Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 1 | /* |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 2 | * Copyright (c) 2017-2018 ARM Limited. |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 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 | #ifndef __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__ |
| 25 | #define __ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__ |
| 26 | |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 27 | #include "tests/AssetsLibrary.h" |
| 28 | #include "tests/Globals.h" |
| 29 | #include "tests/Utils.h" |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 30 | |
| 31 | #include <memory> |
| 32 | |
| 33 | using namespace arm_compute; |
| 34 | using namespace arm_compute::test; |
| 35 | |
| 36 | namespace arm_compute |
| 37 | { |
| 38 | namespace test |
| 39 | { |
| 40 | namespace networks |
| 41 | { |
| 42 | /** Lenet5 model object */ |
| 43 | template <typename TensorType, |
| 44 | typename Accessor, |
| 45 | typename ActivationLayerFunction, |
| 46 | typename ConvolutionLayerFunction, |
| 47 | typename FullyConnectedLayerFunction, |
| 48 | typename PoolingLayerFunction, |
| 49 | typename SoftmaxLayerFunction> |
| 50 | class LeNet5Network |
| 51 | { |
| 52 | public: |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 53 | /** Initialize the network. |
| 54 | * |
| 55 | * @param[in] batches Number of batches. |
| 56 | */ |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 57 | void init(int batches) |
| 58 | { |
| 59 | _batches = batches; |
| 60 | |
| 61 | // Initialize input, output, weights and biases |
| 62 | input.allocator()->init(TensorInfo(TensorShape(28U, 28U, 1U, _batches), 1, DataType::F32)); |
| 63 | output.allocator()->init(TensorInfo(TensorShape(10U, _batches), 1, DataType::F32)); |
| 64 | w[0].allocator()->init(TensorInfo(TensorShape(5U, 5U, 1U, 20U), 1, DataType::F32)); |
| 65 | b[0].allocator()->init(TensorInfo(TensorShape(20U), 1, DataType::F32)); |
| 66 | w[1].allocator()->init(TensorInfo(TensorShape(5U, 5U, 20U, 50U), 1, DataType::F32)); |
| 67 | b[1].allocator()->init(TensorInfo(TensorShape(50U), 1, DataType::F32)); |
| 68 | w[2].allocator()->init(TensorInfo(TensorShape(800U, 500U), 1, DataType::F32)); |
| 69 | b[2].allocator()->init(TensorInfo(TensorShape(500U), 1, DataType::F32)); |
| 70 | w[3].allocator()->init(TensorInfo(TensorShape(500U, 10U), 1, DataType::F32)); |
| 71 | b[3].allocator()->init(TensorInfo(TensorShape(10U), 1, DataType::F32)); |
| 72 | } |
| 73 | |
| 74 | /** Build the model. */ |
| 75 | void build() |
| 76 | { |
| 77 | // Initialize intermediate tensors |
| 78 | // Layer 1 |
| 79 | conv1_out.allocator()->init(TensorInfo(TensorShape(24U, 24U, 20U, _batches), 1, DataType::F32)); |
| 80 | pool1_out.allocator()->init(TensorInfo(TensorShape(12U, 12U, 20U, _batches), 1, DataType::F32)); |
| 81 | // Layer 2 |
| 82 | conv2_out.allocator()->init(TensorInfo(TensorShape(8U, 8U, 50U, _batches), 1, DataType::F32)); |
| 83 | pool2_out.allocator()->init(TensorInfo(TensorShape(4U, 4U, 50U, _batches), 1, DataType::F32)); |
| 84 | // Layer 3 |
| 85 | fc1_out.allocator()->init(TensorInfo(TensorShape(500U, _batches), 1, DataType::F32)); |
| 86 | act1_out.allocator()->init(TensorInfo(TensorShape(500U, _batches), 1, DataType::F32)); |
| 87 | // Layer 6 |
| 88 | fc2_out.allocator()->init(TensorInfo(TensorShape(10U, _batches), 1, DataType::F32)); |
| 89 | |
| 90 | // Configure Layers |
| 91 | conv1.configure(&input, &w[0], &b[0], &conv1_out, PadStrideInfo(1, 1, 0, 0)); |
| 92 | pool1.configure(&conv1_out, &pool1_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))); |
| 93 | conv2.configure(&pool1_out, &w[1], &b[1], &conv2_out, PadStrideInfo(1, 1, 0, 0)); |
| 94 | pool2.configure(&conv2_out, &pool2_out, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))); |
| 95 | fc1.configure(&pool2_out, &w[2], &b[2], &fc1_out); |
| 96 | act1.configure(&fc1_out, &act1_out, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 97 | fc2.configure(&act1_out, &w[3], &b[3], &fc2_out); |
| 98 | smx.configure(&fc2_out, &output); |
| 99 | } |
| 100 | |
Alex Gilday | c357c47 | 2018-03-21 13:54:09 +0000 | [diff] [blame] | 101 | /** Allocate the network */ |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 102 | void allocate() |
| 103 | { |
| 104 | // Allocate tensors |
| 105 | input.allocator()->allocate(); |
| 106 | output.allocator()->allocate(); |
| 107 | for(auto &wi : w) |
| 108 | { |
| 109 | wi.allocator()->allocate(); |
| 110 | } |
| 111 | for(auto &bi : b) |
| 112 | { |
| 113 | bi.allocator()->allocate(); |
| 114 | } |
| 115 | conv1_out.allocator()->allocate(); |
| 116 | pool1_out.allocator()->allocate(); |
| 117 | conv2_out.allocator()->allocate(); |
| 118 | pool2_out.allocator()->allocate(); |
| 119 | fc1_out.allocator()->allocate(); |
| 120 | act1_out.allocator()->allocate(); |
| 121 | fc2_out.allocator()->allocate(); |
| 122 | } |
| 123 | |
| 124 | /** Fills the trainable parameters and input with random data. */ |
| 125 | void fill_random() |
| 126 | { |
| 127 | std::uniform_real_distribution<> distribution(-1, 1); |
| 128 | library->fill(Accessor(input), distribution, 0); |
| 129 | for(unsigned int i = 0; i < w.size(); ++i) |
| 130 | { |
| 131 | library->fill(Accessor(w[i]), distribution, i + 1); |
| 132 | library->fill(Accessor(b[i]), distribution, i + 10); |
| 133 | } |
| 134 | } |
| 135 | |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 136 | /** Fills the trainable parameters from binary files |
| 137 | * |
| 138 | * @param weights Files names containing the weights data |
| 139 | * @param biases Files names containing the bias data |
| 140 | */ |
| 141 | void fill(std::vector<std::string> weights, std::vector<std::string> biases) |
| 142 | { |
| 143 | ARM_COMPUTE_ERROR_ON(weights.size() != w.size()); |
| 144 | ARM_COMPUTE_ERROR_ON(biases.size() != b.size()); |
| 145 | |
| 146 | for(unsigned int i = 0; i < weights.size(); ++i) |
| 147 | { |
| 148 | library->fill_layer_data(Accessor(w[i]), weights[i]); |
| 149 | library->fill_layer_data(Accessor(b[i]), biases[i]); |
| 150 | } |
| 151 | } |
| 152 | |
| 153 | /** Feed input to network from file. |
| 154 | * |
| 155 | * @param name File name of containing the input data. |
| 156 | */ |
| 157 | void feed(std::string name) |
| 158 | { |
| 159 | library->fill_layer_data(Accessor(input), name); |
| 160 | } |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 161 | |
| 162 | /** Get the classification results. |
| 163 | * |
| 164 | * @return Vector containing the classified labels |
| 165 | */ |
| 166 | std::vector<unsigned int> get_classifications() |
| 167 | { |
| 168 | std::vector<unsigned int> classified_labels; |
| 169 | Accessor output_accessor(output); |
| 170 | |
| 171 | Window window; |
| 172 | window.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| 173 | for(unsigned int d = 1; d < output_accessor.shape().num_dimensions(); ++d) |
| 174 | { |
| 175 | window.set(d, Window::Dimension(0, output_accessor.shape()[d], 1)); |
| 176 | } |
| 177 | |
| 178 | execute_window_loop(window, [&](const Coordinates & id) |
| 179 | { |
| 180 | int max_idx = 0; |
| 181 | float val = 0; |
| 182 | const void *const out_ptr = output_accessor(id); |
| 183 | for(unsigned int l = 0; l < output_accessor.shape().x(); ++l) |
| 184 | { |
| 185 | float curr_val = reinterpret_cast<const float *>(out_ptr)[l]; |
| 186 | if(curr_val > val) |
| 187 | { |
| 188 | max_idx = l; |
| 189 | val = curr_val; |
| 190 | } |
| 191 | } |
| 192 | classified_labels.push_back(max_idx); |
| 193 | }); |
| 194 | return classified_labels; |
| 195 | } |
| 196 | |
| 197 | /** Clear all allocated memory from the tensor objects */ |
| 198 | void clear() |
| 199 | { |
| 200 | input.allocator()->free(); |
| 201 | output.allocator()->free(); |
| 202 | for(auto &wi : w) |
| 203 | { |
| 204 | wi.allocator()->free(); |
| 205 | } |
| 206 | for(auto &bi : b) |
| 207 | { |
| 208 | bi.allocator()->free(); |
| 209 | } |
| 210 | |
| 211 | conv1_out.allocator()->free(); |
| 212 | pool1_out.allocator()->free(); |
| 213 | conv2_out.allocator()->free(); |
| 214 | pool2_out.allocator()->free(); |
| 215 | fc1_out.allocator()->free(); |
| 216 | act1_out.allocator()->free(); |
| 217 | fc2_out.allocator()->free(); |
| 218 | } |
| 219 | |
| 220 | /** Runs the model */ |
| 221 | void run() |
| 222 | { |
| 223 | // Layer 1 |
| 224 | conv1.run(); |
| 225 | pool1.run(); |
| 226 | // Layer 2 |
| 227 | conv2.run(); |
| 228 | pool2.run(); |
| 229 | // Layer 3 |
| 230 | fc1.run(); |
| 231 | act1.run(); |
| 232 | // Layer 4 |
| 233 | fc2.run(); |
| 234 | // Softmax |
| 235 | smx.run(); |
| 236 | } |
| 237 | |
Joel Liang | 1c5ffd6 | 2017-12-28 10:09:51 +0800 | [diff] [blame] | 238 | /** Sync the results */ |
| 239 | void sync() |
| 240 | { |
| 241 | sync_if_necessary<TensorType>(); |
| 242 | sync_tensor_if_necessary<TensorType>(output); |
| 243 | } |
| 244 | |
Moritz Pflanzer | ee493ae | 2017-07-05 10:52:21 +0100 | [diff] [blame] | 245 | private: |
| 246 | unsigned int _batches{ 0 }; |
| 247 | |
| 248 | ActivationLayerFunction act1{}; |
| 249 | ConvolutionLayerFunction conv1{}, conv2{}; |
| 250 | FullyConnectedLayerFunction fc1{}, fc2{}; |
| 251 | PoolingLayerFunction pool1{}, pool2{}; |
| 252 | SoftmaxLayerFunction smx{}; |
| 253 | |
| 254 | TensorType input{}, output{}; |
| 255 | std::array<TensorType, 4> w{ {} }, b{ {} }; |
| 256 | |
| 257 | TensorType conv1_out{}, pool1_out{}; |
| 258 | TensorType conv2_out{}, pool2_out{}; |
| 259 | TensorType fc1_out{}, act1_out{}; |
| 260 | TensorType fc2_out{}; |
| 261 | }; |
| 262 | } // namespace networks |
| 263 | } // namespace test |
| 264 | } // namespace arm_compute |
| 265 | #endif //__ARM_COMPUTE_TEST_MODEL_OBJECTS_LENET5_H__ |