├── MobileFaceNet.prototxt
├── MobileFaceNet_deploy.prototxt
├── README.md
├── face_snapshot
    └── MobileFaceNet.caffemodel
├── face_solver.prototxt
├── mobileFaceNet.log
├── result.png
└── start_train.sh


/MobileFaceNet.prototxt:
--------------------------------------------------------------------------------
   1 | name:"Caffe_MobileFaceNet"
   2 | 
   3 | ############## CNN Architecture ###############
   4 | layer {
   5 |   name: "data"
   6 |   type: "ImageData"
   7 |   top: "data"
   8 |   top: "label"
   9 |   transform_param {
  10 |     mean_value: 127.5
  11 |     mean_value: 127.5
  12 |     mean_value: 127.5
  13 |     scale: 0.0078125
  14 |     mirror: true
  15 |   }
  16 |   image_data_param {
  17 |     source: "/data/vggface2/VGGFACE_train_list-112X96-add.txt"
  18 |     batch_size:64
  19 |     shuffle: true
  20 |   }
  21 | }
  22 | 
  23 | layer {
  24 |   name: "conv1"
  25 |   type: "Convolution"
  26 |   bottom: "data"
  27 |   top: "conv1"
  28 |   param {
  29 |     lr_mult: 1
  30 |     decay_mult: 1
  31 |   }
  32 |   convolution_param {
  33 |     num_output: 64
  34 |     kernel_size: 3
  35 |     stride: 2
  36 |     pad: 1
  37 |     weight_filler {
  38 |       type: "xavier"
  39 |     }
  40 |     bias_term:false
  41 |   }
  42 | }
  43 | layer {
  44 |   name: "conv1/bn"
  45 |   type: "BatchNorm"
  46 |   bottom: "conv1"
  47 |   top: "conv1"
  48 |   param {
  49 |     lr_mult: 0
  50 |     decay_mult: 0
  51 |   }
  52 |   param {
  53 |     lr_mult: 0
  54 |     decay_mult: 0
  55 |   }
  56 |   param {
  57 |     lr_mult: 0
  58 |     decay_mult: 0
  59 |   }
  60 | }
  61 | layer {
  62 |   name: "conv1/scale"
  63 |   type: "Scale"
  64 |   bottom: "conv1"
  65 |   top: "conv1"
  66 |   param {
  67 |     lr_mult: 1
  68 |     decay_mult: 0
  69 |   }
  70 |   param {
  71 |     lr_mult: 1
  72 |     decay_mult: 0
  73 |   }
  74 |   scale_param {
  75 |     bias_term: true
  76 |   }
  77 | }
  78 | layer {
  79 |   name: "relu1"
  80 |   type: "PReLU"
  81 |   bottom: "conv1"
  82 |   top: "conv1"
  83 | }
  84 | layer {
  85 |   name: "conv1_dw"
  86 |   type: "DepthwiseConvolution"
  87 |   bottom: "conv1"
  88 |   top: "conv1_dw"
  89 |   param {
  90 |     lr_mult: 1
  91 |     decay_mult: 1
  92 |   }
  93 |   convolution_param {
  94 |     num_output: 64
  95 |     group:64
  96 |     kernel_size: 3
  97 |     stride: 1
  98 |     pad: 1
  99 |     weight_filler {
 100 |       type: "xavier"
 101 |     }
 102 |     bias_term:false
 103 |   }
 104 | }
 105 | layer {
 106 |   name: "conv1_dw/bn"
 107 |   type: "BatchNorm"
 108 |   bottom: "conv1_dw"
 109 |   top: "conv1_dw"
 110 |   param {
 111 |     lr_mult: 0
 112 |     decay_mult: 0
 113 |   }
 114 |   param {
 115 |     lr_mult: 0
 116 |     decay_mult: 0
 117 |   }
 118 |   param {
 119 |     lr_mult: 0
 120 |     decay_mult: 0
 121 |   }
 122 | }
 123 | layer {
 124 |   name: "conv1_dw/scale"
 125 |   type: "Scale"
 126 |   bottom: "conv1_dw"
 127 |   top: "conv1_dw"
 128 |   param {
 129 |     lr_mult: 1
 130 |     decay_mult: 0
 131 |   }
 132 |   param {
 133 |     lr_mult: 1
 134 |     decay_mult: 0
 135 |   }
 136 |   scale_param {
 137 |     bias_term: true
 138 |   }
 139 | }
 140 | layer {
 141 |   name: "relu1_dw"
 142 |   type: "PReLU"
 143 |   bottom: "conv1_dw"
 144 |   top: "conv1_dw"
 145 | }
 146 | layer {
 147 |   name: "conv2_ex"
 148 |   type: "Convolution"
 149 |   bottom: "conv1_dw"
 150 |   top: "conv2_ex"
 151 |   param {
 152 |     lr_mult: 1
 153 |     decay_mult: 1
 154 |   }
 155 |   convolution_param {
 156 |     num_output: 128
 157 |     kernel_size: 1
 158 |     stride: 1
 159 |     pad: 0
 160 |     weight_filler {
 161 |       type: "xavier"
 162 |     }
 163 |     bias_term:false
 164 |   }
 165 | }
 166 | layer {
 167 |   name: "conv2_ex/bn"
 168 |   type: "BatchNorm"
 169 |   bottom: "conv2_ex"
 170 |   top: "conv2_ex"
 171 |   param {
 172 |     lr_mult: 0
 173 |     decay_mult: 0
 174 |   }
 175 |   param {
 176 |     lr_mult: 0
 177 |     decay_mult: 0
 178 |   }
 179 |   param {
 180 |     lr_mult: 0
 181 |     decay_mult: 0
 182 |   }
 183 | }
 184 | layer {
 185 |   name: "conv2_ex/scale"
 186 |   type: "Scale"
 187 |   bottom: "conv2_ex"
 188 |   top: "conv2_ex"
 189 |   param {
 190 |     lr_mult: 1
 191 |     decay_mult: 0
 192 |   }
 193 |   param {
 194 |     lr_mult: 1
 195 |     decay_mult: 0
 196 |   }
 197 |   scale_param {
 198 |     bias_term: true
 199 |   }
 200 | }
 201 | layer {
 202 |   name: "relu2_ex"
 203 |   type: "PReLU"
 204 |   bottom: "conv2_ex"
 205 |   top: "conv2_ex"
 206 | }
 207 | 
 208 | layer {
 209 |   name: "conv2_dw"
 210 |   type: "DepthwiseConvolution"
 211 |   bottom: "conv2_ex"
 212 |   top: "conv2_dw"
 213 |   param {
 214 |     lr_mult: 1
 215 |     decay_mult: 1
 216 |   }
 217 |   convolution_param {
 218 |     num_output: 128
 219 |     group:128
 220 |     kernel_size: 3
 221 |     stride: 2
 222 |     pad: 1
 223 |     weight_filler {
 224 |       type: "xavier"
 225 |     }
 226 |     bias_term:false
 227 |     engine: CAFFE
 228 |   }
 229 | }
 230 | 
 231 | layer {
 232 |   name: "conv2_dw/bn"
 233 |   type: "BatchNorm"
 234 |   bottom: "conv2_dw"
 235 |   top: "conv2_dw"
 236 |   param {
 237 |     lr_mult: 0
 238 |     decay_mult: 0
 239 |   }
 240 |   param {
 241 |     lr_mult: 0
 242 |     decay_mult: 0
 243 |   }
 244 |   param {
 245 |     lr_mult: 0
 246 |     decay_mult: 0
 247 |   }
 248 | }
 249 | layer {
 250 |   name: "conv2_dw/scale"
 251 |   type: "Scale"
 252 |   bottom: "conv2_dw"
 253 |   top: "conv2_dw"
 254 |   param {
 255 |     lr_mult: 1
 256 |     decay_mult: 0
 257 |   }
 258 |   param {
 259 |     lr_mult: 1
 260 |     decay_mult: 0
 261 |   }
 262 |   scale_param {
 263 |     bias_term: true
 264 |   }
 265 | }
 266 | layer {
 267 |   name: "relu2_dw"
 268 |   type: "PReLU"
 269 |   bottom: "conv2_dw"
 270 |   top: "conv2_dw"
 271 | }
 272 | 
 273 | layer {
 274 |   name: "conv2_em"
 275 |   type: "Convolution"
 276 |   bottom: "conv2_dw"
 277 |   top: "conv2_em"
 278 |   param {
 279 |     lr_mult: 1
 280 |     decay_mult: 1
 281 |   }
 282 |   convolution_param {
 283 |     num_output: 64
 284 |     kernel_size: 1
 285 |     stride: 1
 286 |     pad: 0
 287 |     weight_filler {
 288 |       type: "xavier"
 289 |     }
 290 |     bias_term:false
 291 |   }
 292 | }
 293 | 
 294 | 
 295 | layer {
 296 |   name: "conv2_em/bn"
 297 |   type: "BatchNorm"
 298 |   bottom: "conv2_em"
 299 |   top: "conv2_em"
 300 |   param {
 301 |     lr_mult: 0
 302 |     decay_mult: 0
 303 |   }
 304 |   param {
 305 |     lr_mult: 0
 306 |     decay_mult: 0
 307 |   }
 308 |   param {
 309 |     lr_mult: 0
 310 |     decay_mult: 0
 311 |   }
 312 | }
 313 | layer {
 314 |   name: "conv2_em/scale"
 315 |   type: "Scale"
 316 |   bottom: "conv2_em"
 317 |   top: "conv2_em"
 318 |   param {
 319 |     lr_mult: 1
 320 |     decay_mult: 0
 321 |   }
 322 |   param {
 323 |     lr_mult: 1
 324 |     decay_mult: 0
 325 |   }
 326 |   scale_param {
 327 |     bias_term: true
 328 |   }
 329 | }
 330 | 
 331 | layer {
 332 |   name: "conv2_1_ex"
 333 |   type: "Convolution"
 334 |   bottom: "conv2_em"
 335 |   top: "conv2_1_ex"
 336 |   param {
 337 |     lr_mult: 1
 338 |     decay_mult: 1
 339 |   }
 340 |   convolution_param {
 341 |     num_output: 128
 342 |     kernel_size: 1
 343 |     stride: 1
 344 |     pad: 0
 345 |     weight_filler {
 346 |       type: "xavier"
 347 |     }
 348 |     bias_term:false
 349 |   }
 350 | }
 351 | 
 352 | layer {
 353 |   name: "conv2_1_ex/bn"
 354 |   type: "BatchNorm"
 355 |   bottom: "conv2_1_ex"
 356 |   top: "conv2_1_ex"
 357 |   param {
 358 |     lr_mult: 0
 359 |     decay_mult: 0
 360 |   }
 361 |   param {
 362 |     lr_mult: 0
 363 |     decay_mult: 0
 364 |   }
 365 |   param {
 366 |     lr_mult: 0
 367 |     decay_mult: 0
 368 |   }
 369 | }
 370 | layer {
 371 |   name: "conv2_1_ex/scale"
 372 |   type: "Scale"
 373 |   bottom: "conv2_1_ex"
 374 |   top: "conv2_1_ex"
 375 |   param {
 376 |     lr_mult: 1
 377 |     decay_mult: 0
 378 |   }
 379 |   param {
 380 |     lr_mult: 1
 381 |     decay_mult: 0
 382 |   }
 383 |   scale_param {
 384 |     bias_term: true
 385 |   }
 386 | }
 387 | layer {
 388 |   name: "relu2_1_ex"
 389 |   type: "PReLU"
 390 |   bottom: "conv2_1_ex"
 391 |   top: "conv2_1_ex"
 392 | }
 393 | 
 394 | 
 395 | layer {
 396 |   name: "conv2_1_dw"
 397 |   type: "DepthwiseConvolution"
 398 |   bottom: "conv2_1_ex"
 399 |   top: "conv2_1_dw"
 400 |   param {
 401 |     lr_mult: 1
 402 |     decay_mult: 1
 403 |   }
 404 |   convolution_param {
 405 |     num_output: 128
 406 |     group:128
 407 |     kernel_size: 3
 408 |     stride: 1
 409 |     pad: 1
 410 |     weight_filler {
 411 |       type: "xavier"
 412 |     }
 413 |     bias_term:false
 414 |     engine: CAFFE
 415 |   }
 416 | }
 417 | 
 418 | layer {
 419 |   name: "conv2_1_dw/bn"
 420 |   type: "BatchNorm"
 421 |   bottom: "conv2_1_dw"
 422 |   top: "conv2_1_dw"
 423 |   param {
 424 |     lr_mult: 0
 425 |     decay_mult: 0
 426 |   }
 427 |   param {
 428 |     lr_mult: 0
 429 |     decay_mult: 0
 430 |   }
 431 |   param {
 432 |     lr_mult: 0
 433 |     decay_mult: 0
 434 |   }
 435 | }
 436 | layer {
 437 |   name: "conv2_1_dw/scale"
 438 |   type: "Scale"
 439 |   bottom: "conv2_1_dw"
 440 |   top: "conv2_1_dw"
 441 |   param {
 442 |     lr_mult: 1
 443 |     decay_mult: 0
 444 |   }
 445 |   param {
 446 |     lr_mult: 1
 447 |     decay_mult: 0
 448 |   }
 449 |   scale_param {
 450 |     bias_term: true
 451 |   }
 452 | }
 453 | layer {
 454 |   name: "relu2_1_dw"
 455 |   type: "PReLU"
 456 |   bottom: "conv2_1_dw"
 457 |   top: "conv2_1_dw"
 458 | }
 459 | 
 460 | layer {
 461 |   name: "conv2_1_em"
 462 |   type: "Convolution"
 463 |   bottom: "conv2_1_dw"
 464 |   top: "conv2_1_em"
 465 |   param {
 466 |     lr_mult: 1
 467 |     decay_mult: 1
 468 |   }
 469 |   convolution_param {
 470 |     num_output: 64
 471 |     kernel_size: 1
 472 |     stride: 1
 473 |     pad: 0
 474 |     weight_filler {
 475 |       type: "xavier"
 476 |     }
 477 |     bias_term:false
 478 |   }
 479 | }
 480 | 
 481 | 
 482 | layer {
 483 |   name: "conv2_1_em/bn"
 484 |   type: "BatchNorm"
 485 |   bottom: "conv2_1_em"
 486 |   top: "conv2_1_em"
 487 |   param {
 488 |     lr_mult: 0
 489 |     decay_mult: 0
 490 |   }
 491 |   param {
 492 |     lr_mult: 0
 493 |     decay_mult: 0
 494 |   }
 495 |   param {
 496 |     lr_mult: 0
 497 |     decay_mult: 0
 498 |   }
 499 | }
 500 | layer {
 501 |   name: "conv2_1_em/scale"
 502 |   type: "Scale"
 503 |   bottom: "conv2_1_em"
 504 |   top: "conv2_1_em"
 505 |   param {
 506 |     lr_mult: 1
 507 |     decay_mult: 0
 508 |   }
 509 |   param {
 510 |     lr_mult: 1
 511 |     decay_mult: 0
 512 |   }
 513 |   scale_param {
 514 |     bias_term: true
 515 |   }
 516 | }
 517 | 
 518 | layer {
 519 |   name: "res2_1"
 520 |   type: "Eltwise"
 521 |   bottom: "conv2_em"
 522 |   bottom: "conv2_1_em"
 523 |   top: "res2_1"
 524 | }
 525 | 
 526 | layer {
 527 |   name: "conv2_2_ex"
 528 |   type: "Convolution"
 529 |   bottom: "res2_1"
 530 |   top: "conv2_2_ex"
 531 |   param {
 532 |     lr_mult: 1
 533 |     decay_mult: 1
 534 |   }
 535 |   convolution_param {
 536 |     num_output: 128
 537 |     kernel_size: 1
 538 |     stride: 1
 539 |     pad: 0
 540 |     weight_filler {
 541 |       type: "xavier"
 542 |     }
 543 |     bias_term:false
 544 |   }
 545 | }
 546 | 
 547 | layer {
 548 |   name: "conv2_2_ex/bn"
 549 |   type: "BatchNorm"
 550 |   bottom: "conv2_2_ex"
 551 |   top: "conv2_2_ex"
 552 |   param {
 553 |     lr_mult: 0
 554 |     decay_mult: 0
 555 |   }
 556 |   param {
 557 |     lr_mult: 0
 558 |     decay_mult: 0
 559 |   }
 560 |   param {
 561 |     lr_mult: 0
 562 |     decay_mult: 0
 563 |   }
 564 | }
 565 | layer {
 566 |   name: "conv2_2_ex/scale"
 567 |   type: "Scale"
 568 |   bottom: "conv2_2_ex"
 569 |   top: "conv2_2_ex"
 570 |   param {
 571 |     lr_mult: 1
 572 |     decay_mult: 0
 573 |   }
 574 |   param {
 575 |     lr_mult: 1
 576 |     decay_mult: 0
 577 |   }
 578 |   scale_param {
 579 |     bias_term: true
 580 |   }
 581 | }
 582 | layer {
 583 |   name: "relu2_2_ex"
 584 |   type: "PReLU"
 585 |   bottom: "conv2_2_ex"
 586 |   top: "conv2_2_ex"
 587 | }
 588 | 
 589 | 
 590 | layer {
 591 |   name: "conv2_2_dw"
 592 |   type: "DepthwiseConvolution"
 593 |   bottom: "conv2_2_ex"
 594 |   top: "conv2_2_dw"
 595 |   param {
 596 |     lr_mult: 1
 597 |     decay_mult: 1
 598 |   }
 599 |   convolution_param {
 600 |     num_output: 128
 601 |     group:128
 602 |     kernel_size: 3
 603 |     stride: 1
 604 |     pad: 1
 605 |     weight_filler {
 606 |       type: "xavier"
 607 |     }
 608 |     bias_term:false
 609 |     engine: CAFFE
 610 |   }
 611 | }
 612 | 
 613 | layer {
 614 |   name: "conv2_2_dw/bn"
 615 |   type: "BatchNorm"
 616 |   bottom: "conv2_2_dw"
 617 |   top: "conv2_2_dw"
 618 |   param {
 619 |     lr_mult: 0
 620 |     decay_mult: 0
 621 |   }
 622 |   param {
 623 |     lr_mult: 0
 624 |     decay_mult: 0
 625 |   }
 626 |   param {
 627 |     lr_mult: 0
 628 |     decay_mult: 0
 629 |   }
 630 | }
 631 | layer {
 632 |   name: "conv2_2_dw/scale"
 633 |   type: "Scale"
 634 |   bottom: "conv2_2_dw"
 635 |   top: "conv2_2_dw"
 636 |   param {
 637 |     lr_mult: 1
 638 |     decay_mult: 0
 639 |   }
 640 |   param {
 641 |     lr_mult: 1
 642 |     decay_mult: 0
 643 |   }
 644 |   scale_param {
 645 |     bias_term: true
 646 |   }
 647 | }
 648 | layer {
 649 |   name: "relu2_2_dw"
 650 |   type: "PReLU"
 651 |   bottom: "conv2_2_dw"
 652 |   top: "conv2_2_dw"
 653 | }
 654 | 
 655 | layer {
 656 |   name: "conv2_2_em"
 657 |   type: "Convolution"
 658 |   bottom: "conv2_2_dw"
 659 |   top: "conv2_2_em"
 660 |   param {
 661 |     lr_mult: 1
 662 |     decay_mult: 1
 663 |   }
 664 |   convolution_param {
 665 |     num_output: 64
 666 |     kernel_size: 1
 667 |     stride: 1
 668 |     pad: 0
 669 |     weight_filler {
 670 |       type: "xavier"
 671 |     }
 672 |     bias_term:false
 673 |   }
 674 | }
 675 | 
 676 | 
 677 | layer {
 678 |   name: "conv2_2_em/bn"
 679 |   type: "BatchNorm"
 680 |   bottom: "conv2_2_em"
 681 |   top: "conv2_2_em"
 682 |   param {
 683 |     lr_mult: 0
 684 |     decay_mult: 0
 685 |   }
 686 |   param {
 687 |     lr_mult: 0
 688 |     decay_mult: 0
 689 |   }
 690 |   param {
 691 |     lr_mult: 0
 692 |     decay_mult: 0
 693 |   }
 694 | }
 695 | layer {
 696 |   name: "conv2_2_em/scale"
 697 |   type: "Scale"
 698 |   bottom: "conv2_2_em"
 699 |   top: "conv2_2_em"
 700 |   param {
 701 |     lr_mult: 1
 702 |     decay_mult: 0
 703 |   }
 704 |   param {
 705 |     lr_mult: 1
 706 |     decay_mult: 0
 707 |   }
 708 |   scale_param {
 709 |     bias_term: true
 710 |   }
 711 | }
 712 | 
 713 | layer {
 714 |   name: "res2_2"
 715 |   type: "Eltwise"
 716 |   bottom: "res2_1"
 717 |   bottom: "conv2_2_em"
 718 |   top: "res2_2"
 719 | }
 720 | 
 721 | layer {
 722 |   name: "conv2_3_ex"
 723 |   type: "Convolution"
 724 |   bottom: "res2_2"
 725 |   top: "conv2_3_ex"
 726 |   param {
 727 |     lr_mult: 1
 728 |     decay_mult: 1
 729 |   }
 730 |   convolution_param {
 731 |     num_output: 128
 732 |     kernel_size: 1
 733 |     stride: 1
 734 |     pad: 0
 735 |     weight_filler {
 736 |       type: "xavier"
 737 |     }
 738 |     bias_term:false
 739 |   }
 740 | }
 741 | 
 742 | layer {
 743 |   name: "conv2_3_ex/bn"
 744 |   type: "BatchNorm"
 745 |   bottom: "conv2_3_ex"
 746 |   top: "conv2_3_ex"
 747 |   param {
 748 |     lr_mult: 0
 749 |     decay_mult: 0
 750 |   }
 751 |   param {
 752 |     lr_mult: 0
 753 |     decay_mult: 0
 754 |   }
 755 |   param {
 756 |     lr_mult: 0
 757 |     decay_mult: 0
 758 |   }
 759 | }
 760 | layer {
 761 |   name: "conv2_3_ex/scale"
 762 |   type: "Scale"
 763 |   bottom: "conv2_3_ex"
 764 |   top: "conv2_3_ex"
 765 |   param {
 766 |     lr_mult: 1
 767 |     decay_mult: 0
 768 |   }
 769 |   param {
 770 |     lr_mult: 1
 771 |     decay_mult: 0
 772 |   }
 773 |   scale_param {
 774 |     bias_term: true
 775 |   }
 776 | }
 777 | layer {
 778 |   name: "relu2_3_ex"
 779 |   type: "PReLU"
 780 |   bottom: "conv2_3_ex"
 781 |   top: "conv2_3_ex"
 782 | }
 783 | 
 784 | 
 785 | layer {
 786 |   name: "conv2_3_dw"
 787 |   type: "DepthwiseConvolution"
 788 |   bottom: "conv2_3_ex"
 789 |   top: "conv2_3_dw"
 790 |   param {
 791 |     lr_mult: 1
 792 |     decay_mult: 1
 793 |   }
 794 |   convolution_param {
 795 |     num_output: 128
 796 |     group:128
 797 |     kernel_size: 3
 798 |     stride: 1
 799 |     pad: 1
 800 |     weight_filler {
 801 |       type: "xavier"
 802 |     }
 803 |     bias_term:false
 804 |     engine: CAFFE
 805 |   }
 806 | }
 807 | 
 808 | layer {
 809 |   name: "conv2_3_dw/bn"
 810 |   type: "BatchNorm"
 811 |   bottom: "conv2_3_dw"
 812 |   top: "conv2_3_dw"
 813 |   param {
 814 |     lr_mult: 0
 815 |     decay_mult: 0
 816 |   }
 817 |   param {
 818 |     lr_mult: 0
 819 |     decay_mult: 0
 820 |   }
 821 |   param {
 822 |     lr_mult: 0
 823 |     decay_mult: 0
 824 |   }
 825 | }
 826 | layer {
 827 |   name: "conv2_3_dw/scale"
 828 |   type: "Scale"
 829 |   bottom: "conv2_3_dw"
 830 |   top: "conv2_3_dw"
 831 |   param {
 832 |     lr_mult: 1
 833 |     decay_mult: 0
 834 |   }
 835 |   param {
 836 |     lr_mult: 1
 837 |     decay_mult: 0
 838 |   }
 839 |   scale_param {
 840 |     bias_term: true
 841 |   }
 842 | }
 843 | layer {
 844 |   name: "relu2_3_dw"
 845 |   type: "PReLU"
 846 |   bottom: "conv2_3_dw"
 847 |   top: "conv2_3_dw"
 848 | }
 849 | 
 850 | layer {
 851 |   name: "conv2_3_em"
 852 |   type: "Convolution"
 853 |   bottom: "conv2_3_dw"
 854 |   top: "conv2_3_em"
 855 |   param {
 856 |     lr_mult: 1
 857 |     decay_mult: 1
 858 |   }
 859 |   convolution_param {
 860 |     num_output: 64
 861 |     kernel_size: 1
 862 |     stride: 1
 863 |     pad: 0
 864 |     weight_filler {
 865 |       type: "xavier"
 866 |     }
 867 |     bias_term:false
 868 |   }
 869 | }
 870 | 
 871 | 
 872 | layer {
 873 |   name: "conv2_3_em/bn"
 874 |   type: "BatchNorm"
 875 |   bottom: "conv2_3_em"
 876 |   top: "conv2_3_em"
 877 |   param {
 878 |     lr_mult: 0
 879 |     decay_mult: 0
 880 |   }
 881 |   param {
 882 |     lr_mult: 0
 883 |     decay_mult: 0
 884 |   }
 885 |   param {
 886 |     lr_mult: 0
 887 |     decay_mult: 0
 888 |   }
 889 | }
 890 | layer {
 891 |   name: "conv2_3_em/scale"
 892 |   type: "Scale"
 893 |   bottom: "conv2_3_em"
 894 |   top: "conv2_3_em"
 895 |   param {
 896 |     lr_mult: 1
 897 |     decay_mult: 0
 898 |   }
 899 |   param {
 900 |     lr_mult: 1
 901 |     decay_mult: 0
 902 |   }
 903 |   scale_param {
 904 |     bias_term: true
 905 |   }
 906 | }
 907 | 
 908 | layer {
 909 |   name: "res2_3"
 910 |   type: "Eltwise"
 911 |   bottom: "res2_2"
 912 |   bottom: "conv2_3_em"
 913 |   top: "res2_3"
 914 | }
 915 | 
 916 | layer {
 917 |   name: "conv2_4_ex"
 918 |   type: "Convolution"
 919 |   bottom: "res2_3"
 920 |   top: "conv2_4_ex"
 921 |   param {
 922 |     lr_mult: 1
 923 |     decay_mult: 1
 924 |   }
 925 | 
 926 |   convolution_param {
 927 |     num_output: 128
 928 |     kernel_size: 1
 929 |     stride: 1
 930 |     pad: 0
 931 |     weight_filler {
 932 |       type: "xavier"
 933 |     }
 934 |     bias_term:false
 935 |   }
 936 | }
 937 | 
 938 | layer {
 939 |   name: "conv2_4_ex/bn"
 940 |   type: "BatchNorm"
 941 |   bottom: "conv2_4_ex"
 942 |   top: "conv2_4_ex"
 943 |   param {
 944 |     lr_mult: 0
 945 |     decay_mult: 0
 946 |   }
 947 |   param {
 948 |     lr_mult: 0
 949 |     decay_mult: 0
 950 |   }
 951 |   param {
 952 |     lr_mult: 0
 953 |     decay_mult: 0
 954 |   }
 955 | }
 956 | layer {
 957 |   name: "conv2_4_ex/scale"
 958 |   type: "Scale"
 959 |   bottom: "conv2_4_ex"
 960 |   top: "conv2_4_ex"
 961 |   param {
 962 |     lr_mult: 1
 963 |     decay_mult: 0
 964 |   }
 965 |   param {
 966 |     lr_mult: 1
 967 |     decay_mult: 0
 968 |   }
 969 |   scale_param {
 970 |     bias_term: true
 971 |   }
 972 | }
 973 | layer {
 974 |   name: "relu2_4_ex"
 975 |   type: "PReLU"
 976 |   bottom: "conv2_4_ex"
 977 |   top: "conv2_4_ex"
 978 | }
 979 | 
 980 | 
 981 | layer {
 982 |   name: "conv2_4_dw"
 983 |   type: "DepthwiseConvolution"
 984 |   bottom: "conv2_4_ex"
 985 |   top: "conv2_4_dw"
 986 |   param {
 987 |     lr_mult: 1
 988 |     decay_mult: 1
 989 |   }
 990 |   convolution_param {
 991 |     num_output: 128
 992 |     group:128
 993 |     kernel_size: 3
 994 |     stride: 1
 995 |     pad: 1
 996 |     weight_filler {
 997 |       type: "xavier"
 998 |     }
 999 |     bias_term:false
1000 |     engine: CAFFE
1001 |   }
1002 | }
1003 | 
1004 | layer {
1005 |   name: "conv2_4_dw/bn"
1006 |   type: "BatchNorm"
1007 |   bottom: "conv2_4_dw"
1008 |   top: "conv2_4_dw"
1009 |   param {
1010 |     lr_mult: 0
1011 |     decay_mult: 0
1012 |   }
1013 |   param {
1014 |     lr_mult: 0
1015 |     decay_mult: 0
1016 |   }
1017 |   param {
1018 |     lr_mult: 0
1019 |     decay_mult: 0
1020 |   }
1021 | }
1022 | layer {
1023 |   name: "conv2_4_dw/scale"
1024 |   type: "Scale"
1025 |   bottom: "conv2_4_dw"
1026 |   top: "conv2_4_dw"
1027 |   param {
1028 |     lr_mult: 1
1029 |     decay_mult: 0
1030 |   }
1031 |   param {
1032 |     lr_mult: 1
1033 |     decay_mult: 0
1034 |   }
1035 |   scale_param {
1036 |     bias_term: true
1037 |   }
1038 | }
1039 | layer {
1040 |   name: "relu2_4_dw"
1041 |   type: "PReLU"
1042 |   bottom: "conv2_4_dw"
1043 |   top: "conv2_4_dw"
1044 | }
1045 | 
1046 | layer {
1047 |   name: "conv2_4_em"
1048 |   type: "Convolution"
1049 |   bottom: "conv2_4_dw"
1050 |   top: "conv2_4_em"
1051 |   param {
1052 |     lr_mult: 1
1053 |     decay_mult: 1
1054 |   }
1055 |   convolution_param {
1056 |     num_output: 64
1057 |     kernel_size: 1
1058 |     stride: 1
1059 |     pad: 0
1060 |     weight_filler {
1061 |       type: "xavier"
1062 |     }
1063 |     bias_term:false
1064 |   }
1065 | }
1066 | 
1067 | 
1068 | layer {
1069 |   name: "conv2_4_em/bn"
1070 |   type: "BatchNorm"
1071 |   bottom: "conv2_4_em"
1072 |   top: "conv2_4_em"
1073 |   param {
1074 |     lr_mult: 0
1075 |     decay_mult: 0
1076 |   }
1077 |   param {
1078 |     lr_mult: 0
1079 |     decay_mult: 0
1080 |   }
1081 |   param {
1082 |     lr_mult: 0
1083 |     decay_mult: 0
1084 |   }
1085 | }
1086 | layer {
1087 |   name: "conv2_4_em/scale"
1088 |   type: "Scale"
1089 |   bottom: "conv2_4_em"
1090 |   top: "conv2_4_em"
1091 |   param {
1092 |     lr_mult: 1
1093 |     decay_mult: 0
1094 |   }
1095 |   param {
1096 |     lr_mult: 1
1097 |     decay_mult: 0
1098 |   }
1099 |   scale_param {
1100 |     bias_term: true
1101 |   }
1102 | }
1103 | 
1104 | layer {
1105 |   name: "res2_4"
1106 |   type: "Eltwise"
1107 |   bottom: "res2_3"
1108 |   bottom: "conv2_4_em"
1109 |   top: "res2_4"
1110 | }
1111 | 
1112 | 
1113 | layer {
1114 |   name: "conv3_ex"
1115 |   type: "Convolution"
1116 |   bottom: "res2_4"
1117 |   top: "conv3_ex"
1118 |   param {
1119 |     lr_mult: 1
1120 |     decay_mult: 1
1121 |   }
1122 |   convolution_param {
1123 |     num_output: 256
1124 |     kernel_size: 1
1125 |     stride: 1
1126 |     pad: 0
1127 |     weight_filler {
1128 |       type: "xavier"
1129 |     }
1130 |     bias_term:false
1131 |   }
1132 | }
1133 | 
1134 | layer {
1135 |   name: "conv3_ex/bn"
1136 |   type: "BatchNorm"
1137 |   bottom: "conv3_ex"
1138 |   top: "conv3_ex"
1139 |   param {
1140 |     lr_mult: 0
1141 |     decay_mult: 0
1142 |   }
1143 |   param {
1144 |     lr_mult: 0
1145 |     decay_mult: 0
1146 |   }
1147 |   param {
1148 |     lr_mult: 0
1149 |     decay_mult: 0
1150 |   }
1151 | }
1152 | layer {
1153 |   name: "conv3_ex/scale"
1154 |   type: "Scale"
1155 |   bottom: "conv3_ex"
1156 |   top: "conv3_ex"
1157 |   param {
1158 |     lr_mult: 1
1159 |     decay_mult: 0
1160 |   }
1161 |   param {
1162 |     lr_mult: 1
1163 |     decay_mult: 0
1164 |   }
1165 |   scale_param {
1166 |     bias_term: true
1167 |   }
1168 | }
1169 | layer {
1170 |   name: "relu3_ex"
1171 |   type: "PReLU"
1172 |   bottom: "conv3_ex"
1173 |   top: "conv3_ex"
1174 | }
1175 | 
1176 | 
1177 | layer {
1178 |   name: "conv3_dw"
1179 |   type: "DepthwiseConvolution"
1180 |   bottom: "conv3_ex"
1181 |   top: "conv3_dw"
1182 |   param {
1183 |     lr_mult: 1
1184 |     decay_mult: 1
1185 |   }
1186 |   convolution_param {
1187 |     num_output: 256
1188 |     group:256
1189 |     kernel_size: 3
1190 |     stride: 2
1191 |     pad: 1
1192 |     weight_filler {
1193 |       type: "xavier"
1194 |     }
1195 |     bias_term:false
1196 |     engine: CAFFE
1197 |   }
1198 | }
1199 | 
1200 | layer {
1201 |   name: "conv3_dw/bn"
1202 |   type: "BatchNorm"
1203 |   bottom: "conv3_dw"
1204 |   top: "conv3_dw"
1205 |   param {
1206 |     lr_mult: 0
1207 |     decay_mult: 0
1208 |   }
1209 |   param {
1210 |     lr_mult: 0
1211 |     decay_mult: 0
1212 |   }
1213 |   param {
1214 |     lr_mult: 0
1215 |     decay_mult: 0
1216 |   }
1217 | }
1218 | layer {
1219 |   name: "conv3_dw/scale"
1220 |   type: "Scale"
1221 |   bottom: "conv3_dw"
1222 |   top: "conv3_dw"
1223 |   param {
1224 |     lr_mult: 1
1225 |     decay_mult: 0
1226 |   }
1227 |   param {
1228 |     lr_mult: 1
1229 |     decay_mult: 0
1230 |   }
1231 |   scale_param {
1232 |     bias_term: true
1233 |   }
1234 | }
1235 | layer {
1236 |   name: "relu3_dw"
1237 |   type: "PReLU"
1238 |   bottom: "conv3_dw"
1239 |   top: "conv3_dw"
1240 | }
1241 | 
1242 | layer {
1243 |   name: "conv3_em"
1244 |   type: "Convolution"
1245 |   bottom: "conv3_dw"
1246 |   top: "conv3_em"
1247 |   param {
1248 |     lr_mult: 1
1249 |     decay_mult: 1
1250 |   }
1251 |   convolution_param {
1252 |     num_output: 128
1253 |     kernel_size: 1
1254 |     stride: 1
1255 |     pad: 0
1256 |     weight_filler {
1257 |       type: "xavier"
1258 |     }
1259 |     bias_term:false
1260 |   }
1261 | }
1262 | 
1263 | 
1264 | layer {
1265 |   name: "conv3_em/bn"
1266 |   type: "BatchNorm"
1267 |   bottom: "conv3_em"
1268 |   top: "conv3_em"
1269 |   param {
1270 |     lr_mult: 0
1271 |     decay_mult: 0
1272 |   }
1273 |   param {
1274 |     lr_mult: 0
1275 |     decay_mult: 0
1276 |   }
1277 |   param {
1278 |     lr_mult: 0
1279 |     decay_mult: 0
1280 |   }
1281 | }
1282 | layer {
1283 |   name: "conv3_em/scale"
1284 |   type: "Scale"
1285 |   bottom: "conv3_em"
1286 |   top: "conv3_em"
1287 |   param {
1288 |     lr_mult: 1
1289 |     decay_mult: 0
1290 |   }
1291 |   param {
1292 |     lr_mult: 1
1293 |     decay_mult: 0
1294 |   }
1295 |   scale_param {
1296 |     bias_term: true
1297 |   }
1298 | }
1299 | 
1300 | layer {
1301 |   name: "conv3_1_ex"
1302 |   type: "Convolution"
1303 |   bottom: "conv3_em"
1304 |   top: "conv3_1_ex"
1305 |   param {
1306 |     lr_mult: 1
1307 |     decay_mult: 1
1308 |   }
1309 |   convolution_param {
1310 |     num_output: 256
1311 |     kernel_size: 1
1312 |     stride: 1
1313 |     pad: 0
1314 |     weight_filler {
1315 |       type: "xavier"
1316 |     }
1317 |     bias_term:false
1318 |   }
1319 | }
1320 | 
1321 | layer {
1322 |   name: "conv3_1_ex/bn"
1323 |   type: "BatchNorm"
1324 |   bottom: "conv3_1_ex"
1325 |   top: "conv3_1_ex"
1326 |   param {
1327 |     lr_mult: 0
1328 |     decay_mult: 0
1329 |   }
1330 |   param {
1331 |     lr_mult: 0
1332 |     decay_mult: 0
1333 |   }
1334 |   param {
1335 |     lr_mult: 0
1336 |     decay_mult: 0
1337 |   }
1338 | }
1339 | layer {
1340 |   name: "conv3_1_ex/scale"
1341 |   type: "Scale"
1342 |   bottom: "conv3_1_ex"
1343 |   top: "conv3_1_ex"
1344 |   param {
1345 |     lr_mult: 1
1346 |     decay_mult: 0
1347 |   }
1348 |   param {
1349 |     lr_mult: 1
1350 |     decay_mult: 0
1351 |   }
1352 |   scale_param {
1353 |     bias_term: true
1354 |   }
1355 | }
1356 | layer {
1357 |   name: "relu3_1_ex"
1358 |   type: "PReLU"
1359 |   bottom: "conv3_1_ex"
1360 |   top: "conv3_1_ex"
1361 | }
1362 | 
1363 | 
1364 | layer {
1365 |   name: "conv3_1_dw"
1366 |   type: "DepthwiseConvolution"
1367 |   bottom: "conv3_1_ex"
1368 |   top: "conv3_1_dw"
1369 |   param {
1370 |     lr_mult: 1
1371 |     decay_mult: 1
1372 |   }
1373 |   convolution_param {
1374 |     num_output: 256
1375 |     group:256
1376 |     kernel_size: 3
1377 |     stride: 1
1378 |     pad: 1
1379 |     weight_filler {
1380 |       type: "xavier"
1381 |     }
1382 |     bias_term:false
1383 |     engine: CAFFE
1384 |   }
1385 | }
1386 | 
1387 | layer {
1388 |   name: "conv3_1_dw/bn"
1389 |   type: "BatchNorm"
1390 |   bottom: "conv3_1_dw"
1391 |   top: "conv3_1_dw"
1392 |   param {
1393 |     lr_mult: 0
1394 |     decay_mult: 0
1395 |   }
1396 |   param {
1397 |     lr_mult: 0
1398 |     decay_mult: 0
1399 |   }
1400 |   param {
1401 |     lr_mult: 0
1402 |     decay_mult: 0
1403 |   }
1404 | }
1405 | layer {
1406 |   name: "conv3_1_dw/scale"
1407 |   type: "Scale"
1408 |   bottom: "conv3_1_dw"
1409 |   top: "conv3_1_dw"
1410 |   param {
1411 |     lr_mult: 1
1412 |     decay_mult: 0
1413 |   }
1414 |   param {
1415 |     lr_mult: 1
1416 |     decay_mult: 0
1417 |   }
1418 |   scale_param {
1419 |     bias_term: true
1420 |   }
1421 | }
1422 | layer {
1423 |   name: "relu3_1_dw"
1424 |   type: "PReLU"
1425 |   bottom: "conv3_1_dw"
1426 |   top: "conv3_1_dw"
1427 | }
1428 | 
1429 | layer {
1430 |   name: "conv3_1_em"
1431 |   type: "Convolution"
1432 |   bottom: "conv3_1_dw"
1433 |   top: "conv3_1_em"
1434 |   param {
1435 |     lr_mult: 1
1436 |     decay_mult: 1
1437 |   }
1438 |   convolution_param {
1439 |     num_output: 128
1440 |     kernel_size: 1
1441 |     stride: 1
1442 |     pad: 0
1443 |     weight_filler {
1444 |       type: "xavier"
1445 |     }
1446 |     bias_term:false
1447 |   }
1448 | }
1449 | 
1450 | 
1451 | layer {
1452 |   name: "conv3_1_em/bn"
1453 |   type: "BatchNorm"
1454 |   bottom: "conv3_1_em"
1455 |   top: "conv3_1_em"
1456 |   param {
1457 |     lr_mult: 0
1458 |     decay_mult: 0
1459 |   }
1460 |   param {
1461 |     lr_mult: 0
1462 |     decay_mult: 0
1463 |   }
1464 |   param {
1465 |     lr_mult: 0
1466 |     decay_mult: 0
1467 |   }
1468 | }
1469 | layer {
1470 |   name: "conv3_1_em/scale"
1471 |   type: "Scale"
1472 |   bottom: "conv3_1_em"
1473 |   top: "conv3_1_em"
1474 |   param {
1475 |     lr_mult: 1
1476 |     decay_mult: 0
1477 |   }
1478 |   param {
1479 |     lr_mult: 1
1480 |     decay_mult: 0
1481 |   }
1482 |   scale_param {
1483 |     bias_term: true
1484 |   }
1485 | }
1486 | 
1487 | layer {
1488 |   name: "res3_1"
1489 |   type: "Eltwise"
1490 |   bottom: "conv3_em"
1491 |   bottom: "conv3_1_em"
1492 |   top: "res3_1"
1493 | }
1494 | 
1495 | 
1496 | layer {
1497 |   name: "conv3_2_ex"
1498 |   type: "Convolution"
1499 |   bottom: "res3_1"
1500 |   top: "conv3_2_ex"
1501 |   param {
1502 |     lr_mult: 1
1503 |     decay_mult: 1
1504 |   }
1505 |   convolution_param {
1506 |     num_output: 256
1507 |     kernel_size: 1
1508 |     stride: 1
1509 |     pad: 0
1510 |     weight_filler {
1511 |       type: "xavier"
1512 |     }
1513 |     bias_term:false
1514 |   }
1515 | }
1516 | 
1517 | layer {
1518 |   name: "conv3_2_ex/bn"
1519 |   type: "BatchNorm"
1520 |   bottom: "conv3_2_ex"
1521 |   top: "conv3_2_ex"
1522 |   param {
1523 |     lr_mult: 0
1524 |     decay_mult: 0
1525 |   }
1526 |   param {
1527 |     lr_mult: 0
1528 |     decay_mult: 0
1529 |   }
1530 |   param {
1531 |     lr_mult: 0
1532 |     decay_mult: 0
1533 |   }
1534 | }
1535 | layer {
1536 |   name: "conv3_2_ex/scale"
1537 |   type: "Scale"
1538 |   bottom: "conv3_2_ex"
1539 |   top: "conv3_2_ex"
1540 |   param {
1541 |     lr_mult: 1
1542 |     decay_mult: 0
1543 |   }
1544 |   param {
1545 |     lr_mult: 1
1546 |     decay_mult: 0
1547 |   }
1548 |   scale_param {
1549 |     bias_term: true
1550 |   }
1551 | }
1552 | layer {
1553 |   name: "relu3_2_ex"
1554 |   type: "PReLU"
1555 |   bottom: "conv3_2_ex"
1556 |   top: "conv3_2_ex"
1557 | }
1558 | 
1559 | 
1560 | layer {
1561 |   name: "conv3_2_dw"
1562 |   type: "DepthwiseConvolution"
1563 |   bottom: "conv3_2_ex"
1564 |   top: "conv3_2_dw"
1565 |   param {
1566 |     lr_mult: 1
1567 |     decay_mult: 1
1568 |   }
1569 |   convolution_param {
1570 |     num_output: 256
1571 |     group:256
1572 |     kernel_size: 3
1573 |     stride: 1
1574 |     pad: 1
1575 |     weight_filler {
1576 |       type: "xavier"
1577 |     }
1578 |     bias_term:false
1579 |     engine: CAFFE
1580 |   }
1581 | }
1582 | 
1583 | layer {
1584 |   name: "conv3_2_dw/bn"
1585 |   type: "BatchNorm"
1586 |   bottom: "conv3_2_dw"
1587 |   top: "conv3_2_dw"
1588 |   param {
1589 |     lr_mult: 0
1590 |     decay_mult: 0
1591 |   }
1592 |   param {
1593 |     lr_mult: 0
1594 |     decay_mult: 0
1595 |   }
1596 |   param {
1597 |     lr_mult: 0
1598 |     decay_mult: 0
1599 |   }
1600 | }
1601 | layer {
1602 |   name: "conv3_2_dw/scale"
1603 |   type: "Scale"
1604 |   bottom: "conv3_2_dw"
1605 |   top: "conv3_2_dw"
1606 |   param {
1607 |     lr_mult: 1
1608 |     decay_mult: 0
1609 |   }
1610 |   param {
1611 |     lr_mult: 1
1612 |     decay_mult: 0
1613 |   }
1614 |   scale_param {
1615 |     bias_term: true
1616 |   }
1617 | }
1618 | layer {
1619 |   name: "relu3_2_dw"
1620 |   type: "PReLU"
1621 |   bottom: "conv3_2_dw"
1622 |   top: "conv3_2_dw"
1623 | }
1624 | 
1625 | layer {
1626 |   name: "conv3_2_em"
1627 |   type: "Convolution"
1628 |   bottom: "conv3_2_dw"
1629 |   top: "conv3_2_em"
1630 |   param {
1631 |     lr_mult: 1
1632 |     decay_mult: 1
1633 |   }
1634 |   convolution_param {
1635 |     num_output: 128
1636 |     kernel_size: 1
1637 |     stride: 1
1638 |     pad: 0
1639 |     weight_filler {
1640 |       type: "xavier"
1641 |     }
1642 |     bias_term:false
1643 |   }
1644 | }
1645 | 
1646 | 
1647 | layer {
1648 |   name: "conv3_2_em/bn"
1649 |   type: "BatchNorm"
1650 |   bottom: "conv3_2_em"
1651 |   top: "conv3_2_em"
1652 |   param {
1653 |     lr_mult: 0
1654 |     decay_mult: 0
1655 |   }
1656 |   param {
1657 |     lr_mult: 0
1658 |     decay_mult: 0
1659 |   }
1660 |   param {
1661 |     lr_mult: 0
1662 |     decay_mult: 0
1663 |   }
1664 | }
1665 | layer {
1666 |   name: "conv3_2_em/scale"
1667 |   type: "Scale"
1668 |   bottom: "conv3_2_em"
1669 |   top: "conv3_2_em"
1670 |   param {
1671 |     lr_mult: 1
1672 |     decay_mult: 0
1673 |   }
1674 |   param {
1675 |     lr_mult: 1
1676 |     decay_mult: 0
1677 |   }
1678 |   scale_param {
1679 |     bias_term: true
1680 |   }
1681 | }
1682 | 
1683 | layer {
1684 |   name: "res3_2"
1685 |   type: "Eltwise"
1686 |   bottom: "res3_1"
1687 |   bottom: "conv3_2_em"
1688 |   top: "res3_2"
1689 | }
1690 | 
1691 | layer {
1692 |   name: "conv3_3_ex"
1693 |   type: "Convolution"
1694 |   bottom: "res3_2"
1695 |   top: "conv3_3_ex"
1696 |   param {
1697 |     lr_mult: 1
1698 |     decay_mult: 1
1699 |   }
1700 |   convolution_param {
1701 |     num_output: 256
1702 |     kernel_size: 1
1703 |     stride: 1
1704 |     pad: 0
1705 |     weight_filler {
1706 |       type: "xavier"
1707 |     }
1708 |     bias_term:false
1709 |   }
1710 | }
1711 | 
1712 | layer {
1713 |   name: "conv3_3_ex/bn"
1714 |   type: "BatchNorm"
1715 |   bottom: "conv3_3_ex"
1716 |   top: "conv3_3_ex"
1717 |   param {
1718 |     lr_mult: 0
1719 |     decay_mult: 0
1720 |   }
1721 |   param {
1722 |     lr_mult: 0
1723 |     decay_mult: 0
1724 |   }
1725 |   param {
1726 |     lr_mult: 0
1727 |     decay_mult: 0
1728 |   }
1729 | }
1730 | layer {
1731 |   name: "conv3_3_ex/scale"
1732 |   type: "Scale"
1733 |   bottom: "conv3_3_ex"
1734 |   top: "conv3_3_ex"
1735 |   param {
1736 |     lr_mult: 1
1737 |     decay_mult: 0
1738 |   }
1739 |   param {
1740 |     lr_mult: 1
1741 |     decay_mult: 0
1742 |   }
1743 |   scale_param {
1744 |     bias_term: true
1745 |   }
1746 | }
1747 | layer {
1748 |   name: "relu3_3_ex"
1749 |   type: "PReLU"
1750 |   bottom: "conv3_3_ex"
1751 |   top: "conv3_3_ex"
1752 | }
1753 | 
1754 | 
1755 | layer {
1756 |   name: "conv3_3_dw"
1757 |   type: "DepthwiseConvolution"
1758 |   bottom: "conv3_3_ex"
1759 |   top: "conv3_3_dw"
1760 |   param {
1761 |     lr_mult: 1
1762 |     decay_mult: 1
1763 |   }
1764 |   convolution_param {
1765 |     num_output: 256
1766 |     group:256
1767 |     kernel_size: 3
1768 |     stride: 1
1769 |     pad: 1
1770 |     weight_filler {
1771 |       type: "xavier"
1772 |     }
1773 |     bias_term:false
1774 |     engine: CAFFE
1775 |   }
1776 | }
1777 | 
1778 | layer {
1779 |   name: "conv3_3_dw/bn"
1780 |   type: "BatchNorm"
1781 |   bottom: "conv3_3_dw"
1782 |   top: "conv3_3_dw"
1783 |   param {
1784 |     lr_mult: 0
1785 |     decay_mult: 0
1786 |   }
1787 |   param {
1788 |     lr_mult: 0
1789 |     decay_mult: 0
1790 |   }
1791 |   param {
1792 |     lr_mult: 0
1793 |     decay_mult: 0
1794 |   }
1795 | }
1796 | layer {
1797 |   name: "conv3_3_dw/scale"
1798 |   type: "Scale"
1799 |   bottom: "conv3_3_dw"
1800 |   top: "conv3_3_dw"
1801 |   param {
1802 |     lr_mult: 1
1803 |     decay_mult: 0
1804 |   }
1805 |   param {
1806 |     lr_mult: 1
1807 |     decay_mult: 0
1808 |   }
1809 |   scale_param {
1810 |     bias_term: true
1811 |   }
1812 | }
1813 | layer {
1814 |   name: "relu3_3_dw"
1815 |   type: "PReLU"
1816 |   bottom: "conv3_3_dw"
1817 |   top: "conv3_3_dw"
1818 | }
1819 | 
1820 | layer {
1821 |   name: "conv3_3_em"
1822 |   type: "Convolution"
1823 |   bottom: "conv3_3_dw"
1824 |   top: "conv3_3_em"
1825 |   param {
1826 |     lr_mult: 1
1827 |     decay_mult: 1
1828 |   }
1829 |   convolution_param {
1830 |     num_output: 128
1831 |     kernel_size: 1
1832 |     stride: 1
1833 |     pad: 0
1834 |     weight_filler {
1835 |       type: "xavier"
1836 |     }
1837 |     bias_term:false
1838 |   }
1839 | }
1840 | 
1841 | 
1842 | layer {
1843 |   name: "conv3_3_em/bn"
1844 |   type: "BatchNorm"
1845 |   bottom: "conv3_3_em"
1846 |   top: "conv3_3_em"
1847 |   param {
1848 |     lr_mult: 0
1849 |     decay_mult: 0
1850 |   }
1851 |   param {
1852 |     lr_mult: 0
1853 |     decay_mult: 0
1854 |   }
1855 |   param {
1856 |     lr_mult: 0
1857 |     decay_mult: 0
1858 |   }
1859 | }
1860 | layer {
1861 |   name: "conv3_3_em/scale"
1862 |   type: "Scale"
1863 |   bottom: "conv3_3_em"
1864 |   top: "conv3_3_em"
1865 |   param {
1866 |     lr_mult: 1
1867 |     decay_mult: 0
1868 |   }
1869 |   param {
1870 |     lr_mult: 1
1871 |     decay_mult: 0
1872 |   }
1873 |   scale_param {
1874 |     bias_term: true
1875 |   }
1876 | }
1877 | 
1878 | layer {
1879 |   name: "res3_3"
1880 |   type: "Eltwise"
1881 |   bottom: "res3_2"
1882 |   bottom: "conv3_3_em"
1883 |   top: "res3_3"
1884 | }
1885 | 
1886 | 
1887 | layer {
1888 |   name: "conv3_4_ex"
1889 |   type: "Convolution"
1890 |   bottom: "res3_3"
1891 |   top: "conv3_4_ex"
1892 |   param {
1893 |     lr_mult: 1
1894 |     decay_mult: 1
1895 |   }
1896 |   convolution_param {
1897 |     num_output: 256
1898 |     kernel_size: 1
1899 |     stride: 1
1900 |     pad: 0
1901 |     weight_filler {
1902 |       type: "xavier"
1903 |     }
1904 |     bias_term:false
1905 |   }
1906 | }
1907 | 
1908 | layer {
1909 |   name: "conv3_4_ex/bn"
1910 |   type: "BatchNorm"
1911 |   bottom: "conv3_4_ex"
1912 |   top: "conv3_4_ex"
1913 |   param {
1914 |     lr_mult: 0
1915 |     decay_mult: 0
1916 |   }
1917 |   param {
1918 |     lr_mult: 0
1919 |     decay_mult: 0
1920 |   }
1921 |   param {
1922 |     lr_mult: 0
1923 |     decay_mult: 0
1924 |   }
1925 | }
1926 | layer {
1927 |   name: "conv3_4_ex/scale"
1928 |   type: "Scale"
1929 |   bottom: "conv3_4_ex"
1930 |   top: "conv3_4_ex"
1931 |   param {
1932 |     lr_mult: 1
1933 |     decay_mult: 0
1934 |   }
1935 |   param {
1936 |     lr_mult: 1
1937 |     decay_mult: 0
1938 |   }
1939 |   scale_param {
1940 |     bias_term: true
1941 |   }
1942 | }
1943 | layer {
1944 |   name: "relu3_4_ex"
1945 |   type: "PReLU"
1946 |   bottom: "conv3_4_ex"
1947 |   top: "conv3_4_ex"
1948 | }
1949 | 
1950 | 
1951 | layer {
1952 |   name: "conv3_4_dw"
1953 |   type: "DepthwiseConvolution"
1954 |   bottom: "conv3_4_ex"
1955 |   top: "conv3_4_dw"
1956 |   param {
1957 |     lr_mult: 1
1958 |     decay_mult: 1
1959 |   }
1960 |   convolution_param {
1961 |     num_output: 256
1962 |     group:256
1963 |     kernel_size: 3
1964 |     stride: 1
1965 |     pad: 1
1966 |     weight_filler {
1967 |       type: "xavier"
1968 |     }
1969 |     bias_term:false
1970 |     engine: CAFFE
1971 |   }
1972 | }
1973 | 
1974 | layer {
1975 |   name: "conv3_4_dw/bn"
1976 |   type: "BatchNorm"
1977 |   bottom: "conv3_4_dw"
1978 |   top: "conv3_4_dw"
1979 |   param {
1980 |     lr_mult: 0
1981 |     decay_mult: 0
1982 |   }
1983 |   param {
1984 |     lr_mult: 0
1985 |     decay_mult: 0
1986 |   }
1987 |   param {
1988 |     lr_mult: 0
1989 |     decay_mult: 0
1990 |   }
1991 | }
1992 | layer {
1993 |   name: "conv3_4_dw/scale"
1994 |   type: "Scale"
1995 |   bottom: "conv3_4_dw"
1996 |   top: "conv3_4_dw"
1997 |   param {
1998 |     lr_mult: 1
1999 |     decay_mult: 0
2000 |   }
2001 |   param {
2002 |     lr_mult: 1
2003 |     decay_mult: 0
2004 |   }
2005 |   scale_param {
2006 |     bias_term: true
2007 |   }
2008 | }
2009 | layer {
2010 |   name: "relu3_4_dw"
2011 |   type: "PReLU"
2012 |   bottom: "conv3_4_dw"
2013 |   top: "conv3_4_dw"
2014 | }
2015 | 
2016 | layer {
2017 |   name: "conv3_4_em"
2018 |   type: "Convolution"
2019 |   bottom: "conv3_4_dw"
2020 |   top: "conv3_4_em"
2021 |   param {
2022 |     lr_mult: 1
2023 |     decay_mult: 1
2024 |   }
2025 |   convolution_param {
2026 |     num_output: 128
2027 |     kernel_size: 1
2028 |     stride: 1
2029 |     pad: 0
2030 |     weight_filler {
2031 |       type: "xavier"
2032 |     }
2033 |     bias_term:false
2034 |   }
2035 | }
2036 | 
2037 | 
2038 | layer {
2039 |   name: "conv3_4_em/bn"
2040 |   type: "BatchNorm"
2041 |   bottom: "conv3_4_em"
2042 |   top: "conv3_4_em"
2043 |   param {
2044 |     lr_mult: 0
2045 |     decay_mult: 0
2046 |   }
2047 |   param {
2048 |     lr_mult: 0
2049 |     decay_mult: 0
2050 |   }
2051 |   param {
2052 |     lr_mult: 0
2053 |     decay_mult: 0
2054 |   }
2055 | }
2056 | layer {
2057 |   name: "conv3_4_em/scale"
2058 |   type: "Scale"
2059 |   bottom: "conv3_4_em"
2060 |   top: "conv3_4_em"
2061 |   param {
2062 |     lr_mult: 1
2063 |     decay_mult: 0
2064 |   }
2065 |   param {
2066 |     lr_mult: 1
2067 |     decay_mult: 0
2068 |   }
2069 |   scale_param {
2070 |     bias_term: true
2071 |   }
2072 | }
2073 | 
2074 | layer {
2075 |   name: "res3_4"
2076 |   type: "Eltwise"
2077 |   bottom: "res3_3"
2078 |   bottom: "conv3_4_em"
2079 |   top: "res3_4"
2080 | }
2081 | 
2082 | 
2083 | layer {
2084 |   name: "conv3_5_ex"
2085 |   type: "Convolution"
2086 |   bottom: "res3_4"
2087 |   top: "conv3_5_ex"
2088 |   param {
2089 |     lr_mult: 1
2090 |     decay_mult: 1
2091 |   }
2092 |   convolution_param {
2093 |     num_output: 256
2094 |     kernel_size: 1
2095 |     stride: 1
2096 |     pad: 0
2097 |     weight_filler {
2098 |       type: "xavier"
2099 |     }
2100 |     bias_term:false
2101 |   }
2102 | }
2103 | 
2104 | layer {
2105 |   name: "conv3_5_ex/bn"
2106 |   type: "BatchNorm"
2107 |   bottom: "conv3_5_ex"
2108 |   top: "conv3_5_ex"
2109 |   param {
2110 |     lr_mult: 0
2111 |     decay_mult: 0
2112 |   }
2113 |   param {
2114 |     lr_mult: 0
2115 |     decay_mult: 0
2116 |   }
2117 |   param {
2118 |     lr_mult: 0
2119 |     decay_mult: 0
2120 |   }
2121 | }
2122 | layer {
2123 |   name: "conv3_5_ex/scale"
2124 |   type: "Scale"
2125 |   bottom: "conv3_5_ex"
2126 |   top: "conv3_5_ex"
2127 |   param {
2128 |     lr_mult: 1
2129 |     decay_mult: 0
2130 |   }
2131 |   param {
2132 |     lr_mult: 1
2133 |     decay_mult: 0
2134 |   }
2135 |   scale_param {
2136 |     bias_term: true
2137 |   }
2138 | }
2139 | layer {
2140 |   name: "relu3_5_ex"
2141 |   type: "PReLU"
2142 |   bottom: "conv3_5_ex"
2143 |   top: "conv3_5_ex"
2144 | }
2145 | 
2146 | 
2147 | layer {
2148 |   name: "conv3_5_dw"
2149 |   type: "DepthwiseConvolution"
2150 |   bottom: "conv3_5_ex"
2151 |   top: "conv3_5_dw"
2152 |   param {
2153 |     lr_mult: 1
2154 |     decay_mult: 1
2155 |   }
2156 |   convolution_param {
2157 |     num_output: 256
2158 |     group:256
2159 |     kernel_size: 3
2160 |     stride: 1
2161 |     pad: 1
2162 |     weight_filler {
2163 |       type: "xavier"
2164 |     }
2165 |     bias_term:false
2166 |     engine: CAFFE
2167 |   }
2168 | }
2169 | 
2170 | layer {
2171 |   name: "conv3_5_dw/bn"
2172 |   type: "BatchNorm"
2173 |   bottom: "conv3_5_dw"
2174 |   top: "conv3_5_dw"
2175 |   param {
2176 |     lr_mult: 0
2177 |     decay_mult: 0
2178 |   }
2179 |   param {
2180 |     lr_mult: 0
2181 |     decay_mult: 0
2182 |   }
2183 |   param {
2184 |     lr_mult: 0
2185 |     decay_mult: 0
2186 |   }
2187 | }
2188 | layer {
2189 |   name: "conv3_5_dw/scale"
2190 |   type: "Scale"
2191 |   bottom: "conv3_5_dw"
2192 |   top: "conv3_5_dw"
2193 |   param {
2194 |     lr_mult: 1
2195 |     decay_mult: 0
2196 |   }
2197 |   param {
2198 |     lr_mult: 1
2199 |     decay_mult: 0
2200 |   }
2201 |   scale_param {
2202 |     bias_term: true
2203 |   }
2204 | }
2205 | layer {
2206 |   name: "relu3_5_dw"
2207 |   type: "PReLU"
2208 |   bottom: "conv3_5_dw"
2209 |   top: "conv3_5_dw"
2210 | }
2211 | 
2212 | layer {
2213 |   name: "conv3_5_em"
2214 |   type: "Convolution"
2215 |   bottom: "conv3_5_dw"
2216 |   top: "conv3_5_em"
2217 |   param {
2218 |     lr_mult: 1
2219 |     decay_mult: 1
2220 |   }
2221 |   convolution_param {
2222 |     num_output: 128
2223 |     kernel_size: 1
2224 |     stride: 1
2225 |     pad: 0
2226 |     weight_filler {
2227 |       type: "xavier"
2228 |     }
2229 |     bias_term:false
2230 |   }
2231 | }
2232 | 
2233 | 
2234 | layer {
2235 |   name: "conv3_5_em/bn"
2236 |   type: "BatchNorm"
2237 |   bottom: "conv3_5_em"
2238 |   top: "conv3_5_em"
2239 |   param {
2240 |     lr_mult: 0
2241 |     decay_mult: 0
2242 |   }
2243 |   param {
2244 |     lr_mult: 0
2245 |     decay_mult: 0
2246 |   }
2247 |   param {
2248 |     lr_mult: 0
2249 |     decay_mult: 0
2250 |   }
2251 | }
2252 | layer {
2253 |   name: "conv3_5_em/scale"
2254 |   type: "Scale"
2255 |   bottom: "conv3_5_em"
2256 |   top: "conv3_5_em"
2257 |   param {
2258 |     lr_mult: 1
2259 |     decay_mult: 0
2260 |   }
2261 |   param {
2262 |     lr_mult: 1
2263 |     decay_mult: 0
2264 |   }
2265 |   scale_param {
2266 |     bias_term: true
2267 |   }
2268 | }
2269 | 
2270 | layer {
2271 |   name: "res3_5"
2272 |   type: "Eltwise"
2273 |   bottom: "res3_4"
2274 |   bottom: "conv3_5_em"
2275 |   top: "res3_5"
2276 | }
2277 | 
2278 | 
2279 | layer {
2280 |   name: "conv3_6_ex"
2281 |   type: "Convolution"
2282 |   bottom: "res3_5"
2283 |   top: "conv3_6_ex"
2284 |   param {
2285 |     lr_mult: 1
2286 |     decay_mult: 1
2287 |   }
2288 |   convolution_param {
2289 |     num_output: 256
2290 |     kernel_size: 1
2291 |     stride: 1
2292 |     pad: 0
2293 |     weight_filler {
2294 |       type: "xavier"
2295 |     }
2296 |     bias_term:false
2297 |   }
2298 | }
2299 | 
2300 | layer {
2301 |   name: "conv3_6_ex/bn"
2302 |   type: "BatchNorm"
2303 |   bottom: "conv3_6_ex"
2304 |   top: "conv3_6_ex"
2305 |   param {
2306 |     lr_mult: 0
2307 |     decay_mult: 0
2308 |   }
2309 |   param {
2310 |     lr_mult: 0
2311 |     decay_mult: 0
2312 |   }
2313 |   param {
2314 |     lr_mult: 0
2315 |     decay_mult: 0
2316 |   }
2317 | }
2318 | layer {
2319 |   name: "conv3_6_ex/scale"
2320 |   type: "Scale"
2321 |   bottom: "conv3_6_ex"
2322 |   top: "conv3_6_ex"
2323 |   param {
2324 |     lr_mult: 1
2325 |     decay_mult: 0
2326 |   }
2327 |   param {
2328 |     lr_mult: 1
2329 |     decay_mult: 0
2330 |   }
2331 |   scale_param {
2332 |     bias_term: true
2333 |   }
2334 | }
2335 | layer {
2336 |   name: "relu3_6_ex"
2337 |   type: "PReLU"
2338 |   bottom: "conv3_6_ex"
2339 |   top: "conv3_6_ex"
2340 | }
2341 | 
2342 | 
2343 | layer {
2344 |   name: "conv3_6_dw"
2345 |   type: "DepthwiseConvolution"
2346 |   bottom: "conv3_6_ex"
2347 |   top: "conv3_6_dw"
2348 |   param {
2349 |     lr_mult: 1
2350 |     decay_mult: 1
2351 |   }
2352 |   convolution_param {
2353 |     num_output: 256
2354 |     group:256
2355 |     kernel_size: 3
2356 |     stride: 1
2357 |     pad: 1
2358 |     weight_filler {
2359 |       type: "xavier"
2360 |     }
2361 |     bias_term:false
2362 |     engine: CAFFE
2363 |   }
2364 | }
2365 | 
2366 | layer {
2367 |   name: "conv3_6_dw/bn"
2368 |   type: "BatchNorm"
2369 |   bottom: "conv3_6_dw"
2370 |   top: "conv3_6_dw"
2371 |   param {
2372 |     lr_mult: 0
2373 |     decay_mult: 0
2374 |   }
2375 |   param {
2376 |     lr_mult: 0
2377 |     decay_mult: 0
2378 |   }
2379 |   param {
2380 |     lr_mult: 0
2381 |     decay_mult: 0
2382 |   }
2383 | }
2384 | layer {
2385 |   name: "conv3_6_dw/scale"
2386 |   type: "Scale"
2387 |   bottom: "conv3_6_dw"
2388 |   top: "conv3_6_dw"
2389 |   param {
2390 |     lr_mult: 1
2391 |     decay_mult: 0
2392 |   }
2393 |   param {
2394 |     lr_mult: 1
2395 |     decay_mult: 0
2396 |   }
2397 |   scale_param {
2398 |     bias_term: true
2399 |   }
2400 | }
2401 | layer {
2402 |   name: "relu3_6_dw"
2403 |   type: "PReLU"
2404 |   bottom: "conv3_6_dw"
2405 |   top: "conv3_6_dw"
2406 | }
2407 | 
2408 | layer {
2409 |   name: "conv3_6_em"
2410 |   type: "Convolution"
2411 |   bottom: "conv3_6_dw"
2412 |   top: "conv3_6_em"
2413 |   param {
2414 |     lr_mult: 1
2415 |     decay_mult: 1
2416 |   }
2417 |   convolution_param {
2418 |     num_output: 128
2419 |     kernel_size: 1
2420 |     stride: 1
2421 |     pad: 0
2422 |     weight_filler {
2423 |       type: "xavier"
2424 |     }
2425 |     bias_term:false
2426 |   }
2427 | }
2428 | 
2429 | 
2430 | layer {
2431 |   name: "conv3_6_em/bn"
2432 |   type: "BatchNorm"
2433 |   bottom: "conv3_6_em"
2434 |   top: "conv3_6_em"
2435 |   param {
2436 |     lr_mult: 0
2437 |     decay_mult: 0
2438 |   }
2439 |   param {
2440 |     lr_mult: 0
2441 |     decay_mult: 0
2442 |   }
2443 |   param {
2444 |     lr_mult: 0
2445 |     decay_mult: 0
2446 |   }
2447 | }
2448 | layer {
2449 |   name: "conv3_6_em/scale"
2450 |   type: "Scale"
2451 |   bottom: "conv3_6_em"
2452 |   top: "conv3_6_em"
2453 |   param {
2454 |     lr_mult: 1
2455 |     decay_mult: 0
2456 |   }
2457 |   param {
2458 |     lr_mult: 1
2459 |     decay_mult: 0
2460 |   }
2461 |   scale_param {
2462 |     bias_term: true
2463 |   }
2464 | }
2465 | 
2466 | layer {
2467 |   name: "res3_6"
2468 |   type: "Eltwise"
2469 |   bottom: "res3_5"
2470 |   bottom: "conv3_6_em"
2471 |   top: "res3_6"
2472 | }
2473 | 
2474 | layer {
2475 |   name: "conv4_ex"
2476 |   type: "Convolution"
2477 |   bottom: "res3_6"
2478 |   top: "conv4_ex"
2479 |   param {
2480 |     lr_mult: 1
2481 |     decay_mult: 1
2482 |   }
2483 |   convolution_param {
2484 |     num_output: 512
2485 |     kernel_size: 1
2486 |     stride: 1
2487 |     pad: 0
2488 |     weight_filler {
2489 |       type: "xavier"
2490 |     }
2491 |     bias_term:false
2492 |   }
2493 | }
2494 | 
2495 | layer {
2496 |   name: "conv4_ex/bn"
2497 |   type: "BatchNorm"
2498 |   bottom: "conv4_ex"
2499 |   top: "conv4_ex"
2500 |   param {
2501 |     lr_mult: 0
2502 |     decay_mult: 0
2503 |   }
2504 |   param {
2505 |     lr_mult: 0
2506 |     decay_mult: 0
2507 |   }
2508 |   param {
2509 |     lr_mult: 0
2510 |     decay_mult: 0
2511 |   }
2512 | }
2513 | layer {
2514 |   name: "conv4_ex/scale"
2515 |   type: "Scale"
2516 |   bottom: "conv4_ex"
2517 |   top: "conv4_ex"
2518 |   param {
2519 |     lr_mult: 1
2520 |     decay_mult: 0
2521 |   }
2522 |   param {
2523 |     lr_mult: 1
2524 |     decay_mult: 0
2525 |   }
2526 |   scale_param {
2527 |     bias_term: true
2528 |   }
2529 | }
2530 | layer {
2531 |   name: "relu4_ex"
2532 |   type: "PReLU"
2533 |   bottom: "conv4_ex"
2534 |   top: "conv4_ex"
2535 | }
2536 | 
2537 | 
2538 | layer {
2539 |   name: "conv4_dw"
2540 |   type: "DepthwiseConvolution"
2541 |   bottom: "conv4_ex"
2542 |   top: "conv4_dw"
2543 |   param {
2544 |     lr_mult: 1
2545 |     decay_mult: 1
2546 |   }
2547 |   convolution_param {
2548 |     num_output: 512
2549 |     group:512
2550 |     kernel_size: 3
2551 |     stride: 2
2552 |     pad: 1
2553 |     weight_filler {
2554 |       type: "xavier"
2555 |     }
2556 |     bias_term:false
2557 |     engine: CAFFE
2558 |   }
2559 | }
2560 | 
2561 | layer {
2562 |   name: "conv4_dw/bn"
2563 |   type: "BatchNorm"
2564 |   bottom: "conv4_dw"
2565 |   top: "conv4_dw"
2566 |   param {
2567 |     lr_mult: 0
2568 |     decay_mult: 0
2569 |   }
2570 |   param {
2571 |     lr_mult: 0
2572 |     decay_mult: 0
2573 |   }
2574 |   param {
2575 |     lr_mult: 0
2576 |     decay_mult: 0
2577 |   }
2578 | }
2579 | layer {
2580 |   name: "conv4_dw/scale"
2581 |   type: "Scale"
2582 |   bottom: "conv4_dw"
2583 |   top: "conv4_dw"
2584 |   param {
2585 |     lr_mult: 1
2586 |     decay_mult: 0
2587 |   }
2588 |   param {
2589 |     lr_mult: 1
2590 |     decay_mult: 0
2591 |   }
2592 |   scale_param {
2593 |     bias_term: true
2594 |   }
2595 | }
2596 | layer {
2597 |   name: "relu4_dw"
2598 |   type: "PReLU"
2599 |   bottom: "conv4_dw"
2600 |   top: "conv4_dw"
2601 | }
2602 | 
2603 | layer {
2604 |   name: "conv4_em"
2605 |   type: "Convolution"
2606 |   bottom: "conv4_dw"
2607 |   top: "conv4_em"
2608 |   param {
2609 |     lr_mult: 1
2610 |     decay_mult: 1
2611 |   }
2612 |   convolution_param {
2613 |     num_output: 128
2614 |     kernel_size: 1
2615 |     stride: 1
2616 |     pad: 0
2617 |     weight_filler {
2618 |       type: "xavier"
2619 |     }
2620 |     bias_term:false
2621 |   }
2622 | }
2623 | 
2624 | 
2625 | layer {
2626 |   name: "conv4_em/bn"
2627 |   type: "BatchNorm"
2628 |   bottom: "conv4_em"
2629 |   top: "conv4_em"
2630 |   param {
2631 |     lr_mult: 0
2632 |     decay_mult: 0
2633 |   }
2634 |   param {
2635 |     lr_mult: 0
2636 |     decay_mult: 0
2637 |   }
2638 |   param {
2639 |     lr_mult: 0
2640 |     decay_mult: 0
2641 |   }
2642 | }
2643 | layer {
2644 |   name: "conv4_em/scale"
2645 |   type: "Scale"
2646 |   bottom: "conv4_em"
2647 |   top: "conv4_em"
2648 |   param {
2649 |     lr_mult: 1
2650 |     decay_mult: 0
2651 |   }
2652 |   param {
2653 |     lr_mult: 1
2654 |     decay_mult: 0
2655 |   }
2656 |   scale_param {
2657 |     bias_term: true
2658 |   }
2659 | }
2660 | 
2661 | layer {
2662 |   name: "conv4_1_ex"
2663 |   type: "Convolution"
2664 |   bottom: "conv4_em"
2665 |   top: "conv4_1_ex"
2666 |   param {
2667 |     lr_mult: 1
2668 |     decay_mult: 1
2669 |   }
2670 |   convolution_param {
2671 |     num_output: 256
2672 |     kernel_size: 1
2673 |     stride: 1
2674 |     pad: 0
2675 |     weight_filler {
2676 |       type: "xavier"
2677 |     }
2678 |     bias_term:false
2679 |   }
2680 | }
2681 | 
2682 | layer {
2683 |   name: "conv4_1_ex/bn"
2684 |   type: "BatchNorm"
2685 |   bottom: "conv4_1_ex"
2686 |   top: "conv4_1_ex"
2687 |   param {
2688 |     lr_mult: 0
2689 |     decay_mult: 0
2690 |   }
2691 |   param {
2692 |     lr_mult: 0
2693 |     decay_mult: 0
2694 |   }
2695 |   param {
2696 |     lr_mult: 0
2697 |     decay_mult: 0
2698 |   }
2699 | }
2700 | layer {
2701 |   name: "conv4_1_ex/scale"
2702 |   type: "Scale"
2703 |   bottom: "conv4_1_ex"
2704 |   top: "conv4_1_ex"
2705 |   param {
2706 |     lr_mult: 1
2707 |     decay_mult: 0
2708 |   }
2709 |   param {
2710 |     lr_mult: 1
2711 |     decay_mult: 0
2712 |   }
2713 |   scale_param {
2714 |     bias_term: true
2715 |   }
2716 | }
2717 | layer {
2718 |   name: "relu4_1_ex"
2719 |   type: "PReLU"
2720 |   bottom: "conv4_1_ex"
2721 |   top: "conv4_1_ex"
2722 | }
2723 | 
2724 | 
2725 | layer {
2726 |   name: "conv4_1_dw"
2727 |   type: "DepthwiseConvolution"
2728 |   bottom: "conv4_1_ex"
2729 |   top: "conv4_1_dw"
2730 |   param {
2731 |     lr_mult: 1
2732 |     decay_mult: 1
2733 |   }
2734 |   convolution_param {
2735 |     num_output: 256
2736 |     group:256
2737 |     kernel_size: 3
2738 |     stride: 1
2739 |     pad: 1
2740 |     weight_filler {
2741 |       type: "xavier"
2742 |     }
2743 |     bias_term:false
2744 |     engine: CAFFE
2745 |   }
2746 | }
2747 | 
2748 | layer {
2749 |   name: "conv4_1_dw/bn"
2750 |   type: "BatchNorm"
2751 |   bottom: "conv4_1_dw"
2752 |   top: "conv4_1_dw"
2753 |   param {
2754 |     lr_mult: 0
2755 |     decay_mult: 0
2756 |   }
2757 |   param {
2758 |     lr_mult: 0
2759 |     decay_mult: 0
2760 |   }
2761 |   param {
2762 |     lr_mult: 0
2763 |     decay_mult: 0
2764 |   }
2765 | }
2766 | layer {
2767 |   name: "conv4_1_dw/scale"
2768 |   type: "Scale"
2769 |   bottom: "conv4_1_dw"
2770 |   top: "conv4_1_dw"
2771 |   param {
2772 |     lr_mult: 1
2773 |     decay_mult: 0
2774 |   }
2775 |   param {
2776 |     lr_mult: 1
2777 |     decay_mult: 0
2778 |   }
2779 |   scale_param {
2780 |     bias_term: true
2781 |   }
2782 | }
2783 | layer {
2784 |   name: "relu4_1_dw"
2785 |   type: "PReLU"
2786 |   bottom: "conv4_1_dw"
2787 |   top: "conv4_1_dw"
2788 | }
2789 | 
2790 | layer {
2791 |   name: "conv4_1_em"
2792 |   type: "Convolution"
2793 |   bottom: "conv4_1_dw"
2794 |   top: "conv4_1_em"
2795 |   param {
2796 |     lr_mult: 1
2797 |     decay_mult: 1
2798 |   }
2799 |   convolution_param {
2800 |     num_output: 128
2801 |     kernel_size: 1
2802 |     stride: 1
2803 |     pad: 0
2804 |     weight_filler {
2805 |       type: "xavier"
2806 |     }
2807 |     bias_term:false
2808 |   }
2809 | }
2810 | 
2811 | 
2812 | layer {
2813 |   name: "conv4_1_em/bn"
2814 |   type: "BatchNorm"
2815 |   bottom: "conv4_1_em"
2816 |   top: "conv4_1_em"
2817 |   param {
2818 |     lr_mult: 0
2819 |     decay_mult: 0
2820 |   }
2821 |   param {
2822 |     lr_mult: 0
2823 |     decay_mult: 0
2824 |   }
2825 |   param {
2826 |     lr_mult: 0
2827 |     decay_mult: 0
2828 |   }
2829 | }
2830 | layer {
2831 |   name: "conv4_1_em/scale"
2832 |   type: "Scale"
2833 |   bottom: "conv4_1_em"
2834 |   top: "conv4_1_em"
2835 |   param {
2836 |     lr_mult: 1
2837 |     decay_mult: 0
2838 |   }
2839 |   param {
2840 |     lr_mult: 1
2841 |     decay_mult: 0
2842 |   }
2843 |   scale_param {
2844 |     bias_term: true
2845 |   }
2846 | }
2847 | 
2848 | layer {
2849 |   name: "res4_1"
2850 |   type: "Eltwise"
2851 |   bottom: "conv4_em"
2852 |   bottom: "conv4_1_em"
2853 |   top: "res4_1"
2854 | }
2855 | 
2856 | 
2857 | layer {
2858 |   name: "conv4_2_ex"
2859 |   type: "Convolution"
2860 |   bottom: "res4_1"
2861 |   top: "conv4_2_ex"
2862 |   param {
2863 |     lr_mult: 1
2864 |     decay_mult: 1
2865 |   }
2866 |   convolution_param {
2867 |     num_output: 256
2868 |     kernel_size: 1
2869 |     stride: 1
2870 |     pad: 0
2871 |     weight_filler {
2872 |       type: "xavier"
2873 |     }
2874 |     bias_term:false
2875 |   }
2876 | }
2877 | 
2878 | layer {
2879 |   name: "conv4_2_ex/bn"
2880 |   type: "BatchNorm"
2881 |   bottom: "conv4_2_ex"
2882 |   top: "conv4_2_ex"
2883 |   param {
2884 |     lr_mult: 0
2885 |     decay_mult: 0
2886 |   }
2887 |   param {
2888 |     lr_mult: 0
2889 |     decay_mult: 0
2890 |   }
2891 |   param {
2892 |     lr_mult: 0
2893 |     decay_mult: 0
2894 |   }
2895 | }
2896 | layer {
2897 |   name: "conv4_2_ex/scale"
2898 |   type: "Scale"
2899 |   bottom: "conv4_2_ex"
2900 |   top: "conv4_2_ex"
2901 |   param {
2902 |     lr_mult: 1
2903 |     decay_mult: 0
2904 |   }
2905 |   param {
2906 |     lr_mult: 1
2907 |     decay_mult: 0
2908 |   }
2909 |   scale_param {
2910 |     bias_term: true
2911 |   }
2912 | }
2913 | layer {
2914 |   name: "relu4_2_ex"
2915 |   type: "PReLU"
2916 |   bottom: "conv4_2_ex"
2917 |   top: "conv4_2_ex"
2918 | }
2919 | 
2920 | 
2921 | layer {
2922 |   name: "conv4_2_dw"
2923 |   type: "DepthwiseConvolution"
2924 |   bottom: "conv4_2_ex"
2925 |   top: "conv4_2_dw"
2926 |   param {
2927 |     lr_mult: 1
2928 |     decay_mult: 1
2929 |   }
2930 |   convolution_param {
2931 |     num_output: 256
2932 |     group:256
2933 |     kernel_size: 3
2934 |     stride: 1
2935 |     pad: 1
2936 |     weight_filler {
2937 |       type: "xavier"
2938 |     }
2939 |     bias_term:false
2940 |     engine: CAFFE
2941 |   }
2942 | }
2943 | 
2944 | layer {
2945 |   name: "conv4_2_dw/bn"
2946 |   type: "BatchNorm"
2947 |   bottom: "conv4_2_dw"
2948 |   top: "conv4_2_dw"
2949 |   param {
2950 |     lr_mult: 0
2951 |     decay_mult: 0
2952 |   }
2953 |   param {
2954 |     lr_mult: 0
2955 |     decay_mult: 0
2956 |   }
2957 |   param {
2958 |     lr_mult: 0
2959 |     decay_mult: 0
2960 |   }
2961 | }
2962 | layer {
2963 |   name: "conv4_2_dw/scale"
2964 |   type: "Scale"
2965 |   bottom: "conv4_2_dw"
2966 |   top: "conv4_2_dw"
2967 |   param {
2968 |     lr_mult: 1
2969 |     decay_mult: 0
2970 |   }
2971 |   param {
2972 |     lr_mult: 1
2973 |     decay_mult: 0
2974 |   }
2975 |   scale_param {
2976 |     bias_term: true
2977 |   }
2978 | }
2979 | layer {
2980 |   name: "relu4_2_dw"
2981 |   type: "PReLU"
2982 |   bottom: "conv4_2_dw"
2983 |   top: "conv4_2_dw"
2984 | }
2985 | 
2986 | layer {
2987 |   name: "conv4_2_em"
2988 |   type: "Convolution"
2989 |   bottom: "conv4_2_dw"
2990 |   top: "conv4_2_em"
2991 |   param {
2992 |     lr_mult: 1
2993 |     decay_mult: 1
2994 |   }
2995 |   convolution_param {
2996 |     num_output: 128
2997 |     kernel_size: 1
2998 |     stride: 1
2999 |     pad: 0
3000 |     weight_filler {
3001 |       type: "xavier"
3002 |     }
3003 |     bias_term:false
3004 |   }
3005 | }
3006 | 
3007 | 
3008 | layer {
3009 |   name: "conv4_2_em/bn"
3010 |   type: "BatchNorm"
3011 |   bottom: "conv4_2_em"
3012 |   top: "conv4_2_em"
3013 |   param {
3014 |     lr_mult: 0
3015 |     decay_mult: 0
3016 |   }
3017 |   param {
3018 |     lr_mult: 0
3019 |     decay_mult: 0
3020 |   }
3021 |   param {
3022 |     lr_mult: 0
3023 |     decay_mult: 0
3024 |   }
3025 | }
3026 | layer {
3027 |   name: "conv4_2_em/scale"
3028 |   type: "Scale"
3029 |   bottom: "conv4_2_em"
3030 |   top: "conv4_2_em"
3031 |   param {
3032 |     lr_mult: 1
3033 |     decay_mult: 0
3034 |   }
3035 |   param {
3036 |     lr_mult: 1
3037 |     decay_mult: 0
3038 |   }
3039 |   scale_param {
3040 |     bias_term: true
3041 |   }
3042 | }
3043 | 
3044 | layer {
3045 |   name: "res4_2"
3046 |   type: "Eltwise"
3047 |   bottom: "res4_1"
3048 |   bottom: "conv4_2_em"
3049 |   top: "res4_2"
3050 | }
3051 | 
3052 | layer {
3053 |   name: "conv5_ex"
3054 |   type: "Convolution"
3055 |   bottom: "res4_2"
3056 |   top: "conv5_ex"
3057 |   param {
3058 |     lr_mult: 1
3059 |     decay_mult: 1
3060 |   }
3061 |   convolution_param {
3062 |     num_output: 512
3063 |     kernel_size: 1
3064 |     stride: 1
3065 |     pad: 0
3066 |     weight_filler {
3067 |       type: "xavier"
3068 |     }
3069 |     bias_term:false
3070 |   }
3071 | }
3072 | 
3073 | 
3074 | layer {
3075 |   name: "conv5_ex/bn"
3076 |   type: "BatchNorm"
3077 |   bottom: "conv5_ex"
3078 |   top: "conv5_ex"
3079 |   param {
3080 |     lr_mult: 0
3081 |     decay_mult: 0
3082 |   }
3083 |   param {
3084 |     lr_mult: 0
3085 |     decay_mult: 0
3086 |   }
3087 |   param {
3088 |     lr_mult: 0
3089 |     decay_mult: 0
3090 |   }
3091 | }
3092 | layer {
3093 |   name: "conv5_ex/scale"
3094 |   type: "Scale"
3095 |   bottom: "conv5_ex"
3096 |   top: "conv5_ex"
3097 |   param {
3098 |     lr_mult: 1
3099 |     decay_mult: 0
3100 |   }
3101 |   param {
3102 |     lr_mult: 1
3103 |     decay_mult: 0
3104 |   }
3105 |   scale_param {
3106 |     bias_term: true
3107 |   }
3108 | }
3109 | layer {
3110 |   name: "relu5_ex"
3111 |   type: "PReLU"
3112 |   bottom: "conv5_ex"
3113 |   top: "conv5_ex"
3114 | }
3115 | 
3116 | layer {
3117 |   name: "conv5_dw"
3118 |   type: "DepthwiseConvolution"
3119 |   bottom: "conv5_ex"
3120 |   top: "conv5_dw"
3121 |   param {
3122 |     lr_mult: 1
3123 |     decay_mult: 1
3124 |   }
3125 |   convolution_param {
3126 |     num_output: 512
3127 |     group:512
3128 |     kernel_h: 7
3129 |     kernel_w:6
3130 |     stride: 1
3131 |     pad: 0
3132 |     weight_filler {
3133 |       type: "xavier"
3134 |     }
3135 |     bias_term:false
3136 |     engine: CAFFE
3137 |   }
3138 | }
3139 | 
3140 | layer {
3141 |   name: "conv5_dw/bn"
3142 |   type: "BatchNorm"
3143 |   bottom: "conv5_dw"
3144 |   top: "conv5_dw"
3145 |   param {
3146 |     lr_mult: 0
3147 |     decay_mult: 0
3148 |   }
3149 |   param {
3150 |     lr_mult: 0
3151 |     decay_mult: 0
3152 |   }
3153 |   param {
3154 |     lr_mult: 0
3155 |     decay_mult: 0
3156 |   }
3157 | }
3158 | layer {
3159 |   name: "conv5_dw/scale"
3160 |   type: "Scale"
3161 |   bottom: "conv5_dw"
3162 |   top: "conv5_dw"
3163 |   param {
3164 |     lr_mult: 1
3165 |     decay_mult: 0
3166 |   }
3167 |   param {
3168 |     lr_mult: 1
3169 |     decay_mult: 0
3170 |   }
3171 |   scale_param {
3172 |     bias_term: true
3173 |   }
3174 | }
3175 | 
3176 | 
3177 | layer {
3178 |   name: "fc5"
3179 |   type: "InnerProduct"
3180 |   bottom: "conv5_dw"
3181 |   top: "fc5"
3182 |   param {
3183 |     lr_mult: 1
3184 |     decay_mult: 10
3185 |   }
3186 |   inner_product_param {
3187 |     num_output: 128
3188 |     weight_filler {
3189 |       type: "xavier"
3190 |     }
3191 |     bias_term:false
3192 |   }
3193 | }
3194 | 
3195 | 
3196 | layer {
3197 |   name: "norm1"
3198 |   type: "Normalize"
3199 |   bottom: "fc5"
3200 |   top: "norm1"
3201 | }
3202 | 
3203 | layer {
3204 |   name: "fc6_l2"
3205 |   type: "InnerProduct"
3206 |   bottom: "norm1"
3207 |   top: "fc6"
3208 |   param {
3209 |     lr_mult: 1
3210 |     decay_mult:10
3211 |   }
3212 |   inner_product_param{
3213 |     num_output: 9131
3214 |     normalize: true
3215 |     weight_filler {
3216 |       type: "xavier"
3217 |     }
3218 |     bias_term: false
3219 |   }
3220 | }
3221 | 
3222 | layer{
3223 |   name: "label_specific_margin"
3224 |   type: "LabelSpecificAdd"
3225 |   bottom: "fc6"
3226 |   bottom: "label"
3227 |   top: "fc6_margin"
3228 |   label_specific_add_param {
3229 |     bias: -0.3
3230 |   }
3231 | }
3232 | 
3233 | layer{
3234 |   name: "fc6_margin_scale"
3235 |   type: "Scale"
3236 |   bottom: "fc6_margin"
3237 |   top: "fc6_margin_scale"
3238 |   param {
3239 |     lr_mult: 0
3240 |     decay_mult:0
3241 |   }
3242 |   scale_param{
3243 |     min_value: 30
3244 |     filler{
3245 |       type:"constant"
3246 |       value:30
3247 |     }
3248 |   }
3249 | }
3250 | 
3251 | layer{
3252 |   name: "softmax_loss"
3253 |   type: "SoftmaxWithLoss"
3254 |   bottom: "fc6_margin_scale"
3255 |   bottom:"label"
3256 |   top: "softmax_loss"
3257 | }
3258 | 
3259 | 
3260 | 
3261 | 


--------------------------------------------------------------------------------
/MobileFaceNet_deploy.prototxt:
--------------------------------------------------------------------------------
   1 | input:"data"
   2 | input_dim:1
   3 | input_dim:3
   4 | input_dim:112
   5 | input_dim:96
   6 | ############## CNN Architecture ###############
   7 | layer {
   8 |   name: "conv1"
   9 |   type: "Convolution"
  10 |   bottom: "data"
  11 |   top: "conv1"
  12 |   param {
  13 |     lr_mult: 1
  14 |     decay_mult: 1
  15 |   }
  16 |   convolution_param {
  17 |     num_output: 64
  18 |     kernel_size: 3
  19 |     stride: 2
  20 |     pad: 1
  21 |     weight_filler {
  22 |       type: "xavier"
  23 |     }
  24 |     bias_term:false
  25 |   }
  26 | }
  27 | layer {
  28 |   name: "conv1/bn"
  29 |   type: "BatchNorm"
  30 |   bottom: "conv1"
  31 |   top: "conv1"
  32 |   param {
  33 |     lr_mult: 0
  34 |     decay_mult: 0
  35 |   }
  36 |   param {
  37 |     lr_mult: 0
  38 |     decay_mult: 0
  39 |   }
  40 |   param {
  41 |     lr_mult: 0
  42 |     decay_mult: 0
  43 |   }
  44 | }
  45 | layer {
  46 |   name: "conv1/scale"
  47 |   type: "Scale"
  48 |   bottom: "conv1"
  49 |   top: "conv1"
  50 |   param {
  51 |     lr_mult: 1
  52 |     decay_mult: 0
  53 |   }
  54 |   param {
  55 |     lr_mult: 1
  56 |     decay_mult: 0
  57 |   }
  58 |   scale_param {
  59 |     bias_term: true
  60 |   }
  61 | }
  62 | layer {
  63 |   name: "relu1"
  64 |   type: "PReLU"
  65 |   bottom: "conv1"
  66 |   top: "conv1"
  67 | }
  68 | layer {
  69 |   name: "conv1_dw"
  70 |   type: "DepthwiseConvolution"
  71 |   bottom: "conv1"
  72 |   top: "conv1_dw"
  73 |   param {
  74 |     lr_mult: 1
  75 |     decay_mult: 1
  76 |   }
  77 |   convolution_param {
  78 |     num_output: 64
  79 |     group:64
  80 |     kernel_size: 3
  81 |     stride: 1
  82 |     pad: 1
  83 |     weight_filler {
  84 |       type: "xavier"
  85 |     }
  86 |     bias_term:false
  87 |   }
  88 | }
  89 | layer {
  90 |   name: "conv1_dw/bn"
  91 |   type: "BatchNorm"
  92 |   bottom: "conv1_dw"
  93 |   top: "conv1_dw"
  94 |   param {
  95 |     lr_mult: 0
  96 |     decay_mult: 0
  97 |   }
  98 |   param {
  99 |     lr_mult: 0
 100 |     decay_mult: 0
 101 |   }
 102 |   param {
 103 |     lr_mult: 0
 104 |     decay_mult: 0
 105 |   }
 106 | }
 107 | layer {
 108 |   name: "conv1_dw/scale"
 109 |   type: "Scale"
 110 |   bottom: "conv1_dw"
 111 |   top: "conv1_dw"
 112 |   param {
 113 |     lr_mult: 1
 114 |     decay_mult: 0
 115 |   }
 116 |   param {
 117 |     lr_mult: 1
 118 |     decay_mult: 0
 119 |   }
 120 |   scale_param {
 121 |     bias_term: true
 122 |   }
 123 | }
 124 | layer {
 125 |   name: "relu1_dw"
 126 |   type: "PReLU"
 127 |   bottom: "conv1_dw"
 128 |   top: "conv1_dw"
 129 | }
 130 | layer {
 131 |   name: "conv2_ex"
 132 |   type: "Convolution"
 133 |   bottom: "conv1_dw"
 134 |   top: "conv2_ex"
 135 |   param {
 136 |     lr_mult: 1
 137 |     decay_mult: 1
 138 |   }
 139 |   convolution_param {
 140 |     num_output: 128
 141 |     kernel_size: 1
 142 |     stride: 1
 143 |     pad: 0
 144 |     weight_filler {
 145 |       type: "xavier"
 146 |     }
 147 |     bias_term:false
 148 |   }
 149 | }
 150 | layer {
 151 |   name: "conv2_ex/bn"
 152 |   type: "BatchNorm"
 153 |   bottom: "conv2_ex"
 154 |   top: "conv2_ex"
 155 |   param {
 156 |     lr_mult: 0
 157 |     decay_mult: 0
 158 |   }
 159 |   param {
 160 |     lr_mult: 0
 161 |     decay_mult: 0
 162 |   }
 163 |   param {
 164 |     lr_mult: 0
 165 |     decay_mult: 0
 166 |   }
 167 | }
 168 | layer {
 169 |   name: "conv2_ex/scale"
 170 |   type: "Scale"
 171 |   bottom: "conv2_ex"
 172 |   top: "conv2_ex"
 173 |   param {
 174 |     lr_mult: 1
 175 |     decay_mult: 0
 176 |   }
 177 |   param {
 178 |     lr_mult: 1
 179 |     decay_mult: 0
 180 |   }
 181 |   scale_param {
 182 |     bias_term: true
 183 |   }
 184 | }
 185 | layer {
 186 |   name: "relu2_ex"
 187 |   type: "PReLU"
 188 |   bottom: "conv2_ex"
 189 |   top: "conv2_ex"
 190 | }
 191 | 
 192 | layer {
 193 |   name: "conv2_dw"
 194 |   type: "DepthwiseConvolution"
 195 |   bottom: "conv2_ex"
 196 |   top: "conv2_dw"
 197 |   param {
 198 |     lr_mult: 1
 199 |     decay_mult: 1
 200 |   }
 201 |   convolution_param {
 202 |     num_output: 128
 203 |     group:128
 204 |     kernel_size: 3
 205 |     stride: 2
 206 |     pad: 1
 207 |     weight_filler {
 208 |       type: "xavier"
 209 |     }
 210 |     bias_term:false
 211 |     engine: CAFFE
 212 |   }
 213 | }
 214 | 
 215 | layer {
 216 |   name: "conv2_dw/bn"
 217 |   type: "BatchNorm"
 218 |   bottom: "conv2_dw"
 219 |   top: "conv2_dw"
 220 |   param {
 221 |     lr_mult: 0
 222 |     decay_mult: 0
 223 |   }
 224 |   param {
 225 |     lr_mult: 0
 226 |     decay_mult: 0
 227 |   }
 228 |   param {
 229 |     lr_mult: 0
 230 |     decay_mult: 0
 231 |   }
 232 | }
 233 | layer {
 234 |   name: "conv2_dw/scale"
 235 |   type: "Scale"
 236 |   bottom: "conv2_dw"
 237 |   top: "conv2_dw"
 238 |   param {
 239 |     lr_mult: 1
 240 |     decay_mult: 0
 241 |   }
 242 |   param {
 243 |     lr_mult: 1
 244 |     decay_mult: 0
 245 |   }
 246 |   scale_param {
 247 |     bias_term: true
 248 |   }
 249 | }
 250 | layer {
 251 |   name: "relu2_dw"
 252 |   type: "PReLU"
 253 |   bottom: "conv2_dw"
 254 |   top: "conv2_dw"
 255 | }
 256 | 
 257 | layer {
 258 |   name: "conv2_em"
 259 |   type: "Convolution"
 260 |   bottom: "conv2_dw"
 261 |   top: "conv2_em"
 262 |   param {
 263 |     lr_mult: 1
 264 |     decay_mult: 1
 265 |   }
 266 |   convolution_param {
 267 |     num_output: 64
 268 |     kernel_size: 1
 269 |     stride: 1
 270 |     pad: 0
 271 |     weight_filler {
 272 |       type: "xavier"
 273 |     }
 274 |     bias_term:false
 275 |   }
 276 | }
 277 | 
 278 | 
 279 | layer {
 280 |   name: "conv2_em/bn"
 281 |   type: "BatchNorm"
 282 |   bottom: "conv2_em"
 283 |   top: "conv2_em"
 284 |   param {
 285 |     lr_mult: 0
 286 |     decay_mult: 0
 287 |   }
 288 |   param {
 289 |     lr_mult: 0
 290 |     decay_mult: 0
 291 |   }
 292 |   param {
 293 |     lr_mult: 0
 294 |     decay_mult: 0
 295 |   }
 296 | }
 297 | layer {
 298 |   name: "conv2_em/scale"
 299 |   type: "Scale"
 300 |   bottom: "conv2_em"
 301 |   top: "conv2_em"
 302 |   param {
 303 |     lr_mult: 1
 304 |     decay_mult: 0
 305 |   }
 306 |   param {
 307 |     lr_mult: 1
 308 |     decay_mult: 0
 309 |   }
 310 |   scale_param {
 311 |     bias_term: true
 312 |   }
 313 | }
 314 | 
 315 | layer {
 316 |   name: "conv2_1_ex"
 317 |   type: "Convolution"
 318 |   bottom: "conv2_em"
 319 |   top: "conv2_1_ex"
 320 |   param {
 321 |     lr_mult: 1
 322 |     decay_mult: 1
 323 |   }
 324 |   convolution_param {
 325 |     num_output: 128
 326 |     kernel_size: 1
 327 |     stride: 1
 328 |     pad: 0
 329 |     weight_filler {
 330 |       type: "xavier"
 331 |     }
 332 |     bias_term:false
 333 |   }
 334 | }
 335 | 
 336 | layer {
 337 |   name: "conv2_1_ex/bn"
 338 |   type: "BatchNorm"
 339 |   bottom: "conv2_1_ex"
 340 |   top: "conv2_1_ex"
 341 |   param {
 342 |     lr_mult: 0
 343 |     decay_mult: 0
 344 |   }
 345 |   param {
 346 |     lr_mult: 0
 347 |     decay_mult: 0
 348 |   }
 349 |   param {
 350 |     lr_mult: 0
 351 |     decay_mult: 0
 352 |   }
 353 | }
 354 | layer {
 355 |   name: "conv2_1_ex/scale"
 356 |   type: "Scale"
 357 |   bottom: "conv2_1_ex"
 358 |   top: "conv2_1_ex"
 359 |   param {
 360 |     lr_mult: 1
 361 |     decay_mult: 0
 362 |   }
 363 |   param {
 364 |     lr_mult: 1
 365 |     decay_mult: 0
 366 |   }
 367 |   scale_param {
 368 |     bias_term: true
 369 |   }
 370 | }
 371 | layer {
 372 |   name: "relu2_1_ex"
 373 |   type: "PReLU"
 374 |   bottom: "conv2_1_ex"
 375 |   top: "conv2_1_ex"
 376 | }
 377 | 
 378 | 
 379 | layer {
 380 |   name: "conv2_1_dw"
 381 |   type: "DepthwiseConvolution"
 382 |   bottom: "conv2_1_ex"
 383 |   top: "conv2_1_dw"
 384 |   param {
 385 |     lr_mult: 1
 386 |     decay_mult: 1
 387 |   }
 388 |   convolution_param {
 389 |     num_output: 128
 390 |     group:128
 391 |     kernel_size: 3
 392 |     stride: 1
 393 |     pad: 1
 394 |     weight_filler {
 395 |       type: "xavier"
 396 |     }
 397 |     bias_term:false
 398 |     engine: CAFFE
 399 |   }
 400 | }
 401 | 
 402 | layer {
 403 |   name: "conv2_1_dw/bn"
 404 |   type: "BatchNorm"
 405 |   bottom: "conv2_1_dw"
 406 |   top: "conv2_1_dw"
 407 |   param {
 408 |     lr_mult: 0
 409 |     decay_mult: 0
 410 |   }
 411 |   param {
 412 |     lr_mult: 0
 413 |     decay_mult: 0
 414 |   }
 415 |   param {
 416 |     lr_mult: 0
 417 |     decay_mult: 0
 418 |   }
 419 | }
 420 | layer {
 421 |   name: "conv2_1_dw/scale"
 422 |   type: "Scale"
 423 |   bottom: "conv2_1_dw"
 424 |   top: "conv2_1_dw"
 425 |   param {
 426 |     lr_mult: 1
 427 |     decay_mult: 0
 428 |   }
 429 |   param {
 430 |     lr_mult: 1
 431 |     decay_mult: 0
 432 |   }
 433 |   scale_param {
 434 |     bias_term: true
 435 |   }
 436 | }
 437 | layer {
 438 |   name: "relu2_1_dw"
 439 |   type: "PReLU"
 440 |   bottom: "conv2_1_dw"
 441 |   top: "conv2_1_dw"
 442 | }
 443 | 
 444 | layer {
 445 |   name: "conv2_1_em"
 446 |   type: "Convolution"
 447 |   bottom: "conv2_1_dw"
 448 |   top: "conv2_1_em"
 449 |   param {
 450 |     lr_mult: 1
 451 |     decay_mult: 1
 452 |   }
 453 |   convolution_param {
 454 |     num_output: 64
 455 |     kernel_size: 1
 456 |     stride: 1
 457 |     pad: 0
 458 |     weight_filler {
 459 |       type: "xavier"
 460 |     }
 461 |     bias_term:false
 462 |   }
 463 | }
 464 | 
 465 | 
 466 | layer {
 467 |   name: "conv2_1_em/bn"
 468 |   type: "BatchNorm"
 469 |   bottom: "conv2_1_em"
 470 |   top: "conv2_1_em"
 471 |   param {
 472 |     lr_mult: 0
 473 |     decay_mult: 0
 474 |   }
 475 |   param {
 476 |     lr_mult: 0
 477 |     decay_mult: 0
 478 |   }
 479 |   param {
 480 |     lr_mult: 0
 481 |     decay_mult: 0
 482 |   }
 483 | }
 484 | layer {
 485 |   name: "conv2_1_em/scale"
 486 |   type: "Scale"
 487 |   bottom: "conv2_1_em"
 488 |   top: "conv2_1_em"
 489 |   param {
 490 |     lr_mult: 1
 491 |     decay_mult: 0
 492 |   }
 493 |   param {
 494 |     lr_mult: 1
 495 |     decay_mult: 0
 496 |   }
 497 |   scale_param {
 498 |     bias_term: true
 499 |   }
 500 | }
 501 | 
 502 | layer {
 503 |   name: "res2_1"
 504 |   type: "Eltwise"
 505 |   bottom: "conv2_em"
 506 |   bottom: "conv2_1_em"
 507 |   top: "res2_1"
 508 | }
 509 | 
 510 | layer {
 511 |   name: "conv2_2_ex"
 512 |   type: "Convolution"
 513 |   bottom: "res2_1"
 514 |   top: "conv2_2_ex"
 515 |   param {
 516 |     lr_mult: 1
 517 |     decay_mult: 1
 518 |   }
 519 |   convolution_param {
 520 |     num_output: 128
 521 |     kernel_size: 1
 522 |     stride: 1
 523 |     pad: 0
 524 |     weight_filler {
 525 |       type: "xavier"
 526 |     }
 527 |     bias_term:false
 528 |   }
 529 | }
 530 | 
 531 | layer {
 532 |   name: "conv2_2_ex/bn"
 533 |   type: "BatchNorm"
 534 |   bottom: "conv2_2_ex"
 535 |   top: "conv2_2_ex"
 536 |   param {
 537 |     lr_mult: 0
 538 |     decay_mult: 0
 539 |   }
 540 |   param {
 541 |     lr_mult: 0
 542 |     decay_mult: 0
 543 |   }
 544 |   param {
 545 |     lr_mult: 0
 546 |     decay_mult: 0
 547 |   }
 548 | }
 549 | layer {
 550 |   name: "conv2_2_ex/scale"
 551 |   type: "Scale"
 552 |   bottom: "conv2_2_ex"
 553 |   top: "conv2_2_ex"
 554 |   param {
 555 |     lr_mult: 1
 556 |     decay_mult: 0
 557 |   }
 558 |   param {
 559 |     lr_mult: 1
 560 |     decay_mult: 0
 561 |   }
 562 |   scale_param {
 563 |     bias_term: true
 564 |   }
 565 | }
 566 | layer {
 567 |   name: "relu2_2_ex"
 568 |   type: "PReLU"
 569 |   bottom: "conv2_2_ex"
 570 |   top: "conv2_2_ex"
 571 | }
 572 | 
 573 | 
 574 | layer {
 575 |   name: "conv2_2_dw"
 576 |   type: "DepthwiseConvolution"
 577 |   bottom: "conv2_2_ex"
 578 |   top: "conv2_2_dw"
 579 |   param {
 580 |     lr_mult: 1
 581 |     decay_mult: 1
 582 |   }
 583 |   convolution_param {
 584 |     num_output: 128
 585 |     group:128
 586 |     kernel_size: 3
 587 |     stride: 1
 588 |     pad: 1
 589 |     weight_filler {
 590 |       type: "xavier"
 591 |     }
 592 |     bias_term:false
 593 |     engine: CAFFE
 594 |   }
 595 | }
 596 | 
 597 | layer {
 598 |   name: "conv2_2_dw/bn"
 599 |   type: "BatchNorm"
 600 |   bottom: "conv2_2_dw"
 601 |   top: "conv2_2_dw"
 602 |   param {
 603 |     lr_mult: 0
 604 |     decay_mult: 0
 605 |   }
 606 |   param {
 607 |     lr_mult: 0
 608 |     decay_mult: 0
 609 |   }
 610 |   param {
 611 |     lr_mult: 0
 612 |     decay_mult: 0
 613 |   }
 614 | }
 615 | layer {
 616 |   name: "conv2_2_dw/scale"
 617 |   type: "Scale"
 618 |   bottom: "conv2_2_dw"
 619 |   top: "conv2_2_dw"
 620 |   param {
 621 |     lr_mult: 1
 622 |     decay_mult: 0
 623 |   }
 624 |   param {
 625 |     lr_mult: 1
 626 |     decay_mult: 0
 627 |   }
 628 |   scale_param {
 629 |     bias_term: true
 630 |   }
 631 | }
 632 | layer {
 633 |   name: "relu2_2_dw"
 634 |   type: "PReLU"
 635 |   bottom: "conv2_2_dw"
 636 |   top: "conv2_2_dw"
 637 | }
 638 | 
 639 | layer {
 640 |   name: "conv2_2_em"
 641 |   type: "Convolution"
 642 |   bottom: "conv2_2_dw"
 643 |   top: "conv2_2_em"
 644 |   param {
 645 |     lr_mult: 1
 646 |     decay_mult: 1
 647 |   }
 648 |   convolution_param {
 649 |     num_output: 64
 650 |     kernel_size: 1
 651 |     stride: 1
 652 |     pad: 0
 653 |     weight_filler {
 654 |       type: "xavier"
 655 |     }
 656 |     bias_term:false
 657 |   }
 658 | }
 659 | 
 660 | 
 661 | layer {
 662 |   name: "conv2_2_em/bn"
 663 |   type: "BatchNorm"
 664 |   bottom: "conv2_2_em"
 665 |   top: "conv2_2_em"
 666 |   param {
 667 |     lr_mult: 0
 668 |     decay_mult: 0
 669 |   }
 670 |   param {
 671 |     lr_mult: 0
 672 |     decay_mult: 0
 673 |   }
 674 |   param {
 675 |     lr_mult: 0
 676 |     decay_mult: 0
 677 |   }
 678 | }
 679 | layer {
 680 |   name: "conv2_2_em/scale"
 681 |   type: "Scale"
 682 |   bottom: "conv2_2_em"
 683 |   top: "conv2_2_em"
 684 |   param {
 685 |     lr_mult: 1
 686 |     decay_mult: 0
 687 |   }
 688 |   param {
 689 |     lr_mult: 1
 690 |     decay_mult: 0
 691 |   }
 692 |   scale_param {
 693 |     bias_term: true
 694 |   }
 695 | }
 696 | 
 697 | layer {
 698 |   name: "res2_2"
 699 |   type: "Eltwise"
 700 |   bottom: "res2_1"
 701 |   bottom: "conv2_2_em"
 702 |   top: "res2_2"
 703 | }
 704 | 
 705 | layer {
 706 |   name: "conv2_3_ex"
 707 |   type: "Convolution"
 708 |   bottom: "res2_2"
 709 |   top: "conv2_3_ex"
 710 |   param {
 711 |     lr_mult: 1
 712 |     decay_mult: 1
 713 |   }
 714 |   convolution_param {
 715 |     num_output: 128
 716 |     kernel_size: 1
 717 |     stride: 1
 718 |     pad: 0
 719 |     weight_filler {
 720 |       type: "xavier"
 721 |     }
 722 |     bias_term:false
 723 |   }
 724 | }
 725 | 
 726 | layer {
 727 |   name: "conv2_3_ex/bn"
 728 |   type: "BatchNorm"
 729 |   bottom: "conv2_3_ex"
 730 |   top: "conv2_3_ex"
 731 |   param {
 732 |     lr_mult: 0
 733 |     decay_mult: 0
 734 |   }
 735 |   param {
 736 |     lr_mult: 0
 737 |     decay_mult: 0
 738 |   }
 739 |   param {
 740 |     lr_mult: 0
 741 |     decay_mult: 0
 742 |   }
 743 | }
 744 | layer {
 745 |   name: "conv2_3_ex/scale"
 746 |   type: "Scale"
 747 |   bottom: "conv2_3_ex"
 748 |   top: "conv2_3_ex"
 749 |   param {
 750 |     lr_mult: 1
 751 |     decay_mult: 0
 752 |   }
 753 |   param {
 754 |     lr_mult: 1
 755 |     decay_mult: 0
 756 |   }
 757 |   scale_param {
 758 |     bias_term: true
 759 |   }
 760 | }
 761 | layer {
 762 |   name: "relu2_3_ex"
 763 |   type: "PReLU"
 764 |   bottom: "conv2_3_ex"
 765 |   top: "conv2_3_ex"
 766 | }
 767 | 
 768 | 
 769 | layer {
 770 |   name: "conv2_3_dw"
 771 |   type: "DepthwiseConvolution"
 772 |   bottom: "conv2_3_ex"
 773 |   top: "conv2_3_dw"
 774 |   param {
 775 |     lr_mult: 1
 776 |     decay_mult: 1
 777 |   }
 778 |   convolution_param {
 779 |     num_output: 128
 780 |     group:128
 781 |     kernel_size: 3
 782 |     stride: 1
 783 |     pad: 1
 784 |     weight_filler {
 785 |       type: "xavier"
 786 |     }
 787 |     bias_term:false
 788 |     engine: CAFFE
 789 |   }
 790 | }
 791 | 
 792 | layer {
 793 |   name: "conv2_3_dw/bn"
 794 |   type: "BatchNorm"
 795 |   bottom: "conv2_3_dw"
 796 |   top: "conv2_3_dw"
 797 |   param {
 798 |     lr_mult: 0
 799 |     decay_mult: 0
 800 |   }
 801 |   param {
 802 |     lr_mult: 0
 803 |     decay_mult: 0
 804 |   }
 805 |   param {
 806 |     lr_mult: 0
 807 |     decay_mult: 0
 808 |   }
 809 | }
 810 | layer {
 811 |   name: "conv2_3_dw/scale"
 812 |   type: "Scale"
 813 |   bottom: "conv2_3_dw"
 814 |   top: "conv2_3_dw"
 815 |   param {
 816 |     lr_mult: 1
 817 |     decay_mult: 0
 818 |   }
 819 |   param {
 820 |     lr_mult: 1
 821 |     decay_mult: 0
 822 |   }
 823 |   scale_param {
 824 |     bias_term: true
 825 |   }
 826 | }
 827 | layer {
 828 |   name: "relu2_3_dw"
 829 |   type: "PReLU"
 830 |   bottom: "conv2_3_dw"
 831 |   top: "conv2_3_dw"
 832 | }
 833 | 
 834 | layer {
 835 |   name: "conv2_3_em"
 836 |   type: "Convolution"
 837 |   bottom: "conv2_3_dw"
 838 |   top: "conv2_3_em"
 839 |   param {
 840 |     lr_mult: 1
 841 |     decay_mult: 1
 842 |   }
 843 |   convolution_param {
 844 |     num_output: 64
 845 |     kernel_size: 1
 846 |     stride: 1
 847 |     pad: 0
 848 |     weight_filler {
 849 |       type: "xavier"
 850 |     }
 851 |     bias_term:false
 852 |   }
 853 | }
 854 | 
 855 | 
 856 | layer {
 857 |   name: "conv2_3_em/bn"
 858 |   type: "BatchNorm"
 859 |   bottom: "conv2_3_em"
 860 |   top: "conv2_3_em"
 861 |   param {
 862 |     lr_mult: 0
 863 |     decay_mult: 0
 864 |   }
 865 |   param {
 866 |     lr_mult: 0
 867 |     decay_mult: 0
 868 |   }
 869 |   param {
 870 |     lr_mult: 0
 871 |     decay_mult: 0
 872 |   }
 873 | }
 874 | layer {
 875 |   name: "conv2_3_em/scale"
 876 |   type: "Scale"
 877 |   bottom: "conv2_3_em"
 878 |   top: "conv2_3_em"
 879 |   param {
 880 |     lr_mult: 1
 881 |     decay_mult: 0
 882 |   }
 883 |   param {
 884 |     lr_mult: 1
 885 |     decay_mult: 0
 886 |   }
 887 |   scale_param {
 888 |     bias_term: true
 889 |   }
 890 | }
 891 | 
 892 | layer {
 893 |   name: "res2_3"
 894 |   type: "Eltwise"
 895 |   bottom: "res2_2"
 896 |   bottom: "conv2_3_em"
 897 |   top: "res2_3"
 898 | }
 899 | 
 900 | layer {
 901 |   name: "conv2_4_ex"
 902 |   type: "Convolution"
 903 |   bottom: "res2_3"
 904 |   top: "conv2_4_ex"
 905 |   param {
 906 |     lr_mult: 1
 907 |     decay_mult: 1
 908 |   }
 909 | 
 910 |   convolution_param {
 911 |     num_output: 128
 912 |     kernel_size: 1
 913 |     stride: 1
 914 |     pad: 0
 915 |     weight_filler {
 916 |       type: "xavier"
 917 |     }
 918 |     bias_term:false
 919 |   }
 920 | }
 921 | 
 922 | layer {
 923 |   name: "conv2_4_ex/bn"
 924 |   type: "BatchNorm"
 925 |   bottom: "conv2_4_ex"
 926 |   top: "conv2_4_ex"
 927 |   param {
 928 |     lr_mult: 0
 929 |     decay_mult: 0
 930 |   }
 931 |   param {
 932 |     lr_mult: 0
 933 |     decay_mult: 0
 934 |   }
 935 |   param {
 936 |     lr_mult: 0
 937 |     decay_mult: 0
 938 |   }
 939 | }
 940 | layer {
 941 |   name: "conv2_4_ex/scale"
 942 |   type: "Scale"
 943 |   bottom: "conv2_4_ex"
 944 |   top: "conv2_4_ex"
 945 |   param {
 946 |     lr_mult: 1
 947 |     decay_mult: 0
 948 |   }
 949 |   param {
 950 |     lr_mult: 1
 951 |     decay_mult: 0
 952 |   }
 953 |   scale_param {
 954 |     bias_term: true
 955 |   }
 956 | }
 957 | layer {
 958 |   name: "relu2_4_ex"
 959 |   type: "PReLU"
 960 |   bottom: "conv2_4_ex"
 961 |   top: "conv2_4_ex"
 962 | }
 963 | 
 964 | 
 965 | layer {
 966 |   name: "conv2_4_dw"
 967 |   type: "DepthwiseConvolution"
 968 |   bottom: "conv2_4_ex"
 969 |   top: "conv2_4_dw"
 970 |   param {
 971 |     lr_mult: 1
 972 |     decay_mult: 1
 973 |   }
 974 |   convolution_param {
 975 |     num_output: 128
 976 |     group:128
 977 |     kernel_size: 3
 978 |     stride: 1
 979 |     pad: 1
 980 |     weight_filler {
 981 |       type: "xavier"
 982 |     }
 983 |     bias_term:false
 984 |     engine: CAFFE
 985 |   }
 986 | }
 987 | 
 988 | layer {
 989 |   name: "conv2_4_dw/bn"
 990 |   type: "BatchNorm"
 991 |   bottom: "conv2_4_dw"
 992 |   top: "conv2_4_dw"
 993 |   param {
 994 |     lr_mult: 0
 995 |     decay_mult: 0
 996 |   }
 997 |   param {
 998 |     lr_mult: 0
 999 |     decay_mult: 0
1000 |   }
1001 |   param {
1002 |     lr_mult: 0
1003 |     decay_mult: 0
1004 |   }
1005 | }
1006 | layer {
1007 |   name: "conv2_4_dw/scale"
1008 |   type: "Scale"
1009 |   bottom: "conv2_4_dw"
1010 |   top: "conv2_4_dw"
1011 |   param {
1012 |     lr_mult: 1
1013 |     decay_mult: 0
1014 |   }
1015 |   param {
1016 |     lr_mult: 1
1017 |     decay_mult: 0
1018 |   }
1019 |   scale_param {
1020 |     bias_term: true
1021 |   }
1022 | }
1023 | layer {
1024 |   name: "relu2_4_dw"
1025 |   type: "PReLU"
1026 |   bottom: "conv2_4_dw"
1027 |   top: "conv2_4_dw"
1028 | }
1029 | 
1030 | layer {
1031 |   name: "conv2_4_em"
1032 |   type: "Convolution"
1033 |   bottom: "conv2_4_dw"
1034 |   top: "conv2_4_em"
1035 |   param {
1036 |     lr_mult: 1
1037 |     decay_mult: 1
1038 |   }
1039 |   convolution_param {
1040 |     num_output: 64
1041 |     kernel_size: 1
1042 |     stride: 1
1043 |     pad: 0
1044 |     weight_filler {
1045 |       type: "xavier"
1046 |     }
1047 |     bias_term:false
1048 |   }
1049 | }
1050 | 
1051 | 
1052 | layer {
1053 |   name: "conv2_4_em/bn"
1054 |   type: "BatchNorm"
1055 |   bottom: "conv2_4_em"
1056 |   top: "conv2_4_em"
1057 |   param {
1058 |     lr_mult: 0
1059 |     decay_mult: 0
1060 |   }
1061 |   param {
1062 |     lr_mult: 0
1063 |     decay_mult: 0
1064 |   }
1065 |   param {
1066 |     lr_mult: 0
1067 |     decay_mult: 0
1068 |   }
1069 | }
1070 | layer {
1071 |   name: "conv2_4_em/scale"
1072 |   type: "Scale"
1073 |   bottom: "conv2_4_em"
1074 |   top: "conv2_4_em"
1075 |   param {
1076 |     lr_mult: 1
1077 |     decay_mult: 0
1078 |   }
1079 |   param {
1080 |     lr_mult: 1
1081 |     decay_mult: 0
1082 |   }
1083 |   scale_param {
1084 |     bias_term: true
1085 |   }
1086 | }
1087 | 
1088 | layer {
1089 |   name: "res2_4"
1090 |   type: "Eltwise"
1091 |   bottom: "res2_3"
1092 |   bottom: "conv2_4_em"
1093 |   top: "res2_4"
1094 | }
1095 | 
1096 | 
1097 | layer {
1098 |   name: "conv3_ex"
1099 |   type: "Convolution"
1100 |   bottom: "res2_4"
1101 |   top: "conv3_ex"
1102 |   param {
1103 |     lr_mult: 1
1104 |     decay_mult: 1
1105 |   }
1106 |   convolution_param {
1107 |     num_output: 256
1108 |     kernel_size: 1
1109 |     stride: 1
1110 |     pad: 0
1111 |     weight_filler {
1112 |       type: "xavier"
1113 |     }
1114 |     bias_term:false
1115 |   }
1116 | }
1117 | 
1118 | layer {
1119 |   name: "conv3_ex/bn"
1120 |   type: "BatchNorm"
1121 |   bottom: "conv3_ex"
1122 |   top: "conv3_ex"
1123 |   param {
1124 |     lr_mult: 0
1125 |     decay_mult: 0
1126 |   }
1127 |   param {
1128 |     lr_mult: 0
1129 |     decay_mult: 0
1130 |   }
1131 |   param {
1132 |     lr_mult: 0
1133 |     decay_mult: 0
1134 |   }
1135 | }
1136 | layer {
1137 |   name: "conv3_ex/scale"
1138 |   type: "Scale"
1139 |   bottom: "conv3_ex"
1140 |   top: "conv3_ex"
1141 |   param {
1142 |     lr_mult: 1
1143 |     decay_mult: 0
1144 |   }
1145 |   param {
1146 |     lr_mult: 1
1147 |     decay_mult: 0
1148 |   }
1149 |   scale_param {
1150 |     bias_term: true
1151 |   }
1152 | }
1153 | layer {
1154 |   name: "relu3_ex"
1155 |   type: "PReLU"
1156 |   bottom: "conv3_ex"
1157 |   top: "conv3_ex"
1158 | }
1159 | 
1160 | 
1161 | layer {
1162 |   name: "conv3_dw"
1163 |   type: "DepthwiseConvolution"
1164 |   bottom: "conv3_ex"
1165 |   top: "conv3_dw"
1166 |   param {
1167 |     lr_mult: 1
1168 |     decay_mult: 1
1169 |   }
1170 |   convolution_param {
1171 |     num_output: 256
1172 |     group:256
1173 |     kernel_size: 3
1174 |     stride: 2
1175 |     pad: 1
1176 |     weight_filler {
1177 |       type: "xavier"
1178 |     }
1179 |     bias_term:false
1180 |     engine: CAFFE
1181 |   }
1182 | }
1183 | 
1184 | layer {
1185 |   name: "conv3_dw/bn"
1186 |   type: "BatchNorm"
1187 |   bottom: "conv3_dw"
1188 |   top: "conv3_dw"
1189 |   param {
1190 |     lr_mult: 0
1191 |     decay_mult: 0
1192 |   }
1193 |   param {
1194 |     lr_mult: 0
1195 |     decay_mult: 0
1196 |   }
1197 |   param {
1198 |     lr_mult: 0
1199 |     decay_mult: 0
1200 |   }
1201 | }
1202 | layer {
1203 |   name: "conv3_dw/scale"
1204 |   type: "Scale"
1205 |   bottom: "conv3_dw"
1206 |   top: "conv3_dw"
1207 |   param {
1208 |     lr_mult: 1
1209 |     decay_mult: 0
1210 |   }
1211 |   param {
1212 |     lr_mult: 1
1213 |     decay_mult: 0
1214 |   }
1215 |   scale_param {
1216 |     bias_term: true
1217 |   }
1218 | }
1219 | layer {
1220 |   name: "relu3_dw"
1221 |   type: "PReLU"
1222 |   bottom: "conv3_dw"
1223 |   top: "conv3_dw"
1224 | }
1225 | 
1226 | layer {
1227 |   name: "conv3_em"
1228 |   type: "Convolution"
1229 |   bottom: "conv3_dw"
1230 |   top: "conv3_em"
1231 |   param {
1232 |     lr_mult: 1
1233 |     decay_mult: 1
1234 |   }
1235 |   convolution_param {
1236 |     num_output: 128
1237 |     kernel_size: 1
1238 |     stride: 1
1239 |     pad: 0
1240 |     weight_filler {
1241 |       type: "xavier"
1242 |     }
1243 |     bias_term:false
1244 |   }
1245 | }
1246 | 
1247 | 
1248 | layer {
1249 |   name: "conv3_em/bn"
1250 |   type: "BatchNorm"
1251 |   bottom: "conv3_em"
1252 |   top: "conv3_em"
1253 |   param {
1254 |     lr_mult: 0
1255 |     decay_mult: 0
1256 |   }
1257 |   param {
1258 |     lr_mult: 0
1259 |     decay_mult: 0
1260 |   }
1261 |   param {
1262 |     lr_mult: 0
1263 |     decay_mult: 0
1264 |   }
1265 | }
1266 | layer {
1267 |   name: "conv3_em/scale"
1268 |   type: "Scale"
1269 |   bottom: "conv3_em"
1270 |   top: "conv3_em"
1271 |   param {
1272 |     lr_mult: 1
1273 |     decay_mult: 0
1274 |   }
1275 |   param {
1276 |     lr_mult: 1
1277 |     decay_mult: 0
1278 |   }
1279 |   scale_param {
1280 |     bias_term: true
1281 |   }
1282 | }
1283 | 
1284 | layer {
1285 |   name: "conv3_1_ex"
1286 |   type: "Convolution"
1287 |   bottom: "conv3_em"
1288 |   top: "conv3_1_ex"
1289 |   param {
1290 |     lr_mult: 1
1291 |     decay_mult: 1
1292 |   }
1293 |   convolution_param {
1294 |     num_output: 256
1295 |     kernel_size: 1
1296 |     stride: 1
1297 |     pad: 0
1298 |     weight_filler {
1299 |       type: "xavier"
1300 |     }
1301 |     bias_term:false
1302 |   }
1303 | }
1304 | 
1305 | layer {
1306 |   name: "conv3_1_ex/bn"
1307 |   type: "BatchNorm"
1308 |   bottom: "conv3_1_ex"
1309 |   top: "conv3_1_ex"
1310 |   param {
1311 |     lr_mult: 0
1312 |     decay_mult: 0
1313 |   }
1314 |   param {
1315 |     lr_mult: 0
1316 |     decay_mult: 0
1317 |   }
1318 |   param {
1319 |     lr_mult: 0
1320 |     decay_mult: 0
1321 |   }
1322 | }
1323 | layer {
1324 |   name: "conv3_1_ex/scale"
1325 |   type: "Scale"
1326 |   bottom: "conv3_1_ex"
1327 |   top: "conv3_1_ex"
1328 |   param {
1329 |     lr_mult: 1
1330 |     decay_mult: 0
1331 |   }
1332 |   param {
1333 |     lr_mult: 1
1334 |     decay_mult: 0
1335 |   }
1336 |   scale_param {
1337 |     bias_term: true
1338 |   }
1339 | }
1340 | layer {
1341 |   name: "relu3_1_ex"
1342 |   type: "PReLU"
1343 |   bottom: "conv3_1_ex"
1344 |   top: "conv3_1_ex"
1345 | }
1346 | 
1347 | 
1348 | layer {
1349 |   name: "conv3_1_dw"
1350 |   type: "DepthwiseConvolution"
1351 |   bottom: "conv3_1_ex"
1352 |   top: "conv3_1_dw"
1353 |   param {
1354 |     lr_mult: 1
1355 |     decay_mult: 1
1356 |   }
1357 |   convolution_param {
1358 |     num_output: 256
1359 |     group:256
1360 |     kernel_size: 3
1361 |     stride: 1
1362 |     pad: 1
1363 |     weight_filler {
1364 |       type: "xavier"
1365 |     }
1366 |     bias_term:false
1367 |     engine: CAFFE
1368 |   }
1369 | }
1370 | 
1371 | layer {
1372 |   name: "conv3_1_dw/bn"
1373 |   type: "BatchNorm"
1374 |   bottom: "conv3_1_dw"
1375 |   top: "conv3_1_dw"
1376 |   param {
1377 |     lr_mult: 0
1378 |     decay_mult: 0
1379 |   }
1380 |   param {
1381 |     lr_mult: 0
1382 |     decay_mult: 0
1383 |   }
1384 |   param {
1385 |     lr_mult: 0
1386 |     decay_mult: 0
1387 |   }
1388 | }
1389 | layer {
1390 |   name: "conv3_1_dw/scale"
1391 |   type: "Scale"
1392 |   bottom: "conv3_1_dw"
1393 |   top: "conv3_1_dw"
1394 |   param {
1395 |     lr_mult: 1
1396 |     decay_mult: 0
1397 |   }
1398 |   param {
1399 |     lr_mult: 1
1400 |     decay_mult: 0
1401 |   }
1402 |   scale_param {
1403 |     bias_term: true
1404 |   }
1405 | }
1406 | layer {
1407 |   name: "relu3_1_dw"
1408 |   type: "PReLU"
1409 |   bottom: "conv3_1_dw"
1410 |   top: "conv3_1_dw"
1411 | }
1412 | 
1413 | layer {
1414 |   name: "conv3_1_em"
1415 |   type: "Convolution"
1416 |   bottom: "conv3_1_dw"
1417 |   top: "conv3_1_em"
1418 |   param {
1419 |     lr_mult: 1
1420 |     decay_mult: 1
1421 |   }
1422 |   convolution_param {
1423 |     num_output: 128
1424 |     kernel_size: 1
1425 |     stride: 1
1426 |     pad: 0
1427 |     weight_filler {
1428 |       type: "xavier"
1429 |     }
1430 |     bias_term:false
1431 |   }
1432 | }
1433 | 
1434 | 
1435 | layer {
1436 |   name: "conv3_1_em/bn"
1437 |   type: "BatchNorm"
1438 |   bottom: "conv3_1_em"
1439 |   top: "conv3_1_em"
1440 |   param {
1441 |     lr_mult: 0
1442 |     decay_mult: 0
1443 |   }
1444 |   param {
1445 |     lr_mult: 0
1446 |     decay_mult: 0
1447 |   }
1448 |   param {
1449 |     lr_mult: 0
1450 |     decay_mult: 0
1451 |   }
1452 | }
1453 | layer {
1454 |   name: "conv3_1_em/scale"
1455 |   type: "Scale"
1456 |   bottom: "conv3_1_em"
1457 |   top: "conv3_1_em"
1458 |   param {
1459 |     lr_mult: 1
1460 |     decay_mult: 0
1461 |   }
1462 |   param {
1463 |     lr_mult: 1
1464 |     decay_mult: 0
1465 |   }
1466 |   scale_param {
1467 |     bias_term: true
1468 |   }
1469 | }
1470 | 
1471 | layer {
1472 |   name: "res3_1"
1473 |   type: "Eltwise"
1474 |   bottom: "conv3_em"
1475 |   bottom: "conv3_1_em"
1476 |   top: "res3_1"
1477 | }
1478 | 
1479 | 
1480 | layer {
1481 |   name: "conv3_2_ex"
1482 |   type: "Convolution"
1483 |   bottom: "res3_1"
1484 |   top: "conv3_2_ex"
1485 |   param {
1486 |     lr_mult: 1
1487 |     decay_mult: 1
1488 |   }
1489 |   convolution_param {
1490 |     num_output: 256
1491 |     kernel_size: 1
1492 |     stride: 1
1493 |     pad: 0
1494 |     weight_filler {
1495 |       type: "xavier"
1496 |     }
1497 |     bias_term:false
1498 |   }
1499 | }
1500 | 
1501 | layer {
1502 |   name: "conv3_2_ex/bn"
1503 |   type: "BatchNorm"
1504 |   bottom: "conv3_2_ex"
1505 |   top: "conv3_2_ex"
1506 |   param {
1507 |     lr_mult: 0
1508 |     decay_mult: 0
1509 |   }
1510 |   param {
1511 |     lr_mult: 0
1512 |     decay_mult: 0
1513 |   }
1514 |   param {
1515 |     lr_mult: 0
1516 |     decay_mult: 0
1517 |   }
1518 | }
1519 | layer {
1520 |   name: "conv3_2_ex/scale"
1521 |   type: "Scale"
1522 |   bottom: "conv3_2_ex"
1523 |   top: "conv3_2_ex"
1524 |   param {
1525 |     lr_mult: 1
1526 |     decay_mult: 0
1527 |   }
1528 |   param {
1529 |     lr_mult: 1
1530 |     decay_mult: 0
1531 |   }
1532 |   scale_param {
1533 |     bias_term: true
1534 |   }
1535 | }
1536 | layer {
1537 |   name: "relu3_2_ex"
1538 |   type: "PReLU"
1539 |   bottom: "conv3_2_ex"
1540 |   top: "conv3_2_ex"
1541 | }
1542 | 
1543 | 
1544 | layer {
1545 |   name: "conv3_2_dw"
1546 |   type: "DepthwiseConvolution"
1547 |   bottom: "conv3_2_ex"
1548 |   top: "conv3_2_dw"
1549 |   param {
1550 |     lr_mult: 1
1551 |     decay_mult: 1
1552 |   }
1553 |   convolution_param {
1554 |     num_output: 256
1555 |     group:256
1556 |     kernel_size: 3
1557 |     stride: 1
1558 |     pad: 1
1559 |     weight_filler {
1560 |       type: "xavier"
1561 |     }
1562 |     bias_term:false
1563 |     engine: CAFFE
1564 |   }
1565 | }
1566 | 
1567 | layer {
1568 |   name: "conv3_2_dw/bn"
1569 |   type: "BatchNorm"
1570 |   bottom: "conv3_2_dw"
1571 |   top: "conv3_2_dw"
1572 |   param {
1573 |     lr_mult: 0
1574 |     decay_mult: 0
1575 |   }
1576 |   param {
1577 |     lr_mult: 0
1578 |     decay_mult: 0
1579 |   }
1580 |   param {
1581 |     lr_mult: 0
1582 |     decay_mult: 0
1583 |   }
1584 | }
1585 | layer {
1586 |   name: "conv3_2_dw/scale"
1587 |   type: "Scale"
1588 |   bottom: "conv3_2_dw"
1589 |   top: "conv3_2_dw"
1590 |   param {
1591 |     lr_mult: 1
1592 |     decay_mult: 0
1593 |   }
1594 |   param {
1595 |     lr_mult: 1
1596 |     decay_mult: 0
1597 |   }
1598 |   scale_param {
1599 |     bias_term: true
1600 |   }
1601 | }
1602 | layer {
1603 |   name: "relu3_2_dw"
1604 |   type: "PReLU"
1605 |   bottom: "conv3_2_dw"
1606 |   top: "conv3_2_dw"
1607 | }
1608 | 
1609 | layer {
1610 |   name: "conv3_2_em"
1611 |   type: "Convolution"
1612 |   bottom: "conv3_2_dw"
1613 |   top: "conv3_2_em"
1614 |   param {
1615 |     lr_mult: 1
1616 |     decay_mult: 1
1617 |   }
1618 |   convolution_param {
1619 |     num_output: 128
1620 |     kernel_size: 1
1621 |     stride: 1
1622 |     pad: 0
1623 |     weight_filler {
1624 |       type: "xavier"
1625 |     }
1626 |     bias_term:false
1627 |   }
1628 | }
1629 | 
1630 | 
1631 | layer {
1632 |   name: "conv3_2_em/bn"
1633 |   type: "BatchNorm"
1634 |   bottom: "conv3_2_em"
1635 |   top: "conv3_2_em"
1636 |   param {
1637 |     lr_mult: 0
1638 |     decay_mult: 0
1639 |   }
1640 |   param {
1641 |     lr_mult: 0
1642 |     decay_mult: 0
1643 |   }
1644 |   param {
1645 |     lr_mult: 0
1646 |     decay_mult: 0
1647 |   }
1648 | }
1649 | layer {
1650 |   name: "conv3_2_em/scale"
1651 |   type: "Scale"
1652 |   bottom: "conv3_2_em"
1653 |   top: "conv3_2_em"
1654 |   param {
1655 |     lr_mult: 1
1656 |     decay_mult: 0
1657 |   }
1658 |   param {
1659 |     lr_mult: 1
1660 |     decay_mult: 0
1661 |   }
1662 |   scale_param {
1663 |     bias_term: true
1664 |   }
1665 | }
1666 | 
1667 | layer {
1668 |   name: "res3_2"
1669 |   type: "Eltwise"
1670 |   bottom: "res3_1"
1671 |   bottom: "conv3_2_em"
1672 |   top: "res3_2"
1673 | }
1674 | 
1675 | layer {
1676 |   name: "conv3_3_ex"
1677 |   type: "Convolution"
1678 |   bottom: "res3_2"
1679 |   top: "conv3_3_ex"
1680 |   param {
1681 |     lr_mult: 1
1682 |     decay_mult: 1
1683 |   }
1684 |   convolution_param {
1685 |     num_output: 256
1686 |     kernel_size: 1
1687 |     stride: 1
1688 |     pad: 0
1689 |     weight_filler {
1690 |       type: "xavier"
1691 |     }
1692 |     bias_term:false
1693 |   }
1694 | }
1695 | 
1696 | layer {
1697 |   name: "conv3_3_ex/bn"
1698 |   type: "BatchNorm"
1699 |   bottom: "conv3_3_ex"
1700 |   top: "conv3_3_ex"
1701 |   param {
1702 |     lr_mult: 0
1703 |     decay_mult: 0
1704 |   }
1705 |   param {
1706 |     lr_mult: 0
1707 |     decay_mult: 0
1708 |   }
1709 |   param {
1710 |     lr_mult: 0
1711 |     decay_mult: 0
1712 |   }
1713 | }
1714 | layer {
1715 |   name: "conv3_3_ex/scale"
1716 |   type: "Scale"
1717 |   bottom: "conv3_3_ex"
1718 |   top: "conv3_3_ex"
1719 |   param {
1720 |     lr_mult: 1
1721 |     decay_mult: 0
1722 |   }
1723 |   param {
1724 |     lr_mult: 1
1725 |     decay_mult: 0
1726 |   }
1727 |   scale_param {
1728 |     bias_term: true
1729 |   }
1730 | }
1731 | layer {
1732 |   name: "relu3_3_ex"
1733 |   type: "PReLU"
1734 |   bottom: "conv3_3_ex"
1735 |   top: "conv3_3_ex"
1736 | }
1737 | 
1738 | 
1739 | layer {
1740 |   name: "conv3_3_dw"
1741 |   type: "DepthwiseConvolution"
1742 |   bottom: "conv3_3_ex"
1743 |   top: "conv3_3_dw"
1744 |   param {
1745 |     lr_mult: 1
1746 |     decay_mult: 1
1747 |   }
1748 |   convolution_param {
1749 |     num_output: 256
1750 |     group:256
1751 |     kernel_size: 3
1752 |     stride: 1
1753 |     pad: 1
1754 |     weight_filler {
1755 |       type: "xavier"
1756 |     }
1757 |     bias_term:false
1758 |     engine: CAFFE
1759 |   }
1760 | }
1761 | 
1762 | layer {
1763 |   name: "conv3_3_dw/bn"
1764 |   type: "BatchNorm"
1765 |   bottom: "conv3_3_dw"
1766 |   top: "conv3_3_dw"
1767 |   param {
1768 |     lr_mult: 0
1769 |     decay_mult: 0
1770 |   }
1771 |   param {
1772 |     lr_mult: 0
1773 |     decay_mult: 0
1774 |   }
1775 |   param {
1776 |     lr_mult: 0
1777 |     decay_mult: 0
1778 |   }
1779 | }
1780 | layer {
1781 |   name: "conv3_3_dw/scale"
1782 |   type: "Scale"
1783 |   bottom: "conv3_3_dw"
1784 |   top: "conv3_3_dw"
1785 |   param {
1786 |     lr_mult: 1
1787 |     decay_mult: 0
1788 |   }
1789 |   param {
1790 |     lr_mult: 1
1791 |     decay_mult: 0
1792 |   }
1793 |   scale_param {
1794 |     bias_term: true
1795 |   }
1796 | }
1797 | layer {
1798 |   name: "relu3_3_dw"
1799 |   type: "PReLU"
1800 |   bottom: "conv3_3_dw"
1801 |   top: "conv3_3_dw"
1802 | }
1803 | 
1804 | layer {
1805 |   name: "conv3_3_em"
1806 |   type: "Convolution"
1807 |   bottom: "conv3_3_dw"
1808 |   top: "conv3_3_em"
1809 |   param {
1810 |     lr_mult: 1
1811 |     decay_mult: 1
1812 |   }
1813 |   convolution_param {
1814 |     num_output: 128
1815 |     kernel_size: 1
1816 |     stride: 1
1817 |     pad: 0
1818 |     weight_filler {
1819 |       type: "xavier"
1820 |     }
1821 |     bias_term:false
1822 |   }
1823 | }
1824 | 
1825 | 
1826 | layer {
1827 |   name: "conv3_3_em/bn"
1828 |   type: "BatchNorm"
1829 |   bottom: "conv3_3_em"
1830 |   top: "conv3_3_em"
1831 |   param {
1832 |     lr_mult: 0
1833 |     decay_mult: 0
1834 |   }
1835 |   param {
1836 |     lr_mult: 0
1837 |     decay_mult: 0
1838 |   }
1839 |   param {
1840 |     lr_mult: 0
1841 |     decay_mult: 0
1842 |   }
1843 | }
1844 | layer {
1845 |   name: "conv3_3_em/scale"
1846 |   type: "Scale"
1847 |   bottom: "conv3_3_em"
1848 |   top: "conv3_3_em"
1849 |   param {
1850 |     lr_mult: 1
1851 |     decay_mult: 0
1852 |   }
1853 |   param {
1854 |     lr_mult: 1
1855 |     decay_mult: 0
1856 |   }
1857 |   scale_param {
1858 |     bias_term: true
1859 |   }
1860 | }
1861 | 
1862 | layer {
1863 |   name: "res3_3"
1864 |   type: "Eltwise"
1865 |   bottom: "res3_2"
1866 |   bottom: "conv3_3_em"
1867 |   top: "res3_3"
1868 | }
1869 | 
1870 | 
1871 | layer {
1872 |   name: "conv3_4_ex"
1873 |   type: "Convolution"
1874 |   bottom: "res3_3"
1875 |   top: "conv3_4_ex"
1876 |   param {
1877 |     lr_mult: 1
1878 |     decay_mult: 1
1879 |   }
1880 |   convolution_param {
1881 |     num_output: 256
1882 |     kernel_size: 1
1883 |     stride: 1
1884 |     pad: 0
1885 |     weight_filler {
1886 |       type: "xavier"
1887 |     }
1888 |     bias_term:false
1889 |   }
1890 | }
1891 | 
1892 | layer {
1893 |   name: "conv3_4_ex/bn"
1894 |   type: "BatchNorm"
1895 |   bottom: "conv3_4_ex"
1896 |   top: "conv3_4_ex"
1897 |   param {
1898 |     lr_mult: 0
1899 |     decay_mult: 0
1900 |   }
1901 |   param {
1902 |     lr_mult: 0
1903 |     decay_mult: 0
1904 |   }
1905 |   param {
1906 |     lr_mult: 0
1907 |     decay_mult: 0
1908 |   }
1909 | }
1910 | layer {
1911 |   name: "conv3_4_ex/scale"
1912 |   type: "Scale"
1913 |   bottom: "conv3_4_ex"
1914 |   top: "conv3_4_ex"
1915 |   param {
1916 |     lr_mult: 1
1917 |     decay_mult: 0
1918 |   }
1919 |   param {
1920 |     lr_mult: 1
1921 |     decay_mult: 0
1922 |   }
1923 |   scale_param {
1924 |     bias_term: true
1925 |   }
1926 | }
1927 | layer {
1928 |   name: "relu3_4_ex"
1929 |   type: "PReLU"
1930 |   bottom: "conv3_4_ex"
1931 |   top: "conv3_4_ex"
1932 | }
1933 | 
1934 | 
1935 | layer {
1936 |   name: "conv3_4_dw"
1937 |   type: "DepthwiseConvolution"
1938 |   bottom: "conv3_4_ex"
1939 |   top: "conv3_4_dw"
1940 |   param {
1941 |     lr_mult: 1
1942 |     decay_mult: 1
1943 |   }
1944 |   convolution_param {
1945 |     num_output: 256
1946 |     group:256
1947 |     kernel_size: 3
1948 |     stride: 1
1949 |     pad: 1
1950 |     weight_filler {
1951 |       type: "xavier"
1952 |     }
1953 |     bias_term:false
1954 |     engine: CAFFE
1955 |   }
1956 | }
1957 | 
1958 | layer {
1959 |   name: "conv3_4_dw/bn"
1960 |   type: "BatchNorm"
1961 |   bottom: "conv3_4_dw"
1962 |   top: "conv3_4_dw"
1963 |   param {
1964 |     lr_mult: 0
1965 |     decay_mult: 0
1966 |   }
1967 |   param {
1968 |     lr_mult: 0
1969 |     decay_mult: 0
1970 |   }
1971 |   param {
1972 |     lr_mult: 0
1973 |     decay_mult: 0
1974 |   }
1975 | }
1976 | layer {
1977 |   name: "conv3_4_dw/scale"
1978 |   type: "Scale"
1979 |   bottom: "conv3_4_dw"
1980 |   top: "conv3_4_dw"
1981 |   param {
1982 |     lr_mult: 1
1983 |     decay_mult: 0
1984 |   }
1985 |   param {
1986 |     lr_mult: 1
1987 |     decay_mult: 0
1988 |   }
1989 |   scale_param {
1990 |     bias_term: true
1991 |   }
1992 | }
1993 | layer {
1994 |   name: "relu3_4_dw"
1995 |   type: "PReLU"
1996 |   bottom: "conv3_4_dw"
1997 |   top: "conv3_4_dw"
1998 | }
1999 | 
2000 | layer {
2001 |   name: "conv3_4_em"
2002 |   type: "Convolution"
2003 |   bottom: "conv3_4_dw"
2004 |   top: "conv3_4_em"
2005 |   param {
2006 |     lr_mult: 1
2007 |     decay_mult: 1
2008 |   }
2009 |   convolution_param {
2010 |     num_output: 128
2011 |     kernel_size: 1
2012 |     stride: 1
2013 |     pad: 0
2014 |     weight_filler {
2015 |       type: "xavier"
2016 |     }
2017 |     bias_term:false
2018 |   }
2019 | }
2020 | 
2021 | 
2022 | layer {
2023 |   name: "conv3_4_em/bn"
2024 |   type: "BatchNorm"
2025 |   bottom: "conv3_4_em"
2026 |   top: "conv3_4_em"
2027 |   param {
2028 |     lr_mult: 0
2029 |     decay_mult: 0
2030 |   }
2031 |   param {
2032 |     lr_mult: 0
2033 |     decay_mult: 0
2034 |   }
2035 |   param {
2036 |     lr_mult: 0
2037 |     decay_mult: 0
2038 |   }
2039 | }
2040 | layer {
2041 |   name: "conv3_4_em/scale"
2042 |   type: "Scale"
2043 |   bottom: "conv3_4_em"
2044 |   top: "conv3_4_em"
2045 |   param {
2046 |     lr_mult: 1
2047 |     decay_mult: 0
2048 |   }
2049 |   param {
2050 |     lr_mult: 1
2051 |     decay_mult: 0
2052 |   }
2053 |   scale_param {
2054 |     bias_term: true
2055 |   }
2056 | }
2057 | 
2058 | layer {
2059 |   name: "res3_4"
2060 |   type: "Eltwise"
2061 |   bottom: "res3_3"
2062 |   bottom: "conv3_4_em"
2063 |   top: "res3_4"
2064 | }
2065 | 
2066 | 
2067 | layer {
2068 |   name: "conv3_5_ex"
2069 |   type: "Convolution"
2070 |   bottom: "res3_4"
2071 |   top: "conv3_5_ex"
2072 |   param {
2073 |     lr_mult: 1
2074 |     decay_mult: 1
2075 |   }
2076 |   convolution_param {
2077 |     num_output: 256
2078 |     kernel_size: 1
2079 |     stride: 1
2080 |     pad: 0
2081 |     weight_filler {
2082 |       type: "xavier"
2083 |     }
2084 |     bias_term:false
2085 |   }
2086 | }
2087 | 
2088 | layer {
2089 |   name: "conv3_5_ex/bn"
2090 |   type: "BatchNorm"
2091 |   bottom: "conv3_5_ex"
2092 |   top: "conv3_5_ex"
2093 |   param {
2094 |     lr_mult: 0
2095 |     decay_mult: 0
2096 |   }
2097 |   param {
2098 |     lr_mult: 0
2099 |     decay_mult: 0
2100 |   }
2101 |   param {
2102 |     lr_mult: 0
2103 |     decay_mult: 0
2104 |   }
2105 | }
2106 | layer {
2107 |   name: "conv3_5_ex/scale"
2108 |   type: "Scale"
2109 |   bottom: "conv3_5_ex"
2110 |   top: "conv3_5_ex"
2111 |   param {
2112 |     lr_mult: 1
2113 |     decay_mult: 0
2114 |   }
2115 |   param {
2116 |     lr_mult: 1
2117 |     decay_mult: 0
2118 |   }
2119 |   scale_param {
2120 |     bias_term: true
2121 |   }
2122 | }
2123 | layer {
2124 |   name: "relu3_5_ex"
2125 |   type: "PReLU"
2126 |   bottom: "conv3_5_ex"
2127 |   top: "conv3_5_ex"
2128 | }
2129 | 
2130 | 
2131 | layer {
2132 |   name: "conv3_5_dw"
2133 |   type: "DepthwiseConvolution"
2134 |   bottom: "conv3_5_ex"
2135 |   top: "conv3_5_dw"
2136 |   param {
2137 |     lr_mult: 1
2138 |     decay_mult: 1
2139 |   }
2140 |   convolution_param {
2141 |     num_output: 256
2142 |     group:256
2143 |     kernel_size: 3
2144 |     stride: 1
2145 |     pad: 1
2146 |     weight_filler {
2147 |       type: "xavier"
2148 |     }
2149 |     bias_term:false
2150 |     engine: CAFFE
2151 |   }
2152 | }
2153 | 
2154 | layer {
2155 |   name: "conv3_5_dw/bn"
2156 |   type: "BatchNorm"
2157 |   bottom: "conv3_5_dw"
2158 |   top: "conv3_5_dw"
2159 |   param {
2160 |     lr_mult: 0
2161 |     decay_mult: 0
2162 |   }
2163 |   param {
2164 |     lr_mult: 0
2165 |     decay_mult: 0
2166 |   }
2167 |   param {
2168 |     lr_mult: 0
2169 |     decay_mult: 0
2170 |   }
2171 | }
2172 | layer {
2173 |   name: "conv3_5_dw/scale"
2174 |   type: "Scale"
2175 |   bottom: "conv3_5_dw"
2176 |   top: "conv3_5_dw"
2177 |   param {
2178 |     lr_mult: 1
2179 |     decay_mult: 0
2180 |   }
2181 |   param {
2182 |     lr_mult: 1
2183 |     decay_mult: 0
2184 |   }
2185 |   scale_param {
2186 |     bias_term: true
2187 |   }
2188 | }
2189 | layer {
2190 |   name: "relu3_5_dw"
2191 |   type: "PReLU"
2192 |   bottom: "conv3_5_dw"
2193 |   top: "conv3_5_dw"
2194 | }
2195 | 
2196 | layer {
2197 |   name: "conv3_5_em"
2198 |   type: "Convolution"
2199 |   bottom: "conv3_5_dw"
2200 |   top: "conv3_5_em"
2201 |   param {
2202 |     lr_mult: 1
2203 |     decay_mult: 1
2204 |   }
2205 |   convolution_param {
2206 |     num_output: 128
2207 |     kernel_size: 1
2208 |     stride: 1
2209 |     pad: 0
2210 |     weight_filler {
2211 |       type: "xavier"
2212 |     }
2213 |     bias_term:false
2214 |   }
2215 | }
2216 | 
2217 | 
2218 | layer {
2219 |   name: "conv3_5_em/bn"
2220 |   type: "BatchNorm"
2221 |   bottom: "conv3_5_em"
2222 |   top: "conv3_5_em"
2223 |   param {
2224 |     lr_mult: 0
2225 |     decay_mult: 0
2226 |   }
2227 |   param {
2228 |     lr_mult: 0
2229 |     decay_mult: 0
2230 |   }
2231 |   param {
2232 |     lr_mult: 0
2233 |     decay_mult: 0
2234 |   }
2235 | }
2236 | layer {
2237 |   name: "conv3_5_em/scale"
2238 |   type: "Scale"
2239 |   bottom: "conv3_5_em"
2240 |   top: "conv3_5_em"
2241 |   param {
2242 |     lr_mult: 1
2243 |     decay_mult: 0
2244 |   }
2245 |   param {
2246 |     lr_mult: 1
2247 |     decay_mult: 0
2248 |   }
2249 |   scale_param {
2250 |     bias_term: true
2251 |   }
2252 | }
2253 | 
2254 | layer {
2255 |   name: "res3_5"
2256 |   type: "Eltwise"
2257 |   bottom: "res3_4"
2258 |   bottom: "conv3_5_em"
2259 |   top: "res3_5"
2260 | }
2261 | 
2262 | 
2263 | layer {
2264 |   name: "conv3_6_ex"
2265 |   type: "Convolution"
2266 |   bottom: "res3_5"
2267 |   top: "conv3_6_ex"
2268 |   param {
2269 |     lr_mult: 1
2270 |     decay_mult: 1
2271 |   }
2272 |   convolution_param {
2273 |     num_output: 256
2274 |     kernel_size: 1
2275 |     stride: 1
2276 |     pad: 0
2277 |     weight_filler {
2278 |       type: "xavier"
2279 |     }
2280 |     bias_term:false
2281 |   }
2282 | }
2283 | 
2284 | layer {
2285 |   name: "conv3_6_ex/bn"
2286 |   type: "BatchNorm"
2287 |   bottom: "conv3_6_ex"
2288 |   top: "conv3_6_ex"
2289 |   param {
2290 |     lr_mult: 0
2291 |     decay_mult: 0
2292 |   }
2293 |   param {
2294 |     lr_mult: 0
2295 |     decay_mult: 0
2296 |   }
2297 |   param {
2298 |     lr_mult: 0
2299 |     decay_mult: 0
2300 |   }
2301 | }
2302 | layer {
2303 |   name: "conv3_6_ex/scale"
2304 |   type: "Scale"
2305 |   bottom: "conv3_6_ex"
2306 |   top: "conv3_6_ex"
2307 |   param {
2308 |     lr_mult: 1
2309 |     decay_mult: 0
2310 |   }
2311 |   param {
2312 |     lr_mult: 1
2313 |     decay_mult: 0
2314 |   }
2315 |   scale_param {
2316 |     bias_term: true
2317 |   }
2318 | }
2319 | layer {
2320 |   name: "relu3_6_ex"
2321 |   type: "PReLU"
2322 |   bottom: "conv3_6_ex"
2323 |   top: "conv3_6_ex"
2324 | }
2325 | 
2326 | 
2327 | layer {
2328 |   name: "conv3_6_dw"
2329 |   type: "DepthwiseConvolution"
2330 |   bottom: "conv3_6_ex"
2331 |   top: "conv3_6_dw"
2332 |   param {
2333 |     lr_mult: 1
2334 |     decay_mult: 1
2335 |   }
2336 |   convolution_param {
2337 |     num_output: 256
2338 |     group:256
2339 |     kernel_size: 3
2340 |     stride: 1
2341 |     pad: 1
2342 |     weight_filler {
2343 |       type: "xavier"
2344 |     }
2345 |     bias_term:false
2346 |     engine: CAFFE
2347 |   }
2348 | }
2349 | 
2350 | layer {
2351 |   name: "conv3_6_dw/bn"
2352 |   type: "BatchNorm"
2353 |   bottom: "conv3_6_dw"
2354 |   top: "conv3_6_dw"
2355 |   param {
2356 |     lr_mult: 0
2357 |     decay_mult: 0
2358 |   }
2359 |   param {
2360 |     lr_mult: 0
2361 |     decay_mult: 0
2362 |   }
2363 |   param {
2364 |     lr_mult: 0
2365 |     decay_mult: 0
2366 |   }
2367 | }
2368 | layer {
2369 |   name: "conv3_6_dw/scale"
2370 |   type: "Scale"
2371 |   bottom: "conv3_6_dw"
2372 |   top: "conv3_6_dw"
2373 |   param {
2374 |     lr_mult: 1
2375 |     decay_mult: 0
2376 |   }
2377 |   param {
2378 |     lr_mult: 1
2379 |     decay_mult: 0
2380 |   }
2381 |   scale_param {
2382 |     bias_term: true
2383 |   }
2384 | }
2385 | layer {
2386 |   name: "relu3_6_dw"
2387 |   type: "PReLU"
2388 |   bottom: "conv3_6_dw"
2389 |   top: "conv3_6_dw"
2390 | }
2391 | 
2392 | layer {
2393 |   name: "conv3_6_em"
2394 |   type: "Convolution"
2395 |   bottom: "conv3_6_dw"
2396 |   top: "conv3_6_em"
2397 |   param {
2398 |     lr_mult: 1
2399 |     decay_mult: 1
2400 |   }
2401 |   convolution_param {
2402 |     num_output: 128
2403 |     kernel_size: 1
2404 |     stride: 1
2405 |     pad: 0
2406 |     weight_filler {
2407 |       type: "xavier"
2408 |     }
2409 |     bias_term:false
2410 |   }
2411 | }
2412 | 
2413 | 
2414 | layer {
2415 |   name: "conv3_6_em/bn"
2416 |   type: "BatchNorm"
2417 |   bottom: "conv3_6_em"
2418 |   top: "conv3_6_em"
2419 |   param {
2420 |     lr_mult: 0
2421 |     decay_mult: 0
2422 |   }
2423 |   param {
2424 |     lr_mult: 0
2425 |     decay_mult: 0
2426 |   }
2427 |   param {
2428 |     lr_mult: 0
2429 |     decay_mult: 0
2430 |   }
2431 | }
2432 | layer {
2433 |   name: "conv3_6_em/scale"
2434 |   type: "Scale"
2435 |   bottom: "conv3_6_em"
2436 |   top: "conv3_6_em"
2437 |   param {
2438 |     lr_mult: 1
2439 |     decay_mult: 0
2440 |   }
2441 |   param {
2442 |     lr_mult: 1
2443 |     decay_mult: 0
2444 |   }
2445 |   scale_param {
2446 |     bias_term: true
2447 |   }
2448 | }
2449 | 
2450 | layer {
2451 |   name: "res3_6"
2452 |   type: "Eltwise"
2453 |   bottom: "res3_5"
2454 |   bottom: "conv3_6_em"
2455 |   top: "res3_6"
2456 | }
2457 | 
2458 | layer {
2459 |   name: "conv4_ex"
2460 |   type: "Convolution"
2461 |   bottom: "res3_6"
2462 |   top: "conv4_ex"
2463 |   param {
2464 |     lr_mult: 1
2465 |     decay_mult: 1
2466 |   }
2467 |   convolution_param {
2468 |     num_output: 512
2469 |     kernel_size: 1
2470 |     stride: 1
2471 |     pad: 0
2472 |     weight_filler {
2473 |       type: "xavier"
2474 |     }
2475 |     bias_term:false
2476 |   }
2477 | }
2478 | 
2479 | layer {
2480 |   name: "conv4_ex/bn"
2481 |   type: "BatchNorm"
2482 |   bottom: "conv4_ex"
2483 |   top: "conv4_ex"
2484 |   param {
2485 |     lr_mult: 0
2486 |     decay_mult: 0
2487 |   }
2488 |   param {
2489 |     lr_mult: 0
2490 |     decay_mult: 0
2491 |   }
2492 |   param {
2493 |     lr_mult: 0
2494 |     decay_mult: 0
2495 |   }
2496 | }
2497 | layer {
2498 |   name: "conv4_ex/scale"
2499 |   type: "Scale"
2500 |   bottom: "conv4_ex"
2501 |   top: "conv4_ex"
2502 |   param {
2503 |     lr_mult: 1
2504 |     decay_mult: 0
2505 |   }
2506 |   param {
2507 |     lr_mult: 1
2508 |     decay_mult: 0
2509 |   }
2510 |   scale_param {
2511 |     bias_term: true
2512 |   }
2513 | }
2514 | layer {
2515 |   name: "relu4_ex"
2516 |   type: "PReLU"
2517 |   bottom: "conv4_ex"
2518 |   top: "conv4_ex"
2519 | }
2520 | 
2521 | 
2522 | layer {
2523 |   name: "conv4_dw"
2524 |   type: "DepthwiseConvolution"
2525 |   bottom: "conv4_ex"
2526 |   top: "conv4_dw"
2527 |   param {
2528 |     lr_mult: 1
2529 |     decay_mult: 1
2530 |   }
2531 |   convolution_param {
2532 |     num_output: 512
2533 |     group:512
2534 |     kernel_size: 3
2535 |     stride: 2
2536 |     pad: 1
2537 |     weight_filler {
2538 |       type: "xavier"
2539 |     }
2540 |     bias_term:false
2541 |     engine: CAFFE
2542 |   }
2543 | }
2544 | 
2545 | layer {
2546 |   name: "conv4_dw/bn"
2547 |   type: "BatchNorm"
2548 |   bottom: "conv4_dw"
2549 |   top: "conv4_dw"
2550 |   param {
2551 |     lr_mult: 0
2552 |     decay_mult: 0
2553 |   }
2554 |   param {
2555 |     lr_mult: 0
2556 |     decay_mult: 0
2557 |   }
2558 |   param {
2559 |     lr_mult: 0
2560 |     decay_mult: 0
2561 |   }
2562 | }
2563 | layer {
2564 |   name: "conv4_dw/scale"
2565 |   type: "Scale"
2566 |   bottom: "conv4_dw"
2567 |   top: "conv4_dw"
2568 |   param {
2569 |     lr_mult: 1
2570 |     decay_mult: 0
2571 |   }
2572 |   param {
2573 |     lr_mult: 1
2574 |     decay_mult: 0
2575 |   }
2576 |   scale_param {
2577 |     bias_term: true
2578 |   }
2579 | }
2580 | layer {
2581 |   name: "relu4_dw"
2582 |   type: "PReLU"
2583 |   bottom: "conv4_dw"
2584 |   top: "conv4_dw"
2585 | }
2586 | 
2587 | layer {
2588 |   name: "conv4_em"
2589 |   type: "Convolution"
2590 |   bottom: "conv4_dw"
2591 |   top: "conv4_em"
2592 |   param {
2593 |     lr_mult: 1
2594 |     decay_mult: 1
2595 |   }
2596 |   convolution_param {
2597 |     num_output: 128
2598 |     kernel_size: 1
2599 |     stride: 1
2600 |     pad: 0
2601 |     weight_filler {
2602 |       type: "xavier"
2603 |     }
2604 |     bias_term:false
2605 |   }
2606 | }
2607 | 
2608 | 
2609 | layer {
2610 |   name: "conv4_em/bn"
2611 |   type: "BatchNorm"
2612 |   bottom: "conv4_em"
2613 |   top: "conv4_em"
2614 |   param {
2615 |     lr_mult: 0
2616 |     decay_mult: 0
2617 |   }
2618 |   param {
2619 |     lr_mult: 0
2620 |     decay_mult: 0
2621 |   }
2622 |   param {
2623 |     lr_mult: 0
2624 |     decay_mult: 0
2625 |   }
2626 | }
2627 | layer {
2628 |   name: "conv4_em/scale"
2629 |   type: "Scale"
2630 |   bottom: "conv4_em"
2631 |   top: "conv4_em"
2632 |   param {
2633 |     lr_mult: 1
2634 |     decay_mult: 0
2635 |   }
2636 |   param {
2637 |     lr_mult: 1
2638 |     decay_mult: 0
2639 |   }
2640 |   scale_param {
2641 |     bias_term: true
2642 |   }
2643 | }
2644 | 
2645 | layer {
2646 |   name: "conv4_1_ex"
2647 |   type: "Convolution"
2648 |   bottom: "conv4_em"
2649 |   top: "conv4_1_ex"
2650 |   param {
2651 |     lr_mult: 1
2652 |     decay_mult: 1
2653 |   }
2654 |   convolution_param {
2655 |     num_output: 256
2656 |     kernel_size: 1
2657 |     stride: 1
2658 |     pad: 0
2659 |     weight_filler {
2660 |       type: "xavier"
2661 |     }
2662 |     bias_term:false
2663 |   }
2664 | }
2665 | 
2666 | layer {
2667 |   name: "conv4_1_ex/bn"
2668 |   type: "BatchNorm"
2669 |   bottom: "conv4_1_ex"
2670 |   top: "conv4_1_ex"
2671 |   param {
2672 |     lr_mult: 0
2673 |     decay_mult: 0
2674 |   }
2675 |   param {
2676 |     lr_mult: 0
2677 |     decay_mult: 0
2678 |   }
2679 |   param {
2680 |     lr_mult: 0
2681 |     decay_mult: 0
2682 |   }
2683 | }
2684 | layer {
2685 |   name: "conv4_1_ex/scale"
2686 |   type: "Scale"
2687 |   bottom: "conv4_1_ex"
2688 |   top: "conv4_1_ex"
2689 |   param {
2690 |     lr_mult: 1
2691 |     decay_mult: 0
2692 |   }
2693 |   param {
2694 |     lr_mult: 1
2695 |     decay_mult: 0
2696 |   }
2697 |   scale_param {
2698 |     bias_term: true
2699 |   }
2700 | }
2701 | layer {
2702 |   name: "relu4_1_ex"
2703 |   type: "PReLU"
2704 |   bottom: "conv4_1_ex"
2705 |   top: "conv4_1_ex"
2706 | }
2707 | 
2708 | 
2709 | layer {
2710 |   name: "conv4_1_dw"
2711 |   type: "DepthwiseConvolution"
2712 |   bottom: "conv4_1_ex"
2713 |   top: "conv4_1_dw"
2714 |   param {
2715 |     lr_mult: 1
2716 |     decay_mult: 1
2717 |   }
2718 |   convolution_param {
2719 |     num_output: 256
2720 |     group:256
2721 |     kernel_size: 3
2722 |     stride: 1
2723 |     pad: 1
2724 |     weight_filler {
2725 |       type: "xavier"
2726 |     }
2727 |     bias_term:false
2728 |     engine: CAFFE
2729 |   }
2730 | }
2731 | 
2732 | layer {
2733 |   name: "conv4_1_dw/bn"
2734 |   type: "BatchNorm"
2735 |   bottom: "conv4_1_dw"
2736 |   top: "conv4_1_dw"
2737 |   param {
2738 |     lr_mult: 0
2739 |     decay_mult: 0
2740 |   }
2741 |   param {
2742 |     lr_mult: 0
2743 |     decay_mult: 0
2744 |   }
2745 |   param {
2746 |     lr_mult: 0
2747 |     decay_mult: 0
2748 |   }
2749 | }
2750 | layer {
2751 |   name: "conv4_1_dw/scale"
2752 |   type: "Scale"
2753 |   bottom: "conv4_1_dw"
2754 |   top: "conv4_1_dw"
2755 |   param {
2756 |     lr_mult: 1
2757 |     decay_mult: 0
2758 |   }
2759 |   param {
2760 |     lr_mult: 1
2761 |     decay_mult: 0
2762 |   }
2763 |   scale_param {
2764 |     bias_term: true
2765 |   }
2766 | }
2767 | layer {
2768 |   name: "relu4_1_dw"
2769 |   type: "PReLU"
2770 |   bottom: "conv4_1_dw"
2771 |   top: "conv4_1_dw"
2772 | }
2773 | 
2774 | layer {
2775 |   name: "conv4_1_em"
2776 |   type: "Convolution"
2777 |   bottom: "conv4_1_dw"
2778 |   top: "conv4_1_em"
2779 |   param {
2780 |     lr_mult: 1
2781 |     decay_mult: 1
2782 |   }
2783 |   convolution_param {
2784 |     num_output: 128
2785 |     kernel_size: 1
2786 |     stride: 1
2787 |     pad: 0
2788 |     weight_filler {
2789 |       type: "xavier"
2790 |     }
2791 |     bias_term:false
2792 |   }
2793 | }
2794 | 
2795 | 
2796 | layer {
2797 |   name: "conv4_1_em/bn"
2798 |   type: "BatchNorm"
2799 |   bottom: "conv4_1_em"
2800 |   top: "conv4_1_em"
2801 |   param {
2802 |     lr_mult: 0
2803 |     decay_mult: 0
2804 |   }
2805 |   param {
2806 |     lr_mult: 0
2807 |     decay_mult: 0
2808 |   }
2809 |   param {
2810 |     lr_mult: 0
2811 |     decay_mult: 0
2812 |   }
2813 | }
2814 | layer {
2815 |   name: "conv4_1_em/scale"
2816 |   type: "Scale"
2817 |   bottom: "conv4_1_em"
2818 |   top: "conv4_1_em"
2819 |   param {
2820 |     lr_mult: 1
2821 |     decay_mult: 0
2822 |   }
2823 |   param {
2824 |     lr_mult: 1
2825 |     decay_mult: 0
2826 |   }
2827 |   scale_param {
2828 |     bias_term: true
2829 |   }
2830 | }
2831 | 
2832 | layer {
2833 |   name: "res4_1"
2834 |   type: "Eltwise"
2835 |   bottom: "conv4_em"
2836 |   bottom: "conv4_1_em"
2837 |   top: "res4_1"
2838 | }
2839 | 
2840 | 
2841 | layer {
2842 |   name: "conv4_2_ex"
2843 |   type: "Convolution"
2844 |   bottom: "res4_1"
2845 |   top: "conv4_2_ex"
2846 |   param {
2847 |     lr_mult: 1
2848 |     decay_mult: 1
2849 |   }
2850 |   convolution_param {
2851 |     num_output: 256
2852 |     kernel_size: 1
2853 |     stride: 1
2854 |     pad: 0
2855 |     weight_filler {
2856 |       type: "xavier"
2857 |     }
2858 |     bias_term:false
2859 |   }
2860 | }
2861 | 
2862 | layer {
2863 |   name: "conv4_2_ex/bn"
2864 |   type: "BatchNorm"
2865 |   bottom: "conv4_2_ex"
2866 |   top: "conv4_2_ex"
2867 |   param {
2868 |     lr_mult: 0
2869 |     decay_mult: 0
2870 |   }
2871 |   param {
2872 |     lr_mult: 0
2873 |     decay_mult: 0
2874 |   }
2875 |   param {
2876 |     lr_mult: 0
2877 |     decay_mult: 0
2878 |   }
2879 | }
2880 | layer {
2881 |   name: "conv4_2_ex/scale"
2882 |   type: "Scale"
2883 |   bottom: "conv4_2_ex"
2884 |   top: "conv4_2_ex"
2885 |   param {
2886 |     lr_mult: 1
2887 |     decay_mult: 0
2888 |   }
2889 |   param {
2890 |     lr_mult: 1
2891 |     decay_mult: 0
2892 |   }
2893 |   scale_param {
2894 |     bias_term: true
2895 |   }
2896 | }
2897 | layer {
2898 |   name: "relu4_2_ex"
2899 |   type: "PReLU"
2900 |   bottom: "conv4_2_ex"
2901 |   top: "conv4_2_ex"
2902 | }
2903 | 
2904 | 
2905 | layer {
2906 |   name: "conv4_2_dw"
2907 |   type: "DepthwiseConvolution"
2908 |   bottom: "conv4_2_ex"
2909 |   top: "conv4_2_dw"
2910 |   param {
2911 |     lr_mult: 1
2912 |     decay_mult: 1
2913 |   }
2914 |   convolution_param {
2915 |     num_output: 256
2916 |     group:256
2917 |     kernel_size: 3
2918 |     stride: 1
2919 |     pad: 1
2920 |     weight_filler {
2921 |       type: "xavier"
2922 |     }
2923 |     bias_term:false
2924 |     engine: CAFFE
2925 |   }
2926 | }
2927 | 
2928 | layer {
2929 |   name: "conv4_2_dw/bn"
2930 |   type: "BatchNorm"
2931 |   bottom: "conv4_2_dw"
2932 |   top: "conv4_2_dw"
2933 |   param {
2934 |     lr_mult: 0
2935 |     decay_mult: 0
2936 |   }
2937 |   param {
2938 |     lr_mult: 0
2939 |     decay_mult: 0
2940 |   }
2941 |   param {
2942 |     lr_mult: 0
2943 |     decay_mult: 0
2944 |   }
2945 | }
2946 | layer {
2947 |   name: "conv4_2_dw/scale"
2948 |   type: "Scale"
2949 |   bottom: "conv4_2_dw"
2950 |   top: "conv4_2_dw"
2951 |   param {
2952 |     lr_mult: 1
2953 |     decay_mult: 0
2954 |   }
2955 |   param {
2956 |     lr_mult: 1
2957 |     decay_mult: 0
2958 |   }
2959 |   scale_param {
2960 |     bias_term: true
2961 |   }
2962 | }
2963 | layer {
2964 |   name: "relu4_2_dw"
2965 |   type: "PReLU"
2966 |   bottom: "conv4_2_dw"
2967 |   top: "conv4_2_dw"
2968 | }
2969 | 
2970 | layer {
2971 |   name: "conv4_2_em"
2972 |   type: "Convolution"
2973 |   bottom: "conv4_2_dw"
2974 |   top: "conv4_2_em"
2975 |   param {
2976 |     lr_mult: 1
2977 |     decay_mult: 1
2978 |   }
2979 |   convolution_param {
2980 |     num_output: 128
2981 |     kernel_size: 1
2982 |     stride: 1
2983 |     pad: 0
2984 |     weight_filler {
2985 |       type: "xavier"
2986 |     }
2987 |     bias_term:false
2988 |   }
2989 | }
2990 | 
2991 | 
2992 | layer {
2993 |   name: "conv4_2_em/bn"
2994 |   type: "BatchNorm"
2995 |   bottom: "conv4_2_em"
2996 |   top: "conv4_2_em"
2997 |   param {
2998 |     lr_mult: 0
2999 |     decay_mult: 0
3000 |   }
3001 |   param {
3002 |     lr_mult: 0
3003 |     decay_mult: 0
3004 |   }
3005 |   param {
3006 |     lr_mult: 0
3007 |     decay_mult: 0
3008 |   }
3009 | }
3010 | layer {
3011 |   name: "conv4_2_em/scale"
3012 |   type: "Scale"
3013 |   bottom: "conv4_2_em"
3014 |   top: "conv4_2_em"
3015 |   param {
3016 |     lr_mult: 1
3017 |     decay_mult: 0
3018 |   }
3019 |   param {
3020 |     lr_mult: 1
3021 |     decay_mult: 0
3022 |   }
3023 |   scale_param {
3024 |     bias_term: true
3025 |   }
3026 | }
3027 | 
3028 | layer {
3029 |   name: "res4_2"
3030 |   type: "Eltwise"
3031 |   bottom: "res4_1"
3032 |   bottom: "conv4_2_em"
3033 |   top: "res4_2"
3034 | }
3035 | 
3036 | layer {
3037 |   name: "conv5_ex"
3038 |   type: "Convolution"
3039 |   bottom: "res4_2"
3040 |   top: "conv5_ex"
3041 |   param {
3042 |     lr_mult: 1
3043 |     decay_mult: 1
3044 |   }
3045 |   convolution_param {
3046 |     num_output: 512
3047 |     kernel_size: 1
3048 |     stride: 1
3049 |     pad: 0
3050 |     weight_filler {
3051 |       type: "xavier"
3052 |     }
3053 |     bias_term:false
3054 |   }
3055 | }
3056 | 
3057 | 
3058 | layer {
3059 |   name: "conv5_ex/bn"
3060 |   type: "BatchNorm"
3061 |   bottom: "conv5_ex"
3062 |   top: "conv5_ex"
3063 |   param {
3064 |     lr_mult: 0
3065 |     decay_mult: 0
3066 |   }
3067 |   param {
3068 |     lr_mult: 0
3069 |     decay_mult: 0
3070 |   }
3071 |   param {
3072 |     lr_mult: 0
3073 |     decay_mult: 0
3074 |   }
3075 | }
3076 | layer {
3077 |   name: "conv5_ex/scale"
3078 |   type: "Scale"
3079 |   bottom: "conv5_ex"
3080 |   top: "conv5_ex"
3081 |   param {
3082 |     lr_mult: 1
3083 |     decay_mult: 0
3084 |   }
3085 |   param {
3086 |     lr_mult: 1
3087 |     decay_mult: 0
3088 |   }
3089 |   scale_param {
3090 |     bias_term: true
3091 |   }
3092 | }
3093 | layer {
3094 |   name: "relu5_ex"
3095 |   type: "PReLU"
3096 |   bottom: "conv5_ex"
3097 |   top: "conv5_ex"
3098 | }
3099 | 
3100 | layer {
3101 |   name: "conv5_dw"
3102 |   type: "DepthwiseConvolution"
3103 |   bottom: "conv5_ex"
3104 |   top: "conv5_dw"
3105 |   param {
3106 |     lr_mult: 1
3107 |     decay_mult: 1
3108 |   }
3109 |   convolution_param {
3110 |     num_output: 512
3111 |     group:512
3112 |     kernel_h: 7
3113 |     kernel_w:6
3114 |     stride: 1
3115 |     pad: 0
3116 |     weight_filler {
3117 |       type: "xavier"
3118 |     }
3119 |     bias_term:false
3120 |     engine: CAFFE
3121 |   }
3122 | }
3123 | 
3124 | layer {
3125 |   name: "conv5_dw/bn"
3126 |   type: "BatchNorm"
3127 |   bottom: "conv5_dw"
3128 |   top: "conv5_dw"
3129 |   param {
3130 |     lr_mult: 0
3131 |     decay_mult: 0
3132 |   }
3133 |   param {
3134 |     lr_mult: 0
3135 |     decay_mult: 0
3136 |   }
3137 |   param {
3138 |     lr_mult: 0
3139 |     decay_mult: 0
3140 |   }
3141 | }
3142 | layer {
3143 |   name: "conv5_dw/scale"
3144 |   type: "Scale"
3145 |   bottom: "conv5_dw"
3146 |   top: "conv5_dw"
3147 |   param {
3148 |     lr_mult: 1
3149 |     decay_mult: 0
3150 |   }
3151 |   param {
3152 |     lr_mult: 1
3153 |     decay_mult: 0
3154 |   }
3155 |   scale_param {
3156 |     bias_term: true
3157 |   }
3158 | }
3159 | 
3160 | 
3161 | layer {
3162 |   name: "fc5"
3163 |   type: "InnerProduct"
3164 |   bottom: "conv5_dw"
3165 |   top: "fc5"
3166 |   param {
3167 |     lr_mult: 1
3168 |     decay_mult: 1
3169 |   }
3170 |   inner_product_param {
3171 |     num_output: 128
3172 |     weight_filler {
3173 |       type: "xavier"
3174 |     }
3175 |     bias_term:false
3176 |   }
3177 | }
3178 | 
3179 | 
3180 | 
3181 | 
3182 | 
3183 | 
3184 | 
3185 | 
3186 | 
3187 | 
3188 | 
3189 | 
3190 | 
3191 | 
3192 | 
3193 | 
3194 | 
3195 | 
3196 | 
3197 | 
3198 | 
3199 | 
3200 | 
3201 | 
3202 | 
3203 | 
3204 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # MobileFaceNet
 2 |   论文 ： [MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices](https://arxiv.org/abs/1804.07573) 的AMsoftmaxLoss实现
 3 | 
 4 | ## 环境需要
 5 | 
 6 | - Amsoftmax的配置
 7 | 
 8 | - DepthwiseConvolution层的添加
 9 | 
10 | ## 训练数据集
11 |   
12 |   VggFace2数据集，图像size：112X96
13 | 
14 | ## LFW准确率
15 | 
16 | ![](https://github.com/zhanglaplace/MobileFaceNet/blob/master/result.png)
17 | 
18 | ## 参考：
19 | 
20 | [mobilefacenet-caffe](https://github.com/KaleidoZhouYN/mobilefacenet-caffe)
21 | 
22 | [AMSoftmax](https://github.com/happynear/AMSoftmax)
23 | 
24 | [depthwise convolution](https://github.com/yonghenglh6/DepthwiseConvolution)
25 | 
26 | [mobilenetv2-caffe](https://github.com/shicai/MobileNet-Caffe)
27 | 
28 | [MobileFaceNet-caffe](https://github.com/imistyrain/MobileFaceNet)


--------------------------------------------------------------------------------
/face_snapshot/MobileFaceNet.caffemodel:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhanglaplace/MobileFaceNet/14ed6328989eecacceca8906e242148361ab5a2c/face_snapshot/MobileFaceNet.caffemodel


--------------------------------------------------------------------------------
/face_solver.prototxt:
--------------------------------------------------------------------------------
 1 | net: "MobileFaceNet.prototxt"
 2 | #test_iter: 100 
 3 | #test_interval: 500
 4 | #test_iter: 10
 5 | #test_interval: 500000
 6 | #test_initialization: false
 7 | 
 8 | base_lr: 0.1
 9 | lr_policy: "multistep"
10 | gamma: 0.1
11 | 
12 | stepvalue: 80000
13 | stepvalue: 120000
14 | stepvalue: 140000
15 | max_iter:  160000
16 | iter_size: 1
17 | 
18 | display: 100
19 | momentum: 0.9
20 | weight_decay: 0.0005
21 | snapshot: 5000
22 | snapshot_prefix: "face_snapshot/MobileFaceNet_16w_0pt3_model"
23 | 
24 | solver_mode: GPU
25 | 


--------------------------------------------------------------------------------
/result.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/zhanglaplace/MobileFaceNet/14ed6328989eecacceca8906e242148361ab5a2c/result.png


--------------------------------------------------------------------------------
/start_train.sh:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | # Usage:
 3 | # ./start_train.sh 
 4 | #
 5 | # Example:
 6 | # ./start_train.sh 
 7 | mkdir -p face_snapshot
 8 | mkdir -p result
 9 | ../../../../build/tools/caffe train -solver face_solver.prototxt -gpu all 2>&1 | tee result/mobileFaceNet_20180507_0pt3.log
10 | 


--------------------------------------------------------------------------------