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Diffstat (limited to 'tests/validation/NEON/BatchNormalizationLayer.cpp')
| -rw-r--r-- | tests/validation/NEON/BatchNormalizationLayer.cpp | 195 |
1 files changed, 195 insertions, 0 deletions
diff --git a/tests/validation/NEON/BatchNormalizationLayer.cpp b/tests/validation/NEON/BatchNormalizationLayer.cpp new file mode 100644 index 0000000000..7656b2f392 --- /dev/null +++ b/tests/validation/NEON/BatchNormalizationLayer.cpp @@ -0,0 +1,195 @@ +/* + * 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/Helper.h" +#include "NEON/NEAccessor.h" +#include "TypePrinter.h" +#include "dataset/BatchNormalizationLayerDataset.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_q = 3; /**< 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(shape0, dt, 1, fixed_point_position); + Tensor dst = create_tensor(shape0, dt, 1, fixed_point_position); + Tensor mean = create_tensor(shape1, dt, 1, fixed_point_position); + Tensor var = create_tensor(shape1, dt, 1, fixed_point_position); + Tensor beta = create_tensor(shape1, dt, 1, fixed_point_position); + Tensor gamma = create_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; + std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_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::F32) + boost::unit_test::data::make(DataType::QS8)), 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(obj.shape0, dt, 1, fixed_point_position); + Tensor dst = create_tensor(obj.shape0, dt, 1, fixed_point_position); + Tensor mean = create_tensor(obj.shape1, dt, 1, fixed_point_position); + Tensor var = create_tensor(obj.shape1, dt, 1, fixed_point_position); + Tensor beta = create_tensor(obj.shape1, dt, 1, fixed_point_position); + Tensor gamma = create_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_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_q, 0); +} +BOOST_AUTO_TEST_SUITE_END() + +BOOST_AUTO_TEST_SUITE_END() +BOOST_AUTO_TEST_SUITE_END() +#endif |