configuration of NEMeanStdDevNormalizatio...
RelativeTolerance< half_float::half > tolerance_f16(half(0.2))
F16 Tolerance value for comparing reference&#39;s output against implementation&#39;s output for floating poi...
zip(zip(framework::dataset::make("Weights", { TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 2U, 1U), 1, DataType::F32), }), framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32), TensorInfo(TensorShape(2U), 1, DataType::F16), TensorInfo(TensorShape(5U), 1, DataType::F32), })), framework::dataset::make("Expected", { true, false, false}))
TEST_SUITE(QASYMM8_to_F32) FIXTURE_DATA_TEST_CASE(RunSmall
DataType
Available data types.
Definition: Types.h:79
combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))
Definition: AbsLayer.cpp:65