├── FoF-TSN.prototxt
├── FoF.prototxt
├── README.md
├── SBN-FDP.prototxt
├── TSN-FDP.prototxt
├── fractalnet-AvgPool.prototxt
├── fractalnet.prototxt
├── freeze_drop_path.cpp
├── freeze_drop_path.cu
├── freeze_drop_path.hpp
└── solver.prototxt


/FoF.prototxt:
--------------------------------------------------------------------------------
   1 | name: "FractalOfFractalNet"
   2 | layer {
   3 |   name: "cifar"
   4 |   type: "Data"
   5 |   top: "data"
   6 |   top: "label"
   7 |   include {
   8 |     phase: TRAIN
   9 |   }
  10 |   transform_param {
  11 |     mirror: true
  12 |     mean_file: "examples/cifar10/mean.binaryproto"
  13 | #    crop_size: 32
  14 | #    mean_value: 126
  15 | #    mean_value: 126
  16 | #    mean_value: 126
  17 |   }
  18 |   data_param {
  19 |     source: "examples/cifar10/cifar10_train_lmdb"
  20 |     batch_size: 25
  21 |     backend: LMDB
  22 |   }
  23 | }
  24 | layer {
  25 |   name: "cifar"
  26 |   type: "Data"
  27 |   top: "data"
  28 |   top: "label"
  29 |   include {
  30 |     phase: TEST
  31 |   }
  32 |   transform_param {
  33 |     mean_file: "examples/cifar10/mean.binaryproto"
  34 |     mirror: false
  35 | #    shuffle: true
  36 | #    crop_size: 32
  37 |   }
  38 |   data_param {
  39 |     source: "examples/cifar10/cifar10_test_lmdb"
  40 |     batch_size: 10
  41 |     backend: LMDB
  42 |   }
  43 | }
  44 | # Input size: 32
  45 | layer {
  46 |   bottom: "data"
  47 |   top: "conv2_0"
  48 |   name: "conv2_0"
  49 |   param {
  50 |     lr_mult: 1
  51 |     decay_mult: 1
  52 |     name: "conv2_0"
  53 |   }
  54 |   param {
  55 |     lr_mult: 2
  56 |     decay_mult: 0
  57 |     name: "conv2_0_b"
  58 |   }
  59 |   type: "Convolution"
  60 |   convolution_param {
  61 |     num_output: 64
  62 |     pad: 1
  63 |     kernel_size: 3
  64 |     weight_filler {
  65 |       type: "xavier"
  66 |     }
  67 |     bias_filler {
  68 |       type: "constant"
  69 |     }
  70 |   }
  71 | }
  72 | layer {
  73 |   name: "dropout_conv2_0"
  74 |   type: "Dropout"
  75 |   bottom: "conv2_0"
  76 |   top: "conv2_0"
  77 |   dropout_param {
  78 |     dropout_ratio: 0.1
  79 |   }
  80 | }
  81 | layer {
  82 |   name: "batch_conv2_0"
  83 |   type: "BatchNorm"
  84 |   bottom: "conv2_0"
  85 |   top: "conv2_0"
  86 |   param { lr_mult: 0 }
  87 |   param { lr_mult: 0 }
  88 |   param { lr_mult: 0 }
  89 | }
  90 | layer {
  91 |   name: "relu_conv2_0"
  92 |   type: "ReLU"
  93 |   bottom: "conv2_0"
  94 |   top: "conv2_0"
  95 | }
  96 | layer {
  97 |   bottom: "conv2_0"
  98 |   top: "conv2_1"
  99 |   name: "conv2_1"
 100 |   param {
 101 |     lr_mult: 1
 102 |     decay_mult: 1
 103 |     name: "conv2_1"
 104 |   }
 105 |   param {
 106 |     lr_mult: 2
 107 |     decay_mult: 0
 108 |     name: "conv2_1_b"
 109 |   }
 110 |   type: "Convolution"
 111 |   convolution_param {
 112 |     num_output: 64
 113 |     pad: 1
 114 |     kernel_size: 3
 115 |     weight_filler {
 116 |       type: "xavier"
 117 |     }
 118 |     bias_filler {
 119 |       type: "constant"
 120 |     }
 121 |   }
 122 | }
 123 | layer {
 124 |   name: "dropout_conv2_1"
 125 |   type: "Dropout"
 126 |   bottom: "conv2_1"
 127 |   top: "conv2_1"
 128 |   dropout_param {
 129 |     dropout_ratio: 0.1
 130 |   }
 131 | }
 132 | layer {
 133 |   name: "batch_conv2_1"
 134 |   type: "BatchNorm"
 135 |   bottom: "conv2_1"
 136 |   top: "conv2_1"
 137 |   param { lr_mult: 0 }
 138 |   param { lr_mult: 0 }
 139 |   param { lr_mult: 0 }
 140 | }
 141 | layer {
 142 |   name: "relu_conv2_1"
 143 |   type: "ReLU"
 144 |   bottom: "conv2_1"
 145 |   top: "conv2_1"
 146 | }
 147 | layer {
 148 |   bottom: "data"
 149 |   top: "conv1_0"
 150 |   name: "conv1_0"
 151 |   param {
 152 |     lr_mult: 1
 153 |     decay_mult: 1
 154 |     name: "conv1_0"
 155 |   }
 156 |   param {
 157 |     lr_mult: 2
 158 |     decay_mult: 0
 159 |     name: "conv1_0_b"
 160 |   }
 161 |   type: "Convolution"
 162 |   convolution_param {
 163 |     num_output: 64
 164 |     pad: 1
 165 |     kernel_size: 3
 166 |     weight_filler {
 167 |       type: "xavier"
 168 |     }
 169 |     bias_filler {
 170 |       type: "constant"
 171 |     }
 172 |   }
 173 | }
 174 | layer {
 175 |   name: "dropout_conv1_0"
 176 |   type: "Dropout"
 177 |   bottom: "conv1_0"
 178 |   top: "conv1_0"
 179 |   dropout_param {
 180 |     dropout_ratio: 0.1
 181 |   }
 182 | }
 183 | layer {
 184 |   name: "batch_conv1_0"
 185 |   type: "BatchNorm"
 186 |   bottom: "conv1_0"
 187 |   top: "conv1_0"
 188 |   param { lr_mult: 0 }
 189 |   param { lr_mult: 0 }
 190 |   param { lr_mult: 0 }
 191 | }
 192 | layer {
 193 |   name: "relu_conv1_0"
 194 |   type: "ReLU"
 195 |   bottom: "conv1_0"
 196 |   top: "conv1_0"
 197 | }
 198 | layer {
 199 |   name: "join_conv2_1_plus"
 200 |   type: "FractalJoin"
 201 |   bottom: "conv1_0"
 202 |   bottom: "conv2_1"
 203 |   top: "conv2_1_plus"
 204 |   fractal_join_param {
 205 |     drop_path_ratio: 0.15
 206 |   }
 207 | }
 208 | layer {
 209 |   bottom: "conv2_1_plus"
 210 |   top: "conv2_2"
 211 |   name: "conv2_2"
 212 |   param {
 213 |     lr_mult: 1
 214 |     decay_mult: 1
 215 |     name: "conv2_2"
 216 |   }
 217 |   param {
 218 |     lr_mult: 2
 219 |     decay_mult: 0
 220 |     name: "conv2_2_b"
 221 |   }
 222 |   type: "Convolution"
 223 |   convolution_param {
 224 |     num_output: 64
 225 |     pad: 1
 226 |     kernel_size: 3
 227 |     weight_filler {
 228 |       type: "xavier"
 229 |     }
 230 |     bias_filler {
 231 |       type: "constant"
 232 |     }
 233 |   }
 234 | }
 235 | layer {
 236 |   name: "dropout_conv2_2"
 237 |   type: "Dropout"
 238 |   bottom: "conv2_2"
 239 |   top: "conv2_2"
 240 |   dropout_param {
 241 |     dropout_ratio: 0.1
 242 |   }
 243 | }
 244 | layer {
 245 |   name: "batch_conv2_2"
 246 |   type: "BatchNorm"
 247 |   bottom: "conv2_2"
 248 |   top: "conv2_2"
 249 |   param { lr_mult: 0 }
 250 |   param { lr_mult: 0 }
 251 |   param { lr_mult: 0 }
 252 | }
 253 | layer {
 254 |   name: "relu_conv2_2"
 255 |   type: "ReLU"
 256 |   bottom: "conv2_2"
 257 |   top: "conv2_2"
 258 | }
 259 | layer {
 260 |   bottom: "conv2_2"
 261 |   top: "conv2_3"
 262 |   name: "conv2_3"
 263 |   param {
 264 |     lr_mult: 1
 265 |     decay_mult: 1
 266 |     name: "conv2_3"
 267 |   }
 268 |   param {
 269 |     lr_mult: 2
 270 |     decay_mult: 0
 271 |     name: "conv2_3_b"
 272 |   }
 273 |   type: "Convolution"
 274 |   convolution_param {
 275 |     num_output: 64
 276 |     pad: 1
 277 |     kernel_size: 3
 278 |     weight_filler {
 279 |       type: "xavier"
 280 |     }
 281 |     bias_filler {
 282 |       type: "constant"
 283 |     }
 284 |   }
 285 | }
 286 | layer {
 287 |   name: "dropout_conv2_3"
 288 |   type: "Dropout"
 289 |   bottom: "conv2_3"
 290 |   top: "conv2_3"
 291 |   dropout_param {
 292 |     dropout_ratio: 0.1
 293 |   }
 294 | }
 295 | layer {
 296 |   name: "batch_conv2_3"
 297 |   type: "BatchNorm"
 298 |   bottom: "conv2_3"
 299 |   top: "conv2_3"
 300 |   param { lr_mult: 0 }
 301 |   param { lr_mult: 0 }
 302 |   param { lr_mult: 0 }
 303 | }
 304 | layer {
 305 |   name: "relu_conv2_3"
 306 |   type: "ReLU"
 307 |   bottom: "conv2_3"
 308 |   top: "conv2_3"
 309 | }
 310 | layer {
 311 |   bottom: "conv2_1_plus"
 312 |   top: "conv1_1"
 313 |   name: "conv1_1"
 314 |   param {
 315 |     lr_mult: 1
 316 |     decay_mult: 1
 317 |     name: "conv1_1"
 318 |   }
 319 |   param {
 320 |     lr_mult: 2
 321 |     decay_mult: 0
 322 |     name: "conv1_1_b"
 323 |   }
 324 |   type: "Convolution"
 325 |   convolution_param {
 326 |     num_output: 64
 327 |     pad: 1
 328 |     kernel_size: 3
 329 |     weight_filler {
 330 |       type: "xavier"
 331 |     }
 332 |     bias_filler {
 333 |       type: "constant"
 334 |     }
 335 |   }
 336 | }
 337 | layer {
 338 |   name: "dropout_conv1_1"
 339 |   type: "Dropout"
 340 |   bottom: "conv1_1"
 341 |   top: "conv1_1"
 342 |   dropout_param {
 343 |     dropout_ratio: 0.1
 344 |   }
 345 | }
 346 | layer {
 347 |   name: "batch_conv1_1"
 348 |   type: "BatchNorm"
 349 |   bottom: "conv1_1"
 350 |   top: "conv1_1"
 351 |   param { lr_mult: 0 }
 352 |   param { lr_mult: 0 }
 353 |   param { lr_mult: 0 }
 354 | }
 355 | layer {
 356 |   name: "relu_conv1_1"
 357 |   type: "ReLU"
 358 |   bottom: "conv1_1"
 359 |   top: "conv1_1"
 360 | }
 361 | layer {
 362 |   bottom: "data"
 363 |   top: "conv0_0"
 364 |   name: "conv0_0"
 365 |   param {
 366 |     lr_mult: 1
 367 |     decay_mult: 1
 368 |     name: "conv0_0"
 369 |   }
 370 |   param {
 371 |     lr_mult: 2
 372 |     decay_mult: 0
 373 |     name: "conv0_0_b"
 374 |   }
 375 |   type: "Convolution"
 376 |   convolution_param {
 377 |     num_output: 64
 378 |     pad: 1
 379 |     kernel_size: 3
 380 |     weight_filler {
 381 |       type: "xavier"
 382 |     }
 383 |     bias_filler {
 384 |       type: "constant"
 385 |     }
 386 |   }
 387 | }
 388 | layer {
 389 |   name: "dropout_conv0_0"
 390 |   type: "Dropout"
 391 |   bottom: "conv0_0"
 392 |   top: "conv0_0"
 393 |   dropout_param {
 394 |     dropout_ratio: 0.1
 395 |   }
 396 | }
 397 | layer {
 398 |   name: "batch_conv0_0"
 399 |   type: "BatchNorm"
 400 |   bottom: "conv0_0"
 401 |   top: "conv0_0"
 402 |   param { lr_mult: 0 }
 403 |   param { lr_mult: 0 }
 404 |   param { lr_mult: 0 }
 405 | }
 406 | layer {
 407 |   name: "relu_conv0_0"
 408 |   type: "ReLU"
 409 |   bottom: "conv0_0"
 410 |   top: "conv0_0"
 411 | }
 412 | layer {
 413 |   bottom: "conv0_0"
 414 |   top: "pool0_0"
 415 |   name: "pool0_0"
 416 |   type: "Pooling"
 417 |   pooling_param {
 418 |     pool: MAX
 419 |     kernel_size: 2
 420 |     stride: 2
 421 |   }
 422 | }
 423 | layer {
 424 |   bottom: "conv1_1"
 425 |   top: "pool1_1"
 426 |   name: "pool1_1"
 427 |   type: "Pooling"
 428 |   pooling_param {
 429 |     pool: MAX
 430 |     kernel_size: 2
 431 |     stride: 2
 432 |   }
 433 | }
 434 | layer {
 435 |   bottom: "conv2_3"
 436 |   top: "pool2_3"
 437 |   name: "pool2_3"
 438 |   type: "Pooling"
 439 |   pooling_param {
 440 |     pool: MAX
 441 |     kernel_size: 2
 442 |     stride: 2
 443 |   }
 444 | }
 445 | layer {
 446 |   name: "join_pool2_3_plus"
 447 |   type: "FractalJoin"
 448 |   bottom: "pool0_0"
 449 |   bottom: "pool1_1"
 450 |   bottom: "pool2_3"
 451 |   top: "pool2_3_plus"
 452 |   fractal_join_param {
 453 |     drop_path_ratio: 0.15
 454 |   }
 455 | }
 456 | # Reduction: 1, spatial size: 16
 457 | layer {
 458 |   bottom: "pool2_3_plus"
 459 |   top: "conv2_4"
 460 |   name: "conv2_4"
 461 |   param {
 462 |     lr_mult: 1
 463 |     decay_mult: 1
 464 |     name: "conv2_4"
 465 |   }
 466 |   param {
 467 |     lr_mult: 2
 468 |     decay_mult: 0
 469 |     name: "conv2_4_b"
 470 |   }
 471 |   type: "Convolution"
 472 |   convolution_param {
 473 |     num_output: 128
 474 |     pad: 1
 475 |     kernel_size: 3
 476 |     weight_filler {
 477 |       type: "xavier"
 478 |     }
 479 |     bias_filler {
 480 |       type: "constant"
 481 |     }
 482 |   }
 483 | }
 484 | layer {
 485 |   name: "dropout_conv2_4"
 486 |   type: "Dropout"
 487 |   bottom: "conv2_4"
 488 |   top: "conv2_4"
 489 |   dropout_param {
 490 |     dropout_ratio: 0.2
 491 |   }
 492 | }
 493 | layer {
 494 |   name: "batch_conv2_4"
 495 |   type: "BatchNorm"
 496 |   bottom: "conv2_4"
 497 |   top: "conv2_4"
 498 |   param { lr_mult: 0 }
 499 |   param { lr_mult: 0 }
 500 |   param { lr_mult: 0 }
 501 | }
 502 | layer {
 503 |   name: "relu_conv2_4"
 504 |   type: "ReLU"
 505 |   bottom: "conv2_4"
 506 |   top: "conv2_4"
 507 | }
 508 | layer {
 509 |   bottom: "conv2_4"
 510 |   top: "conv2_5"
 511 |   name: "conv2_5"
 512 |   param {
 513 |     lr_mult: 1
 514 |     decay_mult: 1
 515 |     name: "conv2_5"
 516 |   }
 517 |   param {
 518 |     lr_mult: 2
 519 |     decay_mult: 0
 520 |     name: "conv2_5_b"
 521 |   }
 522 |   type: "Convolution"
 523 |   convolution_param {
 524 |     num_output: 128
 525 |     pad: 1
 526 |     kernel_size: 3
 527 |     weight_filler {
 528 |       type: "xavier"
 529 |     }
 530 |     bias_filler {
 531 |       type: "constant"
 532 |     }
 533 |   }
 534 | }
 535 | layer {
 536 |   name: "dropout_conv2_5"
 537 |   type: "Dropout"
 538 |   bottom: "conv2_5"
 539 |   top: "conv2_5"
 540 |   dropout_param {
 541 |     dropout_ratio: 0.2
 542 |   }
 543 | }
 544 | layer {
 545 |   name: "batch_conv2_5"
 546 |   type: "BatchNorm"
 547 |   bottom: "conv2_5"
 548 |   top: "conv2_5"
 549 |   param { lr_mult: 0 }
 550 |   param { lr_mult: 0 }
 551 |   param { lr_mult: 0 }
 552 | }
 553 | layer {
 554 |   name: "relu_conv2_5"
 555 |   type: "ReLU"
 556 |   bottom: "conv2_5"
 557 |   top: "conv2_5"
 558 | }
 559 | layer {
 560 |   bottom: "pool2_3_plus"
 561 |   top: "conv1_2"
 562 |   name: "conv1_2"
 563 |   param {
 564 |     lr_mult: 1
 565 |     decay_mult: 1
 566 |     name: "conv1_2"
 567 |   }
 568 |   param {
 569 |     lr_mult: 2
 570 |     decay_mult: 0
 571 |     name: "conv1_2_b"
 572 |   }
 573 |   type: "Convolution"
 574 |   convolution_param {
 575 |     num_output: 128
 576 |     pad: 1
 577 |     kernel_size: 3
 578 |     weight_filler {
 579 |       type: "xavier"
 580 |     }
 581 |     bias_filler {
 582 |       type: "constant"
 583 |     }
 584 |   }
 585 | }
 586 | layer {
 587 |   name: "dropout_conv1_2"
 588 |   type: "Dropout"
 589 |   bottom: "conv1_2"
 590 |   top: "conv1_2"
 591 |   dropout_param {
 592 |     dropout_ratio: 0.2
 593 |   }
 594 | }
 595 | layer {
 596 |   name: "batch_conv1_2"
 597 |   type: "BatchNorm"
 598 |   bottom: "conv1_2"
 599 |   top: "conv1_2"
 600 |   param { lr_mult: 0 }
 601 |   param { lr_mult: 0 }
 602 |   param { lr_mult: 0 }
 603 | }
 604 | layer {
 605 |   name: "relu_conv1_2"
 606 |   type: "ReLU"
 607 |   bottom: "conv1_2"
 608 |   top: "conv1_2"
 609 | }
 610 | layer {
 611 |   name: "join_conv2_5_plus"
 612 |   type: "FractalJoin"
 613 |   bottom: "conv1_2"
 614 |   bottom: "conv2_5"
 615 |   top: "conv2_5_plus"
 616 |   fractal_join_param {
 617 |     drop_path_ratio: 0.15
 618 |   }
 619 | }
 620 | layer {
 621 |   bottom: "conv2_5_plus"
 622 |   top: "conv2_6"
 623 |   name: "conv2_6"
 624 |   param {
 625 |     lr_mult: 1
 626 |     decay_mult: 1
 627 |     name: "conv2_6"
 628 |   }
 629 |   param {
 630 |     lr_mult: 2
 631 |     decay_mult: 0
 632 |     name: "conv2_6_b"
 633 |   }
 634 |   type: "Convolution"
 635 |   convolution_param {
 636 |     num_output: 128
 637 |     pad: 1
 638 |     kernel_size: 3
 639 |     weight_filler {
 640 |       type: "xavier"
 641 |     }
 642 |     bias_filler {
 643 |       type: "constant"
 644 |     }
 645 |   }
 646 | }
 647 | layer {
 648 |   name: "dropout_conv2_6"
 649 |   type: "Dropout"
 650 |   bottom: "conv2_6"
 651 |   top: "conv2_6"
 652 |   dropout_param {
 653 |     dropout_ratio: 0.2
 654 |   }
 655 | }
 656 | layer {
 657 |   name: "batch_conv2_6"
 658 |   type: "BatchNorm"
 659 |   bottom: "conv2_6"
 660 |   top: "conv2_6"
 661 |   param { lr_mult: 0 }
 662 |   param { lr_mult: 0 }
 663 |   param { lr_mult: 0 }
 664 | }
 665 | layer {
 666 |   name: "relu_conv2_6"
 667 |   type: "ReLU"
 668 |   bottom: "conv2_6"
 669 |   top: "conv2_6"
 670 | }
 671 | layer {
 672 |   bottom: "conv2_6"
 673 |   top: "conv2_7"
 674 |   name: "conv2_7"
 675 |   param {
 676 |     lr_mult: 1
 677 |     decay_mult: 1
 678 |     name: "conv2_7"
 679 |   }
 680 |   param {
 681 |     lr_mult: 2
 682 |     decay_mult: 0
 683 |     name: "conv2_7_b"
 684 |   }
 685 |   type: "Convolution"
 686 |   convolution_param {
 687 |     num_output: 128
 688 |     pad: 1
 689 |     kernel_size: 3
 690 |     weight_filler {
 691 |       type: "xavier"
 692 |     }
 693 |     bias_filler {
 694 |       type: "constant"
 695 |     }
 696 |   }
 697 | }
 698 | layer {
 699 |   name: "dropout_conv2_7"
 700 |   type: "Dropout"
 701 |   bottom: "conv2_7"
 702 |   top: "conv2_7"
 703 |   dropout_param {
 704 |     dropout_ratio: 0.2
 705 |   }
 706 | }
 707 | layer {
 708 |   name: "batch_conv2_7"
 709 |   type: "BatchNorm"
 710 |   bottom: "conv2_7"
 711 |   top: "conv2_7"
 712 |   param { lr_mult: 0 }
 713 |   param { lr_mult: 0 }
 714 |   param { lr_mult: 0 }
 715 | }
 716 | layer {
 717 |   name: "relu_conv2_7"
 718 |   type: "ReLU"
 719 |   bottom: "conv2_7"
 720 |   top: "conv2_7"
 721 | }
 722 | layer {
 723 |   bottom: "conv2_5_plus"
 724 |   top: "conv1_3"
 725 |   name: "conv1_3"
 726 |   param {
 727 |     lr_mult: 1
 728 |     decay_mult: 1
 729 |     name: "conv1_3"
 730 |   }
 731 |   param {
 732 |     lr_mult: 2
 733 |     decay_mult: 0
 734 |     name: "conv1_3_b"
 735 |   }
 736 |   type: "Convolution"
 737 |   convolution_param {
 738 |     num_output: 128
 739 |     pad: 1
 740 |     kernel_size: 3
 741 |     weight_filler {
 742 |       type: "xavier"
 743 |     }
 744 |     bias_filler {
 745 |       type: "constant"
 746 |     }
 747 |   }
 748 | }
 749 | layer {
 750 |   name: "dropout_conv1_3"
 751 |   type: "Dropout"
 752 |   bottom: "conv1_3"
 753 |   top: "conv1_3"
 754 |   dropout_param {
 755 |     dropout_ratio: 0.2
 756 |   }
 757 | }
 758 | layer {
 759 |   name: "batch_conv1_3"
 760 |   type: "BatchNorm"
 761 |   bottom: "conv1_3"
 762 |   top: "conv1_3"
 763 |   param { lr_mult: 0 }
 764 |   param { lr_mult: 0 }
 765 |   param { lr_mult: 0 }
 766 | }
 767 | layer {
 768 |   name: "relu_conv1_3"
 769 |   type: "ReLU"
 770 |   bottom: "conv1_3"
 771 |   top: "conv1_3"
 772 | }
 773 | layer {
 774 |   bottom: "pool2_3_plus"
 775 |   top: "conv0_1"
 776 |   name: "conv0_1"
 777 |   param {
 778 |     lr_mult: 1
 779 |     decay_mult: 1
 780 |     name: "conv0_1"
 781 |   }
 782 |   param {
 783 |     lr_mult: 2
 784 |     decay_mult: 0
 785 |     name: "conv0_1_b"
 786 |   }
 787 |   type: "Convolution"
 788 |   convolution_param {
 789 |     num_output: 128
 790 |     pad: 1
 791 |     kernel_size: 3
 792 |     weight_filler {
 793 |       type: "xavier"
 794 |     }
 795 |     bias_filler {
 796 |       type: "constant"
 797 |     }
 798 |   }
 799 | }
 800 | layer {
 801 |   name: "dropout_conv0_1"
 802 |   type: "Dropout"
 803 |   bottom: "conv0_1"
 804 |   top: "conv0_1"
 805 |   dropout_param {
 806 |     dropout_ratio: 0.2
 807 |   }
 808 | }
 809 | layer {
 810 |   name: "batch_conv0_1"
 811 |   type: "BatchNorm"
 812 |   bottom: "conv0_1"
 813 |   top: "conv0_1"
 814 |   param { lr_mult: 0 }
 815 |   param { lr_mult: 0 }
 816 |   param { lr_mult: 0 }
 817 | }
 818 | layer {
 819 |   name: "relu_conv0_1"
 820 |   type: "ReLU"
 821 |   bottom: "conv0_1"
 822 |   top: "conv0_1"
 823 | }
 824 | layer {
 825 |   bottom: "conv0_1"
 826 |   top: "pool0_1"
 827 |   name: "pool0_1"
 828 |   type: "Pooling"
 829 |   pooling_param {
 830 |     pool: MAX
 831 |     kernel_size: 2
 832 |     stride: 2
 833 |   }
 834 | }
 835 | layer {
 836 |   bottom: "conv1_3"
 837 |   top: "pool1_3"
 838 |   name: "pool1_3"
 839 |   type: "Pooling"
 840 |   pooling_param {
 841 |     pool: MAX
 842 |     kernel_size: 2
 843 |     stride: 2
 844 |   }
 845 | }
 846 | layer {
 847 |   bottom: "conv2_7"
 848 |   top: "pool2_7"
 849 |   name: "pool2_7"
 850 |   type: "Pooling"
 851 |   pooling_param {
 852 |     pool: MAX
 853 |     kernel_size: 2
 854 |     stride: 2
 855 |   }
 856 | }
 857 | layer {
 858 |   name: "join_pool2_7_plus"
 859 |   type: "FractalJoin"
 860 |   bottom: "pool0_1"
 861 |   bottom: "pool1_3"
 862 |   bottom: "pool2_7"
 863 |   top: "pool2_7_plus"
 864 |   fractal_join_param {
 865 |     drop_path_ratio: 0.15
 866 |   }
 867 | }
 868 | # Reduction: 2, spatial size: 8
 869 | layer {
 870 |   bottom: "data"
 871 |   top: "conv2_8"
 872 |   name: "conv2_8"
 873 |   param {
 874 |     lr_mult: 1
 875 |     decay_mult: 1
 876 |     name: "conv2_8"
 877 |   }
 878 |   param {
 879 |     lr_mult: 2
 880 |     decay_mult: 0
 881 |     name: "conv2_8_b"
 882 |   }
 883 |   type: "Convolution"
 884 |   convolution_param {
 885 |     num_output: 128
 886 |     pad: 1
 887 |     kernel_size: 3
 888 |     weight_filler {
 889 |       type: "xavier"
 890 |     }
 891 |     bias_filler {
 892 |       type: "constant"
 893 |     }
 894 |   }
 895 | }
 896 | layer {
 897 |   name: "dropout_conv2_8"
 898 |   type: "Dropout"
 899 |   bottom: "conv2_8"
 900 |   top: "conv2_8"
 901 |   dropout_param {
 902 |     dropout_ratio: 0.1
 903 |   }
 904 | }
 905 | layer {
 906 |   name: "batch_conv2_8"
 907 |   type: "BatchNorm"
 908 |   bottom: "conv2_8"
 909 |   top: "conv2_8"
 910 |   param { lr_mult: 0 }
 911 |   param { lr_mult: 0 }
 912 |   param { lr_mult: 0 }
 913 | }
 914 | layer {
 915 |   name: "relu_conv2_8"
 916 |   type: "ReLU"
 917 |   bottom: "conv2_8"
 918 |   top: "conv2_8"
 919 | }
 920 | layer {
 921 |   bottom: "conv2_8"
 922 |   top: "conv2_9"
 923 |   name: "conv2_9"
 924 |   param {
 925 |     lr_mult: 1
 926 |     decay_mult: 1
 927 |     name: "conv2_9"
 928 |   }
 929 |   param {
 930 |     lr_mult: 2
 931 |     decay_mult: 0
 932 |     name: "conv2_9_b"
 933 |   }
 934 |   type: "Convolution"
 935 |   convolution_param {
 936 |     num_output: 128
 937 |     pad: 1
 938 |     kernel_size: 3
 939 |     weight_filler {
 940 |       type: "xavier"
 941 |     }
 942 |     bias_filler {
 943 |       type: "constant"
 944 |     }
 945 |   }
 946 | }
 947 | layer {
 948 |   name: "dropout_conv2_9"
 949 |   type: "Dropout"
 950 |   bottom: "conv2_9"
 951 |   top: "conv2_9"
 952 |   dropout_param {
 953 |     dropout_ratio: 0.1
 954 |   }
 955 | }
 956 | layer {
 957 |   name: "batch_conv2_9"
 958 |   type: "BatchNorm"
 959 |   bottom: "conv2_9"
 960 |   top: "conv2_9"
 961 |   param { lr_mult: 0 }
 962 |   param { lr_mult: 0 }
 963 |   param { lr_mult: 0 }
 964 | }
 965 | layer {
 966 |   name: "relu_conv2_9"
 967 |   type: "ReLU"
 968 |   bottom: "conv2_9"
 969 |   top: "conv2_9"
 970 | }
 971 | layer {
 972 |   bottom: "data"
 973 |   top: "conv1_4"
 974 |   name: "conv1_4"
 975 |   param {
 976 |     lr_mult: 1
 977 |     decay_mult: 1
 978 |     name: "conv1_4"
 979 |   }
 980 |   param {
 981 |     lr_mult: 2
 982 |     decay_mult: 0
 983 |     name: "conv1_4_b"
 984 |   }
 985 |   type: "Convolution"
 986 |   convolution_param {
 987 |     num_output: 128
 988 |     pad: 1
 989 |     kernel_size: 3
 990 |     weight_filler {
 991 |       type: "xavier"
 992 |     }
 993 |     bias_filler {
 994 |       type: "constant"
 995 |     }
 996 |   }
 997 | }
 998 | layer {
 999 |   name: "dropout_conv1_4"
1000 |   type: "Dropout"
1001 |   bottom: "conv1_4"
1002 |   top: "conv1_4"
1003 |   dropout_param {
1004 |     dropout_ratio: 0.1
1005 |   }
1006 | }
1007 | layer {
1008 |   name: "batch_conv1_4"
1009 |   type: "BatchNorm"
1010 |   bottom: "conv1_4"
1011 |   top: "conv1_4"
1012 |   param { lr_mult: 0 }
1013 |   param { lr_mult: 0 }
1014 |   param { lr_mult: 0 }
1015 | }
1016 | layer {
1017 |   name: "relu_conv1_4"
1018 |   type: "ReLU"
1019 |   bottom: "conv1_4"
1020 |   top: "conv1_4"
1021 | }
1022 | layer {
1023 |   name: "join_conv2_9_plus"
1024 |   type: "FractalJoin"
1025 |   bottom: "conv1_4"
1026 |   bottom: "conv2_9"
1027 |   top: "conv2_9_plus"
1028 |   fractal_join_param {
1029 |     drop_path_ratio: 0.15
1030 |   }
1031 | }
1032 | layer {
1033 |   bottom: "conv2_9_plus"
1034 |   top: "conv2_10"
1035 |   name: "conv2_10"
1036 |   param {
1037 |     lr_mult: 1
1038 |     decay_mult: 1
1039 |     name: "conv2_10"
1040 |   }
1041 |   param {
1042 |     lr_mult: 2
1043 |     decay_mult: 0
1044 |     name: "conv2_10_b"
1045 |   }
1046 |   type: "Convolution"
1047 |   convolution_param {
1048 |     num_output: 128
1049 |     pad: 1
1050 |     kernel_size: 3
1051 |     weight_filler {
1052 |       type: "xavier"
1053 |     }
1054 |     bias_filler {
1055 |       type: "constant"
1056 |     }
1057 |   }
1058 | }
1059 | layer {
1060 |   name: "dropout_conv2_10"
1061 |   type: "Dropout"
1062 |   bottom: "conv2_10"
1063 |   top: "conv2_10"
1064 |   dropout_param {
1065 |     dropout_ratio: 0.1
1066 |   }
1067 | }
1068 | layer {
1069 |   name: "batch_conv2_10"
1070 |   type: "BatchNorm"
1071 |   bottom: "conv2_10"
1072 |   top: "conv2_10"
1073 |   param { lr_mult: 0 }
1074 |   param { lr_mult: 0 }
1075 |   param { lr_mult: 0 }
1076 | }
1077 | layer {
1078 |   name: "relu_conv2_10"
1079 |   type: "ReLU"
1080 |   bottom: "conv2_10"
1081 |   top: "conv2_10"
1082 | }
1083 | layer {
1084 |   bottom: "conv2_10"
1085 |   top: "conv2_11"
1086 |   name: "conv2_11"
1087 |   param {
1088 |     lr_mult: 1
1089 |     decay_mult: 1
1090 |     name: "conv2_11"
1091 |   }
1092 |   param {
1093 |     lr_mult: 2
1094 |     decay_mult: 0
1095 |     name: "conv2_11_b"
1096 |   }
1097 |   type: "Convolution"
1098 |   convolution_param {
1099 |     num_output: 128
1100 |     pad: 1
1101 |     kernel_size: 3
1102 |     weight_filler {
1103 |       type: "xavier"
1104 |     }
1105 |     bias_filler {
1106 |       type: "constant"
1107 |     }
1108 |   }
1109 | }
1110 | layer {
1111 |   name: "dropout_conv2_11"
1112 |   type: "Dropout"
1113 |   bottom: "conv2_11"
1114 |   top: "conv2_11"
1115 |   dropout_param {
1116 |     dropout_ratio: 0.1
1117 |   }
1118 | }
1119 | layer {
1120 |   name: "batch_conv2_11"
1121 |   type: "BatchNorm"
1122 |   bottom: "conv2_11"
1123 |   top: "conv2_11"
1124 |   param { lr_mult: 0 }
1125 |   param { lr_mult: 0 }
1126 |   param { lr_mult: 0 }
1127 | }
1128 | layer {
1129 |   name: "relu_conv2_11"
1130 |   type: "ReLU"
1131 |   bottom: "conv2_11"
1132 |   top: "conv2_11"
1133 | }
1134 | layer {
1135 |   bottom: "conv2_9_plus"
1136 |   top: "conv1_5"
1137 |   name: "conv1_5"
1138 |   param {
1139 |     lr_mult: 1
1140 |     decay_mult: 1
1141 |     name: "conv1_5"
1142 |   }
1143 |   param {
1144 |     lr_mult: 2
1145 |     decay_mult: 0
1146 |     name: "conv1_5_b"
1147 |   }
1148 |   type: "Convolution"
1149 |   convolution_param {
1150 |     num_output: 128
1151 |     pad: 1
1152 |     kernel_size: 3
1153 |     weight_filler {
1154 |       type: "xavier"
1155 |     }
1156 |     bias_filler {
1157 |       type: "constant"
1158 |     }
1159 |   }
1160 | }
1161 | layer {
1162 |   name: "dropout_conv1_5"
1163 |   type: "Dropout"
1164 |   bottom: "conv1_5"
1165 |   top: "conv1_5"
1166 |   dropout_param {
1167 |     dropout_ratio: 0.1
1168 |   }
1169 | }
1170 | layer {
1171 |   name: "batch_conv1_5"
1172 |   type: "BatchNorm"
1173 |   bottom: "conv1_5"
1174 |   top: "conv1_5"
1175 |   param { lr_mult: 0 }
1176 |   param { lr_mult: 0 }
1177 |   param { lr_mult: 0 }
1178 | }
1179 | layer {
1180 |   name: "relu_conv1_5"
1181 |   type: "ReLU"
1182 |   bottom: "conv1_5"
1183 |   top: "conv1_5"
1184 | }
1185 | layer {
1186 |   bottom: "data"
1187 |   top: "conv0_2"
1188 |   name: "conv0_2"
1189 |   param {
1190 |     lr_mult: 1
1191 |     decay_mult: 1
1192 |     name: "conv0_2"
1193 |   }
1194 |   param {
1195 |     lr_mult: 2
1196 |     decay_mult: 0
1197 |     name: "conv0_2_b"
1198 |   }
1199 |   type: "Convolution"
1200 |   convolution_param {
1201 |     num_output: 128
1202 |     pad: 1
1203 |     kernel_size: 3
1204 |     weight_filler {
1205 |       type: "xavier"
1206 |     }
1207 |     bias_filler {
1208 |       type: "constant"
1209 |     }
1210 |   }
1211 | }
1212 | layer {
1213 |   name: "dropout_conv0_2"
1214 |   type: "Dropout"
1215 |   bottom: "conv0_2"
1216 |   top: "conv0_2"
1217 |   dropout_param {
1218 |     dropout_ratio: 0.1
1219 |   }
1220 | }
1221 | layer {
1222 |   name: "batch_conv0_2"
1223 |   type: "BatchNorm"
1224 |   bottom: "conv0_2"
1225 |   top: "conv0_2"
1226 |   param { lr_mult: 0 }
1227 |   param { lr_mult: 0 }
1228 |   param { lr_mult: 0 }
1229 | }
1230 | layer {
1231 |   name: "relu_conv0_2"
1232 |   type: "ReLU"
1233 |   bottom: "conv0_2"
1234 |   top: "conv0_2"
1235 | }
1236 | layer {
1237 |   bottom: "conv0_2"
1238 |   top: "pool0_2_0"
1239 |   name: "pool0_2_0"
1240 |   type: "Pooling"
1241 |   pooling_param {
1242 |     pool: MAX
1243 |     kernel_size: 2
1244 |     stride: 2
1245 |   }
1246 | }
1247 | layer {
1248 |   bottom: "pool0_2_0"
1249 |   top: "pool0_2_1"
1250 |   name: "pool0_2_1"
1251 |   type: "Pooling"
1252 |   pooling_param {
1253 |     pool: MAX
1254 |     kernel_size: 2
1255 |     stride: 2
1256 |   }
1257 | }
1258 | layer {
1259 |   bottom: "conv1_5"
1260 |   top: "pool1_5_0"
1261 |   name: "pool1_5_0"
1262 |   type: "Pooling"
1263 |   pooling_param {
1264 |     pool: MAX
1265 |     kernel_size: 2
1266 |     stride: 2
1267 |   }
1268 | }
1269 | layer {
1270 |   bottom: "pool1_5_0"
1271 |   top: "pool1_5_1"
1272 |   name: "pool1_5_1"
1273 |   type: "Pooling"
1274 |   pooling_param {
1275 |     pool: MAX
1276 |     kernel_size: 2
1277 |     stride: 2
1278 |   }
1279 | }
1280 | layer {
1281 |   bottom: "conv2_11"
1282 |   top: "pool2_11_0"
1283 |   name: "pool2_11_0"
1284 |   type: "Pooling"
1285 |   pooling_param {
1286 |     pool: MAX
1287 |     kernel_size: 2
1288 |     stride: 2
1289 |   }
1290 | }
1291 | layer {
1292 |   bottom: "pool2_11_0"
1293 |   top: "pool2_11_1"
1294 |   name: "pool2_11_1"
1295 |   type: "Pooling"
1296 |   pooling_param {
1297 |     pool: MAX
1298 |     kernel_size: 2
1299 |     stride: 2
1300 |   }
1301 | }
1302 | layer {
1303 |   name: "join_pool2_11_plus"
1304 |   type: "FractalJoin"
1305 |   bottom: "pool0_2_1"
1306 |   bottom: "pool1_5_1"
1307 |   bottom: "pool2_11_1"
1308 |   top: "pool2_11_plus"
1309 |   fractal_join_param {
1310 |     drop_path_ratio: 0.15
1311 |   }
1312 | }
1313 | layer {
1314 |   name: "join_extra_mid_join"
1315 |   type: "FractalJoin"
1316 |   bottom: "pool2_7_plus"
1317 |   bottom: "pool2_11_plus"
1318 |   top: "extra_mid_join"
1319 |   fractal_join_param {
1320 |     drop_path_ratio: 0.15
1321 |   }
1322 | }
1323 | # Reduction: 3, spatial size: 4
1324 | layer {
1325 |   bottom: "extra_mid_join"
1326 |   top: "conv2_12"
1327 |   name: "conv2_12"
1328 |   param {
1329 |     lr_mult: 1
1330 |     decay_mult: 1
1331 |     name: "conv2_12"
1332 |   }
1333 |   param {
1334 |     lr_mult: 2
1335 |     decay_mult: 0
1336 |     name: "conv2_12_b"
1337 |   }
1338 |   type: "Convolution"
1339 |   convolution_param {
1340 |     num_output: 256
1341 |     pad: 1
1342 |     kernel_size: 3
1343 |     weight_filler {
1344 |       type: "xavier"
1345 |     }
1346 |     bias_filler {
1347 |       type: "constant"
1348 |     }
1349 |   }
1350 | }
1351 | layer {
1352 |   name: "dropout_conv2_12"
1353 |   type: "Dropout"
1354 |   bottom: "conv2_12"
1355 |   top: "conv2_12"
1356 |   dropout_param {
1357 |     dropout_ratio: 0.0
1358 |   }
1359 | }
1360 | layer {
1361 |   name: "batch_conv2_12"
1362 |   type: "BatchNorm"
1363 |   bottom: "conv2_12"
1364 |   top: "conv2_12"
1365 |   param { lr_mult: 0 }
1366 |   param { lr_mult: 0 }
1367 |   param { lr_mult: 0 }
1368 | }
1369 | layer {
1370 |   name: "relu_conv2_12"
1371 |   type: "ReLU"
1372 |   bottom: "conv2_12"
1373 |   top: "conv2_12"
1374 | }
1375 | layer {
1376 |   bottom: "conv2_12"
1377 |   top: "conv2_13"
1378 |   name: "conv2_13"
1379 |   param {
1380 |     lr_mult: 1
1381 |     decay_mult: 1
1382 |     name: "conv2_13"
1383 |   }
1384 |   param {
1385 |     lr_mult: 2
1386 |     decay_mult: 0
1387 |     name: "conv2_13_b"
1388 |   }
1389 |   type: "Convolution"
1390 |   convolution_param {
1391 |     num_output: 256
1392 |     pad: 1
1393 |     kernel_size: 3
1394 |     weight_filler {
1395 |       type: "xavier"
1396 |     }
1397 |     bias_filler {
1398 |       type: "constant"
1399 |     }
1400 |   }
1401 | }
1402 | layer {
1403 |   name: "dropout_conv2_13"
1404 |   type: "Dropout"
1405 |   bottom: "conv2_13"
1406 |   top: "conv2_13"
1407 |   dropout_param {
1408 |     dropout_ratio: 0.0
1409 |   }
1410 | }
1411 | layer {
1412 |   name: "batch_conv2_13"
1413 |   type: "BatchNorm"
1414 |   bottom: "conv2_13"
1415 |   top: "conv2_13"
1416 |   param { lr_mult: 0 }
1417 |   param { lr_mult: 0 }
1418 |   param { lr_mult: 0 }
1419 | }
1420 | layer {
1421 |   name: "relu_conv2_13"
1422 |   type: "ReLU"
1423 |   bottom: "conv2_13"
1424 |   top: "conv2_13"
1425 | }
1426 | layer {
1427 |   bottom: "extra_mid_join"
1428 |   top: "conv1_6"
1429 |   name: "conv1_6"
1430 |   param {
1431 |     lr_mult: 1
1432 |     decay_mult: 1
1433 |     name: "conv1_6"
1434 |   }
1435 |   param {
1436 |     lr_mult: 2
1437 |     decay_mult: 0
1438 |     name: "conv1_6_b"
1439 |   }
1440 |   type: "Convolution"
1441 |   convolution_param {
1442 |     num_output: 256
1443 |     pad: 1
1444 |     kernel_size: 3
1445 |     weight_filler {
1446 |       type: "xavier"
1447 |     }
1448 |     bias_filler {
1449 |       type: "constant"
1450 |     }
1451 |   }
1452 | }
1453 | layer {
1454 |   name: "dropout_conv1_6"
1455 |   type: "Dropout"
1456 |   bottom: "conv1_6"
1457 |   top: "conv1_6"
1458 |   dropout_param {
1459 |     dropout_ratio: 0.3
1460 |   }
1461 | }
1462 | layer {
1463 |   name: "batch_conv1_6"
1464 |   type: "BatchNorm"
1465 |   bottom: "conv1_6"
1466 |   top: "conv1_6"
1467 |   param { lr_mult: 0 }
1468 |   param { lr_mult: 0 }
1469 |   param { lr_mult: 0 }
1470 | }
1471 | layer {
1472 |   name: "relu_conv1_6"
1473 |   type: "ReLU"
1474 |   bottom: "conv1_6"
1475 |   top: "conv1_6"
1476 | }
1477 | layer {
1478 |   name: "join_conv2_13_plus"
1479 |   type: "FractalJoin"
1480 |   bottom: "conv1_6"
1481 |   bottom: "conv2_13"
1482 |   top: "conv2_13_plus"
1483 |   fractal_join_param {
1484 |     drop_path_ratio: 0.15
1485 |   }
1486 | }
1487 | layer {
1488 |   bottom: "conv2_13_plus"
1489 |   top: "conv2_14"
1490 |   name: "conv2_14"
1491 |   param {
1492 |     lr_mult: 1
1493 |     decay_mult: 1
1494 |     name: "conv2_14"
1495 |   }
1496 |   param {
1497 |     lr_mult: 2
1498 |     decay_mult: 0
1499 |     name: "conv2_14_b"
1500 |   }
1501 |   type: "Convolution"
1502 |   convolution_param {
1503 |     num_output: 256
1504 |     pad: 1
1505 |     kernel_size: 3
1506 |     weight_filler {
1507 |       type: "xavier"
1508 |     }
1509 |     bias_filler {
1510 |       type: "constant"
1511 |     }
1512 |   }
1513 | }
1514 | layer {
1515 |   name: "dropout_conv2_14"
1516 |   type: "Dropout"
1517 |   bottom: "conv2_14"
1518 |   top: "conv2_14"
1519 |   dropout_param {
1520 |     dropout_ratio: 0.3
1521 |   }
1522 | }
1523 | layer {
1524 |   name: "batch_conv2_14"
1525 |   type: "BatchNorm"
1526 |   bottom: "conv2_14"
1527 |   top: "conv2_14"
1528 |   param { lr_mult: 0 }
1529 |   param { lr_mult: 0 }
1530 |   param { lr_mult: 0 }
1531 | }
1532 | layer {
1533 |   name: "relu_conv2_14"
1534 |   type: "ReLU"
1535 |   bottom: "conv2_14"
1536 |   top: "conv2_14"
1537 | }
1538 | layer {
1539 |   bottom: "conv2_14"
1540 |   top: "conv2_15"
1541 |   name: "conv2_15"
1542 |   param {
1543 |     lr_mult: 1
1544 |     decay_mult: 1
1545 |     name: "conv2_15"
1546 |   }
1547 |   param {
1548 |     lr_mult: 2
1549 |     decay_mult: 0
1550 |     name: "conv2_15_b"
1551 |   }
1552 |   type: "Convolution"
1553 |   convolution_param {
1554 |     num_output: 256
1555 |     pad: 1
1556 |     kernel_size: 3
1557 |     weight_filler {
1558 |       type: "xavier"
1559 |     }
1560 |     bias_filler {
1561 |       type: "constant"
1562 |     }
1563 |   }
1564 | }
1565 | layer {
1566 |   name: "dropout_conv2_15"
1567 |   type: "Dropout"
1568 |   bottom: "conv2_15"
1569 |   top: "conv2_15"
1570 |   dropout_param {
1571 |     dropout_ratio: 0.3
1572 |   }
1573 | }
1574 | layer {
1575 |   name: "batch_conv2_15"
1576 |   type: "BatchNorm"
1577 |   bottom: "conv2_15"
1578 |   top: "conv2_15"
1579 |   param { lr_mult: 0 }
1580 |   param { lr_mult: 0 }
1581 |   param { lr_mult: 0 }
1582 | }
1583 | layer {
1584 |   name: "relu_conv2_15"
1585 |   type: "ReLU"
1586 |   bottom: "conv2_15"
1587 |   top: "conv2_15"
1588 | }
1589 | layer {
1590 |   bottom: "conv2_13_plus"
1591 |   top: "conv1_7"
1592 |   name: "conv1_7"
1593 |   param {
1594 |     lr_mult: 1
1595 |     decay_mult: 1
1596 |     name: "conv1_7"
1597 |   }
1598 |   param {
1599 |     lr_mult: 2
1600 |     decay_mult: 0
1601 |     name: "conv1_7_b"
1602 |   }
1603 |   type: "Convolution"
1604 |   convolution_param {
1605 |     num_output: 256
1606 |     pad: 1
1607 |     kernel_size: 3
1608 |     weight_filler {
1609 |       type: "xavier"
1610 |     }
1611 |     bias_filler {
1612 |       type: "constant"
1613 |     }
1614 |   }
1615 | }
1616 | layer {
1617 |   name: "dropout_conv1_7"
1618 |   type: "Dropout"
1619 |   bottom: "conv1_7"
1620 |   top: "conv1_7"
1621 |   dropout_param {
1622 |     dropout_ratio: 0.3
1623 |   }
1624 | }
1625 | layer {
1626 |   name: "batch_conv1_7"
1627 |   type: "BatchNorm"
1628 |   bottom: "conv1_7"
1629 |   top: "conv1_7"
1630 |   param { lr_mult: 0 }
1631 |   param { lr_mult: 0 }
1632 |   param { lr_mult: 0 }
1633 | }
1634 | layer {
1635 |   name: "relu_conv1_7"
1636 |   type: "ReLU"
1637 |   bottom: "conv1_7"
1638 |   top: "conv1_7"
1639 | }
1640 | layer {
1641 |   bottom: "extra_mid_join"
1642 |   top: "conv0_3"
1643 |   name: "conv0_3"
1644 |   param {
1645 |     lr_mult: 1
1646 |     decay_mult: 1
1647 |     name: "conv0_3"
1648 |   }
1649 |   param {
1650 |     lr_mult: 2
1651 |     decay_mult: 0
1652 |     name: "conv0_3_b"
1653 |   }
1654 |   type: "Convolution"
1655 |   convolution_param {
1656 |     num_output: 256
1657 |     pad: 1
1658 |     kernel_size: 3
1659 |     weight_filler {
1660 |       type: "xavier"
1661 |     }
1662 |     bias_filler {
1663 |       type: "constant"
1664 |     }
1665 |   }
1666 | }
1667 | layer {
1668 |   name: "dropout_conv0_3"
1669 |   type: "Dropout"
1670 |   bottom: "conv0_3"
1671 |   top: "conv0_3"
1672 |   dropout_param {
1673 |     dropout_ratio: 0.3
1674 |   }
1675 | }
1676 | layer {
1677 |   name: "batch_conv0_3"
1678 |   type: "BatchNorm"
1679 |   bottom: "conv0_3"
1680 |   top: "conv0_3"
1681 |   param { lr_mult: 0 }
1682 |   param { lr_mult: 0 }
1683 |   param { lr_mult: 0 }
1684 | }
1685 | layer {
1686 |   name: "relu_conv0_3"
1687 |   type: "ReLU"
1688 |   bottom: "conv0_3"
1689 |   top: "conv0_3"
1690 | }
1691 | layer {
1692 |   bottom: "conv0_3"
1693 |   top: "pool0_3"
1694 |   name: "pool0_3"
1695 |   type: "Pooling"
1696 |   pooling_param {
1697 |     pool: MAX
1698 |     kernel_size: 2
1699 |     stride: 2
1700 |   }
1701 | }
1702 | layer {
1703 |   bottom: "conv1_7"
1704 |   top: "pool1_7"
1705 |   name: "pool1_7"
1706 |   type: "Pooling"
1707 |   pooling_param {
1708 |     pool: MAX
1709 |     kernel_size: 2
1710 |     stride: 2
1711 |   }
1712 | }
1713 | layer {
1714 |   bottom: "conv2_15"
1715 |   top: "pool2_15"
1716 |   name: "pool2_15"
1717 |   type: "Pooling"
1718 |   pooling_param {
1719 |     pool: MAX
1720 |     kernel_size: 2
1721 |     stride: 2
1722 |   }
1723 | }
1724 | layer {
1725 |   name: "join_pool2_15_plus"
1726 |   type: "FractalJoin"
1727 |   bottom: "pool0_3"
1728 |   bottom: "pool1_7"
1729 |   bottom: "pool2_15"
1730 |   top: "pool2_15_plus"
1731 |   fractal_join_param {
1732 |     drop_path_ratio: 0.15
1733 |   }
1734 | }
1735 | # Reduction: 4, spatial size: 2
1736 | layer {
1737 |   bottom: "pool2_15_plus"
1738 |   top: "conv2_16"
1739 |   name: "conv2_16"
1740 |   param {
1741 |     lr_mult: 1
1742 |     decay_mult: 1
1743 |     name: "conv2_16"
1744 |   }
1745 |   param {
1746 |     lr_mult: 2
1747 |     decay_mult: 0
1748 |     name: "conv2_16_b"
1749 |   }
1750 |   type: "Convolution"
1751 |   convolution_param {
1752 |     num_output: 512
1753 |     pad: 1
1754 |     kernel_size: 3
1755 |     weight_filler {
1756 |       type: "xavier"
1757 |     }
1758 |     bias_filler {
1759 |       type: "constant"
1760 |     }
1761 |   }
1762 | }
1763 | layer {
1764 |   name: "dropout_conv2_16"
1765 |   type: "Dropout"
1766 |   bottom: "conv2_16"
1767 |   top: "conv2_16"
1768 |   dropout_param {
1769 |     dropout_ratio: 0.3
1770 |   }
1771 | }
1772 | layer {
1773 |   name: "batch_conv2_16"
1774 |   type: "BatchNorm"
1775 |   bottom: "conv2_16"
1776 |   top: "conv2_16"
1777 |   param { lr_mult: 0 }
1778 |   param { lr_mult: 0 }
1779 |   param { lr_mult: 0 }
1780 | }
1781 | layer {
1782 |   name: "relu_conv2_16"
1783 |   type: "ReLU"
1784 |   bottom: "conv2_16"
1785 |   top: "conv2_16"
1786 | }
1787 | layer {
1788 |   bottom: "conv2_16"
1789 |   top: "conv2_17"
1790 |   name: "conv2_17"
1791 |   param {
1792 |     lr_mult: 1
1793 |     decay_mult: 1
1794 |     name: "conv2_17"
1795 |   }
1796 |   param {
1797 |     lr_mult: 2
1798 |     decay_mult: 0
1799 |     name: "conv2_17_b"
1800 |   }
1801 |   type: "Convolution"
1802 |   convolution_param {
1803 |     num_output: 512
1804 |     pad: 1
1805 |     kernel_size: 3
1806 |     weight_filler {
1807 |       type: "xavier"
1808 |     }
1809 |     bias_filler {
1810 |       type: "constant"
1811 |     }
1812 |   }
1813 | }
1814 | layer {
1815 |   name: "dropout_conv2_17"
1816 |   type: "Dropout"
1817 |   bottom: "conv2_17"
1818 |   top: "conv2_17"
1819 |   dropout_param {
1820 |     dropout_ratio: 0.3
1821 |   }
1822 | }
1823 | layer {
1824 |   name: "batch_conv2_17"
1825 |   type: "BatchNorm"
1826 |   bottom: "conv2_17"
1827 |   top: "conv2_17"
1828 |   param { lr_mult: 0 }
1829 |   param { lr_mult: 0 }
1830 |   param { lr_mult: 0 }
1831 | }
1832 | layer {
1833 |   name: "relu_conv2_17"
1834 |   type: "ReLU"
1835 |   bottom: "conv2_17"
1836 |   top: "conv2_17"
1837 | }
1838 | layer {
1839 |   bottom: "pool2_15_plus"
1840 |   top: "conv1_8"
1841 |   name: "conv1_8"
1842 |   param {
1843 |     lr_mult: 1
1844 |     decay_mult: 1
1845 |     name: "conv1_8"
1846 |   }
1847 |   param {
1848 |     lr_mult: 2
1849 |     decay_mult: 0
1850 |     name: "conv1_8_b"
1851 |   }
1852 |   type: "Convolution"
1853 |   convolution_param {
1854 |     num_output: 512
1855 |     pad: 1
1856 |     kernel_size: 3
1857 |     weight_filler {
1858 |       type: "xavier"
1859 |     }
1860 |     bias_filler {
1861 |       type: "constant"
1862 |     }
1863 |   }
1864 | }
1865 | layer {
1866 |   name: "dropout_conv1_8"
1867 |   type: "Dropout"
1868 |   bottom: "conv1_8"
1869 |   top: "conv1_8"
1870 |   dropout_param {
1871 |     dropout_ratio: 0.3
1872 |   }
1873 | }
1874 | layer {
1875 |   name: "batch_conv1_8"
1876 |   type: "BatchNorm"
1877 |   bottom: "conv1_8"
1878 |   top: "conv1_8"
1879 |   param { lr_mult: 0 }
1880 |   param { lr_mult: 0 }
1881 |   param { lr_mult: 0 }
1882 | }
1883 | layer {
1884 |   name: "relu_conv1_8"
1885 |   type: "ReLU"
1886 |   bottom: "conv1_8"
1887 |   top: "conv1_8"
1888 | }
1889 | layer {
1890 |   name: "join_conv2_17_plus"
1891 |   type: "FractalJoin"
1892 |   bottom: "conv1_8"
1893 |   bottom: "conv2_17"
1894 |   top: "conv2_17_plus"
1895 |   fractal_join_param {
1896 |     drop_path_ratio: 0.15
1897 |   }
1898 | }
1899 | layer {
1900 |   bottom: "conv2_17_plus"
1901 |   top: "conv2_18"
1902 |   name: "conv2_18"
1903 |   param {
1904 |     lr_mult: 1
1905 |     decay_mult: 1
1906 |     name: "conv2_18"
1907 |   }
1908 |   param {
1909 |     lr_mult: 2
1910 |     decay_mult: 0
1911 |     name: "conv2_18_b"
1912 |   }
1913 |   type: "Convolution"
1914 |   convolution_param {
1915 |     num_output: 512
1916 |     pad: 1
1917 |     kernel_size: 3
1918 |     weight_filler {
1919 |       type: "xavier"
1920 |     }
1921 |     bias_filler {
1922 |       type: "constant"
1923 |     }
1924 |   }
1925 | }
1926 | layer {
1927 |   name: "dropout_conv2_18"
1928 |   type: "Dropout"
1929 |   bottom: "conv2_18"
1930 |   top: "conv2_18"
1931 |   dropout_param {
1932 |     dropout_ratio: 0.3
1933 |   }
1934 | }
1935 | layer {
1936 |   name: "batch_conv2_18"
1937 |   type: "BatchNorm"
1938 |   bottom: "conv2_18"
1939 |   top: "conv2_18"
1940 |   param { lr_mult: 0 }
1941 |   param { lr_mult: 0 }
1942 |   param { lr_mult: 0 }
1943 | }
1944 | layer {
1945 |   name: "relu_conv2_18"
1946 |   type: "ReLU"
1947 |   bottom: "conv2_18"
1948 |   top: "conv2_18"
1949 | }
1950 | layer {
1951 |   bottom: "conv2_18"
1952 |   top: "conv2_19"
1953 |   name: "conv2_19"
1954 |   param {
1955 |     lr_mult: 1
1956 |     decay_mult: 1
1957 |     name: "conv2_19"
1958 |   }
1959 |   param {
1960 |     lr_mult: 2
1961 |     decay_mult: 0
1962 |     name: "conv2_19_b"
1963 |   }
1964 |   type: "Convolution"
1965 |   convolution_param {
1966 |     num_output: 512
1967 |     pad: 1
1968 |     kernel_size: 3
1969 |     weight_filler {
1970 |       type: "xavier"
1971 |     }
1972 |     bias_filler {
1973 |       type: "constant"
1974 |     }
1975 |   }
1976 | }
1977 | layer {
1978 |   name: "dropout_conv2_19"
1979 |   type: "Dropout"
1980 |   bottom: "conv2_19"
1981 |   top: "conv2_19"
1982 |   dropout_param {
1983 |     dropout_ratio: 0.3
1984 |   }
1985 | }
1986 | layer {
1987 |   name: "batch_conv2_19"
1988 |   type: "BatchNorm"
1989 |   bottom: "conv2_19"
1990 |   top: "conv2_19"
1991 |   param { lr_mult: 0 }
1992 |   param { lr_mult: 0 }
1993 |   param { lr_mult: 0 }
1994 | }
1995 | layer {
1996 |   name: "relu_conv2_19"
1997 |   type: "ReLU"
1998 |   bottom: "conv2_19"
1999 |   top: "conv2_19"
2000 | }
2001 | layer {
2002 |   bottom: "conv2_17_plus"
2003 |   top: "conv1_9"
2004 |   name: "conv1_9"
2005 |   param {
2006 |     lr_mult: 1
2007 |     decay_mult: 1
2008 |     name: "conv1_9"
2009 |   }
2010 |   param {
2011 |     lr_mult: 2
2012 |     decay_mult: 0
2013 |     name: "conv1_9_b"
2014 |   }
2015 |   type: "Convolution"
2016 |   convolution_param {
2017 |     num_output: 512
2018 |     pad: 1
2019 |     kernel_size: 3
2020 |     weight_filler {
2021 |       type: "xavier"
2022 |     }
2023 |     bias_filler {
2024 |       type: "constant"
2025 |     }
2026 |   }
2027 | }
2028 | layer {
2029 |   name: "dropout_conv1_9"
2030 |   type: "Dropout"
2031 |   bottom: "conv1_9"
2032 |   top: "conv1_9"
2033 |   dropout_param {
2034 |     dropout_ratio: 0.3
2035 |   }
2036 | }
2037 | layer {
2038 |   name: "batch_conv1_9"
2039 |   type: "BatchNorm"
2040 |   bottom: "conv1_9"
2041 |   top: "conv1_9"
2042 |   param { lr_mult: 0 }
2043 |   param { lr_mult: 0 }
2044 |   param { lr_mult: 0 }
2045 | }
2046 | layer {
2047 |   name: "relu_conv1_9"
2048 |   type: "ReLU"
2049 |   bottom: "conv1_9"
2050 |   top: "conv1_9"
2051 | }
2052 | layer {
2053 |   bottom: "pool2_15_plus"
2054 |   top: "conv0_4"
2055 |   name: "conv0_4"
2056 |   param {
2057 |     lr_mult: 1
2058 |     decay_mult: 1
2059 |     name: "conv0_4"
2060 |   }
2061 |   param {
2062 |     lr_mult: 2
2063 |     decay_mult: 0
2064 |     name: "conv0_4_b"
2065 |   }
2066 |   type: "Convolution"
2067 |   convolution_param {
2068 |     num_output: 512
2069 |     pad: 1
2070 |     kernel_size: 3
2071 |     weight_filler {
2072 |       type: "xavier"
2073 |     }
2074 |     bias_filler {
2075 |       type: "constant"
2076 |     }
2077 |   }
2078 | }
2079 | layer {
2080 |   name: "dropout_conv0_4"
2081 |   type: "Dropout"
2082 |   bottom: "conv0_4"
2083 |   top: "conv0_4"
2084 |   dropout_param {
2085 |     dropout_ratio: 0.3
2086 |   }
2087 | }
2088 | layer {
2089 |   name: "batch_conv0_4"
2090 |   type: "BatchNorm"
2091 |   bottom: "conv0_4"
2092 |   top: "conv0_4"
2093 |   param { lr_mult: 0 }
2094 |   param { lr_mult: 0 }
2095 |   param { lr_mult: 0 }
2096 | }
2097 | layer {
2098 |   name: "relu_conv0_4"
2099 |   type: "ReLU"
2100 |   bottom: "conv0_4"
2101 |   top: "conv0_4"
2102 | }
2103 | layer {
2104 |   bottom: "conv0_4"
2105 |   top: "pool0_4"
2106 |   name: "pool0_4"
2107 |   type: "Pooling"
2108 |   pooling_param {
2109 |     pool: MAX
2110 |     kernel_size: 2
2111 |     stride: 2
2112 |   }
2113 | }
2114 | layer {
2115 |   bottom: "conv1_9"
2116 |   top: "pool1_9"
2117 |   name: "pool1_9"
2118 |   type: "Pooling"
2119 |   pooling_param {
2120 |     pool: MAX
2121 |     kernel_size: 2
2122 |     stride: 2
2123 |   }
2124 | }
2125 | layer {
2126 |   bottom: "conv2_19"
2127 |   top: "pool2_19"
2128 |   name: "pool2_19"
2129 |   type: "Pooling"
2130 |   pooling_param {
2131 |     pool: MAX
2132 |     kernel_size: 2
2133 |     stride: 2
2134 |   }
2135 | }
2136 | layer {
2137 |   name: "join_pool2_19_plus"
2138 |   type: "FractalJoin"
2139 |   bottom: "pool0_4"
2140 |   bottom: "pool1_9"
2141 |   bottom: "pool2_19"
2142 |   top: "pool2_19_plus"
2143 |   fractal_join_param {
2144 |     drop_path_ratio: 0.15
2145 |   }
2146 | }
2147 | # Reduction: 5, spatial size: 1
2148 | layer {
2149 |   bottom: "extra_mid_join"
2150 |   top: "conv2_20"
2151 |   name: "conv2_20"
2152 |   param {
2153 |     lr_mult: 1
2154 |     decay_mult: 1
2155 |     name: "conv2_20"
2156 |   }
2157 |   param {
2158 |     lr_mult: 2
2159 |     decay_mult: 0
2160 |     name: "conv2_20_b"
2161 |   }
2162 |   type: "Convolution"
2163 |   convolution_param {
2164 |     num_output: 512
2165 |     pad: 1
2166 |     kernel_size: 3
2167 |     weight_filler {
2168 |       type: "xavier"
2169 |     }
2170 |     bias_filler {
2171 |       type: "constant"
2172 |     }
2173 |   }
2174 | }
2175 | layer {
2176 |   name: "dropout_conv2_20"
2177 |   type: "Dropout"
2178 |   bottom: "conv2_20"
2179 |   top: "conv2_20"
2180 |   dropout_param {
2181 |     dropout_ratio: 0.3
2182 |   }
2183 | }
2184 | layer {
2185 |   name: "batch_conv2_20"
2186 |   type: "BatchNorm"
2187 |   bottom: "conv2_20"
2188 |   top: "conv2_20"
2189 |   param { lr_mult: 0 }
2190 |   param { lr_mult: 0 }
2191 |   param { lr_mult: 0 }
2192 | }
2193 | layer {
2194 |   name: "relu_conv2_20"
2195 |   type: "ReLU"
2196 |   bottom: "conv2_20"
2197 |   top: "conv2_20"
2198 | }
2199 | layer {
2200 |   bottom: "conv2_20"
2201 |   top: "conv2_21"
2202 |   name: "conv2_21"
2203 |   param {
2204 |     lr_mult: 1
2205 |     decay_mult: 1
2206 |     name: "conv2_21"
2207 |   }
2208 |   param {
2209 |     lr_mult: 2
2210 |     decay_mult: 0
2211 |     name: "conv2_21_b"
2212 |   }
2213 |   type: "Convolution"
2214 |   convolution_param {
2215 |     num_output: 512
2216 |     pad: 1
2217 |     kernel_size: 3
2218 |     weight_filler {
2219 |       type: "xavier"
2220 |     }
2221 |     bias_filler {
2222 |       type: "constant"
2223 |     }
2224 |   }
2225 | }
2226 | layer {
2227 |   name: "dropout_conv2_21"
2228 |   type: "Dropout"
2229 |   bottom: "conv2_21"
2230 |   top: "conv2_21"
2231 |   dropout_param {
2232 |     dropout_ratio: 0.3
2233 |   }
2234 | }
2235 | layer {
2236 |   name: "batch_conv2_21"
2237 |   type: "BatchNorm"
2238 |   bottom: "conv2_21"
2239 |   top: "conv2_21"
2240 |   param { lr_mult: 0 }
2241 |   param { lr_mult: 0 }
2242 |   param { lr_mult: 0 }
2243 | }
2244 | layer {
2245 |   name: "relu_conv2_21"
2246 |   type: "ReLU"
2247 |   bottom: "conv2_21"
2248 |   top: "conv2_21"
2249 | }
2250 | layer {
2251 |   bottom: "extra_mid_join"
2252 |   top: "conv1_10"
2253 |   name: "conv1_10"
2254 |   param {
2255 |     lr_mult: 1
2256 |     decay_mult: 1
2257 |     name: "conv1_10"
2258 |   }
2259 |   param {
2260 |     lr_mult: 2
2261 |     decay_mult: 0
2262 |     name: "conv1_10_b"
2263 |   }
2264 |   type: "Convolution"
2265 |   convolution_param {
2266 |     num_output: 512
2267 |     pad: 1
2268 |     kernel_size: 3
2269 |     weight_filler {
2270 |       type: "xavier"
2271 |     }
2272 |     bias_filler {
2273 |       type: "constant"
2274 |     }
2275 |   }
2276 | }
2277 | layer {
2278 |   name: "dropout_conv1_10"
2279 |   type: "Dropout"
2280 |   bottom: "conv1_10"
2281 |   top: "conv1_10"
2282 |   dropout_param {
2283 |     dropout_ratio: 0.3
2284 |   }
2285 | }
2286 | layer {
2287 |   name: "batch_conv1_10"
2288 |   type: "BatchNorm"
2289 |   bottom: "conv1_10"
2290 |   top: "conv1_10"
2291 |   param { lr_mult: 0 }
2292 |   param { lr_mult: 0 }
2293 |   param { lr_mult: 0 }
2294 | }
2295 | layer {
2296 |   name: "relu_conv1_10"
2297 |   type: "ReLU"
2298 |   bottom: "conv1_10"
2299 |   top: "conv1_10"
2300 | }
2301 | layer {
2302 |   name: "join_conv2_21_plus"
2303 |   type: "FractalJoin"
2304 |   bottom: "conv1_10"
2305 |   bottom: "conv2_21"
2306 |   top: "conv2_21_plus"
2307 |   fractal_join_param {
2308 |     drop_path_ratio: 0.15
2309 |   }
2310 | }
2311 | layer {
2312 |   bottom: "conv2_21_plus"
2313 |   top: "conv2_22"
2314 |   name: "conv2_22"
2315 |   param {
2316 |     lr_mult: 1
2317 |     decay_mult: 1
2318 |     name: "conv2_22"
2319 |   }
2320 |   param {
2321 |     lr_mult: 2
2322 |     decay_mult: 0
2323 |     name: "conv2_22_b"
2324 |   }
2325 |   type: "Convolution"
2326 |   convolution_param {
2327 |     num_output: 512
2328 |     pad: 1
2329 |     kernel_size: 3
2330 |     weight_filler {
2331 |       type: "xavier"
2332 |     }
2333 |     bias_filler {
2334 |       type: "constant"
2335 |     }
2336 |   }
2337 | }
2338 | layer {
2339 |   name: "dropout_conv2_22"
2340 |   type: "Dropout"
2341 |   bottom: "conv2_22"
2342 |   top: "conv2_22"
2343 |   dropout_param {
2344 |     dropout_ratio: 0.3
2345 |   }
2346 | }
2347 | layer {
2348 |   name: "batch_conv2_22"
2349 |   type: "BatchNorm"
2350 |   bottom: "conv2_22"
2351 |   top: "conv2_22"
2352 |   param { lr_mult: 0 }
2353 |   param { lr_mult: 0 }
2354 |   param { lr_mult: 0 }
2355 | }
2356 | layer {
2357 |   name: "relu_conv2_22"
2358 |   type: "ReLU"
2359 |   bottom: "conv2_22"
2360 |   top: "conv2_22"
2361 | }
2362 | layer {
2363 |   bottom: "conv2_22"
2364 |   top: "conv2_23"
2365 |   name: "conv2_23"
2366 |   param {
2367 |     lr_mult: 1
2368 |     decay_mult: 1
2369 |     name: "conv2_23"
2370 |   }
2371 |   param {
2372 |     lr_mult: 2
2373 |     decay_mult: 0
2374 |     name: "conv2_23_b"
2375 |   }
2376 |   type: "Convolution"
2377 |   convolution_param {
2378 |     num_output: 512
2379 |     pad: 1
2380 |     kernel_size: 3
2381 |     weight_filler {
2382 |       type: "xavier"
2383 |     }
2384 |     bias_filler {
2385 |       type: "constant"
2386 |     }
2387 |   }
2388 | }
2389 | layer {
2390 |   name: "dropout_conv2_23"
2391 |   type: "Dropout"
2392 |   bottom: "conv2_23"
2393 |   top: "conv2_23"
2394 |   dropout_param {
2395 |     dropout_ratio: 0.3
2396 |   }
2397 | }
2398 | layer {
2399 |   name: "batch_conv2_23"
2400 |   type: "BatchNorm"
2401 |   bottom: "conv2_23"
2402 |   top: "conv2_23"
2403 |   param { lr_mult: 0 }
2404 |   param { lr_mult: 0 }
2405 |   param { lr_mult: 0 }
2406 | }
2407 | layer {
2408 |   name: "relu_conv2_23"
2409 |   type: "ReLU"
2410 |   bottom: "conv2_23"
2411 |   top: "conv2_23"
2412 | }
2413 | layer {
2414 |   bottom: "conv2_21_plus"
2415 |   top: "conv1_11"
2416 |   name: "conv1_11"
2417 |   param {
2418 |     lr_mult: 1
2419 |     decay_mult: 1
2420 |     name: "conv1_11"
2421 |   }
2422 |   param {
2423 |     lr_mult: 2
2424 |     decay_mult: 0
2425 |     name: "conv1_11_b"
2426 |   }
2427 |   type: "Convolution"
2428 |   convolution_param {
2429 |     num_output: 512
2430 |     pad: 1
2431 |     kernel_size: 3
2432 |     weight_filler {
2433 |       type: "xavier"
2434 |     }
2435 |     bias_filler {
2436 |       type: "constant"
2437 |     }
2438 |   }
2439 | }
2440 | layer {
2441 |   name: "dropout_conv1_11"
2442 |   type: "Dropout"
2443 |   bottom: "conv1_11"
2444 |   top: "conv1_11"
2445 |   dropout_param {
2446 |     dropout_ratio: 0.3
2447 |   }
2448 | }
2449 | layer {
2450 |   name: "batch_conv1_11"
2451 |   type: "BatchNorm"
2452 |   bottom: "conv1_11"
2453 |   top: "conv1_11"
2454 |   param { lr_mult: 0 }
2455 |   param { lr_mult: 0 }
2456 |   param { lr_mult: 0 }
2457 | }
2458 | layer {
2459 |   name: "relu_conv1_11"
2460 |   type: "ReLU"
2461 |   bottom: "conv1_11"
2462 |   top: "conv1_11"
2463 | }
2464 | layer {
2465 |   bottom: "extra_mid_join"
2466 |   top: "conv0_5"
2467 |   name: "conv0_5"
2468 |   param {
2469 |     lr_mult: 1
2470 |     decay_mult: 1
2471 |     name: "conv0_5"
2472 |   }
2473 |   param {
2474 |     lr_mult: 2
2475 |     decay_mult: 0
2476 |     name: "conv0_5_b"
2477 |   }
2478 |   type: "Convolution"
2479 |   convolution_param {
2480 |     num_output: 512
2481 |     pad: 1
2482 |     kernel_size: 3
2483 |     weight_filler {
2484 |       type: "xavier"
2485 |     }
2486 |     bias_filler {
2487 |       type: "constant"
2488 |     }
2489 |   }
2490 | }
2491 | layer {
2492 |   name: "dropout_conv0_5"
2493 |   type: "Dropout"
2494 |   bottom: "conv0_5"
2495 |   top: "conv0_5"
2496 |   dropout_param {
2497 |     dropout_ratio: 0.3
2498 |   }
2499 | }
2500 | layer {
2501 |   name: "batch_conv0_5"
2502 |   type: "BatchNorm"
2503 |   bottom: "conv0_5"
2504 |   top: "conv0_5"
2505 |   param { lr_mult: 0 }
2506 |   param { lr_mult: 0 }
2507 |   param { lr_mult: 0 }
2508 | }
2509 | layer {
2510 |   name: "relu_conv0_5"
2511 |   type: "ReLU"
2512 |   bottom: "conv0_5"
2513 |   top: "conv0_5"
2514 | }
2515 | layer {
2516 |   bottom: "conv0_5"
2517 |   top: "pool0_5_0"
2518 |   name: "pool0_5_0"
2519 |   type: "Pooling"
2520 |   pooling_param {
2521 |     pool: MAX
2522 |     kernel_size: 2
2523 |     stride: 2
2524 |   }
2525 | }
2526 | layer {
2527 |   bottom: "pool0_5_0"
2528 |   top: "pool0_5_1"
2529 |   name: "pool0_5_1"
2530 |   type: "Pooling"
2531 |   pooling_param {
2532 |     pool: MAX
2533 |     kernel_size: 2
2534 |     stride: 2
2535 |   }
2536 | }
2537 | layer {
2538 |   bottom: "conv1_11"
2539 |   top: "pool1_11_0"
2540 |   name: "pool1_11_0"
2541 |   type: "Pooling"
2542 |   pooling_param {
2543 |     pool: MAX
2544 |     kernel_size: 2
2545 |     stride: 2
2546 |   }
2547 | }
2548 | layer {
2549 |   bottom: "pool1_11_0"
2550 |   top: "pool1_11_1"
2551 |   name: "pool1_11_1"
2552 |   type: "Pooling"
2553 |   pooling_param {
2554 |     pool: MAX
2555 |     kernel_size: 2
2556 |     stride: 2
2557 |   }
2558 | }
2559 | layer {
2560 |   bottom: "conv2_23"
2561 |   top: "pool2_23_0"
2562 |   name: "pool2_23_0"
2563 |   type: "Pooling"
2564 |   pooling_param {
2565 |     pool: MAX
2566 |     kernel_size: 2
2567 |     stride: 2
2568 |   }
2569 | }
2570 | layer {
2571 |   bottom: "pool2_23_0"
2572 |   top: "pool2_23_1"
2573 |   name: "pool2_23_1"
2574 |   type: "Pooling"
2575 |   pooling_param {
2576 |     pool: MAX
2577 |     kernel_size: 2
2578 |     stride: 2
2579 |   }
2580 | }
2581 | layer {
2582 |   name: "join_pool2_23_plus"
2583 |   type: "FractalJoin"
2584 |   bottom: "pool0_5_1"
2585 |   bottom: "pool1_11_1"
2586 |   bottom: "pool2_23_1"
2587 |   top: "pool2_23_plus"
2588 |   fractal_join_param {
2589 |     drop_path_ratio: 0.15
2590 |   }
2591 | }
2592 | # Reduction: 6, spatial size: 0
2593 | layer {
2594 |   bottom: "data"
2595 |   top: "conv2_24"
2596 |   name: "conv2_24"
2597 |   param {
2598 |     lr_mult: 1
2599 |     decay_mult: 1
2600 |     name: "conv2_24"
2601 |   }
2602 |   param {
2603 |     lr_mult: 2
2604 |     decay_mult: 0
2605 |     name: "conv2_24_b"
2606 |   }
2607 |   type: "Convolution"
2608 |   convolution_param {
2609 |     num_output: 512
2610 |     pad: 1
2611 |     kernel_size: 3
2612 |     weight_filler {
2613 |       type: "xavier"
2614 |     }
2615 |     bias_filler {
2616 |       type: "constant"
2617 |     }
2618 |   }
2619 | }
2620 | layer {
2621 |   name: "dropout_conv2_24"
2622 |   type: "Dropout"
2623 |   bottom: "conv2_24"
2624 |   top: "conv2_24"
2625 |   dropout_param {
2626 |     dropout_ratio: 0.0
2627 |   }
2628 | }
2629 | layer {
2630 |   name: "batch_conv2_24"
2631 |   type: "BatchNorm"
2632 |   bottom: "conv2_24"
2633 |   top: "conv2_24"
2634 |   param { lr_mult: 0 }
2635 |   param { lr_mult: 0 }
2636 |   param { lr_mult: 0 }
2637 | }
2638 | layer {
2639 |   name: "relu_conv2_24"
2640 |   type: "ReLU"
2641 |   bottom: "conv2_24"
2642 |   top: "conv2_24"
2643 | }
2644 | layer {
2645 |   bottom: "conv2_24"
2646 |   top: "conv2_25"
2647 |   name: "conv2_25"
2648 |   param {
2649 |     lr_mult: 1
2650 |     decay_mult: 1
2651 |     name: "conv2_25"
2652 |   }
2653 |   param {
2654 |     lr_mult: 2
2655 |     decay_mult: 0
2656 |     name: "conv2_25_b"
2657 |   }
2658 |   type: "Convolution"
2659 |   convolution_param {
2660 |     num_output: 512
2661 |     pad: 1
2662 |     kernel_size: 3
2663 |     weight_filler {
2664 |       type: "xavier"
2665 |     }
2666 |     bias_filler {
2667 |       type: "constant"
2668 |     }
2669 |   }
2670 | }
2671 | layer {
2672 |   name: "dropout_conv2_25"
2673 |   type: "Dropout"
2674 |   bottom: "conv2_25"
2675 |   top: "conv2_25"
2676 |   dropout_param {
2677 |     dropout_ratio: 0.0
2678 |   }
2679 | }
2680 | layer {
2681 |   name: "batch_conv2_25"
2682 |   type: "BatchNorm"
2683 |   bottom: "conv2_25"
2684 |   top: "conv2_25"
2685 |   param { lr_mult: 0 }
2686 |   param { lr_mult: 0 }
2687 |   param { lr_mult: 0 }
2688 | }
2689 | layer {
2690 |   name: "relu_conv2_25"
2691 |   type: "ReLU"
2692 |   bottom: "conv2_25"
2693 |   top: "conv2_25"
2694 | }
2695 | layer {
2696 |   bottom: "data"
2697 |   top: "conv1_12"
2698 |   name: "conv1_12"
2699 |   param {
2700 |     lr_mult: 1
2701 |     decay_mult: 1
2702 |     name: "conv1_12"
2703 |   }
2704 |   param {
2705 |     lr_mult: 2
2706 |     decay_mult: 0
2707 |     name: "conv1_12_b"
2708 |   }
2709 |   type: "Convolution"
2710 |   convolution_param {
2711 |     num_output: 512
2712 |     pad: 1
2713 |     kernel_size: 3
2714 |     weight_filler {
2715 |       type: "xavier"
2716 |     }
2717 |     bias_filler {
2718 |       type: "constant"
2719 |     }
2720 |   }
2721 | }
2722 | layer {
2723 |   name: "dropout_conv1_12"
2724 |   type: "Dropout"
2725 |   bottom: "conv1_12"
2726 |   top: "conv1_12"
2727 |   dropout_param {
2728 |     dropout_ratio: 0.0
2729 |   }
2730 | }
2731 | layer {
2732 |   name: "batch_conv1_12"
2733 |   type: "BatchNorm"
2734 |   bottom: "conv1_12"
2735 |   top: "conv1_12"
2736 |   param { lr_mult: 0 }
2737 |   param { lr_mult: 0 }
2738 |   param { lr_mult: 0 }
2739 | }
2740 | layer {
2741 |   name: "relu_conv1_12"
2742 |   type: "ReLU"
2743 |   bottom: "conv1_12"
2744 |   top: "conv1_12"
2745 | }
2746 | layer {
2747 |   name: "join_conv2_25_plus"
2748 |   type: "FractalJoin"
2749 |   bottom: "conv1_12"
2750 |   bottom: "conv2_25"
2751 |   top: "conv2_25_plus"
2752 |   fractal_join_param {
2753 |     drop_path_ratio: 0.15
2754 |   }
2755 | }
2756 | layer {
2757 |   bottom: "conv2_25_plus"
2758 |   top: "conv2_26"
2759 |   name: "conv2_26"
2760 |   param {
2761 |     lr_mult: 1
2762 |     decay_mult: 1
2763 |     name: "conv2_26"
2764 |   }
2765 |   param {
2766 |     lr_mult: 2
2767 |     decay_mult: 0
2768 |     name: "conv2_26_b"
2769 |   }
2770 |   type: "Convolution"
2771 |   convolution_param {
2772 |     num_output: 512
2773 |     pad: 1
2774 |     kernel_size: 3
2775 |     weight_filler {
2776 |       type: "xavier"
2777 |     }
2778 |     bias_filler {
2779 |       type: "constant"
2780 |     }
2781 |   }
2782 | }
2783 | layer {
2784 |   name: "dropout_conv2_26"
2785 |   type: "Dropout"
2786 |   bottom: "conv2_26"
2787 |   top: "conv2_26"
2788 |   dropout_param {
2789 |     dropout_ratio: 0.0
2790 |   }
2791 | }
2792 | layer {
2793 |   name: "batch_conv2_26"
2794 |   type: "BatchNorm"
2795 |   bottom: "conv2_26"
2796 |   top: "conv2_26"
2797 |   param { lr_mult: 0 }
2798 |   param { lr_mult: 0 }
2799 |   param { lr_mult: 0 }
2800 | }
2801 | layer {
2802 |   name: "relu_conv2_26"
2803 |   type: "ReLU"
2804 |   bottom: "conv2_26"
2805 |   top: "conv2_26"
2806 | }
2807 | layer {
2808 |   bottom: "conv2_26"
2809 |   top: "conv2_27"
2810 |   name: "conv2_27"
2811 |   param {
2812 |     lr_mult: 1
2813 |     decay_mult: 1
2814 |     name: "conv2_27"
2815 |   }
2816 |   param {
2817 |     lr_mult: 2
2818 |     decay_mult: 0
2819 |     name: "conv2_27_b"
2820 |   }
2821 |   type: "Convolution"
2822 |   convolution_param {
2823 |     num_output: 512
2824 |     pad: 1
2825 |     kernel_size: 3
2826 |     weight_filler {
2827 |       type: "xavier"
2828 |     }
2829 |     bias_filler {
2830 |       type: "constant"
2831 |     }
2832 |   }
2833 | }
2834 | layer {
2835 |   name: "dropout_conv2_27"
2836 |   type: "Dropout"
2837 |   bottom: "conv2_27"
2838 |   top: "conv2_27"
2839 |   dropout_param {
2840 |     dropout_ratio: 0.0
2841 |   }
2842 | }
2843 | layer {
2844 |   name: "batch_conv2_27"
2845 |   type: "BatchNorm"
2846 |   bottom: "conv2_27"
2847 |   top: "conv2_27"
2848 |   param { lr_mult: 0 }
2849 |   param { lr_mult: 0 }
2850 |   param { lr_mult: 0 }
2851 | }
2852 | layer {
2853 |   name: "relu_conv2_27"
2854 |   type: "ReLU"
2855 |   bottom: "conv2_27"
2856 |   top: "conv2_27"
2857 | }
2858 | layer {
2859 |   bottom: "conv2_25_plus"
2860 |   top: "conv1_13"
2861 |   name: "conv1_13"
2862 |   param {
2863 |     lr_mult: 1
2864 |     decay_mult: 1
2865 |     name: "conv1_13"
2866 |   }
2867 |   param {
2868 |     lr_mult: 2
2869 |     decay_mult: 0
2870 |     name: "conv1_13_b"
2871 |   }
2872 |   type: "Convolution"
2873 |   convolution_param {
2874 |     num_output: 512
2875 |     pad: 1
2876 |     kernel_size: 3
2877 |     weight_filler {
2878 |       type: "xavier"
2879 |     }
2880 |     bias_filler {
2881 |       type: "constant"
2882 |     }
2883 |   }
2884 | }
2885 | layer {
2886 |   name: "dropout_conv1_13"
2887 |   type: "Dropout"
2888 |   bottom: "conv1_13"
2889 |   top: "conv1_13"
2890 |   dropout_param {
2891 |     dropout_ratio: 0.0
2892 |   }
2893 | }
2894 | layer {
2895 |   name: "batch_conv1_13"
2896 |   type: "BatchNorm"
2897 |   bottom: "conv1_13"
2898 |   top: "conv1_13"
2899 |   param { lr_mult: 0 }
2900 |   param { lr_mult: 0 }
2901 |   param { lr_mult: 0 }
2902 | }
2903 | layer {
2904 |   name: "relu_conv1_13"
2905 |   type: "ReLU"
2906 |   bottom: "conv1_13"
2907 |   top: "conv1_13"
2908 | }
2909 | layer {
2910 |   bottom: "data"
2911 |   top: "conv0_6"
2912 |   name: "conv0_6"
2913 |   param {
2914 |     lr_mult: 1
2915 |     decay_mult: 1
2916 |     name: "conv0_6"
2917 |   }
2918 |   param {
2919 |     lr_mult: 2
2920 |     decay_mult: 0
2921 |     name: "conv0_6_b"
2922 |   }
2923 |   type: "Convolution"
2924 |   convolution_param {
2925 |     num_output: 512
2926 |     pad: 1
2927 |     kernel_size: 3
2928 |     weight_filler {
2929 |       type: "xavier"
2930 |     }
2931 |     bias_filler {
2932 |       type: "constant"
2933 |     }
2934 |   }
2935 | }
2936 | layer {
2937 |   name: "dropout_conv0_6"
2938 |   type: "Dropout"
2939 |   bottom: "conv0_6"
2940 |   top: "conv0_6"
2941 |   dropout_param {
2942 |     dropout_ratio: 0.0
2943 |   }
2944 | }
2945 | layer {
2946 |   name: "batch_conv0_6"
2947 |   type: "BatchNorm"
2948 |   bottom: "conv0_6"
2949 |   top: "conv0_6"
2950 |   param { lr_mult: 0 }
2951 |   param { lr_mult: 0 }
2952 |   param { lr_mult: 0 }
2953 | }
2954 | layer {
2955 |   name: "relu_conv0_6"
2956 |   type: "ReLU"
2957 |   bottom: "conv0_6"
2958 |   top: "conv0_6"
2959 | }
2960 | layer {
2961 |   bottom: "conv0_6"
2962 |   top: "pool0_6_0"
2963 |   name: "pool0_6_0"
2964 |   type: "Pooling"
2965 |   pooling_param {
2966 |     pool: MAX
2967 |     kernel_size: 2
2968 |     stride: 2
2969 |   }
2970 | }
2971 | layer {
2972 |   bottom: "pool0_6_0"
2973 |   top: "pool0_6_1"
2974 |   name: "pool0_6_1"
2975 |   type: "Pooling"
2976 |   pooling_param {
2977 |     pool: MAX
2978 |     kernel_size: 2
2979 |     stride: 2
2980 |   }
2981 | }
2982 | layer {
2983 |   bottom: "pool0_6_1"
2984 |   top: "pool0_6_2"
2985 |   name: "pool0_6_2"
2986 |   type: "Pooling"
2987 |   pooling_param {
2988 |     pool: MAX
2989 |     kernel_size: 2
2990 |     stride: 2
2991 |   }
2992 | }
2993 | layer {
2994 |   bottom: "pool0_6_2"
2995 |   top: "pool0_6_3"
2996 |   name: "pool0_6_3"
2997 |   type: "Pooling"
2998 |   pooling_param {
2999 |     pool: MAX
3000 |     kernel_size: 2
3001 |     stride: 2
3002 |   }
3003 | }
3004 | layer {
3005 |   bottom: "conv1_13"
3006 |   top: "pool1_13_0"
3007 |   name: "pool1_13_0"
3008 |   type: "Pooling"
3009 |   pooling_param {
3010 |     pool: MAX
3011 |     kernel_size: 2
3012 |     stride: 2
3013 |   }
3014 | }
3015 | layer {
3016 |   bottom: "pool1_13_0"
3017 |   top: "pool1_13_1"
3018 |   name: "pool1_13_1"
3019 |   type: "Pooling"
3020 |   pooling_param {
3021 |     pool: MAX
3022 |     kernel_size: 2
3023 |     stride: 2
3024 |   }
3025 | }
3026 | layer {
3027 |   bottom: "pool1_13_1"
3028 |   top: "pool1_13_2"
3029 |   name: "pool1_13_2"
3030 |   type: "Pooling"
3031 |   pooling_param {
3032 |     pool: MAX
3033 |     kernel_size: 2
3034 |     stride: 2
3035 |   }
3036 | }
3037 | layer {
3038 |   bottom: "pool1_13_2"
3039 |   top: "pool1_13_3"
3040 |   name: "pool1_13_3"
3041 |   type: "Pooling"
3042 |   pooling_param {
3043 |     pool: MAX
3044 |     kernel_size: 2
3045 |     stride: 2
3046 |   }
3047 | }
3048 | layer {
3049 |   bottom: "conv2_27"
3050 |   top: "pool2_27_0"
3051 |   name: "pool2_27_0"
3052 |   type: "Pooling"
3053 |   pooling_param {
3054 |     pool: MAX
3055 |     kernel_size: 2
3056 |     stride: 2
3057 |   }
3058 | }
3059 | layer {
3060 |   bottom: "pool2_27_0"
3061 |   top: "pool2_27_1"
3062 |   name: "pool2_27_1"
3063 |   type: "Pooling"
3064 |   pooling_param {
3065 |     pool: MAX
3066 |     kernel_size: 2
3067 |     stride: 2
3068 |   }
3069 | }
3070 | layer {
3071 |   bottom: "pool2_27_1"
3072 |   top: "pool2_27_2"
3073 |   name: "pool2_27_2"
3074 |   type: "Pooling"
3075 |   pooling_param {
3076 |     pool: MAX
3077 |     kernel_size: 2
3078 |     stride: 2
3079 |   }
3080 | }
3081 | layer {
3082 |   bottom: "pool2_27_2"
3083 |   top: "pool2_27_3"
3084 |   name: "pool2_27_3"
3085 |   type: "Pooling"
3086 |   pooling_param {
3087 |     pool: MAX
3088 |     kernel_size: 2
3089 |     stride: 2
3090 |   }
3091 | }
3092 | layer {
3093 |   name: "join_pool2_27_plus"
3094 |   type: "FractalJoin"
3095 |   bottom: "pool0_6_3"
3096 |   bottom: "pool1_13_3"
3097 |   bottom: "pool2_27_3"
3098 |   top: "pool2_27_plus"
3099 |   fractal_join_param {
3100 |     drop_path_ratio: 0.15
3101 |   }
3102 | }
3103 | layer {
3104 |   name: "join_extra_mid_join2"
3105 |   type: "FractalJoin"
3106 |   bottom: "pool2_19_plus"
3107 |   bottom: "pool2_23_plus"
3108 |   bottom: "pool2_27_plus"
3109 |   top: "extra_mid_join2"
3110 |   fractal_join_param {
3111 |     drop_path_ratio: 0.15
3112 |   }
3113 | }
3114 | # Reduction: 7, spatial size: 0
3115 | layer {
3116 |   name: "prediction0"
3117 |   type: "InnerProduct"
3118 |   bottom: "extra_mid_join2"
3119 |   top: "prediction0"
3120 |   param {
3121 |     lr_mult: 1
3122 |     decay_mult: 1
3123 |     name: "prediction0"
3124 |   }
3125 |   param {
3126 |     lr_mult: 2
3127 |     decay_mult: 0
3128 |     name: "prediction0_b"
3129 |   }
3130 |   inner_product_param {
3131 |     num_output: 10
3132 |     weight_filler {
3133 |       type: "xavier"
3134 |     }
3135 |     bias_filler {
3136 |       type: "constant"
3137 |     }
3138 |   }
3139 | }
3140 | layer {
3141 |   name: "loss0"
3142 |   type: "SoftmaxWithLoss"
3143 |   bottom: "prediction0"
3144 |   bottom: "label"
3145 |   top: "loss0"
3146 |   loss_weight: 1.0
3147 |   include: { phase: TRAIN }
3148 | }
3149 | 
3150 | layer {
3151 |   name: "accuracy_loss0"
3152 |   type: "Accuracy"
3153 |   bottom: "prediction0"
3154 |   bottom: "label"
3155 |   top: "accuracy_loss0"
3156 |   include: { phase: TEST }
3157 | }
3158 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # CNNDesignPatterns
 2 | 
 3 | This repository hosts the Caffe code and prototxt files for the CNN Design Patterns paper available at https://arxiv.org/abs/1611.00847.  If you use this work in your research, please cite this paper.
 4 | 
 5 | 
 6 | In order to use the prototxt files, you should install Caffe along with the fractalnet code located at https://github.com/gustavla/fractalnet/tree/master/caffe.
 7 | 
 8 | To use the SBN and TSN prototxt files, you will need to also install the freeze-drop-path code and the instructions are below.
 9 | 
10 | Prototxt files
11 | ==============
12 | 
13 |     solver.prototxt: The solver file.
14 |     fractalnet.prototxt: The original fractalnet architecture
15 |     fractalnet-AvgPool.prototxt: Replaces max pooling with average pooling in the original fractalnet architecture
16 |     FoF.prototxt: The fractal of fractalnet architecture.
17 |     FoF-TSN.prototxt: Taylor series modification where branch 2 is squared and branch 3 cubed prior to the final fractal join.
18 |     SBN-FDP.prototxt: Stagewise Boosting network architecture with freeze-drop-path as the final join.
19 |     TSN-FDP.prototxt: Taylor series modification of the SBN architecture.
20 | 
21 | 
22 | Freeze-drop-path
23 | ================
24 | 
25 | Copy the hpp file into your include/caffe/layers folder and copy the cpp and cu files into your src/caffe/layers folder. 
26 | 
27 | Then, add the following to your ``src/caffe/proto/caffe.proto`` file in ``LayerParameter``:
28 | 
29 |   optional FreezeDropPathParameter freeze_drop_path_param = 148;
30 | 
31 | Set ``148`` to whatever you want that is not in conflict with another layer's parameters. Also add the following to the bottom ``caffe.proto``:
32 | 
33 |     message FreezeDropPathParameter {
34 |       optional uint32 num_iter_per_cycle = 1 [default = 0];
35 |       optional uint32 interval_type = 2 [default = 0];
36 |     }
37 | 
38 | Re-compile and you should now have access to the ``freezedroppath`` unit.
39 | 
40 | Usage
41 | -----
42 | Here is an example of how to use freeze-drop-path with two layers, with the stochastic option and intervals that increase with the square of the branch number, respectively::
43 | 
44 |     layer {
45 |       name: "freeze_drop_path"
46 |       type: "FreezeDropPath"
47 |       bottom: "pool2_27_plus"
48 |       bottom: "extra_mid_join2a"
49 |       top: "extra_mid_join2"
50 |       freeze_drop_path_param {
51 |         num_iter_per_cycle: 0
52 |         interval_type: 0
53 |       }
54 |     }
55 | 
56 | If you want to use freeze-drop-path deterministically, set num_iter_per_cycle to the number of iterations for a cycle through the all the branches.
57 | 
58 | 


--------------------------------------------------------------------------------
/SBN-FDP.prototxt:
--------------------------------------------------------------------------------
   1 | name: "FractalOfFractalNet"
   2 | layer {
   3 |   name: "cifar"
   4 |   type: "Data"
   5 |   top: "data"
   6 |   top: "label"
   7 |   include {
   8 |     phase: TRAIN
   9 |   }
  10 |   transform_param {
  11 |     mirror: true
  12 |     mean_file: "examples/cifar10/mean.binaryproto"
  13 |   }
  14 |   data_param {
  15 |     source: "examples/cifar10/cifar10_train_lmdb"
  16 |     batch_size: 25
  17 |     backend: LMDB
  18 |   }
  19 | }
  20 | layer {
  21 |   name: "cifar"
  22 |   type: "Data"
  23 |   top: "data"
  24 |   top: "label"
  25 |   include {
  26 |     phase: TEST
  27 |   }
  28 |   transform_param {
  29 |     mean_file: "examples/cifar10/mean.binaryproto"
  30 |     mirror: false
  31 | #    shuffle: true
  32 |   }
  33 |   data_param {
  34 |     source: "examples/cifar10/cifar10_test_lmdb"
  35 |     batch_size: 10
  36 |     backend: LMDB
  37 |   }
  38 | }
  39 | # Input size: 32
  40 | layer {
  41 |   bottom: "data"
  42 |   top: "conv2_0"
  43 |   name: "conv2_0"
  44 |   param {
  45 |     lr_mult: 1
  46 |     decay_mult: 1
  47 |     name: "conv2_0"
  48 |   }
  49 |   param {
  50 |     lr_mult: 2
  51 |     decay_mult: 0
  52 |     name: "conv2_0_b"
  53 |   }
  54 |   type: "Convolution"
  55 |   convolution_param {
  56 |     num_output: 64
  57 |     pad: 1
  58 |     kernel_size: 3
  59 |     weight_filler {
  60 |       type: "xavier"
  61 |     }
  62 |     bias_filler {
  63 |       type: "constant"
  64 |     }
  65 |   }
  66 | }
  67 | layer {
  68 |   name: "dropout_conv2_0"
  69 |   type: "Dropout"
  70 |   bottom: "conv2_0"
  71 |   top: "conv2_0"
  72 |   dropout_param {
  73 |     dropout_ratio: 0.0
  74 |   }
  75 | }
  76 | layer {
  77 |   name: "batch_conv2_0"
  78 |   type: "BatchNorm"
  79 |   bottom: "conv2_0"
  80 |   top: "conv2_0"
  81 |   param { lr_mult: 0 }
  82 |   param { lr_mult: 0 }
  83 |   param { lr_mult: 0 }
  84 | }
  85 | layer {
  86 |   name: "relu_conv2_0"
  87 |   type: "ReLU"
  88 |   bottom: "conv2_0"
  89 |   top: "conv2_0"
  90 | }
  91 | layer {
  92 |   bottom: "conv2_0"
  93 |   top: "conv2_1"
  94 |   name: "conv2_1"
  95 |   param {
  96 |     lr_mult: 1
  97 |     decay_mult: 1
  98 |     name: "conv2_1"
  99 |   }
 100 |   param {
 101 |     lr_mult: 2
 102 |     decay_mult: 0
 103 |     name: "conv2_1_b"
 104 |   }
 105 |   type: "Convolution"
 106 |   convolution_param {
 107 |     num_output: 64
 108 |     pad: 1
 109 |     kernel_size: 3
 110 |     weight_filler {
 111 |       type: "xavier"
 112 |     }
 113 |     bias_filler {
 114 |       type: "constant"
 115 |     }
 116 |   }
 117 | }
 118 | layer {
 119 |   name: "dropout_conv2_1"
 120 |   type: "Dropout"
 121 |   bottom: "conv2_1"
 122 |   top: "conv2_1"
 123 |   dropout_param {
 124 |     dropout_ratio: 0.0
 125 |   }
 126 | }
 127 | layer {
 128 |   name: "batch_conv2_1"
 129 |   type: "BatchNorm"
 130 |   bottom: "conv2_1"
 131 |   top: "conv2_1"
 132 |   param { lr_mult: 0 }
 133 |   param { lr_mult: 0 }
 134 |   param { lr_mult: 0 }
 135 | }
 136 | layer {
 137 |   name: "relu_conv2_1"
 138 |   type: "ReLU"
 139 |   bottom: "conv2_1"
 140 |   top: "conv2_1"
 141 | }
 142 | layer {
 143 |   bottom: "data"
 144 |   top: "conv1_0"
 145 |   name: "conv1_0"
 146 |   param {
 147 |     lr_mult: 1
 148 |     decay_mult: 1
 149 |     name: "conv1_0"
 150 |   }
 151 |   param {
 152 |     lr_mult: 2
 153 |     decay_mult: 0
 154 |     name: "conv1_0_b"
 155 |   }
 156 |   type: "Convolution"
 157 |   convolution_param {
 158 |     num_output: 64
 159 |     pad: 1
 160 |     kernel_size: 3
 161 |     weight_filler {
 162 |       type: "xavier"
 163 |     }
 164 |     bias_filler {
 165 |       type: "constant"
 166 |     }
 167 |   }
 168 | }
 169 | layer {
 170 |   name: "dropout_conv1_0"
 171 |   type: "Dropout"
 172 |   bottom: "conv1_0"
 173 |   top: "conv1_0"
 174 |   dropout_param {
 175 |     dropout_ratio: 0.0
 176 |   }
 177 | }
 178 | layer {
 179 |   name: "batch_conv1_0"
 180 |   type: "BatchNorm"
 181 |   bottom: "conv1_0"
 182 |   top: "conv1_0"
 183 |   param { lr_mult: 0 }
 184 |   param { lr_mult: 0 }
 185 |   param { lr_mult: 0 }
 186 | }
 187 | layer {
 188 |   name: "relu_conv1_0"
 189 |   type: "ReLU"
 190 |   bottom: "conv1_0"
 191 |   top: "conv1_0"
 192 | }
 193 | layer {
 194 |   name: "join_conv2_1_plus"
 195 |   type: "FractalJoin"
 196 |   bottom: "conv1_0"
 197 |   bottom: "conv2_1"
 198 |   top: "conv2_1_plus"
 199 |   fractal_join_param {
 200 |     drop_path_ratio: 0.15
 201 |   }
 202 | }
 203 | layer {
 204 |   bottom: "conv2_1_plus"
 205 |   top: "conv2_2"
 206 |   name: "conv2_2"
 207 |   param {
 208 |     lr_mult: 1
 209 |     decay_mult: 1
 210 |     name: "conv2_2"
 211 |   }
 212 |   param {
 213 |     lr_mult: 2
 214 |     decay_mult: 0
 215 |     name: "conv2_2_b"
 216 |   }
 217 |   type: "Convolution"
 218 |   convolution_param {
 219 |     num_output: 64
 220 |     pad: 1
 221 |     kernel_size: 3
 222 |     weight_filler {
 223 |       type: "xavier"
 224 |     }
 225 |     bias_filler {
 226 |       type: "constant"
 227 |     }
 228 |   }
 229 | }
 230 | layer {
 231 |   name: "dropout_conv2_2"
 232 |   type: "Dropout"
 233 |   bottom: "conv2_2"
 234 |   top: "conv2_2"
 235 |   dropout_param {
 236 |     dropout_ratio: 0.0
 237 |   }
 238 | }
 239 | layer {
 240 |   name: "batch_conv2_2"
 241 |   type: "BatchNorm"
 242 |   bottom: "conv2_2"
 243 |   top: "conv2_2"
 244 |   param { lr_mult: 0 }
 245 |   param { lr_mult: 0 }
 246 |   param { lr_mult: 0 }
 247 | }
 248 | layer {
 249 |   name: "relu_conv2_2"
 250 |   type: "ReLU"
 251 |   bottom: "conv2_2"
 252 |   top: "conv2_2"
 253 | }
 254 | layer {
 255 |   bottom: "conv2_2"
 256 |   top: "conv2_3"
 257 |   name: "conv2_3"
 258 |   param {
 259 |     lr_mult: 1
 260 |     decay_mult: 1
 261 |     name: "conv2_3"
 262 |   }
 263 |   param {
 264 |     lr_mult: 2
 265 |     decay_mult: 0
 266 |     name: "conv2_3_b"
 267 |   }
 268 |   type: "Convolution"
 269 |   convolution_param {
 270 |     num_output: 64
 271 |     pad: 1
 272 |     kernel_size: 3
 273 |     weight_filler {
 274 |       type: "xavier"
 275 |     }
 276 |     bias_filler {
 277 |       type: "constant"
 278 |     }
 279 |   }
 280 | }
 281 | layer {
 282 |   name: "dropout_conv2_3"
 283 |   type: "Dropout"
 284 |   bottom: "conv2_3"
 285 |   top: "conv2_3"
 286 |   dropout_param {
 287 |     dropout_ratio: 0.0
 288 |   }
 289 | }
 290 | layer {
 291 |   name: "batch_conv2_3"
 292 |   type: "BatchNorm"
 293 |   bottom: "conv2_3"
 294 |   top: "conv2_3"
 295 |   param { lr_mult: 0 }
 296 |   param { lr_mult: 0 }
 297 |   param { lr_mult: 0 }
 298 | }
 299 | layer {
 300 |   name: "relu_conv2_3"
 301 |   type: "ReLU"
 302 |   bottom: "conv2_3"
 303 |   top: "conv2_3"
 304 | }
 305 | layer {
 306 |   bottom: "conv2_1_plus"
 307 |   top: "conv1_1"
 308 |   name: "conv1_1"
 309 |   param {
 310 |     lr_mult: 1
 311 |     decay_mult: 1
 312 |     name: "conv1_1"
 313 |   }
 314 |   param {
 315 |     lr_mult: 2
 316 |     decay_mult: 0
 317 |     name: "conv1_1_b"
 318 |   }
 319 |   type: "Convolution"
 320 |   convolution_param {
 321 |     num_output: 64
 322 |     pad: 1
 323 |     kernel_size: 3
 324 |     weight_filler {
 325 |       type: "xavier"
 326 |     }
 327 |     bias_filler {
 328 |       type: "constant"
 329 |     }
 330 |   }
 331 | }
 332 | layer {
 333 |   name: "dropout_conv1_1"
 334 |   type: "Dropout"
 335 |   bottom: "conv1_1"
 336 |   top: "conv1_1"
 337 |   dropout_param {
 338 |     dropout_ratio: 0.0
 339 |   }
 340 | }
 341 | layer {
 342 |   name: "batch_conv1_1"
 343 |   type: "BatchNorm"
 344 |   bottom: "conv1_1"
 345 |   top: "conv1_1"
 346 |   param { lr_mult: 0 }
 347 |   param { lr_mult: 0 }
 348 |   param { lr_mult: 0 }
 349 | }
 350 | layer {
 351 |   name: "relu_conv1_1"
 352 |   type: "ReLU"
 353 |   bottom: "conv1_1"
 354 |   top: "conv1_1"
 355 | }
 356 | layer {
 357 |   bottom: "data"
 358 |   top: "conv0_0"
 359 |   name: "conv0_0"
 360 |   param {
 361 |     lr_mult: 1
 362 |     decay_mult: 1
 363 |     name: "conv0_0"
 364 |   }
 365 |   param {
 366 |     lr_mult: 2
 367 |     decay_mult: 0
 368 |     name: "conv0_0_b"
 369 |   }
 370 |   type: "Convolution"
 371 |   convolution_param {
 372 |     num_output: 64
 373 |     pad: 1
 374 |     kernel_size: 3
 375 |     weight_filler {
 376 |       type: "xavier"
 377 |     }
 378 |     bias_filler {
 379 |       type: "constant"
 380 |     }
 381 |   }
 382 | }
 383 | layer {
 384 |   name: "dropout_conv0_0"
 385 |   type: "Dropout"
 386 |   bottom: "conv0_0"
 387 |   top: "conv0_0"
 388 |   dropout_param {
 389 |     dropout_ratio: 0.0
 390 |   }
 391 | }
 392 | layer {
 393 |   name: "batch_conv0_0"
 394 |   type: "BatchNorm"
 395 |   bottom: "conv0_0"
 396 |   top: "conv0_0"
 397 |   param { lr_mult: 0 }
 398 |   param { lr_mult: 0 }
 399 |   param { lr_mult: 0 }
 400 | }
 401 | layer {
 402 |   name: "relu_conv0_0"
 403 |   type: "ReLU"
 404 |   bottom: "conv0_0"
 405 |   top: "conv0_0"
 406 | }
 407 | layer {
 408 |   bottom: "conv0_0"
 409 |   top: "pool0_0"
 410 |   name: "pool0_0"
 411 |   type: "Pooling"
 412 |   pooling_param {
 413 |     pool: MAX
 414 |     kernel_size: 2
 415 |     stride: 2
 416 |   }
 417 | }
 418 | layer {
 419 |   bottom: "conv1_1"
 420 |   top: "pool1_1"
 421 |   name: "pool1_1"
 422 |   type: "Pooling"
 423 |   pooling_param {
 424 |     pool: MAX
 425 |     kernel_size: 2
 426 |     stride: 2
 427 |   }
 428 | }
 429 | layer {
 430 |   bottom: "conv2_3"
 431 |   top: "pool2_3"
 432 |   name: "pool2_3"
 433 |   type: "Pooling"
 434 |   pooling_param {
 435 |     pool: MAX
 436 |     kernel_size: 2
 437 |     stride: 2
 438 |   }
 439 | }
 440 | layer {
 441 |   name: "join_pool2_3_plus"
 442 |   type: "FractalJoin"
 443 |   bottom: "pool0_0"
 444 |   bottom: "pool1_1"
 445 |   bottom: "pool2_3"
 446 |   top: "pool2_3_plus"
 447 |   fractal_join_param {
 448 |     drop_path_ratio: 0.15
 449 |   }
 450 | }
 451 | # Reduction: 1, spatial size: 16
 452 | layer {
 453 |   bottom: "pool2_3_plus"
 454 |   top: "conv2_4"
 455 |   name: "conv2_4"
 456 |   param {
 457 |     lr_mult: 1
 458 |     decay_mult: 1
 459 |     name: "conv2_4"
 460 |   }
 461 |   param {
 462 |     lr_mult: 2
 463 |     decay_mult: 0
 464 |     name: "conv2_4_b"
 465 |   }
 466 |   type: "Convolution"
 467 |   convolution_param {
 468 |     num_output: 128
 469 |     pad: 1
 470 |     kernel_size: 3
 471 |     weight_filler {
 472 |       type: "xavier"
 473 |     }
 474 |     bias_filler {
 475 |       type: "constant"
 476 |     }
 477 |   }
 478 | }
 479 | layer {
 480 |   name: "dropout_conv2_4"
 481 |   type: "Dropout"
 482 |   bottom: "conv2_4"
 483 |   top: "conv2_4"
 484 |   dropout_param {
 485 |     dropout_ratio: 0.1
 486 |   }
 487 | }
 488 | layer {
 489 |   name: "batch_conv2_4"
 490 |   type: "BatchNorm"
 491 |   bottom: "conv2_4"
 492 |   top: "conv2_4"
 493 |   param { lr_mult: 0 }
 494 |   param { lr_mult: 0 }
 495 |   param { lr_mult: 0 }
 496 | }
 497 | layer {
 498 |   name: "relu_conv2_4"
 499 |   type: "ReLU"
 500 |   bottom: "conv2_4"
 501 |   top: "conv2_4"
 502 | }
 503 | layer {
 504 |   bottom: "conv2_4"
 505 |   top: "conv2_5"
 506 |   name: "conv2_5"
 507 |   param {
 508 |     lr_mult: 1
 509 |     decay_mult: 1
 510 |     name: "conv2_5"
 511 |   }
 512 |   param {
 513 |     lr_mult: 2
 514 |     decay_mult: 0
 515 |     name: "conv2_5_b"
 516 |   }
 517 |   type: "Convolution"
 518 |   convolution_param {
 519 |     num_output: 128
 520 |     pad: 1
 521 |     kernel_size: 3
 522 |     weight_filler {
 523 |       type: "xavier"
 524 |     }
 525 |     bias_filler {
 526 |       type: "constant"
 527 |     }
 528 |   }
 529 | }
 530 | layer {
 531 |   name: "dropout_conv2_5"
 532 |   type: "Dropout"
 533 |   bottom: "conv2_5"
 534 |   top: "conv2_5"
 535 |   dropout_param {
 536 |     dropout_ratio: 0.1
 537 |   }
 538 | }
 539 | layer {
 540 |   name: "batch_conv2_5"
 541 |   type: "BatchNorm"
 542 |   bottom: "conv2_5"
 543 |   top: "conv2_5"
 544 |   param { lr_mult: 0 }
 545 |   param { lr_mult: 0 }
 546 |   param { lr_mult: 0 }
 547 | }
 548 | layer {
 549 |   name: "relu_conv2_5"
 550 |   type: "ReLU"
 551 |   bottom: "conv2_5"
 552 |   top: "conv2_5"
 553 | }
 554 | layer {
 555 |   bottom: "pool2_3_plus"
 556 |   top: "conv1_2"
 557 |   name: "conv1_2"
 558 |   param {
 559 |     lr_mult: 1
 560 |     decay_mult: 1
 561 |     name: "conv1_2"
 562 |   }
 563 |   param {
 564 |     lr_mult: 2
 565 |     decay_mult: 0
 566 |     name: "conv1_2_b"
 567 |   }
 568 |   type: "Convolution"
 569 |   convolution_param {
 570 |     num_output: 128
 571 |     pad: 1
 572 |     kernel_size: 3
 573 |     weight_filler {
 574 |       type: "xavier"
 575 |     }
 576 |     bias_filler {
 577 |       type: "constant"
 578 |     }
 579 |   }
 580 | }
 581 | layer {
 582 |   name: "dropout_conv1_2"
 583 |   type: "Dropout"
 584 |   bottom: "conv1_2"
 585 |   top: "conv1_2"
 586 |   dropout_param {
 587 |     dropout_ratio: 0.1
 588 |   }
 589 | }
 590 | layer {
 591 |   name: "batch_conv1_2"
 592 |   type: "BatchNorm"
 593 |   bottom: "conv1_2"
 594 |   top: "conv1_2"
 595 |   param { lr_mult: 0 }
 596 |   param { lr_mult: 0 }
 597 |   param { lr_mult: 0 }
 598 | }
 599 | layer {
 600 |   name: "relu_conv1_2"
 601 |   type: "ReLU"
 602 |   bottom: "conv1_2"
 603 |   top: "conv1_2"
 604 | }
 605 | layer {
 606 |   name: "join_conv2_5_plus"
 607 |   type: "FractalJoin"
 608 |   bottom: "conv1_2"
 609 |   bottom: "conv2_5"
 610 |   top: "conv2_5_plus"
 611 |   fractal_join_param {
 612 |     drop_path_ratio: 0.15
 613 |   }
 614 | }
 615 | layer {
 616 |   bottom: "conv2_5_plus"
 617 |   top: "conv2_6"
 618 |   name: "conv2_6"
 619 |   param {
 620 |     lr_mult: 1
 621 |     decay_mult: 1
 622 |     name: "conv2_6"
 623 |   }
 624 |   param {
 625 |     lr_mult: 2
 626 |     decay_mult: 0
 627 |     name: "conv2_6_b"
 628 |   }
 629 |   type: "Convolution"
 630 |   convolution_param {
 631 |     num_output: 128
 632 |     pad: 1
 633 |     kernel_size: 3
 634 |     weight_filler {
 635 |       type: "xavier"
 636 |     }
 637 |     bias_filler {
 638 |       type: "constant"
 639 |     }
 640 |   }
 641 | }
 642 | layer {
 643 |   name: "dropout_conv2_6"
 644 |   type: "Dropout"
 645 |   bottom: "conv2_6"
 646 |   top: "conv2_6"
 647 |   dropout_param {
 648 |     dropout_ratio: 0.1
 649 |   }
 650 | }
 651 | layer {
 652 |   name: "batch_conv2_6"
 653 |   type: "BatchNorm"
 654 |   bottom: "conv2_6"
 655 |   top: "conv2_6"
 656 |   param { lr_mult: 0 }
 657 |   param { lr_mult: 0 }
 658 |   param { lr_mult: 0 }
 659 | }
 660 | layer {
 661 |   name: "relu_conv2_6"
 662 |   type: "ReLU"
 663 |   bottom: "conv2_6"
 664 |   top: "conv2_6"
 665 | }
 666 | layer {
 667 |   bottom: "conv2_6"
 668 |   top: "conv2_7"
 669 |   name: "conv2_7"
 670 |   param {
 671 |     lr_mult: 1
 672 |     decay_mult: 1
 673 |     name: "conv2_7"
 674 |   }
 675 |   param {
 676 |     lr_mult: 2
 677 |     decay_mult: 0
 678 |     name: "conv2_7_b"
 679 |   }
 680 |   type: "Convolution"
 681 |   convolution_param {
 682 |     num_output: 128
 683 |     pad: 1
 684 |     kernel_size: 3
 685 |     weight_filler {
 686 |       type: "xavier"
 687 |     }
 688 |     bias_filler {
 689 |       type: "constant"
 690 |     }
 691 |   }
 692 | }
 693 | layer {
 694 |   name: "dropout_conv2_7"
 695 |   type: "Dropout"
 696 |   bottom: "conv2_7"
 697 |   top: "conv2_7"
 698 |   dropout_param {
 699 |     dropout_ratio: 0.1
 700 |   }
 701 | }
 702 | layer {
 703 |   name: "batch_conv2_7"
 704 |   type: "BatchNorm"
 705 |   bottom: "conv2_7"
 706 |   top: "conv2_7"
 707 |   param { lr_mult: 0 }
 708 |   param { lr_mult: 0 }
 709 |   param { lr_mult: 0 }
 710 | }
 711 | layer {
 712 |   name: "relu_conv2_7"
 713 |   type: "ReLU"
 714 |   bottom: "conv2_7"
 715 |   top: "conv2_7"
 716 | }
 717 | layer {
 718 |   bottom: "conv2_5_plus"
 719 |   top: "conv1_3"
 720 |   name: "conv1_3"
 721 |   param {
 722 |     lr_mult: 1
 723 |     decay_mult: 1
 724 |     name: "conv1_3"
 725 |   }
 726 |   param {
 727 |     lr_mult: 2
 728 |     decay_mult: 0
 729 |     name: "conv1_3_b"
 730 |   }
 731 |   type: "Convolution"
 732 |   convolution_param {
 733 |     num_output: 128
 734 |     pad: 1
 735 |     kernel_size: 3
 736 |     weight_filler {
 737 |       type: "xavier"
 738 |     }
 739 |     bias_filler {
 740 |       type: "constant"
 741 |     }
 742 |   }
 743 | }
 744 | layer {
 745 |   name: "dropout_conv1_3"
 746 |   type: "Dropout"
 747 |   bottom: "conv1_3"
 748 |   top: "conv1_3"
 749 |   dropout_param {
 750 |     dropout_ratio: 0.1
 751 |   }
 752 | }
 753 | layer {
 754 |   name: "batch_conv1_3"
 755 |   type: "BatchNorm"
 756 |   bottom: "conv1_3"
 757 |   top: "conv1_3"
 758 |   param { lr_mult: 0 }
 759 |   param { lr_mult: 0 }
 760 |   param { lr_mult: 0 }
 761 | }
 762 | layer {
 763 |   name: "relu_conv1_3"
 764 |   type: "ReLU"
 765 |   bottom: "conv1_3"
 766 |   top: "conv1_3"
 767 | }
 768 | layer {
 769 |   bottom: "pool2_3_plus"
 770 |   top: "conv0_1"
 771 |   name: "conv0_1"
 772 |   param {
 773 |     lr_mult: 1
 774 |     decay_mult: 1
 775 |     name: "conv0_1"
 776 |   }
 777 |   param {
 778 |     lr_mult: 2
 779 |     decay_mult: 0
 780 |     name: "conv0_1_b"
 781 |   }
 782 |   type: "Convolution"
 783 |   convolution_param {
 784 |     num_output: 128
 785 |     pad: 1
 786 |     kernel_size: 3
 787 |     weight_filler {
 788 |       type: "xavier"
 789 |     }
 790 |     bias_filler {
 791 |       type: "constant"
 792 |     }
 793 |   }
 794 | }
 795 | layer {
 796 |   name: "dropout_conv0_1"
 797 |   type: "Dropout"
 798 |   bottom: "conv0_1"
 799 |   top: "conv0_1"
 800 |   dropout_param {
 801 |     dropout_ratio: 0.1
 802 |   }
 803 | }
 804 | layer {
 805 |   name: "batch_conv0_1"
 806 |   type: "BatchNorm"
 807 |   bottom: "conv0_1"
 808 |   top: "conv0_1"
 809 |   param { lr_mult: 0 }
 810 |   param { lr_mult: 0 }
 811 |   param { lr_mult: 0 }
 812 | }
 813 | layer {
 814 |   name: "relu_conv0_1"
 815 |   type: "ReLU"
 816 |   bottom: "conv0_1"
 817 |   top: "conv0_1"
 818 | }
 819 | layer {
 820 |   bottom: "conv0_1"
 821 |   top: "pool0_1"
 822 |   name: "pool0_1"
 823 |   type: "Pooling"
 824 |   pooling_param {
 825 |     pool: MAX
 826 |     kernel_size: 2
 827 |     stride: 2
 828 |   }
 829 | }
 830 | layer {
 831 |   bottom: "conv1_3"
 832 |   top: "pool1_3"
 833 |   name: "pool1_3"
 834 |   type: "Pooling"
 835 |   pooling_param {
 836 |     pool: MAX
 837 |     kernel_size: 2
 838 |     stride: 2
 839 |   }
 840 | }
 841 | layer {
 842 |   bottom: "conv2_7"
 843 |   top: "pool2_7"
 844 |   name: "pool2_7"
 845 |   type: "Pooling"
 846 |   pooling_param {
 847 |     pool: MAX
 848 |     kernel_size: 2
 849 |     stride: 2
 850 |   }
 851 | }
 852 | layer {
 853 |   name: "join_pool2_7_plus"
 854 |   type: "FractalJoin"
 855 |   bottom: "pool0_1"
 856 |   bottom: "pool1_3"
 857 |   bottom: "pool2_7"
 858 |   top: "pool2_7_plus"
 859 |   fractal_join_param {
 860 |     drop_path_ratio: 0.15
 861 |   }
 862 | }
 863 | # Reduction: 2, spatial size: 8
 864 | layer {
 865 |   bottom: "data"
 866 |   top: "conv2_8"
 867 |   name: "conv2_8"
 868 |   param {
 869 |     lr_mult: 1
 870 |     decay_mult: 1
 871 |     name: "conv2_8"
 872 |   }
 873 |   param {
 874 |     lr_mult: 2
 875 |     decay_mult: 0
 876 |     name: "conv2_8_b"
 877 |   }
 878 |   type: "Convolution"
 879 |   convolution_param {
 880 |     num_output: 128
 881 |     pad: 1
 882 |     kernel_size: 3
 883 |     weight_filler {
 884 |       type: "xavier"
 885 |     }
 886 |     bias_filler {
 887 |       type: "constant"
 888 |     }
 889 |   }
 890 | }
 891 | layer {
 892 |   name: "dropout_conv2_8"
 893 |   type: "Dropout"
 894 |   bottom: "conv2_8"
 895 |   top: "conv2_8"
 896 |   dropout_param {
 897 |     dropout_ratio: 0.0
 898 |   }
 899 | }
 900 | layer {
 901 |   name: "batch_conv2_8"
 902 |   type: "BatchNorm"
 903 |   bottom: "conv2_8"
 904 |   top: "conv2_8"
 905 |   param { lr_mult: 0 }
 906 |   param { lr_mult: 0 }
 907 |   param { lr_mult: 0 }
 908 | }
 909 | layer {
 910 |   name: "relu_conv2_8"
 911 |   type: "ReLU"
 912 |   bottom: "conv2_8"
 913 |   top: "conv2_8"
 914 | }
 915 | layer {
 916 |   bottom: "conv2_8"
 917 |   top: "conv2_9"
 918 |   name: "conv2_9"
 919 |   param {
 920 |     lr_mult: 1
 921 |     decay_mult: 1
 922 |     name: "conv2_9"
 923 |   }
 924 |   param {
 925 |     lr_mult: 2
 926 |     decay_mult: 0
 927 |     name: "conv2_9_b"
 928 |   }
 929 |   type: "Convolution"
 930 |   convolution_param {
 931 |     num_output: 128
 932 |     pad: 1
 933 |     kernel_size: 3
 934 |     weight_filler {
 935 |       type: "xavier"
 936 |     }
 937 |     bias_filler {
 938 |       type: "constant"
 939 |     }
 940 |   }
 941 | }
 942 | layer {
 943 |   name: "dropout_conv2_9"
 944 |   type: "Dropout"
 945 |   bottom: "conv2_9"
 946 |   top: "conv2_9"
 947 |   dropout_param {
 948 |     dropout_ratio: 0.0
 949 |   }
 950 | }
 951 | layer {
 952 |   name: "batch_conv2_9"
 953 |   type: "BatchNorm"
 954 |   bottom: "conv2_9"
 955 |   top: "conv2_9"
 956 |   param { lr_mult: 0 }
 957 |   param { lr_mult: 0 }
 958 |   param { lr_mult: 0 }
 959 | }
 960 | layer {
 961 |   name: "relu_conv2_9"
 962 |   type: "ReLU"
 963 |   bottom: "conv2_9"
 964 |   top: "conv2_9"
 965 | }
 966 | layer {
 967 |   bottom: "data"
 968 |   top: "conv1_4"
 969 |   name: "conv1_4"
 970 |   param {
 971 |     lr_mult: 1
 972 |     decay_mult: 1
 973 |     name: "conv1_4"
 974 |   }
 975 |   param {
 976 |     lr_mult: 2
 977 |     decay_mult: 0
 978 |     name: "conv1_4_b"
 979 |   }
 980 |   type: "Convolution"
 981 |   convolution_param {
 982 |     num_output: 128
 983 |     pad: 1
 984 |     kernel_size: 3
 985 |     weight_filler {
 986 |       type: "xavier"
 987 |     }
 988 |     bias_filler {
 989 |       type: "constant"
 990 |     }
 991 |   }
 992 | }
 993 | layer {
 994 |   name: "dropout_conv1_4"
 995 |   type: "Dropout"
 996 |   bottom: "conv1_4"
 997 |   top: "conv1_4"
 998 |   dropout_param {
 999 |     dropout_ratio: 0.0
1000 |   }
1001 | }
1002 | layer {
1003 |   name: "batch_conv1_4"
1004 |   type: "BatchNorm"
1005 |   bottom: "conv1_4"
1006 |   top: "conv1_4"
1007 |   param { lr_mult: 0 }
1008 |   param { lr_mult: 0 }
1009 |   param { lr_mult: 0 }
1010 | }
1011 | layer {
1012 |   name: "relu_conv1_4"
1013 |   type: "ReLU"
1014 |   bottom: "conv1_4"
1015 |   top: "conv1_4"
1016 | }
1017 | layer {
1018 |   name: "join_conv2_9_plus"
1019 |   type: "FractalJoin"
1020 |   bottom: "conv1_4"
1021 |   bottom: "conv2_9"
1022 |   top: "conv2_9_plus"
1023 |   fractal_join_param {
1024 |     drop_path_ratio: 0.15
1025 |   }
1026 | }
1027 | layer {
1028 |   bottom: "conv2_9_plus"
1029 |   top: "conv2_10"
1030 |   name: "conv2_10"
1031 |   param {
1032 |     lr_mult: 1
1033 |     decay_mult: 1
1034 |     name: "conv2_10"
1035 |   }
1036 |   param {
1037 |     lr_mult: 2
1038 |     decay_mult: 0
1039 |     name: "conv2_10_b"
1040 |   }
1041 |   type: "Convolution"
1042 |   convolution_param {
1043 |     num_output: 128
1044 |     pad: 1
1045 |     kernel_size: 3
1046 |     weight_filler {
1047 |       type: "xavier"
1048 |     }
1049 |     bias_filler {
1050 |       type: "constant"
1051 |     }
1052 |   }
1053 | }
1054 | layer {
1055 |   name: "dropout_conv2_10"
1056 |   type: "Dropout"
1057 |   bottom: "conv2_10"
1058 |   top: "conv2_10"
1059 |   dropout_param {
1060 |     dropout_ratio: 0.0
1061 |   }
1062 | }
1063 | layer {
1064 |   name: "batch_conv2_10"
1065 |   type: "BatchNorm"
1066 |   bottom: "conv2_10"
1067 |   top: "conv2_10"
1068 |   param { lr_mult: 0 }
1069 |   param { lr_mult: 0 }
1070 |   param { lr_mult: 0 }
1071 | }
1072 | layer {
1073 |   name: "relu_conv2_10"
1074 |   type: "ReLU"
1075 |   bottom: "conv2_10"
1076 |   top: "conv2_10"
1077 | }
1078 | layer {
1079 |   bottom: "conv2_10"
1080 |   top: "conv2_11"
1081 |   name: "conv2_11"
1082 |   param {
1083 |     lr_mult: 1
1084 |     decay_mult: 1
1085 |     name: "conv2_11"
1086 |   }
1087 |   param {
1088 |     lr_mult: 2
1089 |     decay_mult: 0
1090 |     name: "conv2_11_b"
1091 |   }
1092 |   type: "Convolution"
1093 |   convolution_param {
1094 |     num_output: 128
1095 |     pad: 1
1096 |     kernel_size: 3
1097 |     weight_filler {
1098 |       type: "xavier"
1099 |     }
1100 |     bias_filler {
1101 |       type: "constant"
1102 |     }
1103 |   }
1104 | }
1105 | layer {
1106 |   name: "dropout_conv2_11"
1107 |   type: "Dropout"
1108 |   bottom: "conv2_11"
1109 |   top: "conv2_11"
1110 |   dropout_param {
1111 |     dropout_ratio: 0.0
1112 |   }
1113 | }
1114 | layer {
1115 |   name: "batch_conv2_11"
1116 |   type: "BatchNorm"
1117 |   bottom: "conv2_11"
1118 |   top: "conv2_11"
1119 |   param { lr_mult: 0 }
1120 |   param { lr_mult: 0 }
1121 |   param { lr_mult: 0 }
1122 | }
1123 | layer {
1124 |   name: "relu_conv2_11"
1125 |   type: "ReLU"
1126 |   bottom: "conv2_11"
1127 |   top: "conv2_11"
1128 | }
1129 | layer {
1130 |   bottom: "conv2_9_plus"
1131 |   top: "conv1_5"
1132 |   name: "conv1_5"
1133 |   param {
1134 |     lr_mult: 1
1135 |     decay_mult: 1
1136 |     name: "conv1_5"
1137 |   }
1138 |   param {
1139 |     lr_mult: 2
1140 |     decay_mult: 0
1141 |     name: "conv1_5_b"
1142 |   }
1143 |   type: "Convolution"
1144 |   convolution_param {
1145 |     num_output: 128
1146 |     pad: 1
1147 |     kernel_size: 3
1148 |     weight_filler {
1149 |       type: "xavier"
1150 |     }
1151 |     bias_filler {
1152 |       type: "constant"
1153 |     }
1154 |   }
1155 | }
1156 | layer {
1157 |   name: "dropout_conv1_5"
1158 |   type: "Dropout"
1159 |   bottom: "conv1_5"
1160 |   top: "conv1_5"
1161 |   dropout_param {
1162 |     dropout_ratio: 0.0
1163 |   }
1164 | }
1165 | layer {
1166 |   name: "batch_conv1_5"
1167 |   type: "BatchNorm"
1168 |   bottom: "conv1_5"
1169 |   top: "conv1_5"
1170 |   param { lr_mult: 0 }
1171 |   param { lr_mult: 0 }
1172 |   param { lr_mult: 0 }
1173 | }
1174 | layer {
1175 |   name: "relu_conv1_5"
1176 |   type: "ReLU"
1177 |   bottom: "conv1_5"
1178 |   top: "conv1_5"
1179 | }
1180 | layer {
1181 |   bottom: "data"
1182 |   top: "conv0_2"
1183 |   name: "conv0_2"
1184 |   param {
1185 |     lr_mult: 1
1186 |     decay_mult: 1
1187 |     name: "conv0_2"
1188 |   }
1189 |   param {
1190 |     lr_mult: 2
1191 |     decay_mult: 0
1192 |     name: "conv0_2_b"
1193 |   }
1194 |   type: "Convolution"
1195 |   convolution_param {
1196 |     num_output: 128
1197 |     pad: 1
1198 |     kernel_size: 3
1199 |     weight_filler {
1200 |       type: "xavier"
1201 |     }
1202 |     bias_filler {
1203 |       type: "constant"
1204 |     }
1205 |   }
1206 | }
1207 | layer {
1208 |   name: "dropout_conv0_2"
1209 |   type: "Dropout"
1210 |   bottom: "conv0_2"
1211 |   top: "conv0_2"
1212 |   dropout_param {
1213 |     dropout_ratio: 0.0
1214 |   }
1215 | }
1216 | layer {
1217 |   name: "batch_conv0_2"
1218 |   type: "BatchNorm"
1219 |   bottom: "conv0_2"
1220 |   top: "conv0_2"
1221 |   param { lr_mult: 0 }
1222 |   param { lr_mult: 0 }
1223 |   param { lr_mult: 0 }
1224 | }
1225 | layer {
1226 |   name: "relu_conv0_2"
1227 |   type: "ReLU"
1228 |   bottom: "conv0_2"
1229 |   top: "conv0_2"
1230 | }
1231 | layer {
1232 |   bottom: "conv0_2"
1233 |   top: "pool0_2_0"
1234 |   name: "pool0_2_0"
1235 |   type: "Pooling"
1236 |   pooling_param {
1237 |     pool: MAX
1238 |     kernel_size: 2
1239 |     stride: 2
1240 |   }
1241 | }
1242 | layer {
1243 |   bottom: "pool0_2_0"
1244 |   top: "pool0_2_1"
1245 |   name: "pool0_2_1"
1246 |   type: "Pooling"
1247 |   pooling_param {
1248 |     pool: MAX
1249 |     kernel_size: 2
1250 |     stride: 2
1251 |   }
1252 | }
1253 | layer {
1254 |   bottom: "conv1_5"
1255 |   top: "pool1_5_0"
1256 |   name: "pool1_5_0"
1257 |   type: "Pooling"
1258 |   pooling_param {
1259 |     pool: MAX
1260 |     kernel_size: 2
1261 |     stride: 2
1262 |   }
1263 | }
1264 | layer {
1265 |   bottom: "pool1_5_0"
1266 |   top: "pool1_5_1"
1267 |   name: "pool1_5_1"
1268 |   type: "Pooling"
1269 |   pooling_param {
1270 |     pool: MAX
1271 |     kernel_size: 2
1272 |     stride: 2
1273 |   }
1274 | }
1275 | layer {
1276 |   bottom: "conv2_11"
1277 |   top: "pool2_11_0"
1278 |   name: "pool2_11_0"
1279 |   type: "Pooling"
1280 |   pooling_param {
1281 |     pool: MAX
1282 |     kernel_size: 2
1283 |     stride: 2
1284 |   }
1285 | }
1286 | layer {
1287 |   bottom: "pool2_11_0"
1288 |   top: "pool2_11_1"
1289 |   name: "pool2_11_1"
1290 |   type: "Pooling"
1291 |   pooling_param {
1292 |     pool: MAX
1293 |     kernel_size: 2
1294 |     stride: 2
1295 |   }
1296 | }
1297 | layer {
1298 |   name: "join_pool2_11_plus"
1299 |   type: "FractalJoin"
1300 |   bottom: "pool0_2_1"
1301 |   bottom: "pool1_5_1"
1302 |   bottom: "pool2_11_1"
1303 |   top: "pool2_11_plus"
1304 |   fractal_join_param {
1305 |     drop_path_ratio: 0.15
1306 |   }
1307 | }
1308 | layer {
1309 |   name: "join_extra_mid_join"
1310 |   type: "FractalJoin"
1311 |   bottom: "pool2_7_plus"
1312 |   bottom: "pool2_11_plus"
1313 |   top: "extra_mid_join"
1314 |   fractal_join_param {
1315 |     drop_path_ratio: 0.15
1316 |   }
1317 | }
1318 | # Reduction: 3, spatial size: 4
1319 | layer {
1320 |   bottom: "extra_mid_join"
1321 |   top: "conv2_12"
1322 |   name: "conv2_12"
1323 |   param {
1324 |     lr_mult: 1
1325 |     decay_mult: 1
1326 |     name: "conv2_12"
1327 |   }
1328 |   param {
1329 |     lr_mult: 2
1330 |     decay_mult: 0
1331 |     name: "conv2_12_b"
1332 |   }
1333 |   type: "Convolution"
1334 |   convolution_param {
1335 |     num_output: 256
1336 |     pad: 1
1337 |     kernel_size: 3
1338 |     weight_filler {
1339 |       type: "xavier"
1340 |     }
1341 |     bias_filler {
1342 |       type: "constant"
1343 |     }
1344 |   }
1345 | }
1346 | layer {
1347 |   name: "dropout_conv2_12"
1348 |   type: "Dropout"
1349 |   bottom: "conv2_12"
1350 |   top: "conv2_12"
1351 |   dropout_param {
1352 |     dropout_ratio: 0.2
1353 |   }
1354 | }
1355 | layer {
1356 |   name: "batch_conv2_12"
1357 |   type: "BatchNorm"
1358 |   bottom: "conv2_12"
1359 |   top: "conv2_12"
1360 |   param { lr_mult: 0 }
1361 |   param { lr_mult: 0 }
1362 |   param { lr_mult: 0 }
1363 | }
1364 | layer {
1365 |   name: "relu_conv2_12"
1366 |   type: "ReLU"
1367 |   bottom: "conv2_12"
1368 |   top: "conv2_12"
1369 | }
1370 | layer {
1371 |   bottom: "conv2_12"
1372 |   top: "conv2_13"
1373 |   name: "conv2_13"
1374 |   param {
1375 |     lr_mult: 1
1376 |     decay_mult: 1
1377 |     name: "conv2_13"
1378 |   }
1379 |   param {
1380 |     lr_mult: 2
1381 |     decay_mult: 0
1382 |     name: "conv2_13_b"
1383 |   }
1384 |   type: "Convolution"
1385 |   convolution_param {
1386 |     num_output: 256
1387 |     pad: 1
1388 |     kernel_size: 3
1389 |     weight_filler {
1390 |       type: "xavier"
1391 |     }
1392 |     bias_filler {
1393 |       type: "constant"
1394 |     }
1395 |   }
1396 | }
1397 | layer {
1398 |   name: "dropout_conv2_13"
1399 |   type: "Dropout"
1400 |   bottom: "conv2_13"
1401 |   top: "conv2_13"
1402 |   dropout_param {
1403 |     dropout_ratio: 0.2
1404 |   }
1405 | }
1406 | layer {
1407 |   name: "batch_conv2_13"
1408 |   type: "BatchNorm"
1409 |   bottom: "conv2_13"
1410 |   top: "conv2_13"
1411 |   param { lr_mult: 0 }
1412 |   param { lr_mult: 0 }
1413 |   param { lr_mult: 0 }
1414 | }
1415 | layer {
1416 |   name: "relu_conv2_13"
1417 |   type: "ReLU"
1418 |   bottom: "conv2_13"
1419 |   top: "conv2_13"
1420 | }
1421 | layer {
1422 |   bottom: "extra_mid_join"
1423 |   top: "conv1_6"
1424 |   name: "conv1_6"
1425 |   param {
1426 |     lr_mult: 1
1427 |     decay_mult: 1
1428 |     name: "conv1_6"
1429 |   }
1430 |   param {
1431 |     lr_mult: 2
1432 |     decay_mult: 0
1433 |     name: "conv1_6_b"
1434 |   }
1435 |   type: "Convolution"
1436 |   convolution_param {
1437 |     num_output: 256
1438 |     pad: 1
1439 |     kernel_size: 3
1440 |     weight_filler {
1441 |       type: "xavier"
1442 |     }
1443 |     bias_filler {
1444 |       type: "constant"
1445 |     }
1446 |   }
1447 | }
1448 | layer {
1449 |   name: "dropout_conv1_6"
1450 |   type: "Dropout"
1451 |   bottom: "conv1_6"
1452 |   top: "conv1_6"
1453 |   dropout_param {
1454 |     dropout_ratio: 0.2
1455 |   }
1456 | }
1457 | layer {
1458 |   name: "batch_conv1_6"
1459 |   type: "BatchNorm"
1460 |   bottom: "conv1_6"
1461 |   top: "conv1_6"
1462 |   param { lr_mult: 0 }
1463 |   param { lr_mult: 0 }
1464 |   param { lr_mult: 0 }
1465 | }
1466 | layer {
1467 |   name: "relu_conv1_6"
1468 |   type: "ReLU"
1469 |   bottom: "conv1_6"
1470 |   top: "conv1_6"
1471 | }
1472 | layer {
1473 |   name: "join_conv2_13_plus"
1474 |   type: "FractalJoin"
1475 |   bottom: "conv1_6"
1476 |   bottom: "conv2_13"
1477 |   top: "conv2_13_plus"
1478 |   fractal_join_param {
1479 |     drop_path_ratio: 0.15
1480 |   }
1481 | }
1482 | layer {
1483 |   bottom: "conv2_13_plus"
1484 |   top: "conv2_14"
1485 |   name: "conv2_14"
1486 |   param {
1487 |     lr_mult: 1
1488 |     decay_mult: 1
1489 |     name: "conv2_14"
1490 |   }
1491 |   param {
1492 |     lr_mult: 2
1493 |     decay_mult: 0
1494 |     name: "conv2_14_b"
1495 |   }
1496 |   type: "Convolution"
1497 |   convolution_param {
1498 |     num_output: 256
1499 |     pad: 1
1500 |     kernel_size: 3
1501 |     weight_filler {
1502 |       type: "xavier"
1503 |     }
1504 |     bias_filler {
1505 |       type: "constant"
1506 |     }
1507 |   }
1508 | }
1509 | layer {
1510 |   name: "dropout_conv2_14"
1511 |   type: "Dropout"
1512 |   bottom: "conv2_14"
1513 |   top: "conv2_14"
1514 |   dropout_param {
1515 |     dropout_ratio: 0.2
1516 |   }
1517 | }
1518 | layer {
1519 |   name: "batch_conv2_14"
1520 |   type: "BatchNorm"
1521 |   bottom: "conv2_14"
1522 |   top: "conv2_14"
1523 |   param { lr_mult: 0 }
1524 |   param { lr_mult: 0 }
1525 |   param { lr_mult: 0 }
1526 | }
1527 | layer {
1528 |   name: "relu_conv2_14"
1529 |   type: "ReLU"
1530 |   bottom: "conv2_14"
1531 |   top: "conv2_14"
1532 | }
1533 | layer {
1534 |   bottom: "conv2_14"
1535 |   top: "conv2_15"
1536 |   name: "conv2_15"
1537 |   param {
1538 |     lr_mult: 1
1539 |     decay_mult: 1
1540 |     name: "conv2_15"
1541 |   }
1542 |   param {
1543 |     lr_mult: 2
1544 |     decay_mult: 0
1545 |     name: "conv2_15_b"
1546 |   }
1547 |   type: "Convolution"
1548 |   convolution_param {
1549 |     num_output: 256
1550 |     pad: 1
1551 |     kernel_size: 3
1552 |     weight_filler {
1553 |       type: "xavier"
1554 |     }
1555 |     bias_filler {
1556 |       type: "constant"
1557 |     }
1558 |   }
1559 | }
1560 | layer {
1561 |   name: "dropout_conv2_15"
1562 |   type: "Dropout"
1563 |   bottom: "conv2_15"
1564 |   top: "conv2_15"
1565 |   dropout_param {
1566 |     dropout_ratio: 0.2
1567 |   }
1568 | }
1569 | layer {
1570 |   name: "batch_conv2_15"
1571 |   type: "BatchNorm"
1572 |   bottom: "conv2_15"
1573 |   top: "conv2_15"
1574 |   param { lr_mult: 0 }
1575 |   param { lr_mult: 0 }
1576 |   param { lr_mult: 0 }
1577 | }
1578 | layer {
1579 |   name: "relu_conv2_15"
1580 |   type: "ReLU"
1581 |   bottom: "conv2_15"
1582 |   top: "conv2_15"
1583 | }
1584 | layer {
1585 |   bottom: "conv2_13_plus"
1586 |   top: "conv1_7"
1587 |   name: "conv1_7"
1588 |   param {
1589 |     lr_mult: 1
1590 |     decay_mult: 1
1591 |     name: "conv1_7"
1592 |   }
1593 |   param {
1594 |     lr_mult: 2
1595 |     decay_mult: 0
1596 |     name: "conv1_7_b"
1597 |   }
1598 |   type: "Convolution"
1599 |   convolution_param {
1600 |     num_output: 256
1601 |     pad: 1
1602 |     kernel_size: 3
1603 |     weight_filler {
1604 |       type: "xavier"
1605 |     }
1606 |     bias_filler {
1607 |       type: "constant"
1608 |     }
1609 |   }
1610 | }
1611 | layer {
1612 |   name: "dropout_conv1_7"
1613 |   type: "Dropout"
1614 |   bottom: "conv1_7"
1615 |   top: "conv1_7"
1616 |   dropout_param {
1617 |     dropout_ratio: 0.2
1618 |   }
1619 | }
1620 | layer {
1621 |   name: "batch_conv1_7"
1622 |   type: "BatchNorm"
1623 |   bottom: "conv1_7"
1624 |   top: "conv1_7"
1625 |   param { lr_mult: 0 }
1626 |   param { lr_mult: 0 }
1627 |   param { lr_mult: 0 }
1628 | }
1629 | layer {
1630 |   name: "relu_conv1_7"
1631 |   type: "ReLU"
1632 |   bottom: "conv1_7"
1633 |   top: "conv1_7"
1634 | }
1635 | layer {
1636 |   bottom: "extra_mid_join"
1637 |   top: "conv0_3"
1638 |   name: "conv0_3"
1639 |   param {
1640 |     lr_mult: 1
1641 |     decay_mult: 1
1642 |     name: "conv0_3"
1643 |   }
1644 |   param {
1645 |     lr_mult: 2
1646 |     decay_mult: 0
1647 |     name: "conv0_3_b"
1648 |   }
1649 |   type: "Convolution"
1650 |   convolution_param {
1651 |     num_output: 256
1652 |     pad: 1
1653 |     kernel_size: 3
1654 |     weight_filler {
1655 |       type: "xavier"
1656 |     }
1657 |     bias_filler {
1658 |       type: "constant"
1659 |     }
1660 |   }
1661 | }
1662 | layer {
1663 |   name: "dropout_conv0_3"
1664 |   type: "Dropout"
1665 |   bottom: "conv0_3"
1666 |   top: "conv0_3"
1667 |   dropout_param {
1668 |     dropout_ratio: 0.2
1669 |   }
1670 | }
1671 | layer {
1672 |   name: "batch_conv0_3"
1673 |   type: "BatchNorm"
1674 |   bottom: "conv0_3"
1675 |   top: "conv0_3"
1676 |   param { lr_mult: 0 }
1677 |   param { lr_mult: 0 }
1678 |   param { lr_mult: 0 }
1679 | }
1680 | layer {
1681 |   name: "relu_conv0_3"
1682 |   type: "ReLU"
1683 |   bottom: "conv0_3"
1684 |   top: "conv0_3"
1685 | }
1686 | layer {
1687 |   bottom: "conv0_3"
1688 |   top: "pool0_3"
1689 |   name: "pool0_3"
1690 |   type: "Pooling"
1691 |   pooling_param {
1692 |     pool: MAX
1693 |     kernel_size: 2
1694 |     stride: 2
1695 |   }
1696 | }
1697 | layer {
1698 |   bottom: "conv1_7"
1699 |   top: "pool1_7"
1700 |   name: "pool1_7"
1701 |   type: "Pooling"
1702 |   pooling_param {
1703 |     pool: MAX
1704 |     kernel_size: 2
1705 |     stride: 2
1706 |   }
1707 | }
1708 | layer {
1709 |   bottom: "conv2_15"
1710 |   top: "pool2_15"
1711 |   name: "pool2_15"
1712 |   type: "Pooling"
1713 |   pooling_param {
1714 |     pool: MAX
1715 |     kernel_size: 2
1716 |     stride: 2
1717 |   }
1718 | }
1719 | layer {
1720 |   name: "join_pool2_15_plus"
1721 |   type: "FractalJoin"
1722 |   bottom: "pool0_3"
1723 |   bottom: "pool1_7"
1724 |   bottom: "pool2_15"
1725 |   top: "pool2_15_plus"
1726 |   fractal_join_param {
1727 |     drop_path_ratio: 0.15
1728 |   }
1729 | }
1730 | # Reduction: 4, spatial size: 2
1731 | layer {
1732 |   bottom: "pool2_15_plus"
1733 |   top: "conv2_16"
1734 |   name: "conv2_16"
1735 |   param {
1736 |     lr_mult: 1
1737 |     decay_mult: 1
1738 |     name: "conv2_16"
1739 |   }
1740 |   param {
1741 |     lr_mult: 2
1742 |     decay_mult: 0
1743 |     name: "conv2_16_b"
1744 |   }
1745 |   type: "Convolution"
1746 |   convolution_param {
1747 |     num_output: 512
1748 |     pad: 1
1749 |     kernel_size: 3
1750 |     weight_filler {
1751 |       type: "xavier"
1752 |     }
1753 |     bias_filler {
1754 |       type: "constant"
1755 |     }
1756 |   }
1757 | }
1758 | layer {
1759 |   name: "dropout_conv2_16"
1760 |   type: "Dropout"
1761 |   bottom: "conv2_16"
1762 |   top: "conv2_16"
1763 |   dropout_param {
1764 |     dropout_ratio: 0.3
1765 |   }
1766 | }
1767 | layer {
1768 |   name: "batch_conv2_16"
1769 |   type: "BatchNorm"
1770 |   bottom: "conv2_16"
1771 |   top: "conv2_16"
1772 |   param { lr_mult: 0 }
1773 |   param { lr_mult: 0 }
1774 |   param { lr_mult: 0 }
1775 | }
1776 | layer {
1777 |   name: "relu_conv2_16"
1778 |   type: "ReLU"
1779 |   bottom: "conv2_16"
1780 |   top: "conv2_16"
1781 | }
1782 | layer {
1783 |   bottom: "conv2_16"
1784 |   top: "conv2_17"
1785 |   name: "conv2_17"
1786 |   param {
1787 |     lr_mult: 1
1788 |     decay_mult: 1
1789 |     name: "conv2_17"
1790 |   }
1791 |   param {
1792 |     lr_mult: 2
1793 |     decay_mult: 0
1794 |     name: "conv2_17_b"
1795 |   }
1796 |   type: "Convolution"
1797 |   convolution_param {
1798 |     num_output: 512
1799 |     pad: 1
1800 |     kernel_size: 3
1801 |     weight_filler {
1802 |       type: "xavier"
1803 |     }
1804 |     bias_filler {
1805 |       type: "constant"
1806 |     }
1807 |   }
1808 | }
1809 | layer {
1810 |   name: "dropout_conv2_17"
1811 |   type: "Dropout"
1812 |   bottom: "conv2_17"
1813 |   top: "conv2_17"
1814 |   dropout_param {
1815 |     dropout_ratio: 0.3
1816 |   }
1817 | }
1818 | layer {
1819 |   name: "batch_conv2_17"
1820 |   type: "BatchNorm"
1821 |   bottom: "conv2_17"
1822 |   top: "conv2_17"
1823 |   param { lr_mult: 0 }
1824 |   param { lr_mult: 0 }
1825 |   param { lr_mult: 0 }
1826 | }
1827 | layer {
1828 |   name: "relu_conv2_17"
1829 |   type: "ReLU"
1830 |   bottom: "conv2_17"
1831 |   top: "conv2_17"
1832 | }
1833 | layer {
1834 |   bottom: "pool2_15_plus"
1835 |   top: "conv1_8"
1836 |   name: "conv1_8"
1837 |   param {
1838 |     lr_mult: 1
1839 |     decay_mult: 1
1840 |     name: "conv1_8"
1841 |   }
1842 |   param {
1843 |     lr_mult: 2
1844 |     decay_mult: 0
1845 |     name: "conv1_8_b"
1846 |   }
1847 |   type: "Convolution"
1848 |   convolution_param {
1849 |     num_output: 512
1850 |     pad: 1
1851 |     kernel_size: 3
1852 |     weight_filler {
1853 |       type: "xavier"
1854 |     }
1855 |     bias_filler {
1856 |       type: "constant"
1857 |     }
1858 |   }
1859 | }
1860 | layer {
1861 |   name: "dropout_conv1_8"
1862 |   type: "Dropout"
1863 |   bottom: "conv1_8"
1864 |   top: "conv1_8"
1865 |   dropout_param {
1866 |     dropout_ratio: 0.3
1867 |   }
1868 | }
1869 | layer {
1870 |   name: "batch_conv1_8"
1871 |   type: "BatchNorm"
1872 |   bottom: "conv1_8"
1873 |   top: "conv1_8"
1874 |   param { lr_mult: 0 }
1875 |   param { lr_mult: 0 }
1876 |   param { lr_mult: 0 }
1877 | }
1878 | layer {
1879 |   name: "relu_conv1_8"
1880 |   type: "ReLU"
1881 |   bottom: "conv1_8"
1882 |   top: "conv1_8"
1883 | }
1884 | layer {
1885 |   name: "join_conv2_17_plus"
1886 |   type: "FractalJoin"
1887 |   bottom: "conv1_8"
1888 |   bottom: "conv2_17"
1889 |   top: "conv2_17_plus"
1890 |   fractal_join_param {
1891 |     drop_path_ratio: 0.15
1892 |   }
1893 | }
1894 | layer {
1895 |   bottom: "conv2_17_plus"
1896 |   top: "conv2_18"
1897 |   name: "conv2_18"
1898 |   param {
1899 |     lr_mult: 1
1900 |     decay_mult: 1
1901 |     name: "conv2_18"
1902 |   }
1903 |   param {
1904 |     lr_mult: 2
1905 |     decay_mult: 0
1906 |     name: "conv2_18_b"
1907 |   }
1908 |   type: "Convolution"
1909 |   convolution_param {
1910 |     num_output: 512
1911 |     pad: 1
1912 |     kernel_size: 3
1913 |     weight_filler {
1914 |       type: "xavier"
1915 |     }
1916 |     bias_filler {
1917 |       type: "constant"
1918 |     }
1919 |   }
1920 | }
1921 | layer {
1922 |   name: "dropout_conv2_18"
1923 |   type: "Dropout"
1924 |   bottom: "conv2_18"
1925 |   top: "conv2_18"
1926 |   dropout_param {
1927 |     dropout_ratio: 0.3
1928 |   }
1929 | }
1930 | layer {
1931 |   name: "batch_conv2_18"
1932 |   type: "BatchNorm"
1933 |   bottom: "conv2_18"
1934 |   top: "conv2_18"
1935 |   param { lr_mult: 0 }
1936 |   param { lr_mult: 0 }
1937 |   param { lr_mult: 0 }
1938 | }
1939 | layer {
1940 |   name: "relu_conv2_18"
1941 |   type: "ReLU"
1942 |   bottom: "conv2_18"
1943 |   top: "conv2_18"
1944 | }
1945 | layer {
1946 |   bottom: "conv2_18"
1947 |   top: "conv2_19"
1948 |   name: "conv2_19"
1949 |   param {
1950 |     lr_mult: 1
1951 |     decay_mult: 1
1952 |     name: "conv2_19"
1953 |   }
1954 |   param {
1955 |     lr_mult: 2
1956 |     decay_mult: 0
1957 |     name: "conv2_19_b"
1958 |   }
1959 |   type: "Convolution"
1960 |   convolution_param {
1961 |     num_output: 512
1962 |     pad: 1
1963 |     kernel_size: 3
1964 |     weight_filler {
1965 |       type: "xavier"
1966 |     }
1967 |     bias_filler {
1968 |       type: "constant"
1969 |     }
1970 |   }
1971 | }
1972 | layer {
1973 |   name: "dropout_conv2_19"
1974 |   type: "Dropout"
1975 |   bottom: "conv2_19"
1976 |   top: "conv2_19"
1977 |   dropout_param {
1978 |     dropout_ratio: 0.3
1979 |   }
1980 | }
1981 | layer {
1982 |   name: "batch_conv2_19"
1983 |   type: "BatchNorm"
1984 |   bottom: "conv2_19"
1985 |   top: "conv2_19"
1986 |   param { lr_mult: 0 }
1987 |   param { lr_mult: 0 }
1988 |   param { lr_mult: 0 }
1989 | }
1990 | layer {
1991 |   name: "relu_conv2_19"
1992 |   type: "ReLU"
1993 |   bottom: "conv2_19"
1994 |   top: "conv2_19"
1995 | }
1996 | layer {
1997 |   bottom: "conv2_17_plus"
1998 |   top: "conv1_9"
1999 |   name: "conv1_9"
2000 |   param {
2001 |     lr_mult: 1
2002 |     decay_mult: 1
2003 |     name: "conv1_9"
2004 |   }
2005 |   param {
2006 |     lr_mult: 2
2007 |     decay_mult: 0
2008 |     name: "conv1_9_b"
2009 |   }
2010 |   type: "Convolution"
2011 |   convolution_param {
2012 |     num_output: 512
2013 |     pad: 1
2014 |     kernel_size: 3
2015 |     weight_filler {
2016 |       type: "xavier"
2017 |     }
2018 |     bias_filler {
2019 |       type: "constant"
2020 |     }
2021 |   }
2022 | }
2023 | layer {
2024 |   name: "dropout_conv1_9"
2025 |   type: "Dropout"
2026 |   bottom: "conv1_9"
2027 |   top: "conv1_9"
2028 |   dropout_param {
2029 |     dropout_ratio: 0.3
2030 |   }
2031 | }
2032 | layer {
2033 |   name: "batch_conv1_9"
2034 |   type: "BatchNorm"
2035 |   bottom: "conv1_9"
2036 |   top: "conv1_9"
2037 |   param { lr_mult: 0 }
2038 |   param { lr_mult: 0 }
2039 |   param { lr_mult: 0 }
2040 | }
2041 | layer {
2042 |   name: "relu_conv1_9"
2043 |   type: "ReLU"
2044 |   bottom: "conv1_9"
2045 |   top: "conv1_9"
2046 | }
2047 | layer {
2048 |   bottom: "pool2_15_plus"
2049 |   top: "conv0_4"
2050 |   name: "conv0_4"
2051 |   param {
2052 |     lr_mult: 1
2053 |     decay_mult: 1
2054 |     name: "conv0_4"
2055 |   }
2056 |   param {
2057 |     lr_mult: 2
2058 |     decay_mult: 0
2059 |     name: "conv0_4_b"
2060 |   }
2061 |   type: "Convolution"
2062 |   convolution_param {
2063 |     num_output: 512
2064 |     pad: 1
2065 |     kernel_size: 3
2066 |     weight_filler {
2067 |       type: "xavier"
2068 |     }
2069 |     bias_filler {
2070 |       type: "constant"
2071 |     }
2072 |   }
2073 | }
2074 | layer {
2075 |   name: "dropout_conv0_4"
2076 |   type: "Dropout"
2077 |   bottom: "conv0_4"
2078 |   top: "conv0_4"
2079 |   dropout_param {
2080 |     dropout_ratio: 0.3
2081 |   }
2082 | }
2083 | layer {
2084 |   name: "batch_conv0_4"
2085 |   type: "BatchNorm"
2086 |   bottom: "conv0_4"
2087 |   top: "conv0_4"
2088 |   param { lr_mult: 0 }
2089 |   param { lr_mult: 0 }
2090 |   param { lr_mult: 0 }
2091 | }
2092 | layer {
2093 |   name: "relu_conv0_4"
2094 |   type: "ReLU"
2095 |   bottom: "conv0_4"
2096 |   top: "conv0_4"
2097 | }
2098 | layer {
2099 |   bottom: "conv0_4"
2100 |   top: "pool0_4"
2101 |   name: "pool0_4"
2102 |   type: "Pooling"
2103 |   pooling_param {
2104 |     pool: MAX
2105 |     kernel_size: 2
2106 |     stride: 2
2107 |   }
2108 | }
2109 | layer {
2110 |   bottom: "conv1_9"
2111 |   top: "pool1_9"
2112 |   name: "pool1_9"
2113 |   type: "Pooling"
2114 |   pooling_param {
2115 |     pool: MAX
2116 |     kernel_size: 2
2117 |     stride: 2
2118 |   }
2119 | }
2120 | layer {
2121 |   bottom: "conv2_19"
2122 |   top: "pool2_19"
2123 |   name: "pool2_19"
2124 |   type: "Pooling"
2125 |   pooling_param {
2126 |     pool: MAX
2127 |     kernel_size: 2
2128 |     stride: 2
2129 |   }
2130 | }
2131 | layer {
2132 |   name: "join_pool2_19_plus"
2133 |   type: "FractalJoin"
2134 |   bottom: "pool0_4"
2135 |   bottom: "pool1_9"
2136 |   bottom: "pool2_19"
2137 |   top: "pool2_19_plus"
2138 |   fractal_join_param {
2139 |     drop_path_ratio: 0.15
2140 |   }
2141 | }
2142 | # Reduction: 5, spatial size: 1
2143 | layer {
2144 |   bottom: "extra_mid_join"
2145 |   top: "conv2_20"
2146 |   name: "conv2_20"
2147 |   param {
2148 |     lr_mult: 1
2149 |     decay_mult: 1
2150 |     name: "conv2_20"
2151 |   }
2152 |   param {
2153 |     lr_mult: 2
2154 |     decay_mult: 0
2155 |     name: "conv2_20_b"
2156 |   }
2157 |   type: "Convolution"
2158 |   convolution_param {
2159 |     num_output: 512
2160 |     pad: 1
2161 |     kernel_size: 3
2162 |     weight_filler {
2163 |       type: "xavier"
2164 |     }
2165 |     bias_filler {
2166 |       type: "constant"
2167 |     }
2168 |   }
2169 | }
2170 | layer {
2171 |   name: "dropout_conv2_20"
2172 |   type: "Dropout"
2173 |   bottom: "conv2_20"
2174 |   top: "conv2_20"
2175 |   dropout_param {
2176 |     dropout_ratio: 0.2
2177 |   }
2178 | }
2179 | layer {
2180 |   name: "batch_conv2_20"
2181 |   type: "BatchNorm"
2182 |   bottom: "conv2_20"
2183 |   top: "conv2_20"
2184 |   param { lr_mult: 0 }
2185 |   param { lr_mult: 0 }
2186 |   param { lr_mult: 0 }
2187 | }
2188 | layer {
2189 |   name: "relu_conv2_20"
2190 |   type: "ReLU"
2191 |   bottom: "conv2_20"
2192 |   top: "conv2_20"
2193 | }
2194 | layer {
2195 |   bottom: "conv2_20"
2196 |   top: "conv2_21"
2197 |   name: "conv2_21"
2198 |   param {
2199 |     lr_mult: 1
2200 |     decay_mult: 1
2201 |     name: "conv2_21"
2202 |   }
2203 |   param {
2204 |     lr_mult: 2
2205 |     decay_mult: 0
2206 |     name: "conv2_21_b"
2207 |   }
2208 |   type: "Convolution"
2209 |   convolution_param {
2210 |     num_output: 512
2211 |     pad: 1
2212 |     kernel_size: 3
2213 |     weight_filler {
2214 |       type: "xavier"
2215 |     }
2216 |     bias_filler {
2217 |       type: "constant"
2218 |     }
2219 |   }
2220 | }
2221 | layer {
2222 |   name: "dropout_conv2_21"
2223 |   type: "Dropout"
2224 |   bottom: "conv2_21"
2225 |   top: "conv2_21"
2226 |   dropout_param {
2227 |     dropout_ratio: 0.2
2228 |   }
2229 | }
2230 | layer {
2231 |   name: "batch_conv2_21"
2232 |   type: "BatchNorm"
2233 |   bottom: "conv2_21"
2234 |   top: "conv2_21"
2235 |   param { lr_mult: 0 }
2236 |   param { lr_mult: 0 }
2237 |   param { lr_mult: 0 }
2238 | }
2239 | layer {
2240 |   name: "relu_conv2_21"
2241 |   type: "ReLU"
2242 |   bottom: "conv2_21"
2243 |   top: "conv2_21"
2244 | }
2245 | layer {
2246 |   bottom: "extra_mid_join"
2247 |   top: "conv1_10"
2248 |   name: "conv1_10"
2249 |   param {
2250 |     lr_mult: 1
2251 |     decay_mult: 1
2252 |     name: "conv1_10"
2253 |   }
2254 |   param {
2255 |     lr_mult: 2
2256 |     decay_mult: 0
2257 |     name: "conv1_10_b"
2258 |   }
2259 |   type: "Convolution"
2260 |   convolution_param {
2261 |     num_output: 512
2262 |     pad: 1
2263 |     kernel_size: 3
2264 |     weight_filler {
2265 |       type: "xavier"
2266 |     }
2267 |     bias_filler {
2268 |       type: "constant"
2269 |     }
2270 |   }
2271 | }
2272 | layer {
2273 |   name: "dropout_conv1_10"
2274 |   type: "Dropout"
2275 |   bottom: "conv1_10"
2276 |   top: "conv1_10"
2277 |   dropout_param {
2278 |     dropout_ratio: 0.2
2279 |   }
2280 | }
2281 | layer {
2282 |   name: "batch_conv1_10"
2283 |   type: "BatchNorm"
2284 |   bottom: "conv1_10"
2285 |   top: "conv1_10"
2286 |   param { lr_mult: 0 }
2287 |   param { lr_mult: 0 }
2288 |   param { lr_mult: 0 }
2289 | }
2290 | layer {
2291 |   name: "relu_conv1_10"
2292 |   type: "ReLU"
2293 |   bottom: "conv1_10"
2294 |   top: "conv1_10"
2295 | }
2296 | layer {
2297 |   name: "join_conv2_21_plus"
2298 |   type: "FractalJoin"
2299 |   bottom: "conv1_10"
2300 |   bottom: "conv2_21"
2301 |   top: "conv2_21_plus"
2302 |   fractal_join_param {
2303 |     drop_path_ratio: 0.15
2304 |   }
2305 | }
2306 | layer {
2307 |   bottom: "conv2_21_plus"
2308 |   top: "conv2_22"
2309 |   name: "conv2_22"
2310 |   param {
2311 |     lr_mult: 1
2312 |     decay_mult: 1
2313 |     name: "conv2_22"
2314 |   }
2315 |   param {
2316 |     lr_mult: 2
2317 |     decay_mult: 0
2318 |     name: "conv2_22_b"
2319 |   }
2320 |   type: "Convolution"
2321 |   convolution_param {
2322 |     num_output: 512
2323 |     pad: 1
2324 |     kernel_size: 3
2325 |     weight_filler {
2326 |       type: "xavier"
2327 |     }
2328 |     bias_filler {
2329 |       type: "constant"
2330 |     }
2331 |   }
2332 | }
2333 | layer {
2334 |   name: "dropout_conv2_22"
2335 |   type: "Dropout"
2336 |   bottom: "conv2_22"
2337 |   top: "conv2_22"
2338 |   dropout_param {
2339 |     dropout_ratio: 0.2
2340 |   }
2341 | }
2342 | layer {
2343 |   name: "batch_conv2_22"
2344 |   type: "BatchNorm"
2345 |   bottom: "conv2_22"
2346 |   top: "conv2_22"
2347 |   param { lr_mult: 0 }
2348 |   param { lr_mult: 0 }
2349 |   param { lr_mult: 0 }
2350 | }
2351 | layer {
2352 |   name: "relu_conv2_22"
2353 |   type: "ReLU"
2354 |   bottom: "conv2_22"
2355 |   top: "conv2_22"
2356 | }
2357 | layer {
2358 |   bottom: "conv2_22"
2359 |   top: "conv2_23"
2360 |   name: "conv2_23"
2361 |   param {
2362 |     lr_mult: 1
2363 |     decay_mult: 1
2364 |     name: "conv2_23"
2365 |   }
2366 |   param {
2367 |     lr_mult: 2
2368 |     decay_mult: 0
2369 |     name: "conv2_23_b"
2370 |   }
2371 |   type: "Convolution"
2372 |   convolution_param {
2373 |     num_output: 512
2374 |     pad: 1
2375 |     kernel_size: 3
2376 |     weight_filler {
2377 |       type: "xavier"
2378 |     }
2379 |     bias_filler {
2380 |       type: "constant"
2381 |     }
2382 |   }
2383 | }
2384 | layer {
2385 |   name: "dropout_conv2_23"
2386 |   type: "Dropout"
2387 |   bottom: "conv2_23"
2388 |   top: "conv2_23"
2389 |   dropout_param {
2390 |     dropout_ratio: 0.2
2391 |   }
2392 | }
2393 | layer {
2394 |   name: "batch_conv2_23"
2395 |   type: "BatchNorm"
2396 |   bottom: "conv2_23"
2397 |   top: "conv2_23"
2398 |   param { lr_mult: 0 }
2399 |   param { lr_mult: 0 }
2400 |   param { lr_mult: 0 }
2401 | }
2402 | layer {
2403 |   name: "relu_conv2_23"
2404 |   type: "ReLU"
2405 |   bottom: "conv2_23"
2406 |   top: "conv2_23"
2407 | }
2408 | layer {
2409 |   bottom: "conv2_21_plus"
2410 |   top: "conv1_11"
2411 |   name: "conv1_11"
2412 |   param {
2413 |     lr_mult: 1
2414 |     decay_mult: 1
2415 |     name: "conv1_11"
2416 |   }
2417 |   param {
2418 |     lr_mult: 2
2419 |     decay_mult: 0
2420 |     name: "conv1_11_b"
2421 |   }
2422 |   type: "Convolution"
2423 |   convolution_param {
2424 |     num_output: 512
2425 |     pad: 1
2426 |     kernel_size: 3
2427 |     weight_filler {
2428 |       type: "xavier"
2429 |     }
2430 |     bias_filler {
2431 |       type: "constant"
2432 |     }
2433 |   }
2434 | }
2435 | layer {
2436 |   name: "dropout_conv1_11"
2437 |   type: "Dropout"
2438 |   bottom: "conv1_11"
2439 |   top: "conv1_11"
2440 |   dropout_param {
2441 |     dropout_ratio: 0.2
2442 |   }
2443 | }
2444 | layer {
2445 |   name: "batch_conv1_11"
2446 |   type: "BatchNorm"
2447 |   bottom: "conv1_11"
2448 |   top: "conv1_11"
2449 |   param { lr_mult: 0 }
2450 |   param { lr_mult: 0 }
2451 |   param { lr_mult: 0 }
2452 | }
2453 | layer {
2454 |   name: "relu_conv1_11"
2455 |   type: "ReLU"
2456 |   bottom: "conv1_11"
2457 |   top: "conv1_11"
2458 | }
2459 | layer {
2460 |   bottom: "extra_mid_join"
2461 |   top: "conv0_5"
2462 |   name: "conv0_5"
2463 |   param {
2464 |     lr_mult: 1
2465 |     decay_mult: 1
2466 |     name: "conv0_5"
2467 |   }
2468 |   param {
2469 |     lr_mult: 2
2470 |     decay_mult: 0
2471 |     name: "conv0_5_b"
2472 |   }
2473 |   type: "Convolution"
2474 |   convolution_param {
2475 |     num_output: 512
2476 |     pad: 1
2477 |     kernel_size: 3
2478 |     weight_filler {
2479 |       type: "xavier"
2480 |     }
2481 |     bias_filler {
2482 |       type: "constant"
2483 |     }
2484 |   }
2485 | }
2486 | layer {
2487 |   name: "dropout_conv0_5"
2488 |   type: "Dropout"
2489 |   bottom: "conv0_5"
2490 |   top: "conv0_5"
2491 |   dropout_param {
2492 |     dropout_ratio: 0.2
2493 |   }
2494 | }
2495 | layer {
2496 |   name: "batch_conv0_5"
2497 |   type: "BatchNorm"
2498 |   bottom: "conv0_5"
2499 |   top: "conv0_5"
2500 |   param { lr_mult: 0 }
2501 |   param { lr_mult: 0 }
2502 |   param { lr_mult: 0 }
2503 | }
2504 | layer {
2505 |   name: "relu_conv0_5"
2506 |   type: "ReLU"
2507 |   bottom: "conv0_5"
2508 |   top: "conv0_5"
2509 | }
2510 | layer {
2511 |   bottom: "conv0_5"
2512 |   top: "pool0_5_0"
2513 |   name: "pool0_5_0"
2514 |   type: "Pooling"
2515 |   pooling_param {
2516 |     pool: MAX
2517 |     kernel_size: 2
2518 |     stride: 2
2519 |   }
2520 | }
2521 | layer {
2522 |   bottom: "pool0_5_0"
2523 |   top: "pool0_5_1"
2524 |   name: "pool0_5_1"
2525 |   type: "Pooling"
2526 |   pooling_param {
2527 |     pool: MAX
2528 |     kernel_size: 2
2529 |     stride: 2
2530 |   }
2531 | }
2532 | layer {
2533 |   bottom: "conv1_11"
2534 |   top: "pool1_11_0"
2535 |   name: "pool1_11_0"
2536 |   type: "Pooling"
2537 |   pooling_param {
2538 |     pool: MAX
2539 |     kernel_size: 2
2540 |     stride: 2
2541 |   }
2542 | }
2543 | layer {
2544 |   bottom: "pool1_11_0"
2545 |   top: "pool1_11_1"
2546 |   name: "pool1_11_1"
2547 |   type: "Pooling"
2548 |   pooling_param {
2549 |     pool: MAX
2550 |     kernel_size: 2
2551 |     stride: 2
2552 |   }
2553 | }
2554 | layer {
2555 |   bottom: "conv2_23"
2556 |   top: "pool2_23_0"
2557 |   name: "pool2_23_0"
2558 |   type: "Pooling"
2559 |   pooling_param {
2560 |     pool: MAX
2561 |     kernel_size: 2
2562 |     stride: 2
2563 |   }
2564 | }
2565 | layer {
2566 |   bottom: "pool2_23_0"
2567 |   top: "pool2_23_1"
2568 |   name: "pool2_23_1"
2569 |   type: "Pooling"
2570 |   pooling_param {
2571 |     pool: MAX
2572 |     kernel_size: 2
2573 |     stride: 2
2574 |   }
2575 | }
2576 | layer {
2577 |   name: "join_pool2_23_plus"
2578 |   type: "FractalJoin"
2579 |   bottom: "pool0_5_1"
2580 |   bottom: "pool1_11_1"
2581 |   bottom: "pool2_23_1"
2582 |   top: "pool2_23_plus"
2583 |   fractal_join_param {
2584 |     drop_path_ratio: 0.15
2585 |   }
2586 | }
2587 | # Reduction: 6, spatial size: 0
2588 | layer {
2589 |   bottom: "data"
2590 |   top: "conv2_24"
2591 |   name: "conv2_24"
2592 |   param {
2593 |     lr_mult: 1
2594 |     decay_mult: 1
2595 |     name: "conv2_24"
2596 |   }
2597 |   param {
2598 |     lr_mult: 2
2599 |     decay_mult: 0
2600 |     name: "conv2_24_b"
2601 |   }
2602 |   type: "Convolution"
2603 |   convolution_param {
2604 |     num_output: 512
2605 |     pad: 1
2606 |     kernel_size: 3
2607 |     weight_filler {
2608 |       type: "xavier"
2609 |     }
2610 |     bias_filler {
2611 |       type: "constant"
2612 |     }
2613 |   }
2614 | }
2615 | layer {
2616 |   name: "dropout_conv2_24"
2617 |   type: "Dropout"
2618 |   bottom: "conv2_24"
2619 |   top: "conv2_24"
2620 |   dropout_param {
2621 |     dropout_ratio: 0.0
2622 |   }
2623 | }
2624 | layer {
2625 |   name: "batch_conv2_24"
2626 |   type: "BatchNorm"
2627 |   bottom: "conv2_24"
2628 |   top: "conv2_24"
2629 |   param { lr_mult: 0 }
2630 |   param { lr_mult: 0 }
2631 |   param { lr_mult: 0 }
2632 | }
2633 | layer {
2634 |   name: "relu_conv2_24"
2635 |   type: "ReLU"
2636 |   bottom: "conv2_24"
2637 |   top: "conv2_24"
2638 | }
2639 | layer {
2640 |   bottom: "conv2_24"
2641 |   top: "conv2_25"
2642 |   name: "conv2_25"
2643 |   param {
2644 |     lr_mult: 1
2645 |     decay_mult: 1
2646 |     name: "conv2_25"
2647 |   }
2648 |   param {
2649 |     lr_mult: 2
2650 |     decay_mult: 0
2651 |     name: "conv2_25_b"
2652 |   }
2653 |   type: "Convolution"
2654 |   convolution_param {
2655 |     num_output: 512
2656 |     pad: 1
2657 |     kernel_size: 3
2658 |     weight_filler {
2659 |       type: "xavier"
2660 |     }
2661 |     bias_filler {
2662 |       type: "constant"
2663 |     }
2664 |   }
2665 | }
2666 | layer {
2667 |   name: "dropout_conv2_25"
2668 |   type: "Dropout"
2669 |   bottom: "conv2_25"
2670 |   top: "conv2_25"
2671 |   dropout_param {
2672 |     dropout_ratio: 0.0
2673 |   }
2674 | }
2675 | layer {
2676 |   name: "batch_conv2_25"
2677 |   type: "BatchNorm"
2678 |   bottom: "conv2_25"
2679 |   top: "conv2_25"
2680 |   param { lr_mult: 0 }
2681 |   param { lr_mult: 0 }
2682 |   param { lr_mult: 0 }
2683 | }
2684 | layer {
2685 |   name: "relu_conv2_25"
2686 |   type: "ReLU"
2687 |   bottom: "conv2_25"
2688 |   top: "conv2_25"
2689 | }
2690 | layer {
2691 |   bottom: "data"
2692 |   top: "conv1_12"
2693 |   name: "conv1_12"
2694 |   param {
2695 |     lr_mult: 1
2696 |     decay_mult: 1
2697 |     name: "conv1_12"
2698 |   }
2699 |   param {
2700 |     lr_mult: 2
2701 |     decay_mult: 0
2702 |     name: "conv1_12_b"
2703 |   }
2704 |   type: "Convolution"
2705 |   convolution_param {
2706 |     num_output: 512
2707 |     pad: 1
2708 |     kernel_size: 3
2709 |     weight_filler {
2710 |       type: "xavier"
2711 |     }
2712 |     bias_filler {
2713 |       type: "constant"
2714 |     }
2715 |   }
2716 | }
2717 | layer {
2718 |   name: "dropout_conv1_12"
2719 |   type: "Dropout"
2720 |   bottom: "conv1_12"
2721 |   top: "conv1_12"
2722 |   dropout_param {
2723 |     dropout_ratio: 0.0
2724 |   }
2725 | }
2726 | layer {
2727 |   name: "batch_conv1_12"
2728 |   type: "BatchNorm"
2729 |   bottom: "conv1_12"
2730 |   top: "conv1_12"
2731 |   param { lr_mult: 0 }
2732 |   param { lr_mult: 0 }
2733 |   param { lr_mult: 0 }
2734 | }
2735 | layer {
2736 |   name: "relu_conv1_12"
2737 |   type: "ReLU"
2738 |   bottom: "conv1_12"
2739 |   top: "conv1_12"
2740 | }
2741 | layer {
2742 |   name: "join_conv2_25_plus"
2743 |   type: "FractalJoin"
2744 |   bottom: "conv1_12"
2745 |   bottom: "conv2_25"
2746 |   top: "conv2_25_plus"
2747 |   fractal_join_param {
2748 |     drop_path_ratio: 0.15
2749 |   }
2750 | }
2751 | layer {
2752 |   bottom: "conv2_25_plus"
2753 |   top: "conv2_26"
2754 |   name: "conv2_26"
2755 |   param {
2756 |     lr_mult: 1
2757 |     decay_mult: 1
2758 |     name: "conv2_26"
2759 |   }
2760 |   param {
2761 |     lr_mult: 2
2762 |     decay_mult: 0
2763 |     name: "conv2_26_b"
2764 |   }
2765 |   type: "Convolution"
2766 |   convolution_param {
2767 |     num_output: 512
2768 |     pad: 1
2769 |     kernel_size: 3
2770 |     weight_filler {
2771 |       type: "xavier"
2772 |     }
2773 |     bias_filler {
2774 |       type: "constant"
2775 |     }
2776 |   }
2777 | }
2778 | layer {
2779 |   name: "dropout_conv2_26"
2780 |   type: "Dropout"
2781 |   bottom: "conv2_26"
2782 |   top: "conv2_26"
2783 |   dropout_param {
2784 |     dropout_ratio: 0.0
2785 |   }
2786 | }
2787 | layer {
2788 |   name: "batch_conv2_26"
2789 |   type: "BatchNorm"
2790 |   bottom: "conv2_26"
2791 |   top: "conv2_26"
2792 |   param { lr_mult: 0 }
2793 |   param { lr_mult: 0 }
2794 |   param { lr_mult: 0 }
2795 | }
2796 | layer {
2797 |   name: "relu_conv2_26"
2798 |   type: "ReLU"
2799 |   bottom: "conv2_26"
2800 |   top: "conv2_26"
2801 | }
2802 | layer {
2803 |   bottom: "conv2_26"
2804 |   top: "conv2_27"
2805 |   name: "conv2_27"
2806 |   param {
2807 |     lr_mult: 1
2808 |     decay_mult: 1
2809 |     name: "conv2_27"
2810 |   }
2811 |   param {
2812 |     lr_mult: 2
2813 |     decay_mult: 0
2814 |     name: "conv2_27_b"
2815 |   }
2816 |   type: "Convolution"
2817 |   convolution_param {
2818 |     num_output: 512
2819 |     pad: 1
2820 |     kernel_size: 3
2821 |     weight_filler {
2822 |       type: "xavier"
2823 |     }
2824 |     bias_filler {
2825 |       type: "constant"
2826 |     }
2827 |   }
2828 | }
2829 | layer {
2830 |   name: "dropout_conv2_27"
2831 |   type: "Dropout"
2832 |   bottom: "conv2_27"
2833 |   top: "conv2_27"
2834 |   dropout_param {
2835 |     dropout_ratio: 0.0
2836 |   }
2837 | }
2838 | layer {
2839 |   name: "batch_conv2_27"
2840 |   type: "BatchNorm"
2841 |   bottom: "conv2_27"
2842 |   top: "conv2_27"
2843 |   param { lr_mult: 0 }
2844 |   param { lr_mult: 0 }
2845 |   param { lr_mult: 0 }
2846 | }
2847 | layer {
2848 |   name: "relu_conv2_27"
2849 |   type: "ReLU"
2850 |   bottom: "conv2_27"
2851 |   top: "conv2_27"
2852 | }
2853 | layer {
2854 |   bottom: "conv2_25_plus"
2855 |   top: "conv1_13"
2856 |   name: "conv1_13"
2857 |   param {
2858 |     lr_mult: 1
2859 |     decay_mult: 1
2860 |     name: "conv1_13"
2861 |   }
2862 |   param {
2863 |     lr_mult: 2
2864 |     decay_mult: 0
2865 |     name: "conv1_13_b"
2866 |   }
2867 |   type: "Convolution"
2868 |   convolution_param {
2869 |     num_output: 512
2870 |     pad: 1
2871 |     kernel_size: 3
2872 |     weight_filler {
2873 |       type: "xavier"
2874 |     }
2875 |     bias_filler {
2876 |       type: "constant"
2877 |     }
2878 |   }
2879 | }
2880 | layer {
2881 |   name: "dropout_conv1_13"
2882 |   type: "Dropout"
2883 |   bottom: "conv1_13"
2884 |   top: "conv1_13"
2885 |   dropout_param {
2886 |     dropout_ratio: 0.0
2887 |   }
2888 | }
2889 | layer {
2890 |   name: "batch_conv1_13"
2891 |   type: "BatchNorm"
2892 |   bottom: "conv1_13"
2893 |   top: "conv1_13"
2894 |   param { lr_mult: 0 }
2895 |   param { lr_mult: 0 }
2896 |   param { lr_mult: 0 }
2897 | }
2898 | layer {
2899 |   name: "relu_conv1_13"
2900 |   type: "ReLU"
2901 |   bottom: "conv1_13"
2902 |   top: "conv1_13"
2903 | }
2904 | layer {
2905 |   bottom: "data"
2906 |   top: "conv0_6"
2907 |   name: "conv0_6"
2908 |   param {
2909 |     lr_mult: 1
2910 |     decay_mult: 1
2911 |     name: "conv0_6"
2912 |   }
2913 |   param {
2914 |     lr_mult: 2
2915 |     decay_mult: 0
2916 |     name: "conv0_6_b"
2917 |   }
2918 |   type: "Convolution"
2919 |   convolution_param {
2920 |     num_output: 512
2921 |     pad: 1
2922 |     kernel_size: 3
2923 |     weight_filler {
2924 |       type: "xavier"
2925 |     }
2926 |     bias_filler {
2927 |       type: "constant"
2928 |     }
2929 |   }
2930 | }
2931 | layer {
2932 |   name: "dropout_conv0_6"
2933 |   type: "Dropout"
2934 |   bottom: "conv0_6"
2935 |   top: "conv0_6"
2936 |   dropout_param {
2937 |     dropout_ratio: 0.0
2938 |   }
2939 | }
2940 | layer {
2941 |   name: "batch_conv0_6"
2942 |   type: "BatchNorm"
2943 |   bottom: "conv0_6"
2944 |   top: "conv0_6"
2945 |   param { lr_mult: 0 }
2946 |   param { lr_mult: 0 }
2947 |   param { lr_mult: 0 }
2948 | }
2949 | layer {
2950 |   name: "relu_conv0_6"
2951 |   type: "ReLU"
2952 |   bottom: "conv0_6"
2953 |   top: "conv0_6"
2954 | }
2955 | layer {
2956 |   bottom: "conv0_6"
2957 |   top: "pool0_6_0"
2958 |   name: "pool0_6_0"
2959 |   type: "Pooling"
2960 |   pooling_param {
2961 |     pool: MAX
2962 |     kernel_size: 2
2963 |     stride: 2
2964 |   }
2965 | }
2966 | layer {
2967 |   bottom: "pool0_6_0"
2968 |   top: "pool0_6_1"
2969 |   name: "pool0_6_1"
2970 |   type: "Pooling"
2971 |   pooling_param {
2972 |     pool: MAX
2973 |     kernel_size: 2
2974 |     stride: 2
2975 |   }
2976 | }
2977 | layer {
2978 |   bottom: "pool0_6_1"
2979 |   top: "pool0_6_2"
2980 |   name: "pool0_6_2"
2981 |   type: "Pooling"
2982 |   pooling_param {
2983 |     pool: MAX
2984 |     kernel_size: 2
2985 |     stride: 2
2986 |   }
2987 | }
2988 | layer {
2989 |   bottom: "pool0_6_2"
2990 |   top: "pool0_6_3"
2991 |   name: "pool0_6_3"
2992 |   type: "Pooling"
2993 |   pooling_param {
2994 |     pool: MAX
2995 |     kernel_size: 2
2996 |     stride: 2
2997 |   }
2998 | }
2999 | layer {
3000 |   bottom: "conv1_13"
3001 |   top: "pool1_13_0"
3002 |   name: "pool1_13_0"
3003 |   type: "Pooling"
3004 |   pooling_param {
3005 |     pool: MAX
3006 |     kernel_size: 2
3007 |     stride: 2
3008 |   }
3009 | }
3010 | layer {
3011 |   bottom: "pool1_13_0"
3012 |   top: "pool1_13_1"
3013 |   name: "pool1_13_1"
3014 |   type: "Pooling"
3015 |   pooling_param {
3016 |     pool: MAX
3017 |     kernel_size: 2
3018 |     stride: 2
3019 |   }
3020 | }
3021 | layer {
3022 |   bottom: "pool1_13_1"
3023 |   top: "pool1_13_2"
3024 |   name: "pool1_13_2"
3025 |   type: "Pooling"
3026 |   pooling_param {
3027 |     pool: MAX
3028 |     kernel_size: 2
3029 |     stride: 2
3030 |   }
3031 | }
3032 | layer {
3033 |   bottom: "pool1_13_2"
3034 |   top: "pool1_13_3"
3035 |   name: "pool1_13_3"
3036 |   type: "Pooling"
3037 |   pooling_param {
3038 |     pool: MAX
3039 |     kernel_size: 2
3040 |     stride: 2
3041 |   }
3042 | }
3043 | layer {
3044 |   bottom: "conv2_27"
3045 |   top: "pool2_27_0"
3046 |   name: "pool2_27_0"
3047 |   type: "Pooling"
3048 |   pooling_param {
3049 |     pool: MAX
3050 |     kernel_size: 2
3051 |     stride: 2
3052 |   }
3053 | }
3054 | layer {
3055 |   bottom: "pool2_27_0"
3056 |   top: "pool2_27_1"
3057 |   name: "pool2_27_1"
3058 |   type: "Pooling"
3059 |   pooling_param {
3060 |     pool: MAX
3061 |     kernel_size: 2
3062 |     stride: 2
3063 |   }
3064 | }
3065 | layer {
3066 |   bottom: "pool2_27_1"
3067 |   top: "pool2_27_2"
3068 |   name: "pool2_27_2"
3069 |   type: "Pooling"
3070 |   pooling_param {
3071 |     pool: MAX
3072 |     kernel_size: 2
3073 |     stride: 2
3074 |   }
3075 | }
3076 | layer {
3077 |   bottom: "pool2_27_2"
3078 |   top: "pool2_27_3"
3079 |   name: "pool2_27_3"
3080 |   type: "Pooling"
3081 |   pooling_param {
3082 |     pool: MAX
3083 |     kernel_size: 2
3084 |     stride: 2
3085 |   }
3086 | }
3087 | layer {
3088 |   name: "join_pool2_27_plus"
3089 |   type: "FractalJoin"
3090 |   bottom: "pool0_6_3"
3091 |   bottom: "pool1_13_3"
3092 |   bottom: "pool2_27_3"
3093 |   top: "pool2_27_plus"
3094 |   fractal_join_param {
3095 |     drop_path_ratio: 0.15
3096 |   }
3097 | }
3098 | layer {
3099 |   name: "join_extra_mid_join2"
3100 |   type: "FractalJoin"
3101 |   bottom: "pool2_23_plus"
3102 |   bottom: "pool2_19_plus"
3103 | #  bottom: "pool2_27_plus"
3104 |   top: "extra_mid_join2a"
3105 |   fractal_join_param {
3106 |     drop_path_ratio: 0.0
3107 |   }
3108 | }
3109 | layer {
3110 |   name: "freeze_drop_path"
3111 |   type: "FreezeDropPath"
3112 |   bottom: "pool2_27_plus"
3113 |   bottom: "extra_mid_join2a"
3114 |   top: "extra_mid_join2"
3115 |   freeze_drop_path_param {
3116 |     num_iter_per_cycle: 0
3117 |     interval_type: 0
3118 |   }
3119 | }
3120 | layer {
3121 |   name: "batch_extra_mid_join2"
3122 |   type: "BatchNorm"
3123 |   bottom: "extra_mid_join2"
3124 |   top: "extra_mid_join2"
3125 |   param { lr_mult: 0 }
3126 |   param { lr_mult: 0 }
3127 |   param { lr_mult: 0 }
3128 | }
3129 | 
3130 | # Reduction: 7, spatial size: 0
3131 | layer {
3132 |   name: "prediction0"
3133 |   type: "InnerProduct"
3134 |   bottom: "extra_mid_join2"
3135 |   top: "prediction0"
3136 |   param {
3137 |     lr_mult: 1
3138 |     decay_mult: 1
3139 |     name: "prediction0"
3140 |   }
3141 |   param {
3142 |     lr_mult: 2
3143 |     decay_mult: 0
3144 |     name: "prediction0_b"
3145 |   }
3146 |   inner_product_param {
3147 |     num_output: 10
3148 |     weight_filler {
3149 |       type: "xavier"
3150 |     }
3151 |     bias_filler {
3152 |       type: "constant"
3153 |     }
3154 |   }
3155 | }
3156 | layer {
3157 |   name: "loss0"
3158 |   type: "SoftmaxWithLoss"
3159 |   bottom: "prediction0"
3160 |   bottom: "label"
3161 |   top: "loss0"
3162 |   loss_weight: 1.0
3163 |   include: { phase: TRAIN }
3164 | }
3165 | 
3166 | layer {
3167 |   name: "accuracy_loss0"
3168 |   type: "Accuracy"
3169 |   bottom: "prediction0"
3170 |   bottom: "label"
3171 |   top: "accuracy_loss0"
3172 |   include: { phase: TEST }
3173 | }
3174 | 


--------------------------------------------------------------------------------
/TSN-FDP.prototxt:
--------------------------------------------------------------------------------
   1 | name: "FractalOfFractalNet"
   2 | layer {
   3 |   name: "cifar"
   4 |   type: "Data"
   5 |   top: "data"
   6 |   top: "label"
   7 |   include {
   8 |     phase: TRAIN
   9 |   }
  10 |   transform_param {
  11 |     mirror: true
  12 |     mean_file: "examples/cifar10/mean.binaryproto"
  13 |   }
  14 |   data_param {
  15 |     source: "examples/cifar10/cifar10_train_lmdb"
  16 |     batch_size: 25
  17 |     backend: LMDB
  18 |   }
  19 | }
  20 | layer {
  21 |   name: "cifar"
  22 |   type: "Data"
  23 |   top: "data"
  24 |   top: "label"
  25 |   include {
  26 |     phase: TEST
  27 |   }
  28 |   transform_param {
  29 |     mean_file: "examples/cifar10/mean.binaryproto"
  30 |     mirror: false
  31 | #    shuffle: true
  32 |   }
  33 |   data_param {
  34 |     source: "examples/cifar10/cifar10_test_lmdb"
  35 |     batch_size: 10
  36 |     backend: LMDB
  37 |   }
  38 | }
  39 | # Input size: 32
  40 | layer {
  41 |   bottom: "data"
  42 |   top: "conv2_0"
  43 |   name: "conv2_0"
  44 |   param {
  45 |     lr_mult: 1
  46 |     decay_mult: 1
  47 |     name: "conv2_0"
  48 |   }
  49 |   param {
  50 |     lr_mult: 2
  51 |     decay_mult: 0
  52 |     name: "conv2_0_b"
  53 |   }
  54 |   type: "Convolution"
  55 |   convolution_param {
  56 |     num_output: 64
  57 |     pad: 1
  58 |     kernel_size: 3
  59 |     weight_filler {
  60 |       type: "xavier"
  61 |     }
  62 |     bias_filler {
  63 |       type: "constant"
  64 |     }
  65 |   }
  66 | }
  67 | layer {
  68 |   name: "dropout_conv2_0"
  69 |   type: "Dropout"
  70 |   bottom: "conv2_0"
  71 |   top: "conv2_0"
  72 |   dropout_param {
  73 |     dropout_ratio: 0.0
  74 |   }
  75 | }
  76 | layer {
  77 |   name: "batch_conv2_0"
  78 |   type: "BatchNorm"
  79 |   bottom: "conv2_0"
  80 |   top: "conv2_0"
  81 |   param { lr_mult: 0 }
  82 |   param { lr_mult: 0 }
  83 |   param { lr_mult: 0 }
  84 | }
  85 | layer {
  86 |   name: "relu_conv2_0"
  87 |   type: "ReLU"
  88 |   bottom: "conv2_0"
  89 |   top: "conv2_0"
  90 | }
  91 | layer {
  92 |   bottom: "conv2_0"
  93 |   top: "conv2_1"
  94 |   name: "conv2_1"
  95 |   param {
  96 |     lr_mult: 1
  97 |     decay_mult: 1
  98 |     name: "conv2_1"
  99 |   }
 100 |   param {
 101 |     lr_mult: 2
 102 |     decay_mult: 0
 103 |     name: "conv2_1_b"
 104 |   }
 105 |   type: "Convolution"
 106 |   convolution_param {
 107 |     num_output: 64
 108 |     pad: 1
 109 |     kernel_size: 3
 110 |     weight_filler {
 111 |       type: "xavier"
 112 |     }
 113 |     bias_filler {
 114 |       type: "constant"
 115 |     }
 116 |   }
 117 | }
 118 | layer {
 119 |   name: "dropout_conv2_1"
 120 |   type: "Dropout"
 121 |   bottom: "conv2_1"
 122 |   top: "conv2_1"
 123 |   dropout_param {
 124 |     dropout_ratio: 0.0
 125 |   }
 126 | }
 127 | layer {
 128 |   name: "batch_conv2_1"
 129 |   type: "BatchNorm"
 130 |   bottom: "conv2_1"
 131 |   top: "conv2_1"
 132 |   param { lr_mult: 0 }
 133 |   param { lr_mult: 0 }
 134 |   param { lr_mult: 0 }
 135 | }
 136 | layer {
 137 |   name: "relu_conv2_1"
 138 |   type: "ReLU"
 139 |   bottom: "conv2_1"
 140 |   top: "conv2_1"
 141 | }
 142 | layer {
 143 |   bottom: "data"
 144 |   top: "conv1_0"
 145 |   name: "conv1_0"
 146 |   param {
 147 |     lr_mult: 1
 148 |     decay_mult: 1
 149 |     name: "conv1_0"
 150 |   }
 151 |   param {
 152 |     lr_mult: 2
 153 |     decay_mult: 0
 154 |     name: "conv1_0_b"
 155 |   }
 156 |   type: "Convolution"
 157 |   convolution_param {
 158 |     num_output: 64
 159 |     pad: 1
 160 |     kernel_size: 3
 161 |     weight_filler {
 162 |       type: "xavier"
 163 |     }
 164 |     bias_filler {
 165 |       type: "constant"
 166 |     }
 167 |   }
 168 | }
 169 | layer {
 170 |   name: "dropout_conv1_0"
 171 |   type: "Dropout"
 172 |   bottom: "conv1_0"
 173 |   top: "conv1_0"
 174 |   dropout_param {
 175 |     dropout_ratio: 0.0
 176 |   }
 177 | }
 178 | layer {
 179 |   name: "batch_conv1_0"
 180 |   type: "BatchNorm"
 181 |   bottom: "conv1_0"
 182 |   top: "conv1_0"
 183 |   param { lr_mult: 0 }
 184 |   param { lr_mult: 0 }
 185 |   param { lr_mult: 0 }
 186 | }
 187 | layer {
 188 |   name: "relu_conv1_0"
 189 |   type: "ReLU"
 190 |   bottom: "conv1_0"
 191 |   top: "conv1_0"
 192 | }
 193 | layer {
 194 |   name: "join_conv2_1_plus"
 195 |   type: "FractalJoin"
 196 |   bottom: "conv1_0"
 197 |   bottom: "conv2_1"
 198 |   top: "conv2_1_plus"
 199 |   fractal_join_param {
 200 |     drop_path_ratio: 0.15
 201 |   }
 202 | }
 203 | layer {
 204 |   bottom: "conv2_1_plus"
 205 |   top: "conv2_2"
 206 |   name: "conv2_2"
 207 |   param {
 208 |     lr_mult: 1
 209 |     decay_mult: 1
 210 |     name: "conv2_2"
 211 |   }
 212 |   param {
 213 |     lr_mult: 2
 214 |     decay_mult: 0
 215 |     name: "conv2_2_b"
 216 |   }
 217 |   type: "Convolution"
 218 |   convolution_param {
 219 |     num_output: 64
 220 |     pad: 1
 221 |     kernel_size: 3
 222 |     weight_filler {
 223 |       type: "xavier"
 224 |     }
 225 |     bias_filler {
 226 |       type: "constant"
 227 |     }
 228 |   }
 229 | }
 230 | layer {
 231 |   name: "dropout_conv2_2"
 232 |   type: "Dropout"
 233 |   bottom: "conv2_2"
 234 |   top: "conv2_2"
 235 |   dropout_param {
 236 |     dropout_ratio: 0.0
 237 |   }
 238 | }
 239 | layer {
 240 |   name: "batch_conv2_2"
 241 |   type: "BatchNorm"
 242 |   bottom: "conv2_2"
 243 |   top: "conv2_2"
 244 |   param { lr_mult: 0 }
 245 |   param { lr_mult: 0 }
 246 |   param { lr_mult: 0 }
 247 | }
 248 | layer {
 249 |   name: "relu_conv2_2"
 250 |   type: "ReLU"
 251 |   bottom: "conv2_2"
 252 |   top: "conv2_2"
 253 | }
 254 | layer {
 255 |   bottom: "conv2_2"
 256 |   top: "conv2_3"
 257 |   name: "conv2_3"
 258 |   param {
 259 |     lr_mult: 1
 260 |     decay_mult: 1
 261 |     name: "conv2_3"
 262 |   }
 263 |   param {
 264 |     lr_mult: 2
 265 |     decay_mult: 0
 266 |     name: "conv2_3_b"
 267 |   }
 268 |   type: "Convolution"
 269 |   convolution_param {
 270 |     num_output: 64
 271 |     pad: 1
 272 |     kernel_size: 3
 273 |     weight_filler {
 274 |       type: "xavier"
 275 |     }
 276 |     bias_filler {
 277 |       type: "constant"
 278 |     }
 279 |   }
 280 | }
 281 | layer {
 282 |   name: "dropout_conv2_3"
 283 |   type: "Dropout"
 284 |   bottom: "conv2_3"
 285 |   top: "conv2_3"
 286 |   dropout_param {
 287 |     dropout_ratio: 0.0
 288 |   }
 289 | }
 290 | layer {
 291 |   name: "batch_conv2_3"
 292 |   type: "BatchNorm"
 293 |   bottom: "conv2_3"
 294 |   top: "conv2_3"
 295 |   param { lr_mult: 0 }
 296 |   param { lr_mult: 0 }
 297 |   param { lr_mult: 0 }
 298 | }
 299 | layer {
 300 |   name: "relu_conv2_3"
 301 |   type: "ReLU"
 302 |   bottom: "conv2_3"
 303 |   top: "conv2_3"
 304 | }
 305 | layer {
 306 |   bottom: "conv2_1_plus"
 307 |   top: "conv1_1"
 308 |   name: "conv1_1"
 309 |   param {
 310 |     lr_mult: 1
 311 |     decay_mult: 1
 312 |     name: "conv1_1"
 313 |   }
 314 |   param {
 315 |     lr_mult: 2
 316 |     decay_mult: 0
 317 |     name: "conv1_1_b"
 318 |   }
 319 |   type: "Convolution"
 320 |   convolution_param {
 321 |     num_output: 64
 322 |     pad: 1
 323 |     kernel_size: 3
 324 |     weight_filler {
 325 |       type: "xavier"
 326 |     }
 327 |     bias_filler {
 328 |       type: "constant"
 329 |     }
 330 |   }
 331 | }
 332 | layer {
 333 |   name: "dropout_conv1_1"
 334 |   type: "Dropout"
 335 |   bottom: "conv1_1"
 336 |   top: "conv1_1"
 337 |   dropout_param {
 338 |     dropout_ratio: 0.0
 339 |   }
 340 | }
 341 | layer {
 342 |   name: "batch_conv1_1"
 343 |   type: "BatchNorm"
 344 |   bottom: "conv1_1"
 345 |   top: "conv1_1"
 346 |   param { lr_mult: 0 }
 347 |   param { lr_mult: 0 }
 348 |   param { lr_mult: 0 }
 349 | }
 350 | layer {
 351 |   name: "relu_conv1_1"
 352 |   type: "ReLU"
 353 |   bottom: "conv1_1"
 354 |   top: "conv1_1"
 355 | }
 356 | layer {
 357 |   bottom: "data"
 358 |   top: "conv0_0"
 359 |   name: "conv0_0"
 360 |   param {
 361 |     lr_mult: 1
 362 |     decay_mult: 1
 363 |     name: "conv0_0"
 364 |   }
 365 |   param {
 366 |     lr_mult: 2
 367 |     decay_mult: 0
 368 |     name: "conv0_0_b"
 369 |   }
 370 |   type: "Convolution"
 371 |   convolution_param {
 372 |     num_output: 64
 373 |     pad: 1
 374 |     kernel_size: 3
 375 |     weight_filler {
 376 |       type: "xavier"
 377 |     }
 378 |     bias_filler {
 379 |       type: "constant"
 380 |     }
 381 |   }
 382 | }
 383 | layer {
 384 |   name: "dropout_conv0_0"
 385 |   type: "Dropout"
 386 |   bottom: "conv0_0"
 387 |   top: "conv0_0"
 388 |   dropout_param {
 389 |     dropout_ratio: 0.0
 390 |   }
 391 | }
 392 | layer {
 393 |   name: "batch_conv0_0"
 394 |   type: "BatchNorm"
 395 |   bottom: "conv0_0"
 396 |   top: "conv0_0"
 397 |   param { lr_mult: 0 }
 398 |   param { lr_mult: 0 }
 399 |   param { lr_mult: 0 }
 400 | }
 401 | layer {
 402 |   name: "relu_conv0_0"
 403 |   type: "ReLU"
 404 |   bottom: "conv0_0"
 405 |   top: "conv0_0"
 406 | }
 407 | layer {
 408 |   bottom: "conv0_0"
 409 |   top: "pool0_0"
 410 |   name: "pool0_0"
 411 |   type: "Pooling"
 412 |   pooling_param {
 413 |     pool: MAX
 414 |     kernel_size: 2
 415 |     stride: 2
 416 |   }
 417 | }
 418 | layer {
 419 |   bottom: "conv1_1"
 420 |   top: "pool1_1"
 421 |   name: "pool1_1"
 422 |   type: "Pooling"
 423 |   pooling_param {
 424 |     pool: MAX
 425 |     kernel_size: 2
 426 |     stride: 2
 427 |   }
 428 | }
 429 | layer {
 430 |   bottom: "conv2_3"
 431 |   top: "pool2_3"
 432 |   name: "pool2_3"
 433 |   type: "Pooling"
 434 |   pooling_param {
 435 |     pool: MAX
 436 |     kernel_size: 2
 437 |     stride: 2
 438 |   }
 439 | }
 440 | layer {
 441 |   name: "join_pool2_3_plus"
 442 |   type: "FractalJoin"
 443 |   bottom: "pool0_0"
 444 |   bottom: "pool1_1"
 445 |   bottom: "pool2_3"
 446 |   top: "pool2_3_plus"
 447 |   fractal_join_param {
 448 |     drop_path_ratio: 0.15
 449 |   }
 450 | }
 451 | # Reduction: 1, spatial size: 16
 452 | layer {
 453 |   bottom: "pool2_3_plus"
 454 |   top: "conv2_4"
 455 |   name: "conv2_4"
 456 |   param {
 457 |     lr_mult: 1
 458 |     decay_mult: 1
 459 |     name: "conv2_4"
 460 |   }
 461 |   param {
 462 |     lr_mult: 2
 463 |     decay_mult: 0
 464 |     name: "conv2_4_b"
 465 |   }
 466 |   type: "Convolution"
 467 |   convolution_param {
 468 |     num_output: 128
 469 |     pad: 1
 470 |     kernel_size: 3
 471 |     weight_filler {
 472 |       type: "xavier"
 473 |     }
 474 |     bias_filler {
 475 |       type: "constant"
 476 |     }
 477 |   }
 478 | }
 479 | layer {
 480 |   name: "dropout_conv2_4"
 481 |   type: "Dropout"
 482 |   bottom: "conv2_4"
 483 |   top: "conv2_4"
 484 |   dropout_param {
 485 |     dropout_ratio: 0.1
 486 |   }
 487 | }
 488 | layer {
 489 |   name: "batch_conv2_4"
 490 |   type: "BatchNorm"
 491 |   bottom: "conv2_4"
 492 |   top: "conv2_4"
 493 |   param { lr_mult: 0 }
 494 |   param { lr_mult: 0 }
 495 |   param { lr_mult: 0 }
 496 | }
 497 | layer {
 498 |   name: "relu_conv2_4"
 499 |   type: "ReLU"
 500 |   bottom: "conv2_4"
 501 |   top: "conv2_4"
 502 | }
 503 | layer {
 504 |   bottom: "conv2_4"
 505 |   top: "conv2_5"
 506 |   name: "conv2_5"
 507 |   param {
 508 |     lr_mult: 1
 509 |     decay_mult: 1
 510 |     name: "conv2_5"
 511 |   }
 512 |   param {
 513 |     lr_mult: 2
 514 |     decay_mult: 0
 515 |     name: "conv2_5_b"
 516 |   }
 517 |   type: "Convolution"
 518 |   convolution_param {
 519 |     num_output: 128
 520 |     pad: 1
 521 |     kernel_size: 3
 522 |     weight_filler {
 523 |       type: "xavier"
 524 |     }
 525 |     bias_filler {
 526 |       type: "constant"
 527 |     }
 528 |   }
 529 | }
 530 | layer {
 531 |   name: "dropout_conv2_5"
 532 |   type: "Dropout"
 533 |   bottom: "conv2_5"
 534 |   top: "conv2_5"
 535 |   dropout_param {
 536 |     dropout_ratio: 0.1
 537 |   }
 538 | }
 539 | layer {
 540 |   name: "batch_conv2_5"
 541 |   type: "BatchNorm"
 542 |   bottom: "conv2_5"
 543 |   top: "conv2_5"
 544 |   param { lr_mult: 0 }
 545 |   param { lr_mult: 0 }
 546 |   param { lr_mult: 0 }
 547 | }
 548 | layer {
 549 |   name: "relu_conv2_5"
 550 |   type: "ReLU"
 551 |   bottom: "conv2_5"
 552 |   top: "conv2_5"
 553 | }
 554 | layer {
 555 |   bottom: "pool2_3_plus"
 556 |   top: "conv1_2"
 557 |   name: "conv1_2"
 558 |   param {
 559 |     lr_mult: 1
 560 |     decay_mult: 1
 561 |     name: "conv1_2"
 562 |   }
 563 |   param {
 564 |     lr_mult: 2
 565 |     decay_mult: 0
 566 |     name: "conv1_2_b"
 567 |   }
 568 |   type: "Convolution"
 569 |   convolution_param {
 570 |     num_output: 128
 571 |     pad: 1
 572 |     kernel_size: 3
 573 |     weight_filler {
 574 |       type: "xavier"
 575 |     }
 576 |     bias_filler {
 577 |       type: "constant"
 578 |     }
 579 |   }
 580 | }
 581 | layer {
 582 |   name: "dropout_conv1_2"
 583 |   type: "Dropout"
 584 |   bottom: "conv1_2"
 585 |   top: "conv1_2"
 586 |   dropout_param {
 587 |     dropout_ratio: 0.1
 588 |   }
 589 | }
 590 | layer {
 591 |   name: "batch_conv1_2"
 592 |   type: "BatchNorm"
 593 |   bottom: "conv1_2"
 594 |   top: "conv1_2"
 595 |   param { lr_mult: 0 }
 596 |   param { lr_mult: 0 }
 597 |   param { lr_mult: 0 }
 598 | }
 599 | layer {
 600 |   name: "relu_conv1_2"
 601 |   type: "ReLU"
 602 |   bottom: "conv1_2"
 603 |   top: "conv1_2"
 604 | }
 605 | layer {
 606 |   name: "join_conv2_5_plus"
 607 |   type: "FractalJoin"
 608 |   bottom: "conv1_2"
 609 |   bottom: "conv2_5"
 610 |   top: "conv2_5_plus"
 611 |   fractal_join_param {
 612 |     drop_path_ratio: 0.15
 613 |   }
 614 | }
 615 | layer {
 616 |   bottom: "conv2_5_plus"
 617 |   top: "conv2_6"
 618 |   name: "conv2_6"
 619 |   param {
 620 |     lr_mult: 1
 621 |     decay_mult: 1
 622 |     name: "conv2_6"
 623 |   }
 624 |   param {
 625 |     lr_mult: 2
 626 |     decay_mult: 0
 627 |     name: "conv2_6_b"
 628 |   }
 629 |   type: "Convolution"
 630 |   convolution_param {
 631 |     num_output: 128
 632 |     pad: 1
 633 |     kernel_size: 3
 634 |     weight_filler {
 635 |       type: "xavier"
 636 |     }
 637 |     bias_filler {
 638 |       type: "constant"
 639 |     }
 640 |   }
 641 | }
 642 | layer {
 643 |   name: "dropout_conv2_6"
 644 |   type: "Dropout"
 645 |   bottom: "conv2_6"
 646 |   top: "conv2_6"
 647 |   dropout_param {
 648 |     dropout_ratio: 0.1
 649 |   }
 650 | }
 651 | layer {
 652 |   name: "batch_conv2_6"
 653 |   type: "BatchNorm"
 654 |   bottom: "conv2_6"
 655 |   top: "conv2_6"
 656 |   param { lr_mult: 0 }
 657 |   param { lr_mult: 0 }
 658 |   param { lr_mult: 0 }
 659 | }
 660 | layer {
 661 |   name: "relu_conv2_6"
 662 |   type: "ReLU"
 663 |   bottom: "conv2_6"
 664 |   top: "conv2_6"
 665 | }
 666 | layer {
 667 |   bottom: "conv2_6"
 668 |   top: "conv2_7"
 669 |   name: "conv2_7"
 670 |   param {
 671 |     lr_mult: 1
 672 |     decay_mult: 1
 673 |     name: "conv2_7"
 674 |   }
 675 |   param {
 676 |     lr_mult: 2
 677 |     decay_mult: 0
 678 |     name: "conv2_7_b"
 679 |   }
 680 |   type: "Convolution"
 681 |   convolution_param {
 682 |     num_output: 128
 683 |     pad: 1
 684 |     kernel_size: 3
 685 |     weight_filler {
 686 |       type: "xavier"
 687 |     }
 688 |     bias_filler {
 689 |       type: "constant"
 690 |     }
 691 |   }
 692 | }
 693 | layer {
 694 |   name: "dropout_conv2_7"
 695 |   type: "Dropout"
 696 |   bottom: "conv2_7"
 697 |   top: "conv2_7"
 698 |   dropout_param {
 699 |     dropout_ratio: 0.1
 700 |   }
 701 | }
 702 | layer {
 703 |   name: "batch_conv2_7"
 704 |   type: "BatchNorm"
 705 |   bottom: "conv2_7"
 706 |   top: "conv2_7"
 707 |   param { lr_mult: 0 }
 708 |   param { lr_mult: 0 }
 709 |   param { lr_mult: 0 }
 710 | }
 711 | layer {
 712 |   name: "relu_conv2_7"
 713 |   type: "ReLU"
 714 |   bottom: "conv2_7"
 715 |   top: "conv2_7"
 716 | }
 717 | layer {
 718 |   bottom: "conv2_5_plus"
 719 |   top: "conv1_3"
 720 |   name: "conv1_3"
 721 |   param {
 722 |     lr_mult: 1
 723 |     decay_mult: 1
 724 |     name: "conv1_3"
 725 |   }
 726 |   param {
 727 |     lr_mult: 2
 728 |     decay_mult: 0
 729 |     name: "conv1_3_b"
 730 |   }
 731 |   type: "Convolution"
 732 |   convolution_param {
 733 |     num_output: 128
 734 |     pad: 1
 735 |     kernel_size: 3
 736 |     weight_filler {
 737 |       type: "xavier"
 738 |     }
 739 |     bias_filler {
 740 |       type: "constant"
 741 |     }
 742 |   }
 743 | }
 744 | layer {
 745 |   name: "dropout_conv1_3"
 746 |   type: "Dropout"
 747 |   bottom: "conv1_3"
 748 |   top: "conv1_3"
 749 |   dropout_param {
 750 |     dropout_ratio: 0.1
 751 |   }
 752 | }
 753 | layer {
 754 |   name: "batch_conv1_3"
 755 |   type: "BatchNorm"
 756 |   bottom: "conv1_3"
 757 |   top: "conv1_3"
 758 |   param { lr_mult: 0 }
 759 |   param { lr_mult: 0 }
 760 |   param { lr_mult: 0 }
 761 | }
 762 | layer {
 763 |   name: "relu_conv1_3"
 764 |   type: "ReLU"
 765 |   bottom: "conv1_3"
 766 |   top: "conv1_3"
 767 | }
 768 | layer {
 769 |   bottom: "pool2_3_plus"
 770 |   top: "conv0_1"
 771 |   name: "conv0_1"
 772 |   param {
 773 |     lr_mult: 1
 774 |     decay_mult: 1
 775 |     name: "conv0_1"
 776 |   }
 777 |   param {
 778 |     lr_mult: 2
 779 |     decay_mult: 0
 780 |     name: "conv0_1_b"
 781 |   }
 782 |   type: "Convolution"
 783 |   convolution_param {
 784 |     num_output: 128
 785 |     pad: 1
 786 |     kernel_size: 3
 787 |     weight_filler {
 788 |       type: "xavier"
 789 |     }
 790 |     bias_filler {
 791 |       type: "constant"
 792 |     }
 793 |   }
 794 | }
 795 | layer {
 796 |   name: "dropout_conv0_1"
 797 |   type: "Dropout"
 798 |   bottom: "conv0_1"
 799 |   top: "conv0_1"
 800 |   dropout_param {
 801 |     dropout_ratio: 0.1
 802 |   }
 803 | }
 804 | layer {
 805 |   name: "batch_conv0_1"
 806 |   type: "BatchNorm"
 807 |   bottom: "conv0_1"
 808 |   top: "conv0_1"
 809 |   param { lr_mult: 0 }
 810 |   param { lr_mult: 0 }
 811 |   param { lr_mult: 0 }
 812 | }
 813 | layer {
 814 |   name: "relu_conv0_1"
 815 |   type: "ReLU"
 816 |   bottom: "conv0_1"
 817 |   top: "conv0_1"
 818 | }
 819 | layer {
 820 |   bottom: "conv0_1"
 821 |   top: "pool0_1"
 822 |   name: "pool0_1"
 823 |   type: "Pooling"
 824 |   pooling_param {
 825 |     pool: MAX
 826 |     kernel_size: 2
 827 |     stride: 2
 828 |   }
 829 | }
 830 | layer {
 831 |   bottom: "conv1_3"
 832 |   top: "pool1_3"
 833 |   name: "pool1_3"
 834 |   type: "Pooling"
 835 |   pooling_param {
 836 |     pool: MAX
 837 |     kernel_size: 2
 838 |     stride: 2
 839 |   }
 840 | }
 841 | layer {
 842 |   bottom: "conv2_7"
 843 |   top: "pool2_7"
 844 |   name: "pool2_7"
 845 |   type: "Pooling"
 846 |   pooling_param {
 847 |     pool: MAX
 848 |     kernel_size: 2
 849 |     stride: 2
 850 |   }
 851 | }
 852 | layer {
 853 |   name: "join_pool2_7_plus"
 854 |   type: "FractalJoin"
 855 |   bottom: "pool0_1"
 856 |   bottom: "pool1_3"
 857 |   bottom: "pool2_7"
 858 |   top: "pool2_7_plus"
 859 |   fractal_join_param {
 860 |     drop_path_ratio: 0.15
 861 |   }
 862 | }
 863 | # Reduction: 2, spatial size: 8
 864 | layer {
 865 |   bottom: "data"
 866 |   top: "conv2_8"
 867 |   name: "conv2_8"
 868 |   param {
 869 |     lr_mult: 1
 870 |     decay_mult: 1
 871 |     name: "conv2_8"
 872 |   }
 873 |   param {
 874 |     lr_mult: 2
 875 |     decay_mult: 0
 876 |     name: "conv2_8_b"
 877 |   }
 878 |   type: "Convolution"
 879 |   convolution_param {
 880 |     num_output: 128
 881 |     pad: 1
 882 |     kernel_size: 3
 883 |     weight_filler {
 884 |       type: "xavier"
 885 |     }
 886 |     bias_filler {
 887 |       type: "constant"
 888 |     }
 889 |   }
 890 | }
 891 | layer {
 892 |   name: "dropout_conv2_8"
 893 |   type: "Dropout"
 894 |   bottom: "conv2_8"
 895 |   top: "conv2_8"
 896 |   dropout_param {
 897 |     dropout_ratio: 0.0
 898 |   }
 899 | }
 900 | layer {
 901 |   name: "batch_conv2_8"
 902 |   type: "BatchNorm"
 903 |   bottom: "conv2_8"
 904 |   top: "conv2_8"
 905 |   param { lr_mult: 0 }
 906 |   param { lr_mult: 0 }
 907 |   param { lr_mult: 0 }
 908 | }
 909 | layer {
 910 |   name: "relu_conv2_8"
 911 |   type: "ReLU"
 912 |   bottom: "conv2_8"
 913 |   top: "conv2_8"
 914 | }
 915 | layer {
 916 |   bottom: "conv2_8"
 917 |   top: "conv2_9"
 918 |   name: "conv2_9"
 919 |   param {
 920 |     lr_mult: 1
 921 |     decay_mult: 1
 922 |     name: "conv2_9"
 923 |   }
 924 |   param {
 925 |     lr_mult: 2
 926 |     decay_mult: 0
 927 |     name: "conv2_9_b"
 928 |   }
 929 |   type: "Convolution"
 930 |   convolution_param {
 931 |     num_output: 128
 932 |     pad: 1
 933 |     kernel_size: 3
 934 |     weight_filler {
 935 |       type: "xavier"
 936 |     }
 937 |     bias_filler {
 938 |       type: "constant"
 939 |     }
 940 |   }
 941 | }
 942 | layer {
 943 |   name: "dropout_conv2_9"
 944 |   type: "Dropout"
 945 |   bottom: "conv2_9"
 946 |   top: "conv2_9"
 947 |   dropout_param {
 948 |     dropout_ratio: 0.0
 949 |   }
 950 | }
 951 | layer {
 952 |   name: "batch_conv2_9"
 953 |   type: "BatchNorm"
 954 |   bottom: "conv2_9"
 955 |   top: "conv2_9"
 956 |   param { lr_mult: 0 }
 957 |   param { lr_mult: 0 }
 958 |   param { lr_mult: 0 }
 959 | }
 960 | layer {
 961 |   name: "relu_conv2_9"
 962 |   type: "ReLU"
 963 |   bottom: "conv2_9"
 964 |   top: "conv2_9"
 965 | }
 966 | layer {
 967 |   bottom: "data"
 968 |   top: "conv1_4"
 969 |   name: "conv1_4"
 970 |   param {
 971 |     lr_mult: 1
 972 |     decay_mult: 1
 973 |     name: "conv1_4"
 974 |   }
 975 |   param {
 976 |     lr_mult: 2
 977 |     decay_mult: 0
 978 |     name: "conv1_4_b"
 979 |   }
 980 |   type: "Convolution"
 981 |   convolution_param {
 982 |     num_output: 128
 983 |     pad: 1
 984 |     kernel_size: 3
 985 |     weight_filler {
 986 |       type: "xavier"
 987 |     }
 988 |     bias_filler {
 989 |       type: "constant"
 990 |     }
 991 |   }
 992 | }
 993 | layer {
 994 |   name: "dropout_conv1_4"
 995 |   type: "Dropout"
 996 |   bottom: "conv1_4"
 997 |   top: "conv1_4"
 998 |   dropout_param {
 999 |     dropout_ratio: 0.0
1000 |   }
1001 | }
1002 | layer {
1003 |   name: "batch_conv1_4"
1004 |   type: "BatchNorm"
1005 |   bottom: "conv1_4"
1006 |   top: "conv1_4"
1007 |   param { lr_mult: 0 }
1008 |   param { lr_mult: 0 }
1009 |   param { lr_mult: 0 }
1010 | }
1011 | layer {
1012 |   name: "relu_conv1_4"
1013 |   type: "ReLU"
1014 |   bottom: "conv1_4"
1015 |   top: "conv1_4"
1016 | }
1017 | layer {
1018 |   name: "join_conv2_9_plus"
1019 |   type: "FractalJoin"
1020 |   bottom: "conv1_4"
1021 |   bottom: "conv2_9"
1022 |   top: "conv2_9_plus"
1023 |   fractal_join_param {
1024 |     drop_path_ratio: 0.15
1025 |   }
1026 | }
1027 | layer {
1028 |   bottom: "conv2_9_plus"
1029 |   top: "conv2_10"
1030 |   name: "conv2_10"
1031 |   param {
1032 |     lr_mult: 1
1033 |     decay_mult: 1
1034 |     name: "conv2_10"
1035 |   }
1036 |   param {
1037 |     lr_mult: 2
1038 |     decay_mult: 0
1039 |     name: "conv2_10_b"
1040 |   }
1041 |   type: "Convolution"
1042 |   convolution_param {
1043 |     num_output: 128
1044 |     pad: 1
1045 |     kernel_size: 3
1046 |     weight_filler {
1047 |       type: "xavier"
1048 |     }
1049 |     bias_filler {
1050 |       type: "constant"
1051 |     }
1052 |   }
1053 | }
1054 | layer {
1055 |   name: "dropout_conv2_10"
1056 |   type: "Dropout"
1057 |   bottom: "conv2_10"
1058 |   top: "conv2_10"
1059 |   dropout_param {
1060 |     dropout_ratio: 0.0
1061 |   }
1062 | }
1063 | layer {
1064 |   name: "batch_conv2_10"
1065 |   type: "BatchNorm"
1066 |   bottom: "conv2_10"
1067 |   top: "conv2_10"
1068 |   param { lr_mult: 0 }
1069 |   param { lr_mult: 0 }
1070 |   param { lr_mult: 0 }
1071 | }
1072 | layer {
1073 |   name: "relu_conv2_10"
1074 |   type: "ReLU"
1075 |   bottom: "conv2_10"
1076 |   top: "conv2_10"
1077 | }
1078 | layer {
1079 |   bottom: "conv2_10"
1080 |   top: "conv2_11"
1081 |   name: "conv2_11"
1082 |   param {
1083 |     lr_mult: 1
1084 |     decay_mult: 1
1085 |     name: "conv2_11"
1086 |   }
1087 |   param {
1088 |     lr_mult: 2
1089 |     decay_mult: 0
1090 |     name: "conv2_11_b"
1091 |   }
1092 |   type: "Convolution"
1093 |   convolution_param {
1094 |     num_output: 128
1095 |     pad: 1
1096 |     kernel_size: 3
1097 |     weight_filler {
1098 |       type: "xavier"
1099 |     }
1100 |     bias_filler {
1101 |       type: "constant"
1102 |     }
1103 |   }
1104 | }
1105 | layer {
1106 |   name: "dropout_conv2_11"
1107 |   type: "Dropout"
1108 |   bottom: "conv2_11"
1109 |   top: "conv2_11"
1110 |   dropout_param {
1111 |     dropout_ratio: 0.0
1112 |   }
1113 | }
1114 | layer {
1115 |   name: "batch_conv2_11"
1116 |   type: "BatchNorm"
1117 |   bottom: "conv2_11"
1118 |   top: "conv2_11"
1119 |   param { lr_mult: 0 }
1120 |   param { lr_mult: 0 }
1121 |   param { lr_mult: 0 }
1122 | }
1123 | layer {
1124 |   name: "relu_conv2_11"
1125 |   type: "ReLU"
1126 |   bottom: "conv2_11"
1127 |   top: "conv2_11"
1128 | }
1129 | layer {
1130 |   bottom: "conv2_9_plus"
1131 |   top: "conv1_5"
1132 |   name: "conv1_5"
1133 |   param {
1134 |     lr_mult: 1
1135 |     decay_mult: 1
1136 |     name: "conv1_5"
1137 |   }
1138 |   param {
1139 |     lr_mult: 2
1140 |     decay_mult: 0
1141 |     name: "conv1_5_b"
1142 |   }
1143 |   type: "Convolution"
1144 |   convolution_param {
1145 |     num_output: 128
1146 |     pad: 1
1147 |     kernel_size: 3
1148 |     weight_filler {
1149 |       type: "xavier"
1150 |     }
1151 |     bias_filler {
1152 |       type: "constant"
1153 |     }
1154 |   }
1155 | }
1156 | layer {
1157 |   name: "dropout_conv1_5"
1158 |   type: "Dropout"
1159 |   bottom: "conv1_5"
1160 |   top: "conv1_5"
1161 |   dropout_param {
1162 |     dropout_ratio: 0.0
1163 |   }
1164 | }
1165 | layer {
1166 |   name: "batch_conv1_5"
1167 |   type: "BatchNorm"
1168 |   bottom: "conv1_5"
1169 |   top: "conv1_5"
1170 |   param { lr_mult: 0 }
1171 |   param { lr_mult: 0 }
1172 |   param { lr_mult: 0 }
1173 | }
1174 | layer {
1175 |   name: "relu_conv1_5"
1176 |   type: "ReLU"
1177 |   bottom: "conv1_5"
1178 |   top: "conv1_5"
1179 | }
1180 | layer {
1181 |   bottom: "data"
1182 |   top: "conv0_2"
1183 |   name: "conv0_2"
1184 |   param {
1185 |     lr_mult: 1
1186 |     decay_mult: 1
1187 |     name: "conv0_2"
1188 |   }
1189 |   param {
1190 |     lr_mult: 2
1191 |     decay_mult: 0
1192 |     name: "conv0_2_b"
1193 |   }
1194 |   type: "Convolution"
1195 |   convolution_param {
1196 |     num_output: 128
1197 |     pad: 1
1198 |     kernel_size: 3
1199 |     weight_filler {
1200 |       type: "xavier"
1201 |     }
1202 |     bias_filler {
1203 |       type: "constant"
1204 |     }
1205 |   }
1206 | }
1207 | layer {
1208 |   name: "dropout_conv0_2"
1209 |   type: "Dropout"
1210 |   bottom: "conv0_2"
1211 |   top: "conv0_2"
1212 |   dropout_param {
1213 |     dropout_ratio: 0.0
1214 |   }
1215 | }
1216 | layer {
1217 |   name: "batch_conv0_2"
1218 |   type: "BatchNorm"
1219 |   bottom: "conv0_2"
1220 |   top: "conv0_2"
1221 |   param { lr_mult: 0 }
1222 |   param { lr_mult: 0 }
1223 |   param { lr_mult: 0 }
1224 | }
1225 | layer {
1226 |   name: "relu_conv0_2"
1227 |   type: "ReLU"
1228 |   bottom: "conv0_2"
1229 |   top: "conv0_2"
1230 | }
1231 | layer {
1232 |   bottom: "conv0_2"
1233 |   top: "pool0_2_0"
1234 |   name: "pool0_2_0"
1235 |   type: "Pooling"
1236 |   pooling_param {
1237 |     pool: MAX
1238 |     kernel_size: 2
1239 |     stride: 2
1240 |   }
1241 | }
1242 | layer {
1243 |   bottom: "pool0_2_0"
1244 |   top: "pool0_2_1"
1245 |   name: "pool0_2_1"
1246 |   type: "Pooling"
1247 |   pooling_param {
1248 |     pool: MAX
1249 |     kernel_size: 2
1250 |     stride: 2
1251 |   }
1252 | }
1253 | layer {
1254 |   bottom: "conv1_5"
1255 |   top: "pool1_5_0"
1256 |   name: "pool1_5_0"
1257 |   type: "Pooling"
1258 |   pooling_param {
1259 |     pool: MAX
1260 |     kernel_size: 2
1261 |     stride: 2
1262 |   }
1263 | }
1264 | layer {
1265 |   bottom: "pool1_5_0"
1266 |   top: "pool1_5_1"
1267 |   name: "pool1_5_1"
1268 |   type: "Pooling"
1269 |   pooling_param {
1270 |     pool: MAX
1271 |     kernel_size: 2
1272 |     stride: 2
1273 |   }
1274 | }
1275 | layer {
1276 |   bottom: "conv2_11"
1277 |   top: "pool2_11_0"
1278 |   name: "pool2_11_0"
1279 |   type: "Pooling"
1280 |   pooling_param {
1281 |     pool: MAX
1282 |     kernel_size: 2
1283 |     stride: 2
1284 |   }
1285 | }
1286 | layer {
1287 |   bottom: "pool2_11_0"
1288 |   top: "pool2_11_1"
1289 |   name: "pool2_11_1"
1290 |   type: "Pooling"
1291 |   pooling_param {
1292 |     pool: MAX
1293 |     kernel_size: 2
1294 |     stride: 2
1295 |   }
1296 | }
1297 | layer {
1298 |   name: "join_pool2_11_plus"
1299 |   type: "FractalJoin"
1300 |   bottom: "pool0_2_1"
1301 |   bottom: "pool1_5_1"
1302 |   bottom: "pool2_11_1"
1303 |   top: "pool2_11_plus"
1304 |   fractal_join_param {
1305 |     drop_path_ratio: 0.15
1306 |   }
1307 | }
1308 | layer {
1309 |   name: "join_extra_mid_join"
1310 |   type: "FractalJoin"
1311 |   bottom: "pool2_7_plus"
1312 |   bottom: "pool2_11_plus"
1313 |   top: "extra_mid_join"
1314 |   fractal_join_param {
1315 |     drop_path_ratio: 0.15
1316 |   }
1317 | }
1318 | # Reduction: 3, spatial size: 4
1319 | layer {
1320 |   bottom: "extra_mid_join"
1321 |   top: "conv2_12"
1322 |   name: "conv2_12"
1323 |   param {
1324 |     lr_mult: 1
1325 |     decay_mult: 1
1326 |     name: "conv2_12"
1327 |   }
1328 |   param {
1329 |     lr_mult: 2
1330 |     decay_mult: 0
1331 |     name: "conv2_12_b"
1332 |   }
1333 |   type: "Convolution"
1334 |   convolution_param {
1335 |     num_output: 256
1336 |     pad: 1
1337 |     kernel_size: 3
1338 |     weight_filler {
1339 |       type: "xavier"
1340 |     }
1341 |     bias_filler {
1342 |       type: "constant"
1343 |     }
1344 |   }
1345 | }
1346 | layer {
1347 |   name: "dropout_conv2_12"
1348 |   type: "Dropout"
1349 |   bottom: "conv2_12"
1350 |   top: "conv2_12"
1351 |   dropout_param {
1352 |     dropout_ratio: 0.2
1353 |   }
1354 | }
1355 | layer {
1356 |   name: "batch_conv2_12"
1357 |   type: "BatchNorm"
1358 |   bottom: "conv2_12"
1359 |   top: "conv2_12"
1360 |   param { lr_mult: 0 }
1361 |   param { lr_mult: 0 }
1362 |   param { lr_mult: 0 }
1363 | }
1364 | layer {
1365 |   name: "relu_conv2_12"
1366 |   type: "ReLU"
1367 |   bottom: "conv2_12"
1368 |   top: "conv2_12"
1369 | }
1370 | layer {
1371 |   bottom: "conv2_12"
1372 |   top: "conv2_13"
1373 |   name: "conv2_13"
1374 |   param {
1375 |     lr_mult: 1
1376 |     decay_mult: 1
1377 |     name: "conv2_13"
1378 |   }
1379 |   param {
1380 |     lr_mult: 2
1381 |     decay_mult: 0
1382 |     name: "conv2_13_b"
1383 |   }
1384 |   type: "Convolution"
1385 |   convolution_param {
1386 |     num_output: 256
1387 |     pad: 1
1388 |     kernel_size: 3
1389 |     weight_filler {
1390 |       type: "xavier"
1391 |     }
1392 |     bias_filler {
1393 |       type: "constant"
1394 |     }
1395 |   }
1396 | }
1397 | layer {
1398 |   name: "dropout_conv2_13"
1399 |   type: "Dropout"
1400 |   bottom: "conv2_13"
1401 |   top: "conv2_13"
1402 |   dropout_param {
1403 |     dropout_ratio: 0.2
1404 |   }
1405 | }
1406 | layer {
1407 |   name: "batch_conv2_13"
1408 |   type: "BatchNorm"
1409 |   bottom: "conv2_13"
1410 |   top: "conv2_13"
1411 |   param { lr_mult: 0 }
1412 |   param { lr_mult: 0 }
1413 |   param { lr_mult: 0 }
1414 | }
1415 | layer {
1416 |   name: "relu_conv2_13"
1417 |   type: "ReLU"
1418 |   bottom: "conv2_13"
1419 |   top: "conv2_13"
1420 | }
1421 | layer {
1422 |   bottom: "extra_mid_join"
1423 |   top: "conv1_6"
1424 |   name: "conv1_6"
1425 |   param {
1426 |     lr_mult: 1
1427 |     decay_mult: 1
1428 |     name: "conv1_6"
1429 |   }
1430 |   param {
1431 |     lr_mult: 2
1432 |     decay_mult: 0
1433 |     name: "conv1_6_b"
1434 |   }
1435 |   type: "Convolution"
1436 |   convolution_param {
1437 |     num_output: 256
1438 |     pad: 1
1439 |     kernel_size: 3
1440 |     weight_filler {
1441 |       type: "xavier"
1442 |     }
1443 |     bias_filler {
1444 |       type: "constant"
1445 |     }
1446 |   }
1447 | }
1448 | layer {
1449 |   name: "dropout_conv1_6"
1450 |   type: "Dropout"
1451 |   bottom: "conv1_6"
1452 |   top: "conv1_6"
1453 |   dropout_param {
1454 |     dropout_ratio: 0.2
1455 |   }
1456 | }
1457 | layer {
1458 |   name: "batch_conv1_6"
1459 |   type: "BatchNorm"
1460 |   bottom: "conv1_6"
1461 |   top: "conv1_6"
1462 |   param { lr_mult: 0 }
1463 |   param { lr_mult: 0 }
1464 |   param { lr_mult: 0 }
1465 | }
1466 | layer {
1467 |   name: "relu_conv1_6"
1468 |   type: "ReLU"
1469 |   bottom: "conv1_6"
1470 |   top: "conv1_6"
1471 | }
1472 | layer {
1473 |   name: "join_conv2_13_plus"
1474 |   type: "FractalJoin"
1475 |   bottom: "conv1_6"
1476 |   bottom: "conv2_13"
1477 |   top: "conv2_13_plus"
1478 |   fractal_join_param {
1479 |     drop_path_ratio: 0.15
1480 |   }
1481 | }
1482 | layer {
1483 |   bottom: "conv2_13_plus"
1484 |   top: "conv2_14"
1485 |   name: "conv2_14"
1486 |   param {
1487 |     lr_mult: 1
1488 |     decay_mult: 1
1489 |     name: "conv2_14"
1490 |   }
1491 |   param {
1492 |     lr_mult: 2
1493 |     decay_mult: 0
1494 |     name: "conv2_14_b"
1495 |   }
1496 |   type: "Convolution"
1497 |   convolution_param {
1498 |     num_output: 256
1499 |     pad: 1
1500 |     kernel_size: 3
1501 |     weight_filler {
1502 |       type: "xavier"
1503 |     }
1504 |     bias_filler {
1505 |       type: "constant"
1506 |     }
1507 |   }
1508 | }
1509 | layer {
1510 |   name: "dropout_conv2_14"
1511 |   type: "Dropout"
1512 |   bottom: "conv2_14"
1513 |   top: "conv2_14"
1514 |   dropout_param {
1515 |     dropout_ratio: 0.2
1516 |   }
1517 | }
1518 | layer {
1519 |   name: "batch_conv2_14"
1520 |   type: "BatchNorm"
1521 |   bottom: "conv2_14"
1522 |   top: "conv2_14"
1523 |   param { lr_mult: 0 }
1524 |   param { lr_mult: 0 }
1525 |   param { lr_mult: 0 }
1526 | }
1527 | layer {
1528 |   name: "relu_conv2_14"
1529 |   type: "ReLU"
1530 |   bottom: "conv2_14"
1531 |   top: "conv2_14"
1532 | }
1533 | layer {
1534 |   bottom: "conv2_14"
1535 |   top: "conv2_15"
1536 |   name: "conv2_15"
1537 |   param {
1538 |     lr_mult: 1
1539 |     decay_mult: 1
1540 |     name: "conv2_15"
1541 |   }
1542 |   param {
1543 |     lr_mult: 2
1544 |     decay_mult: 0
1545 |     name: "conv2_15_b"
1546 |   }
1547 |   type: "Convolution"
1548 |   convolution_param {
1549 |     num_output: 256
1550 |     pad: 1
1551 |     kernel_size: 3
1552 |     weight_filler {
1553 |       type: "xavier"
1554 |     }
1555 |     bias_filler {
1556 |       type: "constant"
1557 |     }
1558 |   }
1559 | }
1560 | layer {
1561 |   name: "dropout_conv2_15"
1562 |   type: "Dropout"
1563 |   bottom: "conv2_15"
1564 |   top: "conv2_15"
1565 |   dropout_param {
1566 |     dropout_ratio: 0.2
1567 |   }
1568 | }
1569 | layer {
1570 |   name: "batch_conv2_15"
1571 |   type: "BatchNorm"
1572 |   bottom: "conv2_15"
1573 |   top: "conv2_15"
1574 |   param { lr_mult: 0 }
1575 |   param { lr_mult: 0 }
1576 |   param { lr_mult: 0 }
1577 | }
1578 | layer {
1579 |   name: "relu_conv2_15"
1580 |   type: "ReLU"
1581 |   bottom: "conv2_15"
1582 |   top: "conv2_15"
1583 | }
1584 | layer {
1585 |   bottom: "conv2_13_plus"
1586 |   top: "conv1_7"
1587 |   name: "conv1_7"
1588 |   param {
1589 |     lr_mult: 1
1590 |     decay_mult: 1
1591 |     name: "conv1_7"
1592 |   }
1593 |   param {
1594 |     lr_mult: 2
1595 |     decay_mult: 0
1596 |     name: "conv1_7_b"
1597 |   }
1598 |   type: "Convolution"
1599 |   convolution_param {
1600 |     num_output: 256
1601 |     pad: 1
1602 |     kernel_size: 3
1603 |     weight_filler {
1604 |       type: "xavier"
1605 |     }
1606 |     bias_filler {
1607 |       type: "constant"
1608 |     }
1609 |   }
1610 | }
1611 | layer {
1612 |   name: "dropout_conv1_7"
1613 |   type: "Dropout"
1614 |   bottom: "conv1_7"
1615 |   top: "conv1_7"
1616 |   dropout_param {
1617 |     dropout_ratio: 0.2
1618 |   }
1619 | }
1620 | layer {
1621 |   name: "batch_conv1_7"
1622 |   type: "BatchNorm"
1623 |   bottom: "conv1_7"
1624 |   top: "conv1_7"
1625 |   param { lr_mult: 0 }
1626 |   param { lr_mult: 0 }
1627 |   param { lr_mult: 0 }
1628 | }
1629 | layer {
1630 |   name: "relu_conv1_7"
1631 |   type: "ReLU"
1632 |   bottom: "conv1_7"
1633 |   top: "conv1_7"
1634 | }
1635 | layer {
1636 |   bottom: "extra_mid_join"
1637 |   top: "conv0_3"
1638 |   name: "conv0_3"
1639 |   param {
1640 |     lr_mult: 1
1641 |     decay_mult: 1
1642 |     name: "conv0_3"
1643 |   }
1644 |   param {
1645 |     lr_mult: 2
1646 |     decay_mult: 0
1647 |     name: "conv0_3_b"
1648 |   }
1649 |   type: "Convolution"
1650 |   convolution_param {
1651 |     num_output: 256
1652 |     pad: 1
1653 |     kernel_size: 3
1654 |     weight_filler {
1655 |       type: "xavier"
1656 |     }
1657 |     bias_filler {
1658 |       type: "constant"
1659 |     }
1660 |   }
1661 | }
1662 | layer {
1663 |   name: "dropout_conv0_3"
1664 |   type: "Dropout"
1665 |   bottom: "conv0_3"
1666 |   top: "conv0_3"
1667 |   dropout_param {
1668 |     dropout_ratio: 0.2
1669 |   }
1670 | }
1671 | layer {
1672 |   name: "batch_conv0_3"
1673 |   type: "BatchNorm"
1674 |   bottom: "conv0_3"
1675 |   top: "conv0_3"
1676 |   param { lr_mult: 0 }
1677 |   param { lr_mult: 0 }
1678 |   param { lr_mult: 0 }
1679 | }
1680 | layer {
1681 |   name: "relu_conv0_3"
1682 |   type: "ReLU"
1683 |   bottom: "conv0_3"
1684 |   top: "conv0_3"
1685 | }
1686 | layer {
1687 |   bottom: "conv0_3"
1688 |   top: "pool0_3"
1689 |   name: "pool0_3"
1690 |   type: "Pooling"
1691 |   pooling_param {
1692 |     pool: MAX
1693 |     kernel_size: 2
1694 |     stride: 2
1695 |   }
1696 | }
1697 | layer {
1698 |   bottom: "conv1_7"
1699 |   top: "pool1_7"
1700 |   name: "pool1_7"
1701 |   type: "Pooling"
1702 |   pooling_param {
1703 |     pool: MAX
1704 |     kernel_size: 2
1705 |     stride: 2
1706 |   }
1707 | }
1708 | layer {
1709 |   bottom: "conv2_15"
1710 |   top: "pool2_15"
1711 |   name: "pool2_15"
1712 |   type: "Pooling"
1713 |   pooling_param {
1714 |     pool: MAX
1715 |     kernel_size: 2
1716 |     stride: 2
1717 |   }
1718 | }
1719 | layer {
1720 |   name: "join_pool2_15_plus"
1721 |   type: "FractalJoin"
1722 |   bottom: "pool0_3"
1723 |   bottom: "pool1_7"
1724 |   bottom: "pool2_15"
1725 |   top: "pool2_15_plus"
1726 |   fractal_join_param {
1727 |     drop_path_ratio: 0.15
1728 |   }
1729 | }
1730 | # Reduction: 4, spatial size: 2
1731 | layer {
1732 |   bottom: "pool2_15_plus"
1733 |   top: "conv2_16"
1734 |   name: "conv2_16"
1735 |   param {
1736 |     lr_mult: 1
1737 |     decay_mult: 1
1738 |     name: "conv2_16"
1739 |   }
1740 |   param {
1741 |     lr_mult: 2
1742 |     decay_mult: 0
1743 |     name: "conv2_16_b"
1744 |   }
1745 |   type: "Convolution"
1746 |   convolution_param {
1747 |     num_output: 512
1748 |     pad: 1
1749 |     kernel_size: 3
1750 |     weight_filler {
1751 |       type: "xavier"
1752 |     }
1753 |     bias_filler {
1754 |       type: "constant"
1755 |     }
1756 |   }
1757 | }
1758 | layer {
1759 |   name: "dropout_conv2_16"
1760 |   type: "Dropout"
1761 |   bottom: "conv2_16"
1762 |   top: "conv2_16"
1763 |   dropout_param {
1764 |     dropout_ratio: 0.3
1765 |   }
1766 | }
1767 | layer {
1768 |   name: "batch_conv2_16"
1769 |   type: "BatchNorm"
1770 |   bottom: "conv2_16"
1771 |   top: "conv2_16"
1772 |   param { lr_mult: 0 }
1773 |   param { lr_mult: 0 }
1774 |   param { lr_mult: 0 }
1775 | }
1776 | layer {
1777 |   name: "relu_conv2_16"
1778 |   type: "ReLU"
1779 |   bottom: "conv2_16"
1780 |   top: "conv2_16"
1781 | }
1782 | layer {
1783 |   bottom: "conv2_16"
1784 |   top: "conv2_17"
1785 |   name: "conv2_17"
1786 |   param {
1787 |     lr_mult: 1
1788 |     decay_mult: 1
1789 |     name: "conv2_17"
1790 |   }
1791 |   param {
1792 |     lr_mult: 2
1793 |     decay_mult: 0
1794 |     name: "conv2_17_b"
1795 |   }
1796 |   type: "Convolution"
1797 |   convolution_param {
1798 |     num_output: 512
1799 |     pad: 1
1800 |     kernel_size: 3
1801 |     weight_filler {
1802 |       type: "xavier"
1803 |     }
1804 |     bias_filler {
1805 |       type: "constant"
1806 |     }
1807 |   }
1808 | }
1809 | layer {
1810 |   name: "dropout_conv2_17"
1811 |   type: "Dropout"
1812 |   bottom: "conv2_17"
1813 |   top: "conv2_17"
1814 |   dropout_param {
1815 |     dropout_ratio: 0.3
1816 |   }
1817 | }
1818 | layer {
1819 |   name: "batch_conv2_17"
1820 |   type: "BatchNorm"
1821 |   bottom: "conv2_17"
1822 |   top: "conv2_17"
1823 |   param { lr_mult: 0 }
1824 |   param { lr_mult: 0 }
1825 |   param { lr_mult: 0 }
1826 | }
1827 | layer {
1828 |   name: "relu_conv2_17"
1829 |   type: "ReLU"
1830 |   bottom: "conv2_17"
1831 |   top: "conv2_17"
1832 | }
1833 | layer {
1834 |   bottom: "pool2_15_plus"
1835 |   top: "conv1_8"
1836 |   name: "conv1_8"
1837 |   param {
1838 |     lr_mult: 1
1839 |     decay_mult: 1
1840 |     name: "conv1_8"
1841 |   }
1842 |   param {
1843 |     lr_mult: 2
1844 |     decay_mult: 0
1845 |     name: "conv1_8_b"
1846 |   }
1847 |   type: "Convolution"
1848 |   convolution_param {
1849 |     num_output: 512
1850 |     pad: 1
1851 |     kernel_size: 3
1852 |     weight_filler {
1853 |       type: "xavier"
1854 |     }
1855 |     bias_filler {
1856 |       type: "constant"
1857 |     }
1858 |   }
1859 | }
1860 | layer {
1861 |   name: "dropout_conv1_8"
1862 |   type: "Dropout"
1863 |   bottom: "conv1_8"
1864 |   top: "conv1_8"
1865 |   dropout_param {
1866 |     dropout_ratio: 0.3
1867 |   }
1868 | }
1869 | layer {
1870 |   name: "batch_conv1_8"
1871 |   type: "BatchNorm"
1872 |   bottom: "conv1_8"
1873 |   top: "conv1_8"
1874 |   param { lr_mult: 0 }
1875 |   param { lr_mult: 0 }
1876 |   param { lr_mult: 0 }
1877 | }
1878 | layer {
1879 |   name: "relu_conv1_8"
1880 |   type: "ReLU"
1881 |   bottom: "conv1_8"
1882 |   top: "conv1_8"
1883 | }
1884 | layer {
1885 |   name: "join_conv2_17_plus"
1886 |   type: "FractalJoin"
1887 |   bottom: "conv1_8"
1888 |   bottom: "conv2_17"
1889 |   top: "conv2_17_plus"
1890 |   fractal_join_param {
1891 |     drop_path_ratio: 0.15
1892 |   }
1893 | }
1894 | layer {
1895 |   bottom: "conv2_17_plus"
1896 |   top: "conv2_18"
1897 |   name: "conv2_18"
1898 |   param {
1899 |     lr_mult: 1
1900 |     decay_mult: 1
1901 |     name: "conv2_18"
1902 |   }
1903 |   param {
1904 |     lr_mult: 2
1905 |     decay_mult: 0
1906 |     name: "conv2_18_b"
1907 |   }
1908 |   type: "Convolution"
1909 |   convolution_param {
1910 |     num_output: 512
1911 |     pad: 1
1912 |     kernel_size: 3
1913 |     weight_filler {
1914 |       type: "xavier"
1915 |     }
1916 |     bias_filler {
1917 |       type: "constant"
1918 |     }
1919 |   }
1920 | }
1921 | layer {
1922 |   name: "dropout_conv2_18"
1923 |   type: "Dropout"
1924 |   bottom: "conv2_18"
1925 |   top: "conv2_18"
1926 |   dropout_param {
1927 |     dropout_ratio: 0.3
1928 |   }
1929 | }
1930 | layer {
1931 |   name: "batch_conv2_18"
1932 |   type: "BatchNorm"
1933 |   bottom: "conv2_18"
1934 |   top: "conv2_18"
1935 |   param { lr_mult: 0 }
1936 |   param { lr_mult: 0 }
1937 |   param { lr_mult: 0 }
1938 | }
1939 | layer {
1940 |   name: "relu_conv2_18"
1941 |   type: "ReLU"
1942 |   bottom: "conv2_18"
1943 |   top: "conv2_18"
1944 | }
1945 | layer {
1946 |   bottom: "conv2_18"
1947 |   top: "conv2_19"
1948 |   name: "conv2_19"
1949 |   param {
1950 |     lr_mult: 1
1951 |     decay_mult: 1
1952 |     name: "conv2_19"
1953 |   }
1954 |   param {
1955 |     lr_mult: 2
1956 |     decay_mult: 0
1957 |     name: "conv2_19_b"
1958 |   }
1959 |   type: "Convolution"
1960 |   convolution_param {
1961 |     num_output: 512
1962 |     pad: 1
1963 |     kernel_size: 3
1964 |     weight_filler {
1965 |       type: "xavier"
1966 |     }
1967 |     bias_filler {
1968 |       type: "constant"
1969 |     }
1970 |   }
1971 | }
1972 | layer {
1973 |   name: "dropout_conv2_19"
1974 |   type: "Dropout"
1975 |   bottom: "conv2_19"
1976 |   top: "conv2_19"
1977 |   dropout_param {
1978 |     dropout_ratio: 0.3
1979 |   }
1980 | }
1981 | layer {
1982 |   name: "batch_conv2_19"
1983 |   type: "BatchNorm"
1984 |   bottom: "conv2_19"
1985 |   top: "conv2_19"
1986 |   param { lr_mult: 0 }
1987 |   param { lr_mult: 0 }
1988 |   param { lr_mult: 0 }
1989 | }
1990 | layer {
1991 |   name: "relu_conv2_19"
1992 |   type: "ReLU"
1993 |   bottom: "conv2_19"
1994 |   top: "conv2_19"
1995 | }
1996 | layer {
1997 |   bottom: "conv2_17_plus"
1998 |   top: "conv1_9"
1999 |   name: "conv1_9"
2000 |   param {
2001 |     lr_mult: 1
2002 |     decay_mult: 1
2003 |     name: "conv1_9"
2004 |   }
2005 |   param {
2006 |     lr_mult: 2
2007 |     decay_mult: 0
2008 |     name: "conv1_9_b"
2009 |   }
2010 |   type: "Convolution"
2011 |   convolution_param {
2012 |     num_output: 512
2013 |     pad: 1
2014 |     kernel_size: 3
2015 |     weight_filler {
2016 |       type: "xavier"
2017 |     }
2018 |     bias_filler {
2019 |       type: "constant"
2020 |     }
2021 |   }
2022 | }
2023 | layer {
2024 |   name: "dropout_conv1_9"
2025 |   type: "Dropout"
2026 |   bottom: "conv1_9"
2027 |   top: "conv1_9"
2028 |   dropout_param {
2029 |     dropout_ratio: 0.3
2030 |   }
2031 | }
2032 | layer {
2033 |   name: "batch_conv1_9"
2034 |   type: "BatchNorm"
2035 |   bottom: "conv1_9"
2036 |   top: "conv1_9"
2037 |   param { lr_mult: 0 }
2038 |   param { lr_mult: 0 }
2039 |   param { lr_mult: 0 }
2040 | }
2041 | layer {
2042 |   name: "relu_conv1_9"
2043 |   type: "ReLU"
2044 |   bottom: "conv1_9"
2045 |   top: "conv1_9"
2046 | }
2047 | layer {
2048 |   bottom: "pool2_15_plus"
2049 |   top: "conv0_4"
2050 |   name: "conv0_4"
2051 |   param {
2052 |     lr_mult: 1
2053 |     decay_mult: 1
2054 |     name: "conv0_4"
2055 |   }
2056 |   param {
2057 |     lr_mult: 2
2058 |     decay_mult: 0
2059 |     name: "conv0_4_b"
2060 |   }
2061 |   type: "Convolution"
2062 |   convolution_param {
2063 |     num_output: 512
2064 |     pad: 1
2065 |     kernel_size: 3
2066 |     weight_filler {
2067 |       type: "xavier"
2068 |     }
2069 |     bias_filler {
2070 |       type: "constant"
2071 |     }
2072 |   }
2073 | }
2074 | layer {
2075 |   name: "dropout_conv0_4"
2076 |   type: "Dropout"
2077 |   bottom: "conv0_4"
2078 |   top: "conv0_4"
2079 |   dropout_param {
2080 |     dropout_ratio: 0.3
2081 |   }
2082 | }
2083 | layer {
2084 |   name: "batch_conv0_4"
2085 |   type: "BatchNorm"
2086 |   bottom: "conv0_4"
2087 |   top: "conv0_4"
2088 |   param { lr_mult: 0 }
2089 |   param { lr_mult: 0 }
2090 |   param { lr_mult: 0 }
2091 | }
2092 | layer {
2093 |   name: "relu_conv0_4"
2094 |   type: "ReLU"
2095 |   bottom: "conv0_4"
2096 |   top: "conv0_4"
2097 | }
2098 | layer {
2099 |   bottom: "conv0_4"
2100 |   top: "pool0_4"
2101 |   name: "pool0_4"
2102 |   type: "Pooling"
2103 |   pooling_param {
2104 |     pool: MAX
2105 |     kernel_size: 2
2106 |     stride: 2
2107 |   }
2108 | }
2109 | layer {
2110 |   bottom: "conv1_9"
2111 |   top: "pool1_9"
2112 |   name: "pool1_9"
2113 |   type: "Pooling"
2114 |   pooling_param {
2115 |     pool: MAX
2116 |     kernel_size: 2
2117 |     stride: 2
2118 |   }
2119 | }
2120 | layer {
2121 |   bottom: "conv2_19"
2122 |   top: "pool2_19"
2123 |   name: "pool2_19"
2124 |   type: "Pooling"
2125 |   pooling_param {
2126 |     pool: MAX
2127 |     kernel_size: 2
2128 |     stride: 2
2129 |   }
2130 | }
2131 | layer {
2132 |   name: "join_pool2_19_plus"
2133 |   type: "FractalJoin"
2134 |   bottom: "pool0_4"
2135 |   bottom: "pool1_9"
2136 |   bottom: "pool2_19"
2137 |   top: "pool2_19_plus"
2138 |   fractal_join_param {
2139 |     drop_path_ratio: 0.15
2140 |   }
2141 | }
2142 | # Reduction: 5, spatial size: 1
2143 | layer {
2144 |   bottom: "extra_mid_join"
2145 |   top: "conv2_20"
2146 |   name: "conv2_20"
2147 |   param {
2148 |     lr_mult: 1
2149 |     decay_mult: 1
2150 |     name: "conv2_20"
2151 |   }
2152 |   param {
2153 |     lr_mult: 2
2154 |     decay_mult: 0
2155 |     name: "conv2_20_b"
2156 |   }
2157 |   type: "Convolution"
2158 |   convolution_param {
2159 |     num_output: 512
2160 |     pad: 1
2161 |     kernel_size: 3
2162 |     weight_filler {
2163 |       type: "xavier"
2164 |     }
2165 |     bias_filler {
2166 |       type: "constant"
2167 |     }
2168 |   }
2169 | }
2170 | layer {
2171 |   name: "dropout_conv2_20"
2172 |   type: "Dropout"
2173 |   bottom: "conv2_20"
2174 |   top: "conv2_20"
2175 |   dropout_param {
2176 |     dropout_ratio: 0.2
2177 |   }
2178 | }
2179 | layer {
2180 |   name: "batch_conv2_20"
2181 |   type: "BatchNorm"
2182 |   bottom: "conv2_20"
2183 |   top: "conv2_20"
2184 |   param { lr_mult: 0 }
2185 |   param { lr_mult: 0 }
2186 |   param { lr_mult: 0 }
2187 | }
2188 | layer {
2189 |   name: "relu_conv2_20"
2190 |   type: "ReLU"
2191 |   bottom: "conv2_20"
2192 |   top: "conv2_20"
2193 | }
2194 | layer {
2195 |   bottom: "conv2_20"
2196 |   top: "conv2_21"
2197 |   name: "conv2_21"
2198 |   param {
2199 |     lr_mult: 1
2200 |     decay_mult: 1
2201 |     name: "conv2_21"
2202 |   }
2203 |   param {
2204 |     lr_mult: 2
2205 |     decay_mult: 0
2206 |     name: "conv2_21_b"
2207 |   }
2208 |   type: "Convolution"
2209 |   convolution_param {
2210 |     num_output: 512
2211 |     pad: 1
2212 |     kernel_size: 3
2213 |     weight_filler {
2214 |       type: "xavier"
2215 |     }
2216 |     bias_filler {
2217 |       type: "constant"
2218 |     }
2219 |   }
2220 | }
2221 | layer {
2222 |   name: "dropout_conv2_21"
2223 |   type: "Dropout"
2224 |   bottom: "conv2_21"
2225 |   top: "conv2_21"
2226 |   dropout_param {
2227 |     dropout_ratio: 0.2
2228 |   }
2229 | }
2230 | layer {
2231 |   name: "batch_conv2_21"
2232 |   type: "BatchNorm"
2233 |   bottom: "conv2_21"
2234 |   top: "conv2_21"
2235 |   param { lr_mult: 0 }
2236 |   param { lr_mult: 0 }
2237 |   param { lr_mult: 0 }
2238 | }
2239 | layer {
2240 |   name: "relu_conv2_21"
2241 |   type: "ReLU"
2242 |   bottom: "conv2_21"
2243 |   top: "conv2_21"
2244 | }
2245 | layer {
2246 |   bottom: "extra_mid_join"
2247 |   top: "conv1_10"
2248 |   name: "conv1_10"
2249 |   param {
2250 |     lr_mult: 1
2251 |     decay_mult: 1
2252 |     name: "conv1_10"
2253 |   }
2254 |   param {
2255 |     lr_mult: 2
2256 |     decay_mult: 0
2257 |     name: "conv1_10_b"
2258 |   }
2259 |   type: "Convolution"
2260 |   convolution_param {
2261 |     num_output: 512
2262 |     pad: 1
2263 |     kernel_size: 3
2264 |     weight_filler {
2265 |       type: "xavier"
2266 |     }
2267 |     bias_filler {
2268 |       type: "constant"
2269 |     }
2270 |   }
2271 | }
2272 | layer {
2273 |   name: "dropout_conv1_10"
2274 |   type: "Dropout"
2275 |   bottom: "conv1_10"
2276 |   top: "conv1_10"
2277 |   dropout_param {
2278 |     dropout_ratio: 0.2
2279 |   }
2280 | }
2281 | layer {
2282 |   name: "batch_conv1_10"
2283 |   type: "BatchNorm"
2284 |   bottom: "conv1_10"
2285 |   top: "conv1_10"
2286 |   param { lr_mult: 0 }
2287 |   param { lr_mult: 0 }
2288 |   param { lr_mult: 0 }
2289 | }
2290 | layer {
2291 |   name: "relu_conv1_10"
2292 |   type: "ReLU"
2293 |   bottom: "conv1_10"
2294 |   top: "conv1_10"
2295 | }
2296 | layer {
2297 |   name: "join_conv2_21_plus"
2298 |   type: "FractalJoin"
2299 |   bottom: "conv1_10"
2300 |   bottom: "conv2_21"
2301 |   top: "conv2_21_plus"
2302 |   fractal_join_param {
2303 |     drop_path_ratio: 0.15
2304 |   }
2305 | }
2306 | layer {
2307 |   bottom: "conv2_21_plus"
2308 |   top: "conv2_22"
2309 |   name: "conv2_22"
2310 |   param {
2311 |     lr_mult: 1
2312 |     decay_mult: 1
2313 |     name: "conv2_22"
2314 |   }
2315 |   param {
2316 |     lr_mult: 2
2317 |     decay_mult: 0
2318 |     name: "conv2_22_b"
2319 |   }
2320 |   type: "Convolution"
2321 |   convolution_param {
2322 |     num_output: 512
2323 |     pad: 1
2324 |     kernel_size: 3
2325 |     weight_filler {
2326 |       type: "xavier"
2327 |     }
2328 |     bias_filler {
2329 |       type: "constant"
2330 |     }
2331 |   }
2332 | }
2333 | layer {
2334 |   name: "dropout_conv2_22"
2335 |   type: "Dropout"
2336 |   bottom: "conv2_22"
2337 |   top: "conv2_22"
2338 |   dropout_param {
2339 |     dropout_ratio: 0.2
2340 |   }
2341 | }
2342 | layer {
2343 |   name: "batch_conv2_22"
2344 |   type: "BatchNorm"
2345 |   bottom: "conv2_22"
2346 |   top: "conv2_22"
2347 |   param { lr_mult: 0 }
2348 |   param { lr_mult: 0 }
2349 |   param { lr_mult: 0 }
2350 | }
2351 | layer {
2352 |   name: "relu_conv2_22"
2353 |   type: "ReLU"
2354 |   bottom: "conv2_22"
2355 |   top: "conv2_22"
2356 | }
2357 | layer {
2358 |   bottom: "conv2_22"
2359 |   top: "conv2_23"
2360 |   name: "conv2_23"
2361 |   param {
2362 |     lr_mult: 1
2363 |     decay_mult: 1
2364 |     name: "conv2_23"
2365 |   }
2366 |   param {
2367 |     lr_mult: 2
2368 |     decay_mult: 0
2369 |     name: "conv2_23_b"
2370 |   }
2371 |   type: "Convolution"
2372 |   convolution_param {
2373 |     num_output: 512
2374 |     pad: 1
2375 |     kernel_size: 3
2376 |     weight_filler {
2377 |       type: "xavier"
2378 |     }
2379 |     bias_filler {
2380 |       type: "constant"
2381 |     }
2382 |   }
2383 | }
2384 | layer {
2385 |   name: "dropout_conv2_23"
2386 |   type: "Dropout"
2387 |   bottom: "conv2_23"
2388 |   top: "conv2_23"
2389 |   dropout_param {
2390 |     dropout_ratio: 0.2
2391 |   }
2392 | }
2393 | layer {
2394 |   name: "batch_conv2_23"
2395 |   type: "BatchNorm"
2396 |   bottom: "conv2_23"
2397 |   top: "conv2_23"
2398 |   param { lr_mult: 0 }
2399 |   param { lr_mult: 0 }
2400 |   param { lr_mult: 0 }
2401 | }
2402 | layer {
2403 |   name: "relu_conv2_23"
2404 |   type: "ReLU"
2405 |   bottom: "conv2_23"
2406 |   top: "conv2_23"
2407 | }
2408 | layer {
2409 |   bottom: "conv2_21_plus"
2410 |   top: "conv1_11"
2411 |   name: "conv1_11"
2412 |   param {
2413 |     lr_mult: 1
2414 |     decay_mult: 1
2415 |     name: "conv1_11"
2416 |   }
2417 |   param {
2418 |     lr_mult: 2
2419 |     decay_mult: 0
2420 |     name: "conv1_11_b"
2421 |   }
2422 |   type: "Convolution"
2423 |   convolution_param {
2424 |     num_output: 512
2425 |     pad: 1
2426 |     kernel_size: 3
2427 |     weight_filler {
2428 |       type: "xavier"
2429 |     }
2430 |     bias_filler {
2431 |       type: "constant"
2432 |     }
2433 |   }
2434 | }
2435 | layer {
2436 |   name: "dropout_conv1_11"
2437 |   type: "Dropout"
2438 |   bottom: "conv1_11"
2439 |   top: "conv1_11"
2440 |   dropout_param {
2441 |     dropout_ratio: 0.2
2442 |   }
2443 | }
2444 | layer {
2445 |   name: "batch_conv1_11"
2446 |   type: "BatchNorm"
2447 |   bottom: "conv1_11"
2448 |   top: "conv1_11"
2449 |   param { lr_mult: 0 }
2450 |   param { lr_mult: 0 }
2451 |   param { lr_mult: 0 }
2452 | }
2453 | layer {
2454 |   name: "relu_conv1_11"
2455 |   type: "ReLU"
2456 |   bottom: "conv1_11"
2457 |   top: "conv1_11"
2458 | }
2459 | layer {
2460 |   bottom: "extra_mid_join"
2461 |   top: "conv0_5"
2462 |   name: "conv0_5"
2463 |   param {
2464 |     lr_mult: 1
2465 |     decay_mult: 1
2466 |     name: "conv0_5"
2467 |   }
2468 |   param {
2469 |     lr_mult: 2
2470 |     decay_mult: 0
2471 |     name: "conv0_5_b"
2472 |   }
2473 |   type: "Convolution"
2474 |   convolution_param {
2475 |     num_output: 512
2476 |     pad: 1
2477 |     kernel_size: 3
2478 |     weight_filler {
2479 |       type: "xavier"
2480 |     }
2481 |     bias_filler {
2482 |       type: "constant"
2483 |     }
2484 |   }
2485 | }
2486 | layer {
2487 |   name: "dropout_conv0_5"
2488 |   type: "Dropout"
2489 |   bottom: "conv0_5"
2490 |   top: "conv0_5"
2491 |   dropout_param {
2492 |     dropout_ratio: 0.2
2493 |   }
2494 | }
2495 | layer {
2496 |   name: "batch_conv0_5"
2497 |   type: "BatchNorm"
2498 |   bottom: "conv0_5"
2499 |   top: "conv0_5"
2500 |   param { lr_mult: 0 }
2501 |   param { lr_mult: 0 }
2502 |   param { lr_mult: 0 }
2503 | }
2504 | layer {
2505 |   name: "relu_conv0_5"
2506 |   type: "ReLU"
2507 |   bottom: "conv0_5"
2508 |   top: "conv0_5"
2509 | }
2510 | layer {
2511 |   bottom: "conv0_5"
2512 |   top: "pool0_5_0"
2513 |   name: "pool0_5_0"
2514 |   type: "Pooling"
2515 |   pooling_param {
2516 |     pool: MAX
2517 |     kernel_size: 2
2518 |     stride: 2
2519 |   }
2520 | }
2521 | layer {
2522 |   bottom: "pool0_5_0"
2523 |   top: "pool0_5_1"
2524 |   name: "pool0_5_1"
2525 |   type: "Pooling"
2526 |   pooling_param {
2527 |     pool: MAX
2528 |     kernel_size: 2
2529 |     stride: 2
2530 |   }
2531 | }
2532 | layer {
2533 |   bottom: "conv1_11"
2534 |   top: "pool1_11_0"
2535 |   name: "pool1_11_0"
2536 |   type: "Pooling"
2537 |   pooling_param {
2538 |     pool: MAX
2539 |     kernel_size: 2
2540 |     stride: 2
2541 |   }
2542 | }
2543 | layer {
2544 |   bottom: "pool1_11_0"
2545 |   top: "pool1_11_1"
2546 |   name: "pool1_11_1"
2547 |   type: "Pooling"
2548 |   pooling_param {
2549 |     pool: MAX
2550 |     kernel_size: 2
2551 |     stride: 2
2552 |   }
2553 | }
2554 | layer {
2555 |   bottom: "conv2_23"
2556 |   top: "pool2_23_0"
2557 |   name: "pool2_23_0"
2558 |   type: "Pooling"
2559 |   pooling_param {
2560 |     pool: MAX
2561 |     kernel_size: 2
2562 |     stride: 2
2563 |   }
2564 | }
2565 | layer {
2566 |   bottom: "pool2_23_0"
2567 |   top: "pool2_23_1"
2568 |   name: "pool2_23_1"
2569 |   type: "Pooling"
2570 |   pooling_param {
2571 |     pool: MAX
2572 |     kernel_size: 2
2573 |     stride: 2
2574 |   }
2575 | }
2576 | layer {
2577 |   name: "join_pool2_23_plus"
2578 |   type: "FractalJoin"
2579 |   bottom: "pool0_5_1"
2580 |   bottom: "pool1_11_1"
2581 |   bottom: "pool2_23_1"
2582 |   top: "pool2_23_plus"
2583 |   fractal_join_param {
2584 |     drop_path_ratio: 0.15
2585 |   }
2586 | }
2587 | # Reduction: 6, spatial size: 0
2588 | layer {
2589 |   bottom: "data"
2590 |   top: "conv2_24"
2591 |   name: "conv2_24"
2592 |   param {
2593 |     lr_mult: 1
2594 |     decay_mult: 1
2595 |     name: "conv2_24"
2596 |   }
2597 |   param {
2598 |     lr_mult: 2
2599 |     decay_mult: 0
2600 |     name: "conv2_24_b"
2601 |   }
2602 |   type: "Convolution"
2603 |   convolution_param {
2604 |     num_output: 512
2605 |     pad: 1
2606 |     kernel_size: 3
2607 |     weight_filler {
2608 |       type: "xavier"
2609 |     }
2610 |     bias_filler {
2611 |       type: "constant"
2612 |     }
2613 |   }
2614 | }
2615 | layer {
2616 |   name: "dropout_conv2_24"
2617 |   type: "Dropout"
2618 |   bottom: "conv2_24"
2619 |   top: "conv2_24"
2620 |   dropout_param {
2621 |     dropout_ratio: 0.0
2622 |   }
2623 | }
2624 | layer {
2625 |   name: "batch_conv2_24"
2626 |   type: "BatchNorm"
2627 |   bottom: "conv2_24"
2628 |   top: "conv2_24"
2629 |   param { lr_mult: 0 }
2630 |   param { lr_mult: 0 }
2631 |   param { lr_mult: 0 }
2632 | }
2633 | layer {
2634 |   name: "relu_conv2_24"
2635 |   type: "ReLU"
2636 |   bottom: "conv2_24"
2637 |   top: "conv2_24"
2638 | }
2639 | layer {
2640 |   bottom: "conv2_24"
2641 |   top: "conv2_25"
2642 |   name: "conv2_25"
2643 |   param {
2644 |     lr_mult: 1
2645 |     decay_mult: 1
2646 |     name: "conv2_25"
2647 |   }
2648 |   param {
2649 |     lr_mult: 2
2650 |     decay_mult: 0
2651 |     name: "conv2_25_b"
2652 |   }
2653 |   type: "Convolution"
2654 |   convolution_param {
2655 |     num_output: 512
2656 |     pad: 1
2657 |     kernel_size: 3
2658 |     weight_filler {
2659 |       type: "xavier"
2660 |     }
2661 |     bias_filler {
2662 |       type: "constant"
2663 |     }
2664 |   }
2665 | }
2666 | layer {
2667 |   name: "dropout_conv2_25"
2668 |   type: "Dropout"
2669 |   bottom: "conv2_25"
2670 |   top: "conv2_25"
2671 |   dropout_param {
2672 |     dropout_ratio: 0.0
2673 |   }
2674 | }
2675 | layer {
2676 |   name: "batch_conv2_25"
2677 |   type: "BatchNorm"
2678 |   bottom: "conv2_25"
2679 |   top: "conv2_25"
2680 |   param { lr_mult: 0 }
2681 |   param { lr_mult: 0 }
2682 |   param { lr_mult: 0 }
2683 | }
2684 | layer {
2685 |   name: "relu_conv2_25"
2686 |   type: "ReLU"
2687 |   bottom: "conv2_25"
2688 |   top: "conv2_25"
2689 | }
2690 | layer {
2691 |   bottom: "data"
2692 |   top: "conv1_12"
2693 |   name: "conv1_12"
2694 |   param {
2695 |     lr_mult: 1
2696 |     decay_mult: 1
2697 |     name: "conv1_12"
2698 |   }
2699 |   param {
2700 |     lr_mult: 2
2701 |     decay_mult: 0
2702 |     name: "conv1_12_b"
2703 |   }
2704 |   type: "Convolution"
2705 |   convolution_param {
2706 |     num_output: 512
2707 |     pad: 1
2708 |     kernel_size: 3
2709 |     weight_filler {
2710 |       type: "xavier"
2711 |     }
2712 |     bias_filler {
2713 |       type: "constant"
2714 |     }
2715 |   }
2716 | }
2717 | layer {
2718 |   name: "dropout_conv1_12"
2719 |   type: "Dropout"
2720 |   bottom: "conv1_12"
2721 |   top: "conv1_12"
2722 |   dropout_param {
2723 |     dropout_ratio: 0.0
2724 |   }
2725 | }
2726 | layer {
2727 |   name: "batch_conv1_12"
2728 |   type: "BatchNorm"
2729 |   bottom: "conv1_12"
2730 |   top: "conv1_12"
2731 |   param { lr_mult: 0 }
2732 |   param { lr_mult: 0 }
2733 |   param { lr_mult: 0 }
2734 | }
2735 | layer {
2736 |   name: "relu_conv1_12"
2737 |   type: "ReLU"
2738 |   bottom: "conv1_12"
2739 |   top: "conv1_12"
2740 | }
2741 | layer {
2742 |   name: "join_conv2_25_plus"
2743 |   type: "FractalJoin"
2744 |   bottom: "conv1_12"
2745 |   bottom: "conv2_25"
2746 |   top: "conv2_25_plus"
2747 |   fractal_join_param {
2748 |     drop_path_ratio: 0.15
2749 |   }
2750 | }
2751 | layer {
2752 |   bottom: "conv2_25_plus"
2753 |   top: "conv2_26"
2754 |   name: "conv2_26"
2755 |   param {
2756 |     lr_mult: 1
2757 |     decay_mult: 1
2758 |     name: "conv2_26"
2759 |   }
2760 |   param {
2761 |     lr_mult: 2
2762 |     decay_mult: 0
2763 |     name: "conv2_26_b"
2764 |   }
2765 |   type: "Convolution"
2766 |   convolution_param {
2767 |     num_output: 512
2768 |     pad: 1
2769 |     kernel_size: 3
2770 |     weight_filler {
2771 |       type: "xavier"
2772 |     }
2773 |     bias_filler {
2774 |       type: "constant"
2775 |     }
2776 |   }
2777 | }
2778 | layer {
2779 |   name: "dropout_conv2_26"
2780 |   type: "Dropout"
2781 |   bottom: "conv2_26"
2782 |   top: "conv2_26"
2783 |   dropout_param {
2784 |     dropout_ratio: 0.0
2785 |   }
2786 | }
2787 | layer {
2788 |   name: "batch_conv2_26"
2789 |   type: "BatchNorm"
2790 |   bottom: "conv2_26"
2791 |   top: "conv2_26"
2792 |   param { lr_mult: 0 }
2793 |   param { lr_mult: 0 }
2794 |   param { lr_mult: 0 }
2795 | }
2796 | layer {
2797 |   name: "relu_conv2_26"
2798 |   type: "ReLU"
2799 |   bottom: "conv2_26"
2800 |   top: "conv2_26"
2801 | }
2802 | layer {
2803 |   bottom: "conv2_26"
2804 |   top: "conv2_27"
2805 |   name: "conv2_27"
2806 |   param {
2807 |     lr_mult: 1
2808 |     decay_mult: 1
2809 |     name: "conv2_27"
2810 |   }
2811 |   param {
2812 |     lr_mult: 2
2813 |     decay_mult: 0
2814 |     name: "conv2_27_b"
2815 |   }
2816 |   type: "Convolution"
2817 |   convolution_param {
2818 |     num_output: 512
2819 |     pad: 1
2820 |     kernel_size: 3
2821 |     weight_filler {
2822 |       type: "xavier"
2823 |     }
2824 |     bias_filler {
2825 |       type: "constant"
2826 |     }
2827 |   }
2828 | }
2829 | layer {
2830 |   name: "dropout_conv2_27"
2831 |   type: "Dropout"
2832 |   bottom: "conv2_27"
2833 |   top: "conv2_27"
2834 |   dropout_param {
2835 |     dropout_ratio: 0.0
2836 |   }
2837 | }
2838 | layer {
2839 |   name: "batch_conv2_27"
2840 |   type: "BatchNorm"
2841 |   bottom: "conv2_27"
2842 |   top: "conv2_27"
2843 |   param { lr_mult: 0 }
2844 |   param { lr_mult: 0 }
2845 |   param { lr_mult: 0 }
2846 | }
2847 | layer {
2848 |   name: "relu_conv2_27"
2849 |   type: "ReLU"
2850 |   bottom: "conv2_27"
2851 |   top: "conv2_27"
2852 | }
2853 | layer {
2854 |   bottom: "conv2_25_plus"
2855 |   top: "conv1_13"
2856 |   name: "conv1_13"
2857 |   param {
2858 |     lr_mult: 1
2859 |     decay_mult: 1
2860 |     name: "conv1_13"
2861 |   }
2862 |   param {
2863 |     lr_mult: 2
2864 |     decay_mult: 0
2865 |     name: "conv1_13_b"
2866 |   }
2867 |   type: "Convolution"
2868 |   convolution_param {
2869 |     num_output: 512
2870 |     pad: 1
2871 |     kernel_size: 3
2872 |     weight_filler {
2873 |       type: "xavier"
2874 |     }
2875 |     bias_filler {
2876 |       type: "constant"
2877 |     }
2878 |   }
2879 | }
2880 | layer {
2881 |   name: "dropout_conv1_13"
2882 |   type: "Dropout"
2883 |   bottom: "conv1_13"
2884 |   top: "conv1_13"
2885 |   dropout_param {
2886 |     dropout_ratio: 0.0
2887 |   }
2888 | }
2889 | layer {
2890 |   name: "batch_conv1_13"
2891 |   type: "BatchNorm"
2892 |   bottom: "conv1_13"
2893 |   top: "conv1_13"
2894 |   param { lr_mult: 0 }
2895 |   param { lr_mult: 0 }
2896 |   param { lr_mult: 0 }
2897 | }
2898 | layer {
2899 |   name: "relu_conv1_13"
2900 |   type: "ReLU"
2901 |   bottom: "conv1_13"
2902 |   top: "conv1_13"
2903 | }
2904 | layer {
2905 |   bottom: "data"
2906 |   top: "conv0_6"
2907 |   name: "conv0_6"
2908 |   param {
2909 |     lr_mult: 1
2910 |     decay_mult: 1
2911 |     name: "conv0_6"
2912 |   }
2913 |   param {
2914 |     lr_mult: 2
2915 |     decay_mult: 0
2916 |     name: "conv0_6_b"
2917 |   }
2918 |   type: "Convolution"
2919 |   convolution_param {
2920 |     num_output: 512
2921 |     pad: 1
2922 |     kernel_size: 3
2923 |     weight_filler {
2924 |       type: "xavier"
2925 |     }
2926 |     bias_filler {
2927 |       type: "constant"
2928 |     }
2929 |   }
2930 | }
2931 | layer {
2932 |   name: "dropout_conv0_6"
2933 |   type: "Dropout"
2934 |   bottom: "conv0_6"
2935 |   top: "conv0_6"
2936 |   dropout_param {
2937 |     dropout_ratio: 0.0
2938 |   }
2939 | }
2940 | layer {
2941 |   name: "batch_conv0_6"
2942 |   type: "BatchNorm"
2943 |   bottom: "conv0_6"
2944 |   top: "conv0_6"
2945 |   param { lr_mult: 0 }
2946 |   param { lr_mult: 0 }
2947 |   param { lr_mult: 0 }
2948 | }
2949 | layer {
2950 |   name: "relu_conv0_6"
2951 |   type: "ReLU"
2952 |   bottom: "conv0_6"
2953 |   top: "conv0_6"
2954 | }
2955 | layer {
2956 |   bottom: "conv0_6"
2957 |   top: "pool0_6_0"
2958 |   name: "pool0_6_0"
2959 |   type: "Pooling"
2960 |   pooling_param {
2961 |     pool: MAX
2962 |     kernel_size: 2
2963 |     stride: 2
2964 |   }
2965 | }
2966 | layer {
2967 |   bottom: "pool0_6_0"
2968 |   top: "pool0_6_1"
2969 |   name: "pool0_6_1"
2970 |   type: "Pooling"
2971 |   pooling_param {
2972 |     pool: MAX
2973 |     kernel_size: 2
2974 |     stride: 2
2975 |   }
2976 | }
2977 | layer {
2978 |   bottom: "pool0_6_1"
2979 |   top: "pool0_6_2"
2980 |   name: "pool0_6_2"
2981 |   type: "Pooling"
2982 |   pooling_param {
2983 |     pool: MAX
2984 |     kernel_size: 2
2985 |     stride: 2
2986 |   }
2987 | }
2988 | layer {
2989 |   bottom: "pool0_6_2"
2990 |   top: "pool0_6_3"
2991 |   name: "pool0_6_3"
2992 |   type: "Pooling"
2993 |   pooling_param {
2994 |     pool: MAX
2995 |     kernel_size: 2
2996 |     stride: 2
2997 |   }
2998 | }
2999 | layer {
3000 |   bottom: "conv1_13"
3001 |   top: "pool1_13_0"
3002 |   name: "pool1_13_0"
3003 |   type: "Pooling"
3004 |   pooling_param {
3005 |     pool: MAX
3006 |     kernel_size: 2
3007 |     stride: 2
3008 |   }
3009 | }
3010 | layer {
3011 |   bottom: "pool1_13_0"
3012 |   top: "pool1_13_1"
3013 |   name: "pool1_13_1"
3014 |   type: "Pooling"
3015 |   pooling_param {
3016 |     pool: MAX
3017 |     kernel_size: 2
3018 |     stride: 2
3019 |   }
3020 | }
3021 | layer {
3022 |   bottom: "pool1_13_1"
3023 |   top: "pool1_13_2"
3024 |   name: "pool1_13_2"
3025 |   type: "Pooling"
3026 |   pooling_param {
3027 |     pool: MAX
3028 |     kernel_size: 2
3029 |     stride: 2
3030 |   }
3031 | }
3032 | layer {
3033 |   bottom: "pool1_13_2"
3034 |   top: "pool1_13_3"
3035 |   name: "pool1_13_3"
3036 |   type: "Pooling"
3037 |   pooling_param {
3038 |     pool: MAX
3039 |     kernel_size: 2
3040 |     stride: 2
3041 |   }
3042 | }
3043 | layer {
3044 |   bottom: "conv2_27"
3045 |   top: "pool2_27_0"
3046 |   name: "pool2_27_0"
3047 |   type: "Pooling"
3048 |   pooling_param {
3049 |     pool: MAX
3050 |     kernel_size: 2
3051 |     stride: 2
3052 |   }
3053 | }
3054 | layer {
3055 |   bottom: "pool2_27_0"
3056 |   top: "pool2_27_1"
3057 |   name: "pool2_27_1"
3058 |   type: "Pooling"
3059 |   pooling_param {
3060 |     pool: MAX
3061 |     kernel_size: 2
3062 |     stride: 2
3063 |   }
3064 | }
3065 | layer {
3066 |   bottom: "pool2_27_1"
3067 |   top: "pool2_27_2"
3068 |   name: "pool2_27_2"
3069 |   type: "Pooling"
3070 |   pooling_param {
3071 |     pool: MAX
3072 |     kernel_size: 2
3073 |     stride: 2
3074 |   }
3075 | }
3076 | layer {
3077 |   bottom: "pool2_27_2"
3078 |   top: "pool2_27_3"
3079 |   name: "pool2_27_3"
3080 |   type: "Pooling"
3081 |   pooling_param {
3082 |     pool: MAX
3083 |     kernel_size: 2
3084 |     stride: 2
3085 |   }
3086 | }
3087 | layer {
3088 |   name: "join_pool2_27_plus"
3089 |   type: "FractalJoin"
3090 |   bottom: "pool0_6_3"
3091 |   bottom: "pool1_13_3"
3092 |   bottom: "pool2_27_3"
3093 |   top: "pool2_27_plus"
3094 |   fractal_join_param {
3095 |     drop_path_ratio: 0.15
3096 |   }
3097 | }
3098 | layer {
3099 |   name: "join_extra_mid_join2"
3100 |   type: "FractalJoin"
3101 |   bottom: "pool2_23_plus"
3102 |   bottom: "pool2_19_plus"
3103 | #  bottom: "pool2_27_plus"
3104 |   top: "extra_mid_join2a"
3105 |   fractal_join_param {
3106 |     drop_path_ratio: 0.0
3107 |   }
3108 | }
3109 | layer { 
3110 |   name: "squared2_23"
3111 |   type: "Eltwise"
3112 |   bottom: "extra_mid_join2a"
3113 |   bottom: "extra_mid_join2a"
3114 |   top: "squared2_23"
3115 |   eltwise_param {
3116 |     operation: PROD
3117 |   }
3118 | }
3119 | layer {
3120 |   name: "freeze_drop_path"
3121 |   type: "FreezeDropPath"
3122 |   bottom: "pool2_27_plus"
3123 |   bottom: "squared2_23"
3124 | #  bottom: "cubed2_19"
3125 |   top: "extra_mid_join2"
3126 |   freeze_drop_path_param {
3127 |     num_iter_per_cycle: 0
3128 |     interval_type: 0
3129 |   }
3130 | }
3131 | layer {
3132 |   name: "batch_extra_mid_join2"
3133 |   type: "BatchNorm"
3134 |   bottom: "extra_mid_join2"
3135 |   top: "extra_mid_join2"
3136 |   param { lr_mult: 0 }
3137 |   param { lr_mult: 0 }
3138 |   param { lr_mult: 0 }
3139 | }
3140 | 
3141 | # Reduction: 7, spatial size: 0
3142 | layer {
3143 |   name: "prediction0"
3144 |   type: "InnerProduct"
3145 |   bottom: "extra_mid_join2"
3146 |   top: "prediction0"
3147 |   param {
3148 |     lr_mult: 1
3149 |     decay_mult: 1
3150 |     name: "prediction0"
3151 |   }
3152 |   param {
3153 |     lr_mult: 2
3154 |     decay_mult: 0
3155 |     name: "prediction0_b"
3156 |   }
3157 |   inner_product_param {
3158 |     num_output: 10
3159 |     weight_filler {
3160 |       type: "xavier"
3161 |     }
3162 |     bias_filler {
3163 |       type: "constant"
3164 |     }
3165 |   }
3166 | }
3167 | layer {
3168 |   name: "loss0"
3169 |   type: "SoftmaxWithLoss"
3170 |   bottom: "prediction0"
3171 |   bottom: "label"
3172 |   top: "loss0"
3173 |   loss_weight: 1.0
3174 |   include: { phase: TRAIN }
3175 | }
3176 | 
3177 | layer {
3178 |   name: "accuracy_loss0"
3179 |   type: "Accuracy"
3180 |   bottom: "prediction0"
3181 |   bottom: "label"
3182 |   top: "accuracy_loss0"
3183 |   include: { phase: TEST }
3184 | }
3185 | 


--------------------------------------------------------------------------------
/freeze_drop_path.cpp:
--------------------------------------------------------------------------------
  1 | #include <vector>
  2 | 
  3 | #include "caffe/layers/freeze_drop_path.hpp"
  4 | #include "caffe/util/math_functions.hpp"
  5 | 
  6 | namespace caffe {
  7 | 
  8 | template <typename Dtype>
  9 | void FreezeDropPathLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
 10 |       const vector<Blob<Dtype>*>& top) {
 11 |   Dtype num_iter_per_cycle = this->layer_param_.freeze_drop_path_param().num_iter_per_cycle();
 12 |   Dtype interval_type = this->layer_param_.freeze_drop_path_param().interval_type();
 13 | 
 14 |   thresholds_.clear();
 15 |   uint_thresholds_.clear();
 16 |   stochastic = false;
 17 |   if ( num_iter_per_cycle == 0) stochastic = true;
 18 |   iteration = 0;
 19 |   lastBranch = 0;
 20 | 
 21 |   Dtype interval[20];
 22 |   Dtype norm = 0.0;
 23 |   interval[0] = 0.0;
 24 |   Dtype x;
 25 |   if (interval_type == 0) {
 26 | 	  for (int i = 1; i <= bottom.size(); ++i) {
 27 | 		x = pow(Dtype(i),2.0);
 28 | 		interval[i] += interval[i-1]  + x;
 29 | 		norm += x;
 30 | 	  }
 31 |   } else if(interval_type == 1) {
 32 | 	  for (int i = 1; i <= bottom.size(); ++i) {
 33 | 		interval[i] = i;
 34 | 	  }
 35 |       norm = bottom.size();
 36 |   } else if(interval_type == 2) {
 37 | 	  for (int i = 0; i <= bottom.size(); ++i) {
 38 | 		x = pow(Dtype(bottom.size()-i),2.0);
 39 | 		interval[i] += interval[i]  + x;
 40 | 		norm += x;
 41 | 	  }
 42 |   } else {
 43 | 	  // Not recognized
 44 | 	  LOG(ERROR) << " interval_type "  << interval_type << " not recognized. ";
 45 |   }
 46 | 	  
 47 | 	  
 48 | //  LOG(INFO) << " num_iter_per_cycle = " << num_iter_per_cycle << " bottom.size = " << bottom.size() << " norm = " << norm;
 49 | 	
 50 |     
 51 |   if (stochastic) {  
 52 | 	  for (int i = 0; i <= bottom.size(); ++i) {
 53 |         thresholds_.push_back(interval[i]/norm);
 54 | 		DCHECK(thresholds_[i] >= 0.);
 55 | 		DCHECK(thresholds_[i] <= 1.);
 56 |         uint_thresholds_.push_back(static_cast<unsigned int>(interval[i]));
 57 | 	  }
 58 |   } else {
 59 | 	  for (int i = 0; i <= bottom.size(); ++i) {
 60 | 		x =  Dtype(num_iter_per_cycle * interval[i])/norm;
 61 | 		thresholds_.push_back(x);
 62 | 	  }
 63 |   } 
 64 | }
 65 | 
 66 | template <typename Dtype>
 67 | void FreezeDropPathLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
 68 |       const vector<Blob<Dtype>*>& top) {
 69 |   for (int i = 1; i < bottom.size(); ++i) {
 70 |     CHECK(bottom[i]->shape() == bottom[0]->shape());
 71 |   }
 72 |   top[0]->ReshapeLike(*bottom[0]);
 73 | }
 74 | 
 75 | template <typename Dtype>
 76 | void FreezeDropPathLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
 77 |       const vector<Blob<Dtype>*>& top) {
 78 |   if (this->phase_ == TRAIN) {
 79 | 	branch = 0;
 80 | 	if (stochastic) {  
 81 |       uint rnd = std::rand() % uint_thresholds_[bottom.size()];
 82 |       for (int i = 0; i < bottom.size(); ++i) {
 83 | 		  if (rnd >= uint_thresholds_[i] && rnd < uint_thresholds_[i+1]) branch = i;
 84 |       }
 85 |     } else {
 86 | 		iteration = (iteration + 1) % this->layer_param_.freeze_drop_path_param().num_iter_per_cycle();
 87 |         for (int i = 0; i < bottom.size(); ++i) {
 88 | 		  if (iteration >= thresholds_[i] && iteration < thresholds_[i+1]) branch = i;
 89 | 		}
 90 | 		if (branch > lastBranch || branch < lastBranch) 
 91 | 		  LOG(INFO) << " Changing from branch " << lastBranch << " to " << branch;
 92 | 	}
 93 |   } else {
 94 | 	  branch = bottom.size() - 1;
 95 |   }
 96 |   
 97 | //	Zero the top layer before adding the bottom data
 98 |   caffe_set(top[0]->count(), Dtype(0), top[0]->mutable_cpu_data());
 99 |   for (int i = 0; i <= branch; ++i) {
100 |       caffe_axpy(top[0]->count(), Dtype(1.0), bottom[i]->cpu_data(), top[0]->mutable_cpu_data());
101 |   }
102 |   lastBranch = branch;
103 | }
104 | 
105 | template <typename Dtype>
106 | void FreezeDropPathLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
107 |       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
108 | 
109 |   for (int i = 0; i < bottom.size(); ++i) {
110 |     if (propagate_down[i]) {
111 |       if (i == branch) {
112 |         caffe_copy(bottom[i]->count(), top[0]->cpu_diff(), bottom[i]->mutable_cpu_diff());
113 |       } else {
114 |         caffe_set(bottom[i]->count(), Dtype(0), bottom[i]->mutable_cpu_diff());
115 |       }
116 |     }
117 |   }
118 | }
119 | 
120 | #ifdef CPU_ONLY
121 | STUB_GPU(FreezeDropPathLayer);
122 | #endif
123 | 
124 | INSTANTIATE_CLASS(FreezeDropPathLayer);
125 | REGISTER_LAYER_CLASS(FreezeDropPath);
126 | 
127 | }  // namespace caffe
128 | 
129 | 


--------------------------------------------------------------------------------
/freeze_drop_path.cu:
--------------------------------------------------------------------------------
 1 | #include <vector>
 2 | 
 3 | #include "caffe/layers/freeze_drop_path.hpp"
 4 | #include "caffe/util/math_functions.hpp"
 5 | #include <stdlib.h>     /* srand, rand */
 6 | 
 7 | namespace caffe {
 8 | 
 9 | template <typename Dtype>
10 | void FreezeDropPathLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
11 |       const vector<Blob<Dtype>*>& top) {
12 |   if (this->phase_ == TRAIN) {
13 | 	branch = 0;
14 | 	if (stochastic) {  
15 |       uint rnd = std::rand() % uint_thresholds_[bottom.size()];
16 |       for (int i = 0; i < bottom.size(); ++i) {
17 | 		  if (rnd >= uint_thresholds_[i] && rnd < uint_thresholds_[i+1]) branch = i;
18 |       }
19 | //	LOG(INFO) << " rnd = " << rnd <<  "branch " << branch;
20 |     } else {
21 | 		iteration = (iteration + 1) % this->layer_param_.freeze_drop_path_param().num_iter_per_cycle();
22 |         for (int i = 0; i < bottom.size(); ++i) {
23 | 		  if (iteration >= thresholds_[i] && iteration < thresholds_[i+1]) branch = i;
24 | 		}
25 | 		if (branch > lastBranch || branch < lastBranch) 
26 | 		  LOG(INFO) << " Changing from branch " << lastBranch << " to " << branch;
27 | //	LOG(INFO) << " iteration  = " << iteration << " bottom.size = " << bottom.size() << "branch " << branch;
28 | 	}
29 |   } else {
30 | 	  branch = bottom.size() - 1;
31 |   }
32 |   
33 | //	Zero the top layer before adding the bottom data
34 |   caffe_gpu_set(top[0]->count(), Dtype(0), top[0]->mutable_cpu_data());
35 |   for (int i = 0; i <= branch; ++i) {
36 |       caffe_gpu_axpy(top[0]->count(), Dtype(1.0), bottom[i]->cpu_data(), top[0]->mutable_cpu_data());
37 |   }
38 |   lastBranch = branch;
39 | }
40 | 
41 | template <typename Dtype>
42 | void FreezeDropPathLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
43 |       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
44 | 	for (int i = 0; i < bottom.size(); ++i) {
45 | 	  if (propagate_down[i]) {
46 |         if (i == branch) {
47 |           caffe_copy(bottom[i]->count(), top[0]->cpu_diff(), bottom[i]->mutable_cpu_diff());
48 |         } else {
49 |           caffe_gpu_set(bottom[i]->count(), Dtype(0), bottom[i]->mutable_cpu_diff());
50 |         }
51 |       }
52 |     }
53 | }
54 | 
55 | INSTANTIATE_LAYER_GPU_FUNCS(FreezeDropPathLayer);
56 | 
57 | }  // namespace caffe
58 | 


--------------------------------------------------------------------------------
/freeze_drop_path.hpp:
--------------------------------------------------------------------------------
 1 | #ifndef CAFFE_FREEZE_DROP_PATH_HPP_
 2 | #define CAFFE_FREEZE_DROP_PATH_HPP_
 3 | 
 4 | #include <vector>
 5 | 
 6 | #include "caffe/blob.hpp"
 7 | #include "caffe/layer.hpp"
 8 | #include "caffe/proto/caffe.pb.h"
 9 | 
10 | namespace caffe {
11 | 
12 | /**
13 |  * \ingroup ttic
14 |  * @brief Only one path is active at a time during training.  Higher paths, if any, are
15 |  *        frozen and lower paths are dropped.
16 |  *        This is used with several network branches, where the lower branches
17 |  *        learn a correction to the upper branches results.
18 |  * 
19 |  * @author Leslie N. Smith
20 |  */
21 | template <typename Dtype>
22 | class FreezeDropPathLayer : public Layer<Dtype> {
23 |  public:
24 |   explicit FreezeDropPathLayer(const LayerParameter& param)
25 |       : Layer<Dtype>(param) {}
26 |   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
27 |       const vector<Blob<Dtype>*>& top);
28 |   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
29 |       const vector<Blob<Dtype>*>& top);
30 | 
31 |   virtual inline const char* type() const { return "FreezeDropPathLayer"; }
32 |   virtual inline int MinBottomBlobs() const { return 2; }
33 |   virtual inline int ExactNumTopBlobs() const { return 1; }
34 | 
35 |  protected:
36 |   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
37 |       const vector<Blob<Dtype>*>& top);
38 |   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
39 |       const vector<Blob<Dtype>*>& top);
40 |   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
41 |       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
42 |   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
43 |       const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);
44 | 
45 | //  std::vector<bool> drops_;
46 |   unsigned int iteration, branch, lastBranch;
47 |   bool stochastic;
48 |   std::vector<Dtype> thresholds_;
49 |   std::vector<unsigned int> uint_thresholds_;
50 | };
51 | 
52 | }  // namespace caffe
53 | 
54 | #endif  // CAFFE_FREEZE_DROP_PATH_HPP_
55 | 


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/solver.prototxt:
--------------------------------------------------------------------------------
 1 | net: "examples/fractalnet/architectures/fractal.prototxt"
 2 | test_iter: 200
 3 | test_interval: 500
 4 | display: 250
 5 | max_iter: 100000
 6 | base_lr: 0.02
 7 | lr_policy: "multistep"
 8 | gamma: 0.1
 9 | momentum: 0.9
10 | weight_decay: 0.0001
11 | snapshot: 50000
12 | snapshot_prefix: "examples/fractalnet/snapshots/fractal"
13 | solver_mode: GPU
14 | random_seed: 831486
15 | stepvalue: 50000
16 | stepvalue: 75000
17 | stepvalue: 87500
18 | 


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