| /* |
| * Copyright (c) 2017-2019 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/NEActivationLayer.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/ActivationFunctionsDataset.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/ActivationLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| /** Define relative tolerance of the activation layer. |
| * |
| * @param[in] data_type The data type used. |
| * @param[in] activation The activation function used. |
| * |
| * @return Relative tolerance depending on the activation function. |
| */ |
| RelativeTolerance<float> relative_tolerance(DataType data_type, ActivationLayerInfo::ActivationFunction activation) |
| { |
| switch(activation) |
| { |
| case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| switch(data_type) |
| { |
| case DataType::F16: |
| return RelativeTolerance<float>(0.1f); |
| default: |
| return RelativeTolerance<float>(0.05f); |
| } |
| default: |
| return RelativeTolerance<float>(0.f); |
| } |
| } |
| |
| /** Define absolute tolerance of the activation layer. |
| * |
| * @param[in] data_type The data type used. |
| * @param[in] activation The activation function used. |
| * |
| * @return Absolute tolerance depending on the activation function. |
| */ |
| AbsoluteTolerance<float> absolute_tolerance(DataType data_type, ActivationLayerInfo::ActivationFunction activation) |
| { |
| switch(activation) |
| { |
| case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| switch(data_type) |
| { |
| case DataType::F16: |
| return AbsoluteTolerance<float>(0.01f); |
| default: |
| return AbsoluteTolerance<float>(0.00001f); |
| } |
| default: |
| return AbsoluteTolerance<float>(0.f); |
| } |
| } |
| |
| /** CNN data types */ |
| const auto CNNDataTypes = framework::dataset::make("DataType", |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| DataType::F16, |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| DataType::F32, |
| }); |
| |
| /** Input data sets. */ |
| const auto ActivationDataset = combine(combine(framework::dataset::make("InPlace", { false, true }), datasets::ActivationFunctions()), framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(ActivationLayer) |
| |
| DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(concat(datasets::SmallShapes(), datasets::LargeShapes()), CNNDataTypes), framework::dataset::make("InPlace", { false, true })), |
| shape, data_type, in_place) |
| { |
| // Create tensors |
| Tensor src = create_tensor<Tensor>(shape, data_type, 1); |
| Tensor dst = create_tensor<Tensor>(shape, data_type, 1); |
| |
| ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); |
| ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS); |
| |
| // Create and configure function |
| NEActivationLayer act_layer; |
| |
| if(in_place) |
| { |
| act_layer.configure(&src, nullptr, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS)); |
| } |
| else |
| { |
| act_layer.configure(&src, &dst, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS)); |
| } |
| |
| // Validate valid region |
| const ValidRegion valid_region = shape_to_valid_region(shape); |
| validate(src.info()->valid_region(), valid_region); |
| |
| if(!in_place) |
| { |
| validate(dst.info()->valid_region(), valid_region); |
| } |
| |
| // Validate padding |
| validate(src.info()->padding(), PaddingSize()); |
| if(!in_place) |
| { |
| validate(dst.info()->padding(), PaddingSize()); |
| } |
| } |
| |
| // *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 types |
| TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching shapes |
| }), |
| framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F16), |
| TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), |
| TensorInfo(TensorShape(32U, 13U, 2U), 1, DataType::F32), |
| })), |
| framework::dataset::make("ActivationInfo", { ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| })), |
| framework::dataset::make("Expected", { false, true, false})), |
| input_info, output_info, act_info, expected) |
| { |
| bool is_valid = bool(NEActivationLayer::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), act_info)); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| |
| template <typename T> |
| using NEActivationLayerFixture = ActivationValidationFixture<Tensor, Accessor, NEActivationLayer, T>; |
| |
| TEST_SUITE(Float) |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| TEST_SUITE(FP16) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), ActivationDataset), |
| framework::dataset::make("DataType", |
| DataType::F16))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function)); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEActivationLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), ActivationDataset), |
| framework::dataset::make("DataType", |
| DataType::F16))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function)); |
| } |
| TEST_SUITE_END() |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(datasets::SmallShapes(), ActivationDataset), framework::dataset::make("DataType", |
| DataType::F32))) |
| |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function)); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEActivationLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(datasets::LargeShapes(), ActivationDataset), |
| framework::dataset::make("DataType", DataType::F32))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function)); |
| } |
| TEST_SUITE_END() // FP32 |
| TEST_SUITE_END() // Float |
| |
| template <typename T> |
| using NEActivationLayerQuantizedFixture = ActivationValidationQuantizedFixture<Tensor, Accessor, NEActivationLayer, T>; |
| |
| /** Input data sets. */ |
| const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationFunction", { ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, |
| ActivationLayerInfo::ActivationFunction::RELU |
| }); |
| |
| const auto QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false, true }), QuantizedActivationFunctionsDataset), |
| framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); |
| |
| TEST_SUITE(Quantized) |
| TEST_SUITE(QASYMM8) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallShapes(), QuantizedActivationDataset), |
| framework::dataset::make("DataType", |
| DataType::QASYMM8)), |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.1f, 128.0f) }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function)); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEActivationLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeShapes(), QuantizedActivationDataset), |
| framework::dataset::make("DataType", |
| DataType::QASYMM8)), |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.1f, 128.0f) }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, relative_tolerance(_data_type, _function), 0.f, absolute_tolerance(_data_type, _function)); |
| } |
| TEST_SUITE_END() // QASYMM8 |
| TEST_SUITE_END() // Quantized |
| |
| TEST_SUITE_END() // ActivationLayer |
| TEST_SUITE_END() // NEON |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |