API tools faq
paste
Advertisement
Guest User

Untitled

a guest
Sep 25th, 2017
59
0
Never
Add comment
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 52.43 KB | None | 0 0
raw download clone embed print report
  1. name: "IRes"
  2.  
  3. layer {
  4. name: "data"
  5. type: "SegData"
  6. top: "data"
  7. top: "label"
  8. seg_data_param {
  9. source: "/home/rhwang/VOC_seg/VOC_arg/train.txt"
  10. root_dir: "/home/rhwang/VOC_seg/VOC_arg"
  11. shuffle: true
  12. batch_size: 2
  13. }
  14. transform_param{
  15. # rand_rotate: -10
  16. # rand_rotate: 10
  17. # gaussian_blur: true
  18. ignore_label: 255
  19. mean_value: 103.939
  20. mean_value: 116.779
  21. mean_value: 123.68
  22. crop_size: 321
  23. scale_ratios: 0.5
  24. scale_ratios: 2
  25. mirror: true
  26. }
  27. }
  28.  
  29. ## NETWORK ###
  30.  
  31. layer {
  32. name: "conv1_1_3x3_s2"
  33. type: "Convolution"
  34. bottom: "data"
  35. top: "conv1_1_3x3_s2"
  36. param {
  37. lr_mult: 1
  38. decay_mult: 1
  39. }
  40. convolution_param {
  41. num_output: 64
  42. pad: 1
  43. kernel_size: 3
  44. stride: 2
  45. weight_filler {
  46. type: "msra"
  47. }
  48. bias_term: false
  49. }
  50. }
  51. layer {
  52. name: "conv1_1_3x3_s2/bn"
  53. type: "SyncBN"
  54. bottom: "conv1_1_3x3_s2"
  55. top: "conv1_1_3x3_s2/bn"
  56. param {
  57. lr_mult: 1
  58. decay_mult: 0
  59. }
  60. param {
  61. lr_mult: 1
  62. decay_mult: 0
  63. }
  64. param {
  65. lr_mult: 0
  66. decay_mult: 0
  67. }
  68. param {
  69. lr_mult: 0
  70. decay_mult: 0
  71. }
  72. bn_param {
  73. slope_filler {
  74. type: "constant"
  75. value: 1
  76. }
  77. bias_filler {
  78. type: "constant"
  79. value: 0
  80. }
  81. moving_average: true
  82. # decay: 0.05
  83. }
  84. }
  85. layer {
  86. name: "conv1_1_3x3_s2/relu"
  87. type: "ReLU"
  88. bottom: "conv1_1_3x3_s2/bn"
  89. top: "conv1_1_3x3_s2/bn"
  90. }
  91. layer {
  92. name: "conv1_2_3x3"
  93. type: "Convolution"
  94. bottom: "conv1_1_3x3_s2/bn"
  95. top: "conv1_2_3x3"
  96. param {
  97. lr_mult: 1
  98. decay_mult: 1
  99. }
  100. convolution_param {
  101. num_output: 64
  102. pad: 1
  103. kernel_size: 3
  104. stride: 1
  105. weight_filler {
  106. type: "msra"
  107. }
  108. bias_term: false
  109. }
  110. }
  111. layer {
  112. name: "conv1_2_3x3/bn"
  113. type: "SyncBN"
  114. bottom: "conv1_2_3x3"
  115. top: "conv1_2_3x3/bn"
  116. param {
  117. lr_mult: 1
  118. decay_mult: 0
  119. }
  120. param {
  121. lr_mult: 1
  122. decay_mult: 0
  123. }
  124. param {
  125. lr_mult: 0
  126. decay_mult: 0
  127. }
  128. param {
  129. lr_mult: 0
  130. decay_mult: 0
  131. }
  132. bn_param {
  133. slope_filler {
  134. type: "constant"
  135. value: 1
  136. }
  137. bias_filler {
  138. type: "constant"
  139. value: 0
  140. }
  141. moving_average: true
  142. # decay: 0.05
  143. }
  144. }
  145. layer {
  146. name: "conv1_2_3x3/relu"
  147. type: "ReLU"
  148. bottom: "conv1_2_3x3/bn"
  149. top: "conv1_2_3x3/bn"
  150. }
  151. layer {
  152. name: "conv1_3_3x3"
  153. type: "Convolution"
  154. bottom: "conv1_2_3x3/bn"
  155. top: "conv1_3_3x3"
  156. param {
  157. lr_mult: 1
  158. decay_mult: 1
  159. }
  160. convolution_param {
  161. num_output: 128
  162. pad: 1
  163. kernel_size: 3
  164. stride: 1
  165. weight_filler {
  166. type: "msra"
  167. }
  168. bias_term: false
  169. }
  170. }
  171. layer {
  172. name: "conv1_3_3x3/bn"
  173. type: "SyncBN"
  174. bottom: "conv1_3_3x3"
  175. top: "conv1_3_3x3/bn"
  176. param {
  177. lr_mult: 1
  178. decay_mult: 0
  179. }
  180. param {
  181. lr_mult: 1
  182. decay_mult: 0
  183. }
  184. param {
  185. lr_mult: 0
  186. decay_mult: 0
  187. }
  188. param {
  189. lr_mult: 0
  190. decay_mult: 0
  191. }
  192. bn_param {
  193. slope_filler {
  194. type: "constant"
  195. value: 1
  196. }
  197. bias_filler {
  198. type: "constant"
  199. value: 0
  200. }
  201. moving_average: true
  202. # decay: 0.05
  203. }
  204. }
  205. layer {
  206. name: "conv1_3_3x3/relu"
  207. type: "ReLU"
  208. bottom: "conv1_3_3x3/bn"
  209. top: "conv1_3_3x3/bn"
  210. }
  211. layer {
  212. name: "pool1_3x3_s2"
  213. type: "Pooling"
  214. bottom: "conv1_3_3x3/bn"
  215. top: "pool1_3x3_s2"
  216. pooling_param {
  217. pool: MAX
  218. kernel_size: 3
  219. stride: 2
  220. pad: 1
  221. }
  222. }
  223. layer {
  224. name: "conv2_1_1x1_reduce"
  225. type: "Convolution"
  226. bottom: "pool1_3x3_s2"
  227. top: "conv2_1_1x1_reduce"
  228. param {
  229. lr_mult: 1
  230. decay_mult: 1
  231. }
  232. convolution_param {
  233. num_output: 64
  234. pad: 0
  235. kernel_size: 1
  236. stride: 1
  237. weight_filler {
  238. type: "msra"
  239. }
  240. bias_term: false
  241. }
  242. }
  243. layer {
  244. name: "conv2_1_1x1_reduce/bn"
  245. type: "SyncBN"
  246. bottom: "conv2_1_1x1_reduce"
  247. top: "conv2_1_1x1_reduce/bn"
  248. param {
  249. lr_mult: 1
  250. decay_mult: 0
  251. }
  252. param {
  253. lr_mult: 1
  254. decay_mult: 0
  255. }
  256. param {
  257. lr_mult: 0
  258. decay_mult: 0
  259. }
  260. param {
  261. lr_mult: 0
  262. decay_mult: 0
  263. }
  264. bn_param {
  265. slope_filler {
  266. type: "constant"
  267. value: 1
  268. }
  269. bias_filler {
  270. type: "constant"
  271. value: 0
  272. }
  273. moving_average: true
  274. # decay: 0.05
  275. }
  276. }
  277. layer {
  278. name: "conv2_1_1x1_reduce/relu"
  279. type: "ReLU"
  280. bottom: "conv2_1_1x1_reduce/bn"
  281. top: "conv2_1_1x1_reduce/bn"
  282. }
  283. layer {
  284. name: "conv2_1_3x3"
  285. type: "Convolution"
  286. bottom: "conv2_1_1x1_reduce/bn"
  287. top: "conv2_1_3x3"
  288. param {
  289. lr_mult: 1
  290. decay_mult: 1
  291. }
  292. convolution_param {
  293. num_output: 64
  294. pad: 1
  295. kernel_size: 3
  296. stride: 1
  297. weight_filler {
  298. type: "msra"
  299. }
  300. bias_term: false
  301. }
  302. }
  303. layer {
  304. name: "conv2_1_3x3/bn"
  305. type: "SyncBN"
  306. bottom: "conv2_1_3x3"
  307. top: "conv2_1_3x3/bn"
  308. param {
  309. lr_mult: 1
  310. decay_mult: 0
  311. }
  312. param {
  313. lr_mult: 1
  314. decay_mult: 0
  315. }
  316. param {
  317. lr_mult: 0
  318. decay_mult: 0
  319. }
  320. param {
  321. lr_mult: 0
  322. decay_mult: 0
  323. }
  324. bn_param {
  325. slope_filler {
  326. type: "constant"
  327. value: 1
  328. }
  329. bias_filler {
  330. type: "constant"
  331. value: 0
  332. }
  333. moving_average: true
  334. # decay: 0.05
  335. }
  336. }
  337. layer {
  338. name: "conv2_1_3x3/relu"
  339. type: "ReLU"
  340. bottom: "conv2_1_3x3/bn"
  341. top: "conv2_1_3x3/bn"
  342. }
  343. layer {
  344. name: "conv2_1_1x1_increase"
  345. type: "Convolution"
  346. bottom: "conv2_1_3x3/bn"
  347. top: "conv2_1_1x1_increase"
  348. param {
  349. lr_mult: 1
  350. decay_mult: 1
  351. }
  352. convolution_param {
  353. num_output: 256
  354. pad: 0
  355. kernel_size: 1
  356. stride: 1
  357. weight_filler {
  358. type: "msra"
  359. }
  360. bias_term: false
  361. }
  362. }
  363. layer {
  364. name: "conv2_1_1x1_increase/bn"
  365. type: "SyncBN"
  366. bottom: "conv2_1_1x1_increase"
  367. top: "conv2_1_1x1_increase/bn"
  368. param {
  369. lr_mult: 1
  370. decay_mult: 0
  371. }
  372. param {
  373. lr_mult: 1
  374. decay_mult: 0
  375. }
  376. param {
  377. lr_mult: 0
  378. decay_mult: 0
  379. }
  380. param {
  381. lr_mult: 0
  382. decay_mult: 0
  383. }
  384. bn_param {
  385. slope_filler {
  386. type: "constant"
  387. value: 1
  388. }
  389. bias_filler {
  390. type: "constant"
  391. value: 0
  392. }
  393. moving_average: true
  394. # decay: 0.05
  395. }
  396. }
  397. layer {
  398. name: "conv2_1_1x1_proj"
  399. type: "Convolution"
  400. bottom: "pool1_3x3_s2"
  401. top: "conv2_1_1x1_proj"
  402. param {
  403. lr_mult: 1
  404. decay_mult: 1
  405. }
  406. convolution_param {
  407. num_output: 256
  408. pad: 0
  409. kernel_size: 1
  410. stride: 1
  411. weight_filler {
  412. type: "msra"
  413. }
  414. bias_term: false
  415. }
  416. }
  417. layer {
  418. name: "conv2_1_1x1_proj/bn"
  419. type: "SyncBN"
  420. bottom: "conv2_1_1x1_proj"
  421. top: "conv2_1_1x1_proj/bn"
  422. param {
  423. lr_mult: 1
  424. decay_mult: 0
  425. }
  426. param {
  427. lr_mult: 1
  428. decay_mult: 0
  429. }
  430. param {
  431. lr_mult: 0
  432. decay_mult: 0
  433. }
  434. param {
  435. lr_mult: 0
  436. decay_mult: 0
  437. }
  438. bn_param {
  439. slope_filler {
  440. type: "constant"
  441. value: 1
  442. }
  443. bias_filler {
  444. type: "constant"
  445. value: 0
  446. }
  447. moving_average: true
  448. # decay: 0.05
  449. }
  450. }
  451. layer {
  452. name: "conv2_1"
  453. type: "Eltwise"
  454. bottom: "conv2_1_1x1_proj/bn"
  455. bottom: "conv2_1_1x1_increase/bn"
  456. top: "conv2_1"
  457. eltwise_param {
  458. operation: SUM
  459. }
  460. }
  461. layer {
  462. name: "conv2_1/relu"
  463. type: "ReLU"
  464. bottom: "conv2_1"
  465. top: "conv2_1"
  466. }
  467. layer {
  468. name: "conv2_2_1x1_reduce"
  469. type: "Convolution"
  470. bottom: "conv2_1"
  471. top: "conv2_2_1x1_reduce"
  472. param {
  473. lr_mult: 1
  474. decay_mult: 1
  475. }
  476. convolution_param {
  477. num_output: 64
  478. pad: 0
  479. kernel_size: 1
  480. stride: 1
  481. weight_filler {
  482. type: "msra"
  483. }
  484. bias_term: false
  485. }
  486. }
  487. layer {
  488. name: "conv2_2_1x1_reduce/bn"
  489. type: "SyncBN"
  490. bottom: "conv2_2_1x1_reduce"
  491. top: "conv2_2_1x1_reduce/bn"
  492. param {
  493. lr_mult: 1
  494. decay_mult: 0
  495. }
  496. param {
  497. lr_mult: 1
  498. decay_mult: 0
  499. }
  500. param {
  501. lr_mult: 0
  502. decay_mult: 0
  503. }
  504. param {
  505. lr_mult: 0
  506. decay_mult: 0
  507. }
  508. bn_param {
  509. slope_filler {
  510. type: "constant"
  511. value: 1
  512. }
  513. bias_filler {
  514. type: "constant"
  515. value: 0
  516. }
  517. moving_average: true
  518. # decay: 0.05
  519. }
  520. }
  521. layer {
  522. name: "conv2_2_1x1_reduce/relu"
  523. type: "ReLU"
  524. bottom: "conv2_2_1x1_reduce/bn"
  525. top: "conv2_2_1x1_reduce/bn"
  526. }
  527. layer {
  528. name: "conv2_2_3x3"
  529. type: "Convolution"
  530. bottom: "conv2_2_1x1_reduce/bn"
  531. top: "conv2_2_3x3"
  532. param {
  533. lr_mult: 1
  534. decay_mult: 1
  535. }
  536. convolution_param {
  537. num_output: 64
  538. pad: 1
  539. kernel_size: 3
  540. stride: 1
  541. weight_filler {
  542. type: "msra"
  543. }
  544. bias_term: false
  545. }
  546. }
  547. layer {
  548. name: "conv2_2_3x3/bn"
  549. type: "SyncBN"
  550. bottom: "conv2_2_3x3"
  551. top: "conv2_2_3x3/bn"
  552. param {
  553. lr_mult: 1
  554. decay_mult: 0
  555. }
  556. param {
  557. lr_mult: 1
  558. decay_mult: 0
  559. }
  560. param {
  561. lr_mult: 0
  562. decay_mult: 0
  563. }
  564. param {
  565. lr_mult: 0
  566. decay_mult: 0
  567. }
  568. bn_param {
  569. slope_filler {
  570. type: "constant"
  571. value: 1
  572. }
  573. bias_filler {
  574. type: "constant"
  575. value: 0
  576. }
  577. moving_average: true
  578. # decay: 0.05
  579. }
  580. }
  581. layer {
  582. name: "conv2_2_3x3/relu"
  583. type: "ReLU"
  584. bottom: "conv2_2_3x3/bn"
  585. top: "conv2_2_3x3/bn"
  586. }
  587. layer {
  588. name: "conv2_2_1x1_increase"
  589. type: "Convolution"
  590. bottom: "conv2_2_3x3/bn"
  591. top: "conv2_2_1x1_increase"
  592. param {
  593. lr_mult: 1
  594. decay_mult: 1
  595. }
  596. convolution_param {
  597. num_output: 256
  598. pad: 0
  599. kernel_size: 1
  600. stride: 1
  601. weight_filler {
  602. type: "msra"
  603. }
  604. bias_term: false
  605. }
  606. }
  607. layer {
  608. name: "conv2_2_1x1_increase/bn"
  609. type: "SyncBN"
  610. bottom: "conv2_2_1x1_increase"
  611. top: "conv2_2_1x1_increase/bn"
  612. param {
  613. lr_mult: 1
  614. decay_mult: 0
  615. }
  616. param {
  617. lr_mult: 1
  618. decay_mult: 0
  619. }
  620. param {
  621. lr_mult: 0
  622. decay_mult: 0
  623. }
  624. param {
  625. lr_mult: 0
  626. decay_mult: 0
  627. }
  628. bn_param {
  629. slope_filler {
  630. type: "constant"
  631. value: 1
  632. }
  633. bias_filler {
  634. type: "constant"
  635. value: 0
  636. }
  637. moving_average: true
  638. # decay: 0.05
  639. }
  640. }
  641. layer {
  642. name: "conv2_2"
  643. type: "Eltwise"
  644. bottom: "conv2_1"
  645. bottom: "conv2_2_1x1_increase/bn"
  646. top: "conv2_2"
  647. eltwise_param {
  648. operation: SUM
  649. }
  650. }
  651. layer {
  652. name: "conv2_2/relu"
  653. type: "ReLU"
  654. bottom: "conv2_2"
  655. top: "conv2_2"
  656. }
  657. layer {
  658. name: "conv2_3_1x1_reduce"
  659. type: "Convolution"
  660. bottom: "conv2_2"
  661. top: "conv2_3_1x1_reduce"
  662. param {
  663. lr_mult: 1
  664. decay_mult: 1
  665. }
  666. convolution_param {
  667. num_output: 64
  668. pad: 0
  669. kernel_size: 1
  670. stride: 1
  671. weight_filler {
  672. type: "msra"
  673. }
  674. bias_term: false
  675. }
  676. }
  677. layer {
  678. name: "conv2_3_1x1_reduce/bn"
  679. type: "SyncBN"
  680. bottom: "conv2_3_1x1_reduce"
  681. top: "conv2_3_1x1_reduce/bn"
  682. param {
  683. lr_mult: 1
  684. decay_mult: 0
  685. }
  686. param {
  687. lr_mult: 1
  688. decay_mult: 0
  689. }
  690. param {
  691. lr_mult: 0
  692. decay_mult: 0
  693. }
  694. param {
  695. lr_mult: 0
  696. decay_mult: 0
  697. }
  698. bn_param {
  699. slope_filler {
  700. type: "constant"
  701. value: 1
  702. }
  703. bias_filler {
  704. type: "constant"
  705. value: 0
  706. }
  707. moving_average: true
  708. # decay: 0.05
  709. }
  710. }
  711. layer {
  712. name: "conv2_3_1x1_reduce/relu"
  713. type: "ReLU"
  714. bottom: "conv2_3_1x1_reduce/bn"
  715. top: "conv2_3_1x1_reduce/bn"
  716. }
  717. layer {
  718. name: "conv2_3_3x3"
  719. type: "Convolution"
  720. bottom: "conv2_3_1x1_reduce/bn"
  721. top: "conv2_3_3x3"
  722. param {
  723. lr_mult: 1
  724. decay_mult: 1
  725. }
  726. convolution_param {
  727. num_output: 64
  728. pad: 1
  729. kernel_size: 3
  730. stride: 1
  731. weight_filler {
  732. type: "msra"
  733. }
  734. bias_term: false
  735. }
  736. }
  737. layer {
  738. name: "conv2_3_3x3/bn"
  739. type: "SyncBN"
  740. bottom: "conv2_3_3x3"
  741. top: "conv2_3_3x3/bn"
  742. param {
  743. lr_mult: 1
  744. decay_mult: 0
  745. }
  746. param {
  747. lr_mult: 1
  748. decay_mult: 0
  749. }
  750. param {
  751. lr_mult: 0
  752. decay_mult: 0
  753. }
  754. param {
  755. lr_mult: 0
  756. decay_mult: 0
  757. }
  758. bn_param {
  759. slope_filler {
  760. type: "constant"
  761. value: 1
  762. }
  763. bias_filler {
  764. type: "constant"
  765. value: 0
  766. }
  767. moving_average: true
  768. # decay: 0.05
  769. }
  770. }
  771. layer {
  772. name: "conv2_3_3x3/relu"
  773. type: "ReLU"
  774. bottom: "conv2_3_3x3/bn"
  775. top: "conv2_3_3x3/bn"
  776. }
  777. layer {
  778. name: "conv2_3_1x1_increase"
  779. type: "Convolution"
  780. bottom: "conv2_3_3x3/bn"
  781. top: "conv2_3_1x1_increase"
  782. param {
  783. lr_mult: 1
  784. decay_mult: 1
  785. }
  786. convolution_param {
  787. num_output: 256
  788. pad: 0
  789. kernel_size: 1
  790. stride: 1
  791. weight_filler {
  792. type: "msra"
  793. }
  794. bias_term: false
  795. }
  796. }
  797. layer {
  798. name: "conv2_3_1x1_increase/bn"
  799. type: "SyncBN"
  800. bottom: "conv2_3_1x1_increase"
  801. top: "conv2_3_1x1_increase/bn"
  802. param {
  803. lr_mult: 1
  804. decay_mult: 0
  805. }
  806. param {
  807. lr_mult: 1
  808. decay_mult: 0
  809. }
  810. param {
  811. lr_mult: 0
  812. decay_mult: 0
  813. }
  814. param {
  815. lr_mult: 0
  816. decay_mult: 0
  817. }
  818. bn_param {
  819. slope_filler {
  820. type: "constant"
  821. value: 1
  822. }
  823. bias_filler {
  824. type: "constant"
  825. value: 0
  826. }
  827. moving_average: true
  828. # decay: 0.05
  829. }
  830. }
  831. layer {
  832. name: "conv2_3"
  833. type: "Eltwise"
  834. bottom: "conv2_2"
  835. bottom: "conv2_3_1x1_increase/bn"
  836. top: "conv2_3"
  837. eltwise_param {
  838. operation: SUM
  839. }
  840. }
  841. layer {
  842. name: "conv2_3/relu"
  843. type: "ReLU"
  844. bottom: "conv2_3"
  845. top: "conv2_3"
  846. }
  847. layer {
  848. name: "conv3_1_1x1_reduce"
  849. type: "Convolution"
  850. bottom: "conv2_3"
  851. top: "conv3_1_1x1_reduce"
  852. param {
  853. lr_mult: 1
  854. decay_mult: 1
  855. }
  856. convolution_param {
  857. num_output: 128
  858. pad: 0
  859. kernel_size: 1
  860. stride: 2
  861. weight_filler {
  862. type: "msra"
  863. }
  864. bias_term: false
  865. }
  866. }
  867. layer {
  868. name: "conv3_1_1x1_reduce/bn"
  869. type: "SyncBN"
  870. bottom: "conv3_1_1x1_reduce"
  871. top: "conv3_1_1x1_reduce/bn"
  872. param {
  873. lr_mult: 1
  874. decay_mult: 0
  875. }
  876. param {
  877. lr_mult: 1
  878. decay_mult: 0
  879. }
  880. param {
  881. lr_mult: 0
  882. decay_mult: 0
  883. }
  884. param {
  885. lr_mult: 0
  886. decay_mult: 0
  887. }
  888. bn_param {
  889. slope_filler {
  890. type: "constant"
  891. value: 1
  892. }
  893. bias_filler {
  894. type: "constant"
  895. value: 0
  896. }
  897. moving_average: true
  898. # decay: 0.05
  899. }
  900. }
  901. layer {
  902. name: "conv3_1_1x1_reduce/relu"
  903. type: "ReLU"
  904. bottom: "conv3_1_1x1_reduce/bn"
  905. top: "conv3_1_1x1_reduce/bn"
  906. }
  907. layer {
  908. name: "conv3_1_3x3"
  909. type: "Convolution"
  910. bottom: "conv3_1_1x1_reduce/bn"
  911. top: "conv3_1_3x3"
  912. param {
  913. lr_mult: 1
  914. decay_mult: 1
  915. }
  916. convolution_param {
  917. num_output: 128
  918. pad: 1
  919. kernel_size: 3
  920. stride: 1
  921. weight_filler {
  922. type: "msra"
  923. }
  924. bias_term: false
  925. }
  926. }
  927. layer {
  928. name: "conv3_1_3x3/bn"
  929. type: "SyncBN"
  930. bottom: "conv3_1_3x3"
  931. top: "conv3_1_3x3/bn"
  932. param {
  933. lr_mult: 1
  934. decay_mult: 0
  935. }
  936. param {
  937. lr_mult: 1
  938. decay_mult: 0
  939. }
  940. param {
  941. lr_mult: 0
  942. decay_mult: 0
  943. }
  944. param {
  945. lr_mult: 0
  946. decay_mult: 0
  947. }
  948. bn_param {
  949. slope_filler {
  950. type: "constant"
  951. value: 1
  952. }
  953. bias_filler {
  954. type: "constant"
  955. value: 0
  956. }
  957. moving_average: true
  958. # decay: 0.05
  959. }
  960. }
  961. layer {
  962. name: "conv3_1_3x3/relu"
  963. type: "ReLU"
  964. bottom: "conv3_1_3x3/bn"
  965. top: "conv3_1_3x3/bn"
  966. }
  967. layer {
  968. name: "conv3_1_1x1_increase"
  969. type: "Convolution"
  970. bottom: "conv3_1_3x3/bn"
  971. top: "conv3_1_1x1_increase"
  972. param {
  973. lr_mult: 1
  974. decay_mult: 1
  975. }
  976. convolution_param {
  977. num_output: 512
  978. pad: 0
  979. kernel_size: 1
  980. stride: 1
  981. weight_filler {
  982. type: "msra"
  983. }
  984. bias_term: false
  985. }
  986. }
  987. layer {
  988. name: "conv3_1_1x1_increase/bn"
  989. type: "SyncBN"
  990. bottom: "conv3_1_1x1_increase"
  991. top: "conv3_1_1x1_increase/bn"
  992. param {
  993. lr_mult: 1
  994. decay_mult: 0
  995. }
  996. param {
  997. lr_mult: 1
  998. decay_mult: 0
  999. }
  1000. param {
  1001. lr_mult: 0
  1002. decay_mult: 0
  1003. }
  1004. param {
  1005. lr_mult: 0
  1006. decay_mult: 0
  1007. }
  1008. bn_param {
  1009. slope_filler {
  1010. type: "constant"
  1011. value: 1
  1012. }
  1013. bias_filler {
  1014. type: "constant"
  1015. value: 0
  1016. }
  1017. moving_average: true
  1018. # decay: 0.05
  1019. }
  1020. }
  1021. layer {
  1022. name: "conv3_1_1x1_proj"
  1023. type: "Convolution"
  1024. bottom: "conv2_3"
  1025. top: "conv3_1_1x1_proj"
  1026. param {
  1027. lr_mult: 1
  1028. decay_mult: 1
  1029. }
  1030. convolution_param {
  1031. num_output: 512
  1032. pad: 0
  1033. kernel_size: 1
  1034. stride: 2
  1035. weight_filler {
  1036. type: "msra"
  1037. }
  1038. bias_term: false
  1039. }
  1040. }
  1041. layer {
  1042. name: "conv3_1_1x1_proj/bn"
  1043. type: "SyncBN"
  1044. bottom: "conv3_1_1x1_proj"
  1045. top: "conv3_1_1x1_proj/bn"
  1046. param {
  1047. lr_mult: 1
  1048. decay_mult: 0
  1049. }
  1050. param {
  1051. lr_mult: 1
  1052. decay_mult: 0
  1053. }
  1054. param {
  1055. lr_mult: 0
  1056. decay_mult: 0
  1057. }
  1058. param {
  1059. lr_mult: 0
  1060. decay_mult: 0
  1061. }
  1062. bn_param {
  1063. slope_filler {
  1064. type: "constant"
  1065. value: 1
  1066. }
  1067. bias_filler {
  1068. type: "constant"
  1069. value: 0
  1070. }
  1071. moving_average: true
  1072. # decay: 0.05
  1073. }
  1074. }
  1075. layer {
  1076. name: "conv3_1"
  1077. type: "Eltwise"
  1078. bottom: "conv3_1_1x1_proj/bn"
  1079. bottom: "conv3_1_1x1_increase/bn"
  1080. top: "conv3_1"
  1081. eltwise_param {
  1082. operation: SUM
  1083. }
  1084. }
  1085. layer {
  1086. name: "conv3_1/relu"
  1087. type: "ReLU"
  1088. bottom: "conv3_1"
  1089. top: "conv3_1"
  1090. }
  1091. layer {
  1092. name: "conv3_2_1x1_reduce"
  1093. type: "Convolution"
  1094. bottom: "conv3_1"
  1095. top: "conv3_2_1x1_reduce"
  1096. param {
  1097. lr_mult: 1
  1098. decay_mult: 1
  1099. }
  1100. convolution_param {
  1101. num_output: 128
  1102. pad: 0
  1103. kernel_size: 1
  1104. stride: 1
  1105. weight_filler {
  1106. type: "msra"
  1107. }
  1108. bias_term: false
  1109. }
  1110. }
  1111. layer {
  1112. name: "conv3_2_1x1_reduce/bn"
  1113. type: "SyncBN"
  1114. bottom: "conv3_2_1x1_reduce"
  1115. top: "conv3_2_1x1_reduce/bn"
  1116. param {
  1117. lr_mult: 1
  1118. decay_mult: 0
  1119. }
  1120. param {
  1121. lr_mult: 1
  1122. decay_mult: 0
  1123. }
  1124. param {
  1125. lr_mult: 0
  1126. decay_mult: 0
  1127. }
  1128. param {
  1129. lr_mult: 0
  1130. decay_mult: 0
  1131. }
  1132. bn_param {
  1133. slope_filler {
  1134. type: "constant"
  1135. value: 1
  1136. }
  1137. bias_filler {
  1138. type: "constant"
  1139. value: 0
  1140. }
  1141. moving_average: true
  1142. # decay: 0.05
  1143. }
  1144. }
  1145. layer {
  1146. name: "conv3_2_1x1_reduce/relu"
  1147. type: "ReLU"
  1148. bottom: "conv3_2_1x1_reduce/bn"
  1149. top: "conv3_2_1x1_reduce/bn"
  1150. }
  1151. layer {
  1152. name: "conv3_2_3x3"
  1153. type: "Convolution"
  1154. bottom: "conv3_2_1x1_reduce/bn"
  1155. top: "conv3_2_3x3"
  1156. param {
  1157. lr_mult: 1
  1158. decay_mult: 1
  1159. }
  1160. convolution_param {
  1161. num_output: 128
  1162. pad: 1
  1163. kernel_size: 3
  1164. stride: 1
  1165. weight_filler {
  1166. type: "msra"
  1167. }
  1168. bias_term: false
  1169. }
  1170. }
  1171. layer {
  1172. name: "conv3_2_3x3/bn"
  1173. type: "SyncBN"
  1174. bottom: "conv3_2_3x3"
  1175. top: "conv3_2_3x3/bn"
  1176. param {
  1177. lr_mult: 1
  1178. decay_mult: 0
  1179. }
  1180. param {
  1181. lr_mult: 1
  1182. decay_mult: 0
  1183. }
  1184. param {
  1185. lr_mult: 0
  1186. decay_mult: 0
  1187. }
  1188. param {
  1189. lr_mult: 0
  1190. decay_mult: 0
  1191. }
  1192. bn_param {
  1193. slope_filler {
  1194. type: "constant"
  1195. value: 1
  1196. }
  1197. bias_filler {
  1198. type: "constant"
  1199. value: 0
  1200. }
  1201. moving_average: true
  1202. # decay: 0.05
  1203. }
  1204. }
  1205. layer {
  1206. name: "conv3_2_3x3/relu"
  1207. type: "ReLU"
  1208. bottom: "conv3_2_3x3/bn"
  1209. top: "conv3_2_3x3/bn"
  1210. }
  1211. layer {
  1212. name: "conv3_2_1x1_increase"
  1213. type: "Convolution"
  1214. bottom: "conv3_2_3x3/bn"
  1215. top: "conv3_2_1x1_increase"
  1216. param {
  1217. lr_mult: 1
  1218. decay_mult: 1
  1219. }
  1220. convolution_param {
  1221. num_output: 512
  1222. pad: 0
  1223. kernel_size: 1
  1224. stride: 1
  1225. weight_filler {
  1226. type: "msra"
  1227. }
  1228. bias_term: false
  1229. }
  1230. }
  1231. layer {
  1232. name: "conv3_2_1x1_increase/bn"
  1233. type: "SyncBN"
  1234. bottom: "conv3_2_1x1_increase"
  1235. top: "conv3_2_1x1_increase/bn"
  1236. param {
  1237. lr_mult: 1
  1238. decay_mult: 0
  1239. }
  1240. param {
  1241. lr_mult: 1
  1242. decay_mult: 0
  1243. }
  1244. param {
  1245. lr_mult: 0
  1246. decay_mult: 0
  1247. }
  1248. param {
  1249. lr_mult: 0
  1250. decay_mult: 0
  1251. }
  1252. bn_param {
  1253. slope_filler {
  1254. type: "constant"
  1255. value: 1
  1256. }
  1257. bias_filler {
  1258. type: "constant"
  1259. value: 0
  1260. }
  1261. moving_average: true
  1262. # decay: 0.05
  1263. }
  1264. }
  1265. layer {
  1266. name: "conv3_2"
  1267. type: "Eltwise"
  1268. bottom: "conv3_1"
  1269. bottom: "conv3_2_1x1_increase/bn"
  1270. top: "conv3_2"
  1271. eltwise_param {
  1272. operation: SUM
  1273. }
  1274. }
  1275. layer {
  1276. name: "conv3_2/relu"
  1277. type: "ReLU"
  1278. bottom: "conv3_2"
  1279. top: "conv3_2"
  1280. }
  1281. layer {
  1282. name: "conv3_3_1x1_reduce"
  1283. type: "Convolution"
  1284. bottom: "conv3_2"
  1285. top: "conv3_3_1x1_reduce"
  1286. param {
  1287. lr_mult: 1
  1288. decay_mult: 1
  1289. }
  1290. convolution_param {
  1291. num_output: 128
  1292. pad: 0
  1293. kernel_size: 1
  1294. stride: 1
  1295. weight_filler {
  1296. type: "msra"
  1297. }
  1298. bias_term: false
  1299. }
  1300. }
  1301. layer {
  1302. name: "conv3_3_1x1_reduce/bn"
  1303. type: "SyncBN"
  1304. bottom: "conv3_3_1x1_reduce"
  1305. top: "conv3_3_1x1_reduce/bn"
  1306. param {
  1307. lr_mult: 1
  1308. decay_mult: 0
  1309. }
  1310. param {
  1311. lr_mult: 1
  1312. decay_mult: 0
  1313. }
  1314. param {
  1315. lr_mult: 0
  1316. decay_mult: 0
  1317. }
  1318. param {
  1319. lr_mult: 0
  1320. decay_mult: 0
  1321. }
  1322. bn_param {
  1323. slope_filler {
  1324. type: "constant"
  1325. value: 1
  1326. }
  1327. bias_filler {
  1328. type: "constant"
  1329. value: 0
  1330. }
  1331. moving_average: true
  1332. # decay: 0.05
  1333. }
  1334. }
  1335. layer {
  1336. name: "conv3_3_1x1_reduce/relu"
  1337. type: "ReLU"
  1338. bottom: "conv3_3_1x1_reduce/bn"
  1339. top: "conv3_3_1x1_reduce/bn"
  1340. }
  1341. layer {
  1342. name: "conv3_3_3x3"
  1343. type: "Convolution"
  1344. bottom: "conv3_3_1x1_reduce/bn"
  1345. top: "conv3_3_3x3"
  1346. param {
  1347. lr_mult: 1
  1348. decay_mult: 1
  1349. }
  1350. convolution_param {
  1351. num_output: 128
  1352. pad: 1
  1353. kernel_size: 3
  1354. stride: 1
  1355. weight_filler {
  1356. type: "msra"
  1357. }
  1358. bias_term: false
  1359. }
  1360. }
  1361. layer {
  1362. name: "conv3_3_3x3/bn"
  1363. type: "SyncBN"
  1364. bottom: "conv3_3_3x3"
  1365. top: "conv3_3_3x3/bn"
  1366. param {
  1367. lr_mult: 1
  1368. decay_mult: 0
  1369. }
  1370. param {
  1371. lr_mult: 1
  1372. decay_mult: 0
  1373. }
  1374. param {
  1375. lr_mult: 0
  1376. decay_mult: 0
  1377. }
  1378. param {
  1379. lr_mult: 0
  1380. decay_mult: 0
  1381. }
  1382. bn_param {
  1383. slope_filler {
  1384. type: "constant"
  1385. value: 1
  1386. }
  1387. bias_filler {
  1388. type: "constant"
  1389. value: 0
  1390. }
  1391. moving_average: true
  1392. # decay: 0.05
  1393. }
  1394. }
  1395. layer {
  1396. name: "conv3_3_3x3/relu"
  1397. type: "ReLU"
  1398. bottom: "conv3_3_3x3/bn"
  1399. top: "conv3_3_3x3/bn"
  1400. }
  1401. layer {
  1402. name: "conv3_3_1x1_increase"
  1403. type: "Convolution"
  1404. bottom: "conv3_3_3x3/bn"
  1405. top: "conv3_3_1x1_increase"
  1406. param {
  1407. lr_mult: 1
  1408. decay_mult: 1
  1409. }
  1410. convolution_param {
  1411. num_output: 512
  1412. pad: 0
  1413. kernel_size: 1
  1414. stride: 1
  1415. weight_filler {
  1416. type: "msra"
  1417. }
  1418. bias_term: false
  1419. }
  1420. }
  1421. layer {
  1422. name: "conv3_3_1x1_increase/bn"
  1423. type: "SyncBN"
  1424. bottom: "conv3_3_1x1_increase"
  1425. top: "conv3_3_1x1_increase/bn"
  1426. param {
  1427. lr_mult: 1
  1428. decay_mult: 0
  1429. }
  1430. param {
  1431. lr_mult: 1
  1432. decay_mult: 0
  1433. }
  1434. param {
  1435. lr_mult: 0
  1436. decay_mult: 0
  1437. }
  1438. param {
  1439. lr_mult: 0
  1440. decay_mult: 0
  1441. }
  1442. bn_param {
  1443. slope_filler {
  1444. type: "constant"
  1445. value: 1
  1446. }
  1447. bias_filler {
  1448. type: "constant"
  1449. value: 0
  1450. }
  1451. moving_average: true
  1452. # decay: 0.05
  1453. }
  1454. }
  1455. layer {
  1456. name: "conv3_3"
  1457. type: "Eltwise"
  1458. bottom: "conv3_2"
  1459. bottom: "conv3_3_1x1_increase/bn"
  1460. top: "conv3_3"
  1461. eltwise_param {
  1462. operation: SUM
  1463. }
  1464. }
  1465. layer {
  1466. name: "conv3_3/relu"
  1467. type: "ReLU"
  1468. bottom: "conv3_3"
  1469. top: "conv3_3"
  1470. }
  1471. layer {
  1472. name: "conv3_4_1x1_reduce"
  1473. type: "Convolution"
  1474. bottom: "conv3_3"
  1475. top: "conv3_4_1x1_reduce"
  1476. param {
  1477. lr_mult: 1
  1478. decay_mult: 1
  1479. }
  1480. convolution_param {
  1481. num_output: 128
  1482. pad: 0
  1483. kernel_size: 1
  1484. stride: 1
  1485. weight_filler {
  1486. type: "msra"
  1487. }
  1488. bias_term: false
  1489. }
  1490. }
  1491. layer {
  1492. name: "conv3_4_1x1_reduce/bn"
  1493. type: "SyncBN"
  1494. bottom: "conv3_4_1x1_reduce"
  1495. top: "conv3_4_1x1_reduce/bn"
  1496. param {
  1497. lr_mult: 1
  1498. decay_mult: 0
  1499. }
  1500. param {
  1501. lr_mult: 1
  1502. decay_mult: 0
  1503. }
  1504. param {
  1505. lr_mult: 0
  1506. decay_mult: 0
  1507. }
  1508. param {
  1509. lr_mult: 0
  1510. decay_mult: 0
  1511. }
  1512. bn_param {
  1513. slope_filler {
  1514. type: "constant"
  1515. value: 1
  1516. }
  1517. bias_filler {
  1518. type: "constant"
  1519. value: 0
  1520. }
  1521. moving_average: true
  1522. # decay: 0.05
  1523. }
  1524. }
  1525. layer {
  1526. name: "conv3_4_1x1_reduce/relu"
  1527. type: "ReLU"
  1528. bottom: "conv3_4_1x1_reduce/bn"
  1529. top: "conv3_4_1x1_reduce/bn"
  1530. }
  1531. layer {
  1532. name: "conv3_4_3x3"
  1533. type: "Convolution"
  1534. bottom: "conv3_4_1x1_reduce/bn"
  1535. top: "conv3_4_3x3"
  1536. param {
  1537. lr_mult: 1
  1538. decay_mult: 1
  1539. }
  1540. convolution_param {
  1541. num_output: 128
  1542. pad: 1
  1543. kernel_size: 3
  1544. stride: 1
  1545. weight_filler {
  1546. type: "msra"
  1547. }
  1548. bias_term: false
  1549. }
  1550. }
  1551. layer {
  1552. name: "conv3_4_3x3/bn"
  1553. type: "SyncBN"
  1554. bottom: "conv3_4_3x3"
  1555. top: "conv3_4_3x3/bn"
  1556. param {
  1557. lr_mult: 1
  1558. decay_mult: 0
  1559. }
  1560. param {
  1561. lr_mult: 1
  1562. decay_mult: 0
  1563. }
  1564. param {
  1565. lr_mult: 0
  1566. decay_mult: 0
  1567. }
  1568. param {
  1569. lr_mult: 0
  1570. decay_mult: 0
  1571. }
  1572. bn_param {
  1573. slope_filler {
  1574. type: "constant"
  1575. value: 1
  1576. }
  1577. bias_filler {
  1578. type: "constant"
  1579. value: 0
  1580. }
  1581. moving_average: true
  1582. # decay: 0.05
  1583. }
  1584. }
  1585. layer {
  1586. name: "conv3_4_3x3/relu"
  1587. type: "ReLU"
  1588. bottom: "conv3_4_3x3/bn"
  1589. top: "conv3_4_3x3/bn"
  1590. }
  1591. layer {
  1592. name: "conv3_4_1x1_increase"
  1593. type: "Convolution"
  1594. bottom: "conv3_4_3x3/bn"
  1595. top: "conv3_4_1x1_increase"
  1596. param {
  1597. lr_mult: 1
  1598. decay_mult: 1
  1599. }
  1600. convolution_param {
  1601. num_output: 512
  1602. pad: 0
  1603. kernel_size: 1
  1604. stride: 1
  1605. weight_filler {
  1606. type: "msra"
  1607. }
  1608. bias_term: false
  1609. }
  1610. }
  1611. layer {
  1612. name: "conv3_4_1x1_increase/bn"
  1613. type: "SyncBN"
  1614. bottom: "conv3_4_1x1_increase"
  1615. top: "conv3_4_1x1_increase/bn"
  1616. param {
  1617. lr_mult: 1
  1618. decay_mult: 0
  1619. }
  1620. param {
  1621. lr_mult: 1
  1622. decay_mult: 0
  1623. }
  1624. param {
  1625. lr_mult: 0
  1626. decay_mult: 0
  1627. }
  1628. param {
  1629. lr_mult: 0
  1630. decay_mult: 0
  1631. }
  1632. bn_param {
  1633. slope_filler {
  1634. type: "constant"
  1635. value: 1
  1636. }
  1637. bias_filler {
  1638. type: "constant"
  1639. value: 0
  1640. }
  1641. moving_average: true
  1642. # decay: 0.05
  1643. }
  1644. }
  1645. layer {
  1646. name: "conv3_4"
  1647. type: "Eltwise"
  1648. bottom: "conv3_3"
  1649. bottom: "conv3_4_1x1_increase/bn"
  1650. top: "conv3_4"
  1651. eltwise_param {
  1652. operation: SUM
  1653. }
  1654. }
  1655. layer {
  1656. name: "conv3_4/relu"
  1657. type: "ReLU"
  1658. bottom: "conv3_4"
  1659. top: "conv3_4"
  1660. }
  1661. layer {
  1662. name: "conv4_1_1x1_reduce"
  1663. type: "Convolution"
  1664. bottom: "conv3_4"
  1665. top: "conv4_1_1x1_reduce"
  1666. param {
  1667. lr_mult: 1
  1668. decay_mult: 1
  1669. }
  1670. convolution_param {
  1671. num_output: 256
  1672. pad: 0
  1673. kernel_size: 1
  1674. stride: 1
  1675. weight_filler {
  1676. type: "msra"
  1677. }
  1678. bias_term: false
  1679. }
  1680. }
  1681. layer {
  1682. name: "conv4_1_1x1_reduce/bn"
  1683. type: "SyncBN"
  1684. bottom: "conv4_1_1x1_reduce"
  1685. top: "conv4_1_1x1_reduce/bn"
  1686. param {
  1687. lr_mult: 1
  1688. decay_mult: 0
  1689. }
  1690. param {
  1691. lr_mult: 1
  1692. decay_mult: 0
  1693. }
  1694. param {
  1695. lr_mult: 0
  1696. decay_mult: 0
  1697. }
  1698. param {
  1699. lr_mult: 0
  1700. decay_mult: 0
  1701. }
  1702. bn_param {
  1703. slope_filler {
  1704. type: "constant"
  1705. value: 1
  1706. }
  1707. bias_filler {
  1708. type: "constant"
  1709. value: 0
  1710. }
  1711. moving_average: true
  1712. # decay: 0.05
  1713. }
  1714. }
  1715. layer {
  1716. name: "conv4_1_1x1_reduce/relu"
  1717. type: "ReLU"
  1718. bottom: "conv4_1_1x1_reduce/bn"
  1719. top: "conv4_1_1x1_reduce/bn"
  1720. }
  1721. layer {
  1722. name: "conv4_1_3x3"
  1723. type: "Convolution"
  1724. bottom: "conv4_1_1x1_reduce/bn"
  1725. top: "conv4_1_3x3"
  1726. param {
  1727. lr_mult: 1
  1728. decay_mult: 1
  1729. }
  1730. convolution_param {
  1731. num_output: 256
  1732. pad: 2
  1733. dilation: 2
  1734. kernel_size: 3
  1735. stride: 1
  1736. weight_filler {
  1737. type: "msra"
  1738. }
  1739. bias_term: false
  1740. }
  1741. }
  1742. layer {
  1743. name: "conv4_1_3x3/bn"
  1744. type: "SyncBN"
  1745. bottom: "conv4_1_3x3"
  1746. top: "conv4_1_3x3/bn"
  1747. param {
  1748. lr_mult: 1
  1749. decay_mult: 0
  1750. }
  1751. param {
  1752. lr_mult: 1
  1753. decay_mult: 0
  1754. }
  1755. param {
  1756. lr_mult: 0
  1757. decay_mult: 0
  1758. }
  1759. param {
  1760. lr_mult: 0
  1761. decay_mult: 0
  1762. }
  1763. bn_param {
  1764. slope_filler {
  1765. type: "constant"
  1766. value: 1
  1767. }
  1768. bias_filler {
  1769. type: "constant"
  1770. value: 0
  1771. }
  1772. moving_average: true
  1773. # decay: 0.05
  1774. }
  1775. }
  1776. layer {
  1777. name: "conv4_1_3x3/relu"
  1778. type: "ReLU"
  1779. bottom: "conv4_1_3x3/bn"
  1780. top: "conv4_1_3x3/bn"
  1781. }
  1782. layer {
  1783. name: "conv4_1_1x1_increase"
  1784. type: "Convolution"
  1785. bottom: "conv4_1_3x3/bn"
  1786. top: "conv4_1_1x1_increase"
  1787. param {
  1788. lr_mult: 1
  1789. decay_mult: 1
  1790. }
  1791. convolution_param {
  1792. num_output: 1024
  1793. pad: 0
  1794. kernel_size: 1
  1795. stride: 1
  1796. weight_filler {
  1797. type: "msra"
  1798. }
  1799. bias_term: false
  1800. }
  1801. }
  1802. layer {
  1803. name: "conv4_1_1x1_increase/bn"
  1804. type: "SyncBN"
  1805. bottom: "conv4_1_1x1_increase"
  1806. top: "conv4_1_1x1_increase/bn"
  1807. param {
  1808. lr_mult: 1
  1809. decay_mult: 0
  1810. }
  1811. param {
  1812. lr_mult: 1
  1813. decay_mult: 0
  1814. }
  1815. param {
  1816. lr_mult: 0
  1817. decay_mult: 0
  1818. }
  1819. param {
  1820. lr_mult: 0
  1821. decay_mult: 0
  1822. }
  1823. bn_param {
  1824. slope_filler {
  1825. type: "constant"
  1826. value: 1
  1827. }
  1828. bias_filler {
  1829. type: "constant"
  1830. value: 0
  1831. }
  1832. moving_average: true
  1833. # decay: 0.05
  1834. }
  1835. }
  1836. layer {
  1837. name: "conv4_1_1x1_proj"
  1838. type: "Convolution"
  1839. bottom: "conv3_4"
  1840. top: "conv4_1_1x1_proj"
  1841. param {
  1842. lr_mult: 1
  1843. decay_mult: 1
  1844. }
  1845. convolution_param {
  1846. num_output: 1024
  1847. pad: 0
  1848. kernel_size: 1
  1849. stride: 1
  1850. weight_filler {
  1851. type: "msra"
  1852. }
  1853. bias_term: false
  1854. }
  1855. }
  1856. layer {
  1857. name: "conv4_1_1x1_proj/bn"
  1858. type: "SyncBN"
  1859. bottom: "conv4_1_1x1_proj"
  1860. top: "conv4_1_1x1_proj/bn"
  1861. param {
  1862. lr_mult: 1
  1863. decay_mult: 0
  1864. }
  1865. param {
  1866. lr_mult: 1
  1867. decay_mult: 0
  1868. }
  1869. param {
  1870. lr_mult: 0
  1871. decay_mult: 0
  1872. }
  1873. param {
  1874. lr_mult: 0
  1875. decay_mult: 0
  1876. }
  1877. bn_param {
  1878. slope_filler {
  1879. type: "constant"
  1880. value: 1
  1881. }
  1882. bias_filler {
  1883. type: "constant"
  1884. value: 0
  1885. }
  1886. moving_average: true
  1887. # decay: 0.05
  1888. }
  1889. }
  1890. layer {
  1891. name: "conv4_1"
  1892. type: "Eltwise"
  1893. bottom: "conv4_1_1x1_proj/bn"
  1894. bottom: "conv4_1_1x1_increase/bn"
  1895. top: "conv4_1"
  1896. eltwise_param {
  1897. operation: SUM
  1898. }
  1899. }
  1900. layer {
  1901. name: "conv4_1/relu"
  1902. type: "ReLU"
  1903. bottom: "conv4_1"
  1904. top: "conv4_1"
  1905. }
  1906. layer {
  1907. name: "conv4_2_1x1_reduce"
  1908. type: "Convolution"
  1909. bottom: "conv4_1"
  1910. top: "conv4_2_1x1_reduce"
  1911. param {
  1912. lr_mult: 1
  1913. decay_mult: 1
  1914. }
  1915. convolution_param {
  1916. num_output: 256
  1917. pad: 0
  1918. kernel_size: 1
  1919. stride: 1
  1920. weight_filler {
  1921. type: "msra"
  1922. }
  1923. bias_term: false
  1924. }
  1925. }
  1926. layer {
  1927. name: "conv4_2_1x1_reduce/bn"
  1928. type: "SyncBN"
  1929. bottom: "conv4_2_1x1_reduce"
  1930. top: "conv4_2_1x1_reduce/bn"
  1931. param {
  1932. lr_mult: 1
  1933. decay_mult: 0
  1934. }
  1935. param {
  1936. lr_mult: 1
  1937. decay_mult: 0
  1938. }
  1939. param {
  1940. lr_mult: 0
  1941. decay_mult: 0
  1942. }
  1943. param {
  1944. lr_mult: 0
  1945. decay_mult: 0
  1946. }
  1947. bn_param {
  1948. slope_filler {
  1949. type: "constant"
  1950. value: 1
  1951. }
  1952. bias_filler {
  1953. type: "constant"
  1954. value: 0
  1955. }
  1956. moving_average: true
  1957. # decay: 0.05
  1958. }
  1959. }
  1960. layer {
  1961. name: "conv4_2_1x1_reduce/relu"
  1962. type: "ReLU"
  1963. bottom: "conv4_2_1x1_reduce/bn"
  1964. top: "conv4_2_1x1_reduce/bn"
  1965. }
  1966. layer {
  1967. name: "conv4_2_3x3"
  1968. type: "Convolution"
  1969. bottom: "conv4_2_1x1_reduce/bn"
  1970. top: "conv4_2_3x3"
  1971. param {
  1972. lr_mult: 1
  1973. decay_mult: 1
  1974. }
  1975. convolution_param {
  1976. num_output: 256
  1977. pad: 2
  1978. dilation: 2
  1979. kernel_size: 3
  1980. stride: 1
  1981. weight_filler {
  1982. type: "msra"
  1983. }
  1984. bias_term: false
  1985. }
  1986. }
  1987. layer {
  1988. name: "conv4_2_3x3/bn"
  1989. type: "SyncBN"
  1990. bottom: "conv4_2_3x3"
  1991. top: "conv4_2_3x3/bn"
  1992. param {
  1993. lr_mult: 1
  1994. decay_mult: 0
  1995. }
  1996. param {
  1997. lr_mult: 1
  1998. decay_mult: 0
  1999. }
  2000. param {
  2001. lr_mult: 0
  2002. decay_mult: 0
  2003. }
  2004. param {
  2005. lr_mult: 0
  2006. decay_mult: 0
  2007. }
  2008. bn_param {
  2009. slope_filler {
  2010. type: "constant"
  2011. value: 1
  2012. }
  2013. bias_filler {
  2014. type: "constant"
  2015. value: 0
  2016. }
  2017. moving_average: true
  2018. # decay: 0.05
  2019. }
  2020. }
  2021. layer {
  2022. name: "conv4_2_3x3/relu"
  2023. type: "ReLU"
  2024. bottom: "conv4_2_3x3/bn"
  2025. top: "conv4_2_3x3/bn"
  2026. }
  2027. layer {
  2028. name: "conv4_2_1x1_increase"
  2029. type: "Convolution"
  2030. bottom: "conv4_2_3x3/bn"
  2031. top: "conv4_2_1x1_increase"
  2032. param {
  2033. lr_mult: 1
  2034. decay_mult: 1
  2035. }
  2036. convolution_param {
  2037. num_output: 1024
  2038. pad: 0
  2039. kernel_size: 1
  2040. stride: 1
  2041. weight_filler {
  2042. type: "msra"
  2043. }
  2044. bias_term: false
  2045. }
  2046. }
  2047. layer {
  2048. name: "conv4_2_1x1_increase/bn"
  2049. type: "SyncBN"
  2050. bottom: "conv4_2_1x1_increase"
  2051. top: "conv4_2_1x1_increase/bn"
  2052. param {
  2053. lr_mult: 1
  2054. decay_mult: 0
  2055. }
  2056. param {
  2057. lr_mult: 1
  2058. decay_mult: 0
  2059. }
  2060. param {
  2061. lr_mult: 0
  2062. decay_mult: 0
  2063. }
  2064. param {
  2065. lr_mult: 0
  2066. decay_mult: 0
  2067. }
  2068. bn_param {
  2069. slope_filler {
  2070. type: "constant"
  2071. value: 1
  2072. }
  2073. bias_filler {
  2074. type: "constant"
  2075. value: 0
  2076. }
  2077. moving_average: true
  2078. # decay: 0.05
  2079. }
  2080. }
  2081. layer {
  2082. name: "conv4_2"
  2083. type: "Eltwise"
  2084. bottom: "conv4_1"
  2085. bottom: "conv4_2_1x1_increase/bn"
  2086. top: "conv4_2"
  2087. eltwise_param {
  2088. operation: SUM
  2089. }
  2090. }
  2091. layer {
  2092. name: "conv4_2/relu"
  2093. type: "ReLU"
  2094. bottom: "conv4_2"
  2095. top: "conv4_2"
  2096. }
  2097. layer {
  2098. name: "conv4_3_1x1_reduce"
  2099. type: "Convolution"
  2100. bottom: "conv4_2"
  2101. top: "conv4_3_1x1_reduce"
  2102. param {
  2103. lr_mult: 1
  2104. decay_mult: 1
  2105. }
  2106. convolution_param {
  2107. num_output: 256
  2108. pad: 0
  2109. kernel_size: 1
  2110. stride: 1
  2111. weight_filler {
  2112. type: "msra"
  2113. }
  2114. bias_term: false
  2115. }
  2116. }
  2117. layer {
  2118. name: "conv4_3_1x1_reduce/bn"
  2119. type: "SyncBN"
  2120. bottom: "conv4_3_1x1_reduce"
  2121. top: "conv4_3_1x1_reduce/bn"
  2122. param {
  2123. lr_mult: 1
  2124. decay_mult: 0
  2125. }
  2126. param {
  2127. lr_mult: 1
  2128. decay_mult: 0
  2129. }
  2130. param {
  2131. lr_mult: 0
  2132. decay_mult: 0
  2133. }
  2134. param {
  2135. lr_mult: 0
  2136. decay_mult: 0
  2137. }
  2138. bn_param {
  2139. slope_filler {
  2140. type: "constant"
  2141. value: 1
  2142. }
  2143. bias_filler {
  2144. type: "constant"
  2145. value: 0
  2146. }
  2147. moving_average: true
  2148. # decay: 0.05
  2149. }
  2150. }
  2151. layer {
  2152. name: "conv4_3_1x1_reduce/relu"
  2153. type: "ReLU"
  2154. bottom: "conv4_3_1x1_reduce/bn"
  2155. top: "conv4_3_1x1_reduce/bn"
  2156. }
  2157. layer {
  2158. name: "conv4_3_3x3"
  2159. type: "Convolution"
  2160. bottom: "conv4_3_1x1_reduce/bn"
  2161. top: "conv4_3_3x3"
  2162. param {
  2163. lr_mult: 1
  2164. decay_mult: 1
  2165. }
  2166. convolution_param {
  2167. num_output: 256
  2168. pad: 2
  2169. dilation: 2
  2170. kernel_size: 3
  2171. stride: 1
  2172. weight_filler {
  2173. type: "msra"
  2174. }
  2175. bias_term: false
  2176. }
  2177. }
  2178. layer {
  2179. name: "conv4_3_3x3/bn"
  2180. type: "SyncBN"
  2181. bottom: "conv4_3_3x3"
  2182. top: "conv4_3_3x3/bn"
  2183. param {
  2184. lr_mult: 1
  2185. decay_mult: 0
  2186. }
  2187. param {
  2188. lr_mult: 1
  2189. decay_mult: 0
  2190. }
  2191. param {
  2192. lr_mult: 0
  2193. decay_mult: 0
  2194. }
  2195. param {
  2196. lr_mult: 0
  2197. decay_mult: 0
  2198. }
  2199. bn_param {
  2200. slope_filler {
  2201. type: "constant"
  2202. value: 1
  2203. }
  2204. bias_filler {
  2205. type: "constant"
  2206. value: 0
  2207. }
  2208. moving_average: true
  2209. # decay: 0.05
  2210. }
  2211. }
  2212. layer {
  2213. name: "conv4_3_3x3/relu"
  2214. type: "ReLU"
  2215. bottom: "conv4_3_3x3/bn"
  2216. top: "conv4_3_3x3/bn"
  2217. }
  2218. layer {
  2219. name: "conv4_3_1x1_increase"
  2220. type: "Convolution"
  2221. bottom: "conv4_3_3x3/bn"
  2222. top: "conv4_3_1x1_increase"
  2223. param {
  2224. lr_mult: 1
  2225. decay_mult: 1
  2226. }
  2227. convolution_param {
  2228. num_output: 1024
  2229. pad: 0
  2230. kernel_size: 1
  2231. stride: 1
  2232. weight_filler {
  2233. type: "msra"
  2234. }
  2235. bias_term: false
  2236. }
  2237. }
  2238. layer {
  2239. name: "conv4_3_1x1_increase/bn"
  2240. type: "SyncBN"
  2241. bottom: "conv4_3_1x1_increase"
  2242. top: "conv4_3_1x1_increase/bn"
  2243. param {
  2244. lr_mult: 1
  2245. decay_mult: 0
  2246. }
  2247. param {
  2248. lr_mult: 1
  2249. decay_mult: 0
  2250. }
  2251. param {
  2252. lr_mult: 0
  2253. decay_mult: 0
  2254. }
  2255. param {
  2256. lr_mult: 0
  2257. decay_mult: 0
  2258. }
  2259. bn_param {
  2260. slope_filler {
  2261. type: "constant"
  2262. value: 1
  2263. }
  2264. bias_filler {
  2265. type: "constant"
  2266. value: 0
  2267. }
  2268. moving_average: true
  2269. # decay: 0.05
  2270. }
  2271. }
  2272. layer {
  2273. name: "conv4_3"
  2274. type: "Eltwise"
  2275. bottom: "conv4_2"
  2276. bottom: "conv4_3_1x1_increase/bn"
  2277. top: "conv4_3"
  2278. eltwise_param {
  2279. operation: SUM
  2280. }
  2281. }
  2282. layer {
  2283. name: "conv4_3/relu"
  2284. type: "ReLU"
  2285. bottom: "conv4_3"
  2286. top: "conv4_3"
  2287. }
  2288. layer {
  2289. name: "conv4_4_1x1_reduce"
  2290. type: "Convolution"
  2291. bottom: "conv4_3"
  2292. top: "conv4_4_1x1_reduce"
  2293. param {
  2294. lr_mult: 1
  2295. decay_mult: 1
  2296. }
  2297. convolution_param {
  2298. num_output: 256
  2299. pad: 0
  2300. kernel_size: 1
  2301. stride: 1
  2302. weight_filler {
  2303. type: "msra"
  2304. }
  2305. bias_term: false
  2306. }
  2307. }
  2308. layer {
  2309. name: "conv4_4_1x1_reduce/bn"
  2310. type: "SyncBN"
  2311. bottom: "conv4_4_1x1_reduce"
  2312. top: "conv4_4_1x1_reduce/bn"
  2313. param {
  2314. lr_mult: 1
  2315. decay_mult: 0
  2316. }
  2317. param {
  2318. lr_mult: 1
  2319. decay_mult: 0
  2320. }
  2321. param {
  2322. lr_mult: 0
  2323. decay_mult: 0
  2324. }
  2325. param {
  2326. lr_mult: 0
  2327. decay_mult: 0
  2328. }
  2329. bn_param {
  2330. slope_filler {
  2331. type: "constant"
  2332. value: 1
  2333. }
  2334. bias_filler {
  2335. type: "constant"
  2336. value: 0
  2337. }
  2338. moving_average: true
  2339. # decay: 0.05
  2340. }
  2341. }
  2342. layer {
  2343. name: "conv4_4_1x1_reduce/relu"
  2344. type: "ReLU"
  2345. bottom: "conv4_4_1x1_reduce/bn"
  2346. top: "conv4_4_1x1_reduce/bn"
  2347. }
  2348. layer {
  2349. name: "conv4_4_3x3"
  2350. type: "Convolution"
  2351. bottom: "conv4_4_1x1_reduce/bn"
  2352. top: "conv4_4_3x3"
  2353. param {
  2354. lr_mult: 1
  2355. decay_mult: 1
  2356. }
  2357. convolution_param {
  2358. num_output: 256
  2359. pad: 2
  2360. dilation: 2
  2361. kernel_size: 3
  2362. stride: 1
  2363. weight_filler {
  2364. type: "msra"
  2365. }
  2366. bias_term: false
  2367. }
  2368. }
  2369. layer {
  2370. name: "conv4_4_3x3/bn"
  2371. type: "SyncBN"
  2372. bottom: "conv4_4_3x3"
  2373. top: "conv4_4_3x3/bn"
  2374. param {
  2375. lr_mult: 1
  2376. decay_mult: 0
  2377. }
  2378. param {
  2379. lr_mult: 1
  2380. decay_mult: 0
  2381. }
  2382. param {
  2383. lr_mult: 0
  2384. decay_mult: 0
  2385. }
  2386. param {
  2387. lr_mult: 0
  2388. decay_mult: 0
  2389. }
  2390. bn_param {
  2391. slope_filler {
  2392. type: "constant"
  2393. value: 1
  2394. }
  2395. bias_filler {
  2396. type: "constant"
  2397. value: 0
  2398. }
  2399. moving_average: true
  2400. # decay: 0.05
  2401. }
  2402. }
  2403. layer {
  2404. name: "conv4_4_3x3/relu"
  2405. type: "ReLU"
  2406. bottom: "conv4_4_3x3/bn"
  2407. top: "conv4_4_3x3/bn"
  2408. }
  2409. layer {
  2410. name: "conv4_4_1x1_increase"
  2411. type: "Convolution"
  2412. bottom: "conv4_4_3x3/bn"
  2413. top: "conv4_4_1x1_increase"
  2414. param {
  2415. lr_mult: 1
  2416. decay_mult: 1
  2417. }
  2418. convolution_param {
  2419. num_output: 1024
  2420. pad: 0
  2421. kernel_size: 1
  2422. stride: 1
  2423. weight_filler {
  2424. type: "msra"
  2425. }
  2426. bias_term: false
  2427. }
  2428. }
  2429. layer {
  2430. name: "conv4_4_1x1_increase/bn"
  2431. type: "SyncBN"
  2432. bottom: "conv4_4_1x1_increase"
  2433. top: "conv4_4_1x1_increase/bn"
  2434. param {
  2435. lr_mult: 1
  2436. decay_mult: 0
  2437. }
  2438. param {
  2439. lr_mult: 1
  2440. decay_mult: 0
  2441. }
  2442. param {
  2443. lr_mult: 0
  2444. decay_mult: 0
  2445. }
  2446. param {
  2447. lr_mult: 0
  2448. decay_mult: 0
  2449. }
  2450. bn_param {
  2451. slope_filler {
  2452. type: "constant"
  2453. value: 1
  2454. }
  2455. bias_filler {
  2456. type: "constant"
  2457. value: 0
  2458. }
  2459. moving_average: true
  2460. # decay: 0.05
  2461. }
  2462. }
  2463. layer {
  2464. name: "conv4_4"
  2465. type: "Eltwise"
  2466. bottom: "conv4_3"
  2467. bottom: "conv4_4_1x1_increase/bn"
  2468. top: "conv4_4"
  2469. eltwise_param {
  2470. operation: SUM
  2471. }
  2472. }
  2473. layer {
  2474. name: "conv4_4/relu"
  2475. type: "ReLU"
  2476. bottom: "conv4_4"
  2477. top: "conv4_4"
  2478. }
  2479. layer {
  2480. name: "conv4_5_1x1_reduce"
  2481. type: "Convolution"
  2482. bottom: "conv4_4"
  2483. top: "conv4_5_1x1_reduce"
  2484. param {
  2485. lr_mult: 1
  2486. decay_mult: 1
  2487. }
  2488. convolution_param {
  2489. num_output: 256
  2490. pad: 0
  2491. kernel_size: 1
  2492. stride: 1
  2493. weight_filler {
  2494. type: "msra"
  2495. }
  2496. bias_term: false
  2497. }
  2498. }
  2499. layer {
  2500. name: "conv4_5_1x1_reduce/bn"
  2501. type: "SyncBN"
  2502. bottom: "conv4_5_1x1_reduce"
  2503. top: "conv4_5_1x1_reduce/bn"
  2504. param {
  2505. lr_mult: 1
  2506. decay_mult: 0
  2507. }
  2508. param {
  2509. lr_mult: 1
  2510. decay_mult: 0
  2511. }
  2512. param {
  2513. lr_mult: 0
  2514. decay_mult: 0
  2515. }
  2516. param {
  2517. lr_mult: 0
  2518. decay_mult: 0
  2519. }
  2520. bn_param {
  2521. slope_filler {
  2522. type: "constant"
  2523. value: 1
  2524. }
  2525. bias_filler {
  2526. type: "constant"
  2527. value: 0
  2528. }
  2529. moving_average: true
  2530. # decay: 0.05
  2531. }
  2532. }
  2533. layer {
  2534. name: "conv4_5_1x1_reduce/relu"
  2535. type: "ReLU"
  2536. bottom: "conv4_5_1x1_reduce/bn"
  2537. top: "conv4_5_1x1_reduce/bn"
  2538. }
  2539. layer {
  2540. name: "conv4_5_3x3"
  2541. type: "Convolution"
  2542. bottom: "conv4_5_1x1_reduce/bn"
  2543. top: "conv4_5_3x3"
  2544. param {
  2545. lr_mult: 1
  2546. decay_mult: 1
  2547. }
  2548. convolution_param {
  2549. num_output: 256
  2550. pad: 2
  2551. dilation: 2
  2552. kernel_size: 3
  2553. stride: 1
  2554. weight_filler {
  2555. type: "msra"
  2556. }
  2557. bias_term: false
  2558. }
  2559. }
  2560. layer {
  2561. name: "conv4_5_3x3/bn"
  2562. type: "SyncBN"
  2563. bottom: "conv4_5_3x3"
  2564. top: "conv4_5_3x3/bn"
  2565. param {
  2566. lr_mult: 1
  2567. decay_mult: 0
  2568. }
  2569. param {
  2570. lr_mult: 1
  2571. decay_mult: 0
  2572. }
  2573. param {
  2574. lr_mult: 0
  2575. decay_mult: 0
  2576. }
  2577. param {
  2578. lr_mult: 0
  2579. decay_mult: 0
  2580. }
  2581. bn_param {
  2582. slope_filler {
  2583. type: "constant"
  2584. value: 1
  2585. }
  2586. bias_filler {
  2587. type: "constant"
  2588. value: 0
  2589. }
  2590. moving_average: true
  2591. # decay: 0.05
  2592. }
  2593. }
  2594. layer {
  2595. name: "conv4_5_3x3/relu"
  2596. type: "ReLU"
  2597. bottom: "conv4_5_3x3/bn"
  2598. top: "conv4_5_3x3/bn"
  2599. }
  2600. layer {
  2601. name: "conv4_5_1x1_increase"
  2602. type: "Convolution"
  2603. bottom: "conv4_5_3x3/bn"
  2604. top: "conv4_5_1x1_increase"
  2605. param {
  2606. lr_mult: 1
  2607. decay_mult: 1
  2608. }
  2609. convolution_param {
  2610. num_output: 1024
  2611. pad: 0
  2612. kernel_size: 1
  2613. stride: 1
  2614. weight_filler {
  2615. type: "msra"
  2616. }
  2617. bias_term: false
  2618. }
  2619. }
  2620. layer {
  2621. name: "conv4_5_1x1_increase/bn"
  2622. type: "SyncBN"
  2623. bottom: "conv4_5_1x1_increase"
  2624. top: "conv4_5_1x1_increase/bn"
  2625. param {
  2626. lr_mult: 1
  2627. decay_mult: 0
  2628. }
  2629. param {
  2630. lr_mult: 1
  2631. decay_mult: 0
  2632. }
  2633. param {
  2634. lr_mult: 0
  2635. decay_mult: 0
  2636. }
  2637. param {
  2638. lr_mult: 0
  2639. decay_mult: 0
  2640. }
  2641. bn_param {
  2642. slope_filler {
  2643. type: "constant"
  2644. value: 1
  2645. }
  2646. bias_filler {
  2647. type: "constant"
  2648. value: 0
  2649. }
  2650. moving_average: true
  2651. # decay: 0.05
  2652. }
  2653. }
  2654. layer {
  2655. name: "conv4_5"
  2656. type: "Eltwise"
  2657. bottom: "conv4_4"
  2658. bottom: "conv4_5_1x1_increase/bn"
  2659. top: "conv4_5"
  2660. eltwise_param {
  2661. operation: SUM
  2662. }
  2663. }
  2664. layer {
  2665. name: "conv4_5/relu"
  2666. type: "ReLU"
  2667. bottom: "conv4_5"
  2668. top: "conv4_5"
  2669. }
  2670. layer {
  2671. name: "conv4_6_1x1_reduce"
  2672. type: "Convolution"
  2673. bottom: "conv4_5"
  2674. top: "conv4_6_1x1_reduce"
  2675. param {
  2676. lr_mult: 1
  2677. decay_mult: 1
  2678. }
  2679. convolution_param {
  2680. num_output: 256
  2681. pad: 0
  2682. kernel_size: 1
  2683. stride: 1
  2684. weight_filler {
  2685. type: "msra"
  2686. }
  2687. bias_term: false
  2688. }
  2689. }
  2690. layer {
  2691. name: "conv4_6_1x1_reduce/bn"
  2692. type: "SyncBN"
  2693. bottom: "conv4_6_1x1_reduce"
  2694. top: "conv4_6_1x1_reduce/bn"
  2695. param {
  2696. lr_mult: 1
  2697. decay_mult: 0
  2698. }
  2699. param {
  2700. lr_mult: 1
  2701. decay_mult: 0
  2702. }
  2703. param {
  2704. lr_mult: 0
  2705. decay_mult: 0
  2706. }
  2707. param {
  2708. lr_mult: 0
  2709. decay_mult: 0
  2710. }
  2711. bn_param {
  2712. slope_filler {
  2713. type: "constant"
  2714. value: 1
  2715. }
  2716. bias_filler {
  2717. type: "constant"
  2718. value: 0
  2719. }
  2720. moving_average: true
  2721. # decay: 0.05
  2722. }
  2723. }
  2724. layer {
  2725. name: "conv4_6_1x1_reduce/relu"
  2726. type: "ReLU"
  2727. bottom: "conv4_6_1x1_reduce/bn"
  2728. top: "conv4_6_1x1_reduce/bn"
  2729. }
  2730. layer {
  2731. name: "conv4_6_3x3"
  2732. type: "Convolution"
  2733. bottom: "conv4_6_1x1_reduce/bn"
  2734. top: "conv4_6_3x3"
  2735. param {
  2736. lr_mult: 1
  2737. decay_mult: 1
  2738. }
  2739. convolution_param {
  2740. num_output: 256
  2741. pad: 2
  2742. dilation: 2
  2743. kernel_size: 3
  2744. stride: 1
  2745. weight_filler {
  2746. type: "msra"
  2747. }
  2748. bias_term: false
  2749. }
  2750. }
  2751. layer {
  2752. name: "conv4_6_3x3/bn"
  2753. type: "SyncBN"
  2754. bottom: "conv4_6_3x3"
  2755. top: "conv4_6_3x3/bn"
  2756. param {
  2757. lr_mult: 1
  2758. decay_mult: 0
  2759. }
  2760. param {
  2761. lr_mult: 1
  2762. decay_mult: 0
  2763. }
  2764. param {
  2765. lr_mult: 0
  2766. decay_mult: 0
  2767. }
  2768. param {
  2769. lr_mult: 0
  2770. decay_mult: 0
  2771. }
  2772. bn_param {
  2773. slope_filler {
  2774. type: "constant"
  2775. value: 1
  2776. }
  2777. bias_filler {
  2778. type: "constant"
  2779. value: 0
  2780. }
  2781. moving_average: true
  2782. # decay: 0.05
  2783. }
  2784. }
  2785. layer {
  2786. name: "conv4_6_3x3/relu"
  2787. type: "ReLU"
  2788. bottom: "conv4_6_3x3/bn"
  2789. top: "conv4_6_3x3/bn"
  2790. }
  2791. layer {
  2792. name: "conv4_6_1x1_increase"
  2793. type: "Convolution"
  2794. bottom: "conv4_6_3x3/bn"
  2795. top: "conv4_6_1x1_increase"
  2796. param {
  2797. lr_mult: 1
  2798. decay_mult: 1
  2799. }
  2800. convolution_param {
  2801. num_output: 1024
  2802. pad: 0
  2803. kernel_size: 1
  2804. stride: 1
  2805. weight_filler {
  2806. type: "msra"
  2807. }
  2808. bias_term: false
  2809. }
  2810. }
  2811. layer {
  2812. name: "conv4_6_1x1_increase/bn"
  2813. type: "SyncBN"
  2814. bottom: "conv4_6_1x1_increase"
  2815. top: "conv4_6_1x1_increase/bn"
  2816. param {
  2817. lr_mult: 1
  2818. decay_mult: 0
  2819. }
  2820. param {
  2821. lr_mult: 1
  2822. decay_mult: 0
  2823. }
  2824. param {
  2825. lr_mult: 0
  2826. decay_mult: 0
  2827. }
  2828. param {
  2829. lr_mult: 0
  2830. decay_mult: 0
  2831. }
  2832. bn_param {
  2833. slope_filler {
  2834. type: "constant"
  2835. value: 1
  2836. }
  2837. bias_filler {
  2838. type: "constant"
  2839. value: 0
  2840. }
  2841. moving_average: true
  2842. # decay: 0.05
  2843. }
  2844. }
  2845. layer {
  2846. name: "conv4_6"
  2847. type: "Eltwise"
  2848. bottom: "conv4_5"
  2849. bottom: "conv4_6_1x1_increase/bn"
  2850. top: "conv4_6"
  2851. eltwise_param {
  2852. operation: SUM
  2853. }
  2854. }
  2855. layer {
  2856. name: "conv4_6/relu"
  2857. type: "ReLU"
  2858. bottom: "conv4_6"
  2859. top: "conv4_6"
  2860. }
  2861. layer {
  2862. name: "conv5_1_1x1_reduce"
  2863. type: "Convolution"
  2864. bottom: "conv4_6"
  2865. top: "conv5_1_1x1_reduce"
  2866. param {
  2867. lr_mult: 1
  2868. decay_mult: 1
  2869. }
  2870. convolution_param {
  2871. num_output: 512
  2872. pad: 0
  2873. kernel_size: 1
  2874. stride: 1
  2875. weight_filler {
  2876. type: "msra"
  2877. }
  2878. bias_term: false
  2879. }
  2880. }
  2881. layer {
  2882. name: "conv5_1_1x1_reduce/bn"
  2883. type: "SyncBN"
  2884. bottom: "conv5_1_1x1_reduce"
  2885. top: "conv5_1_1x1_reduce/bn"
  2886. param {
  2887. lr_mult: 1
  2888. decay_mult: 0
  2889. }
  2890. param {
  2891. lr_mult: 1
  2892. decay_mult: 0
  2893. }
  2894. param {
  2895. lr_mult: 0
  2896. decay_mult: 0
  2897. }
  2898. param {
  2899. lr_mult: 0
  2900. decay_mult: 0
  2901. }
  2902. bn_param {
  2903. slope_filler {
  2904. type: "constant"
  2905. value: 1
  2906. }
  2907. bias_filler {
  2908. type: "constant"
  2909. value: 0
  2910. }
  2911. moving_average: true
  2912. # decay: 0.05
  2913. }
  2914. }
  2915. layer {
  2916. name: "conv5_1_1x1_reduce/relu"
  2917. type: "ReLU"
  2918. bottom: "conv5_1_1x1_reduce/bn"
  2919. top: "conv5_1_1x1_reduce/bn"
  2920. }
  2921. layer {
  2922. name: "conv5_1_3x3"
  2923. type: "Convolution"
  2924. bottom: "conv5_1_1x1_reduce/bn"
  2925. top: "conv5_1_3x3"
  2926. param {
  2927. lr_mult: 1
  2928. decay_mult: 1
  2929. }
  2930. convolution_param {
  2931. num_output: 512
  2932. pad: 4
  2933. dilation: 4
  2934. kernel_size: 3
  2935. stride: 1
  2936. weight_filler {
  2937. type: "msra"
  2938. }
  2939. bias_term: false
  2940. }
  2941. }
  2942. layer {
  2943. name: "conv5_1_3x3/bn"
  2944. type: "SyncBN"
  2945. bottom: "conv5_1_3x3"
  2946. top: "conv5_1_3x3/bn"
  2947. param {
  2948. lr_mult: 1
  2949. decay_mult: 0
  2950. }
  2951. param {
  2952. lr_mult: 1
  2953. decay_mult: 0
  2954. }
  2955. param {
  2956. lr_mult: 0
  2957. decay_mult: 0
  2958. }
  2959. param {
  2960. lr_mult: 0
  2961. decay_mult: 0
  2962. }
  2963. bn_param {
  2964. slope_filler {
  2965. type: "constant"
  2966. value: 1
  2967. }
  2968. bias_filler {
  2969. type: "constant"
  2970. value: 0
  2971. }
  2972. moving_average: true
  2973. # decay: 0.05
  2974. }
  2975. }
  2976. layer {
  2977. name: "conv5_1_3x3/relu"
  2978. type: "ReLU"
  2979. bottom: "conv5_1_3x3/bn"
  2980. top: "conv5_1_3x3/bn"
  2981. }
  2982. layer {
  2983. name: "conv5_1_1x1_increase"
  2984. type: "Convolution"
  2985. bottom: "conv5_1_3x3/bn"
  2986. top: "conv5_1_1x1_increase"
  2987. param {
  2988. lr_mult: 1
  2989. decay_mult: 1
  2990. }
  2991. convolution_param {
  2992. num_output: 2048
  2993. pad: 0
  2994. kernel_size: 1
  2995. stride: 1
  2996. weight_filler {
  2997. type: "msra"
  2998. }
  2999. bias_term: false
  3000. }
  3001. }
  3002. layer {
  3003. name: "conv5_1_1x1_increase/bn"
  3004. type: "SyncBN"
  3005. bottom: "conv5_1_1x1_increase"
  3006. top: "conv5_1_1x1_increase/bn"
  3007. param {
  3008. lr_mult: 1
  3009. decay_mult: 0
  3010. }
  3011. param {
  3012. lr_mult: 1
  3013. decay_mult: 0
  3014. }
  3015. param {
  3016. lr_mult: 0
  3017. decay_mult: 0
  3018. }
  3019. param {
  3020. lr_mult: 0
  3021. decay_mult: 0
  3022. }
  3023. bn_param {
  3024. slope_filler {
  3025. type: "constant"
  3026. value: 1
  3027. }
  3028. bias_filler {
  3029. type: "constant"
  3030. value: 0
  3031. }
  3032. moving_average: true
  3033. # decay: 0.05
  3034. }
  3035. }
  3036. layer {
  3037. name: "conv5_1_1x1_proj"
  3038. type: "Convolution"
  3039. bottom: "conv4_6"
  3040. top: "conv5_1_1x1_proj"
  3041. param {
  3042. lr_mult: 1
  3043. decay_mult: 1
  3044. }
  3045. convolution_param {
  3046. num_output: 2048
  3047. pad: 0
  3048. kernel_size: 1
  3049. stride: 1
  3050. weight_filler {
  3051. type: "msra"
  3052. }
  3053. bias_term: false
  3054. }
  3055. }
  3056. layer {
  3057. name: "conv5_1_1x1_proj/bn"
  3058. type: "SyncBN"
  3059. bottom: "conv5_1_1x1_proj"
  3060. top: "conv5_1_1x1_proj/bn"
  3061. param {
  3062. lr_mult: 1
  3063. decay_mult: 0
  3064. }
  3065. param {
  3066. lr_mult: 1
  3067. decay_mult: 0
  3068. }
  3069. param {
  3070. lr_mult: 0
  3071. decay_mult: 0
  3072. }
  3073. param {
  3074. lr_mult: 0
  3075. decay_mult: 0
  3076. }
  3077. bn_param {
  3078. slope_filler {
  3079. type: "constant"
  3080. value: 1
  3081. }
  3082. bias_filler {
  3083. type: "constant"
  3084. value: 0
  3085. }
  3086. moving_average: true
  3087. # decay: 0.05
  3088. }
  3089. }
  3090. layer {
  3091. name: "conv5_1"
  3092. type: "Eltwise"
  3093. bottom: "conv5_1_1x1_proj/bn"
  3094. bottom: "conv5_1_1x1_increase/bn"
  3095. top: "conv5_1"
  3096. eltwise_param {
  3097. operation: SUM
  3098. }
  3099. }
  3100. layer {
  3101. name: "conv5_1/relu"
  3102. type: "ReLU"
  3103. bottom: "conv5_1"
  3104. top: "conv5_1"
  3105. }
  3106. layer {
  3107. name: "conv5_2_1x1_reduce"
  3108. type: "Convolution"
  3109. bottom: "conv5_1"
  3110. top: "conv5_2_1x1_reduce"
  3111. param {
  3112. lr_mult: 1
  3113. decay_mult: 1
  3114. }
  3115. convolution_param {
  3116. num_output: 512
  3117. pad: 0
  3118. kernel_size: 1
  3119. stride: 1
  3120. weight_filler {
  3121. type: "msra"
  3122. }
  3123. bias_term: false
  3124. }
  3125. }
  3126. layer {
  3127. name: "conv5_2_1x1_reduce/bn"
  3128. type: "SyncBN"
  3129. bottom: "conv5_2_1x1_reduce"
  3130. top: "conv5_2_1x1_reduce/bn"
  3131. param {
  3132. lr_mult: 1
  3133. decay_mult: 0
  3134. }
  3135. param {
  3136. lr_mult: 1
  3137. decay_mult: 0
  3138. }
  3139. param {
  3140. lr_mult: 0
  3141. decay_mult: 0
  3142. }
  3143. param {
  3144. lr_mult: 0
  3145. decay_mult: 0
  3146. }
  3147. bn_param {
  3148. slope_filler {
  3149. type: "constant"
  3150. value: 1
  3151. }
  3152. bias_filler {
  3153. type: "constant"
  3154. value: 0
  3155. }
  3156. moving_average: true
  3157. # decay: 0.05
  3158. }
  3159. }
  3160. layer {
  3161. name: "conv5_2_1x1_reduce/relu"
  3162. type: "ReLU"
  3163. bottom: "conv5_2_1x1_reduce/bn"
  3164. top: "conv5_2_1x1_reduce/bn"
  3165. }
  3166. layer {
  3167. name: "conv5_2_3x3"
  3168. type: "Convolution"
  3169. bottom: "conv5_2_1x1_reduce/bn"
  3170. top: "conv5_2_3x3"
  3171. param {
  3172. lr_mult: 1
  3173. decay_mult: 1
  3174. }
  3175. convolution_param {
  3176. num_output: 512
  3177. pad: 4
  3178. dilation: 4
  3179. kernel_size: 3
  3180. stride: 1
  3181. weight_filler {
  3182. type: "msra"
  3183. }
  3184. bias_term: false
  3185. }
  3186. }
  3187. layer {
  3188. name: "conv5_2_3x3/bn"
  3189. type: "SyncBN"
  3190. bottom: "conv5_2_3x3"
  3191. top: "conv5_2_3x3/bn"
  3192. param {
  3193. lr_mult: 1
  3194. decay_mult: 0
  3195. }
  3196. param {
  3197. lr_mult: 1
  3198. decay_mult: 0
  3199. }
  3200. param {
  3201. lr_mult: 0
  3202. decay_mult: 0
  3203. }
  3204. param {
  3205. lr_mult: 0
  3206. decay_mult: 0
  3207. }
  3208. bn_param {
  3209. slope_filler {
  3210. type: "constant"
  3211. value: 1
  3212. }
  3213. bias_filler {
  3214. type: "constant"
  3215. value: 0
  3216. }
  3217. moving_average: true
  3218. # decay: 0.05
  3219. }
  3220. }
  3221. layer {
  3222. name: "conv5_2_3x3/relu"
  3223. type: "ReLU"
  3224. bottom: "conv5_2_3x3/bn"
  3225. top: "conv5_2_3x3/bn"
  3226. }
  3227. layer {
  3228. name: "conv5_2_1x1_increase"
  3229. type: "Convolution"
  3230. bottom: "conv5_2_3x3/bn"
  3231. top: "conv5_2_1x1_increase"
  3232. param {
  3233. lr_mult: 1
  3234. decay_mult: 1
  3235. }
  3236. convolution_param {
  3237. num_output: 2048
  3238. pad: 0
  3239. kernel_size: 1
  3240. stride: 1
  3241. weight_filler {
  3242. type: "msra"
  3243. }
  3244. bias_term: false
  3245. }
  3246. }
  3247. layer {
  3248. name: "conv5_2_1x1_increase/bn"
  3249. type: "SyncBN"
  3250. bottom: "conv5_2_1x1_increase"
  3251. top: "conv5_2_1x1_increase/bn"
  3252. param {
  3253. lr_mult: 1
  3254. decay_mult: 0
  3255. }
  3256. param {
  3257. lr_mult: 1
  3258. decay_mult: 0
  3259. }
  3260. param {
  3261. lr_mult: 0
  3262. decay_mult: 0
  3263. }
  3264. param {
  3265. lr_mult: 0
  3266. decay_mult: 0
  3267. }
  3268. bn_param {
  3269. slope_filler {
  3270. type: "constant"
  3271. value: 1
  3272. }
  3273. bias_filler {
  3274. type: "constant"
  3275. value: 0
  3276. }
  3277. moving_average: true
  3278. # decay: 0.05
  3279. }
  3280. }
  3281. layer {
  3282. name: "conv5_2"
  3283. type: "Eltwise"
  3284. bottom: "conv5_1"
  3285. bottom: "conv5_2_1x1_increase/bn"
  3286. top: "conv5_2"
  3287. eltwise_param {
  3288. operation: SUM
  3289. }
  3290. }
  3291. layer {
  3292. name: "conv5_2/relu"
  3293. type: "ReLU"
  3294. bottom: "conv5_2"
  3295. top: "conv5_2"
  3296. }
  3297. layer {
  3298. name: "conv5_3_1x1_reduce"
  3299. type: "Convolution"
  3300. bottom: "conv5_2"
  3301. top: "conv5_3_1x1_reduce"
  3302. param {
  3303. lr_mult: 1
  3304. decay_mult: 1
  3305. }
  3306. convolution_param {
  3307. num_output: 512
  3308. pad: 0
  3309. kernel_size: 1
  3310. stride: 1
  3311. weight_filler {
  3312. type: "msra"
  3313. }
  3314. bias_term: false
  3315. }
  3316. }
  3317. layer {
  3318. name: "conv5_3_1x1_reduce/bn"
  3319. type: "SyncBN"
  3320. bottom: "conv5_3_1x1_reduce"
  3321. top: "conv5_3_1x1_reduce/bn"
  3322. param {
  3323. lr_mult: 1
  3324. decay_mult: 0
  3325. }
  3326. param {
  3327. lr_mult: 1
  3328. decay_mult: 0
  3329. }
  3330. param {
  3331. lr_mult: 0
  3332. decay_mult: 0
  3333. }
  3334. param {
  3335. lr_mult: 0
  3336. decay_mult: 0
  3337. }
  3338. bn_param {
  3339. slope_filler {
  3340. type: "constant"
  3341. value: 1
  3342. }
  3343. bias_filler {
  3344. type: "constant"
  3345. value: 0
  3346. }
  3347. moving_average: true
  3348. # decay: 0.05
  3349. }
  3350. }
  3351. layer {
  3352. name: "conv5_3_1x1_reduce/relu"
  3353. type: "ReLU"
  3354. bottom: "conv5_3_1x1_reduce/bn"
  3355. top: "conv5_3_1x1_reduce/bn"
  3356. }
  3357. layer {
  3358. name: "conv5_3_3x3"
  3359. type: "Convolution"
  3360. bottom: "conv5_3_1x1_reduce/bn"
  3361. top: "conv5_3_3x3"
  3362. param {
  3363. lr_mult: 1
  3364. decay_mult: 1
  3365. }
  3366. convolution_param {
  3367. num_output: 512
  3368. pad: 4
  3369. dilation: 4
  3370. kernel_size: 3
  3371. stride: 1
  3372. weight_filler {
  3373. type: "msra"
  3374. }
  3375. bias_term: false
  3376. }
  3377. }
  3378. layer {
  3379. name: "conv5_3_3x3/bn"
  3380. type: "SyncBN"
  3381. bottom: "conv5_3_3x3"
  3382. top: "conv5_3_3x3/bn"
  3383. param {
  3384. lr_mult: 1
  3385. decay_mult: 0
  3386. }
  3387. param {
  3388. lr_mult: 1
  3389. decay_mult: 0
  3390. }
  3391. param {
  3392. lr_mult: 0
  3393. decay_mult: 0
  3394. }
  3395. param {
  3396. lr_mult: 0
  3397. decay_mult: 0
  3398. }
  3399. bn_param {
  3400. slope_filler {
  3401. type: "constant"
  3402. value: 1
  3403. }
  3404. bias_filler {
  3405. type: "constant"
  3406. value: 0
  3407. }
  3408. moving_average: true
  3409. # decay: 0.05
  3410. }
  3411. }
  3412. layer {
  3413. name: "conv5_3_3x3/relu"
  3414. type: "ReLU"
  3415. bottom: "conv5_3_3x3/bn"
  3416. top: "conv5_3_3x3/bn"
  3417. }
  3418. layer {
  3419. name: "conv5_3_1x1_increase"
  3420. type: "Convolution"
  3421. bottom: "conv5_3_3x3/bn"
  3422. top: "conv5_3_1x1_increase"
  3423. param {
  3424. lr_mult: 1
  3425. decay_mult: 1
  3426. }
  3427. convolution_param {
  3428. num_output: 2048
  3429. pad: 0
  3430. kernel_size: 1
  3431. stride: 1
  3432. weight_filler {
  3433. type: "msra"
  3434. }
  3435. bias_term: false
  3436. }
  3437. }
  3438. layer {
  3439. name: "conv5_3_1x1_increase/bn"
  3440. type: "SyncBN"
  3441. bottom: "conv5_3_1x1_increase"
  3442. top: "conv5_3_1x1_increase/bn"
  3443. param {
  3444. lr_mult: 1
  3445. decay_mult: 0
  3446. }
  3447. param {
  3448. lr_mult: 1
  3449. decay_mult: 0
  3450. }
  3451. param {
  3452. lr_mult: 0
  3453. decay_mult: 0
  3454. }
  3455. param {
  3456. lr_mult: 0
  3457. decay_mult: 0
  3458. }
  3459. bn_param {
  3460. slope_filler {
  3461. type: "constant"
  3462. value: 1
  3463. }
  3464. bias_filler {
  3465. type: "constant"
  3466. value: 0
  3467. }
  3468. moving_average: true
  3469. # decay: 0.05
  3470. }
  3471. }
  3472. layer {
  3473. name: "conv5_3"
  3474. type: "Eltwise"
  3475. bottom: "conv5_2"
  3476. bottom: "conv5_3_1x1_increase/bn"
  3477. top: "conv5_3"
  3478. eltwise_param {
  3479. operation: SUM
  3480. }
  3481. }
  3482. layer {
  3483. name: "conv5_3/relu"
  3484. type: "ReLU"
  3485. bottom: "conv5_3"
  3486. top: "conv5_3"
  3487. }
  3488.  
  3489. layer {
  3490. name: "conv6"
  3491. type: "Convolution"
  3492. bottom: "conv5_3"
  3493. top: "conv6"
  3494. param {
  3495. lr_mult: 10
  3496. decay_mult: 1
  3497. }
  3498. param {
  3499. lr_mult: 20
  3500. decay_mult: 1
  3501. }
  3502. convolution_param {
  3503. num_output: 21
  3504. kernel_size: 1
  3505. stride: 1
  3506. weight_filler {
  3507. type: "msra"
  3508. }
  3509. }
  3510. }
  3511. layer {
  3512. name: "dropout6"
  3513. type: "Dropout"
  3514. bottom: "conv6"
  3515. top: "conv6"
  3516. dropout_param {
  3517. dropout_ratio: 0.1
  3518. }
  3519. }
  3520. layer {
  3521. bottom: "label"
  3522. top: "label_shrink"
  3523. name: "label_shrink"
  3524. type: "Interp"
  3525. interp_param {
  3526. shrink_factor: 8
  3527. pad_beg: 0
  3528. pad_end: 0
  3529. }
  3530. }
  3531. layer {
  3532. name: "loss"
  3533. type: "SoftmaxWithLoss"
  3534. bottom: "conv6"
  3535. bottom: "label_shrink"
  3536. top: "loss"
  3537. loss_param {
  3538. ignore_label: 255
  3539. }
  3540. include: {phase: TRAIN}
  3541. }
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement
Public Pastes
Advertisement
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.  OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!