1 /* 2 * Copyright (c) 2018-2021 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/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 MobileNetV2's network using the Compute Library's graph API */ 36 class GraphMobilenetV2Example : public Example 37 { 38 public: GraphMobilenetV2Example()39 GraphMobilenetV2Example() 40 : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2") 41 { 42 } 43 GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete; 44 GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete; 45 ~GraphMobilenetV2Example() override = default; 46 do_setup(int argc,char ** argv)47 bool do_setup(int argc, char **argv) override 48 { 49 // Parse arguments 50 cmd_parser.parse(argc, argv); 51 cmd_parser.validate(); 52 53 // Consume common parameters 54 common_params = consume_common_graph_parameters(common_opts); 55 56 // Return when help menu is requested 57 if(common_params.help) 58 { 59 cmd_parser.print_help(argv[0]); 60 return false; 61 } 62 63 // Print parameter values 64 std::cout << common_params << std::endl; 65 66 // Create input descriptor 67 const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, common_params.data_layout); 68 TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); 69 70 // Set graph hints 71 graph << common_params.target 72 << common_params.fast_math_hint; 73 74 // Create core graph 75 if(arm_compute::is_data_type_float(common_params.data_type)) 76 { 77 create_graph_float(input_descriptor); 78 } 79 else 80 { 81 create_graph_qasymm8(input_descriptor); 82 } 83 // Create common tail 84 graph << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape") 85 << SoftmaxLayer().set_name("Predictions/Softmax") 86 << OutputLayer(get_output_accessor(common_params, 5)); 87 88 // Finalize graph 89 GraphConfig config; 90 config.num_threads = common_params.threads; 91 config.use_tuner = common_params.enable_tuner; 92 config.tuner_mode = common_params.tuner_mode; 93 config.tuner_file = common_params.tuner_file; 94 config.mlgo_file = common_params.mlgo_file; 95 96 graph.finalize(common_params.target, config); 97 98 return true; 99 } 100 do_run()101 void do_run() override 102 { 103 // Run graph 104 graph.run(); 105 } 106 107 private: 108 CommandLineParser cmd_parser; 109 CommonGraphOptions common_opts; 110 CommonGraphParams common_params; 111 Stream graph; 112 113 private: 114 enum class IsResidual 115 { 116 Yes, 117 No 118 }; 119 120 enum class HasExpand 121 { 122 Yes, 123 No 124 }; 125 126 private: create_graph_float(TensorDescriptor & input_descriptor)127 void create_graph_float(TensorDescriptor &input_descriptor) 128 { 129 // Create model path 130 const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_model/"; 131 132 // Create a preprocessor object 133 std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(); 134 135 // Get trainable parameters data path 136 std::string data_path = common_params.data_path; 137 138 // Add model path to data path 139 if(!data_path.empty()) 140 { 141 data_path += model_path; 142 } 143 144 graph << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) 145 << ConvolutionLayer(3U, 3U, 32U, 146 get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW), 147 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), 148 PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)) 149 .set_name("Conv") 150 << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"), 151 get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"), 152 get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"), 153 get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"), 154 0.0010000000474974513f) 155 .set_name("Conv/BatchNorm") 156 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) 157 .set_name("Conv/Relu6"); 158 159 get_expanded_conv_float(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1)); 160 get_expanded_conv_float(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); 161 get_expanded_conv_float(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 162 get_expanded_conv_float(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); 163 get_expanded_conv_float(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 164 get_expanded_conv_float(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 165 get_expanded_conv_float(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); 166 get_expanded_conv_float(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 167 get_expanded_conv_float(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 168 get_expanded_conv_float(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 169 get_expanded_conv_float(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes); 170 get_expanded_conv_float(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 171 get_expanded_conv_float(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 172 get_expanded_conv_float(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes); 173 get_expanded_conv_float(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 174 get_expanded_conv_float(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes); 175 get_expanded_conv_float(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes); 176 177 graph << ConvolutionLayer(1U, 1U, 1280U, 178 get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW), 179 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), 180 PadStrideInfo(1, 1, 0, 0)) 181 .set_name("Conv_1") 182 << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"), 183 get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"), 184 get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"), 185 get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"), 186 0.0010000000474974513f) 187 .set_name("Conv_1/BatchNorm") 188 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) 189 .set_name("Conv_1/Relu6") 190 << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") 191 << ConvolutionLayer(1U, 1U, 1001U, 192 get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), 193 get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), 194 PadStrideInfo(1, 1, 0, 0)) 195 .set_name("Logits/Conv2d_1c_1x1"); 196 } 197 get_expanded_conv_float(const std::string & data_path,std::string && param_path,unsigned int input_channels,unsigned int output_channels,PadStrideInfo dwc_pad_stride_info,HasExpand has_expand=HasExpand::No,IsResidual is_residual=IsResidual::No,unsigned int expansion_size=6)198 void get_expanded_conv_float(const std::string &data_path, std::string &¶m_path, 199 unsigned int input_channels, unsigned int output_channels, 200 PadStrideInfo dwc_pad_stride_info, 201 HasExpand has_expand = HasExpand::No, IsResidual is_residual = IsResidual::No, 202 unsigned int expansion_size = 6) 203 { 204 std::string total_path = param_path + "_"; 205 SubStream left(graph); 206 207 // Add expand node 208 if(has_expand == HasExpand::Yes) 209 { 210 left << ConvolutionLayer(1U, 1U, input_channels * expansion_size, 211 get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW), 212 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) 213 .set_name(param_path + "/expand/Conv2D") 214 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"), 215 get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"), 216 get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"), 217 get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"), 218 0.0010000000474974513f) 219 .set_name(param_path + "/expand/BatchNorm") 220 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) 221 .set_name(param_path + "/expand/Relu6"); 222 } 223 224 // Add depthwise node 225 left << DepthwiseConvolutionLayer(3U, 3U, 226 get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), 227 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), 228 dwc_pad_stride_info) 229 .set_name(param_path + "/depthwise/depthwise") 230 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), 231 get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), 232 get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), 233 get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), 234 0.0010000000474974513f) 235 .set_name(param_path + "/depthwise/BatchNorm") 236 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) 237 .set_name(param_path + "/depthwise/Relu6"); 238 239 // Add project node 240 left << ConvolutionLayer(1U, 1U, output_channels, 241 get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW), 242 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0)) 243 .set_name(param_path + "/project/Conv2D") 244 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"), 245 get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"), 246 get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"), 247 get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"), 248 0.0010000000474974513) 249 .set_name(param_path + "/project/BatchNorm"); 250 251 if(is_residual == IsResidual::Yes) 252 { 253 // Add residual node 254 SubStream right(graph); 255 graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add"); 256 } 257 else 258 { 259 graph.forward_tail(left.tail_node()); 260 } 261 } 262 create_graph_qasymm8(TensorDescriptor & input_descriptor)263 void create_graph_qasymm8(TensorDescriptor &input_descriptor) 264 { 265 // Create model path 266 const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_quantized_model/"; 267 268 // Get trainable parameters data path 269 std::string data_path = common_params.data_path; 270 271 // Add model path to data path 272 if(!data_path.empty()) 273 { 274 data_path += model_path; 275 } 276 277 const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128); 278 const QuantizationInfo mid_quant_info = QuantizationInfo(0.023528477177023888f, 128); 279 280 const std::vector<QuantizationInfo> conv_weights_quant_info = 281 { 282 QuantizationInfo(0.03396892547607422f, 122), // Conv 283 QuantizationInfo(0.005167067516595125f, 125), // Conv1 284 QuantizationInfo(0.0016910821432247758f, 113) // Conv2d_1c_1x1 285 }; 286 287 // Pointwise expand convolution quantization info 288 const std::vector<QuantizationInfo> pwc_q = 289 { 290 QuantizationInfo(0.254282623529f, 129), // expand_0 (Dummy) 291 QuantizationInfo(0.009758507832884789f, 127), // expand_1 292 QuantizationInfo(0.0036556976847350597f, 144), // expand_2 293 QuantizationInfo(0.0029988749884068966f, 104), // expand_3 294 QuantizationInfo(0.0019244228024035692f, 128), // expand_4 295 QuantizationInfo(0.0013649158645421267f, 135), // expand_5 296 QuantizationInfo(0.0019170437008142471f, 127), // expand_6 297 QuantizationInfo(0.0015538912266492844f, 125), // expand_7 298 QuantizationInfo(0.0014702979242429137f, 134), // expand_8 299 QuantizationInfo(0.0013733493397012353f, 127), // expand_9 300 QuantizationInfo(0.0016282502328976989f, 131), // expand_10 301 QuantizationInfo(0.0016309921629726887f, 134), // expand_11 302 QuantizationInfo(0.0018258779309689999f, 138), // expand_12 303 QuantizationInfo(0.0013828007504343987f, 123), // expand_13 304 QuantizationInfo(0.0020222084131091833f, 135), // expand_14 305 QuantizationInfo(0.04281935095787048f, 102), // expand_15 306 QuantizationInfo(0.002046825597062707f, 135) // expand_16 307 }; 308 // Depthwise expand convolution quantization info 309 const std::vector<QuantizationInfo> dwc_q = 310 { 311 QuantizationInfo(0.3436955213546753f, 165), // expand_0 312 QuantizationInfo(0.020969120785593987f, 109), // expand_1 313 QuantizationInfo(0.16981913149356842f, 52), // expand_2 314 QuantizationInfo(0.017202870920300484f, 143), // expand_3 315 QuantizationInfo(0.06525065749883652f, 118), // expand_4 316 QuantizationInfo(0.07909784466028214f, 95), // expand_5 317 QuantizationInfo(0.010087885893881321f, 127), // expand_6 318 QuantizationInfo(0.06092711538076401f, 110), // expand_7 319 QuantizationInfo(0.052407849580049515f, 133), // expand_8 320 QuantizationInfo(0.04077887907624245f, 155), // expand_9 321 QuantizationInfo(0.031107846647500992f, 143), // expand_10 322 QuantizationInfo(0.07080810517072678f, 66), // expand_11 323 QuantizationInfo(0.07448793947696686f, 159), // expand_12 324 QuantizationInfo(0.01525793131440878f, 92), // expand_13 325 QuantizationInfo(0.04166752099990845f, 147), // expand_14 326 QuantizationInfo(0.04281935095787048f, 102), // expand_15 327 QuantizationInfo(0.16456253826618195, 201) // expand_16 328 }; 329 // Project convolution quantization info 330 const std::vector<QuantizationInfo> prwc_q = 331 { 332 QuantizationInfo(0.03737175464630127f, 140), // expand_0 333 QuantizationInfo(0.0225360207259655f, 156), // expand_1 334 QuantizationInfo(0.02740888111293316f, 122), // expand_2 335 QuantizationInfo(0.016844693571329117f, 111), // expand_3 336 QuantizationInfo(0.019062912091612816f, 146), // expand_4 337 QuantizationInfo(0.018293123692274094f, 128), // expand_5 338 QuantizationInfo(0.014601286500692368f, 147), // expand_6 339 QuantizationInfo(0.016782939434051514f, 124), // expand_7 340 QuantizationInfo(0.012898261658847332f, 125), // expand_8 341 QuantizationInfo(0.019561484456062317f, 144), // expand_9 342 QuantizationInfo(0.007436311338096857f, 129), // expand_10 343 QuantizationInfo(0.00838223285973072f, 136), // expand_11 344 QuantizationInfo(0.023982593789696693f, 154), // expand_12 345 QuantizationInfo(0.009447949007153511f, 140), // expand_13 346 QuantizationInfo(0.00789870135486126f, 139), // expand_14 347 QuantizationInfo(0.03697410225868225f, 131), // expand_15 348 QuantizationInfo(0.008009289391338825f, 111) // expand_16 349 }; 350 351 graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info), 352 get_weights_accessor(data_path, common_params.image)) 353 << ConvolutionLayer( 354 3U, 3U, 32U, 355 get_weights_accessor(data_path, "Conv_weights.npy"), 356 get_weights_accessor(data_path, "Conv_bias.npy"), 357 PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR), 358 1, conv_weights_quant_info.at(0), mid_quant_info) 359 .set_name("Conv") 360 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv/Relu6") 361 << DepthwiseConvolutionLayer(3U, 3U, 362 get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_weights.npy"), 363 get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_biases.npy"), 364 PadStrideInfo(1, 1, 1, 1), 1, dwc_q.at(0)) 365 .set_name("expanded_conv/depthwise/depthwise") 366 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("expanded_conv/depthwise/Relu6") 367 << ConvolutionLayer(1U, 1U, 16U, 368 get_weights_accessor(data_path, "expanded_conv_project_weights.npy"), 369 get_weights_accessor(data_path, "expanded_conv_project_biases.npy"), 370 PadStrideInfo(1, 1, 0, 0), 1, prwc_q.at(0)) 371 .set_name("expanded_conv/project/Conv2D"); 372 373 get_expanded_conv_qasymm8(data_path, "expanded_conv_1", IsResidual::No, 96U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), 374 pwc_q.at(1), dwc_q.at(1), prwc_q.at(1)); 375 get_expanded_conv_qasymm8(data_path, "expanded_conv_2", IsResidual::Yes, 144U, 24U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(2), dwc_q.at(2), prwc_q.at(2)); 376 get_expanded_conv_qasymm8(data_path, "expanded_conv_3", IsResidual::No, 144U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), 377 pwc_q.at(3), dwc_q.at(3), prwc_q.at(3)); 378 get_expanded_conv_qasymm8(data_path, "expanded_conv_4", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(4), dwc_q.at(4), prwc_q.at(4)); 379 get_expanded_conv_qasymm8(data_path, "expanded_conv_5", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(5), dwc_q.at(5), prwc_q.at(5)); 380 get_expanded_conv_qasymm8(data_path, "expanded_conv_6", IsResidual::No, 192U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), 381 pwc_q.at(6), dwc_q.at(6), prwc_q.at(6)); 382 get_expanded_conv_qasymm8(data_path, "expanded_conv_7", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(7), dwc_q.at(7), prwc_q.at(7)); 383 get_expanded_conv_qasymm8(data_path, "expanded_conv_8", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(8), dwc_q.at(8), prwc_q.at(8)); 384 get_expanded_conv_qasymm8(data_path, "expanded_conv_9", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(9), dwc_q.at(9), prwc_q.at(9)); 385 get_expanded_conv_qasymm8(data_path, "expanded_conv_10", IsResidual::No, 384U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(10), dwc_q.at(10), prwc_q.at(10)); 386 get_expanded_conv_qasymm8(data_path, "expanded_conv_11", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(11), dwc_q.at(11), prwc_q.at(11)); 387 get_expanded_conv_qasymm8(data_path, "expanded_conv_12", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(12), dwc_q.at(12), prwc_q.at(12)); 388 get_expanded_conv_qasymm8(data_path, "expanded_conv_13", IsResidual::No, 576U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), 389 pwc_q.at(13), dwc_q.at(13), prwc_q.at(13)); 390 get_expanded_conv_qasymm8(data_path, "expanded_conv_14", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(14), dwc_q.at(14), prwc_q.at(14)); 391 get_expanded_conv_qasymm8(data_path, "expanded_conv_15", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(15), dwc_q.at(15), prwc_q.at(15)); 392 get_expanded_conv_qasymm8(data_path, "expanded_conv_16", IsResidual::No, 960U, 320U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(16), dwc_q.at(16), prwc_q.at(16)); 393 394 graph << ConvolutionLayer(1U, 1U, 1280U, 395 get_weights_accessor(data_path, "Conv_1_weights.npy"), 396 get_weights_accessor(data_path, "Conv_1_biases.npy"), 397 PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1)) 398 .set_name("Conv_1") 399 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6") 400 << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool") 401 << ConvolutionLayer(1U, 1U, 1001U, 402 get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"), 403 get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), 404 PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(2)) 405 .set_name("Logits/Conv2d_1c_1x1"); 406 } 407 get_expanded_conv_qasymm8(const std::string & data_path,std::string && param_path,IsResidual is_residual,unsigned int input_channels,unsigned int output_channels,PadStrideInfo dwc_pad_stride_info,const QuantizationInfo & pwi,const QuantizationInfo & dwi,const QuantizationInfo & pji)408 void get_expanded_conv_qasymm8(const std::string &data_path, std::string &¶m_path, IsResidual is_residual, 409 unsigned int input_channels, unsigned int output_channels, 410 PadStrideInfo dwc_pad_stride_info, 411 const QuantizationInfo &pwi, const QuantizationInfo &dwi, const QuantizationInfo &pji) 412 { 413 std::string total_path = param_path + "_"; 414 415 SubStream left(graph); 416 left << ConvolutionLayer(1U, 1U, input_channels, 417 get_weights_accessor(data_path, total_path + "project_weights.npy"), 418 get_weights_accessor(data_path, total_path + "project_biases.npy"), 419 PadStrideInfo(1, 1, 0, 0), 1, pwi) 420 .set_name(param_path + "/Conv2D") 421 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/Conv2D/Relu6") 422 << DepthwiseConvolutionLayer(3U, 3U, 423 get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"), 424 get_weights_accessor(data_path, total_path + "depthwise_depthwise_biases.npy"), 425 dwc_pad_stride_info, 1, dwi) 426 .set_name(param_path + "/depthwise/depthwise") 427 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/depthwise/Relu6") 428 << ConvolutionLayer(1U, 1U, output_channels, 429 get_weights_accessor(data_path, total_path + "project_weights.npy"), 430 get_weights_accessor(data_path, total_path + "project_biases.npy"), 431 PadStrideInfo(1, 1, 0, 0), 1, pji) 432 .set_name(param_path + "/project/Conv2D"); 433 434 if(is_residual == IsResidual::Yes) 435 { 436 // Add residual node 437 SubStream right(graph); 438 graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add"); 439 } 440 else 441 { 442 graph.forward_tail(left.tail_node()); 443 } 444 } 445 }; 446 447 /** Main program for MobileNetV2 448 * 449 * Model is based on: 450 * https://arxiv.org/abs/1801.04381 451 * "MobileNetV2: Inverted Residuals and Linear Bottlenecks" 452 * Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen 453 * 454 * Provenance: https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz 455 * 456 * @note To list all the possible arguments execute the binary appended with the --help option 457 * 458 * @param[in] argc Number of arguments 459 * @param[in] argv Arguments 460 */ main(int argc,char ** argv)461 int main(int argc, char **argv) 462 { 463 return arm_compute::utils::run_example<GraphMobilenetV2Example>(argc, argv); 464 } 465