Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2017 ARM Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #include "NEON/Helper.h" |
| 25 | #include "NEON/NEAccessor.h" |
| 26 | #include "TypePrinter.h" |
| 27 | #include "dataset/BatchNormalizationLayerDataset.h" |
| 28 | #include "tests/validation/Helpers.h" |
| 29 | #include "validation/Datasets.h" |
| 30 | #include "validation/Reference.h" |
| 31 | #include "validation/Validation.h" |
| 32 | |
| 33 | #include "arm_compute/runtime/NEON/functions/NEBatchNormalizationLayer.h" |
| 34 | |
| 35 | #include <random> |
| 36 | |
| 37 | using namespace arm_compute; |
| 38 | using namespace arm_compute::test; |
| 39 | using namespace arm_compute::test::neon; |
| 40 | using namespace arm_compute::test::validation; |
| 41 | |
| 42 | namespace |
| 43 | { |
| 44 | const float tolerance_f = 1e-05; /**< Tolerance value for comparing reference's output against floating point implementation's output */ |
| 45 | const float tolerance_q = 3; /**< Tolerance value for comparing reference's output against quantized implementation's output */ |
| 46 | |
| 47 | /** Compute Neon batch normalization function. |
| 48 | * |
| 49 | * @param[in] shape Shape of the input and output tensors. |
| 50 | * @param[in] dt Data type of input and output tensors. |
| 51 | * @param[in] norm_info Normalization Layer information. |
| 52 | * |
| 53 | * @return Computed output tensor. |
| 54 | */ |
| 55 | Tensor compute_reference_batch_normalization_layer(const TensorShape &shape0, const TensorShape &shape1, DataType dt, float epsilon, int fixed_point_position = 0) |
| 56 | { |
| 57 | // Create tensors |
| 58 | Tensor src = create_tensor(shape0, dt, 1, fixed_point_position); |
| 59 | Tensor dst = create_tensor(shape0, dt, 1, fixed_point_position); |
| 60 | Tensor mean = create_tensor(shape1, dt, 1, fixed_point_position); |
| 61 | Tensor var = create_tensor(shape1, dt, 1, fixed_point_position); |
| 62 | Tensor beta = create_tensor(shape1, dt, 1, fixed_point_position); |
| 63 | Tensor gamma = create_tensor(shape1, dt, 1, fixed_point_position); |
| 64 | |
| 65 | // Create and configure function |
| 66 | NEBatchNormalizationLayer norm; |
| 67 | norm.configure(&src, &dst, &mean, &var, &beta, &gamma, epsilon); |
| 68 | |
| 69 | // Allocate tensors |
| 70 | src.allocator()->allocate(); |
| 71 | dst.allocator()->allocate(); |
| 72 | mean.allocator()->allocate(); |
| 73 | var.allocator()->allocate(); |
| 74 | beta.allocator()->allocate(); |
| 75 | gamma.allocator()->allocate(); |
| 76 | |
| 77 | BOOST_TEST(!src.info()->is_resizable()); |
| 78 | BOOST_TEST(!dst.info()->is_resizable()); |
| 79 | BOOST_TEST(!mean.info()->is_resizable()); |
| 80 | BOOST_TEST(!var.info()->is_resizable()); |
| 81 | BOOST_TEST(!beta.info()->is_resizable()); |
| 82 | BOOST_TEST(!gamma.info()->is_resizable()); |
| 83 | |
| 84 | // Fill tensors |
| 85 | if(dt == DataType::F32) |
| 86 | { |
| 87 | float min_bound = 0.f; |
| 88 | float max_bound = 0.f; |
| 89 | std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<float>(); |
| 90 | std::uniform_real_distribution<> distribution(min_bound, max_bound); |
| 91 | std::uniform_real_distribution<> distribution_var(0, max_bound); |
| 92 | library->fill(NEAccessor(src), distribution, 0); |
| 93 | library->fill(NEAccessor(mean), distribution, 1); |
| 94 | library->fill(NEAccessor(var), distribution_var, 0); |
| 95 | library->fill(NEAccessor(beta), distribution, 3); |
| 96 | library->fill(NEAccessor(gamma), distribution, 4); |
| 97 | } |
| 98 | else |
| 99 | { |
| 100 | int min_bound = 0; |
| 101 | int max_bound = 0; |
| 102 | std::tie(min_bound, max_bound) = get_batchnormalization_layer_test_bounds<int8_t>(fixed_point_position); |
| 103 | std::uniform_int_distribution<> distribution(min_bound, max_bound); |
| 104 | std::uniform_int_distribution<> distribution_var(0, max_bound); |
| 105 | library->fill(NEAccessor(src), distribution, 0); |
| 106 | library->fill(NEAccessor(mean), distribution, 1); |
| 107 | library->fill(NEAccessor(var), distribution_var, 0); |
| 108 | library->fill(NEAccessor(beta), distribution, 3); |
| 109 | library->fill(NEAccessor(gamma), distribution, 4); |
| 110 | } |
| 111 | |
| 112 | // Compute function |
| 113 | norm.run(); |
| 114 | |
| 115 | return dst; |
| 116 | } |
| 117 | } // namespace |
| 118 | |
| 119 | #ifndef DOXYGEN_SKIP_THIS |
| 120 | BOOST_AUTO_TEST_SUITE(NEON) |
| 121 | BOOST_AUTO_TEST_SUITE(BatchNormalizationLayer) |
| 122 | |
| 123 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit") * boost::unit_test::label("nightly")) |
| 124 | BOOST_DATA_TEST_CASE(Configuration, RandomBatchNormalizationLayerDataset() * (boost::unit_test::data::make(DataType::F32) + boost::unit_test::data::make(DataType::QS8)), obj, dt) |
| 125 | { |
| 126 | // Set fixed point position data type allowed |
| 127 | int fixed_point_position = (arm_compute::is_data_type_fixed_point(dt)) ? 3 : 0; |
| 128 | |
| 129 | // Create tensors |
| 130 | Tensor src = create_tensor(obj.shape0, dt, 1, fixed_point_position); |
| 131 | Tensor dst = create_tensor(obj.shape0, dt, 1, fixed_point_position); |
| 132 | Tensor mean = create_tensor(obj.shape1, dt, 1, fixed_point_position); |
| 133 | Tensor var = create_tensor(obj.shape1, dt, 1, fixed_point_position); |
| 134 | Tensor beta = create_tensor(obj.shape1, dt, 1, fixed_point_position); |
| 135 | Tensor gamma = create_tensor(obj.shape1, dt, 1, fixed_point_position); |
| 136 | |
| 137 | BOOST_TEST(src.info()->is_resizable()); |
| 138 | BOOST_TEST(dst.info()->is_resizable()); |
| 139 | BOOST_TEST(mean.info()->is_resizable()); |
| 140 | BOOST_TEST(var.info()->is_resizable()); |
| 141 | BOOST_TEST(beta.info()->is_resizable()); |
| 142 | BOOST_TEST(gamma.info()->is_resizable()); |
| 143 | |
| 144 | // Create and configure function |
| 145 | NEBatchNormalizationLayer norm; |
| 146 | norm.configure(&src, &dst, &mean, &var, &beta, &gamma, obj.epsilon); |
| 147 | |
| 148 | // Validate valid region |
| 149 | const ValidRegion valid_region = shape_to_valid_region(obj.shape0); |
| 150 | const ValidRegion valid_region_vec = shape_to_valid_region(obj.shape1); |
| 151 | validate(src.info()->valid_region(), valid_region); |
| 152 | validate(dst.info()->valid_region(), valid_region); |
| 153 | validate(mean.info()->valid_region(), valid_region_vec); |
| 154 | validate(var.info()->valid_region(), valid_region_vec); |
| 155 | validate(beta.info()->valid_region(), valid_region_vec); |
| 156 | validate(gamma.info()->valid_region(), valid_region_vec); |
| 157 | } |
| 158 | |
| 159 | BOOST_AUTO_TEST_SUITE(Float) |
| 160 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| 161 | BOOST_DATA_TEST_CASE(Random, |
| 162 | RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::F32), |
| 163 | obj, dt) |
| 164 | { |
| 165 | // Compute function |
| 166 | Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon); |
| 167 | |
| 168 | // Compute reference |
| 169 | RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon); |
| 170 | |
| 171 | // Validate output |
| 172 | validate(NEAccessor(dst), ref_dst, tolerance_f, 0); |
| 173 | } |
| 174 | BOOST_AUTO_TEST_SUITE_END() |
| 175 | |
| 176 | BOOST_AUTO_TEST_SUITE(Quantized) |
| 177 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| 178 | BOOST_DATA_TEST_CASE(Random, |
| 179 | RandomBatchNormalizationLayerDataset() * boost::unit_test::data::make(DataType::QS8) * boost::unit_test::data::xrange(1, 6), |
| 180 | obj, dt, fixed_point_position) |
| 181 | { |
| 182 | // Compute function |
| 183 | Tensor dst = compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position); |
| 184 | |
| 185 | // Compute reference |
| 186 | RawTensor ref_dst = Reference::compute_reference_batch_normalization_layer(obj.shape0, obj.shape1, dt, obj.epsilon, fixed_point_position); |
| 187 | |
| 188 | // Validate output |
| 189 | validate(NEAccessor(dst), ref_dst, tolerance_q, 0); |
| 190 | } |
| 191 | BOOST_AUTO_TEST_SUITE_END() |
| 192 | |
| 193 | BOOST_AUTO_TEST_SUITE_END() |
| 194 | BOOST_AUTO_TEST_SUITE_END() |
| 195 | #endif |