tests/validation/NEON/NormalizationLayer.cpp

/*
 * Copyright (c) 2017-2020 Arm Limited.
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 * SPDX-License-Identifier: MIT
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 * of this software and associated documentation files (the "Software"), to
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#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NENormalizationLayer.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/NormalizationTypesDataset.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/NormalizationLayerFixture.h"

namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
/** Tolerance for float operations */
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
constexpr AbsoluteTolerance<float> tolerance_f16(0.1f);
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
constexpr AbsoluteTolerance<float> tolerance_f32(0.00001f);

/** Input data set. */
const auto NormalizationDataset = combine(combine(combine(combine(datasets::SmallShapes(), datasets::NormalizationTypes()), framework::dataset::make("NormalizationSize", 3, 9, 2)),
                                                  framework::dataset::make("Beta", { 0.5f, 1.f, 2.f })),
                                          framework::dataset::make("IsScaled", { true }));
const auto NormalizationDatasetFP32 = combine(combine(combine(datasets::NormalizationTypes(), framework::dataset::make("NormalizationSize", 3, 9, 2)),
                                                      framework::dataset::make("Beta", { 0.5f, 1.f, 2.f })),
                                              framework::dataset::make("IsScaled", { true, false }));
} // namespace

TEST_SUITE(NEON)
TEST_SUITE(NormalizationLayer)

// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
    framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/output
                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes
                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Even normalization
                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non implemented IN_MAP_2D
                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                          }),
    framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16),
                                            TensorInfo(TensorShape(27U, 11U, 2U), 1, DataType::F32),
                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
                                            TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
                                            TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
                                          })),
    framework::dataset::make("NormInfo",  { NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
                                            NormalizationLayerInfo(NormType::IN_MAP_1D, 5),
                                            NormalizationLayerInfo(NormType::IN_MAP_1D, 4),
                                            NormalizationLayerInfo(NormType::IN_MAP_2D, 5),
                                            NormalizationLayerInfo(NormType::CROSS_MAP, 1),
                                           })),
    framework::dataset::make("Expected", { false, false, false, true, true })),
    input_info, output_info, norm_info, expected)
{
    bool is_valid = bool(NENormalizationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), norm_info));
    ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*

DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(datasets::SmallShapes(), framework::dataset::make("DataType", DataType::F32)),
               shape, data_type)
{
    NormalizationLayerInfo info(NormType::IN_MAP_1D, 3U, 5.0f, 2.0f, 1.f, false);

    // Create tensors
    Tensor src = create_tensor<Tensor>(shape, data_type);
    Tensor dst = create_tensor<Tensor>(shape, data_type);

    ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
    ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);

    // Create and configure function
    NENormalizationLayer norm;
    norm.configure(&src, &dst, info);

    validate(src.info()->padding(), PaddingSize(0, 0, 0, 0));
}

template <typename T>
using NENormalizationLayerFixture = NormalizationValidationFixture<Tensor, Accessor, NENormalizationLayer, T>;

TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(NormalizationDataset,
                                                                                                                       framework::dataset::make("DataType", DataType::F16)),
                                                                                                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_f16);
}
FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(NormalizationDataset,
                                                                                                                     framework::dataset::make("DataType", DataType::F16)),
                                                                                                             framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_f16);
}
TEST_SUITE_END() // FP16
#endif           /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */

TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, NENormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallShapes(), NormalizationDatasetFP32),
                                                                                                                        framework::dataset::make("DataType", DataType::F32)),
                                                                                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_f32);
}
FIXTURE_DATA_TEST_CASE(RunLarge, NENormalizationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeShapes(), NormalizationDatasetFP32),
                                                                                                                      framework::dataset::make("DataType", DataType::F32)),
                                                                                                              framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
{
    // Validate output
    validate(Accessor(_target), _reference, tolerance_f32);
}
TEST_SUITE_END() // FP32
TEST_SUITE_END() // Float

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