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 "Globals.h" |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 25 | #include "NEON/NEAccessor.h" |
| 26 | #include "TensorLibrary.h" |
| 27 | #include "TypePrinter.h" |
| 28 | #include "Utils.h" |
| 29 | #include "validation/Datasets.h" |
| 30 | #include "validation/Reference.h" |
| 31 | #include "validation/Validation.h" |
| 32 | |
| 33 | #include "arm_compute/core/Helpers.h" |
| 34 | #include "arm_compute/core/Types.h" |
| 35 | #include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" |
| 36 | #include "arm_compute/runtime/Tensor.h" |
| 37 | #include "arm_compute/runtime/TensorAllocator.h" |
| 38 | |
| 39 | #include "boost_wrapper.h" |
| 40 | |
| 41 | #include <random> |
| 42 | #include <string> |
| 43 | #include <tuple> |
| 44 | |
| 45 | using namespace arm_compute; |
| 46 | using namespace arm_compute::test; |
| 47 | using namespace arm_compute::test::neon; |
| 48 | using namespace arm_compute::test::validation; |
| 49 | |
| 50 | namespace |
| 51 | { |
| 52 | const float tolerance_fp = 1e-3f; /**< Tolerance for floating point tests */ |
| 53 | const float tolerance_qs8 = 1; /**< Tolerance for fixed point tests */ |
| 54 | |
| 55 | /** Compute NEON direct convolution layer function. |
| 56 | * |
| 57 | * @param[in] src_shape Shape of the input tensor. |
| 58 | * @param[in] weights_shape Shape of the weights. |
| 59 | * @param[in] bias_shape Shape of the bias tensor. |
| 60 | * @param[in] dst_shape Shape of the output tensor. |
| 61 | * @param[in] dt Data type of input, convolution matrix and output tensors. |
| 62 | * @param[in] conv_info Padding and stride information. |
| 63 | * @param[in] fixed_point_position (Optional) Number of bits for the fractional part of the fixed point numbers |
| 64 | * |
| 65 | * @return Computed output tensor. |
| 66 | */ |
| 67 | Tensor compute_convolution_layer(const TensorShape &src_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &dst_shape, |
| 68 | DataType dt, PadStrideInfo conv_info, int fixed_point_position = 0) |
| 69 | { |
| 70 | // Create tensors |
Moritz Pflanzer | 94450f1 | 2017-06-30 12:48:43 +0100 | [diff] [blame] | 71 | Tensor src = create_tensor<Tensor>(src_shape, dt, 1, fixed_point_position); |
| 72 | Tensor weights = create_tensor<Tensor>(weights_shape, dt, 1, fixed_point_position); |
| 73 | Tensor bias = create_tensor<Tensor>(bias_shape, dt, 1, fixed_point_position); |
| 74 | Tensor dst = create_tensor<Tensor>(dst_shape, dt, 1, fixed_point_position); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 75 | |
| 76 | // Create and configure function |
| 77 | NEDirectConvolutionLayer conv_layer; |
| 78 | conv_layer.configure(&src, &weights, &bias, &dst, conv_info); |
| 79 | |
| 80 | // Allocate tensors |
| 81 | src.allocator()->allocate(); |
| 82 | weights.allocator()->allocate(); |
| 83 | bias.allocator()->allocate(); |
| 84 | dst.allocator()->allocate(); |
| 85 | |
| 86 | BOOST_TEST(!src.info()->is_resizable()); |
| 87 | BOOST_TEST(!weights.info()->is_resizable()); |
| 88 | BOOST_TEST(!bias.info()->is_resizable()); |
| 89 | BOOST_TEST(!dst.info()->is_resizable()); |
| 90 | |
| 91 | // Fill tensors |
| 92 | if(dt == DataType::F32) |
| 93 | { |
| 94 | std::uniform_real_distribution<> distribution(-1.f, 1.f); |
| 95 | library->fill(NEAccessor(src), distribution, 0); |
| 96 | library->fill(NEAccessor(weights), distribution, 1); |
| 97 | library->fill(NEAccessor(bias), distribution, 2); |
| 98 | } |
| 99 | else |
| 100 | { |
| 101 | library->fill_tensor_uniform(NEAccessor(src), 0); |
| 102 | library->fill_tensor_uniform(NEAccessor(weights), 1); |
| 103 | library->fill_tensor_uniform(NEAccessor(bias), 2); |
| 104 | } |
| 105 | |
| 106 | // Compute function |
| 107 | conv_layer.run(); |
| 108 | |
| 109 | return dst; |
| 110 | } |
| 111 | |
| 112 | TensorShape get_output_shape(TensorShape in_shape, TensorShape kernel_shape, const PadStrideInfo &conv_info) |
| 113 | { |
| 114 | TensorShape out_shape(in_shape); |
| 115 | const std::pair<unsigned int, unsigned int> scaled_dims = arm_compute::scaled_dimensions(in_shape.x(), |
| 116 | in_shape.y(), |
| 117 | kernel_shape.x(), |
Gian Marco Iodice | 4e28869 | 2017-06-27 11:41:59 +0100 | [diff] [blame] | 118 | kernel_shape.y(), |
| 119 | conv_info); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 120 | out_shape.set(0, scaled_dims.first); |
| 121 | out_shape.set(1, scaled_dims.second); |
| 122 | out_shape.set(2, kernel_shape[3]); |
| 123 | return out_shape; |
| 124 | } |
| 125 | |
| 126 | } // namespace |
| 127 | |
| 128 | #ifndef DOXYGEN_SKIP_THIS |
| 129 | BOOST_AUTO_TEST_SUITE(NEON) |
| 130 | BOOST_AUTO_TEST_SUITE(ConvolutionLayer) |
| 131 | BOOST_AUTO_TEST_SUITE(Direct) |
| 132 | |
| 133 | BOOST_AUTO_TEST_SUITE(Float) |
| 134 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| 135 | BOOST_DATA_TEST_CASE(W1x1, |
| 136 | DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), |
| 137 | input_shape, dt, sx, sy, num_kernels) |
| 138 | { |
| 139 | const unsigned int kernel_size = 1; |
| 140 | const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); |
| 141 | const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); |
| 142 | const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); |
| 143 | const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); |
| 144 | |
| 145 | Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); |
| 146 | |
| 147 | RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); |
| 148 | |
| 149 | // Validate output |
| 150 | validate(NEAccessor(dst), ref); |
| 151 | } |
| 152 | |
| 153 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| 154 | BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * CNNFloatDataTypes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, |
| 155 | 1) |
| 156 | * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }), |
| 157 | input_shape, dt, sx, sy, px, py, num_kernels) |
| 158 | { |
| 159 | const unsigned int kernel_size = 3; |
| 160 | const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); |
| 161 | const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); |
| 162 | const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); |
| 163 | const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); |
| 164 | |
| 165 | Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info); |
| 166 | |
| 167 | RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, dt, conv_info, 0); |
| 168 | |
| 169 | // Validate output |
| 170 | validate(NEAccessor(dst), ref, tolerance_fp); |
| 171 | } |
| 172 | BOOST_AUTO_TEST_SUITE_END() |
| 173 | |
| 174 | BOOST_AUTO_TEST_SUITE(Quantized) |
| 175 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| 176 | BOOST_DATA_TEST_CASE(W1x1, |
| 177 | DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }), |
| 178 | input_shape, sx, sy, num_kernels, fixed_point_position) |
| 179 | { |
| 180 | const unsigned int kernel_size = 1; |
| 181 | const PadStrideInfo conv_info(sx, sy, 0, 0, DimensionRoundingType::FLOOR); |
| 182 | const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); |
| 183 | const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); |
| 184 | const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); |
| 185 | |
| 186 | Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); |
| 187 | |
| 188 | RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); |
| 189 | |
| 190 | // Validate output |
| 191 | validate(NEAccessor(dst), ref); |
| 192 | } |
| 193 | |
| 194 | BOOST_TEST_DECORATOR(*boost::unit_test::label("precommit")) |
| 195 | BOOST_DATA_TEST_CASE(W3x3, DirectConvolutionShapes() * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(1, 3, 1) * boost::unit_test::data::xrange(0, 2, 1) |
| 196 | * boost::unit_test::data::xrange(0, 2, 1) * boost::unit_test::data::make({ 1, 4, 8, 16 }) * boost::unit_test::data::make({ 4, 5 }), |
| 197 | input_shape, sx, sy, px, py, num_kernels, fixed_point_position) |
| 198 | { |
| 199 | const unsigned int kernel_size = 3; |
| 200 | const PadStrideInfo conv_info(sx, sy, px, py, DimensionRoundingType::FLOOR); |
| 201 | const TensorShape w_shape(kernel_size, kernel_size, input_shape.z(), static_cast<unsigned int>(num_kernels)); |
| 202 | const TensorShape b_shape(static_cast<unsigned int>(num_kernels)); |
| 203 | const TensorShape d_shape(get_output_shape(input_shape, w_shape, conv_info)); |
| 204 | |
| 205 | Tensor dst = compute_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); |
| 206 | |
| 207 | RawTensor ref = Reference::compute_reference_convolution_layer(input_shape, w_shape, b_shape, d_shape, DataType::QS8, conv_info, fixed_point_position); |
| 208 | |
| 209 | // Validate output |
| 210 | validate(NEAccessor(dst), ref, tolerance_qs8); |
| 211 | } |
| 212 | BOOST_AUTO_TEST_SUITE_END() |
| 213 | |
| 214 | BOOST_AUTO_TEST_SUITE_END() |
| 215 | BOOST_AUTO_TEST_SUITE_END() |
| 216 | BOOST_AUTO_TEST_SUITE_END() |
Anthony Barbier | ac69aa1 | 2017-07-03 17:39:37 +0100 | [diff] [blame^] | 217 | #endif /* DOXYGEN_SKIP_THIS */ |