| /* |
| * 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 "NEON/NEAccessor.h" |
| #include "TypePrinter.h" |
| #include "dataset/BatchNormalizationLayerDataset.h" |
| #include "tests/Globals.h" |
| #include "tests/Utils.h" |
| #include "tests/validation/Helpers.h" |
| #include "validation/Datasets.h" |
| #include "validation/Reference.h" |
| #include "validation/Validation.h" |
| |
| #include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h" |
| |
| #include <random> |
| |
| using namespace arm_compute; |
| using namespace arm_compute::test; |
| using namespace arm_compute::test::neon; |
| using namespace arm_compute::test::validation; |
| |
| namespace |
| { |
| const float tolerance_f = 1e-05; /**< Tolerance value for comparing reference's output against floating point implementation's output */ |
| const float tolerance_qs8 = 6; /**< Tolerance value for comparing reference's output against quantized implementation's output */ |
| const float tolerance_qs16 = 6; /**< Tolerance value for comparing reference's output against quantized implementation's output */ |
| |
| /** Compute Neon batch normalization function. |
| * |
| * @param[in] shape Shape of the input and output tensors. |
| * @param[in] dt Data type of input and output tensors. |
| * @param[in] norm_info Normalization Layer information. |
| * |
| * @return Computed output tensor. |
| */ |
| Tensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0) |
| { |
| // Create tensors |
| Tensor src = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position); |
| Tensor dst = create_tensor<Tensor>(shape0, dt, 1, fixed_point_position); |
| Tensor mean = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position); |
| Tensor var = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position); |
| Tensor beta = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position); |
| Tensor gamma = create_tensor<Tensor>(shape1, dt, 1, fixed_point_position); |
| |
| // Create and configure function |
| NEBatchNormalizationLayer norm; |
| norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon); |
| |
| // Allocate tensors |
| src.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| mean.allocator()->allocate(); |
| var.allocator()->allocate(); |
| beta.allocator()->allocate(); |
| gamma.allocator()->allocate(); |
| |
| BOOST_TEST(!src.info()->is_resizable()); |
| BOOST_TEST(!dst.info()->is_resizable()); |
| BOOST_TEST(!mean.info()->is_resizable()); |
| BOOST_TEST(!var.info()->is_resizable()); |
| BOOST_TEST(!beta.info()->is_resizable()); |
| BOOST_TEST(!gamma.info()->is_resizable()); |
| |
| // Fill tensors |
| if(dt == DataType::F32) |
| { |
| float min_bound = 0.f; |
| float max_bound = 0.f; |
| std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<float>(); |
| std::uniform_real_distribution<> distribution(min_bound, max_bound); |
| std::uniform_real_distribution<> distribution_var(0, max_bound); |
| library->fill(NEAccessor(src), distribution, 0); |
| library->fill(NEAccessor(mean), distribution, 1); |
| library->fill(NEAccessor(var), distribution_var, 0); |
| library->fill(NEAccessor(beta), distribution, 3); |
| library->fill(NEAccessor(gamma), distribution, 4); |
| } |
| else |
| { |
| int min_bound = 0; |
| int max_bound = 0; |
| if(dt == DataType::QS8) |
| { |
| std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position); |
| } |
| else |
| { |
| std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int16_t>(fixed_point_position); |
| } |
| std::uniform_int_distribution<> distribution(min_bound, max_bound); |
| std::uniform_int_distribution<> distribution_var(0, max_bound); |
| library->fill(NEAccessor(src), distribution, 0); |
| library->fill(NEAccessor(mean), distribution, 1); |
| library->fill(NEAccessor(var), distribution_var, 0); |
| library->fill(NEAccessor(beta), distribution, 3); |
| library->fill(NEAccessor(gamma), distribution, 4); |
| } |
| |
| // Compute function |
| norm.run(); |
| |
| return dst; |
| } |
| } // namespace |
| |
| #ifndef DOXYGEN_SKIP_THIS |
| BOOST_AUTO_TEST_SUITE(NEON) |
| BOOST_AUTO_TEST_SUITE(BatchNormalizationLayer) |
| |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) |
| BOOST_DATA_TEST_CASE(Configuration, RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make({ DataType::QS8, DataType::QS16, DataType::F32 }), obj, 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<Tensor>(obj.shape0, dt, 1, fixed_point_position); |
| Tensor dst = create_tensor<Tensor>(obj.shape0, dt, 1, fixed_point_position); |
| Tensor mean = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position); |
| Tensor var = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position); |
| Tensor beta = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position); |
| Tensor gamma = create_tensor<Tensor>(obj.shape1, dt, 1, fixed_point_position); |
| |
| BOOST_TEST(src.info()->is_resizable()); |
| BOOST_TEST(dst.info()->is_resizable()); |
| BOOST_TEST(mean.info()->is_resizable()); |
| BOOST_TEST(var.info()->is_resizable()); |
| BOOST_TEST(beta.info()->is_resizable()); |
| BOOST_TEST(gamma.info()->is_resizable()); |
| |
| // Create and configure function |
| NEBatchNormalizationLayer norm; |
| norm.configure(&src, &dst, &mean, &var, &beta, &gamma, obj.epsilon); |
| |
| // Validate valid region |
| const ValidRegion valid_region = shape_to_valid_region(obj.shape0); |
| const ValidRegion valid_region_vec = shape_to_valid_region(obj.shape1); |
| validate(src.info()->valid_region(), valid_region); |
| validate(dst.info()->valid_region(), valid_region); |
| validate(mean.info()->valid_region(), valid_region_vec); |
| validate(var.info()->valid_region(), valid_region_vec); |
| validate(beta.info()->valid_region(), valid_region_vec); |
| validate(gamma.info()->valid_region(), valid_region_vec); |
| } |
| |
| BOOST_AUTO_TEST_SUITE(Float) |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| BOOST_DATA_TEST_CASE(Random, |
| RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::F32), |
| obj, dt) |
| { |
| // Compute function |
| Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_f, 0); |
| } |
| BOOST_AUTO_TEST_SUITE_END() |
| |
| BOOST_AUTO_TEST_SUITE(Quantized) |
| BOOST_AUTO_TEST_SUITE(QS8) |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| BOOST_DATA_TEST_CASE(Random, |
| RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(1, 6), |
| obj, dt, fixed_point_position) |
| { |
| // Compute function |
| Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_qs8); |
| } |
| BOOST_AUTO_TEST_SUITE_END() |
| |
| BOOST_AUTO_TEST_SUITE(QS16) |
| BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| BOOST_DATA_TEST_CASE(Random, |
| RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS16) * boost::unit_test::data::xrange(1, 14), |
| obj, dt, fixed_point_position) |
| { |
| // Compute function |
| Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position); |
| |
| // Compute reference |
| RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position); |
| |
| // Validate output |
| validate(NEAccessor(dst), ref_dst, tolerance_qs16); |
| } |
| BOOST_AUTO_TEST_SUITE_END() |
| BOOST_AUTO_TEST_SUITE_END() |
| |
| BOOST_AUTO_TEST_SUITE_END() |
| BOOST_AUTO_TEST_SUITE_END() |
| #endif /* DOXYGEN_SKIP_THIS */ |