| /* |
| * Copyright (c) 2017-2022 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/core/utils/misc/Traits.h" |
| #include "arm_compute/runtime/NEON/functions/NEActivationLayer.h" |
| #include "arm_compute/runtime/RuntimeContext.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| #include "src/common/cpuinfo/CpuIsaInfo.h" |
| #include "src/cpu/kernels/CpuActivationKernel.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" |
| |
| #include "arm_compute/Acl.hpp" |
| #include "support/Requires.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| RelativeTolerance<float> tolerance_float_sqrt(0.0001f); |
| |
| /** 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::ELU: |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| case ActivationLayerInfo::ActivationFunction::HARD_SWISH: |
| switch(data_type) |
| { |
| case DataType::F16: |
| #if defined(ENABLE_SVE) |
| return RelativeTolerance<float>(0.25f); |
| #else // !defined(ENABLE_SVE) |
| return RelativeTolerance<float>(0.1f); |
| #endif // defined(ENABLE_SVE) |
| default: |
| return RelativeTolerance<float>(0.05f); |
| } |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| switch(data_type) |
| { |
| case DataType::F16: |
| #if defined(ENABLE_SVE) |
| return RelativeTolerance<float>(0.9f); |
| #else // !defined(ENABLE_SVE) |
| return RelativeTolerance<float>(0.01f); |
| #endif // defined(ENABLE_SVE) |
| default: |
| return RelativeTolerance<float>(0.00001f); |
| } |
| 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::SQRT: |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| case ActivationLayerInfo::ActivationFunction::HARD_SWISH: |
| switch(data_type) |
| { |
| case DataType::F16: |
| #if defined(ENABLE_SVE) |
| return AbsoluteTolerance<float>(0.25f); |
| #else // !defined(ENABLE_SVE) |
| return AbsoluteTolerance<float>(0.01f); |
| #endif // defined(ENABLE_SVE) |
| default: |
| return AbsoluteTolerance<float>(0.00001f); |
| } |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| switch(data_type) |
| { |
| case DataType::F16: |
| #if defined(ENABLE_SVE) |
| return AbsoluteTolerance<float>(0.9f); |
| #else // !defined(ENABLE_SVE) |
| return AbsoluteTolerance<float>(0.01f); |
| #endif // defined(ENABLE_SVE) |
| default: |
| return AbsoluteTolerance<float>(0.00001f); |
| } |
| default: |
| return AbsoluteTolerance<float>(0.f); |
| } |
| } |
| |
| /** Define absolute tolerance of the activation layer for qasymm8. |
| * |
| * @param[in] activation The activation function used. |
| * |
| * @return Absolute tolerance depending on the activation function. |
| */ |
| AbsoluteTolerance<uint8_t> tolerance_qasymm8(ActivationLayerInfo::ActivationFunction activation) |
| { |
| switch(activation) |
| { |
| case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| case ActivationLayerInfo::ActivationFunction::SQRT: |
| case ActivationLayerInfo::ActivationFunction::TANH: |
| case ActivationLayerInfo::ActivationFunction::HARD_SWISH: |
| case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| case ActivationLayerInfo::ActivationFunction::LEAKY_RELU: |
| return AbsoluteTolerance<uint8_t>(1); |
| default: |
| return AbsoluteTolerance<uint8_t>(0); |
| } |
| } |
| |
| constexpr AbsoluteTolerance<int16_t> tolerance_qsymm16(1); |
| |
| /** 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, |
| }); |
| |
| const auto NeonActivationFunctionsDataset = concat(datasets::ActivationFunctions(), framework::dataset::make("ActivationFunction", ActivationLayerInfo::ActivationFunction::HARD_SWISH)); |
| |
| /** Input data sets. */ |
| const auto ActivationDataset = combine(combine(framework::dataset::make("InPlace", { false, true }), NeonActivationFunctionsDataset), framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); |
| |
| template <typename T, ARM_COMPUTE_REQUIRES_TA(arm_compute::utils::traits::is_floating_point<T>::value)> |
| void test_float_sqrt_boundary_value() |
| { |
| constexpr auto vector_size = uint32_t{ 16 }; |
| |
| auto data_type = DataType::F32; |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| data_type = std::is_same<T, half>::value ? DataType::F16 : data_type; |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| const auto boundary_value_vector = std::vector<T> |
| { |
| std::numeric_limits<T>::min(), |
| T(0), |
| std::numeric_limits<T>::epsilon(), |
| std::numeric_limits<T>::max(), |
| }; |
| |
| // the following size ensures that the whole logic (vector + left-over) to be tested |
| // using all boundary values iff boundary_value_vecotr.size() is smaller than vector_size. |
| auto shape = TensorShape{ vector_size + boundary_value_vector.size() }; |
| auto info = ActivationLayerInfo{ ActivationLayerInfo::ActivationFunction::SQRT }; |
| auto src = create_tensor<Tensor>(shape, data_type); |
| |
| auto act = NEActivationLayer{}; |
| act.configure(&src, nullptr, info); |
| src.allocator()->allocate(); |
| library->fill_static_values(Accessor(src), boundary_value_vector); |
| act.run(); |
| |
| auto reference_src = SimpleTensor<T> { shape, data_type }; |
| library->fill_static_values(reference_src, boundary_value_vector); |
| auto reference_dst = reference::activation_layer<T>(reference_src, info); |
| |
| validate(Accessor(src), reference_dst, tolerance_float_sqrt); |
| } |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(ActivationLayer) |
| |
| /** Test case for memory injection in @ref cpu::CpuWinogradConv2d. |
| * |
| * Configure the operator once and inject memory at run-time in multiple executions. |
| * |
| * Checks performed in order: |
| * - Both runs compute the same output |
| */ |
| TEST_CASE(ActivationAPI, framework::DatasetMode::ALL) |
| { |
| acl::StatusCode err = acl::StatusCode::Success; |
| |
| // Create context & Queue |
| acl::Context ctx(acl::Target::Cpu, &err); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| |
| acl::Queue queue(ctx, &err); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| |
| // Create activation operator |
| acl::TensorDescriptor src_info({ 2, 3 }, acl::DataType::Float32); |
| acl::TensorDescriptor dst_info({ 2, 3 }, acl::DataType::Float32); |
| acl::ActivationDesc desc{ AclRelu, 6.f, 0.f, false }; |
| |
| acl::Activation act(ctx, src_info, dst_info, desc, &err); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| |
| // Create tensors and feed |
| acl::Tensor src(ctx, src_info, &err); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| acl::Tensor dst(ctx, dst_info, &err); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| |
| acl::TensorPack pack(ctx); |
| err = pack.add(src, ACL_SRC); |
| err = pack.add(dst, ACL_DST); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| |
| // Execute operator |
| err = act.run(queue, pack); |
| ARM_COMPUTE_ASSERT(err == acl::StatusCode::Success); |
| } |
| |
| // *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); |
| } |
| |
| DATA_TEST_CASE(KernelSelection, framework::DatasetMode::ALL, concat(concat( |
| combine(framework::dataset::make("CpuExt", std::string("NEON")), |
| framework::dataset::make("DataType", { DataType::F32, |
| DataType::F16, |
| DataType::QASYMM8, |
| DataType::QASYMM8_SIGNED, |
| DataType::QSYMM16 |
| })), |
| combine(framework::dataset::make("CpuExt", std::string("SVE")), |
| framework::dataset::make("DataType", { DataType::F32, |
| DataType::F16, |
| }))), |
| combine(framework::dataset::make("CpuExt", std::string("SVE2")), |
| framework::dataset::make("DataType", { DataType::QASYMM8, |
| DataType::QASYMM8_SIGNED, |
| DataType::QSYMM16 |
| }))), |
| cpu_ext, data_type) |
| { |
| using namespace cpu::kernels; |
| |
| cpuinfo::CpuIsaInfo cpu_isa{}; |
| cpu_isa.neon = (cpu_ext == "NEON"); |
| cpu_isa.sve = (cpu_ext == "SVE"); |
| cpu_isa.sve2 = (cpu_ext == "SVE2"); |
| cpu_isa.fp16 = (data_type == DataType::F16); |
| |
| const auto *selected_impl = CpuActivationKernel::get_implementation(DataTypeISASelectorData{data_type, cpu_isa}, cpu::KernelSelectionType::Preferred); |
| |
| ARM_COMPUTE_ERROR_ON_NULLPTR(selected_impl); |
| |
| std::string expected = lower_string(cpu_ext) + "_" + cpu_impl_dt(data_type) + "_activation"; |
| std::string actual = selected_impl->name; |
| |
| ARM_COMPUTE_EXPECT_EQUAL(expected, actual, 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) |
| TEST_CASE(SqrtBoundaryValue, framework::DatasetMode::ALL) |
| { |
| test_float_sqrt_boundary_value<half>(); |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerFixture<half>, framework::DatasetMode::ALL, 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)); |
| } |
| TEST_SUITE_END() // FP16 |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| TEST_SUITE(FP32) |
| TEST_CASE(SqrtBoundaryValue, framework::DatasetMode::ALL) |
| { |
| test_float_sqrt_boundary_value<float>(); |
| } |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerFixture<float>, framework::DatasetMode::ALL, 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)); |
| } |
| 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, |
| ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, |
| ActivationLayerInfo::ActivationFunction::LOGISTIC, |
| ActivationLayerInfo::ActivationFunction::TANH, |
| ActivationLayerInfo::ActivationFunction::LEAKY_RELU, |
| }); |
| |
| const auto QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }), |
| concat(QuantizedActivationFunctionsDataset, framework::dataset::make("ActivationFunction", ActivationLayerInfo::ActivationFunction::HARD_SWISH))), |
| framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); |
| |
| TEST_SUITE(Quantized) |
| TEST_SUITE(QASYMM8) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<uint8_t>, framework::DatasetMode::ALL, 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, tolerance_qasymm8(_function)); |
| } |
| TEST_SUITE_END() // QASYMM8 |
| |
| TEST_SUITE(QASYMM8_SIGNED) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallShapes(), QuantizedActivationDataset), |
| framework::dataset::make("DataType", |
| DataType::QASYMM8_SIGNED)), |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10.0f) }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qasymm8(_function)); |
| } |
| TEST_SUITE_END() // QASYMM8_SIGNED |
| |
| /** Input data sets. */ |
| const auto Int16QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationFunction", { ActivationLayerInfo::ActivationFunction::LOGISTIC, |
| ActivationLayerInfo::ActivationFunction::TANH |
| }); |
| const auto Int16QuantizedActivationDataset = combine(combine(framework::dataset::make("InPlace", { false }), Int16QuantizedActivationFunctionsDataset), |
| framework::dataset::make("AlphaBeta", { 0.5f, 1.f })); |
| |
| TEST_SUITE(QSYMM16) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEActivationLayerQuantizedFixture<int16_t>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallShapes(), Int16QuantizedActivationDataset), |
| framework::dataset::make("DataType", |
| DataType::QSYMM16)), |
| framework::dataset::make("QuantizationInfo", { QuantizationInfo(1.f / 32768.f, 0.f) }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_qsymm16); |
| } |
| TEST_SUITE_END() // QSYMM16 |
| TEST_SUITE_END() // Quantized |
| |
| TEST_SUITE_END() // ActivationLayer |
| TEST_SUITE_END() // Neon |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
