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  1. name: "psp_resnet101-v2"
  2.  
  3. layer {
  4. name: "data"
  5. type: "ImageSegData"
  6. top: "data"
  7. top: "label"
  8. # top: "data_dim"
  9. include {
  10. phase: TRAIN
  11. }
  12. transform_param {
  13. mirror: true
  14. crop_size: 270
  15. mean_value: 5
  16. mean_value: 46
  17. mean_value: 43
  18. # scale_factors: 0.5
  19. scale_factors: 0.75
  20. scale_factors: 1
  21. scale_factors: 1.25
  22. scale_factors: 1.5
  23. scale_factors: 2.0
  24. }
  25. image_data_param {
  26. #root_folder: "/home/priv/Database/VOC_PASCAL/SBD"
  27. source: "/home/ubuntu/Datasets/TUMOR_SUR/train.txt"
  28. batch_size: 1
  29. shuffle: true
  30. label_type: PIXEL
  31. }
  32. }
  33.  
  34. ################ resnet101-v2 ################
  35. layer {
  36. name: "conv1"
  37. type: "Convolution"
  38. bottom: "data"
  39. top: "conv1"
  40. param {
  41. lr_mult: 0
  42. decay_mult: 0
  43. }
  44. convolution_param {
  45. bias_term: false
  46. num_output: 64
  47. pad: 3
  48. kernel_size: 7
  49. stride: 2
  50. }
  51. }
  52. layer {
  53. name: "conv1_scale"
  54. type: "Scale"
  55. bottom: "conv1"
  56. top: "conv1"
  57. scale_param {
  58. bias_term: true
  59. }
  60. param {
  61. lr_mult: 0.0
  62. decay_mult: 0.0
  63. }
  64. param {
  65. lr_mult: 0.0
  66. decay_mult: 0.0
  67. }
  68. }
  69. layer {
  70. name: "conv1_relu"
  71. type: "ReLU"
  72. bottom: "conv1"
  73. top: "conv1"
  74. }
  75. layer {
  76. name: "pool1"
  77. type: "Pooling"
  78. bottom: "conv1"
  79. top: "pool1"
  80. pooling_param {
  81. pool: MAX
  82. kernel_size: 3
  83. stride: 2
  84. }
  85. }
  86. layer {
  87. name: "res1_conv1"
  88. type: "Convolution"
  89. bottom: "pool1"
  90. top: "res1_conv1"
  91. param {
  92. lr_mult: 0
  93. decay_mult: 0
  94. }
  95. convolution_param {
  96. bias_term: false
  97. num_output: 64
  98. pad: 0
  99. kernel_size: 1
  100. stride: 1
  101. }
  102. }
  103. layer {
  104. name: "res1_conv1_scale"
  105. type: "Scale"
  106. bottom: "res1_conv1"
  107. top: "res1_conv1"
  108. scale_param {
  109. bias_term: true
  110. }
  111. param {
  112. lr_mult: 0.0
  113. decay_mult: 0.0
  114. }
  115. param {
  116. lr_mult: 0.0
  117. decay_mult: 0.0
  118. }
  119. }
  120. layer {
  121. name: "res1_conv1_relu"
  122. type: "ReLU"
  123. bottom: "res1_conv1"
  124. top: "res1_conv1"
  125. }
  126. layer {
  127. name: "res1_conv2"
  128. type: "Convolution"
  129. bottom: "res1_conv1"
  130. top: "res1_conv2"
  131. param {
  132. lr_mult: 0
  133. decay_mult: 0
  134. }
  135. convolution_param {
  136. bias_term: false
  137. num_output: 64
  138. pad: 1
  139. kernel_size: 3
  140. stride: 1
  141. }
  142. }
  143. layer {
  144. name: "res1_conv2_scale"
  145. type: "Scale"
  146. bottom: "res1_conv2"
  147. top: "res1_conv2"
  148. scale_param {
  149. bias_term: true
  150. }
  151. param {
  152. lr_mult: 0.0
  153. decay_mult: 0.0
  154. }
  155. param {
  156. lr_mult: 0.0
  157. decay_mult: 0.0
  158. }
  159. }
  160. layer {
  161. name: "res1_conv2_relu"
  162. type: "ReLU"
  163. bottom: "res1_conv2"
  164. top: "res1_conv2"
  165. }
  166. layer {
  167. name: "res1_conv3"
  168. type: "Convolution"
  169. bottom: "res1_conv2"
  170. top: "res1_conv3"
  171. param {
  172. lr_mult: 0
  173. decay_mult: 0
  174. }
  175. convolution_param {
  176. bias_term: false
  177. num_output: 256
  178. pad: 0
  179. kernel_size: 1
  180. stride: 1
  181. }
  182. }
  183. layer {
  184. name: "res1_match_conv"
  185. type: "Convolution"
  186. bottom: "pool1"
  187. top: "res1_match_conv"
  188. param {
  189. lr_mult: 0
  190. decay_mult: 0
  191. }
  192. convolution_param {
  193. bias_term: false
  194. num_output: 256
  195. pad: 0
  196. kernel_size: 1
  197. stride: 1
  198. }
  199. }
  200. layer {
  201. name: "res1_eletwise"
  202. type: "Eltwise"
  203. bottom: "res1_match_conv"
  204. bottom: "res1_conv3"
  205. top: "res1_eletwise"
  206. eltwise_param {
  207. operation: SUM
  208. }
  209. }
  210. layer {
  211. name: "res2_scale"
  212. type: "Scale"
  213. bottom: "res1_eletwise"
  214. top: "res2_scale"
  215. scale_param {
  216. bias_term: true
  217. }
  218. param {
  219. lr_mult: 0.0
  220. decay_mult: 0.0
  221. }
  222. param {
  223. lr_mult: 0.0
  224. decay_mult: 0.0
  225. }
  226. }
  227. layer {
  228. name: "res2_relu"
  229. type: "ReLU"
  230. bottom: "res2_scale"
  231. top: "res2_scale"
  232. }
  233. layer {
  234. name: "res2_conv1"
  235. type: "Convolution"
  236. bottom: "res2_scale"
  237. top: "res2_conv1"
  238. param {
  239. lr_mult: 0
  240. decay_mult: 0
  241. }
  242. convolution_param {
  243. bias_term: false
  244. num_output: 64
  245. pad: 0
  246. kernel_size: 1
  247. stride: 1
  248. }
  249. }
  250.  
  251. layer {
  252. name: "res2_conv1_scale"
  253. type: "Scale"
  254. bottom: "res2_conv1"
  255. top: "res2_conv1"
  256. scale_param {
  257. bias_term: true
  258. }
  259. param {
  260. lr_mult: 0.0
  261. decay_mult: 0.0
  262. }
  263. param {
  264. lr_mult: 0.0
  265. decay_mult: 0.0
  266. }
  267. }
  268. layer {
  269. name: "res2_conv1_relu"
  270. type: "ReLU"
  271. bottom: "res2_conv1"
  272. top: "res2_conv1"
  273. }
  274. layer {
  275. name: "res2_conv2"
  276. type: "Convolution"
  277. bottom: "res2_conv1"
  278. top: "res2_conv2"
  279. param {
  280. lr_mult: 0
  281. decay_mult: 0
  282. }
  283. convolution_param {
  284. bias_term: false
  285. num_output: 64
  286. pad: 1
  287. kernel_size: 3
  288. stride: 1
  289. }
  290. }
  291.  
  292. layer {
  293. name: "res2_conv2_scale"
  294. type: "Scale"
  295. bottom: "res2_conv2"
  296. top: "res2_conv2"
  297. scale_param {
  298. bias_term: true
  299. }
  300. param {
  301. lr_mult: 0.0
  302. decay_mult: 0.0
  303. }
  304. param {
  305. lr_mult: 0.0
  306. decay_mult: 0.0
  307. }
  308. }
  309. layer {
  310. name: "res2_conv2_relu"
  311. type: "ReLU"
  312. bottom: "res2_conv2"
  313. top: "res2_conv2"
  314. }
  315. layer {
  316. name: "res2_conv3"
  317. type: "Convolution"
  318. bottom: "res2_conv2"
  319. top: "res2_conv3"
  320. param {
  321. lr_mult: 0
  322. decay_mult: 0
  323. }
  324. convolution_param {
  325. bias_term: false
  326. num_output: 256
  327. pad: 0
  328. kernel_size: 1
  329. stride: 1
  330. }
  331. }
  332. layer {
  333. name: "res2_eletwise"
  334. type: "Eltwise"
  335. bottom: "res1_eletwise"
  336. bottom: "res2_conv3"
  337. top: "res2_eletwise"
  338. eltwise_param {
  339. operation: SUM
  340. }
  341. }
  342.  
  343. layer {
  344. name: "res3_scale"
  345. type: "Scale"
  346. bottom: "res2_eletwise"
  347. top: "res3_scale"
  348. scale_param {
  349. bias_term: true
  350. }
  351. param {
  352. lr_mult: 0.0
  353. decay_mult: 0.0
  354. }
  355. param {
  356. lr_mult: 0.0
  357. decay_mult: 0.0
  358. }
  359. }
  360. layer {
  361. name: "res3_relu"
  362. type: "ReLU"
  363. bottom: "res3_scale"
  364. top: "res3_scale"
  365. }
  366. layer {
  367. name: "res3_conv1"
  368. type: "Convolution"
  369. bottom: "res3_scale"
  370. top: "res3_conv1"
  371. param {
  372. lr_mult: 0
  373. decay_mult: 0
  374. }
  375. convolution_param {
  376. bias_term: false
  377. num_output: 64
  378. pad: 0
  379. kernel_size: 1
  380. stride: 1
  381. }
  382. }
  383.  
  384. layer {
  385. name: "res3_conv1_scale"
  386. type: "Scale"
  387. bottom: "res3_conv1"
  388. top: "res3_conv1"
  389. scale_param {
  390. bias_term: true
  391. }
  392. param {
  393. lr_mult: 0.0
  394. decay_mult: 0.0
  395. }
  396. param {
  397. lr_mult: 0.0
  398. decay_mult: 0.0
  399. }
  400. }
  401. layer {
  402. name: "res3_conv1_relu"
  403. type: "ReLU"
  404. bottom: "res3_conv1"
  405. top: "res3_conv1"
  406. }
  407. layer {
  408. name: "res3_conv2"
  409. type: "Convolution"
  410. bottom: "res3_conv1"
  411. top: "res3_conv2"
  412. param {
  413. lr_mult: 0
  414. decay_mult: 0
  415. }
  416. convolution_param {
  417. bias_term: false
  418. num_output: 64
  419. pad: 1
  420. kernel_size: 3
  421. stride: 1
  422. }
  423. }
  424.  
  425. layer {
  426. name: "res3_conv2_scale"
  427. type: "Scale"
  428. bottom: "res3_conv2"
  429. top: "res3_conv2"
  430. scale_param {
  431. bias_term: true
  432. }
  433. param {
  434. lr_mult: 0.0
  435. decay_mult: 0.0
  436. }
  437. param {
  438. lr_mult: 0.0
  439. decay_mult: 0.0
  440. }
  441. }
  442. layer {
  443. name: "res3_conv2_relu"
  444. type: "ReLU"
  445. bottom: "res3_conv2"
  446. top: "res3_conv2"
  447. }
  448. layer {
  449. name: "res3_conv3"
  450. type: "Convolution"
  451. bottom: "res3_conv2"
  452. top: "res3_conv3"
  453. param {
  454. lr_mult: 0
  455. decay_mult: 0
  456. }
  457. convolution_param {
  458. bias_term: false
  459. num_output: 256
  460. pad: 0
  461. kernel_size: 1
  462. stride: 1
  463. }
  464. }
  465. layer {
  466. name: "res3_eletwise"
  467. type: "Eltwise"
  468. bottom: "res2_eletwise"
  469. bottom: "res3_conv3"
  470. top: "res3_eletwise"
  471. eltwise_param {
  472. operation: SUM
  473. }
  474. }
  475.  
  476. layer {
  477. name: "res4_scale"
  478. type: "Scale"
  479. bottom: "res3_eletwise"
  480. top: "res4_scale"
  481. scale_param {
  482. bias_term: true
  483. }
  484. param {
  485. lr_mult: 0.0
  486. decay_mult: 0.0
  487. }
  488. param {
  489. lr_mult: 0.0
  490. decay_mult: 0.0
  491. }
  492. }
  493. layer {
  494. name: "res4_relu"
  495. type: "ReLU"
  496. bottom: "res4_scale"
  497. top: "res4_scale"
  498. }
  499. layer {
  500. name: "res4_conv1"
  501. type: "Convolution"
  502. bottom: "res4_scale"
  503. top: "res4_conv1"
  504. param {
  505. lr_mult: 1
  506. decay_mult: 1
  507. }
  508. convolution_param {
  509. bias_term: false
  510. num_output: 128
  511. pad: 0
  512. kernel_size: 1
  513. stride: 1
  514. }
  515. }
  516.  
  517. layer {
  518. name: "res4_conv1_scale"
  519. type: "Scale"
  520. bottom: "res4_conv1"
  521. top: "res4_conv1"
  522. scale_param {
  523. bias_term: true
  524. }
  525. param {
  526. lr_mult: 0.0
  527. decay_mult: 0.0
  528. }
  529. param {
  530. lr_mult: 0.0
  531. decay_mult: 0.0
  532. }
  533. }
  534. layer {
  535. name: "res4_conv1_relu"
  536. type: "ReLU"
  537. bottom: "res4_conv1"
  538. top: "res4_conv1"
  539. }
  540. layer {
  541. name: "res4_conv2"
  542. type: "Convolution"
  543. bottom: "res4_conv1"
  544. top: "res4_conv2"
  545. param {
  546. lr_mult: 1
  547. decay_mult: 1
  548. }
  549. convolution_param {
  550. bias_term: false
  551. num_output: 128
  552. pad: 1
  553. kernel_size: 3
  554. stride: 2
  555. }
  556. }
  557.  
  558. layer {
  559. name: "res4_conv2_scale"
  560. type: "Scale"
  561. bottom: "res4_conv2"
  562. top: "res4_conv2"
  563. scale_param {
  564. bias_term: true
  565. }
  566. param {
  567. lr_mult: 0.0
  568. decay_mult: 0.0
  569. }
  570. param {
  571. lr_mult: 0.0
  572. decay_mult: 0.0
  573. }
  574. }
  575. layer {
  576. name: "res4_conv2_relu"
  577. type: "ReLU"
  578. bottom: "res4_conv2"
  579. top: "res4_conv2"
  580. }
  581. layer {
  582. name: "res4_conv3"
  583. type: "Convolution"
  584. bottom: "res4_conv2"
  585. top: "res4_conv3"
  586. param {
  587. lr_mult: 1
  588. decay_mult: 1
  589. }
  590. convolution_param {
  591. bias_term: false
  592. num_output: 512
  593. pad: 0
  594. kernel_size: 1
  595. stride: 1
  596. }
  597. }
  598. layer {
  599. name: "res4_match_conv"
  600. type: "Convolution"
  601. bottom: "res4_scale"
  602. top: "res4_match_conv"
  603. param {
  604. lr_mult: 1
  605. decay_mult: 1
  606. }
  607. convolution_param {
  608. bias_term: false
  609. num_output: 512
  610. pad: 0
  611. kernel_size: 1
  612. stride: 2
  613. }
  614. }
  615. layer {
  616. name: "res4_eletwise"
  617. type: "Eltwise"
  618. bottom: "res4_match_conv"
  619. bottom: "res4_conv3"
  620. top: "res4_eletwise"
  621. eltwise_param {
  622. operation: SUM
  623. }
  624. }
  625.  
  626. layer {
  627. name: "res5_scale"
  628. type: "Scale"
  629. bottom: "res4_eletwise"
  630. top: "res5_scale"
  631. scale_param {
  632. bias_term: true
  633. }
  634. param {
  635. lr_mult: 0.0
  636. decay_mult: 0.0
  637. }
  638. param {
  639. lr_mult: 0.0
  640. decay_mult: 0.0
  641. }
  642. }
  643. layer {
  644. name: "res5_relu"
  645. type: "ReLU"
  646. bottom: "res5_scale"
  647. top: "res5_scale"
  648. }
  649. layer {
  650. name: "res5_conv1"
  651. type: "Convolution"
  652. bottom: "res5_scale"
  653. top: "res5_conv1"
  654. param {
  655. lr_mult: 1
  656. decay_mult: 1
  657. }
  658. convolution_param {
  659. bias_term: false
  660. num_output: 128
  661. pad: 0
  662. kernel_size: 1
  663. stride: 1
  664. }
  665. }
  666.  
  667. layer {
  668. name: "res5_conv1_scale"
  669. type: "Scale"
  670. bottom: "res5_conv1"
  671. top: "res5_conv1"
  672. scale_param {
  673. bias_term: true
  674. }
  675. param {
  676. lr_mult: 0.0
  677. decay_mult: 0.0
  678. }
  679. param {
  680. lr_mult: 0.0
  681. decay_mult: 0.0
  682. }
  683. }
  684. layer {
  685. name: "res5_conv1_relu"
  686. type: "ReLU"
  687. bottom: "res5_conv1"
  688. top: "res5_conv1"
  689. }
  690. layer {
  691. name: "res5_conv2"
  692. type: "Convolution"
  693. bottom: "res5_conv1"
  694. top: "res5_conv2"
  695. param {
  696. lr_mult: 1
  697. decay_mult: 1
  698. }
  699. convolution_param {
  700. bias_term: false
  701. num_output: 128
  702. pad: 1
  703. kernel_size: 3
  704. stride: 1
  705. }
  706. }
  707.  
  708. layer {
  709. name: "res5_conv2_scale"
  710. type: "Scale"
  711. bottom: "res5_conv2"
  712. top: "res5_conv2"
  713. scale_param {
  714. bias_term: true
  715. }
  716. param {
  717. lr_mult: 0.0
  718. decay_mult: 0.0
  719. }
  720. param {
  721. lr_mult: 0.0
  722. decay_mult: 0.0
  723. }
  724. }
  725. layer {
  726. name: "res5_conv2_relu"
  727. type: "ReLU"
  728. bottom: "res5_conv2"
  729. top: "res5_conv2"
  730. }
  731. layer {
  732. name: "res5_conv3"
  733. type: "Convolution"
  734. bottom: "res5_conv2"
  735. top: "res5_conv3"
  736. param {
  737. lr_mult: 1
  738. decay_mult: 1
  739. }
  740. convolution_param {
  741. bias_term: false
  742. num_output: 512
  743. pad: 0
  744. kernel_size: 1
  745. stride: 1
  746. }
  747. }
  748. layer {
  749. name: "res5_eletwise"
  750. type: "Eltwise"
  751. bottom: "res4_eletwise"
  752. bottom: "res5_conv3"
  753. top: "res5_eletwise"
  754. eltwise_param {
  755. operation: SUM
  756. }
  757. }
  758.  
  759. layer {
  760. name: "res6_scale"
  761. type: "Scale"
  762. bottom: "res5_eletwise"
  763. top: "res6_scale"
  764. scale_param {
  765. bias_term: true
  766. }
  767. param {
  768. lr_mult: 0.0
  769. decay_mult: 0.0
  770. }
  771. param {
  772. lr_mult: 0.0
  773. decay_mult: 0.0
  774. }
  775. }
  776. layer {
  777. name: "res6_relu"
  778. type: "ReLU"
  779. bottom: "res6_scale"
  780. top: "res6_scale"
  781. }
  782. layer {
  783. name: "res6_conv1"
  784. type: "Convolution"
  785. bottom: "res6_scale"
  786. top: "res6_conv1"
  787. param {
  788. lr_mult: 1
  789. decay_mult: 1
  790. }
  791. convolution_param {
  792. bias_term: false
  793. num_output: 128
  794. pad: 0
  795. kernel_size: 1
  796. stride: 1
  797. }
  798. }
  799.  
  800. layer {
  801. name: "res6_conv1_scale"
  802. type: "Scale"
  803. bottom: "res6_conv1"
  804. top: "res6_conv1"
  805. scale_param {
  806. bias_term: true
  807. }
  808. param {
  809. lr_mult: 0.0
  810. decay_mult: 0.0
  811. }
  812. param {
  813. lr_mult: 0.0
  814. decay_mult: 0.0
  815. }
  816. }
  817. layer {
  818. name: "res6_conv1_relu"
  819. type: "ReLU"
  820. bottom: "res6_conv1"
  821. top: "res6_conv1"
  822. }
  823. layer {
  824. name: "res6_conv2"
  825. type: "Convolution"
  826. bottom: "res6_conv1"
  827. top: "res6_conv2"
  828. param {
  829. lr_mult: 1
  830. decay_mult: 1
  831. }
  832. convolution_param {
  833. bias_term: false
  834. num_output: 128
  835. pad: 1
  836. kernel_size: 3
  837. stride: 1
  838. }
  839. }
  840.  
  841. layer {
  842. name: "res6_conv2_scale"
  843. type: "Scale"
  844. bottom: "res6_conv2"
  845. top: "res6_conv2"
  846. scale_param {
  847. bias_term: true
  848. }
  849. param {
  850. lr_mult: 0.0
  851. decay_mult: 0.0
  852. }
  853. param {
  854. lr_mult: 0.0
  855. decay_mult: 0.0
  856. }
  857. }
  858. layer {
  859. name: "res6_conv2_relu"
  860. type: "ReLU"
  861. bottom: "res6_conv2"
  862. top: "res6_conv2"
  863. }
  864. layer {
  865. name: "res6_conv3"
  866. type: "Convolution"
  867. bottom: "res6_conv2"
  868. top: "res6_conv3"
  869. param {
  870. lr_mult: 1
  871. decay_mult: 1
  872. }
  873. convolution_param {
  874. bias_term: false
  875. num_output: 512
  876. pad: 0
  877. kernel_size: 1
  878. stride: 1
  879. }
  880. }
  881. layer {
  882. name: "res6_eletwise"
  883. type: "Eltwise"
  884. bottom: "res5_eletwise"
  885. bottom: "res6_conv3"
  886. top: "res6_eletwise"
  887. eltwise_param {
  888. operation: SUM
  889. }
  890. }
  891.  
  892. layer {
  893. name: "res7_scale"
  894. type: "Scale"
  895. bottom: "res6_eletwise"
  896. top: "res7_scale"
  897. scale_param {
  898. bias_term: true
  899. }
  900. param {
  901. lr_mult: 0.0
  902. decay_mult: 0.0
  903. }
  904. param {
  905. lr_mult: 0.0
  906. decay_mult: 0.0
  907. }
  908. }
  909. layer {
  910. name: "res7_relu"
  911. type: "ReLU"
  912. bottom: "res7_scale"
  913. top: "res7_scale"
  914. }
  915. layer {
  916. name: "res7_conv1"
  917. type: "Convolution"
  918. bottom: "res7_scale"
  919. top: "res7_conv1"
  920. param {
  921. lr_mult: 1
  922. decay_mult: 1
  923. }
  924. convolution_param {
  925. bias_term: false
  926. num_output: 128
  927. pad: 0
  928. kernel_size: 1
  929. stride: 1
  930. }
  931. }
  932.  
  933. layer {
  934. name: "res7_conv1_scale"
  935. type: "Scale"
  936. bottom: "res7_conv1"
  937. top: "res7_conv1"
  938. scale_param {
  939. bias_term: true
  940. }
  941. param {
  942. lr_mult: 0.0
  943. decay_mult: 0.0
  944. }
  945. param {
  946. lr_mult: 0.0
  947. decay_mult: 0.0
  948. }
  949. }
  950. layer {
  951. name: "res7_conv1_relu"
  952. type: "ReLU"
  953. bottom: "res7_conv1"
  954. top: "res7_conv1"
  955. }
  956. layer {
  957. name: "res7_conv2"
  958. type: "Convolution"
  959. bottom: "res7_conv1"
  960. top: "res7_conv2"
  961. param {
  962. lr_mult: 1
  963. decay_mult: 1
  964. }
  965. convolution_param {
  966. bias_term: false
  967. num_output: 128
  968. pad: 1
  969. kernel_size: 3
  970. stride: 1
  971. }
  972. }
  973.  
  974. layer {
  975. name: "res7_conv2_scale"
  976. type: "Scale"
  977. bottom: "res7_conv2"
  978. top: "res7_conv2"
  979. scale_param {
  980. bias_term: true
  981. }
  982. param {
  983. lr_mult: 0.0
  984. decay_mult: 0.0
  985. }
  986. param {
  987. lr_mult: 0.0
  988. decay_mult: 0.0
  989. }
  990. }
  991. layer {
  992. name: "res7_conv2_relu"
  993. type: "ReLU"
  994. bottom: "res7_conv2"
  995. top: "res7_conv2"
  996. }
  997. layer {
  998. name: "res7_conv3"
  999. type: "Convolution"
  1000. bottom: "res7_conv2"
  1001. top: "res7_conv3"
  1002. param {
  1003. lr_mult: 1
  1004. decay_mult: 1
  1005. }
  1006. convolution_param {
  1007. bias_term: false
  1008. num_output: 512
  1009. pad: 0
  1010. kernel_size: 1
  1011. stride: 1
  1012. }
  1013. }
  1014. layer {
  1015. name: "res7_eletwise"
  1016. type: "Eltwise"
  1017. bottom: "res6_eletwise"
  1018. bottom: "res7_conv3"
  1019. top: "res7_eletwise"
  1020. eltwise_param {
  1021. operation: SUM
  1022. }
  1023. }
  1024.  
  1025. layer {
  1026. name: "res8_scale"
  1027. type: "Scale"
  1028. bottom: "res7_eletwise"
  1029. top: "res8_scale"
  1030. scale_param {
  1031. bias_term: true
  1032. }
  1033. param {
  1034. lr_mult: 0.0
  1035. decay_mult: 0.0
  1036. }
  1037. param {
  1038. lr_mult: 0.0
  1039. decay_mult: 0.0
  1040. }
  1041. }
  1042. layer {
  1043. name: "res8_relu"
  1044. type: "ReLU"
  1045. bottom: "res8_scale"
  1046. top: "res8_scale"
  1047. }
  1048. layer {
  1049. name: "res8_conv1"
  1050. type: "Convolution"
  1051. bottom: "res8_scale"
  1052. top: "res8_conv1"
  1053. param {
  1054. lr_mult: 1
  1055. decay_mult: 1
  1056. }
  1057. convolution_param {
  1058. bias_term: false
  1059. num_output: 256
  1060. pad: 0
  1061. kernel_size: 1
  1062. stride: 1
  1063. }
  1064. }
  1065.  
  1066. layer {
  1067. name: "res8_conv1_scale"
  1068. type: "Scale"
  1069. bottom: "res8_conv1"
  1070. top: "res8_conv1"
  1071. scale_param {
  1072. bias_term: true
  1073. }
  1074. param {
  1075. lr_mult: 0.0
  1076. decay_mult: 0.0
  1077. }
  1078. param {
  1079. lr_mult: 0.0
  1080. decay_mult: 0.0
  1081. }
  1082. }
  1083. layer {
  1084. name: "res8_conv1_relu"
  1085. type: "ReLU"
  1086. bottom: "res8_conv1"
  1087. top: "res8_conv1"
  1088. }
  1089. layer {
  1090. name: "res8_conv2"
  1091. type: "Convolution"
  1092. bottom: "res8_conv1"
  1093. top: "res8_conv2"
  1094. param {
  1095. lr_mult: 1
  1096. decay_mult: 1
  1097. }
  1098. convolution_param {
  1099. bias_term: false
  1100. num_output: 256
  1101. pad: 2
  1102. dilation: 2
  1103. kernel_size: 3
  1104. stride: 1
  1105. }
  1106. }
  1107.  
  1108. layer {
  1109. name: "res8_conv2_scale"
  1110. type: "Scale"
  1111. bottom: "res8_conv2"
  1112. top: "res8_conv2"
  1113. scale_param {
  1114. bias_term: true
  1115. }
  1116. param {
  1117. lr_mult: 0.0
  1118. decay_mult: 0.0
  1119. }
  1120. param {
  1121. lr_mult: 0.0
  1122. decay_mult: 0.0
  1123. }
  1124. }
  1125. layer {
  1126. name: "res8_conv2_relu"
  1127. type: "ReLU"
  1128. bottom: "res8_conv2"
  1129. top: "res8_conv2"
  1130. }
  1131. layer {
  1132. name: "res8_conv3"
  1133. type: "Convolution"
  1134. bottom: "res8_conv2"
  1135. top: "res8_conv3"
  1136. param {
  1137. lr_mult: 1
  1138. decay_mult: 1
  1139. }
  1140. convolution_param {
  1141. bias_term: false
  1142. num_output: 1024
  1143. pad: 0
  1144. kernel_size: 1
  1145. stride: 1
  1146. }
  1147. }
  1148. layer {
  1149. name: "res8_match_conv"
  1150. type: "Convolution"
  1151. bottom: "res8_scale"
  1152. top: "res8_match_conv"
  1153. param {
  1154. lr_mult: 1
  1155. decay_mult: 1
  1156. }
  1157. convolution_param {
  1158. bias_term: false
  1159. num_output: 1024
  1160. pad: 0
  1161. kernel_size: 1
  1162. stride: 1
  1163. }
  1164. }
  1165. layer {
  1166. name: "res8_eletwise"
  1167. type: "Eltwise"
  1168. bottom: "res8_match_conv"
  1169. bottom: "res8_conv3"
  1170. top: "res8_eletwise"
  1171. eltwise_param {
  1172. operation: SUM
  1173. }
  1174. }
  1175.  
  1176. layer {
  1177. name: "res9_scale"
  1178. type: "Scale"
  1179. bottom: "res8_eletwise"
  1180. top: "res9_scale"
  1181. scale_param {
  1182. bias_term: true
  1183. }
  1184. param {
  1185. lr_mult: 0.0
  1186. decay_mult: 0.0
  1187. }
  1188. param {
  1189. lr_mult: 0.0
  1190. decay_mult: 0.0
  1191. }
  1192. }
  1193. layer {
  1194. name: "res9_relu"
  1195. type: "ReLU"
  1196. bottom: "res9_scale"
  1197. top: "res9_scale"
  1198. }
  1199. layer {
  1200. name: "res9_conv1"
  1201. type: "Convolution"
  1202. bottom: "res9_scale"
  1203. top: "res9_conv1"
  1204. param {
  1205. lr_mult: 1
  1206. decay_mult: 1
  1207. }
  1208. convolution_param {
  1209. bias_term: false
  1210. num_output: 256
  1211. pad: 0
  1212. kernel_size: 1
  1213. stride: 1
  1214. }
  1215. }
  1216.  
  1217. layer {
  1218. name: "res9_conv1_scale"
  1219. type: "Scale"
  1220. bottom: "res9_conv1"
  1221. top: "res9_conv1"
  1222. scale_param {
  1223. bias_term: true
  1224. }
  1225. param {
  1226. lr_mult: 0.0
  1227. decay_mult: 0.0
  1228. }
  1229. param {
  1230. lr_mult: 0.0
  1231. decay_mult: 0.0
  1232. }
  1233. }
  1234. layer {
  1235. name: "res9_conv1_relu"
  1236. type: "ReLU"
  1237. bottom: "res9_conv1"
  1238. top: "res9_conv1"
  1239. }
  1240. layer {
  1241. name: "res9_conv2"
  1242. type: "Convolution"
  1243. bottom: "res9_conv1"
  1244. top: "res9_conv2"
  1245. param {
  1246. lr_mult: 1
  1247. decay_mult: 1
  1248. }
  1249. convolution_param {
  1250. bias_term: false
  1251. num_output: 256
  1252. pad: 2
  1253. dilation: 2
  1254. kernel_size: 3
  1255. stride: 1
  1256. }
  1257. }
  1258.  
  1259. layer {
  1260. name: "res9_conv2_scale"
  1261. type: "Scale"
  1262. bottom: "res9_conv2"
  1263. top: "res9_conv2"
  1264. scale_param {
  1265. bias_term: true
  1266. }
  1267. param {
  1268. lr_mult: 0.0
  1269. decay_mult: 0.0
  1270. }
  1271. param {
  1272. lr_mult: 0.0
  1273. decay_mult: 0.0
  1274. }
  1275. }
  1276. layer {
  1277. name: "res9_conv2_relu"
  1278. type: "ReLU"
  1279. bottom: "res9_conv2"
  1280. top: "res9_conv2"
  1281. }
  1282. layer {
  1283. name: "res9_conv3"
  1284. type: "Convolution"
  1285. bottom: "res9_conv2"
  1286. top: "res9_conv3"
  1287. param {
  1288. lr_mult: 1
  1289. decay_mult: 1
  1290. }
  1291. convolution_param {
  1292. bias_term: false
  1293. num_output: 1024
  1294. pad: 0
  1295. kernel_size: 1
  1296. stride: 1
  1297. }
  1298. }
  1299. layer {
  1300. name: "res9_eletwise"
  1301. type: "Eltwise"
  1302. bottom: "res8_eletwise"
  1303. bottom: "res9_conv3"
  1304. top: "res9_eletwise"
  1305. eltwise_param {
  1306. operation: SUM
  1307. }
  1308. }
  1309.  
  1310. layer {
  1311. name: "res10_scale"
  1312. type: "Scale"
  1313. bottom: "res9_eletwise"
  1314. top: "res10_scale"
  1315. scale_param {
  1316. bias_term: true
  1317. }
  1318. param {
  1319. lr_mult: 0.0
  1320. decay_mult: 0.0
  1321. }
  1322. param {
  1323. lr_mult: 0.0
  1324. decay_mult: 0.0
  1325. }
  1326. }
  1327. layer {
  1328. name: "res10_relu"
  1329. type: "ReLU"
  1330. bottom: "res10_scale"
  1331. top: "res10_scale"
  1332. }
  1333. layer {
  1334. name: "res10_conv1"
  1335. type: "Convolution"
  1336. bottom: "res10_scale"
  1337. top: "res10_conv1"
  1338. param {
  1339. lr_mult: 1
  1340. decay_mult: 1
  1341. }
  1342. convolution_param {
  1343. bias_term: false
  1344. num_output: 256
  1345. pad: 0
  1346. kernel_size: 1
  1347. stride: 1
  1348. }
  1349. }
  1350.  
  1351. layer {
  1352. name: "res10_conv1_scale"
  1353. type: "Scale"
  1354. bottom: "res10_conv1"
  1355. top: "res10_conv1"
  1356. scale_param {
  1357. bias_term: true
  1358. }
  1359. param {
  1360. lr_mult: 0.0
  1361. decay_mult: 0.0
  1362. }
  1363. param {
  1364. lr_mult: 0.0
  1365. decay_mult: 0.0
  1366. }
  1367. }
  1368. layer {
  1369. name: "res10_conv1_relu"
  1370. type: "ReLU"
  1371. bottom: "res10_conv1"
  1372. top: "res10_conv1"
  1373. }
  1374. layer {
  1375. name: "res10_conv2"
  1376. type: "Convolution"
  1377. bottom: "res10_conv1"
  1378. top: "res10_conv2"
  1379. param {
  1380. lr_mult: 1
  1381. decay_mult: 1
  1382. }
  1383. convolution_param {
  1384. bias_term: false
  1385. num_output: 256
  1386. pad: 2
  1387. dilation: 2
  1388. kernel_size: 3
  1389. stride: 1
  1390. }
  1391. }
  1392.  
  1393. layer {
  1394. name: "res10_conv2_scale"
  1395. type: "Scale"
  1396. bottom: "res10_conv2"
  1397. top: "res10_conv2"
  1398. scale_param {
  1399. bias_term: true
  1400. }
  1401. param {
  1402. lr_mult: 0.0
  1403. decay_mult: 0.0
  1404. }
  1405. param {
  1406. lr_mult: 0.0
  1407. decay_mult: 0.0
  1408. }
  1409. }
  1410. layer {
  1411. name: "res10_conv2_relu"
  1412. type: "ReLU"
  1413. bottom: "res10_conv2"
  1414. top: "res10_conv2"
  1415. }
  1416. layer {
  1417. name: "res10_conv3"
  1418. type: "Convolution"
  1419. bottom: "res10_conv2"
  1420. top: "res10_conv3"
  1421. param {
  1422. lr_mult: 1
  1423. decay_mult: 1
  1424. }
  1425. convolution_param {
  1426. bias_term: false
  1427. num_output: 1024
  1428. pad: 0
  1429. kernel_size: 1
  1430. stride: 1
  1431. }
  1432. }
  1433. layer {
  1434. name: "res10_eletwise"
  1435. type: "Eltwise"
  1436. bottom: "res9_eletwise"
  1437. bottom: "res10_conv3"
  1438. top: "res10_eletwise"
  1439. eltwise_param {
  1440. operation: SUM
  1441. }
  1442. }
  1443.  
  1444. layer {
  1445. name: "res11_scale"
  1446. type: "Scale"
  1447. bottom: "res10_eletwise"
  1448. top: "res11_scale"
  1449. scale_param {
  1450. bias_term: true
  1451. }
  1452. param {
  1453. lr_mult: 0.0
  1454. decay_mult: 0.0
  1455. }
  1456. param {
  1457. lr_mult: 0.0
  1458. decay_mult: 0.0
  1459. }
  1460. }
  1461. layer {
  1462. name: "res11_relu"
  1463. type: "ReLU"
  1464. bottom: "res11_scale"
  1465. top: "res11_scale"
  1466. }
  1467. layer {
  1468. name: "res11_conv1"
  1469. type: "Convolution"
  1470. bottom: "res11_scale"
  1471. top: "res11_conv1"
  1472. param {
  1473. lr_mult: 1
  1474. decay_mult: 1
  1475. }
  1476. convolution_param {
  1477. bias_term: false
  1478. num_output: 256
  1479. pad: 0
  1480. kernel_size: 1
  1481. stride: 1
  1482. }
  1483. }
  1484.  
  1485. layer {
  1486. name: "res11_conv1_scale"
  1487. type: "Scale"
  1488. bottom: "res11_conv1"
  1489. top: "res11_conv1"
  1490. scale_param {
  1491. bias_term: true
  1492. }
  1493. param {
  1494. lr_mult: 0.0
  1495. decay_mult: 0.0
  1496. }
  1497. param {
  1498. lr_mult: 0.0
  1499. decay_mult: 0.0
  1500. }
  1501. }
  1502. layer {
  1503. name: "res11_conv1_relu"
  1504. type: "ReLU"
  1505. bottom: "res11_conv1"
  1506. top: "res11_conv1"
  1507. }
  1508. layer {
  1509. name: "res11_conv2"
  1510. type: "Convolution"
  1511. bottom: "res11_conv1"
  1512. top: "res11_conv2"
  1513. param {
  1514. lr_mult: 1
  1515. decay_mult: 1
  1516. }
  1517. convolution_param {
  1518. bias_term: false
  1519. num_output: 256
  1520. pad: 2
  1521. dilation: 2
  1522. kernel_size: 3
  1523. stride: 1
  1524. }
  1525. }
  1526.  
  1527. layer {
  1528. name: "res11_conv2_scale"
  1529. type: "Scale"
  1530. bottom: "res11_conv2"
  1531. top: "res11_conv2"
  1532. scale_param {
  1533. bias_term: true
  1534. }
  1535. param {
  1536. lr_mult: 0.0
  1537. decay_mult: 0.0
  1538. }
  1539. param {
  1540. lr_mult: 0.0
  1541. decay_mult: 0.0
  1542. }
  1543. }
  1544. layer {
  1545. name: "res11_conv2_relu"
  1546. type: "ReLU"
  1547. bottom: "res11_conv2"
  1548. top: "res11_conv2"
  1549. }
  1550. layer {
  1551. name: "res11_conv3"
  1552. type: "Convolution"
  1553. bottom: "res11_conv2"
  1554. top: "res11_conv3"
  1555. param {
  1556. lr_mult: 1
  1557. decay_mult: 1
  1558. }
  1559. convolution_param {
  1560. bias_term: false
  1561. num_output: 1024
  1562. pad: 0
  1563. kernel_size: 1
  1564. stride: 1
  1565. }
  1566. }
  1567. layer {
  1568. name: "res11_eletwise"
  1569. type: "Eltwise"
  1570. bottom: "res10_eletwise"
  1571. bottom: "res11_conv3"
  1572. top: "res11_eletwise"
  1573. eltwise_param {
  1574. operation: SUM
  1575. }
  1576. }
  1577.  
  1578. layer {
  1579. name: "res12_scale"
  1580. type: "Scale"
  1581. bottom: "res11_eletwise"
  1582. top: "res12_scale"
  1583. scale_param {
  1584. bias_term: true
  1585. }
  1586. param {
  1587. lr_mult: 0.0
  1588. decay_mult: 0.0
  1589. }
  1590. param {
  1591. lr_mult: 0.0
  1592. decay_mult: 0.0
  1593. }
  1594. }
  1595. layer {
  1596. name: "res12_relu"
  1597. type: "ReLU"
  1598. bottom: "res12_scale"
  1599. top: "res12_scale"
  1600. }
  1601. layer {
  1602. name: "res12_conv1"
  1603. type: "Convolution"
  1604. bottom: "res12_scale"
  1605. top: "res12_conv1"
  1606. param {
  1607. lr_mult: 1
  1608. decay_mult: 1
  1609. }
  1610. convolution_param {
  1611. bias_term: false
  1612. num_output: 256
  1613. pad: 0
  1614. kernel_size: 1
  1615. stride: 1
  1616. }
  1617. }
  1618.  
  1619. layer {
  1620. name: "res12_conv1_scale"
  1621. type: "Scale"
  1622. bottom: "res12_conv1"
  1623. top: "res12_conv1"
  1624. scale_param {
  1625. bias_term: true
  1626. }
  1627. param {
  1628. lr_mult: 0.0
  1629. decay_mult: 0.0
  1630. }
  1631. param {
  1632. lr_mult: 0.0
  1633. decay_mult: 0.0
  1634. }
  1635. }
  1636. layer {
  1637. name: "res12_conv1_relu"
  1638. type: "ReLU"
  1639. bottom: "res12_conv1"
  1640. top: "res12_conv1"
  1641. }
  1642. layer {
  1643. name: "res12_conv2"
  1644. type: "Convolution"
  1645. bottom: "res12_conv1"
  1646. top: "res12_conv2"
  1647. param {
  1648. lr_mult: 1
  1649. decay_mult: 1
  1650. }
  1651. convolution_param {
  1652. bias_term: false
  1653. num_output: 256
  1654. pad: 2
  1655. dilation: 2
  1656. kernel_size: 3
  1657. stride: 1
  1658. }
  1659. }
  1660.  
  1661. layer {
  1662. name: "res12_conv2_scale"
  1663. type: "Scale"
  1664. bottom: "res12_conv2"
  1665. top: "res12_conv2"
  1666. scale_param {
  1667. bias_term: true
  1668. }
  1669. param {
  1670. lr_mult: 0.0
  1671. decay_mult: 0.0
  1672. }
  1673. param {
  1674. lr_mult: 0.0
  1675. decay_mult: 0.0
  1676. }
  1677. }
  1678. layer {
  1679. name: "res12_conv2_relu"
  1680. type: "ReLU"
  1681. bottom: "res12_conv2"
  1682. top: "res12_conv2"
  1683. }
  1684. layer {
  1685. name: "res12_conv3"
  1686. type: "Convolution"
  1687. bottom: "res12_conv2"
  1688. top: "res12_conv3"
  1689. param {
  1690. lr_mult: 1
  1691. decay_mult: 1
  1692. }
  1693. convolution_param {
  1694. bias_term: false
  1695. num_output: 1024
  1696. pad: 0
  1697. kernel_size: 1
  1698. stride: 1
  1699. }
  1700. }
  1701. layer {
  1702. name: "res12_eletwise"
  1703. type: "Eltwise"
  1704. bottom: "res11_eletwise"
  1705. bottom: "res12_conv3"
  1706. top: "res12_eletwise"
  1707. eltwise_param {
  1708. operation: SUM
  1709. }
  1710. }
  1711.  
  1712. layer {
  1713. name: "res13_scale"
  1714. type: "Scale"
  1715. bottom: "res12_eletwise"
  1716. top: "res13_scale"
  1717. scale_param {
  1718. bias_term: true
  1719. }
  1720. param {
  1721. lr_mult: 0.0
  1722. decay_mult: 0.0
  1723. }
  1724. param {
  1725. lr_mult: 0.0
  1726. decay_mult: 0.0
  1727. }
  1728. }
  1729. layer {
  1730. name: "res13_relu"
  1731. type: "ReLU"
  1732. bottom: "res13_scale"
  1733. top: "res13_scale"
  1734. }
  1735. layer {
  1736. name: "res13_conv1"
  1737. type: "Convolution"
  1738. bottom: "res13_scale"
  1739. top: "res13_conv1"
  1740. param {
  1741. lr_mult: 1
  1742. decay_mult: 1
  1743. }
  1744. convolution_param {
  1745. bias_term: false
  1746. num_output: 256
  1747. pad: 0
  1748. kernel_size: 1
  1749. stride: 1
  1750. }
  1751. }
  1752.  
  1753. layer {
  1754. name: "res13_conv1_scale"
  1755. type: "Scale"
  1756. bottom: "res13_conv1"
  1757. top: "res13_conv1"
  1758. scale_param {
  1759. bias_term: true
  1760. }
  1761. param {
  1762. lr_mult: 0.0
  1763. decay_mult: 0.0
  1764. }
  1765. param {
  1766. lr_mult: 0.0
  1767. decay_mult: 0.0
  1768. }
  1769. }
  1770. layer {
  1771. name: "res13_conv1_relu"
  1772. type: "ReLU"
  1773. bottom: "res13_conv1"
  1774. top: "res13_conv1"
  1775. }
  1776. layer {
  1777. name: "res13_conv2"
  1778. type: "Convolution"
  1779. bottom: "res13_conv1"
  1780. top: "res13_conv2"
  1781. param {
  1782. lr_mult: 1
  1783. decay_mult: 1
  1784. }
  1785. convolution_param {
  1786. bias_term: false
  1787. num_output: 256
  1788. pad: 2
  1789. dilation: 2
  1790. kernel_size: 3
  1791. stride: 1
  1792. }
  1793. }
  1794.  
  1795. layer {
  1796. name: "res13_conv2_scale"
  1797. type: "Scale"
  1798. bottom: "res13_conv2"
  1799. top: "res13_conv2"
  1800. scale_param {
  1801. bias_term: true
  1802. }
  1803. param {
  1804. lr_mult: 0.0
  1805. decay_mult: 0.0
  1806. }
  1807. param {
  1808. lr_mult: 0.0
  1809. decay_mult: 0.0
  1810. }
  1811. }
  1812. layer {
  1813. name: "res13_conv2_relu"
  1814. type: "ReLU"
  1815. bottom: "res13_conv2"
  1816. top: "res13_conv2"
  1817. }
  1818. layer {
  1819. name: "res13_conv3"
  1820. type: "Convolution"
  1821. bottom: "res13_conv2"
  1822. top: "res13_conv3"
  1823. param {
  1824. lr_mult: 1
  1825. decay_mult: 1
  1826. }
  1827. convolution_param {
  1828. bias_term: false
  1829. num_output: 1024
  1830. pad: 0
  1831. kernel_size: 1
  1832. stride: 1
  1833. }
  1834. }
  1835. layer {
  1836. name: "res13_eletwise"
  1837. type: "Eltwise"
  1838. bottom: "res12_eletwise"
  1839. bottom: "res13_conv3"
  1840. top: "res13_eletwise"
  1841. eltwise_param {
  1842. operation: SUM
  1843. }
  1844. }
  1845.  
  1846. layer {
  1847. name: "res14_scale"
  1848. type: "Scale"
  1849. bottom: "res13_eletwise"
  1850. top: "res14_scale"
  1851. scale_param {
  1852. bias_term: true
  1853. }
  1854. param {
  1855. lr_mult: 0.0
  1856. decay_mult: 0.0
  1857. }
  1858. param {
  1859. lr_mult: 0.0
  1860. decay_mult: 0.0
  1861. }
  1862. }
  1863. layer {
  1864. name: "res14_relu"
  1865. type: "ReLU"
  1866. bottom: "res14_scale"
  1867. top: "res14_scale"
  1868. }
  1869. layer {
  1870. name: "res14_conv1"
  1871. type: "Convolution"
  1872. bottom: "res14_scale"
  1873. top: "res14_conv1"
  1874. param {
  1875. lr_mult: 1
  1876. decay_mult: 1
  1877. }
  1878. convolution_param {
  1879. bias_term: false
  1880. num_output: 256
  1881. pad: 0
  1882. kernel_size: 1
  1883. stride: 1
  1884. }
  1885. }
  1886.  
  1887. layer {
  1888. name: "res14_conv1_scale"
  1889. type: "Scale"
  1890. bottom: "res14_conv1"
  1891. top: "res14_conv1"
  1892. scale_param {
  1893. bias_term: true
  1894. }
  1895. param {
  1896. lr_mult: 0.0
  1897. decay_mult: 0.0
  1898. }
  1899. param {
  1900. lr_mult: 0.0
  1901. decay_mult: 0.0
  1902. }
  1903. }
  1904. layer {
  1905. name: "res14_conv1_relu"
  1906. type: "ReLU"
  1907. bottom: "res14_conv1"
  1908. top: "res14_conv1"
  1909. }
  1910. layer {
  1911. name: "res14_conv2"
  1912. type: "Convolution"
  1913. bottom: "res14_conv1"
  1914. top: "res14_conv2"
  1915. param {
  1916. lr_mult: 1
  1917. decay_mult: 1
  1918. }
  1919. convolution_param {
  1920. bias_term: false
  1921. num_output: 256
  1922. pad: 2
  1923. dilation: 2
  1924. kernel_size: 3
  1925. stride: 1
  1926. }
  1927. }
  1928.  
  1929. layer {
  1930. name: "res14_conv2_scale"
  1931. type: "Scale"
  1932. bottom: "res14_conv2"
  1933. top: "res14_conv2"
  1934. scale_param {
  1935. bias_term: true
  1936. }
  1937. param {
  1938. lr_mult: 0.0
  1939. decay_mult: 0.0
  1940. }
  1941. param {
  1942. lr_mult: 0.0
  1943. decay_mult: 0.0
  1944. }
  1945. }
  1946. layer {
  1947. name: "res14_conv2_relu"
  1948. type: "ReLU"
  1949. bottom: "res14_conv2"
  1950. top: "res14_conv2"
  1951. }
  1952. layer {
  1953. name: "res14_conv3"
  1954. type: "Convolution"
  1955. bottom: "res14_conv2"
  1956. top: "res14_conv3"
  1957. param {
  1958. lr_mult: 1
  1959. decay_mult: 1
  1960. }
  1961. convolution_param {
  1962. bias_term: false
  1963. num_output: 1024
  1964. pad: 0
  1965. kernel_size: 1
  1966. stride: 1
  1967. }
  1968. }
  1969. layer {
  1970. name: "res14_eletwise"
  1971. type: "Eltwise"
  1972. bottom: "res13_eletwise"
  1973. bottom: "res14_conv3"
  1974. top: "res14_eletwise"
  1975. eltwise_param {
  1976. operation: SUM
  1977. }
  1978. }
  1979.  
  1980. layer {
  1981. name: "res15_scale"
  1982. type: "Scale"
  1983. bottom: "res14_eletwise"
  1984. top: "res15_scale"
  1985. scale_param {
  1986. bias_term: true
  1987. }
  1988. param {
  1989. lr_mult: 0.0
  1990. decay_mult: 0.0
  1991. }
  1992. param {
  1993. lr_mult: 0.0
  1994. decay_mult: 0.0
  1995. }
  1996. }
  1997. layer {
  1998. name: "res15_relu"
  1999. type: "ReLU"
  2000. bottom: "res15_scale"
  2001. top: "res15_scale"
  2002. }
  2003. layer {
  2004. name: "res15_conv1"
  2005. type: "Convolution"
  2006. bottom: "res15_scale"
  2007. top: "res15_conv1"
  2008. param {
  2009. lr_mult: 1
  2010. decay_mult: 1
  2011. }
  2012. convolution_param {
  2013. bias_term: false
  2014. num_output: 256
  2015. pad: 0
  2016. kernel_size: 1
  2017. stride: 1
  2018. }
  2019. }
  2020.  
  2021. layer {
  2022. name: "res15_conv1_scale"
  2023. type: "Scale"
  2024. bottom: "res15_conv1"
  2025. top: "res15_conv1"
  2026. scale_param {
  2027. bias_term: true
  2028. }
  2029. param {
  2030. lr_mult: 0.0
  2031. decay_mult: 0.0
  2032. }
  2033. param {
  2034. lr_mult: 0.0
  2035. decay_mult: 0.0
  2036. }
  2037. }
  2038. layer {
  2039. name: "res15_conv1_relu"
  2040. type: "ReLU"
  2041. bottom: "res15_conv1"
  2042. top: "res15_conv1"
  2043. }
  2044. layer {
  2045. name: "res15_conv2"
  2046. type: "Convolution"
  2047. bottom: "res15_conv1"
  2048. top: "res15_conv2"
  2049. param {
  2050. lr_mult: 1
  2051. decay_mult: 1
  2052. }
  2053. convolution_param {
  2054. bias_term: false
  2055. num_output: 256
  2056. pad: 2
  2057. dilation: 2
  2058. kernel_size: 3
  2059. stride: 1
  2060. }
  2061. }
  2062.  
  2063. layer {
  2064. name: "res15_conv2_scale"
  2065. type: "Scale"
  2066. bottom: "res15_conv2"
  2067. top: "res15_conv2"
  2068. scale_param {
  2069. bias_term: true
  2070. }
  2071. param {
  2072. lr_mult: 0.0
  2073. decay_mult: 0.0
  2074. }
  2075. param {
  2076. lr_mult: 0.0
  2077. decay_mult: 0.0
  2078. }
  2079. }
  2080. layer {
  2081. name: "res15_conv2_relu"
  2082. type: "ReLU"
  2083. bottom: "res15_conv2"
  2084. top: "res15_conv2"
  2085. }
  2086. layer {
  2087. name: "res15_conv3"
  2088. type: "Convolution"
  2089. bottom: "res15_conv2"
  2090. top: "res15_conv3"
  2091. param {
  2092. lr_mult: 1
  2093. decay_mult: 1
  2094. }
  2095. convolution_param {
  2096. bias_term: false
  2097. num_output: 1024
  2098. pad: 0
  2099. kernel_size: 1
  2100. stride: 1
  2101. }
  2102. }
  2103. layer {
  2104. name: "res15_eletwise"
  2105. type: "Eltwise"
  2106. bottom: "res14_eletwise"
  2107. bottom: "res15_conv3"
  2108. top: "res15_eletwise"
  2109. eltwise_param {
  2110. operation: SUM
  2111. }
  2112. }
  2113.  
  2114. layer {
  2115. name: "res16_scale"
  2116. type: "Scale"
  2117. bottom: "res15_eletwise"
  2118. top: "res16_scale"
  2119. scale_param {
  2120. bias_term: true
  2121. }
  2122. param {
  2123. lr_mult: 0.0
  2124. decay_mult: 0.0
  2125. }
  2126. param {
  2127. lr_mult: 0.0
  2128. decay_mult: 0.0
  2129. }
  2130. }
  2131. layer {
  2132. name: "res16_relu"
  2133. type: "ReLU"
  2134. bottom: "res16_scale"
  2135. top: "res16_scale"
  2136. }
  2137. layer {
  2138. name: "res16_conv1"
  2139. type: "Convolution"
  2140. bottom: "res16_scale"
  2141. top: "res16_conv1"
  2142. param {
  2143. lr_mult: 1
  2144. decay_mult: 1
  2145. }
  2146. convolution_param {
  2147. bias_term: false
  2148. num_output: 256
  2149. pad: 0
  2150. kernel_size: 1
  2151. stride: 1
  2152. }
  2153. }
  2154.  
  2155. layer {
  2156. name: "res16_conv1_scale"
  2157. type: "Scale"
  2158. bottom: "res16_conv1"
  2159. top: "res16_conv1"
  2160. scale_param {
  2161. bias_term: true
  2162. }
  2163. param {
  2164. lr_mult: 0.0
  2165. decay_mult: 0.0
  2166. }
  2167. param {
  2168. lr_mult: 0.0
  2169. decay_mult: 0.0
  2170. }
  2171. }
  2172. layer {
  2173. name: "res16_conv1_relu"
  2174. type: "ReLU"
  2175. bottom: "res16_conv1"
  2176. top: "res16_conv1"
  2177. }
  2178. layer {
  2179. name: "res16_conv2"
  2180. type: "Convolution"
  2181. bottom: "res16_conv1"
  2182. top: "res16_conv2"
  2183. param {
  2184. lr_mult: 1
  2185. decay_mult: 1
  2186. }
  2187. convolution_param {
  2188. bias_term: false
  2189. num_output: 256
  2190. pad: 2
  2191. dilation: 2
  2192. kernel_size: 3
  2193. stride: 1
  2194. }
  2195. }
  2196.  
  2197. layer {
  2198. name: "res16_conv2_scale"
  2199. type: "Scale"
  2200. bottom: "res16_conv2"
  2201. top: "res16_conv2"
  2202. scale_param {
  2203. bias_term: true
  2204. }
  2205. param {
  2206. lr_mult: 0.0
  2207. decay_mult: 0.0
  2208. }
  2209. param {
  2210. lr_mult: 0.0
  2211. decay_mult: 0.0
  2212. }
  2213. }
  2214. layer {
  2215. name: "res16_conv2_relu"
  2216. type: "ReLU"
  2217. bottom: "res16_conv2"
  2218. top: "res16_conv2"
  2219. }
  2220. layer {
  2221. name: "res16_conv3"
  2222. type: "Convolution"
  2223. bottom: "res16_conv2"
  2224. top: "res16_conv3"
  2225. param {
  2226. lr_mult: 1
  2227. decay_mult: 1
  2228. }
  2229. convolution_param {
  2230. bias_term: false
  2231. num_output: 1024
  2232. pad: 0
  2233. kernel_size: 1
  2234. stride: 1
  2235. }
  2236. }
  2237. layer {
  2238. name: "res16_eletwise"
  2239. type: "Eltwise"
  2240. bottom: "res15_eletwise"
  2241. bottom: "res16_conv3"
  2242. top: "res16_eletwise"
  2243. eltwise_param {
  2244. operation: SUM
  2245. }
  2246. }
  2247.  
  2248. layer {
  2249. name: "res17_scale"
  2250. type: "Scale"
  2251. bottom: "res16_eletwise"
  2252. top: "res17_scale"
  2253. scale_param {
  2254. bias_term: true
  2255. }
  2256. param {
  2257. lr_mult: 0.0
  2258. decay_mult: 0.0
  2259. }
  2260. param {
  2261. lr_mult: 0.0
  2262. decay_mult: 0.0
  2263. }
  2264. }
  2265. layer {
  2266. name: "res17_relu"
  2267. type: "ReLU"
  2268. bottom: "res17_scale"
  2269. top: "res17_scale"
  2270. }
  2271. layer {
  2272. name: "res17_conv1"
  2273. type: "Convolution"
  2274. bottom: "res17_scale"
  2275. top: "res17_conv1"
  2276. param {
  2277. lr_mult: 1
  2278. decay_mult: 1
  2279. }
  2280. convolution_param {
  2281. bias_term: false
  2282. num_output: 256
  2283. pad: 0
  2284. kernel_size: 1
  2285. stride: 1
  2286. }
  2287. }
  2288.  
  2289. layer {
  2290. name: "res17_conv1_scale"
  2291. type: "Scale"
  2292. bottom: "res17_conv1"
  2293. top: "res17_conv1"
  2294. scale_param {
  2295. bias_term: true
  2296. }
  2297. param {
  2298. lr_mult: 0.0
  2299. decay_mult: 0.0
  2300. }
  2301. param {
  2302. lr_mult: 0.0
  2303. decay_mult: 0.0
  2304. }
  2305. }
  2306. layer {
  2307. name: "res17_conv1_relu"
  2308. type: "ReLU"
  2309. bottom: "res17_conv1"
  2310. top: "res17_conv1"
  2311. }
  2312. layer {
  2313. name: "res17_conv2"
  2314. type: "Convolution"
  2315. bottom: "res17_conv1"
  2316. top: "res17_conv2"
  2317. param {
  2318. lr_mult: 1
  2319. decay_mult: 1
  2320. }
  2321. convolution_param {
  2322. bias_term: false
  2323. num_output: 256
  2324. pad: 2
  2325. dilation: 2
  2326. kernel_size: 3
  2327. stride: 1
  2328. }
  2329. }
  2330.  
  2331. layer {
  2332. name: "res17_conv2_scale"
  2333. type: "Scale"
  2334. bottom: "res17_conv2"
  2335. top: "res17_conv2"
  2336. scale_param {
  2337. bias_term: true
  2338. }
  2339. param {
  2340. lr_mult: 0.0
  2341. decay_mult: 0.0
  2342. }
  2343. param {
  2344. lr_mult: 0.0
  2345. decay_mult: 0.0
  2346. }
  2347. }
  2348. layer {
  2349. name: "res17_conv2_relu"
  2350. type: "ReLU"
  2351. bottom: "res17_conv2"
  2352. top: "res17_conv2"
  2353. }
  2354. layer {
  2355. name: "res17_conv3"
  2356. type: "Convolution"
  2357. bottom: "res17_conv2"
  2358. top: "res17_conv3"
  2359. param {
  2360. lr_mult: 1
  2361. decay_mult: 1
  2362. }
  2363. convolution_param {
  2364. bias_term: false
  2365. num_output: 1024
  2366. pad: 0
  2367. kernel_size: 1
  2368. stride: 1
  2369. }
  2370. }
  2371. layer {
  2372. name: "res17_eletwise"
  2373. type: "Eltwise"
  2374. bottom: "res16_eletwise"
  2375. bottom: "res17_conv3"
  2376. top: "res17_eletwise"
  2377. eltwise_param {
  2378. operation: SUM
  2379. }
  2380. }
  2381.  
  2382. layer {
  2383. name: "res18_scale"
  2384. type: "Scale"
  2385. bottom: "res17_eletwise"
  2386. top: "res18_scale"
  2387. scale_param {
  2388. bias_term: true
  2389. }
  2390. param {
  2391. lr_mult: 0.0
  2392. decay_mult: 0.0
  2393. }
  2394. param {
  2395. lr_mult: 0.0
  2396. decay_mult: 0.0
  2397. }
  2398. }
  2399. layer {
  2400. name: "res18_relu"
  2401. type: "ReLU"
  2402. bottom: "res18_scale"
  2403. top: "res18_scale"
  2404. }
  2405. layer {
  2406. name: "res18_conv1"
  2407. type: "Convolution"
  2408. bottom: "res18_scale"
  2409. top: "res18_conv1"
  2410. param {
  2411. lr_mult: 1
  2412. decay_mult: 1
  2413. }
  2414. convolution_param {
  2415. bias_term: false
  2416. num_output: 256
  2417. pad: 0
  2418. kernel_size: 1
  2419. stride: 1
  2420. }
  2421. }
  2422.  
  2423. layer {
  2424. name: "res18_conv1_scale"
  2425. type: "Scale"
  2426. bottom: "res18_conv1"
  2427. top: "res18_conv1"
  2428. scale_param {
  2429. bias_term: true
  2430. }
  2431. param {
  2432. lr_mult: 0.0
  2433. decay_mult: 0.0
  2434. }
  2435. param {
  2436. lr_mult: 0.0
  2437. decay_mult: 0.0
  2438. }
  2439. }
  2440. layer {
  2441. name: "res18_conv1_relu"
  2442. type: "ReLU"
  2443. bottom: "res18_conv1"
  2444. top: "res18_conv1"
  2445. }
  2446. layer {
  2447. name: "res18_conv2"
  2448. type: "Convolution"
  2449. bottom: "res18_conv1"
  2450. top: "res18_conv2"
  2451. param {
  2452. lr_mult: 1
  2453. decay_mult: 1
  2454. }
  2455. convolution_param {
  2456. bias_term: false
  2457. num_output: 256
  2458. pad: 2
  2459. dilation: 2
  2460. kernel_size: 3
  2461. stride: 1
  2462. }
  2463. }
  2464.  
  2465. layer {
  2466. name: "res18_conv2_scale"
  2467. type: "Scale"
  2468. bottom: "res18_conv2"
  2469. top: "res18_conv2"
  2470. scale_param {
  2471. bias_term: true
  2472. }
  2473. param {
  2474. lr_mult: 0.0
  2475. decay_mult: 0.0
  2476. }
  2477. param {
  2478. lr_mult: 0.0
  2479. decay_mult: 0.0
  2480. }
  2481. }
  2482. layer {
  2483. name: "res18_conv2_relu"
  2484. type: "ReLU"
  2485. bottom: "res18_conv2"
  2486. top: "res18_conv2"
  2487. }
  2488. layer {
  2489. name: "res18_conv3"
  2490. type: "Convolution"
  2491. bottom: "res18_conv2"
  2492. top: "res18_conv3"
  2493. param {
  2494. lr_mult: 1
  2495. decay_mult: 1
  2496. }
  2497. convolution_param {
  2498. bias_term: false
  2499. num_output: 1024
  2500. pad: 0
  2501. kernel_size: 1
  2502. stride: 1
  2503. }
  2504. }
  2505. layer {
  2506. name: "res18_eletwise"
  2507. type: "Eltwise"
  2508. bottom: "res17_eletwise"
  2509. bottom: "res18_conv3"
  2510. top: "res18_eletwise"
  2511. eltwise_param {
  2512. operation: SUM
  2513. }
  2514. }
  2515.  
  2516. layer {
  2517. name: "res19_scale"
  2518. type: "Scale"
  2519. bottom: "res18_eletwise"
  2520. top: "res19_scale"
  2521. scale_param {
  2522. bias_term: true
  2523. }
  2524. param {
  2525. lr_mult: 0.0
  2526. decay_mult: 0.0
  2527. }
  2528. param {
  2529. lr_mult: 0.0
  2530. decay_mult: 0.0
  2531. }
  2532. }
  2533. layer {
  2534. name: "res19_relu"
  2535. type: "ReLU"
  2536. bottom: "res19_scale"
  2537. top: "res19_scale"
  2538. }
  2539. layer {
  2540. name: "res19_conv1"
  2541. type: "Convolution"
  2542. bottom: "res19_scale"
  2543. top: "res19_conv1"
  2544. param {
  2545. lr_mult: 1
  2546. decay_mult: 1
  2547. }
  2548. convolution_param {
  2549. bias_term: false
  2550. num_output: 256
  2551. pad: 0
  2552. kernel_size: 1
  2553. stride: 1
  2554. }
  2555. }
  2556.  
  2557. layer {
  2558. name: "res19_conv1_scale"
  2559. type: "Scale"
  2560. bottom: "res19_conv1"
  2561. top: "res19_conv1"
  2562. scale_param {
  2563. bias_term: true
  2564. }
  2565. param {
  2566. lr_mult: 0.0
  2567. decay_mult: 0.0
  2568. }
  2569. param {
  2570. lr_mult: 0.0
  2571. decay_mult: 0.0
  2572. }
  2573. }
  2574. layer {
  2575. name: "res19_conv1_relu"
  2576. type: "ReLU"
  2577. bottom: "res19_conv1"
  2578. top: "res19_conv1"
  2579. }
  2580. layer {
  2581. name: "res19_conv2"
  2582. type: "Convolution"
  2583. bottom: "res19_conv1"
  2584. top: "res19_conv2"
  2585. param {
  2586. lr_mult: 1
  2587. decay_mult: 1
  2588. }
  2589. convolution_param {
  2590. bias_term: false
  2591. num_output: 256
  2592. pad: 2
  2593. dilation: 2
  2594. kernel_size: 3
  2595. stride: 1
  2596. }
  2597. }
  2598.  
  2599. layer {
  2600. name: "res19_conv2_scale"
  2601. type: "Scale"
  2602. bottom: "res19_conv2"
  2603. top: "res19_conv2"
  2604. scale_param {
  2605. bias_term: true
  2606. }
  2607. param {
  2608. lr_mult: 0.0
  2609. decay_mult: 0.0
  2610. }
  2611. param {
  2612. lr_mult: 0.0
  2613. decay_mult: 0.0
  2614. }
  2615. }
  2616. layer {
  2617. name: "res19_conv2_relu"
  2618. type: "ReLU"
  2619. bottom: "res19_conv2"
  2620. top: "res19_conv2"
  2621. }
  2622. layer {
  2623. name: "res19_conv3"
  2624. type: "Convolution"
  2625. bottom: "res19_conv2"
  2626. top: "res19_conv3"
  2627. param {
  2628. lr_mult: 1
  2629. decay_mult: 1
  2630. }
  2631. convolution_param {
  2632. bias_term: false
  2633. num_output: 1024
  2634. pad: 0
  2635. kernel_size: 1
  2636. stride: 1
  2637. }
  2638. }
  2639. layer {
  2640. name: "res19_eletwise"
  2641. type: "Eltwise"
  2642. bottom: "res18_eletwise"
  2643. bottom: "res19_conv3"
  2644. top: "res19_eletwise"
  2645. eltwise_param {
  2646. operation: SUM
  2647. }
  2648. }
  2649.  
  2650. layer {
  2651. name: "res20_scale"
  2652. type: "Scale"
  2653. bottom: "res19_eletwise"
  2654. top: "res20_scale"
  2655. scale_param {
  2656. bias_term: true
  2657. }
  2658. param {
  2659. lr_mult: 0.0
  2660. decay_mult: 0.0
  2661. }
  2662. param {
  2663. lr_mult: 0.0
  2664. decay_mult: 0.0
  2665. }
  2666. }
  2667. layer {
  2668. name: "res20_relu"
  2669. type: "ReLU"
  2670. bottom: "res20_scale"
  2671. top: "res20_scale"
  2672. }
  2673. layer {
  2674. name: "res20_conv1"
  2675. type: "Convolution"
  2676. bottom: "res20_scale"
  2677. top: "res20_conv1"
  2678. param {
  2679. lr_mult: 1
  2680. decay_mult: 1
  2681. }
  2682. convolution_param {
  2683. bias_term: false
  2684. num_output: 256
  2685. pad: 0
  2686. kernel_size: 1
  2687. stride: 1
  2688. }
  2689. }
  2690.  
  2691. layer {
  2692. name: "res20_conv1_scale"
  2693. type: "Scale"
  2694. bottom: "res20_conv1"
  2695. top: "res20_conv1"
  2696. scale_param {
  2697. bias_term: true
  2698. }
  2699. param {
  2700. lr_mult: 0.0
  2701. decay_mult: 0.0
  2702. }
  2703. param {
  2704. lr_mult: 0.0
  2705. decay_mult: 0.0
  2706. }
  2707. }
  2708. layer {
  2709. name: "res20_conv1_relu"
  2710. type: "ReLU"
  2711. bottom: "res20_conv1"
  2712. top: "res20_conv1"
  2713. }
  2714. layer {
  2715. name: "res20_conv2"
  2716. type: "Convolution"
  2717. bottom: "res20_conv1"
  2718. top: "res20_conv2"
  2719. param {
  2720. lr_mult: 1
  2721. decay_mult: 1
  2722. }
  2723. convolution_param {
  2724. bias_term: false
  2725. num_output: 256
  2726. pad: 2
  2727. dilation: 2
  2728. kernel_size: 3
  2729. stride: 1
  2730. }
  2731. }
  2732.  
  2733. layer {
  2734. name: "res20_conv2_scale"
  2735. type: "Scale"
  2736. bottom: "res20_conv2"
  2737. top: "res20_conv2"
  2738. scale_param {
  2739. bias_term: true
  2740. }
  2741. param {
  2742. lr_mult: 0.0
  2743. decay_mult: 0.0
  2744. }
  2745. param {
  2746. lr_mult: 0.0
  2747. decay_mult: 0.0
  2748. }
  2749. }
  2750. layer {
  2751. name: "res20_conv2_relu"
  2752. type: "ReLU"
  2753. bottom: "res20_conv2"
  2754. top: "res20_conv2"
  2755. }
  2756. layer {
  2757. name: "res20_conv3"
  2758. type: "Convolution"
  2759. bottom: "res20_conv2"
  2760. top: "res20_conv3"
  2761. param {
  2762. lr_mult: 1
  2763. decay_mult: 1
  2764. }
  2765. convolution_param {
  2766. bias_term: false
  2767. num_output: 1024
  2768. pad: 0
  2769. kernel_size: 1
  2770. stride: 1
  2771. }
  2772. }
  2773. layer {
  2774. name: "res20_eletwise"
  2775. type: "Eltwise"
  2776. bottom: "res19_eletwise"
  2777. bottom: "res20_conv3"
  2778. top: "res20_eletwise"
  2779. eltwise_param {
  2780. operation: SUM
  2781. }
  2782. }
  2783.  
  2784. layer {
  2785. name: "res21_scale"
  2786. type: "Scale"
  2787. bottom: "res20_eletwise"
  2788. top: "res21_scale"
  2789. scale_param {
  2790. bias_term: true
  2791. }
  2792. param {
  2793. lr_mult: 0.0
  2794. decay_mult: 0.0
  2795. }
  2796. param {
  2797. lr_mult: 0.0
  2798. decay_mult: 0.0
  2799. }
  2800. }
  2801. layer {
  2802. name: "res21_relu"
  2803. type: "ReLU"
  2804. bottom: "res21_scale"
  2805. top: "res21_scale"
  2806. }
  2807. layer {
  2808. name: "res21_conv1"
  2809. type: "Convolution"
  2810. bottom: "res21_scale"
  2811. top: "res21_conv1"
  2812. param {
  2813. lr_mult: 1
  2814. decay_mult: 1
  2815. }
  2816. convolution_param {
  2817. bias_term: false
  2818. num_output: 256
  2819. pad: 0
  2820. kernel_size: 1
  2821. stride: 1
  2822. }
  2823. }
  2824.  
  2825. layer {
  2826. name: "res21_conv1_scale"
  2827. type: "Scale"
  2828. bottom: "res21_conv1"
  2829. top: "res21_conv1"
  2830. scale_param {
  2831. bias_term: true
  2832. }
  2833. param {
  2834. lr_mult: 0.0
  2835. decay_mult: 0.0
  2836. }
  2837. param {
  2838. lr_mult: 0.0
  2839. decay_mult: 0.0
  2840. }
  2841. }
  2842. layer {
  2843. name: "res21_conv1_relu"
  2844. type: "ReLU"
  2845. bottom: "res21_conv1"
  2846. top: "res21_conv1"
  2847. }
  2848. layer {
  2849. name: "res21_conv2"
  2850. type: "Convolution"
  2851. bottom: "res21_conv1"
  2852. top: "res21_conv2"
  2853. param {
  2854. lr_mult: 1
  2855. decay_mult: 1
  2856. }
  2857. convolution_param {
  2858. bias_term: false
  2859. num_output: 256
  2860. pad: 2
  2861. dilation: 2
  2862. kernel_size: 3
  2863. stride: 1
  2864. }
  2865. }
  2866.  
  2867. layer {
  2868. name: "res21_conv2_scale"
  2869. type: "Scale"
  2870. bottom: "res21_conv2"
  2871. top: "res21_conv2"
  2872. scale_param {
  2873. bias_term: true
  2874. }
  2875. param {
  2876. lr_mult: 0.0
  2877. decay_mult: 0.0
  2878. }
  2879. param {
  2880. lr_mult: 0.0
  2881. decay_mult: 0.0
  2882. }
  2883. }
  2884. layer {
  2885. name: "res21_conv2_relu"
  2886. type: "ReLU"
  2887. bottom: "res21_conv2"
  2888. top: "res21_conv2"
  2889. }
  2890. layer {
  2891. name: "res21_conv3"
  2892. type: "Convolution"
  2893. bottom: "res21_conv2"
  2894. top: "res21_conv3"
  2895. param {
  2896. lr_mult: 1
  2897. decay_mult: 1
  2898. }
  2899. convolution_param {
  2900. bias_term: false
  2901. num_output: 1024
  2902. pad: 0
  2903. kernel_size: 1
  2904. stride: 1
  2905. }
  2906. }
  2907. layer {
  2908. name: "res21_eletwise"
  2909. type: "Eltwise"
  2910. bottom: "res20_eletwise"
  2911. bottom: "res21_conv3"
  2912. top: "res21_eletwise"
  2913. eltwise_param {
  2914. operation: SUM
  2915. }
  2916. }
  2917.  
  2918. layer {
  2919. name: "res22_scale"
  2920. type: "Scale"
  2921. bottom: "res21_eletwise"
  2922. top: "res22_scale"
  2923. scale_param {
  2924. bias_term: true
  2925. }
  2926. param {
  2927. lr_mult: 0.0
  2928. decay_mult: 0.0
  2929. }
  2930. param {
  2931. lr_mult: 0.0
  2932. decay_mult: 0.0
  2933. }
  2934. }
  2935. layer {
  2936. name: "res22_relu"
  2937. type: "ReLU"
  2938. bottom: "res22_scale"
  2939. top: "res22_scale"
  2940. }
  2941. layer {
  2942. name: "res22_conv1"
  2943. type: "Convolution"
  2944. bottom: "res22_scale"
  2945. top: "res22_conv1"
  2946. param {
  2947. lr_mult: 1
  2948. decay_mult: 1
  2949. }
  2950. convolution_param {
  2951. bias_term: false
  2952. num_output: 256
  2953. pad: 0
  2954. kernel_size: 1
  2955. stride: 1
  2956. }
  2957. }
  2958.  
  2959. layer {
  2960. name: "res22_conv1_scale"
  2961. type: "Scale"
  2962. bottom: "res22_conv1"
  2963. top: "res22_conv1"
  2964. scale_param {
  2965. bias_term: true
  2966. }
  2967. param {
  2968. lr_mult: 0.0
  2969. decay_mult: 0.0
  2970. }
  2971. param {
  2972. lr_mult: 0.0
  2973. decay_mult: 0.0
  2974. }
  2975. }
  2976. layer {
  2977. name: "res22_conv1_relu"
  2978. type: "ReLU"
  2979. bottom: "res22_conv1"
  2980. top: "res22_conv1"
  2981. }
  2982. layer {
  2983. name: "res22_conv2"
  2984. type: "Convolution"
  2985. bottom: "res22_conv1"
  2986. top: "res22_conv2"
  2987. param {
  2988. lr_mult: 1
  2989. decay_mult: 1
  2990. }
  2991. convolution_param {
  2992. bias_term: false
  2993. num_output: 256
  2994. pad: 2
  2995. dilation: 2
  2996. kernel_size: 3
  2997. stride: 1
  2998. }
  2999. }
  3000.  
  3001. layer {
  3002. name: "res22_conv2_scale"
  3003. type: "Scale"
  3004. bottom: "res22_conv2"
  3005. top: "res22_conv2"
  3006. scale_param {
  3007. bias_term: true
  3008. }
  3009. param {
  3010. lr_mult: 0.0
  3011. decay_mult: 0.0
  3012. }
  3013. param {
  3014. lr_mult: 0.0
  3015. decay_mult: 0.0
  3016. }
  3017. }
  3018. layer {
  3019. name: "res22_conv2_relu"
  3020. type: "ReLU"
  3021. bottom: "res22_conv2"
  3022. top: "res22_conv2"
  3023. }
  3024. layer {
  3025. name: "res22_conv3"
  3026. type: "Convolution"
  3027. bottom: "res22_conv2"
  3028. top: "res22_conv3"
  3029. param {
  3030. lr_mult: 1
  3031. decay_mult: 1
  3032. }
  3033. convolution_param {
  3034. bias_term: false
  3035. num_output: 1024
  3036. pad: 0
  3037. kernel_size: 1
  3038. stride: 1
  3039. }
  3040. }
  3041. layer {
  3042. name: "res22_eletwise"
  3043. type: "Eltwise"
  3044. bottom: "res21_eletwise"
  3045. bottom: "res22_conv3"
  3046. top: "res22_eletwise"
  3047. eltwise_param {
  3048. operation: SUM
  3049. }
  3050. }
  3051.  
  3052. layer {
  3053. name: "res23_scale"
  3054. type: "Scale"
  3055. bottom: "res22_eletwise"
  3056. top: "res23_scale"
  3057. scale_param {
  3058. bias_term: true
  3059. }
  3060. param {
  3061. lr_mult: 0.0
  3062. decay_mult: 0.0
  3063. }
  3064. param {
  3065. lr_mult: 0.0
  3066. decay_mult: 0.0
  3067. }
  3068. }
  3069. layer {
  3070. name: "res23_relu"
  3071. type: "ReLU"
  3072. bottom: "res23_scale"
  3073. top: "res23_scale"
  3074. }
  3075. layer {
  3076. name: "res23_conv1"
  3077. type: "Convolution"
  3078. bottom: "res23_scale"
  3079. top: "res23_conv1"
  3080. param {
  3081. lr_mult: 1
  3082. decay_mult: 1
  3083. }
  3084. convolution_param {
  3085. bias_term: false
  3086. num_output: 256
  3087. pad: 0
  3088. kernel_size: 1
  3089. stride: 1
  3090. }
  3091. }
  3092.  
  3093. layer {
  3094. name: "res23_conv1_scale"
  3095. type: "Scale"
  3096. bottom: "res23_conv1"
  3097. top: "res23_conv1"
  3098. scale_param {
  3099. bias_term: true
  3100. }
  3101. param {
  3102. lr_mult: 0.0
  3103. decay_mult: 0.0
  3104. }
  3105. param {
  3106. lr_mult: 0.0
  3107. decay_mult: 0.0
  3108. }
  3109. }
  3110. layer {
  3111. name: "res23_conv1_relu"
  3112. type: "ReLU"
  3113. bottom: "res23_conv1"
  3114. top: "res23_conv1"
  3115. }
  3116. layer {
  3117. name: "res23_conv2"
  3118. type: "Convolution"
  3119. bottom: "res23_conv1"
  3120. top: "res23_conv2"
  3121. param {
  3122. lr_mult: 1
  3123. decay_mult: 1
  3124. }
  3125. convolution_param {
  3126. bias_term: false
  3127. num_output: 256
  3128. pad: 2
  3129. dilation: 2
  3130. kernel_size: 3
  3131. stride: 1
  3132. }
  3133. }
  3134.  
  3135. layer {
  3136. name: "res23_conv2_scale"
  3137. type: "Scale"
  3138. bottom: "res23_conv2"
  3139. top: "res23_conv2"
  3140. scale_param {
  3141. bias_term: true
  3142. }
  3143. param {
  3144. lr_mult: 0.0
  3145. decay_mult: 0.0
  3146. }
  3147. param {
  3148. lr_mult: 0.0
  3149. decay_mult: 0.0
  3150. }
  3151. }
  3152. layer {
  3153. name: "res23_conv2_relu"
  3154. type: "ReLU"
  3155. bottom: "res23_conv2"
  3156. top: "res23_conv2"
  3157. }
  3158. layer {
  3159. name: "res23_conv3"
  3160. type: "Convolution"
  3161. bottom: "res23_conv2"
  3162. top: "res23_conv3"
  3163. param {
  3164. lr_mult: 1
  3165. decay_mult: 1
  3166. }
  3167. convolution_param {
  3168. bias_term: false
  3169. num_output: 1024
  3170. pad: 0
  3171. kernel_size: 1
  3172. stride: 1
  3173. }
  3174. }
  3175. layer {
  3176. name: "res23_eletwise"
  3177. type: "Eltwise"
  3178. bottom: "res22_eletwise"
  3179. bottom: "res23_conv3"
  3180. top: "res23_eletwise"
  3181. eltwise_param {
  3182. operation: SUM
  3183. }
  3184. }
  3185.  
  3186. layer {
  3187. name: "res24_scale"
  3188. type: "Scale"
  3189. bottom: "res23_eletwise"
  3190. top: "res24_scale"
  3191. scale_param {
  3192. bias_term: true
  3193. }
  3194. param {
  3195. lr_mult: 0.0
  3196. decay_mult: 0.0
  3197. }
  3198. param {
  3199. lr_mult: 0.0
  3200. decay_mult: 0.0
  3201. }
  3202. }
  3203. layer {
  3204. name: "res24_relu"
  3205. type: "ReLU"
  3206. bottom: "res24_scale"
  3207. top: "res24_scale"
  3208. }
  3209. layer {
  3210. name: "res24_conv1"
  3211. type: "Convolution"
  3212. bottom: "res24_scale"
  3213. top: "res24_conv1"
  3214. param {
  3215. lr_mult: 1
  3216. decay_mult: 1
  3217. }
  3218. convolution_param {
  3219. bias_term: false
  3220. num_output: 256
  3221. pad: 0
  3222. kernel_size: 1
  3223. stride: 1
  3224. }
  3225. }
  3226.  
  3227. layer {
  3228. name: "res24_conv1_scale"
  3229. type: "Scale"
  3230. bottom: "res24_conv1"
  3231. top: "res24_conv1"
  3232. scale_param {
  3233. bias_term: true
  3234. }
  3235. param {
  3236. lr_mult: 0.0
  3237. decay_mult: 0.0
  3238. }
  3239. param {
  3240. lr_mult: 0.0
  3241. decay_mult: 0.0
  3242. }
  3243. }
  3244. layer {
  3245. name: "res24_conv1_relu"
  3246. type: "ReLU"
  3247. bottom: "res24_conv1"
  3248. top: "res24_conv1"
  3249. }
  3250. layer {
  3251. name: "res24_conv2"
  3252. type: "Convolution"
  3253. bottom: "res24_conv1"
  3254. top: "res24_conv2"
  3255. param {
  3256. lr_mult: 1
  3257. decay_mult: 1
  3258. }
  3259. convolution_param {
  3260. bias_term: false
  3261. num_output: 256
  3262. pad: 2
  3263. dilation: 2
  3264. kernel_size: 3
  3265. stride: 1
  3266. }
  3267. }
  3268.  
  3269. layer {
  3270. name: "res24_conv2_scale"
  3271. type: "Scale"
  3272. bottom: "res24_conv2"
  3273. top: "res24_conv2"
  3274. scale_param {
  3275. bias_term: true
  3276. }
  3277. param {
  3278. lr_mult: 0.0
  3279. decay_mult: 0.0
  3280. }
  3281. param {
  3282. lr_mult: 0.0
  3283. decay_mult: 0.0
  3284. }
  3285. }
  3286. layer {
  3287. name: "res24_conv2_relu"
  3288. type: "ReLU"
  3289. bottom: "res24_conv2"
  3290. top: "res24_conv2"
  3291. }
  3292. layer {
  3293. name: "res24_conv3"
  3294. type: "Convolution"
  3295. bottom: "res24_conv2"
  3296. top: "res24_conv3"
  3297. param {
  3298. lr_mult: 1
  3299. decay_mult: 1
  3300. }
  3301. convolution_param {
  3302. bias_term: false
  3303. num_output: 1024
  3304. pad: 0
  3305. kernel_size: 1
  3306. stride: 1
  3307. }
  3308. }
  3309. layer {
  3310. name: "res24_eletwise"
  3311. type: "Eltwise"
  3312. bottom: "res23_eletwise"
  3313. bottom: "res24_conv3"
  3314. top: "res24_eletwise"
  3315. eltwise_param {
  3316. operation: SUM
  3317. }
  3318. }
  3319.  
  3320. layer {
  3321. name: "res25_scale"
  3322. type: "Scale"
  3323. bottom: "res24_eletwise"
  3324. top: "res25_scale"
  3325. scale_param {
  3326. bias_term: true
  3327. }
  3328. param {
  3329. lr_mult: 0.0
  3330. decay_mult: 0.0
  3331. }
  3332. param {
  3333. lr_mult: 0.0
  3334. decay_mult: 0.0
  3335. }
  3336. }
  3337. layer {
  3338. name: "res25_relu"
  3339. type: "ReLU"
  3340. bottom: "res25_scale"
  3341. top: "res25_scale"
  3342. }
  3343. layer {
  3344. name: "res25_conv1"
  3345. type: "Convolution"
  3346. bottom: "res25_scale"
  3347. top: "res25_conv1"
  3348. param {
  3349. lr_mult: 1
  3350. decay_mult: 1
  3351. }
  3352. convolution_param {
  3353. bias_term: false
  3354. num_output: 256
  3355. pad: 0
  3356. kernel_size: 1
  3357. stride: 1
  3358. }
  3359. }
  3360.  
  3361. layer {
  3362. name: "res25_conv1_scale"
  3363. type: "Scale"
  3364. bottom: "res25_conv1"
  3365. top: "res25_conv1"
  3366. scale_param {
  3367. bias_term: true
  3368. }
  3369. param {
  3370. lr_mult: 0.0
  3371. decay_mult: 0.0
  3372. }
  3373. param {
  3374. lr_mult: 0.0
  3375. decay_mult: 0.0
  3376. }
  3377. }
  3378. layer {
  3379. name: "res25_conv1_relu"
  3380. type: "ReLU"
  3381. bottom: "res25_conv1"
  3382. top: "res25_conv1"
  3383. }
  3384. layer {
  3385. name: "res25_conv2"
  3386. type: "Convolution"
  3387. bottom: "res25_conv1"
  3388. top: "res25_conv2"
  3389. param {
  3390. lr_mult: 1
  3391. decay_mult: 1
  3392. }
  3393. convolution_param {
  3394. bias_term: false
  3395. num_output: 256
  3396. pad: 2
  3397. dilation: 2
  3398. kernel_size: 3
  3399. stride: 1
  3400. }
  3401. }
  3402.  
  3403. layer {
  3404. name: "res25_conv2_scale"
  3405. type: "Scale"
  3406. bottom: "res25_conv2"
  3407. top: "res25_conv2"
  3408. scale_param {
  3409. bias_term: true
  3410. }
  3411. param {
  3412. lr_mult: 0.0
  3413. decay_mult: 0.0
  3414. }
  3415. param {
  3416. lr_mult: 0.0
  3417. decay_mult: 0.0
  3418. }
  3419. }
  3420. layer {
  3421. name: "res25_conv2_relu"
  3422. type: "ReLU"
  3423. bottom: "res25_conv2"
  3424. top: "res25_conv2"
  3425. }
  3426. layer {
  3427. name: "res25_conv3"
  3428. type: "Convolution"
  3429. bottom: "res25_conv2"
  3430. top: "res25_conv3"
  3431. param {
  3432. lr_mult: 1
  3433. decay_mult: 1
  3434. }
  3435. convolution_param {
  3436. bias_term: false
  3437. num_output: 1024
  3438. pad: 0
  3439. kernel_size: 1
  3440. stride: 1
  3441. }
  3442. }
  3443. layer {
  3444. name: "res25_eletwise"
  3445. type: "Eltwise"
  3446. bottom: "res24_eletwise"
  3447. bottom: "res25_conv3"
  3448. top: "res25_eletwise"
  3449. eltwise_param {
  3450. operation: SUM
  3451. }
  3452. }
  3453.  
  3454. layer {
  3455. name: "res26_scale"
  3456. type: "Scale"
  3457. bottom: "res25_eletwise"
  3458. top: "res26_scale"
  3459. scale_param {
  3460. bias_term: true
  3461. }
  3462. param {
  3463. lr_mult: 0.0
  3464. decay_mult: 0.0
  3465. }
  3466. param {
  3467. lr_mult: 0.0
  3468. decay_mult: 0.0
  3469. }
  3470. }
  3471. layer {
  3472. name: "res26_relu"
  3473. type: "ReLU"
  3474. bottom: "res26_scale"
  3475. top: "res26_scale"
  3476. }
  3477. layer {
  3478. name: "res26_conv1"
  3479. type: "Convolution"
  3480. bottom: "res26_scale"
  3481. top: "res26_conv1"
  3482. param {
  3483. lr_mult: 1
  3484. decay_mult: 1
  3485. }
  3486. convolution_param {
  3487. bias_term: false
  3488. num_output: 256
  3489. pad: 0
  3490. kernel_size: 1
  3491. stride: 1
  3492. }
  3493. }
  3494.  
  3495. layer {
  3496. name: "res26_conv1_scale"
  3497. type: "Scale"
  3498. bottom: "res26_conv1"
  3499. top: "res26_conv1"
  3500. scale_param {
  3501. bias_term: true
  3502. }
  3503. param {
  3504. lr_mult: 0.0
  3505. decay_mult: 0.0
  3506. }
  3507. param {
  3508. lr_mult: 0.0
  3509. decay_mult: 0.0
  3510. }
  3511. }
  3512. layer {
  3513. name: "res26_conv1_relu"
  3514. type: "ReLU"
  3515. bottom: "res26_conv1"
  3516. top: "res26_conv1"
  3517. }
  3518. layer {
  3519. name: "res26_conv2"
  3520. type: "Convolution"
  3521. bottom: "res26_conv1"
  3522. top: "res26_conv2"
  3523. param {
  3524. lr_mult: 1
  3525. decay_mult: 1
  3526. }
  3527. convolution_param {
  3528. bias_term: false
  3529. num_output: 256
  3530. pad: 2
  3531. dilation: 2
  3532. kernel_size: 3
  3533. stride: 1
  3534. }
  3535. }
  3536.  
  3537. layer {
  3538. name: "res26_conv2_scale"
  3539. type: "Scale"
  3540. bottom: "res26_conv2"
  3541. top: "res26_conv2"
  3542. scale_param {
  3543. bias_term: true
  3544. }
  3545. param {
  3546. lr_mult: 0.0
  3547. decay_mult: 0.0
  3548. }
  3549. param {
  3550. lr_mult: 0.0
  3551. decay_mult: 0.0
  3552. }
  3553. }
  3554. layer {
  3555. name: "res26_conv2_relu"
  3556. type: "ReLU"
  3557. bottom: "res26_conv2"
  3558. top: "res26_conv2"
  3559. }
  3560. layer {
  3561. name: "res26_conv3"
  3562. type: "Convolution"
  3563. bottom: "res26_conv2"
  3564. top: "res26_conv3"
  3565. param {
  3566. lr_mult: 1
  3567. decay_mult: 1
  3568. }
  3569. convolution_param {
  3570. bias_term: false
  3571. num_output: 1024
  3572. pad: 0
  3573. kernel_size: 1
  3574. stride: 1
  3575. }
  3576. }
  3577. layer {
  3578. name: "res26_eletwise"
  3579. type: "Eltwise"
  3580. bottom: "res25_eletwise"
  3581. bottom: "res26_conv3"
  3582. top: "res26_eletwise"
  3583. eltwise_param {
  3584. operation: SUM
  3585. }
  3586. }
  3587.  
  3588. layer {
  3589. name: "res27_scale"
  3590. type: "Scale"
  3591. bottom: "res26_eletwise"
  3592. top: "res27_scale"
  3593. scale_param {
  3594. bias_term: true
  3595. }
  3596. param {
  3597. lr_mult: 0.0
  3598. decay_mult: 0.0
  3599. }
  3600. param {
  3601. lr_mult: 0.0
  3602. decay_mult: 0.0
  3603. }
  3604. }
  3605. layer {
  3606. name: "res27_relu"
  3607. type: "ReLU"
  3608. bottom: "res27_scale"
  3609. top: "res27_scale"
  3610. }
  3611. layer {
  3612. name: "res27_conv1"
  3613. type: "Convolution"
  3614. bottom: "res27_scale"
  3615. top: "res27_conv1"
  3616. param {
  3617. lr_mult: 1
  3618. decay_mult: 1
  3619. }
  3620. convolution_param {
  3621. bias_term: false
  3622. num_output: 256
  3623. pad: 0
  3624. kernel_size: 1
  3625. stride: 1
  3626. }
  3627. }
  3628.  
  3629. layer {
  3630. name: "res27_conv1_scale"
  3631. type: "Scale"
  3632. bottom: "res27_conv1"
  3633. top: "res27_conv1"
  3634. scale_param {
  3635. bias_term: true
  3636. }
  3637. param {
  3638. lr_mult: 0.0
  3639. decay_mult: 0.0
  3640. }
  3641. param {
  3642. lr_mult: 0.0
  3643. decay_mult: 0.0
  3644. }
  3645. }
  3646. layer {
  3647. name: "res27_conv1_relu"
  3648. type: "ReLU"
  3649. bottom: "res27_conv1"
  3650. top: "res27_conv1"
  3651. }
  3652. layer {
  3653. name: "res27_conv2"
  3654. type: "Convolution"
  3655. bottom: "res27_conv1"
  3656. top: "res27_conv2"
  3657. param {
  3658. lr_mult: 1
  3659. decay_mult: 1
  3660. }
  3661. convolution_param {
  3662. bias_term: false
  3663. num_output: 256
  3664. pad: 2
  3665. dilation: 2
  3666. kernel_size: 3
  3667. stride: 1
  3668. }
  3669. }
  3670.  
  3671. layer {
  3672. name: "res27_conv2_scale"
  3673. type: "Scale"
  3674. bottom: "res27_conv2"
  3675. top: "res27_conv2"
  3676. scale_param {
  3677. bias_term: true
  3678. }
  3679. param {
  3680. lr_mult: 0.0
  3681. decay_mult: 0.0
  3682. }
  3683. param {
  3684. lr_mult: 0.0
  3685. decay_mult: 0.0
  3686. }
  3687. }
  3688. layer {
  3689. name: "res27_conv2_relu"
  3690. type: "ReLU"
  3691. bottom: "res27_conv2"
  3692. top: "res27_conv2"
  3693. }
  3694. layer {
  3695. name: "res27_conv3"
  3696. type: "Convolution"
  3697. bottom: "res27_conv2"
  3698. top: "res27_conv3"
  3699. param {
  3700. lr_mult: 1
  3701. decay_mult: 1
  3702. }
  3703. convolution_param {
  3704. bias_term: false
  3705. num_output: 1024
  3706. pad: 0
  3707. kernel_size: 1
  3708. stride: 1
  3709. }
  3710. }
  3711. layer {
  3712. name: "res27_eletwise"
  3713. type: "Eltwise"
  3714. bottom: "res26_eletwise"
  3715. bottom: "res27_conv3"
  3716. top: "res27_eletwise"
  3717. eltwise_param {
  3718. operation: SUM
  3719. }
  3720. }
  3721.  
  3722. layer {
  3723. name: "res28_scale"
  3724. type: "Scale"
  3725. bottom: "res27_eletwise"
  3726. top: "res28_scale"
  3727. scale_param {
  3728. bias_term: true
  3729. }
  3730. param {
  3731. lr_mult: 0.0
  3732. decay_mult: 0.0
  3733. }
  3734. param {
  3735. lr_mult: 0.0
  3736. decay_mult: 0.0
  3737. }
  3738. }
  3739. layer {
  3740. name: "res28_relu"
  3741. type: "ReLU"
  3742. bottom: "res28_scale"
  3743. top: "res28_scale"
  3744. }
  3745. layer {
  3746. name: "res28_conv1"
  3747. type: "Convolution"
  3748. bottom: "res28_scale"
  3749. top: "res28_conv1"
  3750. param {
  3751. lr_mult: 1
  3752. decay_mult: 1
  3753. }
  3754. convolution_param {
  3755. bias_term: false
  3756. num_output: 256
  3757. pad: 0
  3758. kernel_size: 1
  3759. stride: 1
  3760. }
  3761. }
  3762.  
  3763. layer {
  3764. name: "res28_conv1_scale"
  3765. type: "Scale"
  3766. bottom: "res28_conv1"
  3767. top: "res28_conv1"
  3768. scale_param {
  3769. bias_term: true
  3770. }
  3771. param {
  3772. lr_mult: 0.0
  3773. decay_mult: 0.0
  3774. }
  3775. param {
  3776. lr_mult: 0.0
  3777. decay_mult: 0.0
  3778. }
  3779. }
  3780. layer {
  3781. name: "res28_conv1_relu"
  3782. type: "ReLU"
  3783. bottom: "res28_conv1"
  3784. top: "res28_conv1"
  3785. }
  3786. layer {
  3787. name: "res28_conv2"
  3788. type: "Convolution"
  3789. bottom: "res28_conv1"
  3790. top: "res28_conv2"
  3791. param {
  3792. lr_mult: 1
  3793. decay_mult: 1
  3794. }
  3795. convolution_param {
  3796. bias_term: false
  3797. num_output: 256
  3798. pad: 2
  3799. dilation: 2
  3800. kernel_size: 3
  3801. stride: 1
  3802. }
  3803. }
  3804.  
  3805. layer {
  3806. name: "res28_conv2_scale"
  3807. type: "Scale"
  3808. bottom: "res28_conv2"
  3809. top: "res28_conv2"
  3810. scale_param {
  3811. bias_term: true
  3812. }
  3813. param {
  3814. lr_mult: 0.0
  3815. decay_mult: 0.0
  3816. }
  3817. param {
  3818. lr_mult: 0.0
  3819. decay_mult: 0.0
  3820. }
  3821. }
  3822. layer {
  3823. name: "res28_conv2_relu"
  3824. type: "ReLU"
  3825. bottom: "res28_conv2"
  3826. top: "res28_conv2"
  3827. }
  3828. layer {
  3829. name: "res28_conv3"
  3830. type: "Convolution"
  3831. bottom: "res28_conv2"
  3832. top: "res28_conv3"
  3833. param {
  3834. lr_mult: 1
  3835. decay_mult: 1
  3836. }
  3837. convolution_param {
  3838. bias_term: false
  3839. num_output: 1024
  3840. pad: 0
  3841. kernel_size: 1
  3842. stride: 1
  3843. }
  3844. }
  3845. layer {
  3846. name: "res28_eletwise"
  3847. type: "Eltwise"
  3848. bottom: "res27_eletwise"
  3849. bottom: "res28_conv3"
  3850. top: "res28_eletwise"
  3851. eltwise_param {
  3852. operation: SUM
  3853. }
  3854. }
  3855.  
  3856. layer {
  3857. name: "res29_scale"
  3858. type: "Scale"
  3859. bottom: "res28_eletwise"
  3860. top: "res29_scale"
  3861. scale_param {
  3862. bias_term: true
  3863. }
  3864. param {
  3865. lr_mult: 0.0
  3866. decay_mult: 0.0
  3867. }
  3868. param {
  3869. lr_mult: 0.0
  3870. decay_mult: 0.0
  3871. }
  3872. }
  3873. layer {
  3874. name: "res29_relu"
  3875. type: "ReLU"
  3876. bottom: "res29_scale"
  3877. top: "res29_scale"
  3878. }
  3879. layer {
  3880. name: "res29_conv1"
  3881. type: "Convolution"
  3882. bottom: "res29_scale"
  3883. top: "res29_conv1"
  3884. param {
  3885. lr_mult: 1
  3886. decay_mult: 1
  3887. }
  3888. convolution_param {
  3889. bias_term: false
  3890. num_output: 256
  3891. pad: 0
  3892. kernel_size: 1
  3893. stride: 1
  3894. }
  3895. }
  3896.  
  3897. layer {
  3898. name: "res29_conv1_scale"
  3899. type: "Scale"
  3900. bottom: "res29_conv1"
  3901. top: "res29_conv1"
  3902. scale_param {
  3903. bias_term: true
  3904. }
  3905. param {
  3906. lr_mult: 0.0
  3907. decay_mult: 0.0
  3908. }
  3909. param {
  3910. lr_mult: 0.0
  3911. decay_mult: 0.0
  3912. }
  3913. }
  3914. layer {
  3915. name: "res29_conv1_relu"
  3916. type: "ReLU"
  3917. bottom: "res29_conv1"
  3918. top: "res29_conv1"
  3919. }
  3920. layer {
  3921. name: "res29_conv2"
  3922. type: "Convolution"
  3923. bottom: "res29_conv1"
  3924. top: "res29_conv2"
  3925. param {
  3926. lr_mult: 1
  3927. decay_mult: 1
  3928. }
  3929. convolution_param {
  3930. bias_term: false
  3931. num_output: 256
  3932. pad: 2
  3933. dilation: 2
  3934. kernel_size: 3
  3935. stride: 1
  3936. }
  3937. }
  3938.  
  3939. layer {
  3940. name: "res29_conv2_scale"
  3941. type: "Scale"
  3942. bottom: "res29_conv2"
  3943. top: "res29_conv2"
  3944. scale_param {
  3945. bias_term: true
  3946. }
  3947. param {
  3948. lr_mult: 0.0
  3949. decay_mult: 0.0
  3950. }
  3951. param {
  3952. lr_mult: 0.0
  3953. decay_mult: 0.0
  3954. }
  3955. }
  3956. layer {
  3957. name: "res29_conv2_relu"
  3958. type: "ReLU"
  3959. bottom: "res29_conv2"
  3960. top: "res29_conv2"
  3961. }
  3962. layer {
  3963. name: "res29_conv3"
  3964. type: "Convolution"
  3965. bottom: "res29_conv2"
  3966. top: "res29_conv3"
  3967. param {
  3968. lr_mult: 1
  3969. decay_mult: 1
  3970. }
  3971. convolution_param {
  3972. bias_term: false
  3973. num_output: 1024
  3974. pad: 0
  3975. kernel_size: 1
  3976. stride: 1
  3977. }
  3978. }
  3979. layer {
  3980. name: "res29_eletwise"
  3981. type: "Eltwise"
  3982. bottom: "res28_eletwise"
  3983. bottom: "res29_conv3"
  3984. top: "res29_eletwise"
  3985. eltwise_param {
  3986. operation: SUM
  3987. }
  3988. }
  3989.  
  3990. layer {
  3991. name: "res30_scale"
  3992. type: "Scale"
  3993. bottom: "res29_eletwise"
  3994. top: "res30_scale"
  3995. scale_param {
  3996. bias_term: true
  3997. }
  3998. param {
  3999. lr_mult: 0.0
  4000. decay_mult: 0.0
  4001. }
  4002. param {
  4003. lr_mult: 0.0
  4004. decay_mult: 0.0
  4005. }
  4006. }
  4007. layer {
  4008. name: "res30_relu"
  4009. type: "ReLU"
  4010. bottom: "res30_scale"
  4011. top: "res30_scale"
  4012. }
  4013. layer {
  4014. name: "res30_conv1"
  4015. type: "Convolution"
  4016. bottom: "res30_scale"
  4017. top: "res30_conv1"
  4018. param {
  4019. lr_mult: 1
  4020. decay_mult: 1
  4021. }
  4022. convolution_param {
  4023. bias_term: false
  4024. num_output: 256
  4025. pad: 0
  4026. kernel_size: 1
  4027. stride: 1
  4028. }
  4029. }
  4030.  
  4031. layer {
  4032. name: "res30_conv1_scale"
  4033. type: "Scale"
  4034. bottom: "res30_conv1"
  4035. top: "res30_conv1"
  4036. scale_param {
  4037. bias_term: true
  4038. }
  4039. param {
  4040. lr_mult: 0.0
  4041. decay_mult: 0.0
  4042. }
  4043. param {
  4044. lr_mult: 0.0
  4045. decay_mult: 0.0
  4046. }
  4047. }
  4048. layer {
  4049. name: "res30_conv1_relu"
  4050. type: "ReLU"
  4051. bottom: "res30_conv1"
  4052. top: "res30_conv1"
  4053. }
  4054. layer {
  4055. name: "res30_conv2"
  4056. type: "Convolution"
  4057. bottom: "res30_conv1"
  4058. top: "res30_conv2"
  4059. param {
  4060. lr_mult: 1
  4061. decay_mult: 1
  4062. }
  4063. convolution_param {
  4064. bias_term: false
  4065. num_output: 256
  4066. pad: 2
  4067. dilation: 2
  4068. kernel_size: 3
  4069. stride: 1
  4070. }
  4071. }
  4072.  
  4073. layer {
  4074. name: "res30_conv2_scale"
  4075. type: "Scale"
  4076. bottom: "res30_conv2"
  4077. top: "res30_conv2"
  4078. scale_param {
  4079. bias_term: true
  4080. }
  4081. param {
  4082. lr_mult: 0.0
  4083. decay_mult: 0.0
  4084. }
  4085. param {
  4086. lr_mult: 0.0
  4087. decay_mult: 0.0
  4088. }
  4089. }
  4090. layer {
  4091. name: "res30_conv2_relu"
  4092. type: "ReLU"
  4093. bottom: "res30_conv2"
  4094. top: "res30_conv2"
  4095. }
  4096. layer {
  4097. name: "res30_conv3"
  4098. type: "Convolution"
  4099. bottom: "res30_conv2"
  4100. top: "res30_conv3"
  4101. param {
  4102. lr_mult: 1
  4103. decay_mult: 1
  4104. }
  4105. convolution_param {
  4106. bias_term: false
  4107. num_output: 1024
  4108. pad: 0
  4109. kernel_size: 1
  4110. stride: 1
  4111. }
  4112. }
  4113. layer {
  4114. name: "res30_eletwise"
  4115. type: "Eltwise"
  4116. bottom: "res29_eletwise"
  4117. bottom: "res30_conv3"
  4118. top: "res30_eletwise"
  4119. eltwise_param {
  4120. operation: SUM
  4121. }
  4122. }
  4123. layer {
  4124. name: "res31_scale"
  4125. type: "Scale"
  4126. bottom: "res30_eletwise"
  4127. top: "res31_scale"
  4128. scale_param {
  4129. bias_term: true
  4130. }
  4131. param {
  4132. lr_mult: 0.0
  4133. decay_mult: 0.0
  4134. }
  4135. param {
  4136. lr_mult: 0.0
  4137. decay_mult: 0.0
  4138. }
  4139. }
  4140. layer {
  4141. name: "res31_relu"
  4142. type: "ReLU"
  4143. bottom: "res31_scale"
  4144. top: "res31_scale"
  4145. }
  4146. layer {
  4147. name: "res31_conv1"
  4148. type: "Convolution"
  4149. bottom: "res31_scale"
  4150. top: "res31_conv1"
  4151. param {
  4152. lr_mult: 1
  4153. decay_mult: 1
  4154. }
  4155. convolution_param {
  4156. bias_term: false
  4157. num_output: 512
  4158. pad: 0
  4159. kernel_size: 1
  4160. stride: 1
  4161. }
  4162. }
  4163. layer {
  4164. name: "res31_conv1_scale"
  4165. type: "Scale"
  4166. bottom: "res31_conv1"
  4167. top: "res31_conv1"
  4168. scale_param {
  4169. bias_term: true
  4170. }
  4171. param {
  4172. lr_mult: 0.0
  4173. decay_mult: 0.0
  4174. }
  4175. param {
  4176. lr_mult: 0.0
  4177. decay_mult: 0.0
  4178. }
  4179. }
  4180. layer {
  4181. name: "res31_conv1_relu"
  4182. type: "ReLU"
  4183. bottom: "res31_conv1"
  4184. top: "res31_conv1"
  4185. }
  4186. layer {
  4187. name: "res31_conv2"
  4188. type: "Convolution"
  4189. bottom: "res31_conv1"
  4190. top: "res31_conv2"
  4191. param {
  4192. lr_mult: 1
  4193. decay_mult: 1
  4194. }
  4195. convolution_param {
  4196. bias_term: false
  4197. num_output: 512
  4198. pad: 4
  4199. dilation: 4
  4200. kernel_size: 3
  4201. stride: 1
  4202. }
  4203. }
  4204. layer {
  4205. name: "res31_conv2_scale"
  4206. type: "Scale"
  4207. bottom: "res31_conv2"
  4208. top: "res31_conv2"
  4209. scale_param {
  4210. bias_term: true
  4211. }
  4212. param {
  4213. lr_mult: 0.0
  4214. decay_mult: 0.0
  4215. }
  4216. param {
  4217. lr_mult: 0.0
  4218. decay_mult: 0.0
  4219. }
  4220. }
  4221. layer {
  4222. name: "res31_conv2_relu"
  4223. type: "ReLU"
  4224. bottom: "res31_conv2"
  4225. top: "res31_conv2"
  4226. }
  4227. layer {
  4228. name: "res31_conv3"
  4229. type: "Convolution"
  4230. bottom: "res31_conv2"
  4231. top: "res31_conv3"
  4232. param {
  4233. lr_mult: 1
  4234. decay_mult: 1
  4235. }
  4236. convolution_param {
  4237. bias_term: false
  4238. num_output: 2048
  4239. pad: 0
  4240. kernel_size: 1
  4241. stride: 1
  4242. }
  4243. }
  4244. layer {
  4245. name: "res31_match_conv"
  4246. type: "Convolution"
  4247. bottom: "res31_scale"
  4248. top: "res31_match_conv"
  4249. param {
  4250. lr_mult: 1
  4251. decay_mult: 1
  4252. }
  4253. convolution_param {
  4254. bias_term: false
  4255. num_output: 2048
  4256. pad: 0
  4257. kernel_size: 1
  4258. stride: 1
  4259. }
  4260. }
  4261. layer {
  4262. name: "res31_eletwise"
  4263. type: "Eltwise"
  4264. bottom: "res31_match_conv"
  4265. bottom: "res31_conv3"
  4266. top: "res31_eletwise"
  4267. eltwise_param {
  4268. operation: SUM
  4269. }
  4270. }
  4271. layer {
  4272. name: "res32_scale"
  4273. type: "Scale"
  4274. bottom: "res31_eletwise"
  4275. top: "res32_scale"
  4276. scale_param {
  4277. bias_term: true
  4278. }
  4279. param {
  4280. lr_mult: 0.0
  4281. decay_mult: 0.0
  4282. }
  4283. param {
  4284. lr_mult: 0.0
  4285. decay_mult: 0.0
  4286. }
  4287. }
  4288. layer {
  4289. name: "res32_relu"
  4290. type: "ReLU"
  4291. bottom: "res32_scale"
  4292. top: "res32_scale"
  4293. }
  4294. layer {
  4295. name: "res32_conv1"
  4296. type: "Convolution"
  4297. bottom: "res32_scale"
  4298. top: "res32_conv1"
  4299. param {
  4300. lr_mult: 1
  4301. decay_mult: 1
  4302. }
  4303. convolution_param {
  4304. bias_term: false
  4305. num_output: 512
  4306. pad: 0
  4307. kernel_size: 1
  4308. stride: 1
  4309. }
  4310. }
  4311. layer {
  4312. name: "res32_conv1_scale"
  4313. type: "Scale"
  4314. bottom: "res32_conv1"
  4315. top: "res32_conv1"
  4316. scale_param {
  4317. bias_term: true
  4318. }
  4319. param {
  4320. lr_mult: 0.0
  4321. decay_mult: 0.0
  4322. }
  4323. param {
  4324. lr_mult: 0.0
  4325. decay_mult: 0.0
  4326. }
  4327. }
  4328. layer {
  4329. name: "res32_conv1_relu"
  4330. type: "ReLU"
  4331. bottom: "res32_conv1"
  4332. top: "res32_conv1"
  4333. }
  4334. layer {
  4335. name: "res32_conv2"
  4336. type: "Convolution"
  4337. bottom: "res32_conv1"
  4338. top: "res32_conv2"
  4339. param {
  4340. lr_mult: 1
  4341. decay_mult: 1
  4342. }
  4343. convolution_param {
  4344. bias_term: false
  4345. num_output: 512
  4346. pad: 8
  4347. dilation: 8
  4348. kernel_size: 3
  4349. stride: 1
  4350. }
  4351. }
  4352. layer {
  4353. name: "res32_conv2_scale"
  4354. type: "Scale"
  4355. bottom: "res32_conv2"
  4356. top: "res32_conv2"
  4357. scale_param {
  4358. bias_term: true
  4359. }
  4360. param {
  4361. lr_mult: 0.0
  4362. decay_mult: 0.0
  4363. }
  4364. param {
  4365. lr_mult: 0.0
  4366. decay_mult: 0.0
  4367. }
  4368. }
  4369. layer {
  4370. name: "res32_conv2_relu"
  4371. type: "ReLU"
  4372. bottom: "res32_conv2"
  4373. top: "res32_conv2"
  4374. }
  4375. layer {
  4376. name: "res32_conv3"
  4377. type: "Convolution"
  4378. bottom: "res32_conv2"
  4379. top: "res32_conv3"
  4380. param {
  4381. lr_mult: 1
  4382. decay_mult: 1
  4383. }
  4384. convolution_param {
  4385. bias_term: false
  4386. num_output: 2048
  4387. pad: 0
  4388. kernel_size: 1
  4389. stride: 1
  4390. }
  4391. }
  4392. layer {
  4393. name: "res32_eletwise"
  4394. type: "Eltwise"
  4395. bottom: "res31_eletwise"
  4396. bottom: "res32_conv3"
  4397. top: "res32_eletwise"
  4398. eltwise_param {
  4399. operation: SUM
  4400. }
  4401. }
  4402. layer {
  4403. name: "res33_scale"
  4404. type: "Scale"
  4405. bottom: "res32_eletwise"
  4406. top: "res33_scale"
  4407. scale_param {
  4408. bias_term: true
  4409. }
  4410. param {
  4411. lr_mult: 0.0
  4412. decay_mult: 0.0
  4413. }
  4414. param {
  4415. lr_mult: 0.0
  4416. decay_mult: 0.0
  4417. }
  4418. }
  4419. layer {
  4420. name: "res33_relu"
  4421. type: "ReLU"
  4422. bottom: "res33_scale"
  4423. top: "res33_scale"
  4424. }
  4425. layer {
  4426. name: "res33_conv1"
  4427. type: "Convolution"
  4428. bottom: "res33_scale"
  4429. top: "res33_conv1"
  4430. param {
  4431. lr_mult: 1
  4432. decay_mult: 1
  4433. }
  4434. convolution_param {
  4435. bias_term: false
  4436. num_output: 512
  4437. pad: 0
  4438. kernel_size: 1
  4439. stride: 1
  4440. }
  4441. }
  4442. layer {
  4443. name: "res33_conv1_scale"
  4444. type: "Scale"
  4445. bottom: "res33_conv1"
  4446. top: "res33_conv1"
  4447. scale_param {
  4448. bias_term: true
  4449. }
  4450. param {
  4451. lr_mult: 0.0
  4452. decay_mult: 0.0
  4453. }
  4454. param {
  4455. lr_mult: 0.0
  4456. decay_mult: 0.0
  4457. }
  4458. }
  4459. layer {
  4460. name: "res33_conv1_relu"
  4461. type: "ReLU"
  4462. bottom: "res33_conv1"
  4463. top: "res33_conv1"
  4464. }
  4465. layer {
  4466. name: "res33_conv2"
  4467. type: "Convolution"
  4468. bottom: "res33_conv1"
  4469. top: "res33_conv2"
  4470. param {
  4471. lr_mult: 1
  4472. decay_mult: 1
  4473. }
  4474. convolution_param {
  4475. bias_term: false
  4476. num_output: 512
  4477. pad: 16
  4478. dilation: 16
  4479. kernel_size: 3
  4480. stride: 1
  4481. }
  4482. }
  4483. layer {
  4484. name: "res33_conv2_scale"
  4485. type: "Scale"
  4486. bottom: "res33_conv2"
  4487. top: "res33_conv2"
  4488. scale_param {
  4489. bias_term: true
  4490. }
  4491. param {
  4492. lr_mult: 0.0
  4493. decay_mult: 0.0
  4494. }
  4495. param {
  4496. lr_mult: 0.0
  4497. decay_mult: 0.0
  4498. }
  4499. }
  4500. layer {
  4501. name: "res33_conv2_relu"
  4502. type: "ReLU"
  4503. bottom: "res33_conv2"
  4504. top: "res33_conv2"
  4505. }
  4506. layer {
  4507. name: "res33_conv3"
  4508. type: "Convolution"
  4509. bottom: "res33_conv2"
  4510. top: "res33_conv3"
  4511. param {
  4512. lr_mult: 1
  4513. decay_mult: 1
  4514. }
  4515. convolution_param {
  4516. bias_term: false
  4517. num_output: 2048
  4518. pad: 0
  4519. kernel_size: 1
  4520. stride: 1
  4521. }
  4522. }
  4523. layer {
  4524. name: "res33_eletwise"
  4525. type: "Eltwise"
  4526. bottom: "res32_eletwise"
  4527. bottom: "res33_conv3"
  4528. top: "res33_eletwise"
  4529. eltwise_param {
  4530. operation: SUM
  4531. }
  4532. }
  4533. layer {
  4534. name: "res33_eletwise_scale"
  4535. type: "Scale"
  4536. bottom: "res33_eletwise"
  4537. top: "res33_eletwise_scale"
  4538. scale_param {
  4539. bias_term: true
  4540. }
  4541. param {
  4542. lr_mult: 0.0
  4543. decay_mult: 0.0
  4544. }
  4545. param {
  4546. lr_mult: 0.0
  4547. decay_mult: 0.0
  4548. }
  4549. }
  4550. layer {
  4551. name: "res33_eletwise_relu"
  4552. type: "ReLU"
  4553. bottom: "res33_eletwise_scale"
  4554. top: "res33_eletwise_scale"
  4555. }
  4556.  
  4557. ###################### auxi psp ######################
  4558. layer {
  4559. name: "auxi_psp_pool1"
  4560. type: "Pooling"
  4561. bottom: "res30_eletwise"
  4562. top: "auxi_psp_pool1"
  4563. pooling_param {
  4564. pool: AVE
  4565. kernel_size: 64
  4566. stride: 64
  4567. }
  4568. }
  4569. layer {
  4570. name: "auxi_psp_pool1_conv"
  4571. type: "Convolution"
  4572. bottom: "auxi_psp_pool1"
  4573. top: "auxi_psp_pool1_conv"
  4574. param {
  4575. lr_mult: 10
  4576. decay_mult: 1
  4577. }
  4578. convolution_param {
  4579. num_output: 256
  4580. kernel_size: 1
  4581. stride: 1
  4582. weight_filler {
  4583. type: "msra"
  4584. }
  4585. bias_term: false
  4586. }
  4587. }
  4588. layer {
  4589. name: "auxi_psp_pool1_conv_relu"
  4590. type: "ReLU"
  4591. bottom: "auxi_psp_pool1_conv"
  4592. top: "auxi_psp_pool1_conv"
  4593. }
  4594. layer {
  4595. name: "auxi_psp_pool1_interp"
  4596. type: "Interp"
  4597. bottom: "auxi_psp_pool1_conv"
  4598. top: "auxi_psp_pool1_interp"
  4599. interp_param {
  4600. height: 34
  4601. width: 34
  4602. }
  4603. }
  4604. layer {
  4605. name: "auxi_psp_pool2"
  4606. type: "Pooling"
  4607. bottom: "res30_eletwise"
  4608. top: "auxi_psp_pool2"
  4609. pooling_param {
  4610. pool: AVE
  4611. kernel_size: 32
  4612. stride: 32
  4613. }
  4614. }
  4615. layer {
  4616. name: "auxi_psp_pool2_conv"
  4617. type: "Convolution"
  4618. bottom: "auxi_psp_pool2"
  4619. top: "auxi_psp_pool2_conv"
  4620. param {
  4621. lr_mult: 10
  4622. decay_mult: 1
  4623. }
  4624. convolution_param {
  4625. num_output: 256
  4626. kernel_size: 1
  4627. stride: 1
  4628. weight_filler {
  4629. type: "msra"
  4630. }
  4631. bias_term: false
  4632. }
  4633. }
  4634. layer {
  4635. name: "auxi_psp_pool2_conv_relu"
  4636. type: "ReLU"
  4637. bottom: "auxi_psp_pool2_conv"
  4638. top: "auxi_psp_pool2_conv"
  4639. }
  4640. layer {
  4641. name: "auxi_psp_pool2_interp"
  4642. type: "Interp"
  4643. bottom: "auxi_psp_pool2_conv"
  4644. top: "auxi_psp_pool2_interp"
  4645. interp_param {
  4646. height: 34
  4647. width: 34
  4648. }
  4649. }
  4650. layer {
  4651. name: "auxi_psp_pool4"
  4652. type: "Pooling"
  4653. bottom: "res30_eletwise"
  4654. top: "auxi_psp_pool4"
  4655. pooling_param {
  4656. pool: AVE
  4657. kernel_size: 16
  4658. stride: 16
  4659. }
  4660. }
  4661. layer {
  4662. name: "auxi_psp_pool4_conv"
  4663. type: "Convolution"
  4664. bottom: "auxi_psp_pool4"
  4665. top: "auxi_psp_pool4_conv"
  4666. param {
  4667. lr_mult: 10
  4668. decay_mult: 1
  4669. }
  4670. convolution_param {
  4671. num_output: 256
  4672. kernel_size: 1
  4673. stride: 1
  4674. weight_filler {
  4675. type: "msra"
  4676. }
  4677. bias_term: false
  4678. }
  4679. }
  4680. layer {
  4681. name: "auxi_psp_pool4_relu"
  4682. type: "ReLU"
  4683. bottom: "auxi_psp_pool4_conv"
  4684. top: "auxi_psp_pool4_conv"
  4685. }
  4686. layer {
  4687. name: "auxi_psp_pool4_interp"
  4688. type: "Interp"
  4689. bottom: "auxi_psp_pool4_conv"
  4690. top: "auxi_psp_pool4_interp"
  4691. interp_param {
  4692. height: 34
  4693. width: 34
  4694. }
  4695. }
  4696. layer {
  4697. name: "auxi_psp_pool8"
  4698. type: "Pooling"
  4699. bottom: "res30_eletwise"
  4700. top: "auxi_psp_pool8"
  4701. pooling_param {
  4702. pool: AVE
  4703. kernel_size: 8
  4704. stride: 8
  4705. }
  4706. }
  4707. layer {
  4708. name: "auxi_psp_pool8_conv"
  4709. type: "Convolution"
  4710. bottom: "auxi_psp_pool8"
  4711. top: "auxi_psp_pool8_conv"
  4712. param {
  4713. lr_mult: 10
  4714. decay_mult: 1
  4715. }
  4716. convolution_param {
  4717. num_output: 256
  4718. kernel_size: 1
  4719. stride: 1
  4720. weight_filler {
  4721. type: "msra"
  4722. }
  4723. bias_term: false
  4724. }
  4725. }
  4726. layer {
  4727. name: "auxi_psp_pool8_relu"
  4728. type: "ReLU"
  4729. bottom: "auxi_psp_pool8_conv"
  4730. top: "auxi_psp_pool8_conv"
  4731. }
  4732. layer {
  4733. name: "auxi_psp_pool8_interp"
  4734. type: "Interp"
  4735. bottom: "auxi_psp_pool8_conv"
  4736. top: "auxi_psp_pool8_interp"
  4737. interp_param {
  4738. height: 34
  4739. width: 34
  4740. }
  4741. }
  4742. layer {
  4743. name: "auxi_psp_concat"
  4744. type: "Concat"
  4745. bottom: "res30_eletwise"
  4746. bottom: "auxi_psp_pool8_interp"
  4747. bottom: "auxi_psp_pool4_interp"
  4748. bottom: "auxi_psp_pool2_interp"
  4749. bottom: "auxi_psp_pool1_interp"
  4750. top: "auxi_psp_concat"
  4751. }
  4752. layer {
  4753. name: "auxi_feat"
  4754. type: "Convolution"
  4755. bottom: "auxi_psp_concat"
  4756. top: "auxi_feat"
  4757. param {
  4758. lr_mult: 10
  4759. decay_mult: 1
  4760. }
  4761. convolution_param {
  4762. num_output: 256
  4763. kernel_size: 3
  4764. stride: 1
  4765. pad: 1
  4766. weight_filler {
  4767. type: "msra"
  4768. }
  4769. bias_term: false
  4770. }
  4771. }
  4772. layer {
  4773. name: "auxi_feat_relu"
  4774. type: "ReLU"
  4775. bottom: "auxi_feat"
  4776. top: "auxi_feat"
  4777. }
  4778. layer {
  4779. name: "auxi_feat_dropout"
  4780. type: "Dropout"
  4781. bottom: "auxi_feat"
  4782. top: "auxi_feat"
  4783. dropout_param {
  4784. dropout_ratio: 0.1
  4785. }
  4786. }
  4787. layer {
  4788. name: "auxi_score_map"
  4789. type: "Convolution"
  4790. bottom: "auxi_feat"
  4791. top: "auxi_score_map"
  4792. param {
  4793. lr_mult: 10
  4794. decay_mult: 1
  4795. }
  4796. param {
  4797. lr_mult: 20
  4798. decay_mult: 1
  4799. }
  4800. convolution_param {
  4801. num_output: 21
  4802. kernel_size: 1
  4803. stride: 1
  4804. weight_filler {
  4805. type: "msra"
  4806. }
  4807. }
  4808. }
  4809. layer {
  4810. name: "auxi_score_map_interp"
  4811. type: "Interp"
  4812. bottom: "auxi_score_map"
  4813. top: "auxi_score_map_interp"
  4814. interp_param {
  4815. height: 270
  4816. width: 270
  4817. }
  4818. }
  4819.  
  4820. ###################### psp ######################
  4821. layer {
  4822. name: "psp_pool1"
  4823. type: "Pooling"
  4824. bottom: "res33_eletwise_scale"
  4825. top: "psp_pool1"
  4826. pooling_param {
  4827. pool: AVE
  4828. kernel_size: 64
  4829. stride: 64
  4830. }
  4831. }
  4832. layer {
  4833. name: "psp_pool1_conv"
  4834. type: "Convolution"
  4835. bottom: "psp_pool1"
  4836. top: "psp_pool1_conv"
  4837. param {
  4838. lr_mult: 10
  4839. decay_mult: 1
  4840. }
  4841. convolution_param {
  4842. num_output: 512
  4843. kernel_size: 1
  4844. stride: 1
  4845. weight_filler {
  4846. type: "msra"
  4847. }
  4848. bias_term: false
  4849. }
  4850. }
  4851. layer {
  4852. name: "psp_pool1_relu"
  4853. type: "ReLU"
  4854. bottom: "psp_pool1_conv"
  4855. top: "psp_pool1_conv"
  4856. }
  4857. layer {
  4858. name: "psp_pool1_interp"
  4859. type: "Interp"
  4860. bottom: "psp_pool1_conv"
  4861. top: "psp_pool1_interp"
  4862. interp_param {
  4863. height: 34
  4864. width: 34
  4865. }
  4866. }
  4867. layer {
  4868. name: "psp_pool2"
  4869. type: "Pooling"
  4870. bottom: "res33_eletwise_scale"
  4871. top: "psp_pool2"
  4872. pooling_param {
  4873. pool: AVE
  4874. kernel_size: 32
  4875. stride: 32
  4876. }
  4877. }
  4878. layer {
  4879. name: "psp_pool2_conv"
  4880. type: "Convolution"
  4881. bottom: "psp_pool2"
  4882. top: "psp_pool2_conv"
  4883. param {
  4884. lr_mult: 10
  4885. decay_mult: 1
  4886. }
  4887. convolution_param {
  4888. num_output: 512
  4889. kernel_size: 1
  4890. stride: 1
  4891. weight_filler {
  4892. type: "msra"
  4893. }
  4894. bias_term: false
  4895. }
  4896. }
  4897. layer {
  4898. name: "psp_pool2_relu"
  4899. type: "ReLU"
  4900. bottom: "psp_pool2_conv"
  4901. top: "psp_pool2_conv"
  4902. }
  4903. layer {
  4904. name: "psp_pool2_interp"
  4905. type: "Interp"
  4906. bottom: "psp_pool2_conv"
  4907. top: "psp_pool2_interp"
  4908. interp_param {
  4909. height: 34
  4910. width: 34
  4911. }
  4912. }
  4913. layer {
  4914. name: "psp_pool4"
  4915. type: "Pooling"
  4916. bottom: "res33_eletwise_scale"
  4917. top: "psp_pool4"
  4918. pooling_param {
  4919. pool: AVE
  4920. kernel_size: 16
  4921. stride: 16
  4922. }
  4923. }
  4924. layer {
  4925. name: "psp_pool4_conv"
  4926. type: "Convolution"
  4927. bottom: "psp_pool4"
  4928. top: "psp_pool4_conv"
  4929. param {
  4930. lr_mult: 10
  4931. decay_mult: 1
  4932. }
  4933. convolution_param {
  4934. num_output: 512
  4935. kernel_size: 1
  4936. stride: 1
  4937. weight_filler {
  4938. type: "msra"
  4939. }
  4940. bias_term: false
  4941. }
  4942. }
  4943. layer {
  4944. name: "psp_pool4_relu"
  4945. type: "ReLU"
  4946. bottom: "psp_pool4_conv"
  4947. top: "psp_pool4_conv"
  4948. }
  4949. layer {
  4950. name: "psp_pool4_interp"
  4951. type: "Interp"
  4952. bottom: "psp_pool4_conv"
  4953. top: "psp_pool4_interp"
  4954. interp_param {
  4955. height: 34
  4956. width: 34
  4957. }
  4958. }
  4959. layer {
  4960. name: "psp_pool8"
  4961. type: "Pooling"
  4962. bottom: "res33_eletwise_scale"
  4963. top: "psp_pool8"
  4964. pooling_param {
  4965. pool: AVE
  4966. kernel_size: 8
  4967. stride: 8
  4968. }
  4969. }
  4970. layer {
  4971. name: "psp_pool8_conv"
  4972. type: "Convolution"
  4973. bottom: "psp_pool8"
  4974. top: "psp_pool8_conv"
  4975. param {
  4976. lr_mult: 10
  4977. decay_mult: 1
  4978. }
  4979. convolution_param {
  4980. num_output: 512
  4981. kernel_size: 1
  4982. stride: 1
  4983. weight_filler {
  4984. type: "msra"
  4985. }
  4986. bias_term: false
  4987. }
  4988. }
  4989. layer {
  4990. name: "psp_pool8_relu"
  4991. type: "ReLU"
  4992. bottom: "psp_pool8_conv"
  4993. top: "psp_pool8_conv"
  4994. }
  4995. layer {
  4996. name: "psp_pool8_interp"
  4997. type: "Interp"
  4998. bottom: "psp_pool8_conv"
  4999. top: "psp_pool8_interp"
  5000. interp_param {
  5001. height: 34
  5002. width: 34
  5003. }
  5004. }
  5005. layer {
  5006. name: "psp_concat"
  5007. type: "Concat"
  5008. bottom: "res33_eletwise_scale"
  5009. bottom: "psp_pool8_interp"
  5010. bottom: "psp_pool4_interp"
  5011. bottom: "psp_pool2_interp"
  5012. bottom: "psp_pool1_interp"
  5013. top: "psp_concat"
  5014. }
  5015. layer {
  5016. name: "feat"
  5017. type: "Convolution"
  5018. bottom: "psp_concat"
  5019. top: "feat"
  5020. param {
  5021. lr_mult: 10
  5022. decay_mult: 1
  5023. }
  5024. convolution_param {
  5025. num_output: 512
  5026. kernel_size: 3
  5027. stride: 1
  5028. pad: 1
  5029. weight_filler {
  5030. type: "msra"
  5031. }
  5032. bias_term: false
  5033. }
  5034. }
  5035. layer {
  5036. name: "feat_relu"
  5037. type: "ReLU"
  5038. bottom: "feat"
  5039. top: "feat"
  5040. }
  5041. layer {
  5042. name: "feat_dropout"
  5043. type: "Dropout"
  5044. bottom: "feat"
  5045. top: "feat"
  5046. dropout_param {
  5047. dropout_ratio: 0.1
  5048. }
  5049. }
  5050. layer {
  5051. name: "score_map"
  5052. type: "Convolution"
  5053. bottom: "feat"
  5054. top: "score_map"
  5055. param {
  5056. lr_mult: 10
  5057. decay_mult: 1
  5058. }
  5059. param {
  5060. lr_mult: 20
  5061. decay_mult: 1
  5062. }
  5063. convolution_param {
  5064. num_output: 2
  5065. kernel_size: 1
  5066. stride: 1
  5067. weight_filler {
  5068. type: "msra"
  5069. }
  5070. }
  5071. }
  5072. layer {
  5073. name: "score_map_interp"
  5074. type: "Interp"
  5075. bottom: "score_map"
  5076. top: "score_map_interp"
  5077. interp_param {
  5078. height: 270
  5079. width: 270
  5080. }
  5081. }
  5082.  
  5083. ###################### compute loss ######################
  5084. ### auxi loss
  5085. layer {
  5086. name: "auxi_loss"
  5087. type: "SoftmaxWithLoss"
  5088. bottom: "auxi_score_map_interp"
  5089. bottom: "label"
  5090. top: "auxi_loss"
  5091. loss_weight: 0.4
  5092. include {
  5093. phase: TRAIN
  5094. }
  5095. loss_param {
  5096. ignore_label: 255
  5097. }
  5098. }
  5099. layer {
  5100. name: "auxi_accuracy"
  5101. type: "SegAccuracy"
  5102. bottom: "auxi_score_map_interp"
  5103. bottom: "label"
  5104. top: "auxi_accuracy"
  5105. seg_accuracy_param {
  5106. ignore_label: 255
  5107. }
  5108. }
  5109.  
  5110. ### loss
  5111. layer {
  5112. name: "loss"
  5113. type: "SoftmaxWithLoss"
  5114. bottom: "score_map_interp"
  5115. bottom: "label"
  5116. top: "loss"
  5117. include {
  5118. phase: TRAIN
  5119. }
  5120. loss_param {
  5121. ignore_label: 255
  5122. }
  5123. }
  5124. layer {
  5125. name: "accuracy"
  5126. type: "SegAccuracy"
  5127. bottom: "score_map_interp"
  5128. bottom: "label"
  5129. top: "accuracy"
  5130. seg_accuracy_param {
  5131. ignore_label: 255
  5132. }
  5133. }
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