| /* |
| * Copyright (c) 2017-2020 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/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(0.01f); /**< 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 |