tests/validation/NEON/BatchNormalizationLayer.cpp

/*
 * Copyright (c) 2017-2018 ARM Limited.
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#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"
#include "arm_compute/runtime/Tensor.h"
#include "arm_compute/runtime/TensorAllocator.h"
#include "tests/NEON/Accessor.h"
#include "tests/PaddingCalculator.h"
#include "tests/datasets/RandomBatchNormalizationLayerDataset.h"
#include "tests/datasets/ShapeDatasets.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/BatchNormalizationLayerFixture.h"

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
constexpr AbsoluteTolerance<float> tolerance_f32(0.00001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
constexpr AbsoluteTolerance<float> tolerance_f16(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
#endif                                                   /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
constexpr AbsoluteTolerance<float> tolerance_qs8(3.0f);  /**< Tolerance value for comparing reference's output against implementation's output for DataType::QS8 */
constexpr AbsoluteTolerance<float> tolerance_qs16(6.0f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::QS16 */
const auto                         act_infos = framework::dataset::make("ActivationInfo",
{
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f),
    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f),
});
} // namespace

TEST_SUITE(NEON)
TEST_SUITE(BatchNormalizationLayer)

template <typename T>
using NEBatchNormalizationLayerFixture = BatchNormalizationLayerValidationFixture<Tensor, Accessor, NEBatchNormalizationLayer, T>;

DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::RandomBatchNormalizationLayerDataset(), framework::dataset::make("DataType", { DataType::QS8, DataType::QS16, DataType::F32 })),
               shape0, shape1, epsilon, dt)
{
    // Set fixed point position data type allowed
    const int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0;

    // Create tensors
    Tensor src   = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
    Tensor dst   = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position);
    Tensor mean  = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
    Tensor var   = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
    Tensor beta  = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);
    Tensor gamma = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position);

    // Create and Configure function
    NEBatchNormalizationLayer norm;
    norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon);

    // Validate valid region
    const ValidRegion valid_region = shape_to_valid_region(shape0);
    validate(dst.info()->valid_region(), valid_region);
}

// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
               framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),    // Window shrink
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),    // Mismatching data types
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),    // Mismatching data types
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),    // Invalid mean/var/beta/gamma shape
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), // Mismatching fixed point position
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2), // Fused activation with fixed point not supported
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),    // Fused activation's a < b
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
                                                     }),
               framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(32U, 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::F16),
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 3),
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 3),
                                                       TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::QS8, 2),
                                                       TensorInfo(),
                                                     })),
               framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32),
                                                     TensorInfo(TensorShape(2U), 1, DataType::F32),
                                                     TensorInfo(TensorShape(2U), 1, DataType::F16),
                                                     TensorInfo(TensorShape(2U), 1, DataType::F32),
                                                     TensorInfo(TensorShape(5U), 1, DataType::F32),
                                                     TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
                                                     TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
                                                     TensorInfo(TensorShape(2U), 1, DataType::F32),
                                                     TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
                                                     TensorInfo(TensorShape(2U), 1, DataType::QS8, 2),
                                                   })),
               framework::dataset::make("ActivationLayerInfo",{ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
                                                    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
                                                     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f),
                                                     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f),
                                                     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f),
                                                     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f, 2.f),
                                                     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f, 2.f),
                                                     ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 2.f, 6.f),
                                                     ActivationLayerInfo(),
                                                     ActivationLayerInfo(),
                                                   })),
               framework::dataset::make("Expected", { true, false, false, false, false, false, false, false, true, true})),
               input_info, output_info, mvbg_info, act_info, expected)
{
    const auto &mean_info = mvbg_info;
    const auto &var_info = mvbg_info;
    const auto &beta_info = mvbg_info;
    const auto &gamma_info = mvbg_info;
    bool has_error = bool(NEBatchNormalizationLayer::validate(
            &input_info.clone()->set_is_resizable(false), output_info.total_size() ? &output_info.clone()->set_is_resizable(false) : nullptr,
            &mean_info.clone()->set_is_resizable(false), &var_info.clone()->set_is_resizable(false),
            &beta_info.clone()->set_is_resizable(false), &gamma_info.clone()->set_is_resizable(false), 1.f, act_info));
    ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*

TEST_SUITE(Float)
FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(),
                                                                                                                   act_infos),
                                                                                                                   framework::dataset::make("DataType", DataType::F32)))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_f32, 0);
}
TEST_SUITE_END()

#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(Float16)
FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::RandomBatchNormalizationLayerDataset(),
                                                                                                                  framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
                                                                                                                  framework::dataset::make("DataType", DataType::F16)))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_f16, 0);
}
TEST_SUITE_END()
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

TEST_SUITE(Quantized)
template <typename T>
using NEBatchNormalizationLayerFixedPointFixture = BatchNormalizationLayerValidationFixedPointFixture<Tensor, Accessor, NEBatchNormalizationLayer, T>;

TEST_SUITE(QS8)
FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(),
                       framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
                       framework::dataset::make("DataType", DataType::QS8)),
                       framework::dataset::make("FractionalBits", 1, 6)))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_qs8, 0);
}
TEST_SUITE_END()

TEST_SUITE(QS16)
FIXTURE_DATA_TEST_CASE(Random, NEBatchNormalizationLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::RandomBatchNormalizationLayerDataset(),
                       framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
                       framework::dataset::make("DataType", DataType::QS16)),
                       framework::dataset::make("FractionalBits", 1, 14)))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_qs16, 0);
}
TEST_SUITE_END()

TEST_SUITE_END()

TEST_SUITE_END()
TEST_SUITE_END()
} // namespace validation
} // namespace test
} // namespace arm_compute