32 #include "tests/datasets/LargeConvolutionLayerDataset.h" 33 #include "tests/datasets/RandomBatchNormalizationLayerDataset.h" 34 #include "tests/datasets/SmallConvolutionLayerDataset.h" 40 #include "tests/validation/fixtures/BatchNormalizationLayerFixture.h" 41 #include "tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h" 51 RelativeTolerance<float> rel_tolerance_f32(0.05f);
156 framework::dataset::
make("Weights", { TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1,
DataType::F32),
176 &weights_in_info.clone()->set_is_resizable(
false), &
mean_info.clone()->set_is_resizable(
false),
179 &
beta_info.clone()->set_is_resizable(
false), &
gamma_info.clone()->set_is_resizable(
false), 1.f));
184 template <
typename T>
BatchNormalizationLayerValidationFixture< CLTensor, CLAccessor, CLBatchNormalizationLayer, T > CLBatchNormalizationLayerFixture
half_float::half half
16-bit floating point type
const auto & fused_bias_info
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.
static Status validate(const ITensorInfo *input_weights, const ITensorInfo *bn_mean, const ITensorInfo *bn_var, const ITensorInfo *fused_weights, const ITensorInfo *fused_bias, const ITensorInfo *input_bias=nullptr, const ITensorInfo *bn_beta=nullptr, const ITensorInfo *bn_gamma=nullptr, float epsilon=0.001f, FuseBatchNormalizationType fbn_type=FuseBatchNormalizationType::CONVOLUTION)
Static function to check if given info will lead to a valid configuration of CLFuseBatchNormalization...
Copyright (c) 2017-2021 Arm Limited.
1 channel, 1 F16 per channel
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)
DatasetMode
Possible dataset modes.
TEST_SUITE_END() FIXTURE_DATA_TEST_CASE(RunSmall
[CLActivationLayer Test snippet]
const auto & conv_bias_info
const auto & fused_weights_info
Accessor implementation for CLTensor objects.
TEST_SUITE(U8_to_S8) FIXTURE_DATA_TEST_CASE(RunSmall
validate(CLAccessor(output_state), expected_output)
Num samples, channels, height, width.
Lower and Upper Bounded Rectifier ( )
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsLayerFixture< half >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
Upper Bounded Rectifier ( )
Num samples, height, width, channels.
Store the tensor's metadata.
static Status validate(const ITensorInfo *input, const ITensorInfo *output, const ITensorInfo *mean, const ITensorInfo *var, const ITensorInfo *beta=nullptr, const ITensorInfo *gamma=nullptr, float epsilon=0.001f, ActivationLayerInfo act_info=ActivationLayerInfo())
Static function to check if given info will lead to a valid configuration of CLBatchNormalizationLaye...
RelativeTolerance< half_float::half > tolerance_f16(half(0.2))
F16 Tolerance value for comparing reference's output against implementation'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}))
DataType
Available data types.
constexpr float abs_tolerance_f32(0.0001f)
F32 Absolute tolerance value for comparing reference's output against implementation's output for flo...
DataLayout
[DataLayout enum definition]
BatchNormalizationLayerFusionValidationFixture< CLTensor, CLAccessor, CLConvolutionLayer, CLFuseBatchNormalization, T > CLBatchNormalizationLayerFusionFixture
combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))