31 #include "tests/datasets/DilatedConvolutionLayerDataset.h" 36 #include "tests/validation/fixtures/ConvolutionLayerFixture.h" 47 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC 48 const AbsoluteTolerance<float> abs_tolerance_f16(0.3f);
49 const RelativeTolerance<half_float::half> rel_tolerance_f16(
half_float::half(0.2f));
52 constexpr AbsoluteTolerance<float> tolerance_qasymm8(0.0);
57 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC 87 PadStrideInfo(1, 1, 0, 0),
88 PadStrideInfo(2, 1, 0, 0),
89 PadStrideInfo(3, 2, 1, 0)
111 template <typename T>
112 using NEGEMMDilatedConvolutionLayerFixture = ConvolutionValidationFixture<Tensor, Accessor, NEConvolutionLayer, T>;
115 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC 140 framework::dataset::
make("ReshapeWeights", {
true })),
160 template <typename T>
161 using NEGEMMDilatedConvolutionLayerQuantizedFixture = ConvolutionValidationQuantizedFixture<Tensor, Accessor, NEGEMMConvolutionLayer, T>;
167 framework::dataset::
make("ReshapeWeights", {
true })),
174 validate(Accessor(_target), _reference, tolerance_qasymm8);
185 validate(Accessor(_target), _reference, tolerance_qasymm8);
RelativeTolerance< float > tolerance_f32(0.001f)
F32 Tolerance value for comparing reference's output against implementation's output for floating poi...
constexpr float tolerance_num_f16
F16 Tolerance number.
half_float::half half
16-bit floating point type
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.
ConvolutionMethod
Available ConvolutionMethod.
Activation Layer Information class.
Copyright (c) 2017-2020 Arm Limited.
1 channel, 1 F16 per channel
Quantization information.
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]
quantized, asymmetric fixed-point 8-bit number unsigned
TEST_SUITE(U8_to_S8) FIXTURE_DATA_TEST_CASE(RunSmall
validate(CLAccessor(output_state), expected_output)
Num samples, channels, height, width.
FIXTURE_DATA_TEST_CASE(RunSmall, CLAbsLayerFixture< half >, framework::DatasetMode::PRECOMMIT, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F16)))
static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info=WeightsInfo(), const Size2D &dilation=Size2D(1U, 1U), const ActivationLayerInfo &act_info=ActivationLayerInfo(), bool enable_fast_math=false)
Static function to check if given info will return the convolution called by NEConvolutionLayer.
Num samples, height, width, channels.
Store the tensor's metadata.
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}))
combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)))