Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 1 | /* |
SiCong Li | 4841c97 | 2021-02-03 12:17:35 +0000 | [diff] [blame] | 2 | * Copyright (c) 2018-2021 Arm Limited. |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [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/graph.h" |
| 25 | #include "support/ToolchainSupport.h" |
| 26 | #include "utils/CommonGraphOptions.h" |
| 27 | #include "utils/GraphUtils.h" |
| 28 | #include "utils/Utils.h" |
| 29 | |
| 30 | using namespace arm_compute; |
| 31 | using namespace arm_compute::utils; |
| 32 | using namespace arm_compute::graph::frontend; |
| 33 | using namespace arm_compute::graph_utils; |
| 34 | |
| 35 | /** Example demonstrating how to implement MobileNetSSD's network using the Compute Library's graph API */ |
| 36 | class GraphSSDMobilenetExample : public Example |
| 37 | { |
| 38 | public: |
| 39 | GraphSSDMobilenetExample() |
| 40 | : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetSSD") |
| 41 | { |
Isabella Gottardi | 7234ed8 | 2018-11-27 08:51:10 +0000 | [diff] [blame] | 42 | // Add topk option |
| 43 | keep_topk_opt = cmd_parser.add_option<SimpleOption<int>>("topk", 100); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 44 | keep_topk_opt->set_help("Top k detections results per image. Used for data type F32."); |
| 45 | // Add output option |
| 46 | detection_boxes_opt = cmd_parser.add_option<SimpleOption<std::string>>("detection_boxes_opt", ""); |
| 47 | detection_boxes_opt->set_help("Filename containing the reference values for the graph output detection_boxes. Used for data type QASYMM8."); |
| 48 | detection_classes_opt = cmd_parser.add_option<SimpleOption<std::string>>("detection_classes_opt", ""); |
| 49 | detection_classes_opt->set_help("Filename containing the reference values for the output detection_classes. Used for data type QASYMM8."); |
| 50 | detection_scores_opt = cmd_parser.add_option<SimpleOption<std::string>>("detection_scores_opt", ""); |
| 51 | detection_scores_opt->set_help("Filename containing the reference values for the output detection_scores. Used for data type QASYMM8."); |
| 52 | num_detections_opt = cmd_parser.add_option<SimpleOption<std::string>>("num_detections_opt", ""); |
| 53 | num_detections_opt->set_help("Filename containing the reference values for the output num_detections. Used with datatype QASYMM8."); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 54 | } |
| 55 | GraphSSDMobilenetExample(const GraphSSDMobilenetExample &) = delete; |
| 56 | GraphSSDMobilenetExample &operator=(const GraphSSDMobilenetExample &) = delete; |
Matthew Bentham | f5f2391 | 2020-03-05 22:32:16 +0000 | [diff] [blame] | 57 | ~GraphSSDMobilenetExample() override = default; |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 58 | bool do_setup(int argc, char **argv) override |
| 59 | { |
| 60 | // Parse arguments |
| 61 | cmd_parser.parse(argc, argv); |
Georgios Pinitas | cd60a5f | 2019-08-21 17:06:54 +0100 | [diff] [blame] | 62 | cmd_parser.validate(); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 63 | |
| 64 | // Consume common parameters |
| 65 | common_params = consume_common_graph_parameters(common_opts); |
| 66 | |
| 67 | // Return when help menu is requested |
| 68 | if(common_params.help) |
| 69 | { |
| 70 | cmd_parser.print_help(argv[0]); |
| 71 | return false; |
| 72 | } |
| 73 | |
| 74 | // Print parameter values |
| 75 | std::cout << common_params << std::endl; |
| 76 | |
| 77 | // Create input descriptor |
| 78 | const TensorShape tensor_shape = permute_shape(TensorShape(300, 300, 3U, 1U), DataLayout::NCHW, common_params.data_layout); |
| 79 | TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); |
| 80 | |
| 81 | // Set graph hints |
| 82 | graph << common_params.target |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 83 | << common_params.fast_math_hint; |
| 84 | |
| 85 | // Create core graph |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 86 | if(arm_compute::is_data_type_float(common_params.data_type)) |
| 87 | { |
| 88 | create_graph_float(input_descriptor); |
| 89 | } |
| 90 | else |
| 91 | { |
| 92 | create_graph_qasymm(input_descriptor); |
| 93 | } |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 94 | |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 95 | // Finalize graph |
| 96 | GraphConfig config; |
| 97 | config.num_threads = common_params.threads; |
| 98 | config.use_tuner = common_params.enable_tuner; |
| 99 | config.tuner_file = common_params.tuner_file; |
SiCong Li | 4841c97 | 2021-02-03 12:17:35 +0000 | [diff] [blame] | 100 | config.mlgo_file = common_params.mlgo_file; |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 101 | |
| 102 | graph.finalize(common_params.target, config); |
| 103 | |
| 104 | return true; |
| 105 | } |
| 106 | void do_run() override |
| 107 | { |
| 108 | // Run graph |
| 109 | graph.run(); |
| 110 | } |
| 111 | |
| 112 | private: |
| 113 | CommandLineParser cmd_parser; |
| 114 | CommonGraphOptions common_opts; |
| 115 | SimpleOption<int> *keep_topk_opt{ nullptr }; |
| 116 | CommonGraphParams common_params; |
| 117 | Stream graph; |
| 118 | |
| 119 | SimpleOption<std::string> *detection_boxes_opt{ nullptr }; |
| 120 | SimpleOption<std::string> *detection_classes_opt{ nullptr }; |
| 121 | SimpleOption<std::string> *detection_scores_opt{ nullptr }; |
| 122 | SimpleOption<std::string> *num_detections_opt{ nullptr }; |
| 123 | |
| 124 | ConcatLayer get_node_A_float(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| 125 | unsigned int conv_filt, |
| 126 | PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info) |
| 127 | { |
| 128 | const std::string total_path = param_path + "_"; |
| 129 | SubStream sg(master_graph); |
| 130 | |
| 131 | sg << DepthwiseConvolutionLayer( |
| 132 | 3U, 3U, |
| 133 | get_weights_accessor(data_path, total_path + "dw_w.npy"), |
| 134 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 135 | dwc_pad_stride_info) |
| 136 | .set_name(param_path + "/dw") |
| 137 | << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "dw_bn_mean.npy"), |
| 138 | get_weights_accessor(data_path, total_path + "dw_bn_var.npy"), |
| 139 | get_weights_accessor(data_path, total_path + "dw_scale_w.npy"), |
| 140 | get_weights_accessor(data_path, total_path + "dw_scale_b.npy"), 0.00001f) |
| 141 | .set_name(param_path + "/dw/bn") |
| 142 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "dw/relu") |
| 143 | |
| 144 | << ConvolutionLayer( |
| 145 | 1U, 1U, conv_filt, |
| 146 | get_weights_accessor(data_path, total_path + "w.npy"), |
| 147 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 148 | conv_pad_stride_info) |
| 149 | .set_name(param_path + "/pw") |
| 150 | << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "bn_mean.npy"), |
| 151 | get_weights_accessor(data_path, total_path + "bn_var.npy"), |
| 152 | get_weights_accessor(data_path, total_path + "scale_w.npy"), |
| 153 | get_weights_accessor(data_path, total_path + "scale_b.npy"), 0.00001f) |
| 154 | .set_name(param_path + "/pw/bn") |
| 155 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "pw/relu"); |
| 156 | |
| 157 | return ConcatLayer(std::move(sg)); |
| 158 | } |
| 159 | |
| 160 | ConcatLayer get_node_B_float(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| 161 | unsigned int conv_filt, |
| 162 | PadStrideInfo conv_pad_stride_info_1, PadStrideInfo conv_pad_stride_info_2) |
| 163 | { |
| 164 | const std::string total_path = param_path + "_"; |
| 165 | SubStream sg(master_graph); |
| 166 | |
| 167 | sg << ConvolutionLayer( |
| 168 | 1, 1, conv_filt / 2, |
| 169 | get_weights_accessor(data_path, total_path + "1_w.npy"), |
| 170 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 171 | conv_pad_stride_info_1) |
| 172 | .set_name(total_path + "1/conv") |
| 173 | << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "1_bn_mean.npy"), |
| 174 | get_weights_accessor(data_path, total_path + "1_bn_var.npy"), |
| 175 | get_weights_accessor(data_path, total_path + "1_scale_w.npy"), |
| 176 | get_weights_accessor(data_path, total_path + "1_scale_b.npy"), 0.00001f) |
| 177 | .set_name(total_path + "1/bn") |
| 178 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "1/relu"); |
| 179 | |
| 180 | sg << ConvolutionLayer( |
| 181 | 3, 3, conv_filt, |
| 182 | get_weights_accessor(data_path, total_path + "2_w.npy"), |
| 183 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 184 | conv_pad_stride_info_2) |
| 185 | .set_name(total_path + "2/conv") |
| 186 | << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "2_bn_mean.npy"), |
| 187 | get_weights_accessor(data_path, total_path + "2_bn_var.npy"), |
| 188 | get_weights_accessor(data_path, total_path + "2_scale_w.npy"), |
| 189 | get_weights_accessor(data_path, total_path + "2_scale_b.npy"), 0.00001f) |
| 190 | .set_name(total_path + "2/bn") |
| 191 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(total_path + "2/relu"); |
| 192 | |
| 193 | return ConcatLayer(std::move(sg)); |
| 194 | } |
| 195 | |
| 196 | ConcatLayer get_node_C_float(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| 197 | unsigned int conv_filt, PadStrideInfo conv_pad_stride_info) |
| 198 | { |
| 199 | const std::string total_path = param_path + "_"; |
| 200 | SubStream sg(master_graph); |
| 201 | sg << ConvolutionLayer( |
| 202 | 1U, 1U, conv_filt, |
| 203 | get_weights_accessor(data_path, total_path + "w.npy"), |
| 204 | get_weights_accessor(data_path, total_path + "b.npy"), |
| 205 | conv_pad_stride_info) |
| 206 | .set_name(param_path + "/conv"); |
| 207 | if(common_params.data_layout == DataLayout::NCHW) |
| 208 | { |
| 209 | sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC).set_name(param_path + "/perm"); |
| 210 | } |
| 211 | sg << FlattenLayer().set_name(param_path + "/flat"); |
| 212 | |
| 213 | return ConcatLayer(std::move(sg)); |
| 214 | } |
| 215 | |
| 216 | void create_graph_float(TensorDescriptor &input_descriptor) |
| 217 | { |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 218 | // Create a preprocessor object |
| 219 | const std::array<float, 3> mean_rgb{ { 127.5f, 127.5f, 127.5f } }; |
Georgios Pinitas | 40f51a6 | 2020-11-21 03:04:18 +0000 | [diff] [blame] | 220 | std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<CaffePreproccessor>(mean_rgb, true, 0.007843f); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 221 | |
| 222 | // Get trainable parameters data path |
| 223 | std::string data_path = common_params.data_path; |
| 224 | |
| 225 | // Add model path to data path |
| 226 | if(!data_path.empty()) |
| 227 | { |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 228 | data_path += "/cnn_data/ssd_mobilenet_model/"; |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 229 | } |
| 230 | |
| 231 | graph << InputLayer(input_descriptor, |
| 232 | get_input_accessor(common_params, std::move(preprocessor))); |
| 233 | |
| 234 | SubStream conv_11(graph); |
| 235 | conv_11 << ConvolutionLayer( |
| 236 | 3U, 3U, 32U, |
| 237 | get_weights_accessor(data_path, "conv0_w.npy"), |
| 238 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 239 | PadStrideInfo(2, 2, 1, 1)) |
| 240 | .set_name("conv0"); |
| 241 | conv_11 << BatchNormalizationLayer(get_weights_accessor(data_path, "conv0_bn_mean.npy"), |
| 242 | get_weights_accessor(data_path, "conv0_bn_var.npy"), |
| 243 | get_weights_accessor(data_path, "conv0_scale_w.npy"), |
| 244 | get_weights_accessor(data_path, "conv0_scale_b.npy"), 0.00001f) |
| 245 | .set_name("conv0/bn") |
| 246 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/relu"); |
| 247 | |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 248 | conv_11 << get_node_A_float(conv_11, data_path, "conv1", 64, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 249 | conv_11 << get_node_A_float(conv_11, data_path, "conv2", 128, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 250 | conv_11 << get_node_A_float(conv_11, data_path, "conv3", 128, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 251 | conv_11 << get_node_A_float(conv_11, data_path, "conv4", 256, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 252 | conv_11 << get_node_A_float(conv_11, data_path, "conv5", 256, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 253 | conv_11 << get_node_A_float(conv_11, data_path, "conv6", 512, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 254 | conv_11 << get_node_A_float(conv_11, data_path, "conv7", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 255 | conv_11 << get_node_A_float(conv_11, data_path, "conv8", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 256 | conv_11 << get_node_A_float(conv_11, data_path, "conv9", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 257 | conv_11 << get_node_A_float(conv_11, data_path, "conv10", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 258 | conv_11 << get_node_A_float(conv_11, data_path, "conv11", 512, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 259 | |
| 260 | SubStream conv_13(conv_11); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 261 | conv_13 << get_node_A_float(conv_11, data_path, "conv12", 1024, PadStrideInfo(2, 2, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
| 262 | conv_13 << get_node_A_float(conv_13, data_path, "conv13", 1024, PadStrideInfo(1, 1, 1, 1), PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 263 | |
| 264 | SubStream conv_14(conv_13); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 265 | conv_14 << get_node_B_float(conv_13, data_path, "conv14", 512, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 266 | |
| 267 | SubStream conv_15(conv_14); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 268 | conv_15 << get_node_B_float(conv_14, data_path, "conv15", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 269 | |
| 270 | SubStream conv_16(conv_15); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 271 | conv_16 << get_node_B_float(conv_15, data_path, "conv16", 256, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 272 | |
| 273 | SubStream conv_17(conv_16); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 274 | conv_17 << get_node_B_float(conv_16, data_path, "conv17", 128, PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 2, 1, 1)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 275 | |
| 276 | //mbox_loc |
| 277 | SubStream conv_11_mbox_loc(conv_11); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 278 | conv_11_mbox_loc << get_node_C_float(conv_11, data_path, "conv11_mbox_loc", 12, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 279 | |
| 280 | SubStream conv_13_mbox_loc(conv_13); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 281 | conv_13_mbox_loc << get_node_C_float(conv_13, data_path, "conv13_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 282 | |
| 283 | SubStream conv_14_2_mbox_loc(conv_14); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 284 | conv_14_2_mbox_loc << get_node_C_float(conv_14, data_path, "conv14_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 285 | |
| 286 | SubStream conv_15_2_mbox_loc(conv_15); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 287 | conv_15_2_mbox_loc << get_node_C_float(conv_15, data_path, "conv15_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 288 | |
| 289 | SubStream conv_16_2_mbox_loc(conv_16); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 290 | conv_16_2_mbox_loc << get_node_C_float(conv_16, data_path, "conv16_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 291 | |
| 292 | SubStream conv_17_2_mbox_loc(conv_17); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 293 | conv_17_2_mbox_loc << get_node_C_float(conv_17, data_path, "conv17_2_mbox_loc", 24, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 294 | |
| 295 | SubStream mbox_loc(graph); |
| 296 | mbox_loc << ConcatLayer(std::move(conv_11_mbox_loc), std::move(conv_13_mbox_loc), conv_14_2_mbox_loc, std::move(conv_15_2_mbox_loc), |
| 297 | std::move(conv_16_2_mbox_loc), std::move(conv_17_2_mbox_loc)); |
| 298 | |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 299 | //mbox_conf |
| 300 | SubStream conv_11_mbox_conf(conv_11); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 301 | conv_11_mbox_conf << get_node_C_float(conv_11, data_path, "conv11_mbox_conf", 63, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 302 | |
| 303 | SubStream conv_13_mbox_conf(conv_13); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 304 | conv_13_mbox_conf << get_node_C_float(conv_13, data_path, "conv13_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 305 | |
| 306 | SubStream conv_14_2_mbox_conf(conv_14); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 307 | conv_14_2_mbox_conf << get_node_C_float(conv_14, data_path, "conv14_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 308 | |
| 309 | SubStream conv_15_2_mbox_conf(conv_15); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 310 | conv_15_2_mbox_conf << get_node_C_float(conv_15, data_path, "conv15_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 311 | |
| 312 | SubStream conv_16_2_mbox_conf(conv_16); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 313 | conv_16_2_mbox_conf << get_node_C_float(conv_16, data_path, "conv16_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 314 | |
| 315 | SubStream conv_17_2_mbox_conf(conv_17); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 316 | conv_17_2_mbox_conf << get_node_C_float(conv_17, data_path, "conv17_2_mbox_conf", 126, PadStrideInfo(1, 1, 0, 0)); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 317 | |
| 318 | SubStream mbox_conf(graph); |
| 319 | mbox_conf << ConcatLayer(std::move(conv_11_mbox_conf), std::move(conv_13_mbox_conf), std::move(conv_14_2_mbox_conf), |
| 320 | std::move(conv_15_2_mbox_conf), std::move(conv_16_2_mbox_conf), std::move(conv_17_2_mbox_conf)); |
| 321 | mbox_conf << ReshapeLayer(TensorShape(21U, 1917U)).set_name("mbox_conf/reshape"); |
| 322 | mbox_conf << SoftmaxLayer().set_name("mbox_conf/softmax"); |
| 323 | mbox_conf << FlattenLayer().set_name("mbox_conf/flat"); |
| 324 | |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 325 | const std::vector<float> priorbox_variances = { 0.1f, 0.1f, 0.2f, 0.2f }; |
| 326 | const float priorbox_offset = 0.5f; |
| 327 | const std::vector<float> priorbox_aspect_ratios = { 2.f, 3.f }; |
| 328 | |
| 329 | //mbox_priorbox branch |
| 330 | SubStream conv_11_mbox_priorbox(conv_11); |
| 331 | |
| 332 | conv_11_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| 333 | PriorBoxLayerInfo({ 60.f }, priorbox_variances, priorbox_offset, true, false, {}, { 2.f })) |
| 334 | .set_name("conv11/priorbox"); |
| 335 | |
| 336 | SubStream conv_13_mbox_priorbox(conv_13); |
| 337 | conv_13_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| 338 | PriorBoxLayerInfo({ 105.f }, priorbox_variances, priorbox_offset, true, false, { 150.f }, priorbox_aspect_ratios)) |
| 339 | .set_name("conv13/priorbox"); |
| 340 | |
| 341 | SubStream conv_14_2_mbox_priorbox(conv_14); |
| 342 | conv_14_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| 343 | PriorBoxLayerInfo({ 150.f }, priorbox_variances, priorbox_offset, true, false, { 195.f }, priorbox_aspect_ratios)) |
| 344 | .set_name("conv14/priorbox"); |
| 345 | |
| 346 | SubStream conv_15_2_mbox_priorbox(conv_15); |
| 347 | conv_15_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| 348 | PriorBoxLayerInfo({ 195.f }, priorbox_variances, priorbox_offset, true, false, { 240.f }, priorbox_aspect_ratios)) |
| 349 | .set_name("conv15/priorbox"); |
| 350 | |
| 351 | SubStream conv_16_2_mbox_priorbox(conv_16); |
| 352 | conv_16_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| 353 | PriorBoxLayerInfo({ 240.f }, priorbox_variances, priorbox_offset, true, false, { 285.f }, priorbox_aspect_ratios)) |
| 354 | .set_name("conv16/priorbox"); |
| 355 | |
| 356 | SubStream conv_17_2_mbox_priorbox(conv_17); |
| 357 | conv_17_2_mbox_priorbox << PriorBoxLayer(SubStream(graph), |
| 358 | PriorBoxLayerInfo({ 285.f }, priorbox_variances, priorbox_offset, true, false, { 300.f }, priorbox_aspect_ratios)) |
| 359 | .set_name("conv17/priorbox"); |
| 360 | |
| 361 | SubStream mbox_priorbox(graph); |
| 362 | |
| 363 | mbox_priorbox << ConcatLayer( |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 364 | (common_params.data_layout == DataLayout::NCHW) ? arm_compute::graph::descriptors::ConcatLayerDescriptor(DataLayoutDimension::WIDTH) : arm_compute::graph::descriptors::ConcatLayerDescriptor( |
| 365 | DataLayoutDimension::CHANNEL), |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 366 | std::move(conv_11_mbox_priorbox), std::move(conv_13_mbox_priorbox), std::move(conv_14_2_mbox_priorbox), |
| 367 | std::move(conv_15_2_mbox_priorbox), std::move(conv_16_2_mbox_priorbox), std::move(conv_17_2_mbox_priorbox)); |
| 368 | |
Isabella Gottardi | 7234ed8 | 2018-11-27 08:51:10 +0000 | [diff] [blame] | 369 | const int num_classes = 21; |
| 370 | const bool share_location = true; |
| 371 | const DetectionOutputLayerCodeType detection_type = DetectionOutputLayerCodeType::CENTER_SIZE; |
| 372 | const int keep_top_k = keep_topk_opt->value(); |
| 373 | const float nms_threshold = 0.45f; |
| 374 | const int label_id_background = 0; |
| 375 | const float conf_thrs = 0.25f; |
| 376 | const int top_k = 100; |
| 377 | |
| 378 | SubStream detection_ouput(mbox_loc); |
| 379 | detection_ouput << DetectionOutputLayer(std::move(mbox_conf), std::move(mbox_priorbox), |
| 380 | DetectionOutputLayerInfo(num_classes, share_location, detection_type, keep_top_k, nms_threshold, top_k, label_id_background, conf_thrs)); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 381 | detection_ouput << OutputLayer(get_detection_output_accessor(common_params, { input_descriptor.shape })); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 382 | } |
| 383 | |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 384 | ConcatLayer get_node_A_qasymm(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| 385 | unsigned int conv_filt, |
| 386 | PadStrideInfo dwc_pad_stride_info, PadStrideInfo conv_pad_stride_info, |
| 387 | std::pair<QuantizationInfo, QuantizationInfo> depth_quant_info, std::pair<QuantizationInfo, QuantizationInfo> point_quant_info) |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 388 | { |
| 389 | const std::string total_path = param_path + "_"; |
| 390 | SubStream sg(master_graph); |
| 391 | |
| 392 | sg << DepthwiseConvolutionLayer( |
| 393 | 3U, 3U, |
| 394 | get_weights_accessor(data_path, total_path + "dw_w.npy"), |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 395 | get_weights_accessor(data_path, total_path + "dw_b.npy"), |
| 396 | dwc_pad_stride_info, 1, depth_quant_info.first, depth_quant_info.second) |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 397 | .set_name(param_path + "/dw") |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 398 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(param_path + "/dw/relu6"); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 399 | |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 400 | sg << ConvolutionLayer( |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 401 | 1U, 1U, conv_filt, |
| 402 | get_weights_accessor(data_path, total_path + "w.npy"), |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 403 | get_weights_accessor(data_path, total_path + "b.npy"), |
| 404 | conv_pad_stride_info, 1, point_quant_info.first, point_quant_info.second) |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 405 | .set_name(param_path + "/pw") |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 406 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(param_path + "/pw/relu6"); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 407 | |
| 408 | return ConcatLayer(std::move(sg)); |
| 409 | } |
| 410 | |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 411 | ConcatLayer get_node_B_qasymm(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| 412 | unsigned int conv_filt, |
| 413 | PadStrideInfo conv_pad_stride_info_1x1, PadStrideInfo conv_pad_stride_info_3x3, |
| 414 | const std::pair<QuantizationInfo, QuantizationInfo> quant_info_1x1, const std::pair<QuantizationInfo, QuantizationInfo> quant_info_3x3) |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 415 | { |
| 416 | const std::string total_path = param_path + "_"; |
| 417 | SubStream sg(master_graph); |
| 418 | |
| 419 | sg << ConvolutionLayer( |
| 420 | 1, 1, conv_filt / 2, |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 421 | get_weights_accessor(data_path, total_path + "1x1_w.npy"), |
| 422 | get_weights_accessor(data_path, total_path + "1x1_b.npy"), |
| 423 | conv_pad_stride_info_1x1, 1, quant_info_1x1.first, quant_info_1x1.second) |
| 424 | .set_name(total_path + "1x1/conv") |
| 425 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "1x1/conv/relu6"); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 426 | |
| 427 | sg << ConvolutionLayer( |
| 428 | 3, 3, conv_filt, |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 429 | get_weights_accessor(data_path, total_path + "3x3_w.npy"), |
| 430 | get_weights_accessor(data_path, total_path + "3x3_b.npy"), |
| 431 | conv_pad_stride_info_3x3, 1, quant_info_3x3.first, quant_info_3x3.second) |
| 432 | .set_name(total_path + "3x3/conv") |
| 433 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name(total_path + "3x3/conv/relu6"); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 434 | |
| 435 | return ConcatLayer(std::move(sg)); |
| 436 | } |
| 437 | |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 438 | ConcatLayer get_node_C_qasymm(IStream &master_graph, const std::string &data_path, std::string &¶m_path, |
| 439 | unsigned int conv_filt, PadStrideInfo conv_pad_stride_info, |
| 440 | const std::pair<QuantizationInfo, QuantizationInfo> quant_info, TensorShape reshape_shape) |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 441 | { |
| 442 | const std::string total_path = param_path + "_"; |
| 443 | SubStream sg(master_graph); |
| 444 | sg << ConvolutionLayer( |
| 445 | 1U, 1U, conv_filt, |
| 446 | get_weights_accessor(data_path, total_path + "w.npy"), |
| 447 | get_weights_accessor(data_path, total_path + "b.npy"), |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 448 | conv_pad_stride_info, 1, quant_info.first, quant_info.second) |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 449 | .set_name(param_path + "/conv"); |
| 450 | if(common_params.data_layout == DataLayout::NCHW) |
| 451 | { |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 452 | sg << PermuteLayer(PermutationVector(2U, 0U, 1U), DataLayout::NHWC); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 453 | } |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 454 | sg << ReshapeLayer(reshape_shape).set_name(param_path + "/reshape"); |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 455 | |
| 456 | return ConcatLayer(std::move(sg)); |
| 457 | } |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 458 | |
| 459 | void create_graph_qasymm(TensorDescriptor &input_descriptor) |
| 460 | { |
| 461 | // Get trainable parameters data path |
| 462 | std::string data_path = common_params.data_path; |
| 463 | |
| 464 | // Add model path to data path |
| 465 | if(!data_path.empty()) |
| 466 | { |
| 467 | data_path += "/cnn_data/ssd_mobilenet_qasymm8_model/"; |
| 468 | } |
| 469 | |
| 470 | // Quantization info are saved as pair for each (pointwise/depthwise) convolution layer: <weight_quant_info, output_quant_info> |
| 471 | const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> conv_quant_info = |
| 472 | { |
| 473 | { QuantizationInfo(0.03624850884079933f, 163), QuantizationInfo(0.22219789028167725f, 113) }, // conv0 |
| 474 | { QuantizationInfo(0.0028752065263688564f, 113), QuantizationInfo(0.05433657020330429f, 128) }, // conv13_2_1_1 |
| 475 | { QuantizationInfo(0.0014862528769299388f, 125), QuantizationInfo(0.05037643015384674f, 131) }, // conv13_2_3_3 |
| 476 | { QuantizationInfo(0.00233650766313076f, 113), QuantizationInfo(0.04468846693634987f, 126) }, // conv13_3_1_1 |
| 477 | { QuantizationInfo(0.002501056529581547f, 120), QuantizationInfo(0.06026708707213402f, 111) }, // conv13_3_3_3 |
| 478 | { QuantizationInfo(0.002896666992455721f, 121), QuantizationInfo(0.037775348871946335f, 117) }, // conv13_4_1_1 |
| 479 | { QuantizationInfo(0.0023875406477600336f, 122), QuantizationInfo(0.03881589323282242f, 108) }, // conv13_4_3_3 |
| 480 | { QuantizationInfo(0.0022081052884459496f, 77), QuantizationInfo(0.025450613349676132f, 125) }, // conv13_5_1_1 |
| 481 | { QuantizationInfo(0.00604657270014286f, 121), QuantizationInfo(0.033533502370119095f, 109) } // conv13_5_3_3 |
| 482 | }; |
| 483 | |
| 484 | const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> depth_quant_info = |
| 485 | { |
| 486 | { QuantizationInfo(0.03408717364072f, 131), QuantizationInfo(0.29286590218544006f, 108) }, // dwsc1 |
| 487 | { QuantizationInfo(0.027518004179000854f, 107), QuantizationInfo(0.20796941220760345, 117) }, // dwsc2 |
| 488 | { QuantizationInfo(0.052489638328552246f, 85), QuantizationInfo(0.4303881824016571f, 142) }, // dwsc3 |
| 489 | { QuantizationInfo(0.016570359468460083f, 79), QuantizationInfo(0.10512150079011917f, 116) }, // dwsc4 |
| 490 | { QuantizationInfo(0.060739465057849884f, 65), QuantizationInfo(0.15331414341926575f, 94) }, // dwsc5 |
| 491 | { QuantizationInfo(0.01324534136801958f, 124), QuantizationInfo(0.13010895252227783f, 153) }, // dwsc6 |
| 492 | { QuantizationInfo(0.032326459884643555f, 124), QuantizationInfo(0.11565316468477249, 156) }, // dwsc7 |
| 493 | { QuantizationInfo(0.029948478564620018f, 155), QuantizationInfo(0.11413891613483429f, 146) }, // dwsc8 |
| 494 | { QuantizationInfo(0.028054025024175644f, 129), QuantizationInfo(0.1142905130982399f, 140) }, // dwsc9 |
| 495 | { QuantizationInfo(0.025204822421073914f, 129), QuantizationInfo(0.14668069779872894f, 149) }, // dwsc10 |
| 496 | { QuantizationInfo(0.019332280382514f, 110), QuantizationInfo(0.1480235457420349f, 91) }, // dwsc11 |
| 497 | { QuantizationInfo(0.0319712869822979f, 88), QuantizationInfo(0.10424695909023285f, 117) }, // dwsc12 |
| 498 | { QuantizationInfo(0.04378943517804146f, 164), QuantizationInfo(0.23176774382591248f, 138) } // dwsc13 |
| 499 | }; |
| 500 | |
| 501 | const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> point_quant_info = |
| 502 | { |
| 503 | { QuantizationInfo(0.028777318075299263f, 144), QuantizationInfo(0.2663874328136444f, 121) }, // pw1 |
| 504 | { QuantizationInfo(0.015796702355146408f, 127), QuantizationInfo(0.1739964485168457f, 111) }, // pw2 |
| 505 | { QuantizationInfo(0.009349990636110306f, 127), QuantizationInfo(0.1805974692106247f, 104) }, // pw3 |
| 506 | { QuantizationInfo(0.012920888140797615f, 106), QuantizationInfo(0.1205204650759697f, 100) }, // pw4 |
| 507 | { QuantizationInfo(0.008119508624076843f, 145), QuantizationInfo(0.12272439152002335f, 97) }, // pw5 |
| 508 | { QuantizationInfo(0.0070041813887655735f, 115), QuantizationInfo(0.0947074219584465f, 101) }, // pw6 |
| 509 | { QuantizationInfo(0.004827278666198254f, 115), QuantizationInfo(0.0842885747551918f, 110) }, // pw7 |
| 510 | { QuantizationInfo(0.004755120258778334f, 128), QuantizationInfo(0.08283159881830215f, 116) }, // pw8 |
| 511 | { QuantizationInfo(0.007527193054556847f, 142), QuantizationInfo(0.12555131316184998f, 137) }, // pw9 |
| 512 | { QuantizationInfo(0.006050156895071268f, 109), QuantizationInfo(0.10871313512325287f, 124) }, // pw10 |
| 513 | { QuantizationInfo(0.00490700313821435f, 127), QuantizationInfo(0.10364262014627457f, 140) }, // pw11 |
| 514 | { QuantizationInfo(0.006063731852918863, 124), QuantizationInfo(0.11241862177848816f, 125) }, // pw12 |
| 515 | { QuantizationInfo(0.007901716977357864f, 139), QuantizationInfo(0.49889302253723145f, 141) } // pw13 |
| 516 | }; |
| 517 | |
| 518 | // Quantization info taken from the TfLite SSD MobileNet example |
| 519 | const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); |
Isabella Gottardi | c755f78 | 2019-07-22 17:40:27 +0100 | [diff] [blame] | 520 | // Create core graph |
| 521 | graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), |
| 522 | get_weights_accessor(data_path, common_params.image, DataLayout::NHWC)); |
| 523 | graph << ConvolutionLayer( |
| 524 | 3U, 3U, 32U, |
| 525 | get_weights_accessor(data_path, "conv0_w.npy"), |
| 526 | get_weights_accessor(data_path, "conv0_b.npy"), |
| 527 | PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), 1, conv_quant_info.at(0).first, conv_quant_info.at(0).second) |
| 528 | .set_name("conv0"); |
| 529 | graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)).set_name("conv0/relu"); |
| 530 | graph << get_node_A_qasymm(graph, data_path, "conv1", 64U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(0), |
| 531 | point_quant_info.at(0)); |
| 532 | graph << get_node_A_qasymm(graph, data_path, "conv2", 128U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(1), |
| 533 | point_quant_info.at(1)); |
| 534 | graph << get_node_A_qasymm(graph, data_path, "conv3", 128U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(2), |
| 535 | point_quant_info.at(2)); |
| 536 | graph << get_node_A_qasymm(graph, data_path, "conv4", 256U, PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(3), |
| 537 | point_quant_info.at(3)); |
| 538 | graph << get_node_A_qasymm(graph, data_path, "conv5", 256U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(4), |
| 539 | point_quant_info.at(4)); |
| 540 | graph << get_node_A_qasymm(graph, data_path, "conv6", 512U, PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(5), |
| 541 | point_quant_info.at(5)); |
| 542 | graph << get_node_A_qasymm(graph, data_path, "conv7", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(6), |
| 543 | point_quant_info.at(6)); |
| 544 | graph << get_node_A_qasymm(graph, data_path, "conv8", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(7), |
| 545 | point_quant_info.at(7)); |
| 546 | graph << get_node_A_qasymm(graph, data_path, "conv9", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(8), |
| 547 | point_quant_info.at(8)); |
| 548 | graph << get_node_A_qasymm(graph, data_path, "conv10", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(9), |
| 549 | point_quant_info.at(9)); |
| 550 | graph << get_node_A_qasymm(graph, data_path, "conv11", 512U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(10), |
| 551 | point_quant_info.at(10)); |
| 552 | |
| 553 | SubStream conv_13(graph); |
| 554 | conv_13 << get_node_A_qasymm(graph, data_path, "conv12", 1024U, PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(11), |
| 555 | point_quant_info.at(11)); |
| 556 | conv_13 << get_node_A_qasymm(conv_13, data_path, "conv13", 1024U, PadStrideInfo(1U, 1U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), PadStrideInfo(1U, 1U, 0U, 0U), depth_quant_info.at(12), |
| 557 | point_quant_info.at(12)); |
| 558 | SubStream conv_14(conv_13); |
| 559 | conv_14 << get_node_B_qasymm(conv_13, data_path, "conv13_2", 512U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(1), |
| 560 | conv_quant_info.at(2)); |
| 561 | SubStream conv_15(conv_14); |
| 562 | conv_15 << get_node_B_qasymm(conv_14, data_path, "conv13_3", 256U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(3), |
| 563 | conv_quant_info.at(4)); |
| 564 | SubStream conv_16(conv_15); |
| 565 | conv_16 << get_node_B_qasymm(conv_15, data_path, "conv13_4", 256U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 1U, 1U, 1U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(5), |
| 566 | conv_quant_info.at(6)); |
| 567 | SubStream conv_17(conv_16); |
| 568 | conv_17 << get_node_B_qasymm(conv_16, data_path, "conv13_5", 128U, PadStrideInfo(1U, 1U, 0U, 0U), PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::CEIL), conv_quant_info.at(7), |
| 569 | conv_quant_info.at(8)); |
| 570 | |
| 571 | // box_predictor |
| 572 | const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> box_enc_pred_quant_info = |
| 573 | { |
| 574 | { QuantizationInfo(0.005202020984143019f, 136), QuantizationInfo(0.08655580133199692f, 183) }, // boxpredictor0_bep |
| 575 | { QuantizationInfo(0.003121797926723957f, 132), QuantizationInfo(0.03218776360154152f, 140) }, // boxpredictor1_bep |
| 576 | { QuantizationInfo(0.002995674265548587f, 130), QuantizationInfo(0.029072262346744537f, 125) }, // boxpredictor2_bep |
| 577 | { QuantizationInfo(0.0023131705820560455f, 130), QuantizationInfo(0.026488754898309708f, 127) }, // boxpredictor3_bep |
| 578 | { QuantizationInfo(0.0013905081432312727f, 132), QuantizationInfo(0.0199890099465847f, 137) }, // boxpredictor4_bep |
| 579 | { QuantizationInfo(0.00216794665902853f, 121), QuantizationInfo(0.019798893481492996f, 151) } // boxpredictor5_bep |
| 580 | }; |
| 581 | |
| 582 | const std::vector<TensorShape> box_reshape = // NHWC |
| 583 | { |
| 584 | TensorShape(4U, 1U, 1083U), // boxpredictor0_bep_reshape |
| 585 | TensorShape(4U, 1U, 600U), // boxpredictor1_bep_reshape |
| 586 | TensorShape(4U, 1U, 150U), // boxpredictor2_bep_reshape |
| 587 | TensorShape(4U, 1U, 54U), // boxpredictor3_bep_reshape |
| 588 | TensorShape(4U, 1U, 24U), // boxpredictor4_bep_reshape |
| 589 | TensorShape(4U, 1U, 6U) // boxpredictor5_bep_reshape |
| 590 | }; |
| 591 | |
| 592 | SubStream conv_11_box_enc_pre(graph); |
| 593 | conv_11_box_enc_pre << get_node_C_qasymm(graph, data_path, "BoxPredictor_0_BEP", 12U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(0), box_reshape.at(0)); |
| 594 | |
| 595 | SubStream conv_13_box_enc_pre(conv_13); |
| 596 | conv_13_box_enc_pre << get_node_C_qasymm(conv_13, data_path, "BoxPredictor_1_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(1), box_reshape.at(1)); |
| 597 | |
| 598 | SubStream conv_14_2_box_enc_pre(conv_14); |
| 599 | conv_14_2_box_enc_pre << get_node_C_qasymm(conv_14, data_path, "BoxPredictor_2_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(2), box_reshape.at(2)); |
| 600 | |
| 601 | SubStream conv_15_2_box_enc_pre(conv_15); |
| 602 | conv_15_2_box_enc_pre << get_node_C_qasymm(conv_15, data_path, "BoxPredictor_3_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(3), box_reshape.at(3)); |
| 603 | |
| 604 | SubStream conv_16_2_box_enc_pre(conv_16); |
| 605 | conv_16_2_box_enc_pre << get_node_C_qasymm(conv_16, data_path, "BoxPredictor_4_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(4), box_reshape.at(4)); |
| 606 | |
| 607 | SubStream conv_17_2_box_enc_pre(conv_17); |
| 608 | conv_17_2_box_enc_pre << get_node_C_qasymm(conv_17, data_path, "BoxPredictor_5_BEP", 24U, PadStrideInfo(1U, 1U, 0U, 0U), box_enc_pred_quant_info.at(5), box_reshape.at(5)); |
| 609 | |
| 610 | SubStream box_enc_pre(graph); |
| 611 | const QuantizationInfo bep_concate_qinfo = QuantizationInfo(0.08655580133199692f, 183); |
| 612 | box_enc_pre << ConcatLayer(arm_compute::graph::descriptors::ConcatLayerDescriptor(DataLayoutDimension::HEIGHT, bep_concate_qinfo), |
| 613 | std::move(conv_11_box_enc_pre), std::move(conv_13_box_enc_pre), conv_14_2_box_enc_pre, std::move(conv_15_2_box_enc_pre), |
| 614 | std::move(conv_16_2_box_enc_pre), std::move(conv_17_2_box_enc_pre)) |
| 615 | .set_name("BoxPredictor/concat"); |
| 616 | box_enc_pre << ReshapeLayer(TensorShape(4U, 1917U)).set_name("BoxPredictor/reshape"); |
| 617 | |
| 618 | // class_predictor |
| 619 | const std::vector<std::pair<QuantizationInfo, QuantizationInfo>> class_pred_quant_info = |
| 620 | { |
| 621 | { QuantizationInfo(0.002744135679677129f, 125), QuantizationInfo(0.05746262148022652f, 234) }, // boxpredictor0_cp |
| 622 | { QuantizationInfo(0.0024326108396053314f, 80), QuantizationInfo(0.03764628246426582f, 217) }, // boxpredictor1_cp |
| 623 | { QuantizationInfo(0.0013898586621508002f, 141), QuantizationInfo(0.034081317484378815f, 214) }, // boxpredictor2_cp |
| 624 | { QuantizationInfo(0.0014176908880472183f, 133), QuantizationInfo(0.033889178186655045f, 215) }, // boxpredictor3_cp |
| 625 | { QuantizationInfo(0.001090311910957098f, 125), QuantizationInfo(0.02646234817802906f, 230) }, // boxpredictor4_cp |
| 626 | { QuantizationInfo(0.001134163816459477f, 115), QuantizationInfo(0.026926767081022263f, 218) } // boxpredictor5_cp |
| 627 | }; |
| 628 | |
| 629 | const std::vector<TensorShape> class_reshape = |
| 630 | { |
| 631 | TensorShape(91U, 1083U), // boxpredictor0_cp_reshape |
| 632 | TensorShape(91U, 600U), // boxpredictor1_cp_reshape |
| 633 | TensorShape(91U, 150U), // boxpredictor2_cp_reshape |
| 634 | TensorShape(91U, 54U), // boxpredictor3_cp_reshape |
| 635 | TensorShape(91U, 24U), // boxpredictor4_cp_reshape |
| 636 | TensorShape(91U, 6U) // boxpredictor5_cp_reshape |
| 637 | }; |
| 638 | |
| 639 | SubStream conv_11_class_pre(graph); |
| 640 | conv_11_class_pre << get_node_C_qasymm(graph, data_path, "BoxPredictor_0_CP", 273U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(0), class_reshape.at(0)); |
| 641 | |
| 642 | SubStream conv_13_class_pre(conv_13); |
| 643 | conv_13_class_pre << get_node_C_qasymm(conv_13, data_path, "BoxPredictor_1_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(1), class_reshape.at(1)); |
| 644 | |
| 645 | SubStream conv_14_2_class_pre(conv_14); |
| 646 | conv_14_2_class_pre << get_node_C_qasymm(conv_14, data_path, "BoxPredictor_2_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(2), class_reshape.at(2)); |
| 647 | |
| 648 | SubStream conv_15_2_class_pre(conv_15); |
| 649 | conv_15_2_class_pre << get_node_C_qasymm(conv_15, data_path, "BoxPredictor_3_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(3), class_reshape.at(3)); |
| 650 | |
| 651 | SubStream conv_16_2_class_pre(conv_16); |
| 652 | conv_16_2_class_pre << get_node_C_qasymm(conv_16, data_path, "BoxPredictor_4_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(4), class_reshape.at(4)); |
| 653 | |
| 654 | SubStream conv_17_2_class_pre(conv_17); |
| 655 | conv_17_2_class_pre << get_node_C_qasymm(conv_17, data_path, "BoxPredictor_5_CP", 546U, PadStrideInfo(1U, 1U, 0U, 0U), class_pred_quant_info.at(5), class_reshape.at(5)); |
| 656 | |
| 657 | const QuantizationInfo cp_concate_qinfo = QuantizationInfo(0.0584389753639698f, 230); |
| 658 | SubStream class_pred(graph); |
| 659 | class_pred << ConcatLayer( |
| 660 | arm_compute::graph::descriptors::ConcatLayerDescriptor(DataLayoutDimension::WIDTH, cp_concate_qinfo), |
| 661 | std::move(conv_11_class_pre), std::move(conv_13_class_pre), std::move(conv_14_2_class_pre), |
| 662 | std::move(conv_15_2_class_pre), std::move(conv_16_2_class_pre), std::move(conv_17_2_class_pre)) |
| 663 | .set_name("ClassPrediction/concat"); |
| 664 | |
| 665 | const QuantizationInfo logistic_out_qinfo = QuantizationInfo(0.00390625f, 0); |
| 666 | class_pred << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC), logistic_out_qinfo).set_name("ClassPrediction/logistic"); |
| 667 | |
| 668 | const int max_detections = 10; |
| 669 | const int max_classes_per_detection = 1; |
| 670 | const float nms_score_threshold = 0.30000001192092896f; |
| 671 | const float nms_iou_threshold = 0.6000000238418579f; |
| 672 | const int num_classes = 90; |
| 673 | const float x_scale = 10.f; |
| 674 | const float y_scale = 10.f; |
| 675 | const float h_scale = 5.f; |
| 676 | const float w_scale = 5.f; |
| 677 | std::array<float, 4> scales = { y_scale, x_scale, w_scale, h_scale }; |
| 678 | const QuantizationInfo anchors_qinfo = QuantizationInfo(0.006453060545027256f, 0); |
| 679 | |
| 680 | SubStream detection_ouput(box_enc_pre); |
| 681 | detection_ouput << DetectionPostProcessLayer(std::move(class_pred), |
| 682 | DetectionPostProcessLayerInfo(max_detections, max_classes_per_detection, nms_score_threshold, nms_iou_threshold, num_classes, scales), |
| 683 | get_weights_accessor(data_path, "anchors.npy"), anchors_qinfo) |
| 684 | .set_name("DetectionPostProcess"); |
| 685 | |
| 686 | SubStream ouput_0(detection_ouput); |
| 687 | ouput_0 << OutputLayer(get_npy_output_accessor(detection_boxes_opt->value(), TensorShape(4U, 10U), DataType::F32), 0); |
| 688 | |
| 689 | SubStream ouput_1(detection_ouput); |
| 690 | ouput_1 << OutputLayer(get_npy_output_accessor(detection_classes_opt->value(), TensorShape(10U), DataType::F32), 1); |
| 691 | |
| 692 | SubStream ouput_2(detection_ouput); |
| 693 | ouput_2 << OutputLayer(get_npy_output_accessor(detection_scores_opt->value(), TensorShape(10U), DataType::F32), 2); |
| 694 | |
| 695 | SubStream ouput_3(detection_ouput); |
| 696 | ouput_3 << OutputLayer(get_npy_output_accessor(num_detections_opt->value(), TensorShape(1U), DataType::F32), 3); |
| 697 | } |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 698 | }; |
| 699 | |
| 700 | /** Main program for MobileNetSSD |
| 701 | * |
| 702 | * Model is based on: |
| 703 | * http://arxiv.org/abs/1512.02325 |
| 704 | * SSD: Single Shot MultiBox Detector |
| 705 | * Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg |
| 706 | * |
Georgios Pinitas | 588ebc5 | 2018-12-21 13:39:07 +0000 | [diff] [blame] | 707 | * Provenance: https://github.com/chuanqi305/MobileNet-SSD |
| 708 | * |
Pablo Tello | fea8ec3 | 2018-11-16 13:25:30 +0000 | [diff] [blame] | 709 | * @note To list all the possible arguments execute the binary appended with the --help option |
| 710 | * |
| 711 | * @param[in] argc Number of arguments |
| 712 | * @param[in] argv Arguments |
| 713 | */ |
| 714 | int main(int argc, char **argv) |
| 715 | { |
| 716 | return arm_compute::utils::run_example<GraphSSDMobilenetExample>(argc, argv); |
| 717 | } |