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  1. name: "FCRN-v3"
  2. input: "data"
  3. input_dim: 1
  4. input_dim: 3
  5. input_dim: 228
  6. input_dim: 304
  7.  
  8. layer {
  9. bottom: "data"
  10. top: "conv1"
  11. name: "conv1"
  12. type: "Convolution"
  13. convolution_param {
  14. num_output: 64
  15. kernel_size: 7
  16. pad: 3
  17. stride: 2
  18. }
  19. }
  20.  
  21. layer {
  22. bottom: "conv1"
  23. top: "conv1"
  24. name: "bn_conv1"
  25. type: "BatchNorm"
  26. batch_norm_param {
  27. use_global_stats: true
  28. }
  29. }
  30.  
  31. layer {
  32. bottom: "conv1"
  33. top: "conv1"
  34. name: "scale_conv1"
  35. type: "Scale"
  36. scale_param {
  37. bias_term: true
  38. }
  39. }
  40.  
  41. layer {
  42. bottom: "conv1"
  43. top: "conv1"
  44. name: "conv1_relu"
  45. type: "ReLU"
  46. }
  47.  
  48. layer {
  49. bottom: "conv1"
  50. top: "pool1"
  51. name: "pool1"
  52. type: "Pooling"
  53. pooling_param {
  54. kernel_size: 3
  55. stride: 2
  56. pool: MAX
  57. }
  58. }
  59.  
  60. layer {
  61. bottom: "pool1"
  62. top: "res2a_branch1"
  63. name: "res2a_branch1"
  64. type: "Convolution"
  65. convolution_param {
  66. num_output: 256
  67. kernel_size: 1
  68. pad: 0
  69. stride: 1
  70. bias_term: false
  71. }
  72. }
  73.  
  74. layer {
  75. bottom: "res2a_branch1"
  76. top: "res2a_branch1"
  77. name: "bn2a_branch1"
  78. type: "BatchNorm"
  79. batch_norm_param {
  80. use_global_stats: true
  81. }
  82. }
  83.  
  84. layer {
  85. bottom: "res2a_branch1"
  86. top: "res2a_branch1"
  87. name: "scale2a_branch1"
  88. type: "Scale"
  89. scale_param {
  90. bias_term: true
  91. }
  92. }
  93.  
  94. layer {
  95. bottom: "pool1"
  96. top: "res2a_branch2a"
  97. name: "res2a_branch2a"
  98. type: "Convolution"
  99. convolution_param {
  100. num_output: 64
  101. kernel_size: 1
  102. pad: 0
  103. stride: 1
  104. bias_term: false
  105. }
  106. }
  107.  
  108. layer {
  109. bottom: "res2a_branch2a"
  110. top: "res2a_branch2a"
  111. name: "bn2a_branch2a"
  112. type: "BatchNorm"
  113. batch_norm_param {
  114. use_global_stats: true
  115. }
  116. }
  117.  
  118. layer {
  119. bottom: "res2a_branch2a"
  120. top: "res2a_branch2a"
  121. name: "scale2a_branch2a"
  122. type: "Scale"
  123. scale_param {
  124. bias_term: true
  125. }
  126. }
  127.  
  128. layer {
  129. bottom: "res2a_branch2a"
  130. top: "res2a_branch2a"
  131. name: "res2a_branch2a_relu"
  132. type: "ReLU"
  133. }
  134.  
  135. layer {
  136. bottom: "res2a_branch2a"
  137. top: "res2a_branch2b"
  138. name: "res2a_branch2b"
  139. type: "Convolution"
  140. convolution_param {
  141. num_output: 64
  142. kernel_size: 3
  143. pad: 1
  144. stride: 1
  145. bias_term: false
  146. }
  147. }
  148.  
  149. layer {
  150. bottom: "res2a_branch2b"
  151. top: "res2a_branch2b"
  152. name: "bn2a_branch2b"
  153. type: "BatchNorm"
  154. batch_norm_param {
  155. use_global_stats: true
  156. }
  157. }
  158.  
  159. layer {
  160. bottom: "res2a_branch2b"
  161. top: "res2a_branch2b"
  162. name: "scale2a_branch2b"
  163. type: "Scale"
  164. scale_param {
  165. bias_term: true
  166. }
  167. }
  168.  
  169. layer {
  170. bottom: "res2a_branch2b"
  171. top: "res2a_branch2b"
  172. name: "res2a_branch2b_relu"
  173. type: "ReLU"
  174. }
  175.  
  176. layer {
  177. bottom: "res2a_branch2b"
  178. top: "res2a_branch2c"
  179. name: "res2a_branch2c"
  180. type: "Convolution"
  181. convolution_param {
  182. num_output: 256
  183. kernel_size: 1
  184. pad: 0
  185. stride: 1
  186. bias_term: false
  187. }
  188. }
  189.  
  190. layer {
  191. bottom: "res2a_branch2c"
  192. top: "res2a_branch2c"
  193. name: "bn2a_branch2c"
  194. type: "BatchNorm"
  195. batch_norm_param {
  196. use_global_stats: true
  197. }
  198. }
  199.  
  200. layer {
  201. bottom: "res2a_branch2c"
  202. top: "res2a_branch2c"
  203. name: "scale2a_branch2c"
  204. type: "Scale"
  205. scale_param {
  206. bias_term: true
  207. }
  208. }
  209.  
  210. layer {
  211. bottom: "res2a_branch1"
  212. bottom: "res2a_branch2c"
  213. top: "res2a"
  214. name: "res2a"
  215. type: "Eltwise"
  216. }
  217.  
  218. layer {
  219. bottom: "res2a"
  220. top: "res2a"
  221. name: "res2a_relu"
  222. type: "ReLU"
  223. }
  224.  
  225. layer {
  226. bottom: "res2a"
  227. top: "res2b_branch2a"
  228. name: "res2b_branch2a"
  229. type: "Convolution"
  230. convolution_param {
  231. num_output: 64
  232. kernel_size: 1
  233. pad: 0
  234. stride: 1
  235. bias_term: false
  236. }
  237. }
  238.  
  239. layer {
  240. bottom: "res2b_branch2a"
  241. top: "res2b_branch2a"
  242. name: "bn2b_branch2a"
  243. type: "BatchNorm"
  244. batch_norm_param {
  245. use_global_stats: true
  246. }
  247. }
  248.  
  249. layer {
  250. bottom: "res2b_branch2a"
  251. top: "res2b_branch2a"
  252. name: "scale2b_branch2a"
  253. type: "Scale"
  254. scale_param {
  255. bias_term: true
  256. }
  257. }
  258.  
  259. layer {
  260. bottom: "res2b_branch2a"
  261. top: "res2b_branch2a"
  262. name: "res2b_branch2a_relu"
  263. type: "ReLU"
  264. }
  265.  
  266. layer {
  267. bottom: "res2b_branch2a"
  268. top: "res2b_branch2b"
  269. name: "res2b_branch2b"
  270. type: "Convolution"
  271. convolution_param {
  272. num_output: 64
  273. kernel_size: 3
  274. pad: 1
  275. stride: 1
  276. bias_term: false
  277. }
  278. }
  279.  
  280. layer {
  281. bottom: "res2b_branch2b"
  282. top: "res2b_branch2b"
  283. name: "bn2b_branch2b"
  284. type: "BatchNorm"
  285. batch_norm_param {
  286. use_global_stats: true
  287. }
  288. }
  289.  
  290. layer {
  291. bottom: "res2b_branch2b"
  292. top: "res2b_branch2b"
  293. name: "scale2b_branch2b"
  294. type: "Scale"
  295. scale_param {
  296. bias_term: true
  297. }
  298. }
  299.  
  300. layer {
  301. bottom: "res2b_branch2b"
  302. top: "res2b_branch2b"
  303. name: "res2b_branch2b_relu"
  304. type: "ReLU"
  305. }
  306.  
  307. layer {
  308. bottom: "res2b_branch2b"
  309. top: "res2b_branch2c"
  310. name: "res2b_branch2c"
  311. type: "Convolution"
  312. convolution_param {
  313. num_output: 256
  314. kernel_size: 1
  315. pad: 0
  316. stride: 1
  317. bias_term: false
  318. }
  319. }
  320.  
  321. layer {
  322. bottom: "res2b_branch2c"
  323. top: "res2b_branch2c"
  324. name: "bn2b_branch2c"
  325. type: "BatchNorm"
  326. batch_norm_param {
  327. use_global_stats: true
  328. }
  329. }
  330.  
  331. layer {
  332. bottom: "res2b_branch2c"
  333. top: "res2b_branch2c"
  334. name: "scale2b_branch2c"
  335. type: "Scale"
  336. scale_param {
  337. bias_term: true
  338. }
  339. }
  340.  
  341. layer {
  342. bottom: "res2a"
  343. bottom: "res2b_branch2c"
  344. top: "res2b"
  345. name: "res2b"
  346. type: "Eltwise"
  347. }
  348.  
  349. layer {
  350. bottom: "res2b"
  351. top: "res2b"
  352. name: "res2b_relu"
  353. type: "ReLU"
  354. }
  355.  
  356. layer {
  357. bottom: "res2b"
  358. top: "res2c_branch2a"
  359. name: "res2c_branch2a"
  360. type: "Convolution"
  361. convolution_param {
  362. num_output: 64
  363. kernel_size: 1
  364. pad: 0
  365. stride: 1
  366. bias_term: false
  367. }
  368. }
  369.  
  370. layer {
  371. bottom: "res2c_branch2a"
  372. top: "res2c_branch2a"
  373. name: "bn2c_branch2a"
  374. type: "BatchNorm"
  375. batch_norm_param {
  376. use_global_stats: true
  377. }
  378. }
  379.  
  380. layer {
  381. bottom: "res2c_branch2a"
  382. top: "res2c_branch2a"
  383. name: "scale2c_branch2a"
  384. type: "Scale"
  385. scale_param {
  386. bias_term: true
  387. }
  388. }
  389.  
  390. layer {
  391. bottom: "res2c_branch2a"
  392. top: "res2c_branch2a"
  393. name: "res2c_branch2a_relu"
  394. type: "ReLU"
  395. }
  396.  
  397. layer {
  398. bottom: "res2c_branch2a"
  399. top: "res2c_branch2b"
  400. name: "res2c_branch2b"
  401. type: "Convolution"
  402. convolution_param {
  403. num_output: 64
  404. kernel_size: 3
  405. pad: 1
  406. stride: 1
  407. bias_term: false
  408. }
  409. }
  410.  
  411. layer {
  412. bottom: "res2c_branch2b"
  413. top: "res2c_branch2b"
  414. name: "bn2c_branch2b"
  415. type: "BatchNorm"
  416. batch_norm_param {
  417. use_global_stats: true
  418. }
  419. }
  420.  
  421. layer {
  422. bottom: "res2c_branch2b"
  423. top: "res2c_branch2b"
  424. name: "scale2c_branch2b"
  425. type: "Scale"
  426. scale_param {
  427. bias_term: true
  428. }
  429. }
  430.  
  431. layer {
  432. bottom: "res2c_branch2b"
  433. top: "res2c_branch2b"
  434. name: "res2c_branch2b_relu"
  435. type: "ReLU"
  436. }
  437.  
  438. layer {
  439. bottom: "res2c_branch2b"
  440. top: "res2c_branch2c"
  441. name: "res2c_branch2c"
  442. type: "Convolution"
  443. convolution_param {
  444. num_output: 256
  445. kernel_size: 1
  446. pad: 0
  447. stride: 1
  448. bias_term: false
  449. }
  450. }
  451.  
  452. layer {
  453. bottom: "res2c_branch2c"
  454. top: "res2c_branch2c"
  455. name: "bn2c_branch2c"
  456. type: "BatchNorm"
  457. batch_norm_param {
  458. use_global_stats: true
  459. }
  460. }
  461.  
  462. layer {
  463. bottom: "res2c_branch2c"
  464. top: "res2c_branch2c"
  465. name: "scale2c_branch2c"
  466. type: "Scale"
  467. scale_param {
  468. bias_term: true
  469. }
  470. }
  471.  
  472. layer {
  473. bottom: "res2b"
  474. bottom: "res2c_branch2c"
  475. top: "res2c"
  476. name: "res2c"
  477. type: "Eltwise"
  478. }
  479.  
  480. layer {
  481. bottom: "res2c"
  482. top: "res2c"
  483. name: "res2c_relu"
  484. type: "ReLU"
  485. }
  486.  
  487. layer {
  488. bottom: "res2c"
  489. top: "res3a_branch1"
  490. name: "res3a_branch1"
  491. type: "Convolution"
  492. convolution_param {
  493. num_output: 512
  494. kernel_size: 1
  495. pad: 0
  496. stride: 2
  497. bias_term: false
  498. }
  499. }
  500.  
  501. layer {
  502. bottom: "res3a_branch1"
  503. top: "res3a_branch1"
  504. name: "bn3a_branch1"
  505. type: "BatchNorm"
  506. batch_norm_param {
  507. use_global_stats: true
  508. }
  509. }
  510.  
  511. layer {
  512. bottom: "res3a_branch1"
  513. top: "res3a_branch1"
  514. name: "scale3a_branch1"
  515. type: "Scale"
  516. scale_param {
  517. bias_term: true
  518. }
  519. }
  520.  
  521. layer {
  522. bottom: "res2c"
  523. top: "res3a_branch2a"
  524. name: "res3a_branch2a"
  525. type: "Convolution"
  526. convolution_param {
  527. num_output: 128
  528. kernel_size: 1
  529. pad: 0
  530. stride: 2
  531. bias_term: false
  532. }
  533. }
  534.  
  535. layer {
  536. bottom: "res3a_branch2a"
  537. top: "res3a_branch2a"
  538. name: "bn3a_branch2a"
  539. type: "BatchNorm"
  540. batch_norm_param {
  541. use_global_stats: true
  542. }
  543. }
  544.  
  545. layer {
  546. bottom: "res3a_branch2a"
  547. top: "res3a_branch2a"
  548. name: "scale3a_branch2a"
  549. type: "Scale"
  550. scale_param {
  551. bias_term: true
  552. }
  553. }
  554.  
  555. layer {
  556. bottom: "res3a_branch2a"
  557. top: "res3a_branch2a"
  558. name: "res3a_branch2a_relu"
  559. type: "ReLU"
  560. }
  561.  
  562. layer {
  563. bottom: "res3a_branch2a"
  564. top: "res3a_branch2b"
  565. name: "res3a_branch2b"
  566. type: "Convolution"
  567. convolution_param {
  568. num_output: 128
  569. kernel_size: 3
  570. pad: 1
  571. stride: 1
  572. bias_term: false
  573. }
  574. }
  575.  
  576. layer {
  577. bottom: "res3a_branch2b"
  578. top: "res3a_branch2b"
  579. name: "bn3a_branch2b"
  580. type: "BatchNorm"
  581. batch_norm_param {
  582. use_global_stats: true
  583. }
  584. }
  585.  
  586. layer {
  587. bottom: "res3a_branch2b"
  588. top: "res3a_branch2b"
  589. name: "scale3a_branch2b"
  590. type: "Scale"
  591. scale_param {
  592. bias_term: true
  593. }
  594. }
  595.  
  596. layer {
  597. bottom: "res3a_branch2b"
  598. top: "res3a_branch2b"
  599. name: "res3a_branch2b_relu"
  600. type: "ReLU"
  601. }
  602.  
  603. layer {
  604. bottom: "res3a_branch2b"
  605. top: "res3a_branch2c"
  606. name: "res3a_branch2c"
  607. type: "Convolution"
  608. convolution_param {
  609. num_output: 512
  610. kernel_size: 1
  611. pad: 0
  612. stride: 1
  613. bias_term: false
  614. }
  615. }
  616.  
  617. layer {
  618. bottom: "res3a_branch2c"
  619. top: "res3a_branch2c"
  620. name: "bn3a_branch2c"
  621. type: "BatchNorm"
  622. batch_norm_param {
  623. use_global_stats: true
  624. }
  625. }
  626.  
  627. layer {
  628. bottom: "res3a_branch2c"
  629. top: "res3a_branch2c"
  630. name: "scale3a_branch2c"
  631. type: "Scale"
  632. scale_param {
  633. bias_term: true
  634. }
  635. }
  636.  
  637. layer {
  638. bottom: "res3a_branch1"
  639. bottom: "res3a_branch2c"
  640. top: "res3a"
  641. name: "res3a"
  642. type: "Eltwise"
  643. }
  644.  
  645. layer {
  646. bottom: "res3a"
  647. top: "res3a"
  648. name: "res3a_relu"
  649. type: "ReLU"
  650. }
  651.  
  652. layer {
  653. bottom: "res3a"
  654. top: "res3b_branch2a"
  655. name: "res3b_branch2a"
  656. type: "Convolution"
  657. convolution_param {
  658. num_output: 128
  659. kernel_size: 1
  660. pad: 0
  661. stride: 1
  662. bias_term: false
  663. }
  664. }
  665.  
  666. layer {
  667. bottom: "res3b_branch2a"
  668. top: "res3b_branch2a"
  669. name: "bn3b_branch2a"
  670. type: "BatchNorm"
  671. batch_norm_param {
  672. use_global_stats: true
  673. }
  674. }
  675.  
  676. layer {
  677. bottom: "res3b_branch2a"
  678. top: "res3b_branch2a"
  679. name: "scale3b_branch2a"
  680. type: "Scale"
  681. scale_param {
  682. bias_term: true
  683. }
  684. }
  685.  
  686. layer {
  687. bottom: "res3b_branch2a"
  688. top: "res3b_branch2a"
  689. name: "res3b_branch2a_relu"
  690. type: "ReLU"
  691. }
  692.  
  693. layer {
  694. bottom: "res3b_branch2a"
  695. top: "res3b_branch2b"
  696. name: "res3b_branch2b"
  697. type: "Convolution"
  698. convolution_param {
  699. num_output: 128
  700. kernel_size: 3
  701. pad: 1
  702. stride: 1
  703. bias_term: false
  704. }
  705. }
  706.  
  707. layer {
  708. bottom: "res3b_branch2b"
  709. top: "res3b_branch2b"
  710. name: "bn3b_branch2b"
  711. type: "BatchNorm"
  712. batch_norm_param {
  713. use_global_stats: true
  714. }
  715. }
  716.  
  717. layer {
  718. bottom: "res3b_branch2b"
  719. top: "res3b_branch2b"
  720. name: "scale3b_branch2b"
  721. type: "Scale"
  722. scale_param {
  723. bias_term: true
  724. }
  725. }
  726.  
  727. layer {
  728. bottom: "res3b_branch2b"
  729. top: "res3b_branch2b"
  730. name: "res3b_branch2b_relu"
  731. type: "ReLU"
  732. }
  733.  
  734. layer {
  735. bottom: "res3b_branch2b"
  736. top: "res3b_branch2c"
  737. name: "res3b_branch2c"
  738. type: "Convolution"
  739. convolution_param {
  740. num_output: 512
  741. kernel_size: 1
  742. pad: 0
  743. stride: 1
  744. bias_term: false
  745. }
  746. }
  747.  
  748. layer {
  749. bottom: "res3b_branch2c"
  750. top: "res3b_branch2c"
  751. name: "bn3b_branch2c"
  752. type: "BatchNorm"
  753. batch_norm_param {
  754. use_global_stats: true
  755. }
  756. }
  757.  
  758. layer {
  759. bottom: "res3b_branch2c"
  760. top: "res3b_branch2c"
  761. name: "scale3b_branch2c"
  762. type: "Scale"
  763. scale_param {
  764. bias_term: true
  765. }
  766. }
  767.  
  768. layer {
  769. bottom: "res3a"
  770. bottom: "res3b_branch2c"
  771. top: "res3b"
  772. name: "res3b"
  773. type: "Eltwise"
  774. }
  775.  
  776. layer {
  777. bottom: "res3b"
  778. top: "res3b"
  779. name: "res3b_relu"
  780. type: "ReLU"
  781. }
  782.  
  783. layer {
  784. bottom: "res3b"
  785. top: "res3c_branch2a"
  786. name: "res3c_branch2a"
  787. type: "Convolution"
  788. convolution_param {
  789. num_output: 128
  790. kernel_size: 1
  791. pad: 0
  792. stride: 1
  793. bias_term: false
  794. }
  795. }
  796.  
  797. layer {
  798. bottom: "res3c_branch2a"
  799. top: "res3c_branch2a"
  800. name: "bn3c_branch2a"
  801. type: "BatchNorm"
  802. batch_norm_param {
  803. use_global_stats: true
  804. }
  805. }
  806.  
  807. layer {
  808. bottom: "res3c_branch2a"
  809. top: "res3c_branch2a"
  810. name: "scale3c_branch2a"
  811. type: "Scale"
  812. scale_param {
  813. bias_term: true
  814. }
  815. }
  816.  
  817. layer {
  818. bottom: "res3c_branch2a"
  819. top: "res3c_branch2a"
  820. name: "res3c_branch2a_relu"
  821. type: "ReLU"
  822. }
  823.  
  824. layer {
  825. bottom: "res3c_branch2a"
  826. top: "res3c_branch2b"
  827. name: "res3c_branch2b"
  828. type: "Convolution"
  829. convolution_param {
  830. num_output: 128
  831. kernel_size: 3
  832. pad: 1
  833. stride: 1
  834. bias_term: false
  835. }
  836. }
  837.  
  838. layer {
  839. bottom: "res3c_branch2b"
  840. top: "res3c_branch2b"
  841. name: "bn3c_branch2b"
  842. type: "BatchNorm"
  843. batch_norm_param {
  844. use_global_stats: true
  845. }
  846. }
  847.  
  848. layer {
  849. bottom: "res3c_branch2b"
  850. top: "res3c_branch2b"
  851. name: "scale3c_branch2b"
  852. type: "Scale"
  853. scale_param {
  854. bias_term: true
  855. }
  856. }
  857.  
  858. layer {
  859. bottom: "res3c_branch2b"
  860. top: "res3c_branch2b"
  861. name: "res3c_branch2b_relu"
  862. type: "ReLU"
  863. }
  864.  
  865. layer {
  866. bottom: "res3c_branch2b"
  867. top: "res3c_branch2c"
  868. name: "res3c_branch2c"
  869. type: "Convolution"
  870. convolution_param {
  871. num_output: 512
  872. kernel_size: 1
  873. pad: 0
  874. stride: 1
  875. bias_term: false
  876. }
  877. }
  878.  
  879. layer {
  880. bottom: "res3c_branch2c"
  881. top: "res3c_branch2c"
  882. name: "bn3c_branch2c"
  883. type: "BatchNorm"
  884. batch_norm_param {
  885. use_global_stats: true
  886. }
  887. }
  888.  
  889. layer {
  890. bottom: "res3c_branch2c"
  891. top: "res3c_branch2c"
  892. name: "scale3c_branch2c"
  893. type: "Scale"
  894. scale_param {
  895. bias_term: true
  896. }
  897. }
  898.  
  899. layer {
  900. bottom: "res3b"
  901. bottom: "res3c_branch2c"
  902. top: "res3c"
  903. name: "res3c"
  904. type: "Eltwise"
  905. }
  906.  
  907. layer {
  908. bottom: "res3c"
  909. top: "res3c"
  910. name: "res3c_relu"
  911. type: "ReLU"
  912. }
  913.  
  914. layer {
  915. bottom: "res3c"
  916. top: "res3d_branch2a"
  917. name: "res3d_branch2a"
  918. type: "Convolution"
  919. convolution_param {
  920. num_output: 128
  921. kernel_size: 1
  922. pad: 0
  923. stride: 1
  924. bias_term: false
  925. }
  926. }
  927.  
  928. layer {
  929. bottom: "res3d_branch2a"
  930. top: "res3d_branch2a"
  931. name: "bn3d_branch2a"
  932. type: "BatchNorm"
  933. batch_norm_param {
  934. use_global_stats: true
  935. }
  936. }
  937.  
  938. layer {
  939. bottom: "res3d_branch2a"
  940. top: "res3d_branch2a"
  941. name: "scale3d_branch2a"
  942. type: "Scale"
  943. scale_param {
  944. bias_term: true
  945. }
  946. }
  947.  
  948. layer {
  949. bottom: "res3d_branch2a"
  950. top: "res3d_branch2a"
  951. name: "res3d_branch2a_relu"
  952. type: "ReLU"
  953. }
  954.  
  955. layer {
  956. bottom: "res3d_branch2a"
  957. top: "res3d_branch2b"
  958. name: "res3d_branch2b"
  959. type: "Convolution"
  960. convolution_param {
  961. num_output: 128
  962. kernel_size: 3
  963. pad: 1
  964. stride: 1
  965. bias_term: false
  966. }
  967. }
  968.  
  969. layer {
  970. bottom: "res3d_branch2b"
  971. top: "res3d_branch2b"
  972. name: "bn3d_branch2b"
  973. type: "BatchNorm"
  974. batch_norm_param {
  975. use_global_stats: true
  976. }
  977. }
  978.  
  979. layer {
  980. bottom: "res3d_branch2b"
  981. top: "res3d_branch2b"
  982. name: "scale3d_branch2b"
  983. type: "Scale"
  984. scale_param {
  985. bias_term: true
  986. }
  987. }
  988.  
  989. layer {
  990. bottom: "res3d_branch2b"
  991. top: "res3d_branch2b"
  992. name: "res3d_branch2b_relu"
  993. type: "ReLU"
  994. }
  995.  
  996. layer {
  997. bottom: "res3d_branch2b"
  998. top: "res3d_branch2c"
  999. name: "res3d_branch2c"
  1000. type: "Convolution"
  1001. convolution_param {
  1002. num_output: 512
  1003. kernel_size: 1
  1004. pad: 0
  1005. stride: 1
  1006. bias_term: false
  1007. }
  1008. }
  1009.  
  1010. layer {
  1011. bottom: "res3d_branch2c"
  1012. top: "res3d_branch2c"
  1013. name: "bn3d_branch2c"
  1014. type: "BatchNorm"
  1015. batch_norm_param {
  1016. use_global_stats: true
  1017. }
  1018. }
  1019.  
  1020. layer {
  1021. bottom: "res3d_branch2c"
  1022. top: "res3d_branch2c"
  1023. name: "scale3d_branch2c"
  1024. type: "Scale"
  1025. scale_param {
  1026. bias_term: true
  1027. }
  1028. }
  1029.  
  1030. layer {
  1031. bottom: "res3c"
  1032. bottom: "res3d_branch2c"
  1033. top: "res3d"
  1034. name: "res3d"
  1035. type: "Eltwise"
  1036. }
  1037.  
  1038. layer {
  1039. bottom: "res3d"
  1040. top: "res3d"
  1041. name: "res3d_relu"
  1042. type: "ReLU"
  1043. }
  1044.  
  1045. layer {
  1046. bottom: "res3d"
  1047. top: "res4a_branch1"
  1048. name: "res4a_branch1"
  1049. type: "Convolution"
  1050. convolution_param {
  1051. num_output: 1024
  1052. kernel_size: 1
  1053. pad: 0
  1054. stride: 2
  1055. bias_term: false
  1056. }
  1057. }
  1058.  
  1059. layer {
  1060. bottom: "res4a_branch1"
  1061. top: "res4a_branch1"
  1062. name: "bn4a_branch1"
  1063. type: "BatchNorm"
  1064. batch_norm_param {
  1065. use_global_stats: true
  1066. }
  1067. }
  1068.  
  1069. layer {
  1070. bottom: "res4a_branch1"
  1071. top: "res4a_branch1"
  1072. name: "scale4a_branch1"
  1073. type: "Scale"
  1074. scale_param {
  1075. bias_term: true
  1076. }
  1077. }
  1078.  
  1079. layer {
  1080. bottom: "res3d"
  1081. top: "res4a_branch2a"
  1082. name: "res4a_branch2a"
  1083. type: "Convolution"
  1084. convolution_param {
  1085. num_output: 256
  1086. kernel_size: 1
  1087. pad: 0
  1088. stride: 2
  1089. bias_term: false
  1090. }
  1091. }
  1092.  
  1093. layer {
  1094. bottom: "res4a_branch2a"
  1095. top: "res4a_branch2a"
  1096. name: "bn4a_branch2a"
  1097. type: "BatchNorm"
  1098. batch_norm_param {
  1099. use_global_stats: true
  1100. }
  1101. }
  1102.  
  1103. layer {
  1104. bottom: "res4a_branch2a"
  1105. top: "res4a_branch2a"
  1106. name: "scale4a_branch2a"
  1107. type: "Scale"
  1108. scale_param {
  1109. bias_term: true
  1110. }
  1111. }
  1112.  
  1113. layer {
  1114. bottom: "res4a_branch2a"
  1115. top: "res4a_branch2a"
  1116. name: "res4a_branch2a_relu"
  1117. type: "ReLU"
  1118. }
  1119.  
  1120. layer {
  1121. bottom: "res4a_branch2a"
  1122. top: "res4a_branch2b"
  1123. name: "res4a_branch2b"
  1124. type: "Convolution"
  1125. convolution_param {
  1126. num_output: 256
  1127. kernel_size: 3
  1128. pad: 1
  1129. stride: 1
  1130. bias_term: false
  1131. }
  1132. }
  1133.  
  1134. layer {
  1135. bottom: "res4a_branch2b"
  1136. top: "res4a_branch2b"
  1137. name: "bn4a_branch2b"
  1138. type: "BatchNorm"
  1139. batch_norm_param {
  1140. use_global_stats: true
  1141. }
  1142. }
  1143.  
  1144. layer {
  1145. bottom: "res4a_branch2b"
  1146. top: "res4a_branch2b"
  1147. name: "scale4a_branch2b"
  1148. type: "Scale"
  1149. scale_param {
  1150. bias_term: true
  1151. }
  1152. }
  1153.  
  1154. layer {
  1155. bottom: "res4a_branch2b"
  1156. top: "res4a_branch2b"
  1157. name: "res4a_branch2b_relu"
  1158. type: "ReLU"
  1159. }
  1160.  
  1161. layer {
  1162. bottom: "res4a_branch2b"
  1163. top: "res4a_branch2c"
  1164. name: "res4a_branch2c"
  1165. type: "Convolution"
  1166. convolution_param {
  1167. num_output: 1024
  1168. kernel_size: 1
  1169. pad: 0
  1170. stride: 1
  1171. bias_term: false
  1172. }
  1173. }
  1174.  
  1175. layer {
  1176. bottom: "res4a_branch2c"
  1177. top: "res4a_branch2c"
  1178. name: "bn4a_branch2c"
  1179. type: "BatchNorm"
  1180. batch_norm_param {
  1181. use_global_stats: true
  1182. }
  1183. }
  1184.  
  1185. layer {
  1186. bottom: "res4a_branch2c"
  1187. top: "res4a_branch2c"
  1188. name: "scale4a_branch2c"
  1189. type: "Scale"
  1190. scale_param {
  1191. bias_term: true
  1192. }
  1193. }
  1194.  
  1195. layer {
  1196. bottom: "res4a_branch1"
  1197. bottom: "res4a_branch2c"
  1198. top: "res4a"
  1199. name: "res4a"
  1200. type: "Eltwise"
  1201. }
  1202.  
  1203. layer {
  1204. bottom: "res4a"
  1205. top: "res4a"
  1206. name: "res4a_relu"
  1207. type: "ReLU"
  1208. }
  1209.  
  1210. layer {
  1211. bottom: "res4a"
  1212. top: "res4b_branch2a"
  1213. name: "res4b_branch2a"
  1214. type: "Convolution"
  1215. convolution_param {
  1216. num_output: 256
  1217. kernel_size: 1
  1218. pad: 0
  1219. stride: 1
  1220. bias_term: false
  1221. }
  1222. }
  1223.  
  1224. layer {
  1225. bottom: "res4b_branch2a"
  1226. top: "res4b_branch2a"
  1227. name: "bn4b_branch2a"
  1228. type: "BatchNorm"
  1229. batch_norm_param {
  1230. use_global_stats: true
  1231. }
  1232. }
  1233.  
  1234. layer {
  1235. bottom: "res4b_branch2a"
  1236. top: "res4b_branch2a"
  1237. name: "scale4b_branch2a"
  1238. type: "Scale"
  1239. scale_param {
  1240. bias_term: true
  1241. }
  1242. }
  1243.  
  1244. layer {
  1245. bottom: "res4b_branch2a"
  1246. top: "res4b_branch2a"
  1247. name: "res4b_branch2a_relu"
  1248. type: "ReLU"
  1249. }
  1250.  
  1251. layer {
  1252. bottom: "res4b_branch2a"
  1253. top: "res4b_branch2b"
  1254. name: "res4b_branch2b"
  1255. type: "Convolution"
  1256. convolution_param {
  1257. num_output: 256
  1258. kernel_size: 3
  1259. pad: 1
  1260. stride: 1
  1261. bias_term: false
  1262. }
  1263. }
  1264.  
  1265. layer {
  1266. bottom: "res4b_branch2b"
  1267. top: "res4b_branch2b"
  1268. name: "bn4b_branch2b"
  1269. type: "BatchNorm"
  1270. batch_norm_param {
  1271. use_global_stats: true
  1272. }
  1273. }
  1274.  
  1275. layer {
  1276. bottom: "res4b_branch2b"
  1277. top: "res4b_branch2b"
  1278. name: "scale4b_branch2b"
  1279. type: "Scale"
  1280. scale_param {
  1281. bias_term: true
  1282. }
  1283. }
  1284.  
  1285. layer {
  1286. bottom: "res4b_branch2b"
  1287. top: "res4b_branch2b"
  1288. name: "res4b_branch2b_relu"
  1289. type: "ReLU"
  1290. }
  1291.  
  1292. layer {
  1293. bottom: "res4b_branch2b"
  1294. top: "res4b_branch2c"
  1295. name: "res4b_branch2c"
  1296. type: "Convolution"
  1297. convolution_param {
  1298. num_output: 1024
  1299. kernel_size: 1
  1300. pad: 0
  1301. stride: 1
  1302. bias_term: false
  1303. }
  1304. }
  1305.  
  1306. layer {
  1307. bottom: "res4b_branch2c"
  1308. top: "res4b_branch2c"
  1309. name: "bn4b_branch2c"
  1310. type: "BatchNorm"
  1311. batch_norm_param {
  1312. use_global_stats: true
  1313. }
  1314. }
  1315.  
  1316. layer {
  1317. bottom: "res4b_branch2c"
  1318. top: "res4b_branch2c"
  1319. name: "scale4b_branch2c"
  1320. type: "Scale"
  1321. scale_param {
  1322. bias_term: true
  1323. }
  1324. }
  1325.  
  1326. layer {
  1327. bottom: "res4a"
  1328. bottom: "res4b_branch2c"
  1329. top: "res4b"
  1330. name: "res4b"
  1331. type: "Eltwise"
  1332. }
  1333.  
  1334. layer {
  1335. bottom: "res4b"
  1336. top: "res4b"
  1337. name: "res4b_relu"
  1338. type: "ReLU"
  1339. }
  1340.  
  1341. layer {
  1342. bottom: "res4b"
  1343. top: "res4c_branch2a"
  1344. name: "res4c_branch2a"
  1345. type: "Convolution"
  1346. convolution_param {
  1347. num_output: 256
  1348. kernel_size: 1
  1349. pad: 0
  1350. stride: 1
  1351. bias_term: false
  1352. }
  1353. }
  1354.  
  1355. layer {
  1356. bottom: "res4c_branch2a"
  1357. top: "res4c_branch2a"
  1358. name: "bn4c_branch2a"
  1359. type: "BatchNorm"
  1360. batch_norm_param {
  1361. use_global_stats: true
  1362. }
  1363. }
  1364.  
  1365. layer {
  1366. bottom: "res4c_branch2a"
  1367. top: "res4c_branch2a"
  1368. name: "scale4c_branch2a"
  1369. type: "Scale"
  1370. scale_param {
  1371. bias_term: true
  1372. }
  1373. }
  1374.  
  1375. layer {
  1376. bottom: "res4c_branch2a"
  1377. top: "res4c_branch2a"
  1378. name: "res4c_branch2a_relu"
  1379. type: "ReLU"
  1380. }
  1381.  
  1382. layer {
  1383. bottom: "res4c_branch2a"
  1384. top: "res4c_branch2b"
  1385. name: "res4c_branch2b"
  1386. type: "Convolution"
  1387. convolution_param {
  1388. num_output: 256
  1389. kernel_size: 3
  1390. pad: 1
  1391. stride: 1
  1392. bias_term: false
  1393. }
  1394. }
  1395.  
  1396. layer {
  1397. bottom: "res4c_branch2b"
  1398. top: "res4c_branch2b"
  1399. name: "bn4c_branch2b"
  1400. type: "BatchNorm"
  1401. batch_norm_param {
  1402. use_global_stats: true
  1403. }
  1404. }
  1405.  
  1406. layer {
  1407. bottom: "res4c_branch2b"
  1408. top: "res4c_branch2b"
  1409. name: "scale4c_branch2b"
  1410. type: "Scale"
  1411. scale_param {
  1412. bias_term: true
  1413. }
  1414. }
  1415.  
  1416. layer {
  1417. bottom: "res4c_branch2b"
  1418. top: "res4c_branch2b"
  1419. name: "res4c_branch2b_relu"
  1420. type: "ReLU"
  1421. }
  1422.  
  1423. layer {
  1424. bottom: "res4c_branch2b"
  1425. top: "res4c_branch2c"
  1426. name: "res4c_branch2c"
  1427. type: "Convolution"
  1428. convolution_param {
  1429. num_output: 1024
  1430. kernel_size: 1
  1431. pad: 0
  1432. stride: 1
  1433. bias_term: false
  1434. }
  1435. }
  1436.  
  1437. layer {
  1438. bottom: "res4c_branch2c"
  1439. top: "res4c_branch2c"
  1440. name: "bn4c_branch2c"
  1441. type: "BatchNorm"
  1442. batch_norm_param {
  1443. use_global_stats: true
  1444. }
  1445. }
  1446.  
  1447. layer {
  1448. bottom: "res4c_branch2c"
  1449. top: "res4c_branch2c"
  1450. name: "scale4c_branch2c"
  1451. type: "Scale"
  1452. scale_param {
  1453. bias_term: true
  1454. }
  1455. }
  1456.  
  1457. layer {
  1458. bottom: "res4b"
  1459. bottom: "res4c_branch2c"
  1460. top: "res4c"
  1461. name: "res4c"
  1462. type: "Eltwise"
  1463. }
  1464.  
  1465. layer {
  1466. bottom: "res4c"
  1467. top: "res4c"
  1468. name: "res4c_relu"
  1469. type: "ReLU"
  1470. }
  1471.  
  1472. layer {
  1473. bottom: "res4c"
  1474. top: "res4d_branch2a"
  1475. name: "res4d_branch2a"
  1476. type: "Convolution"
  1477. convolution_param {
  1478. num_output: 256
  1479. kernel_size: 1
  1480. pad: 0
  1481. stride: 1
  1482. bias_term: false
  1483. }
  1484. }
  1485.  
  1486. layer {
  1487. bottom: "res4d_branch2a"
  1488. top: "res4d_branch2a"
  1489. name: "bn4d_branch2a"
  1490. type: "BatchNorm"
  1491. batch_norm_param {
  1492. use_global_stats: true
  1493. }
  1494. }
  1495.  
  1496. layer {
  1497. bottom: "res4d_branch2a"
  1498. top: "res4d_branch2a"
  1499. name: "scale4d_branch2a"
  1500. type: "Scale"
  1501. scale_param {
  1502. bias_term: true
  1503. }
  1504. }
  1505.  
  1506. layer {
  1507. bottom: "res4d_branch2a"
  1508. top: "res4d_branch2a"
  1509. name: "res4d_branch2a_relu"
  1510. type: "ReLU"
  1511. }
  1512.  
  1513. layer {
  1514. bottom: "res4d_branch2a"
  1515. top: "res4d_branch2b"
  1516. name: "res4d_branch2b"
  1517. type: "Convolution"
  1518. convolution_param {
  1519. num_output: 256
  1520. kernel_size: 3
  1521. pad: 1
  1522. stride: 1
  1523. bias_term: false
  1524. }
  1525. }
  1526.  
  1527. layer {
  1528. bottom: "res4d_branch2b"
  1529. top: "res4d_branch2b"
  1530. name: "bn4d_branch2b"
  1531. type: "BatchNorm"
  1532. batch_norm_param {
  1533. use_global_stats: true
  1534. }
  1535. }
  1536.  
  1537. layer {
  1538. bottom: "res4d_branch2b"
  1539. top: "res4d_branch2b"
  1540. name: "scale4d_branch2b"
  1541. type: "Scale"
  1542. scale_param {
  1543. bias_term: true
  1544. }
  1545. }
  1546.  
  1547. layer {
  1548. bottom: "res4d_branch2b"
  1549. top: "res4d_branch2b"
  1550. name: "res4d_branch2b_relu"
  1551. type: "ReLU"
  1552. }
  1553.  
  1554. layer {
  1555. bottom: "res4d_branch2b"
  1556. top: "res4d_branch2c"
  1557. name: "res4d_branch2c"
  1558. type: "Convolution"
  1559. convolution_param {
  1560. num_output: 1024
  1561. kernel_size: 1
  1562. pad: 0
  1563. stride: 1
  1564. bias_term: false
  1565. }
  1566. }
  1567.  
  1568. layer {
  1569. bottom: "res4d_branch2c"
  1570. top: "res4d_branch2c"
  1571. name: "bn4d_branch2c"
  1572. type: "BatchNorm"
  1573. batch_norm_param {
  1574. use_global_stats: true
  1575. }
  1576. }
  1577.  
  1578. layer {
  1579. bottom: "res4d_branch2c"
  1580. top: "res4d_branch2c"
  1581. name: "scale4d_branch2c"
  1582. type: "Scale"
  1583. scale_param {
  1584. bias_term: true
  1585. }
  1586. }
  1587.  
  1588. layer {
  1589. bottom: "res4c"
  1590. bottom: "res4d_branch2c"
  1591. top: "res4d"
  1592. name: "res4d"
  1593. type: "Eltwise"
  1594. }
  1595.  
  1596. layer {
  1597. bottom: "res4d"
  1598. top: "res4d"
  1599. name: "res4d_relu"
  1600. type: "ReLU"
  1601. }
  1602.  
  1603. layer {
  1604. bottom: "res4d"
  1605. top: "res4e_branch2a"
  1606. name: "res4e_branch2a"
  1607. type: "Convolution"
  1608. convolution_param {
  1609. num_output: 256
  1610. kernel_size: 1
  1611. pad: 0
  1612. stride: 1
  1613. bias_term: false
  1614. }
  1615. }
  1616.  
  1617. layer {
  1618. bottom: "res4e_branch2a"
  1619. top: "res4e_branch2a"
  1620. name: "bn4e_branch2a"
  1621. type: "BatchNorm"
  1622. batch_norm_param {
  1623. use_global_stats: true
  1624. }
  1625. }
  1626.  
  1627. layer {
  1628. bottom: "res4e_branch2a"
  1629. top: "res4e_branch2a"
  1630. name: "scale4e_branch2a"
  1631. type: "Scale"
  1632. scale_param {
  1633. bias_term: true
  1634. }
  1635. }
  1636.  
  1637. layer {
  1638. bottom: "res4e_branch2a"
  1639. top: "res4e_branch2a"
  1640. name: "res4e_branch2a_relu"
  1641. type: "ReLU"
  1642. }
  1643.  
  1644. layer {
  1645. bottom: "res4e_branch2a"
  1646. top: "res4e_branch2b"
  1647. name: "res4e_branch2b"
  1648. type: "Convolution"
  1649. convolution_param {
  1650. num_output: 256
  1651. kernel_size: 3
  1652. pad: 1
  1653. stride: 1
  1654. bias_term: false
  1655. }
  1656. }
  1657.  
  1658. layer {
  1659. bottom: "res4e_branch2b"
  1660. top: "res4e_branch2b"
  1661. name: "bn4e_branch2b"
  1662. type: "BatchNorm"
  1663. batch_norm_param {
  1664. use_global_stats: true
  1665. }
  1666. }
  1667.  
  1668. layer {
  1669. bottom: "res4e_branch2b"
  1670. top: "res4e_branch2b"
  1671. name: "scale4e_branch2b"
  1672. type: "Scale"
  1673. scale_param {
  1674. bias_term: true
  1675. }
  1676. }
  1677.  
  1678. layer {
  1679. bottom: "res4e_branch2b"
  1680. top: "res4e_branch2b"
  1681. name: "res4e_branch2b_relu"
  1682. type: "ReLU"
  1683. }
  1684.  
  1685. layer {
  1686. bottom: "res4e_branch2b"
  1687. top: "res4e_branch2c"
  1688. name: "res4e_branch2c"
  1689. type: "Convolution"
  1690. convolution_param {
  1691. num_output: 1024
  1692. kernel_size: 1
  1693. pad: 0
  1694. stride: 1
  1695. bias_term: false
  1696. }
  1697. }
  1698.  
  1699. layer {
  1700. bottom: "res4e_branch2c"
  1701. top: "res4e_branch2c"
  1702. name: "bn4e_branch2c"
  1703. type: "BatchNorm"
  1704. batch_norm_param {
  1705. use_global_stats: true
  1706. }
  1707. }
  1708.  
  1709. layer {
  1710. bottom: "res4e_branch2c"
  1711. top: "res4e_branch2c"
  1712. name: "scale4e_branch2c"
  1713. type: "Scale"
  1714. scale_param {
  1715. bias_term: true
  1716. }
  1717. }
  1718.  
  1719. layer {
  1720. bottom: "res4d"
  1721. bottom: "res4e_branch2c"
  1722. top: "res4e"
  1723. name: "res4e"
  1724. type: "Eltwise"
  1725. }
  1726.  
  1727. layer {
  1728. bottom: "res4e"
  1729. top: "res4e"
  1730. name: "res4e_relu"
  1731. type: "ReLU"
  1732. }
  1733.  
  1734. layer {
  1735. bottom: "res4e"
  1736. top: "res4f_branch2a"
  1737. name: "res4f_branch2a"
  1738. type: "Convolution"
  1739. convolution_param {
  1740. num_output: 256
  1741. kernel_size: 1
  1742. pad: 0
  1743. stride: 1
  1744. bias_term: false
  1745. }
  1746. }
  1747.  
  1748. layer {
  1749. bottom: "res4f_branch2a"
  1750. top: "res4f_branch2a"
  1751. name: "bn4f_branch2a"
  1752. type: "BatchNorm"
  1753. batch_norm_param {
  1754. use_global_stats: true
  1755. }
  1756. }
  1757.  
  1758. layer {
  1759. bottom: "res4f_branch2a"
  1760. top: "res4f_branch2a"
  1761. name: "scale4f_branch2a"
  1762. type: "Scale"
  1763. scale_param {
  1764. bias_term: true
  1765. }
  1766. }
  1767.  
  1768. layer {
  1769. bottom: "res4f_branch2a"
  1770. top: "res4f_branch2a"
  1771. name: "res4f_branch2a_relu"
  1772. type: "ReLU"
  1773. }
  1774.  
  1775. layer {
  1776. bottom: "res4f_branch2a"
  1777. top: "res4f_branch2b"
  1778. name: "res4f_branch2b"
  1779. type: "Convolution"
  1780. convolution_param {
  1781. num_output: 256
  1782. kernel_size: 3
  1783. pad: 1
  1784. stride: 1
  1785. bias_term: false
  1786. }
  1787. }
  1788.  
  1789. layer {
  1790. bottom: "res4f_branch2b"
  1791. top: "res4f_branch2b"
  1792. name: "bn4f_branch2b"
  1793. type: "BatchNorm"
  1794. batch_norm_param {
  1795. use_global_stats: true
  1796. }
  1797. }
  1798.  
  1799. layer {
  1800. bottom: "res4f_branch2b"
  1801. top: "res4f_branch2b"
  1802. name: "scale4f_branch2b"
  1803. type: "Scale"
  1804. scale_param {
  1805. bias_term: true
  1806. }
  1807. }
  1808.  
  1809. layer {
  1810. bottom: "res4f_branch2b"
  1811. top: "res4f_branch2b"
  1812. name: "res4f_branch2b_relu"
  1813. type: "ReLU"
  1814. }
  1815.  
  1816. layer {
  1817. bottom: "res4f_branch2b"
  1818. top: "res4f_branch2c"
  1819. name: "res4f_branch2c"
  1820. type: "Convolution"
  1821. convolution_param {
  1822. num_output: 1024
  1823. kernel_size: 1
  1824. pad: 0
  1825. stride: 1
  1826. bias_term: false
  1827. }
  1828. }
  1829.  
  1830. layer {
  1831. bottom: "res4f_branch2c"
  1832. top: "res4f_branch2c"
  1833. name: "bn4f_branch2c"
  1834. type: "BatchNorm"
  1835. batch_norm_param {
  1836. use_global_stats: true
  1837. }
  1838. }
  1839.  
  1840. layer {
  1841. bottom: "res4f_branch2c"
  1842. top: "res4f_branch2c"
  1843. name: "scale4f_branch2c"
  1844. type: "Scale"
  1845. scale_param {
  1846. bias_term: true
  1847. }
  1848. }
  1849.  
  1850. layer {
  1851. bottom: "res4e"
  1852. bottom: "res4f_branch2c"
  1853. top: "res4f"
  1854. name: "res4f"
  1855. type: "Eltwise"
  1856. }
  1857.  
  1858. layer {
  1859. bottom: "res4f"
  1860. top: "res4f"
  1861. name: "res4f_relu"
  1862. type: "ReLU"
  1863. }
  1864.  
  1865. layer {
  1866. bottom: "res4f"
  1867. top: "res5a_branch1"
  1868. name: "res5a_branch1"
  1869. type: "Convolution"
  1870. convolution_param {
  1871. num_output: 2048
  1872. kernel_size: 1
  1873. pad: 0
  1874. stride: 2
  1875. bias_term: false
  1876. }
  1877. }
  1878.  
  1879. layer {
  1880. bottom: "res5a_branch1"
  1881. top: "res5a_branch1"
  1882. name: "bn5a_branch1"
  1883. type: "BatchNorm"
  1884. batch_norm_param {
  1885. use_global_stats: true
  1886. }
  1887. }
  1888.  
  1889. layer {
  1890. bottom: "res5a_branch1"
  1891. top: "res5a_branch1"
  1892. name: "scale5a_branch1"
  1893. type: "Scale"
  1894. scale_param {
  1895. bias_term: true
  1896. }
  1897. }
  1898.  
  1899. layer {
  1900. bottom: "res4f"
  1901. top: "res5a_branch2a"
  1902. name: "res5a_branch2a"
  1903. type: "Convolution"
  1904. convolution_param {
  1905. num_output: 512
  1906. kernel_size: 1
  1907. pad: 0
  1908. stride: 2
  1909. bias_term: false
  1910. }
  1911. }
  1912.  
  1913. layer {
  1914. bottom: "res5a_branch2a"
  1915. top: "res5a_branch2a"
  1916. name: "bn5a_branch2a"
  1917. type: "BatchNorm"
  1918. batch_norm_param {
  1919. use_global_stats: true
  1920. }
  1921. }
  1922.  
  1923. layer {
  1924. bottom: "res5a_branch2a"
  1925. top: "res5a_branch2a"
  1926. name: "scale5a_branch2a"
  1927. type: "Scale"
  1928. scale_param {
  1929. bias_term: true
  1930. }
  1931. }
  1932.  
  1933. layer {
  1934. bottom: "res5a_branch2a"
  1935. top: "res5a_branch2a"
  1936. name: "res5a_branch2a_relu"
  1937. type: "ReLU"
  1938. }
  1939.  
  1940. layer {
  1941. bottom: "res5a_branch2a"
  1942. top: "res5a_branch2b"
  1943. name: "res5a_branch2b"
  1944. type: "Convolution"
  1945. convolution_param {
  1946. num_output: 512
  1947. kernel_size: 3
  1948. pad: 1
  1949. stride: 1
  1950. bias_term: false
  1951. }
  1952. }
  1953.  
  1954. layer {
  1955. bottom: "res5a_branch2b"
  1956. top: "res5a_branch2b"
  1957. name: "bn5a_branch2b"
  1958. type: "BatchNorm"
  1959. batch_norm_param {
  1960. use_global_stats: true
  1961. }
  1962. }
  1963.  
  1964. layer {
  1965. bottom: "res5a_branch2b"
  1966. top: "res5a_branch2b"
  1967. name: "scale5a_branch2b"
  1968. type: "Scale"
  1969. scale_param {
  1970. bias_term: true
  1971. }
  1972. }
  1973.  
  1974. layer {
  1975. bottom: "res5a_branch2b"
  1976. top: "res5a_branch2b"
  1977. name: "res5a_branch2b_relu"
  1978. type: "ReLU"
  1979. }
  1980.  
  1981. layer {
  1982. bottom: "res5a_branch2b"
  1983. top: "res5a_branch2c"
  1984. name: "res5a_branch2c"
  1985. type: "Convolution"
  1986. convolution_param {
  1987. num_output: 2048
  1988. kernel_size: 1
  1989. pad: 0
  1990. stride: 1
  1991. bias_term: false
  1992. }
  1993. }
  1994.  
  1995. layer {
  1996. bottom: "res5a_branch2c"
  1997. top: "res5a_branch2c"
  1998. name: "bn5a_branch2c"
  1999. type: "BatchNorm"
  2000. batch_norm_param {
  2001. use_global_stats: true
  2002. }
  2003. }
  2004.  
  2005. layer {
  2006. bottom: "res5a_branch2c"
  2007. top: "res5a_branch2c"
  2008. name: "scale5a_branch2c"
  2009. type: "Scale"
  2010. scale_param {
  2011. bias_term: true
  2012. }
  2013. }
  2014.  
  2015. layer {
  2016. bottom: "res5a_branch1"
  2017. bottom: "res5a_branch2c"
  2018. top: "res5a"
  2019. name: "res5a"
  2020. type: "Eltwise"
  2021. }
  2022.  
  2023. layer {
  2024. bottom: "res5a"
  2025. top: "res5a"
  2026. name: "res5a_relu"
  2027. type: "ReLU"
  2028. }
  2029.  
  2030. layer {
  2031. bottom: "res5a"
  2032. top: "res5b_branch2a"
  2033. name: "res5b_branch2a"
  2034. type: "Convolution"
  2035. convolution_param {
  2036. num_output: 512
  2037. kernel_size: 1
  2038. pad: 0
  2039. stride: 1
  2040. bias_term: false
  2041. }
  2042. }
  2043.  
  2044. layer {
  2045. bottom: "res5b_branch2a"
  2046. top: "res5b_branch2a"
  2047. name: "bn5b_branch2a"
  2048. type: "BatchNorm"
  2049. batch_norm_param {
  2050. use_global_stats: true
  2051. }
  2052. }
  2053.  
  2054. layer {
  2055. bottom: "res5b_branch2a"
  2056. top: "res5b_branch2a"
  2057. name: "scale5b_branch2a"
  2058. type: "Scale"
  2059. scale_param {
  2060. bias_term: true
  2061. }
  2062. }
  2063.  
  2064. layer {
  2065. bottom: "res5b_branch2a"
  2066. top: "res5b_branch2a"
  2067. name: "res5b_branch2a_relu"
  2068. type: "ReLU"
  2069. }
  2070.  
  2071. layer {
  2072. bottom: "res5b_branch2a"
  2073. top: "res5b_branch2b"
  2074. name: "res5b_branch2b"
  2075. type: "Convolution"
  2076. convolution_param {
  2077. num_output: 512
  2078. kernel_size: 3
  2079. pad: 1
  2080. stride: 1
  2081. bias_term: false
  2082. }
  2083. }
  2084.  
  2085. layer {
  2086. bottom: "res5b_branch2b"
  2087. top: "res5b_branch2b"
  2088. name: "bn5b_branch2b"
  2089. type: "BatchNorm"
  2090. batch_norm_param {
  2091. use_global_stats: true
  2092. }
  2093. }
  2094.  
  2095. layer {
  2096. bottom: "res5b_branch2b"
  2097. top: "res5b_branch2b"
  2098. name: "scale5b_branch2b"
  2099. type: "Scale"
  2100. scale_param {
  2101. bias_term: true
  2102. }
  2103. }
  2104.  
  2105. layer {
  2106. bottom: "res5b_branch2b"
  2107. top: "res5b_branch2b"
  2108. name: "res5b_branch2b_relu"
  2109. type: "ReLU"
  2110. }
  2111.  
  2112. layer {
  2113. bottom: "res5b_branch2b"
  2114. top: "res5b_branch2c"
  2115. name: "res5b_branch2c"
  2116. type: "Convolution"
  2117. convolution_param {
  2118. num_output: 2048
  2119. kernel_size: 1
  2120. pad: 0
  2121. stride: 1
  2122. bias_term: false
  2123. }
  2124. }
  2125.  
  2126. layer {
  2127. bottom: "res5b_branch2c"
  2128. top: "res5b_branch2c"
  2129. name: "bn5b_branch2c"
  2130. type: "BatchNorm"
  2131. batch_norm_param {
  2132. use_global_stats: true
  2133. }
  2134. }
  2135.  
  2136. layer {
  2137. bottom: "res5b_branch2c"
  2138. top: "res5b_branch2c"
  2139. name: "scale5b_branch2c"
  2140. type: "Scale"
  2141. scale_param {
  2142. bias_term: true
  2143. }
  2144. }
  2145.  
  2146. layer {
  2147. bottom: "res5a"
  2148. bottom: "res5b_branch2c"
  2149. top: "res5b"
  2150. name: "res5b"
  2151. type: "Eltwise"
  2152. }
  2153.  
  2154. layer {
  2155. bottom: "res5b"
  2156. top: "res5b"
  2157. name: "res5b_relu"
  2158. type: "ReLU"
  2159. }
  2160.  
  2161. layer {
  2162. bottom: "res5b"
  2163. top: "res5c_branch2a"
  2164. name: "res5c_branch2a"
  2165. type: "Convolution"
  2166. convolution_param {
  2167. num_output: 512
  2168. kernel_size: 1
  2169. pad: 0
  2170. stride: 1
  2171. bias_term: false
  2172. }
  2173. }
  2174.  
  2175. layer {
  2176. bottom: "res5c_branch2a"
  2177. top: "res5c_branch2a"
  2178. name: "bn5c_branch2a"
  2179. type: "BatchNorm"
  2180. batch_norm_param {
  2181. use_global_stats: true
  2182. }
  2183. }
  2184.  
  2185. layer {
  2186. bottom: "res5c_branch2a"
  2187. top: "res5c_branch2a"
  2188. name: "scale5c_branch2a"
  2189. type: "Scale"
  2190. scale_param {
  2191. bias_term: true
  2192. }
  2193. }
  2194.  
  2195. layer {
  2196. bottom: "res5c_branch2a"
  2197. top: "res5c_branch2a"
  2198. name: "res5c_branch2a_relu"
  2199. type: "ReLU"
  2200. }
  2201.  
  2202. layer {
  2203. bottom: "res5c_branch2a"
  2204. top: "res5c_branch2b"
  2205. name: "res5c_branch2b"
  2206. type: "Convolution"
  2207. convolution_param {
  2208. num_output: 512
  2209. kernel_size: 3
  2210. pad: 1
  2211. stride: 1
  2212. bias_term: false
  2213. }
  2214. }
  2215.  
  2216. layer {
  2217. bottom: "res5c_branch2b"
  2218. top: "res5c_branch2b"
  2219. name: "bn5c_branch2b"
  2220. type: "BatchNorm"
  2221. batch_norm_param {
  2222. use_global_stats: true
  2223. }
  2224. }
  2225.  
  2226. layer {
  2227. bottom: "res5c_branch2b"
  2228. top: "res5c_branch2b"
  2229. name: "scale5c_branch2b"
  2230. type: "Scale"
  2231. scale_param {
  2232. bias_term: true
  2233. }
  2234. }
  2235.  
  2236. layer {
  2237. bottom: "res5c_branch2b"
  2238. top: "res5c_branch2b"
  2239. name: "res5c_branch2b_relu"
  2240. type: "ReLU"
  2241. }
  2242.  
  2243. layer {
  2244. bottom: "res5c_branch2b"
  2245. top: "res5c_branch2c"
  2246. name: "res5c_branch2c"
  2247. type: "Convolution"
  2248. convolution_param {
  2249. num_output: 2048
  2250. kernel_size: 1
  2251. pad: 0
  2252. stride: 1
  2253. bias_term: false
  2254. }
  2255. }
  2256.  
  2257. layer {
  2258. bottom: "res5c_branch2c"
  2259. top: "res5c_branch2c"
  2260. name: "bn5c_branch2c"
  2261. type: "BatchNorm"
  2262. batch_norm_param {
  2263. use_global_stats: true
  2264. }
  2265. }
  2266.  
  2267. layer {
  2268. bottom: "res5c_branch2c"
  2269. top: "res5c_branch2c"
  2270. name: "scale5c_branch2c"
  2271. type: "Scale"
  2272. scale_param {
  2273. bias_term: true
  2274. }
  2275. }
  2276.  
  2277. layer {
  2278. bottom: "res5b"
  2279. bottom: "res5c_branch2c"
  2280. top: "res5c"
  2281. name: "res5c"
  2282. type: "Eltwise"
  2283. }
  2284.  
  2285. layer {
  2286. bottom: "res5c"
  2287. top: "res5c"
  2288. name: "res5c_relu"
  2289. type: "ReLU"
  2290. }
  2291.  
  2292. layer{
  2293. bottom: "res5c"
  2294. top: "dec1"
  2295. name: "dec1"
  2296. type: "Convolution"
  2297. convolution_param{
  2298. num_output: 1024
  2299. kernel_size: 1
  2300. pad: 0
  2301. stride: 1
  2302. bias_term: false
  2303. }
  2304. }
  2305.  
  2306.  
  2307.  
  2308. layer {
  2309. bottom: "dec1"
  2310. top: "dec1"
  2311. name: "bn6"
  2312. type: "BatchNorm"
  2313. batch_norm_param {
  2314. use_global_stats: true
  2315. }
  2316. }
  2317.  
  2318. layer {
  2319. bottom: "dec1"
  2320. top: "dec1"
  2321. name: "scale6"
  2322. type: "Scale"
  2323. scale_param {
  2324. bias_term: true
  2325. }
  2326. }
  2327.  
  2328. layer{
  2329. bottom: "dec1"
  2330. top: "dec2_branch1a"
  2331. name: "dec2_branch1a"
  2332. type: "Convolution"
  2333. convolution_param{
  2334. num_output: 512
  2335. kernel_size: 3
  2336. pad: 1
  2337. stride: 1
  2338. bias_term: true
  2339. }
  2340. }
  2341.  
  2342. layer{
  2343. bottom: "dec1"
  2344. top: "dec2_branch1b"
  2345. name: "dec2_branch1b"
  2346. type: "Convolution"
  2347. convolution_param{
  2348. num_output: 512
  2349. kernel_h: 3
  2350. kernel_w: 2
  2351. pad: 1
  2352. stride: 1
  2353. bias_term: true
  2354. }
  2355. }
  2356.  
  2357. layer{
  2358. bottom: "dec2_branch1b"
  2359. bottom: "dec1"
  2360. top: "dec2_branch1b"
  2361. name: "crop2_branch1b"
  2362. type: "Crop"
  2363. crop_param{
  2364. axis: 2
  2365. offset: 0
  2366. offset:1
  2367. }
  2368. }
  2369.  
  2370. layer{
  2371. bottom: "dec1"
  2372. top: "dec2_branch1c"
  2373. name: "dec2_branch1c"
  2374. type: "Convolution"
  2375. convolution_param{
  2376. num_output: 512
  2377. kernel_h: 2
  2378. kernel_w: 3
  2379. pad: 1
  2380. stride: 1
  2381. bias_term: true
  2382. }
  2383. }
  2384.  
  2385. layer{
  2386. bottom: "dec2_branch1c"
  2387. bottom: "dec1"
  2388. top: "dec2_branch1c"
  2389. name: "crop2_branch1c"
  2390. type: "Crop"
  2391. crop_param{
  2392. axis: 2
  2393. offset: 1
  2394. offset: 0
  2395. }
  2396. }
  2397.  
  2398. layer{
  2399. bottom: "dec1"
  2400. top: "dec2_branch1d"
  2401. name: "dec2_branch1d"
  2402. type: "Convolution"
  2403. convolution_param{
  2404. num_output: 512
  2405. kernel_size: 2
  2406. pad: 1
  2407. stride: 1
  2408. bias_term: true
  2409. }
  2410. }
  2411.  
  2412. layer{
  2413. bottom: "dec2_branch1d"
  2414. bottom: "dec1"
  2415. top: "dec2_branch1d"
  2416. name: "crop2_branch1d"
  2417. type: "Crop"
  2418. crop_param{
  2419. axis: 2
  2420. offset: 1
  2421. offset:1
  2422. }
  2423. }
  2424.  
  2425. ############### INTERLEAVE ###################
  2426.  
  2427. layer{
  2428. bottom: "dec2_branch1a"
  2429. bottom: "dec2_branch1b"
  2430. top: "con1_branch1a"
  2431. type: "Concat"
  2432. name: "con1_branch1a"
  2433. concat_param{
  2434. axis: 2
  2435. }
  2436.  
  2437. }
  2438.  
  2439. layer{
  2440. bottom: "dec2_branch1c"
  2441. bottom: "dec2_branch1d"
  2442. top: "con1_branch1b"
  2443. type: "Concat"
  2444. name: "con1_branch1b"
  2445. concat_param{
  2446. axis: 2
  2447. }
  2448. }
  2449.  
  2450. layer{
  2451. bottom: "con1_branch1a"
  2452. bottom: "con1_branch1b"
  2453. top: "int1_branch1"
  2454. type: "Concat"
  2455. name: "int1_branch1"
  2456. concat_param{
  2457. axis: 3
  2458. }
  2459. }
  2460.  
  2461. ############### END INTERLEAVE #################
  2462.  
  2463. layer{
  2464. bottom: "int1_branch1"
  2465. top: "int1_branch1"
  2466. type: "ReLU"
  2467. name: "relu1_branch1"
  2468. }
  2469.  
  2470. layer{
  2471. bottom: "int1_branch1"
  2472. top: "dec2_branch1e"
  2473. name: "dec2_branch1e"
  2474. type: "Convolution"
  2475. convolution_param{
  2476. num_output: 512
  2477. kernel_size: 3
  2478. pad: 1
  2479. stride: 1
  2480. bias_term: true
  2481. }
  2482. }
  2483.  
  2484. ######3==============================
  2485.  
  2486. layer{
  2487. bottom: "dec1"
  2488. top: "dec2_branch2a"
  2489. name: "dec2_branch2a"
  2490. type: "Convolution"
  2491. convolution_param{
  2492. num_output: 512
  2493. kernel_size: 3
  2494. pad: 1
  2495. stride: 1
  2496. bias_term: true
  2497. }
  2498. }
  2499.  
  2500. layer{
  2501. bottom: "dec1"
  2502. top: "dec2_branch2b"
  2503. name: "dec2_branch2b"
  2504. type: "Convolution"
  2505. convolution_param{
  2506. num_output: 512
  2507. kernel_h: 3
  2508. kernel_w: 2
  2509. pad: 1
  2510. stride: 1
  2511. bias_term: true
  2512. }
  2513. }
  2514.  
  2515. layer{
  2516. bottom: "dec2_branch2b"
  2517. bottom: "dec1"
  2518. top: "dec2_branch2b"
  2519. name: "crop2_branch2b"
  2520. type: "Crop"
  2521. crop_param{
  2522. axis: 2
  2523. offset: 0
  2524. offset: 1
  2525. }
  2526. }
  2527.  
  2528. layer{
  2529. bottom: "dec1"
  2530. top: "dec2_branch2c"
  2531. name: "dec2_branch2c"
  2532. type: "Convolution"
  2533. convolution_param{
  2534. num_output: 512
  2535. kernel_h: 2
  2536. kernel_w: 3
  2537. pad: 1
  2538. stride: 1
  2539. bias_term: true
  2540. }
  2541. }
  2542.  
  2543. layer{
  2544. bottom: "dec2_branch2c"
  2545. bottom: "dec1"
  2546. top: "dec2_branch2c"
  2547. name: "crop2_branch2c"
  2548. type: "Crop"
  2549. crop_param{
  2550. axis: 2
  2551. offset: 1
  2552. offset: 0
  2553. }
  2554. }
  2555.  
  2556. layer{
  2557. bottom: "dec1"
  2558. top: "dec2_branch2d"
  2559. name: "dec2_branch2d"
  2560. type: "Convolution"
  2561. convolution_param{
  2562. num_output: 512
  2563. kernel_size: 2
  2564. pad: 1
  2565. stride: 1
  2566. bias_term: true
  2567. }
  2568. }
  2569.  
  2570. layer{
  2571. bottom: "dec2_branch2d"
  2572. bottom: "dec1"
  2573. top: "dec2_branch2d"
  2574. name: "crop2_branch2d"
  2575. type: "Crop"
  2576. crop_param{
  2577. axis: 2
  2578. offset: 1
  2579. offset:1
  2580. }
  2581. }
  2582.  
  2583. ############### INTERLEAVE ###################
  2584.  
  2585. layer{
  2586. bottom: "dec2_branch2a"
  2587. bottom: "dec2_branch2b"
  2588. top: "con1_branch2a"
  2589. type: "Concat"
  2590. name: "con1_branch2a"
  2591. concat_param{
  2592. axis: 2
  2593. }
  2594.  
  2595. }
  2596.  
  2597. layer{
  2598. bottom: "dec2_branch2c"
  2599. bottom: "dec2_branch2d"
  2600. top: "con1_branch2b"
  2601. type: "Concat"
  2602. name: "con1_branch2b"
  2603. concat_param{
  2604. axis: 2
  2605. }
  2606. }
  2607.  
  2608. layer{
  2609. bottom: "con1_branch2a"
  2610. bottom: "con1_branch2b"
  2611. top: "int1_branch2"
  2612. type: "Concat"
  2613. name: "int1_branch2"
  2614. concat_param{
  2615. axis: 3
  2616. }
  2617. }
  2618.  
  2619. ############### END INTERLEAVE #################
  2620.  
  2621. layer{
  2622. bottom: "int1_branch2"
  2623. bottom: "dec2_branch1e"
  2624. top: "dec2o"
  2625. name: "dec2o"
  2626. type: "Eltwise"
  2627. eltwise_param{
  2628. operation: SUM
  2629. }
  2630. }
  2631.  
  2632. layer {
  2633. bottom: "dec2o"
  2634. top: "dec2o"
  2635. name: "bn2o"
  2636. type: "BatchNorm"
  2637. batch_norm_param {
  2638. use_global_stats: true
  2639. }
  2640. }
  2641.  
  2642. layer {
  2643. bottom: "dec2o"
  2644. top: "dec2o"
  2645. name: "scale2o"
  2646. type: "Scale"
  2647. scale_param {
  2648. bias_term: true
  2649. }
  2650. }
  2651.  
  2652. layer{
  2653. bottom: "dec2o"
  2654. top: "dec2o"
  2655. name: "scale2o_relu"
  2656. type: "ReLU"
  2657. }
  2658.  
  2659. #======== END OF FIRST DECODE ==================
  2660.  
  2661. layer{
  2662. bottom: "dec2o"
  2663. top: "dec3_branch1a"
  2664. name: "dec3_branch1a"
  2665. type: "Convolution"
  2666. convolution_param{
  2667. num_output: 256
  2668. kernel_size: 3
  2669. pad: 1
  2670. stride: 1
  2671. bias_term: true
  2672. }
  2673. }
  2674.  
  2675. layer{
  2676. bottom: "dec2o"
  2677. top: "dec3_branch1b"
  2678. name: "dec3_branch1b"
  2679. type: "Convolution"
  2680. convolution_param{
  2681. num_output: 256
  2682. kernel_h: 3
  2683. kernel_w: 2
  2684. pad: 1
  2685. stride: 1
  2686. bias_term: true
  2687. }
  2688. }
  2689.  
  2690. layer{
  2691. bottom: "dec3_branch1b"
  2692. bottom: "dec2o"
  2693. top: "dec3_branch1b"
  2694. name: "crop3_branch1b"
  2695. type: "Crop"
  2696. crop_param{
  2697. axis: 2
  2698. offset: 0
  2699. offset:1
  2700. }
  2701. }
  2702.  
  2703. layer{
  2704. bottom: "dec2o"
  2705. top: "dec3_branch1c"
  2706. name: "dec3_branch1c"
  2707. type: "Convolution"
  2708. convolution_param{
  2709. num_output: 256
  2710. kernel_h: 2
  2711. kernel_w: 3
  2712. pad: 1
  2713. stride: 1
  2714. bias_term: true
  2715. }
  2716. }
  2717.  
  2718. layer{
  2719. bottom: "dec3_branch1c"
  2720. bottom: "dec2o"
  2721. top: "dec3_branch1c"
  2722. name: "crop3_branch1c"
  2723. type: "Crop"
  2724. crop_param{
  2725. axis: 2
  2726. offset: 1
  2727. offset: 0
  2728. }
  2729. }
  2730.  
  2731. layer{
  2732. bottom: "dec2o"
  2733. top: "dec3_branch1d"
  2734. name: "dec3_branch1d"
  2735. type: "Convolution"
  2736. convolution_param{
  2737. num_output: 256
  2738. kernel_size: 2
  2739. pad: 1
  2740. stride: 1
  2741. bias_term: true
  2742. }
  2743. }
  2744.  
  2745. layer{
  2746. bottom: "dec3_branch1d"
  2747. bottom: "dec2o"
  2748. top: "dec3_branch1d"
  2749. name: "crop3_branch1d"
  2750. type: "Crop"
  2751. crop_param{
  2752. axis: 2
  2753. offset: 1
  2754. offset:1
  2755. }
  2756. }
  2757.  
  2758. ############### INTERLEAVE ###################
  2759.  
  2760. layer{
  2761. bottom: "dec3_branch1a"
  2762. bottom: "dec3_branch1b"
  2763. top: "con3_branch1a"
  2764. type: "Concat"
  2765. name: "con3_branch1a"
  2766. concat_param{
  2767. axis: 2
  2768. }
  2769.  
  2770. }
  2771.  
  2772. layer{
  2773. bottom: "dec3_branch1c"
  2774. bottom: "dec3_branch1d"
  2775. top: "con3_branch1b"
  2776. type: "Concat"
  2777. name: "con3_branch1b"
  2778. concat_param{
  2779. axis: 2
  2780. }
  2781. }
  2782.  
  2783. layer{
  2784. bottom: "con3_branch1a"
  2785. bottom: "con3_branch1b"
  2786. top: "int3_branch1"
  2787. type: "Concat"
  2788. name: "int3_branch1"
  2789. concat_param{
  2790. axis: 3
  2791. }
  2792. }
  2793.  
  2794. ############### END INTERLEAVE #################
  2795.  
  2796. layer{
  2797. bottom: "int3_branch1"
  2798. top: "int3_branch1"
  2799. type: "ReLU"
  2800. name: "relu1_branch1"
  2801. }
  2802.  
  2803. layer{
  2804. bottom: "int3_branch1"
  2805. top: "dec3_branch1e"
  2806. name: "dec3_branch1e"
  2807. type: "Convolution"
  2808. convolution_param{
  2809. num_output: 256
  2810. kernel_size: 3
  2811. pad: 1
  2812. stride: 1
  2813. bias_term: true
  2814. }
  2815. }
  2816.  
  2817. ######3==============================
  2818.  
  2819. layer{
  2820. bottom: "dec2o"
  2821. top: "dec3_branch2a"
  2822. name: "dec3_branch2a"
  2823. type: "Convolution"
  2824. convolution_param{
  2825. num_output: 256
  2826. kernel_size: 3
  2827. pad: 1
  2828. stride: 1
  2829. bias_term: true
  2830. }
  2831. }
  2832.  
  2833. layer{
  2834. bottom: "dec2o"
  2835. top: "dec3_branch2b"
  2836. name: "dec3_branch2b"
  2837. type: "Convolution"
  2838. convolution_param{
  2839. num_output: 256
  2840. kernel_h: 3
  2841. kernel_w: 2
  2842. pad: 1
  2843. stride: 1
  2844. bias_term: true
  2845. }
  2846. }
  2847.  
  2848. layer{
  2849. bottom: "dec3_branch2b"
  2850. bottom: "dec2o"
  2851. top: "dec3_branch2b"
  2852. name: "crop3_branch2b"
  2853. type: "Crop"
  2854. crop_param{
  2855. axis: 2
  2856. offset: 0
  2857. offset: 1
  2858. }
  2859. }
  2860.  
  2861. layer{
  2862. bottom: "dec2o"
  2863. top: "dec3_branch2c"
  2864. name: "dec3_branch2c"
  2865. type: "Convolution"
  2866. convolution_param{
  2867. num_output: 256
  2868. kernel_h: 2
  2869. kernel_w: 3
  2870. pad: 1
  2871. stride: 1
  2872. bias_term: true
  2873. }
  2874. }
  2875.  
  2876. layer{
  2877. bottom: "dec3_branch2c"
  2878. bottom: "dec2o"
  2879. top: "dec3_branch2c"
  2880. name: "crop3_branch2c"
  2881. type: "Crop"
  2882. crop_param{
  2883. axis: 2
  2884. offset: 1
  2885. offset: 0
  2886. }
  2887. }
  2888.  
  2889. layer{
  2890. bottom: "dec2o"
  2891. top: "dec3_branch2d"
  2892. name: "dec3_branch2d"
  2893. type: "Convolution"
  2894. convolution_param{
  2895. num_output: 256
  2896. kernel_size: 2
  2897. pad: 1
  2898. stride: 1
  2899. bias_term: true
  2900. }
  2901. }
  2902.  
  2903. layer{
  2904. bottom: "dec3_branch2d"
  2905. bottom: "dec2o"
  2906. top: "dec3_branch2d"
  2907. name: "crop3_branch2d"
  2908. type: "Crop"
  2909. crop_param{
  2910. axis: 2
  2911. offset: 1
  2912. offset:1
  2913. }
  2914. }
  2915.  
  2916. ############### INTERLEAVE ###################
  2917.  
  2918. layer{
  2919. bottom: "dec3_branch2a"
  2920. bottom: "dec3_branch2b"
  2921. top: "con3_branch2a"
  2922. type: "Concat"
  2923. name: "con3_branch2a"
  2924. concat_param{
  2925. axis: 2
  2926. }
  2927.  
  2928. }
  2929.  
  2930. layer{
  2931. bottom: "dec3_branch2c"
  2932. bottom: "dec3_branch2d"
  2933. top: "con3_branch2b"
  2934. type: "Concat"
  2935. name: "con3_branch2b"
  2936. concat_param{
  2937. axis: 2
  2938. }
  2939. }
  2940.  
  2941. layer{
  2942. bottom: "con3_branch2a"
  2943. bottom: "con3_branch2b"
  2944. top: "int3_branch2"
  2945. type: "Concat"
  2946. name: "int3_branch2"
  2947. concat_param{
  2948. axis: 3
  2949. }
  2950. }
  2951.  
  2952. ############### END INTERLEAVE #################
  2953.  
  2954. layer{
  2955. bottom: "int3_branch2"
  2956. bottom: "dec3_branch1e"
  2957. top: "dec3o"
  2958. name: "dec3o"
  2959. type: "Eltwise"
  2960. eltwise_param{
  2961. operation: SUM
  2962. }
  2963. }
  2964.  
  2965. layer {
  2966. bottom: "dec3o"
  2967. top: "dec3o"
  2968. name: "bn3o"
  2969. type: "BatchNorm"
  2970. batch_norm_param {
  2971. use_global_stats: true
  2972. }
  2973. }
  2974.  
  2975. layer {
  2976. bottom: "dec3o"
  2977. top: "dec3o"
  2978. name: "scale3o"
  2979. type: "Scale"
  2980. scale_param {
  2981. bias_term: true
  2982. }
  2983. }
  2984.  
  2985. layer{
  2986. bottom: "dec3o"
  2987. top: "dec3o"
  2988. name: "scale3o_relu"
  2989. type: "ReLU"
  2990. }
  2991.  
  2992. #======== END OF SECOND DECODE ==================
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