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/*
* Copyright (c) 2019-2020 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to
* deal in the Software without restriction, including without limitation the
* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
* sell copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in all
* copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/NEON/functions/NEInstanceNormalizationLayer.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/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/InstanceNormalizationLayerFixture.h"
namespace arm_compute
{
namespace test
{
namespace validation
{
namespace
{
/** Tolerance for float operations */
AbsoluteTolerance<float> tolerance_f32(0.0015f);
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
// This precision is chosen based on the precision float16_t can provide
// for the decimal numbers between 16 and 32 and decided based on multiple
// times of execution of tests. Although, with randomly generated numbers
// there is no gaurantee that this tolerance will be always large enough.
AbsoluteTolerance<half> tolerance_f16(static_cast<half>(0.015625f));
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
} // namespace
TEST_SUITE(NEON)
TEST_SUITE(InstanceNormalizationLayer)
// *INDENT-OFF*
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32), // Mismatching data type input/output
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32), // Mismatching shape input/output
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 2, DataType::F32), // Number of Input channels != 1
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::S16), // DataType != F32
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32)
}),
framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F16),
TensorInfo(TensorShape(256U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::S16),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32, DataLayout::NCHW),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32, DataLayout::NHWC),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32),
TensorInfo(TensorShape(128U, 64U, 32U, 4U), 1, DataType::F32)
})),
framework::dataset::make("Expected", { false, false, false, false, true, true, true, true, true, true })),
input_info, output_info, expected)
{
bool is_valid = bool(NEInstanceNormalizationLayer::validate(&input_info.clone()->set_is_resizable(false),
&output_info.clone()->set_is_resizable(false)
));
ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
template <typename T>
using NEInstanceNormalizationLayerFixture = InstanceNormalizationLayerValidationFixture<Tensor, Accessor, NEInstanceNormalizationLayer, T>;
TEST_SUITE(FP32)
FIXTURE_DATA_TEST_CASE(RunSmall, NEInstanceNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::Small4DShapes(),
framework::dataset::make("DataType", DataType::F32)),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
framework::dataset::make("InPlace", { false, true })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
TEST_SUITE_END() // FP32
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
FIXTURE_DATA_TEST_CASE(RunSmall, NEInstanceNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(datasets::SmallShapes(),
framework::dataset::make("DataType", DataType::F16)),
framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
framework::dataset::make("InPlace", { false, true })))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
}
TEST_SUITE_END() // FP16
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE_END() // InstanceNormalizationLayer
TEST_SUITE_END() // NEON
} // namespace validation
} // namespace test
} // namespace arm_compute
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