Compute Library
 22.11
RNNLayer.cpp File Reference
#include "arm_compute/runtime/NEON/functions/NERNNLayer.h"
#include "tests/NEON/Accessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/RNNLayerDataset.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/RNNLayerFixture.h"

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Namespaces

 arm_compute
 Copyright (c) 2017-2022 Arm Limited.
 
 arm_compute::test
 
 arm_compute::test::validation
 

Typedefs

template<typename T >
using NERNNLayerFixture = RNNLayerValidationFixture< Tensor, Accessor, NERNNLayer, T >
 

Functions

 DATA_TEST_CASE (Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U), 1, DataType::U8), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U), 1, DataType::F32), TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), }), framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), TensorInfo(TensorShape(27U, 11U), 1, DataType::F32), TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), })), framework::dataset::make("RecurrentWeightsInfo", { TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), TensorInfo(TensorShape(11U, 11U), 1, DataType::F32), TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), })), framework::dataset::make("BiasInfo", { TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(30U), 1, DataType::F32), TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(32U), 1, DataType::F32), })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), })), framework::dataset::make("HiddenStateInfo", { TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U), 1, DataType::F32), TensorInfo(TensorShape(11U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(32U, 32U), 1, DataType::F32), })), framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), })), framework::dataset::make("Expected", { false, false, false, false, false, false, false, true })), input_info, weights_info, recurrent_weights_info, bias_info, output_info, hidden_output_info, info, expected)
 
 FIXTURE_DATA_TEST_CASE (RunSmall, NERNNLayerFixture< float >, framework::DatasetMode::ALL, combine(datasets::SmallRNNLayerDataset(), framework::dataset::make("DataType", DataType::F32)))