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Diffstat (limited to 'tests/validation/NEON/ActivationLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/ActivationLayer.cpp | 217 |
1 files changed, 217 insertions, 0 deletions
diff --git a/tests/validation/NEON/ActivationLayer.cpp b/tests/validation/NEON/ActivationLayer.cpp new file mode 100644 index 0000000000..da304d8087 --- /dev/null +++ b/tests/validation/NEON/ActivationLayer.cpp @@ -0,0 +1,217 @@ +/* + * Copyright (c) 2017 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 "Globals.h" +#include "NEON/Helper.h" +#include "NEON/NEAccessor.h" +#include "TensorLibrary.h" +#include "TypePrinter.h" +#include "Utils.h" +#include "validation/Datasets.h" +#include "validation/Helpers.h" +#include "validation/Reference.h" +#include "validation/Validation.h" + +#include "arm_compute/core/Helpers.h" +#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 "boost_wrapper.h" + +#include <random> +#include <string> +#include <tuple> + +using namespace arm_compute; +using namespace arm_compute::test; +using namespace arm_compute::test::neon; +using namespace arm_compute::test::validation; + +namespace +{ +/** Define tolerance of the activation layer + * + * @param[in] activation The activation function used. + * @param[in] fixed_point_position Number of bits for the fractional part.. + * + * @return Tolerance depending on the activation function. + */ +float activation_layer_tolerance(ActivationLayerInfo::ActivationFunction activation, int fixed_point_position = 0) +{ + switch(activation) + { + case ActivationLayerInfo::ActivationFunction::LOGISTIC: + case ActivationLayerInfo::ActivationFunction::SOFT_RELU: + case ActivationLayerInfo::ActivationFunction::SQRT: + case ActivationLayerInfo::ActivationFunction::TANH: + return (fixed_point_position != 0) ? 5.f : 0.00001f; + break; + default: + return 0.f; + } +} + +/** Compute Neon activation layer function. + * + * @param[in] shape Shape of the input and output tensors. + * @param[in] dt Shape Data type of tensors. + * @param[in] act_info Activation layer information. + * @param[in] fixed_point_position Number of bits for the fractional part of fixed point numbers. + * + * @return Computed output tensor. + */ +Tensor compute_activation_layer(const TensorShape &shape, DataType dt, ActivationLayerInfo act_info, int fixed_point_position = 0) +{ + // Create tensors + Tensor src = create_tensor(shape, dt, 1, fixed_point_position); + Tensor dst = create_tensor(shape, dt, 1, fixed_point_position); + + // Create and configure function + NEActivationLayer act_layer; + act_layer.configure(&src, &dst, act_info); + + // Allocate tensors + src.allocator()->allocate(); + dst.allocator()->allocate(); + + BOOST_TEST(!src.info()->is_resizable()); + BOOST_TEST(!dst.info()->is_resizable()); + + // Fill tensors + if(dt == DataType::F32) + { + float min_bound = 0; + float max_bound = 0; + std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<float>(act_info.activation()); + std::uniform_real_distribution<> distribution(min_bound, max_bound); + library->fill(NEAccessor(src), distribution, 0); + } + else + { + int min_bound = 0; + int max_bound = 0; + std::tie(min_bound, max_bound) = get_activation_layer_test_bounds<int8_t>(act_info.activation(), fixed_point_position); + std::uniform_int_distribution<> distribution(min_bound, max_bound); + library->fill(NEAccessor(src), distribution, 0); + } + + // Compute function + act_layer.run(); + + return dst; +} +} // namespace + +#ifndef DOXYGEN_SKIP_THIS +BOOST_AUTO_TEST_SUITE(NEON) +BOOST_AUTO_TEST_SUITE(ActivationLayer) + +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) +BOOST_DATA_TEST_CASE(Configuration, (SmallShapes() + LargeShapes()) * CNNDataTypes(), shape, dt) +{ + // Set fixed point position data type allowed + int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0; + + // Create tensors + Tensor src = create_tensor(shape, dt, 1, fixed_point_position); + Tensor dst = create_tensor(shape, dt, 1, fixed_point_position); + + BOOST_TEST(src.info()->is_resizable()); + BOOST_TEST(dst.info()->is_resizable()); + + // Create and configure function + NEActivationLayer act_layer; + 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); + validate(dst.info()->valid_region(), valid_region); + + // Validate padding + const PaddingSize padding(0, required_padding(shape.x(), 16), 0, 0); + validate(src.info()->padding(), padding); + validate(dst.info()->padding(), padding); +} + +BOOST_AUTO_TEST_SUITE(Float) +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(RunSmall, SmallShapes() * CNNFloatDataTypes() * ActivationFunctions(), shape, dt, act_function) +{ + // Create activation layer info + ActivationLayerInfo act_info(act_function, 1.f, 1.f); + + // Compute function + Tensor dst = compute_activation_layer(shape, dt, act_info); + + // Compute reference + RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info); + + // Validate output + validate(NEAccessor(dst), ref_dst, activation_layer_tolerance(act_function)); +} + +BOOST_TEST_DECORATOR(*boost::unit_test::label("nightly")) +BOOST_DATA_TEST_CASE(RunLarge, LargeShapes() * CNNFloatDataTypes() * ActivationFunctions(), shape, dt, act_function) +{ + // Create activation layer info + ActivationLayerInfo act_info(act_function, 1.f, 1.f); + + // Compute function + Tensor dst = compute_activation_layer(shape, dt, act_info); + + // Compute reference + RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, dt, act_info); + + // Validate output + validate(NEAccessor(dst), ref_dst, activation_layer_tolerance(act_function)); +} +BOOST_AUTO_TEST_SUITE_END() + +/** @note We test for fixed point precision [3,5] because [1,2] and [6,7] ranges + * cause overflowing issues in most of the transcendentals functions. + */ +BOOST_AUTO_TEST_SUITE(Quantized) +BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) +BOOST_DATA_TEST_CASE(RunSmall, SmallShapes() * ActivationFunctions() * boost::unit_test::data::xrange(3, 6, 1), + shape, act_function, fixed_point_position) +{ + // Create activation layer info + ActivationLayerInfo act_info(act_function, 1.f, 1.f); + + // Compute function + Tensor dst = compute_activation_layer(shape, DataType::QS8, act_info, fixed_point_position); + + // Compute reference + RawTensor ref_dst = Reference::compute_reference_activation_layer(shape, DataType::QS8, act_info, fixed_point_position); + + // Validate output + validate(NEAccessor(dst), ref_dst, activation_layer_tolerance(act_function, fixed_point_position)); +} +BOOST_AUTO_TEST_SUITE_END() + +BOOST_AUTO_TEST_SUITE_END() +BOOST_AUTO_TEST_SUITE_END() +#endif |