Compute Library
 22.11
RNNLayer.cpp
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25 #include "tests/NEON/Accessor.h"
27 #include "tests/datasets/RNNLayerDataset.h"
29 #include "tests/framework/Macros.h"
32 #include "tests/validation/fixtures/RNNLayerFixture.h"
33 
34 namespace arm_compute
35 {
36 namespace test
37 {
38 namespace validation
39 {
40 namespace
41 {
42 RelativeTolerance<float> tolerance_f32(0.001f); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F32 */
43 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
44 RelativeTolerance<half> tolerance_f16(half(0.1)); /**< Relative tolerance value for comparing reference's output against implementation's output for DataType:F16 */
45 constexpr float abs_tolerance_f16(0.02f); /**< Absolute tolerance value for comparing reference's output against implementation's output for DataType:F16 */
46 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
47 } // namespace
48 
49 TEST_SUITE(NEON)
50 TEST_SUITE(RNNLayer)
51 
52 // *INDENT-OFF*
53 // clang-format off
54 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(
55  framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), // Wrong data type
56  TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Wrong input size
57  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong weights size
58  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong recurrent weights size
59  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong bias size
60  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong output size
61  TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), // Wrong hidden output size
63  }),
64  framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32),
66  TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
72  })),
73  framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32),
76  TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32),
81  })),
90  })),
99  })),
100  framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32),
106  TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32),
108  })),
117  })),
118  framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })),
119  input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected)
120 {
121  ARM_COMPUTE_EXPECT(bool(NERNNLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &recurrent_weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), &hidden_output_info.clone()->set_is_resizable(false), info)) == expected, framework::LogLevel::ERRORS);
122 }
123 // clang-format on
124 // *INDENT-ON*
125 
126 template <typename T>
127 using NERNNLayerFixture = RNNLayerValidationFixture<Tensor, Accessor, NERNNLayer, T>;
128 
129 TEST_SUITE(FP32)
130 FIXTURE_DATA_TEST_CASE(RunSmall, NERNNLayerFixture<float>, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32)))
131 {
132  // Validate output
133  validate(Accessor(_target), _reference, tolerance_f32);
134 }
135 TEST_SUITE_END() // FP32
136 
137 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
138 TEST_SUITE(FP16)
140 {
141  // Validate output
142  validate(Accessor(_target), _reference, tolerance_f16, 0.f, abs_tolerance_f16);
143 }
144 TEST_SUITE_END() // FP16
145 #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
146 TEST_SUITE_END() // RNNLayer
147 TEST_SUITE_END() // Neon
148 } // namespace validation
149 } // namespace test
150 } // namespace arm_compute
Shape of a tensor.
Definition: TensorShape.h:39
1 channel, 1 U8 per channel
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.
Activation Layer Information class.
Definition: Types.h:1639
Copyright (c) 2017-2022 Arm Limited.
1 channel, 1 F16 per channel
RelativeTolerance< half_float::half > tolerance_f16(half_float::half(0.1))
Tolerance value for comparing reference&#39;s output against implementation&#39;s output for DataType::F16...
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
RNNLayerValidationFixture< Tensor, Accessor, NERNNLayer, T > NERNNLayerFixture
Definition: RNNLayer.cpp:127
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
RelativeTolerance< float > tolerance_f32(0.01f)
Tolerance value for comparing reference&#39;s output against implementation&#39;s output for DataType::F32...
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
ScaleKernelInfo info(interpolation_policy, default_border_mode, PixelValue(), sampling_policy, false)
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state, const ITensorInfo *output, const ActivationLayerInfo &info)
Initialize the function.
Definition: NERNNLayer.cpp:45
Store the tensor&#39;s metadata.
Definition: TensorInfo.h:43
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