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  1. #
  2. input: "data"
  3. input_dim: 1
  4. input_dim: 3
  5. input_dim: 713
  6. input_dim: 713
  7.  
  8.  
  9. layer {
  10. name: "conv1_1_3x3_s2"
  11. type: "Convolution"
  12. bottom: "data"
  13. top: "conv1_1_3x3_s2_s1"
  14. param {
  15. lr_mult: 1
  16. decay_mult: 1
  17. }
  18. convolution_param {
  19. num_output: 64
  20. pad: 1
  21. kernel_size: 3
  22. stride: 1
  23. weight_filler {
  24. type: "msra"
  25. }
  26. bias_term: false
  27. }
  28. }
  29.  
  30. layer {
  31. name: "pool_conv1_1_3x3_s2"
  32. type: "Pooling"
  33. bottom: "conv1_1_3x3_s2_s1"
  34. top: "down_conv1_1_3x3_s2"
  35. pooling_param {
  36. pool: AVE
  37. kernel_size:3
  38. stride: 2
  39. pad: 1
  40. }
  41. }
  42.  
  43. layer {
  44. name: "conv1_1_3x3_s2/bn"
  45. type: "BN"
  46. bottom: "down_conv1_1_3x3_s2"
  47. top: "conv1_1_3x3_s2"
  48. param {
  49. lr_mult: 1
  50. decay_mult: 0
  51. }
  52. param {
  53. lr_mult: 1
  54. decay_mult: 0
  55. }
  56. param {
  57. lr_mult: 0
  58. decay_mult: 0
  59. }
  60. param {
  61. lr_mult: 0
  62. decay_mult: 0
  63. }
  64. bn_param {
  65. slope_filler {
  66. type: "constant"
  67. value: 1
  68. }
  69. bias_filler {
  70. type: "constant"
  71. value: 0
  72. }
  73. frozen: true
  74. momentum: 0.95
  75. }
  76. }
  77. layer {
  78. name: "conv1_1_3x3_s2/relu"
  79. type: "ReLU"
  80. bottom: "conv1_1_3x3_s2"
  81. top: "conv1_1_3x3_s2"
  82. }
  83. layer {
  84. name: "conv1_2_3x3"
  85. type: "Convolution"
  86. bottom: "conv1_1_3x3_s2"
  87. top: "conv1_2_3x3"
  88. param {
  89. lr_mult: 1
  90. decay_mult: 1
  91. }
  92. convolution_param {
  93. num_output: 64
  94. pad: 1
  95. kernel_size: 3
  96. stride: 1
  97. weight_filler {
  98. type: "msra"
  99. }
  100. bias_term: false
  101. }
  102. }
  103. layer {
  104. name: "conv1_2_3x3/bn"
  105. type: "BN"
  106. bottom: "conv1_2_3x3"
  107. top: "conv1_2_3x3"
  108. param {
  109. lr_mult: 1
  110. decay_mult: 0
  111. }
  112. param {
  113. lr_mult: 1
  114. decay_mult: 0
  115. }
  116. param {
  117. lr_mult: 0
  118. decay_mult: 0
  119. }
  120. param {
  121. lr_mult: 0
  122. decay_mult: 0
  123. }
  124. bn_param {
  125. slope_filler {
  126. type: "constant"
  127. value: 1
  128. }
  129. bias_filler {
  130. type: "constant"
  131. value: 0
  132. }
  133. frozen: true
  134. momentum: 0.95
  135. }
  136. }
  137. layer {
  138. name: "conv1_2_3x3/relu"
  139. type: "ReLU"
  140. bottom: "conv1_2_3x3"
  141. top: "conv1_2_3x3"
  142. }
  143. layer {
  144. name: "conv1_3_3x3"
  145. type: "Convolution"
  146. bottom: "conv1_2_3x3"
  147. top: "conv1_3_3x3"
  148. param {
  149. lr_mult: 1
  150. decay_mult: 1
  151. }
  152. convolution_param {
  153. num_output: 128
  154. pad: 1
  155. kernel_size: 3
  156. stride: 1
  157. weight_filler {
  158. type: "msra"
  159. }
  160. bias_term: false
  161. }
  162. }
  163. layer {
  164. name: "conv1_3_3x3/bn"
  165. type: "BN"
  166. bottom: "conv1_3_3x3"
  167. top: "conv1_3_3x3"
  168. param {
  169. lr_mult: 1
  170. decay_mult: 0
  171. }
  172. param {
  173. lr_mult: 1
  174. decay_mult: 0
  175. }
  176. param {
  177. lr_mult: 0
  178. decay_mult: 0
  179. }
  180. param {
  181. lr_mult: 0
  182. decay_mult: 0
  183. }
  184. bn_param {
  185. slope_filler {
  186. type: "constant"
  187. value: 1
  188. }
  189. bias_filler {
  190. type: "constant"
  191. value: 0
  192. }
  193. frozen: true
  194. momentum: 0.95
  195. }
  196. }
  197. layer {
  198. name: "conv1_3_3x3/relu"
  199. type: "ReLU"
  200. bottom: "conv1_3_3x3"
  201. top: "conv1_3_3x3"
  202. }
  203. layer {
  204. name: "pool1_3x3_s2"
  205. type: "Pooling"
  206. bottom: "conv1_3_3x3"
  207. top: "pool1_3x3_s2_s"
  208. pooling_param {
  209. pool: MAX
  210. kernel_size: 3
  211. stride: 1
  212. pad: 1
  213. }
  214. }
  215.  
  216. layer {
  217. name: "dwon_pool1_3x3_s2"
  218. type: "Pooling"
  219. bottom: "pool1_3x3_s2_s"
  220. top: "pool1_3x3_s2"
  221. pooling_param {
  222. pool: AVE
  223. kernel_size:3
  224. stride: 2
  225. pad: 1
  226. }
  227. }
  228.  
  229. layer {
  230. name: "conv2_1_1x1_reduce"
  231. type: "Convolution"
  232. bottom: "pool1_3x3_s2"
  233. top: "conv2_1_1x1_reduce"
  234. param {
  235. lr_mult: 1
  236. decay_mult: 1
  237. }
  238. convolution_param {
  239. num_output: 64
  240. pad: 0
  241. kernel_size: 1
  242. stride: 1
  243. weight_filler {
  244. type: "msra"
  245. }
  246. bias_term: false
  247. }
  248. }
  249. layer {
  250. name: "conv2_1_1x1_reduce/bn"
  251. type: "BN"
  252. bottom: "conv2_1_1x1_reduce"
  253. top: "conv2_1_1x1_reduce"
  254. param {
  255. lr_mult: 1
  256. decay_mult: 0
  257. }
  258. param {
  259. lr_mult: 1
  260. decay_mult: 0
  261. }
  262. param {
  263. lr_mult: 0
  264. decay_mult: 0
  265. }
  266. param {
  267. lr_mult: 0
  268. decay_mult: 0
  269. }
  270. bn_param {
  271. slope_filler {
  272. type: "constant"
  273. value: 1
  274. }
  275. bias_filler {
  276. type: "constant"
  277. value: 0
  278. }
  279. frozen: true
  280. momentum: 0.95
  281. }
  282. }
  283. layer {
  284. name: "conv2_1_1x1_reduce/relu"
  285. type: "ReLU"
  286. bottom: "conv2_1_1x1_reduce"
  287. top: "conv2_1_1x1_reduce"
  288. }
  289. layer {
  290. name: "conv2_1_3x3"
  291. type: "Convolution"
  292. bottom: "conv2_1_1x1_reduce"
  293. top: "conv2_1_3x3"
  294. param {
  295. lr_mult: 1
  296. decay_mult: 1
  297. }
  298. convolution_param {
  299. num_output: 64
  300. pad: 1
  301. kernel_size: 3
  302. stride: 1
  303. weight_filler {
  304. type: "msra"
  305. }
  306. bias_term: false
  307. }
  308. }
  309. layer {
  310. name: "conv2_1_3x3/bn"
  311. type: "BN"
  312. bottom: "conv2_1_3x3"
  313. top: "conv2_1_3x3"
  314. param {
  315. lr_mult: 1
  316. decay_mult: 0
  317. }
  318. param {
  319. lr_mult: 1
  320. decay_mult: 0
  321. }
  322. param {
  323. lr_mult: 0
  324. decay_mult: 0
  325. }
  326. param {
  327. lr_mult: 0
  328. decay_mult: 0
  329. }
  330. bn_param {
  331. slope_filler {
  332. type: "constant"
  333. value: 1
  334. }
  335. bias_filler {
  336. type: "constant"
  337. value: 0
  338. }
  339. frozen: true
  340. momentum: 0.95
  341. }
  342. }
  343. layer {
  344. name: "conv2_1_3x3/relu"
  345. type: "ReLU"
  346. bottom: "conv2_1_3x3"
  347. top: "conv2_1_3x3"
  348. }
  349. layer {
  350. name: "conv2_1_1x1_increase"
  351. type: "Convolution"
  352. bottom: "conv2_1_3x3"
  353. top: "conv2_1_1x1_increase"
  354. param {
  355. lr_mult: 1
  356. decay_mult: 1
  357. }
  358. convolution_param {
  359. num_output: 256
  360. pad: 0
  361. kernel_size: 1
  362. stride: 1
  363. weight_filler {
  364. type: "msra"
  365. }
  366. bias_term: false
  367. }
  368. }
  369. layer {
  370. name: "conv2_1_1x1_increase/bn"
  371. type: "BN"
  372. bottom: "conv2_1_1x1_increase"
  373. top: "conv2_1_1x1_increase"
  374. param {
  375. lr_mult: 1
  376. decay_mult: 0
  377. }
  378. param {
  379. lr_mult: 1
  380. decay_mult: 0
  381. }
  382. param {
  383. lr_mult: 0
  384. decay_mult: 0
  385. }
  386. param {
  387. lr_mult: 0
  388. decay_mult: 0
  389. }
  390. bn_param {
  391. slope_filler {
  392. type: "constant"
  393. value: 1
  394. }
  395. bias_filler {
  396. type: "constant"
  397. value: 0
  398. }
  399. frozen: true
  400. momentum: 0.95
  401. }
  402. }
  403. layer {
  404. name: "conv2_1_1x1_proj"
  405. type: "Convolution"
  406. bottom: "pool1_3x3_s2"
  407. top: "conv2_1_1x1_proj"
  408. param {
  409. lr_mult: 1
  410. decay_mult: 1
  411. }
  412. convolution_param {
  413. num_output: 256
  414. pad: 0
  415. kernel_size: 1
  416. stride: 1
  417. weight_filler {
  418. type: "msra"
  419. }
  420. bias_term: false
  421. }
  422. }
  423. layer {
  424. name: "conv2_1_1x1_proj/bn"
  425. type: "BN"
  426. bottom: "conv2_1_1x1_proj"
  427. top: "conv2_1_1x1_proj"
  428. param {
  429. lr_mult: 1
  430. decay_mult: 0
  431. }
  432. param {
  433. lr_mult: 1
  434. decay_mult: 0
  435. }
  436. param {
  437. lr_mult: 0
  438. decay_mult: 0
  439. }
  440. param {
  441. lr_mult: 0
  442. decay_mult: 0
  443. }
  444. bn_param {
  445. slope_filler {
  446. type: "constant"
  447. value: 1
  448. }
  449. bias_filler {
  450. type: "constant"
  451. value: 0
  452. }
  453. frozen: true
  454. momentum: 0.95
  455. }
  456. }
  457. layer {
  458. name: "conv2_1"
  459. type: "Eltwise"
  460. bottom: "conv2_1_1x1_proj"
  461. bottom: "conv2_1_1x1_increase"
  462. top: "conv2_1"
  463. eltwise_param {
  464. operation: SUM
  465. }
  466. }
  467. layer {
  468. name: "conv2_1/relu"
  469. type: "ReLU"
  470. bottom: "conv2_1"
  471. top: "conv2_1"
  472. }
  473. layer {
  474. name: "conv2_2_1x1_reduce"
  475. type: "Convolution"
  476. bottom: "conv2_1"
  477. top: "conv2_2_1x1_reduce"
  478. param {
  479. lr_mult: 1
  480. decay_mult: 1
  481. }
  482. convolution_param {
  483. num_output: 64
  484. pad: 0
  485. kernel_size: 1
  486. stride: 1
  487. weight_filler {
  488. type: "msra"
  489. }
  490. bias_term: false
  491. }
  492. }
  493. layer {
  494. name: "conv2_2_1x1_reduce/bn"
  495. type: "BN"
  496. bottom: "conv2_2_1x1_reduce"
  497. top: "conv2_2_1x1_reduce"
  498. param {
  499. lr_mult: 1
  500. decay_mult: 0
  501. }
  502. param {
  503. lr_mult: 1
  504. decay_mult: 0
  505. }
  506. param {
  507. lr_mult: 0
  508. decay_mult: 0
  509. }
  510. param {
  511. lr_mult: 0
  512. decay_mult: 0
  513. }
  514. bn_param {
  515. slope_filler {
  516. type: "constant"
  517. value: 1
  518. }
  519. bias_filler {
  520. type: "constant"
  521. value: 0
  522. }
  523. frozen: true
  524. momentum: 0.95
  525. }
  526. }
  527. layer {
  528. name: "conv2_2_1x1_reduce/relu"
  529. type: "ReLU"
  530. bottom: "conv2_2_1x1_reduce"
  531. top: "conv2_2_1x1_reduce"
  532. }
  533. layer {
  534. name: "conv2_2_3x3"
  535. type: "Convolution"
  536. bottom: "conv2_2_1x1_reduce"
  537. top: "conv2_2_3x3"
  538. param {
  539. lr_mult: 1
  540. decay_mult: 1
  541. }
  542. convolution_param {
  543. num_output: 64
  544. pad: 1
  545. kernel_size: 3
  546. stride: 1
  547. weight_filler {
  548. type: "msra"
  549. }
  550. bias_term: false
  551. }
  552. }
  553. layer {
  554. name: "conv2_2_3x3/bn"
  555. type: "BN"
  556. bottom: "conv2_2_3x3"
  557. top: "conv2_2_3x3"
  558. param {
  559. lr_mult: 1
  560. decay_mult: 0
  561. }
  562. param {
  563. lr_mult: 1
  564. decay_mult: 0
  565. }
  566. param {
  567. lr_mult: 0
  568. decay_mult: 0
  569. }
  570. param {
  571. lr_mult: 0
  572. decay_mult: 0
  573. }
  574. bn_param {
  575. slope_filler {
  576. type: "constant"
  577. value: 1
  578. }
  579. bias_filler {
  580. type: "constant"
  581. value: 0
  582. }
  583. frozen: true
  584. momentum: 0.95
  585. }
  586. }
  587. layer {
  588. name: "conv2_2_3x3/relu"
  589. type: "ReLU"
  590. bottom: "conv2_2_3x3"
  591. top: "conv2_2_3x3"
  592. }
  593. layer {
  594. name: "conv2_2_1x1_increase"
  595. type: "Convolution"
  596. bottom: "conv2_2_3x3"
  597. top: "conv2_2_1x1_increase"
  598. param {
  599. lr_mult: 1
  600. decay_mult: 1
  601. }
  602. convolution_param {
  603. num_output: 256
  604. pad: 0
  605. kernel_size: 1
  606. stride: 1
  607. weight_filler {
  608. type: "msra"
  609. }
  610. bias_term: false
  611. }
  612. }
  613. layer {
  614. name: "conv2_2_1x1_increase/bn"
  615. type: "BN"
  616. bottom: "conv2_2_1x1_increase"
  617. top: "conv2_2_1x1_increase"
  618. param {
  619. lr_mult: 1
  620. decay_mult: 0
  621. }
  622. param {
  623. lr_mult: 1
  624. decay_mult: 0
  625. }
  626. param {
  627. lr_mult: 0
  628. decay_mult: 0
  629. }
  630. param {
  631. lr_mult: 0
  632. decay_mult: 0
  633. }
  634. bn_param {
  635. slope_filler {
  636. type: "constant"
  637. value: 1
  638. }
  639. bias_filler {
  640. type: "constant"
  641. value: 0
  642. }
  643. frozen: true
  644. momentum: 0.95
  645. }
  646. }
  647. layer {
  648. name: "conv2_2"
  649. type: "Eltwise"
  650. bottom: "conv2_1"
  651. bottom: "conv2_2_1x1_increase"
  652. top: "conv2_2"
  653. eltwise_param {
  654. operation: SUM
  655. }
  656. }
  657. layer {
  658. name: "conv2_2/relu"
  659. type: "ReLU"
  660. bottom: "conv2_2"
  661. top: "conv2_2"
  662. }
  663. layer {
  664. name: "conv2_3_1x1_reduce"
  665. type: "Convolution"
  666. bottom: "conv2_2"
  667. top: "conv2_3_1x1_reduce"
  668. param {
  669. lr_mult: 1
  670. decay_mult: 1
  671. }
  672. convolution_param {
  673. num_output: 64
  674. pad: 0
  675. kernel_size: 1
  676. stride: 1
  677. weight_filler {
  678. type: "msra"
  679. }
  680. bias_term: false
  681. }
  682. }
  683. layer {
  684. name: "conv2_3_1x1_reduce/bn"
  685. type: "BN"
  686. bottom: "conv2_3_1x1_reduce"
  687. top: "conv2_3_1x1_reduce"
  688. param {
  689. lr_mult: 1
  690. decay_mult: 0
  691. }
  692. param {
  693. lr_mult: 1
  694. decay_mult: 0
  695. }
  696. param {
  697. lr_mult: 0
  698. decay_mult: 0
  699. }
  700. param {
  701. lr_mult: 0
  702. decay_mult: 0
  703. }
  704. bn_param {
  705. slope_filler {
  706. type: "constant"
  707. value: 1
  708. }
  709. bias_filler {
  710. type: "constant"
  711. value: 0
  712. }
  713. frozen: true
  714. momentum: 0.95
  715. }
  716. }
  717. layer {
  718. name: "conv2_3_1x1_reduce/relu"
  719. type: "ReLU"
  720. bottom: "conv2_3_1x1_reduce"
  721. top: "conv2_3_1x1_reduce"
  722. }
  723. layer {
  724. name: "conv2_3_3x3"
  725. type: "Convolution"
  726. bottom: "conv2_3_1x1_reduce"
  727. top: "conv2_3_3x3"
  728. param {
  729. lr_mult: 1
  730. decay_mult: 1
  731. }
  732. convolution_param {
  733. num_output: 64
  734. pad: 1
  735. kernel_size: 3
  736. stride: 1
  737. weight_filler {
  738. type: "msra"
  739. }
  740. bias_term: false
  741. }
  742. }
  743. layer {
  744. name: "conv2_3_3x3/bn"
  745. type: "BN"
  746. bottom: "conv2_3_3x3"
  747. top: "conv2_3_3x3"
  748. param {
  749. lr_mult: 1
  750. decay_mult: 0
  751. }
  752. param {
  753. lr_mult: 1
  754. decay_mult: 0
  755. }
  756. param {
  757. lr_mult: 0
  758. decay_mult: 0
  759. }
  760. param {
  761. lr_mult: 0
  762. decay_mult: 0
  763. }
  764. bn_param {
  765. slope_filler {
  766. type: "constant"
  767. value: 1
  768. }
  769. bias_filler {
  770. type: "constant"
  771. value: 0
  772. }
  773. frozen: true
  774. momentum: 0.95
  775. }
  776. }
  777. layer {
  778. name: "conv2_3_3x3/relu"
  779. type: "ReLU"
  780. bottom: "conv2_3_3x3"
  781. top: "conv2_3_3x3"
  782. }
  783. layer {
  784. name: "conv2_3_1x1_increase"
  785. type: "Convolution"
  786. bottom: "conv2_3_3x3"
  787. top: "conv2_3_1x1_increase"
  788. param {
  789. lr_mult: 1
  790. decay_mult: 1
  791. }
  792. convolution_param {
  793. num_output: 256
  794. pad: 0
  795. kernel_size: 1
  796. stride: 1
  797. weight_filler {
  798. type: "msra"
  799. }
  800. bias_term: false
  801. }
  802. }
  803. layer {
  804. name: "conv2_3_1x1_increase/bn"
  805. type: "BN"
  806. bottom: "conv2_3_1x1_increase"
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  1565. top: "conv3_4_1x1_reduce"
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  1569. type: "Convolution"
  1570. bottom: "conv3_4_1x1_reduce"
  1571. top: "conv3_4_3x3"
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  1574. decay_mult: 1
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  1580. stride: 1
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  1590. bottom: "conv3_4_3x3"
  1591. top: "conv3_4_3x3"
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  1618. momentum: 0.95
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  1623. type: "ReLU"
  1624. bottom: "conv3_4_3x3"
  1625. top: "conv3_4_3x3"
  1626. }
  1627. layer {
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  1629. type: "Convolution"
  1630. bottom: "conv3_4_3x3"
  1631. top: "conv3_4_1x1_increase"
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  1634. decay_mult: 1
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  1639. kernel_size: 1
  1640. stride: 1
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  1650. bottom: "conv3_4_1x1_increase"
  1651. top: "conv3_4_1x1_increase"
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  1683. type: "Eltwise"
  1684. bottom: "conv3_3"
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  1686. top: "conv3_4"
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  1695. top: "conv3_4"
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  1755. top: "conv4_1_1x1_reduce"
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  1761. top: "conv4_1_3x3"
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  1815. bottom: "conv4_1_3x3"
  1816. top: "conv4_1_3x3"
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  1875. bottom: "conv3_4"
  1876. top: "conv4_1_1x1_proj"
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  1930. bottom: "conv4_1_1x1_increase"
  1931. top: "conv4_1"
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  2027. top: "conv4_2_3x3"
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  2060. bottom: "conv4_2_3x3"
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  2063. layer {
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  2120. bottom: "conv4_1"
  2121. bottom: "conv4_2_1x1_increase"
  2122. top: "conv4_2"
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  2129. type: "ReLU"
  2130. bottom: "conv4_2"
  2131. top: "conv4_2"
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  2136. bottom: "conv4_2"
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  2191. top: "conv4_3_1x1_reduce"
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  2197. top: "conv4_3_3x3"
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  2312. bottom: "conv4_3_1x1_increase"
  2313. top: "conv4_3"
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  6793. kernel_size: 1
  6794. stride: 1
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  6799. }
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  6803. type: "BN"
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  6820. decay_mult: 0
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  6825. value: 1
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  6829. value: 0
  6830. }
  6831. frozen: true
  6832. momentum: 0.95
  6833. }
  6834. }
  6835. layer {
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  6837. type: "ReLU"
  6838. bottom: "conv5_3_pool1_conv"
  6839. top: "conv5_3_pool1_conv"
  6840. }
  6841. layer {
  6842. name: "conv5_3_pool1_interp"
  6843. type: "Interp"
  6844. bottom: "conv5_3_pool1_conv"
  6845. top: "conv5_3_pool1_interp"
  6846. interp_param {
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  6848. width: 90
  6849. }
  6850. }
  6851. layer {
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  6853. type: "Pooling"
  6854. bottom: "conv5_3"
  6855. top: "conv5_3_pool2"
  6856. pooling_param {
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  6858. kernel_size: 45
  6859. stride: 45
  6860. }
  6861. }
  6862. layer {
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  6864. type: "Convolution"
  6865. bottom: "conv5_3_pool2"
  6866. top: "conv5_3_pool2_conv"
  6867. param {
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  6869. decay_mult: 1
  6870. }
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  6873. kernel_size: 1
  6874. stride: 1
  6875. weight_filler {
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  6877. }
  6878. bias_term: false
  6879. }
  6880. }
  6881. layer {
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  6883. type: "BN"
  6884. bottom: "conv5_3_pool2_conv"
  6885. top: "conv5_3_pool2_conv"
  6886. param {
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  6888. decay_mult: 0
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  6890. param {
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  6892. decay_mult: 0
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  6900. decay_mult: 0
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  6905. value: 1
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  6907. bias_filler {
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  6909. value: 0
  6910. }
  6911. frozen: true
  6912. momentum: 0.95
  6913. }
  6914. }
  6915. layer {
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  6917. type: "ReLU"
  6918. bottom: "conv5_3_pool2_conv"
  6919. top: "conv5_3_pool2_conv"
  6920. }
  6921. layer {
  6922. name: "conv5_3_pool2_interp"
  6923. type: "Interp"
  6924. bottom: "conv5_3_pool2_conv"
  6925. top: "conv5_3_pool2_interp"
  6926. interp_param {
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  6928. width: 90
  6929. }
  6930. }
  6931. layer {
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  6933. type: "Pooling"
  6934. bottom: "conv5_3"
  6935. top: "conv5_3_pool3"
  6936. pooling_param {
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  6938. kernel_size: 30
  6939. stride: 30
  6940. }
  6941. }
  6942. layer {
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  6944. type: "Convolution"
  6945. bottom: "conv5_3_pool3"
  6946. top: "conv5_3_pool3_conv"
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  6949. decay_mult: 1
  6950. }
  6951. convolution_param {
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  6953. kernel_size: 1
  6954. stride: 1
  6955. weight_filler {
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  6957. }
  6958. bias_term: false
  6959. }
  6960. }
  6961. layer {
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  6963. type: "BN"
  6964. bottom: "conv5_3_pool3_conv"
  6965. top: "conv5_3_pool3_conv"
  6966. param {
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  6968. decay_mult: 0
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  6970. param {
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  6972. decay_mult: 0
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  6982. bn_param {
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  6987. bias_filler {
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  6989. value: 0
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  6992. momentum: 0.95
  6993. }
  6994. }
  6995. layer {
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  6997. type: "ReLU"
  6998. bottom: "conv5_3_pool3_conv"
  6999. top: "conv5_3_pool3_conv"
  7000. }
  7001. layer {
  7002. name: "conv5_3_pool3_interp"
  7003. type: "Interp"
  7004. bottom: "conv5_3_pool3_conv"
  7005. top: "conv5_3_pool3_interp"
  7006. interp_param {
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  7008. width: 90
  7009. }
  7010. }
  7011. layer {
  7012. name: "conv5_3_pool6"
  7013. type: "Pooling"
  7014. bottom: "conv5_3"
  7015. top: "conv5_3_pool6"
  7016. pooling_param {
  7017. pool: AVE
  7018. kernel_size: 15
  7019. stride: 15
  7020. }
  7021. }
  7022. layer {
  7023. name: "conv5_3_pool6_conv"
  7024. type: "Convolution"
  7025. bottom: "conv5_3_pool6"
  7026. top: "conv5_3_pool6_conv"
  7027. param {
  7028. lr_mult: 10
  7029. decay_mult: 1
  7030. }
  7031. convolution_param {
  7032. num_output: 512
  7033. kernel_size: 1
  7034. stride: 1
  7035. weight_filler {
  7036. type: "msra"
  7037. }
  7038. bias_term: false
  7039. }
  7040. }
  7041. layer {
  7042. name: "conv5_3_pool6_conv/bn"
  7043. type: "BN"
  7044. bottom: "conv5_3_pool6_conv"
  7045. top: "conv5_3_pool6_conv"
  7046. param {
  7047. lr_mult: 10
  7048. decay_mult: 0
  7049. }
  7050. param {
  7051. lr_mult: 10
  7052. decay_mult: 0
  7053. }
  7054. param {
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  7056. decay_mult: 0
  7057. }
  7058. param {
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  7060. decay_mult: 0
  7061. }
  7062. bn_param {
  7063. slope_filler {
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  7065. value: 1
  7066. }
  7067. bias_filler {
  7068. type: "constant"
  7069. value: 0
  7070. }
  7071. frozen: true
  7072. momentum: 0.95
  7073. }
  7074. }
  7075. layer {
  7076. name: "conv5_3_pool6_conv/relu"
  7077. type: "ReLU"
  7078. bottom: "conv5_3_pool6_conv"
  7079. top: "conv5_3_pool6_conv"
  7080. }
  7081. layer {
  7082. name: "conv5_3_pool6_interp"
  7083. type: "Interp"
  7084. bottom: "conv5_3_pool6_conv"
  7085. top: "conv5_3_pool6_interp"
  7086. interp_param {
  7087. height: 90
  7088. width: 90
  7089. }
  7090. }
  7091. layer {
  7092. name: "conv5_3_concat"
  7093. type: "Concat"
  7094. bottom: "conv5_3"
  7095. bottom: "conv5_3_pool6_interp"
  7096. bottom: "conv5_3_pool3_interp"
  7097. bottom: "conv5_3_pool2_interp"
  7098. bottom: "conv5_3_pool1_interp"
  7099. top: "conv5_3_concat"
  7100. }
  7101. layer {
  7102. name: "conv5_4"
  7103. type: "Convolution"
  7104. bottom: "conv5_3_concat"
  7105. top: "conv5_4"
  7106. param {
  7107. lr_mult: 10
  7108. decay_mult: 1
  7109. }
  7110. convolution_param {
  7111. num_output: 512
  7112. kernel_size: 3
  7113. stride: 1
  7114. pad: 1
  7115. weight_filler {
  7116. type: "msra"
  7117. }
  7118. bias_term: false
  7119. }
  7120. }
  7121. layer {
  7122. name: "conv5_4/bn"
  7123. type: "BN"
  7124. bottom: "conv5_4"
  7125. top: "conv5_4"
  7126. param {
  7127. lr_mult: 10
  7128. decay_mult: 0
  7129. }
  7130. param {
  7131. lr_mult: 10
  7132. decay_mult: 0
  7133. }
  7134. param {
  7135. lr_mult: 0
  7136. decay_mult: 0
  7137. }
  7138. param {
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  7140. decay_mult: 0
  7141. }
  7142. bn_param {
  7143. slope_filler {
  7144. type: "constant"
  7145. value: 1
  7146. }
  7147. bias_filler {
  7148. type: "constant"
  7149. value: 0
  7150. }
  7151. frozen: true
  7152. momentum: 0.95
  7153. }
  7154. }
  7155. layer {
  7156. name: "conv5_4/relu"
  7157. type: "ReLU"
  7158. bottom: "conv5_4"
  7159. top: "conv5_4"
  7160. }
  7161. layer {
  7162. name: "conv5_4/dropout"
  7163. type: "Dropout"
  7164. bottom: "conv5_4"
  7165. top: "conv5_4"
  7166. dropout_param {
  7167. dropout_ratio: 0.1
  7168. }
  7169. }
  7170. layer {
  7171. name: "conv6"
  7172. type: "Convolution"
  7173. bottom: "conv5_4"
  7174. top: "conv6"
  7175. param {
  7176. lr_mult: 10
  7177. decay_mult: 1
  7178. }
  7179. param {
  7180. lr_mult: 20
  7181. decay_mult: 1
  7182. }
  7183. convolution_param {
  7184. num_output: 19
  7185. kernel_size: 1
  7186. stride: 1
  7187. weight_filler {
  7188. type: "msra"
  7189. }
  7190. }
  7191. }
  7192. layer {
  7193. name: "conv6_interp"
  7194. type: "Interp"
  7195. bottom: "conv6"
  7196. top: "conv6_interp"
  7197. interp_param {
  7198. zoom_factor: 8
  7199. }
  7200. }
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