Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1 | /* |
Georgios Pinitas | 12be7ab | 2018-07-03 12:06:23 +0100 | [diff] [blame] | 2 | * Copyright (c) 2016-2018 ARM Limited. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +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 | #include "arm_compute/runtime/NEON/NEFunctions.h" |
| 25 | |
| 26 | #include "arm_compute/core/Types.h" |
Gian Marco Iodice | e7f7b55 | 2017-09-28 10:43:38 +0100 | [diff] [blame] | 27 | #include "arm_compute/runtime/Allocator.h" |
| 28 | #include "arm_compute/runtime/BlobLifetimeManager.h" |
| 29 | #include "arm_compute/runtime/MemoryManagerOnDemand.h" |
| 30 | #include "arm_compute/runtime/PoolManager.h" |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 31 | #include "utils/Utils.h" |
| 32 | |
| 33 | using namespace arm_compute; |
| 34 | using namespace utils; |
| 35 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 36 | class NEONCNNExample : public Example |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 37 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 38 | public: |
Georgios Pinitas | 12be7ab | 2018-07-03 12:06:23 +0100 | [diff] [blame] | 39 | bool do_setup(int argc, char **argv) override |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 40 | { |
| 41 | ARM_COMPUTE_UNUSED(argc); |
| 42 | ARM_COMPUTE_UNUSED(argv); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 43 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 44 | // Create memory manager components |
| 45 | // We need 2 memory managers: 1 for handling the tensors within the functions (mm_layers) and 1 for handling the input and output tensors of the functions (mm_transitions)) |
| 46 | auto lifetime_mgr0 = std::make_shared<BlobLifetimeManager>(); // Create lifetime manager |
| 47 | auto lifetime_mgr1 = std::make_shared<BlobLifetimeManager>(); // Create lifetime manager |
| 48 | auto pool_mgr0 = std::make_shared<PoolManager>(); // Create pool manager |
| 49 | auto pool_mgr1 = std::make_shared<PoolManager>(); // Create pool manager |
| 50 | auto mm_layers = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr0, pool_mgr0); // Create the memory manager |
| 51 | auto mm_transitions = std::make_shared<MemoryManagerOnDemand>(lifetime_mgr1, pool_mgr1); // Create the memory manager |
Gian Marco Iodice | e7f7b55 | 2017-09-28 10:43:38 +0100 | [diff] [blame] | 52 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 53 | // The weights and biases tensors should be initialized with the values inferred with the training |
Gian Marco Iodice | e7f7b55 | 2017-09-28 10:43:38 +0100 | [diff] [blame] | 54 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 55 | // Set memory manager where allowed to manage internal memory requirements |
| 56 | conv0 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers); |
| 57 | conv1 = arm_compute::support::cpp14::make_unique<NEConvolutionLayer>(mm_layers); |
| 58 | fc0 = arm_compute::support::cpp14::make_unique<NEFullyConnectedLayer>(mm_layers); |
| 59 | softmax = arm_compute::support::cpp14::make_unique<NESoftmaxLayer>(mm_layers); |
| 60 | |
| 61 | /* [Initialize tensors] */ |
| 62 | |
| 63 | // Initialize src tensor |
| 64 | constexpr unsigned int width_src_image = 32; |
| 65 | constexpr unsigned int height_src_image = 32; |
| 66 | constexpr unsigned int ifm_src_img = 1; |
| 67 | |
| 68 | const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img); |
| 69 | src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32)); |
| 70 | |
| 71 | // Initialize tensors of conv0 |
| 72 | constexpr unsigned int kernel_x_conv0 = 5; |
| 73 | constexpr unsigned int kernel_y_conv0 = 5; |
| 74 | constexpr unsigned int ofm_conv0 = 8; |
| 75 | |
| 76 | const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0); |
| 77 | const TensorShape biases_shape_conv0(weights_shape_conv0[3]); |
| 78 | const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]); |
| 79 | |
| 80 | weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32)); |
| 81 | biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32)); |
| 82 | out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32)); |
| 83 | |
| 84 | // Initialize tensor of act0 |
| 85 | out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32)); |
| 86 | |
| 87 | // Initialize tensor of pool0 |
| 88 | TensorShape out_shape_pool0 = out_shape_conv0; |
| 89 | out_shape_pool0.set(0, out_shape_pool0.x() / 2); |
| 90 | out_shape_pool0.set(1, out_shape_pool0.y() / 2); |
| 91 | out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32)); |
| 92 | |
| 93 | // Initialize tensors of conv1 |
| 94 | constexpr unsigned int kernel_x_conv1 = 3; |
| 95 | constexpr unsigned int kernel_y_conv1 = 3; |
| 96 | constexpr unsigned int ofm_conv1 = 16; |
| 97 | |
| 98 | const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1); |
| 99 | |
| 100 | const TensorShape biases_shape_conv1(weights_shape_conv1[3]); |
| 101 | const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]); |
| 102 | |
| 103 | weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32)); |
| 104 | biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32)); |
| 105 | out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32)); |
| 106 | |
| 107 | // Initialize tensor of act1 |
| 108 | out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32)); |
| 109 | |
| 110 | // Initialize tensor of pool1 |
| 111 | TensorShape out_shape_pool1 = out_shape_conv1; |
| 112 | out_shape_pool1.set(0, out_shape_pool1.x() / 2); |
| 113 | out_shape_pool1.set(1, out_shape_pool1.y() / 2); |
| 114 | out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32)); |
| 115 | |
| 116 | // Initialize tensor of fc0 |
| 117 | constexpr unsigned int num_labels = 128; |
| 118 | |
| 119 | const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels); |
| 120 | const TensorShape biases_shape_fc0(num_labels); |
| 121 | const TensorShape out_shape_fc0(num_labels); |
| 122 | |
| 123 | weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32)); |
| 124 | biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32)); |
| 125 | out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32)); |
| 126 | |
| 127 | // Initialize tensor of act2 |
| 128 | out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32)); |
| 129 | |
| 130 | // Initialize tensor of softmax |
| 131 | const TensorShape out_shape_softmax(out_shape_fc0.x()); |
| 132 | out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32)); |
| 133 | |
| 134 | /* -----------------------End: [Initialize tensors] */ |
| 135 | |
| 136 | /* [Configure functions] */ |
| 137 | |
| 138 | // in:32x32x1: 5x5 convolution, 8 output features maps (OFM) |
| 139 | conv0->configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */)); |
| 140 | |
| 141 | // in:32x32x8, out:32x32x8, Activation function: relu |
| 142 | act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 143 | |
| 144 | // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max |
| 145 | pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); |
| 146 | |
| 147 | // in:16x16x8: 3x3 convolution, 16 output features maps (OFM) |
| 148 | conv1->configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */)); |
| 149 | |
| 150 | // in:16x16x16, out:16x16x16, Activation function: relu |
| 151 | act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 152 | |
| 153 | // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average |
| 154 | pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); |
| 155 | |
| 156 | // in:8x8x16, out:128 |
| 157 | fc0->configure(&out_pool1, &weights2, &biases2, &out_fc0); |
| 158 | |
| 159 | // in:128, out:128, Activation function: relu |
| 160 | act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 161 | |
| 162 | // in:128, out:128 |
| 163 | softmax->configure(&out_act2, &out_softmax); |
| 164 | |
| 165 | /* -----------------------End: [Configure functions] */ |
| 166 | |
| 167 | /*[ Add tensors to memory manager ]*/ |
| 168 | |
| 169 | // We need 2 memory groups for handling the input and output |
| 170 | // We call explicitly allocate after manage() in order to avoid overlapping lifetimes |
| 171 | memory_group0 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions); |
| 172 | memory_group1 = arm_compute::support::cpp14::make_unique<MemoryGroup>(mm_transitions); |
| 173 | |
| 174 | memory_group0->manage(&out_conv0); |
| 175 | out_conv0.allocator()->allocate(); |
| 176 | memory_group1->manage(&out_act0); |
| 177 | out_act0.allocator()->allocate(); |
| 178 | memory_group0->manage(&out_pool0); |
| 179 | out_pool0.allocator()->allocate(); |
| 180 | memory_group1->manage(&out_conv1); |
| 181 | out_conv1.allocator()->allocate(); |
| 182 | memory_group0->manage(&out_act1); |
| 183 | out_act1.allocator()->allocate(); |
| 184 | memory_group1->manage(&out_pool1); |
| 185 | out_pool1.allocator()->allocate(); |
| 186 | memory_group0->manage(&out_fc0); |
| 187 | out_fc0.allocator()->allocate(); |
| 188 | memory_group1->manage(&out_act2); |
| 189 | out_act2.allocator()->allocate(); |
| 190 | memory_group0->manage(&out_softmax); |
| 191 | out_softmax.allocator()->allocate(); |
| 192 | |
| 193 | /* -----------------------End: [ Add tensors to memory manager ] */ |
| 194 | |
| 195 | /* [Allocate tensors] */ |
| 196 | |
| 197 | // Now that the padding requirements are known we can allocate all tensors |
| 198 | src.allocator()->allocate(); |
| 199 | weights0.allocator()->allocate(); |
| 200 | weights1.allocator()->allocate(); |
| 201 | weights2.allocator()->allocate(); |
| 202 | biases0.allocator()->allocate(); |
| 203 | biases1.allocator()->allocate(); |
| 204 | biases2.allocator()->allocate(); |
| 205 | |
| 206 | /* -----------------------End: [Allocate tensors] */ |
| 207 | |
Georgios Pinitas | 9da19e9 | 2018-10-11 15:33:11 +0100 | [diff] [blame] | 208 | // Populate the layers manager. (Validity checks, memory allocations etc) |
| 209 | mm_layers->populate(allocator, 1 /* num_pools */); |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 210 | |
Georgios Pinitas | 9da19e9 | 2018-10-11 15:33:11 +0100 | [diff] [blame] | 211 | // Populate the transitions manager. (Validity checks, memory allocations etc) |
| 212 | mm_transitions->populate(allocator, 2 /* num_pools */); |
Georgios Pinitas | 12be7ab | 2018-07-03 12:06:23 +0100 | [diff] [blame] | 213 | |
| 214 | return true; |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 215 | } |
| 216 | void do_run() override |
| 217 | { |
| 218 | // Acquire memory for the memory groups |
| 219 | memory_group0->acquire(); |
| 220 | memory_group1->acquire(); |
| 221 | |
| 222 | conv0->run(); |
| 223 | act0.run(); |
| 224 | pool0.run(); |
| 225 | conv1->run(); |
| 226 | act1.run(); |
| 227 | pool1.run(); |
| 228 | fc0->run(); |
| 229 | act2.run(); |
| 230 | softmax->run(); |
| 231 | |
| 232 | // Release memory |
| 233 | memory_group0->release(); |
| 234 | memory_group1->release(); |
| 235 | } |
| 236 | |
| 237 | private: |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 238 | // The src tensor should contain the input image |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 239 | Tensor src{}; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 240 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 241 | // Intermediate tensors used |
| 242 | Tensor weights0{}; |
| 243 | Tensor weights1{}; |
| 244 | Tensor weights2{}; |
| 245 | Tensor biases0{}; |
| 246 | Tensor biases1{}; |
| 247 | Tensor biases2{}; |
| 248 | Tensor out_conv0{}; |
| 249 | Tensor out_conv1{}; |
| 250 | Tensor out_act0{}; |
| 251 | Tensor out_act1{}; |
| 252 | Tensor out_act2{}; |
| 253 | Tensor out_pool0{}; |
| 254 | Tensor out_pool1{}; |
| 255 | Tensor out_fc0{}; |
| 256 | Tensor out_softmax{}; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 257 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 258 | // NEON allocator |
| 259 | Allocator allocator{}; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 260 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 261 | // Memory groups |
| 262 | std::unique_ptr<MemoryGroup> memory_group0{}; |
| 263 | std::unique_ptr<MemoryGroup> memory_group1{}; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 264 | |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 265 | // Layers |
| 266 | std::unique_ptr<NEConvolutionLayer> conv0{}; |
| 267 | std::unique_ptr<NEConvolutionLayer> conv1{}; |
| 268 | std::unique_ptr<NEFullyConnectedLayer> fc0{}; |
| 269 | std::unique_ptr<NESoftmaxLayer> softmax{}; |
| 270 | NEPoolingLayer pool0{}; |
| 271 | NEPoolingLayer pool1{}; |
| 272 | NEActivationLayer act0{}; |
| 273 | NEActivationLayer act1{}; |
| 274 | NEActivationLayer act2{}; |
| 275 | }; |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 276 | |
| 277 | /** Main program for cnn test |
| 278 | * |
| 279 | * The example implements the following CNN architecture: |
| 280 | * |
| 281 | * Input -> conv0:5x5 -> act0:relu -> pool:2x2 -> conv1:3x3 -> act1:relu -> pool:2x2 -> fc0 -> act2:relu -> softmax |
| 282 | * |
| 283 | * @param[in] argc Number of arguments |
| 284 | * @param[in] argv Arguments |
| 285 | */ |
Anthony Barbier | 6db0ff5 | 2018-01-05 10:59:12 +0000 | [diff] [blame] | 286 | int main(int argc, char **argv) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 287 | { |
Michalis Spyrou | 2b5f0f2 | 2018-01-10 14:08:50 +0000 | [diff] [blame] | 288 | return utils::run_example<NEONCNNExample>(argc, argv); |
Anthony Barbier | 6db0ff5 | 2018-01-05 10:59:12 +0000 | [diff] [blame] | 289 | } |