| /* | |
| * Copyright (c) 2017-2021 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/CL/CLTensor.h" | |
| #include "arm_compute/runtime/CL/CLTensorAllocator.h" | |
| #include "arm_compute/runtime/CL/functions/CLBatchNormalizationLayer.h" | |
| #include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h" | |
| #include "arm_compute/runtime/CL/functions/CLFuseBatchNormalization.h" | |
| #include "tests/CL/CLAccessor.h" | |
| #include "tests/PaddingCalculator.h" | |
| #include "tests/datasets/LargeConvolutionLayerDataset.h" | |
| #include "tests/datasets/RandomBatchNormalizationLayerDataset.h" | |
| #include "tests/datasets/SmallConvolutionLayerDataset.h" | |
| #include "tests/framework/Asserts.h" | |
| #include "tests/framework/Macros.h" | |
| #include "tests/framework/datasets/Datasets.h" | |
| #include "tests/validation/Helpers.h" | |
| #include "tests/validation/Validation.h" | |
| #include "tests/validation/fixtures/BatchNormalizationLayerFixture.h" | |
| #include "tests/validation/fixtures/BatchNormalizationLayerFusionFixture.h" | |
| namespace arm_compute | |
| { | |
| namespace test | |
| { | |
| namespace validation | |
| { | |
| namespace | |
| { | |
| RelativeTolerance<float> rel_tolerance_f32(0.05f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ | |
| constexpr AbsoluteTolerance<float> abs_tolerance_f32(0.0001f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F32 */ | |
| constexpr AbsoluteTolerance<float> tolerance_f16(0.02f); /**< Tolerance value for comparing reference's output against implementation's output for DataType::F16 */ | |
| 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), | |
| }); | |
| const auto common_fusion_dataset = combine(combine(combine(framework::dataset::make("UseBias", | |
| { false, true }), | |
| framework::dataset::make("UseBeta", { false, true })), | |
| framework::dataset::make("UseGamma", { false, true })), | |
| framework::dataset::make("Epsilon", { 0.001f })); | |
| } // namespace | |
| TEST_SUITE(CL) | |
| TEST_SUITE(BatchNormalizationLayer) | |
| template <typename T> | |
| using CLBatchNormalizationLayerFixture = BatchNormalizationLayerValidationFixture<CLTensor, CLAccessor, CLBatchNormalizationLayer, T>; | |
| // *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::F32), // Unsupported fused activation | |
| TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), // Fused activation's a < b | |
| }), | |
| 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::F32), | |
| TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), | |
| })), | |
| 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::F32), | |
| TensorInfo(TensorShape(2U), 1, DataType::F32), | |
| })), | |
| 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::TANH), | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 2.f, 6.f), | |
| })), | |
| framework::dataset::make("Expected", { true, false, false, false, false, false, false})), | |
| 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(CLBatchNormalizationLayer::validate(&input_info.clone()->set_is_resizable(false), (output_info.total_size() == 0) ? nullptr : &output_info.clone()->set_is_resizable(false), &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) | |
| TEST_SUITE(FP32) | |
| FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallRandomBatchNormalizationLayerDataset(), | |
| combine(framework::dataset::make("UseBeta", { false, true }), framework::dataset::make("UseGamma", { false, true }))), | |
| act_infos), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, abs_tolerance_f32, 0); | |
| } | |
| TEST_SUITE_END() //FP32 | |
| TEST_SUITE(FP16) | |
| FIXTURE_DATA_TEST_CASE(Random, CLBatchNormalizationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallRandomBatchNormalizationLayerDataset(), | |
| combine(framework::dataset::make("UseBeta", { false, true }), framework::dataset::make("UseGamma", { false, true }))), | |
| framework::dataset::make("ActivationInfo", | |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))), | |
| framework::dataset::make("DataType", DataType::F16)), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, tolerance_f16, 0); | |
| } | |
| TEST_SUITE_END() // FP16 | |
| TEST_SUITE_END() // Float | |
| TEST_SUITE_END() // BatchNormalizationLayer | |
| TEST_SUITE(BatchNormalizationLayerFusion) | |
| // *INDENT-OFF* | |
| // clang-format off | |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip( | |
| framework::dataset::make("Weights", { TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), // Valid | |
| TensorInfo(TensorShape(32U, 13U, 2U, 2U), 1, DataType::F32), // Mismatching data types | |
| TensorInfo(TensorShape(32U, 13U, 2U, 1U), 1, DataType::F32), // Invalid mean/var/beta/gamma shape | |
| }), | |
| framework::dataset::make("MVBGInfo",{ TensorInfo(TensorShape(2U), 1, DataType::F32), | |
| TensorInfo(TensorShape(2U), 1, DataType::F16), | |
| TensorInfo(TensorShape(5U), 1, DataType::F32), | |
| })), | |
| framework::dataset::make("Expected", { true, false, false})), | |
| weights_info, mvbg_info, expected) | |
| { | |
| const auto &weights_in_info = weights_info; | |
| const auto &mean_info = mvbg_info; | |
| const auto &var_info = mvbg_info; | |
| const auto &fused_weights_info = weights_info; | |
| const auto &fused_bias_info = mvbg_info; | |
| const auto &conv_bias_info = mvbg_info; | |
| const auto &beta_info = mvbg_info; | |
| const auto &gamma_info = mvbg_info; | |
| bool has_error = bool(CLFuseBatchNormalization::validate( | |
| &weights_in_info.clone()->set_is_resizable(false), &mean_info.clone()->set_is_resizable(false), | |
| &var_info.clone()->set_is_resizable(false), &fused_weights_info.clone()->set_is_resizable(false), | |
| &fused_bias_info.clone()->set_is_resizable(false), &conv_bias_info.clone()->set_is_resizable(false), | |
| &beta_info.clone()->set_is_resizable(false), &gamma_info.clone()->set_is_resizable(false), 1.f)); | |
| ARM_COMPUTE_EXPECT(has_error == expected, framework::LogLevel::ERRORS); | |
| } | |
| // clang-format on | |
| // *INDENT-ON* | |
| template <typename T> | |
| using CLBatchNormalizationLayerFusionFixture = BatchNormalizationLayerFusionValidationFixture<CLTensor, CLAccessor, CLConvolutionLayer, CLFuseBatchNormalization, T>; | |
| TEST_SUITE(Float) | |
| TEST_SUITE(FP32) | |
| FIXTURE_DATA_TEST_CASE(RunSmall, CLBatchNormalizationLayerFusionFixture<float>, framework::DatasetMode::PRECOMMIT, | |
| combine(combine(combine(datasets::SmallConvolutionLayerReducedDataset(), common_fusion_dataset), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); | |
| } | |
| FIXTURE_DATA_TEST_CASE(RunLarge, CLBatchNormalizationLayerFusionFixture<float>, framework::DatasetMode::NIGHTLY, | |
| combine(combine(combine(datasets::SmallConvolutionLayerDataset(), common_fusion_dataset), | |
| framework::dataset::make("DataType", DataType::F32)), | |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) | |
| { | |
| // Validate output | |
| validate(CLAccessor(_target), _reference, rel_tolerance_f32, 0.f, abs_tolerance_f32); | |
| } | |
| TEST_SUITE_END() // FP32 | |
| TEST_SUITE_END() // Float | |
| TEST_SUITE_END() // BatchNormalizationLayerFusion | |
| TEST_SUITE_END() // CL | |
| } // namespace validation | |
| } // namespace test | |
| } // namespace arm_compute |