226 lines
15 KiB
C++
226 lines
15 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
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#include "arm_compute/runtime/NEON/functions/NEFuseBatchNormalization.h"
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#include "arm_compute/runtime/Tensor.h"
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#include "arm_compute/runtime/TensorAllocator.h"
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#include "tests/NEON/Accessor.h"
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#include "tests/PaddingCalculator.h"
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#include "tests/datasets/RandomBatchNormalizationLayerDataset.h"
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#include "tests/datasets/ShapeDatasets.h"
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#include "tests/datasets/SmallConvolutionLayerDataset.h"
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#include "tests/framework/Asserts.h"
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#include "tests/framework/Macros.h"
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#include "tests/framework/datasets/Datasets.h"
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#include "tests/validation/Helpers.h"
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#include "tests/validation/Validation.h"
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#include "tests/validation/fixtures/BatchNormalizationLayerFixture.h"
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#include "tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h"
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namespace arm_compute
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{
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namespace test
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{
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namespace validation
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{
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namespace
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{
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RelativeTolerance<float> rel_tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
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constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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constexpr AbsoluteTolerance<float> tolerance_f16(0.01f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */
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#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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const auto act_infos = framework::dataset::make("ActivationInfo",
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{
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 8.f, 2.f),
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});
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const auto common_fusion_dataset = combine(combine(combine(framework::dataset::make("UseBias", { false, true }),
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framework::dataset::make("UseBeta", { false, true })),
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framework::dataset::make("UseGamma", { false, true })),
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framework::dataset::make("Epsilon", { 0.001f }));
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} // namespace
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TEST_SUITE(NEON)
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TEST_SUITE(BatchNormalizationLayer)
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template <typename T>
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using NEBatchNormalizationLayerFixture = BatchNormalizationLayerValidationFixture<Tensor, Accessor, NEBatchNormalizationLayer, T>;
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
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framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching data types
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Mismatching data types
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Invalid mean/var/beta/gamma shape
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Fused activation's a < b
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}),
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framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F16),
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
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TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32),
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})),
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framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32),
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TensorInfo(TensorShape(2U), 1, DataType::F16),
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TensorInfo(TensorShape(2U), 1, DataType::F32),
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TensorInfo(TensorShape(5U), 1, DataType::F32),
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TensorInfo(TensorShape(2U), 1, DataType::F32),
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})),
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framework::dataset::make("ActivationLayerInfo",{ ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f),
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ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 2.f, 6.f),
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})),
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framework::dataset::make("Expected", { true, false, false, false, false})),
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input_info, output_info, mvbg_info, act_info, expected)
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{
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const auto &mean_info = mvbg_info;
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const auto &var_info = mvbg_info;
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const auto &beta_info = mvbg_info;
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const auto &gamma_info = mvbg_info;
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bool has_error = bool(NEBatchNormalizationLayer::validate(
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&input_info.clone()->set_is_resizable(false), output_info.total_size() ? &output_info.clone()->set_is_resizable(false) : nullptr,
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&mean_info.clone()->set_is_resizable(false), &var_info.clone()->set_is_resizable(false),
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&beta_info.clone()->set_is_resizable(false), &gamma_info.clone()->set_is_resizable(false), 1.f, act_info));
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ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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TEST_SUITE(Float)
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RandomSmall, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallRandomBatchNormalizationLayerDataset(),
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combine(framework::dataset::make("UseBeta", { false, true }),
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framework::dataset::make("UseGamma", { false, true }))),
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act_infos),
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framework::dataset::make("DataType", DataType::F32)),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, abs_tolerance_f32, 0);
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}
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FIXTURE_DATA_TEST_CASE(RandomLarge, NEBatchNormalizationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeRandomBatchNormalizationLayerDataset(),
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combine(framework::dataset::make("UseBeta", { false, true }),
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framework::dataset::make("UseGamma", { false, true }))),
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act_infos),
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framework::dataset::make("DataType", DataType::F32)),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, abs_tolerance_f32, 0);
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}
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TEST_SUITE_END() // F32
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#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
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TEST_SUITE(FP16)
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FIXTURE_DATA_TEST_CASE(RandomSmall, NEBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallRandomBatchNormalizationLayerDataset(),
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combine(framework::dataset::make("UseBeta", { false, true }),
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framework::dataset::make("UseGamma", { false, true }))),
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framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
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framework::dataset::make("DataType", DataType::F16)),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, tolerance_f16, 0);
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}
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FIXTURE_DATA_TEST_CASE(RandomLarge, NEBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::LargeRandomBatchNormalizationLayerDataset(),
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combine(framework::dataset::make("UseBeta", { false, true }),
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framework::dataset::make("UseGamma", { false, true }))),
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framework::dataset::make("ActivationInfo", ActivationLayerInfo())),
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framework::dataset::make("DataType", DataType::F16)),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, tolerance_f16, 0);
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}
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TEST_SUITE_END() // FP16
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#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
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TEST_SUITE_END() // Float
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TEST_SUITE_END() // BatchNormalizationLayer
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TEST_SUITE(BatchNormalizationLayerFusion)
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template <typename T>
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using NEBatchNormalizationLayerFusionFixture = BatchNormalizationLayerFusionValidationFixture<Tensor, Accessor, NEConvolutionLayer, NEFuseBatchNormalization, T>;
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// *INDENT-OFF*
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// clang-format off
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DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(
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framework::dataset::make("Weights", { TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), // Valid
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TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), // Mismatching data types
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TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F16), // Mismatching data types
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TensorInfo(TensorShape(32U, 13U, 2U, 1U), 1, DataType::F32), // Invalid mean/var/beta/gamma shape
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}),
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framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32),
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TensorInfo(TensorShape(2U), 1, DataType::F16),
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TensorInfo(TensorShape(2U), 1, DataType::F32),
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TensorInfo(TensorShape(5U), 1, DataType::F32),
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})),
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framework::dataset::make("Expected", { true, false, false, false})),
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weights_info, mvbg_info, expected)
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{
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const auto &weights_in_info = weights_info;
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const auto &mean_info = mvbg_info;
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const auto &var_info = mvbg_info;
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const auto &fused_weights_info = weights_info;
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const auto &fused_bias_info = mvbg_info;
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const auto &conv_bias_info = mvbg_info;
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const auto &beta_info = mvbg_info;
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const auto &gamma_info = mvbg_info;
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bool has_error = bool(NEFuseBatchNormalization::validate(
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&weights_in_info.clone()->set_is_resizable(false), &mean_info.clone()->set_is_resizable(false),
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&var_info.clone()->set_is_resizable(false), &fused_weights_info.clone()->set_is_resizable(false),
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&fused_bias_info.clone()->set_is_resizable(false), &conv_bias_info.clone()->set_is_resizable(false),
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&beta_info.clone()->set_is_resizable(false), &gamma_info.clone()->set_is_resizable(false), 1.f));
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ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS);
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}
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// clang-format on
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// *INDENT-ON*
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TEST_SUITE(Float)
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TEST_SUITE(FP32)
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FIXTURE_DATA_TEST_CASE(RunSmall, NEBatchNormalizationLayerFusionFixture<float>, framework::DatasetMode::PRECOMMIT,
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combine(combine(combine(datasets::SmallConvolutionLayerDataset(), common_fusion_dataset),
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framework::dataset::make("DataType", DataType::F32)),
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framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
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{
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// Validate output
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validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32);
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}
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TEST_SUITE_END() // FP32
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TEST_SUITE_END() // Float
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TEST_SUITE_END() // BatchNormalizationLayerFusion
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TEST_SUITE_END() // NEON
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} // namespace validation
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} // namespace test
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} // namespace arm_compute
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