Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2016, 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 "arm_compute/runtime/NEON/NEFunctions.h" |
| 25 | |
| 26 | #include "arm_compute/core/Types.h" |
| 27 | #include "utils/Utils.h" |
| 28 | |
| 29 | using namespace arm_compute; |
| 30 | using namespace utils; |
| 31 | |
| 32 | void main_cnn(int argc, const char **argv) |
| 33 | { |
| 34 | ARM_COMPUTE_UNUSED(argc); |
| 35 | ARM_COMPUTE_UNUSED(argv); |
| 36 | |
| 37 | // The src tensor should contain the input image |
| 38 | Tensor src; |
| 39 | |
| 40 | // The weights and biases tensors should be initialized with the values inferred with the training |
| 41 | Tensor weights0; |
| 42 | Tensor weights1; |
| 43 | Tensor weights2; |
| 44 | Tensor biases0; |
| 45 | Tensor biases1; |
| 46 | Tensor biases2; |
| 47 | |
| 48 | Tensor out_conv0; |
| 49 | Tensor out_conv1; |
| 50 | Tensor out_act0; |
| 51 | Tensor out_act1; |
| 52 | Tensor out_act2; |
| 53 | Tensor out_pool0; |
| 54 | Tensor out_pool1; |
| 55 | Tensor out_fc0; |
| 56 | Tensor out_softmax; |
| 57 | |
| 58 | NEConvolutionLayer conv0; |
| 59 | NEConvolutionLayer conv1; |
| 60 | NEPoolingLayer pool0; |
| 61 | NEPoolingLayer pool1; |
| 62 | NEFullyConnectedLayer fc0; |
| 63 | NEActivationLayer act0; |
| 64 | NEActivationLayer act1; |
| 65 | NEActivationLayer act2; |
| 66 | NESoftmaxLayer softmax; |
| 67 | |
| 68 | /* [Initialize tensors] */ |
| 69 | |
| 70 | // Initialize src tensor |
| 71 | constexpr unsigned int width_src_image = 32; |
| 72 | constexpr unsigned int height_src_image = 32; |
| 73 | constexpr unsigned int ifm_src_img = 1; |
| 74 | |
| 75 | const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img); |
| 76 | src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32)); |
| 77 | |
| 78 | // Initialize tensors of conv0 |
| 79 | constexpr unsigned int kernel_x_conv0 = 5; |
| 80 | constexpr unsigned int kernel_y_conv0 = 5; |
| 81 | constexpr unsigned int ofm_conv0 = 8; |
| 82 | |
| 83 | const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0); |
| 84 | const TensorShape biases_shape_conv0(weights_shape_conv0[3]); |
| 85 | const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]); |
| 86 | |
| 87 | weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32)); |
| 88 | biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32)); |
| 89 | out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32)); |
| 90 | |
| 91 | // Initialize tensor of act0 |
| 92 | out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32)); |
| 93 | |
| 94 | // Initialize tensor of pool0 |
| 95 | TensorShape out_shape_pool0 = out_shape_conv0; |
| 96 | out_shape_pool0.set(0, out_shape_pool0.x() / 2); |
| 97 | out_shape_pool0.set(1, out_shape_pool0.y() / 2); |
| 98 | out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32)); |
| 99 | |
| 100 | // Initialize tensors of conv1 |
| 101 | constexpr unsigned int kernel_x_conv1 = 3; |
| 102 | constexpr unsigned int kernel_y_conv1 = 3; |
| 103 | constexpr unsigned int ofm_conv1 = 16; |
| 104 | |
| 105 | const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1); |
| 106 | |
| 107 | const TensorShape biases_shape_conv1(weights_shape_conv1[3]); |
| 108 | const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]); |
| 109 | |
| 110 | weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32)); |
| 111 | biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32)); |
| 112 | out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32)); |
| 113 | |
| 114 | // Initialize tensor of act1 |
| 115 | out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32)); |
| 116 | |
| 117 | // Initialize tensor of pool1 |
| 118 | TensorShape out_shape_pool1 = out_shape_conv1; |
| 119 | out_shape_pool1.set(0, out_shape_pool1.x() / 2); |
| 120 | out_shape_pool1.set(1, out_shape_pool1.y() / 2); |
| 121 | out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32)); |
| 122 | |
| 123 | // Initialize tensor of fc0 |
| 124 | constexpr unsigned int num_labels = 128; |
| 125 | |
| 126 | const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels); |
| 127 | const TensorShape biases_shape_fc0(num_labels); |
| 128 | const TensorShape out_shape_fc0(num_labels); |
| 129 | |
| 130 | weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32)); |
| 131 | biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32)); |
| 132 | out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32)); |
| 133 | |
| 134 | // Initialize tensor of act2 |
| 135 | out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32)); |
| 136 | |
| 137 | // Initialize tensor of softmax |
| 138 | const TensorShape out_shape_softmax(out_shape_fc0.x()); |
| 139 | out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32)); |
| 140 | |
| 141 | /* -----------------------End: [Initialize tensors] */ |
| 142 | |
| 143 | /* [Configure functions] */ |
| 144 | |
| 145 | // in:32x32x1: 5x5 convolution, 8 output features maps (OFM) |
Georgios Pinitas | d8539b2 | 2017-07-03 16:35:17 +0100 | [diff] [blame] | 146 | conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 2 /* pad_x */, 2 /* pad_y */)); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 147 | |
| 148 | // in:32x32x8, out:32x32x8, Activation function: relu |
| 149 | act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 150 | |
| 151 | // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max |
Georgios Pinitas | d8539b2 | 2017-07-03 16:35:17 +0100 | [diff] [blame] | 152 | pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 153 | |
| 154 | // in:16x16x8: 3x3 convolution, 16 output features maps (OFM) |
Georgios Pinitas | d8539b2 | 2017-07-03 16:35:17 +0100 | [diff] [blame] | 155 | conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo(1 /* stride_x */, 1 /* stride_y */, 1 /* pad_x */, 1 /* pad_y */)); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 156 | |
| 157 | // in:16x16x16, out:16x16x16, Activation function: relu |
| 158 | act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 159 | |
| 160 | // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average |
Georgios Pinitas | d8539b2 | 2017-07-03 16:35:17 +0100 | [diff] [blame] | 161 | pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2, PadStrideInfo(2 /* stride_x */, 2 /* stride_y */))); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 162 | |
| 163 | // in:8x8x16, out:128 |
| 164 | fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0); |
| 165 | |
| 166 | // in:128, out:128, Activation function: relu |
| 167 | act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); |
| 168 | |
| 169 | // in:128, out:128 |
| 170 | softmax.configure(&out_act2, &out_softmax); |
| 171 | |
| 172 | /* -----------------------End: [Configure functions] */ |
| 173 | |
| 174 | /* [Allocate tensors] */ |
| 175 | |
| 176 | // Now that the padding requirements are known we can allocate the images: |
| 177 | src.allocator()->allocate(); |
| 178 | weights0.allocator()->allocate(); |
| 179 | weights1.allocator()->allocate(); |
| 180 | weights2.allocator()->allocate(); |
| 181 | biases0.allocator()->allocate(); |
| 182 | biases1.allocator()->allocate(); |
| 183 | biases2.allocator()->allocate(); |
| 184 | out_conv0.allocator()->allocate(); |
| 185 | out_conv1.allocator()->allocate(); |
| 186 | out_act0.allocator()->allocate(); |
| 187 | out_act1.allocator()->allocate(); |
| 188 | out_act2.allocator()->allocate(); |
| 189 | out_pool0.allocator()->allocate(); |
| 190 | out_pool1.allocator()->allocate(); |
| 191 | out_fc0.allocator()->allocate(); |
| 192 | out_softmax.allocator()->allocate(); |
| 193 | |
| 194 | /* -----------------------End: [Allocate tensors] */ |
| 195 | |
| 196 | /* [Initialize weights and biases tensors] */ |
| 197 | |
| 198 | // Once the tensors have been allocated, the src, weights and biases tensors can be initialized |
| 199 | // ... |
| 200 | |
| 201 | /* -----------------------[Initialize weights and biases tensors] */ |
| 202 | |
| 203 | /* [Execute the functions] */ |
| 204 | |
| 205 | conv0.run(); |
| 206 | act0.run(); |
| 207 | pool0.run(); |
| 208 | conv1.run(); |
| 209 | act1.run(); |
| 210 | pool1.run(); |
| 211 | fc0.run(); |
| 212 | act2.run(); |
| 213 | softmax.run(); |
| 214 | |
| 215 | /* -----------------------End: [Execute the functions] */ |
| 216 | } |
| 217 | |
| 218 | /** Main program for cnn test |
| 219 | * |
| 220 | * The example implements the following CNN architecture: |
| 221 | * |
| 222 | * Input -> conv0:5x5 -> act0:relu -> pool:2x2 -> conv1:3x3 -> act1:relu -> pool:2x2 -> fc0 -> act2:relu -> softmax |
| 223 | * |
| 224 | * @param[in] argc Number of arguments |
| 225 | * @param[in] argv Arguments |
| 226 | */ |
| 227 | int main(int argc, const char **argv) |
| 228 | { |
| 229 | return utils::run_example(argc, argv, main_cnn); |
| 230 | } |