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  1. input: "data"
  2. input_shape {
  3. dim: 1
  4. dim: 3
  5. dim: 852
  6. dim: 1640
  7. }
  8. layer {
  9. name: "conv1_1"
  10. type: "Convolution"
  11. bottom: "data"
  12. top: "conv1_1"
  13. param {
  14. lr_mult: 1
  15. decay_mult: 1
  16. }
  17. param {
  18. lr_mult: 2
  19. decay_mult: 0
  20. }
  21. convolution_param {
  22. num_output: 64
  23. kernel_size: 3
  24. }
  25. }
  26. layer {
  27. name: "relu1_1"
  28. type: "ReLU"
  29. bottom: "conv1_1"
  30. top: "conv1_1"
  31. }
  32. layer {
  33. name: "conv1_2"
  34. type: "Convolution"
  35. bottom: "conv1_1"
  36. top: "conv1_2"
  37. param {
  38. lr_mult: 1
  39. decay_mult: 1
  40. }
  41. param {
  42. lr_mult: 2
  43. decay_mult: 0
  44. }
  45. convolution_param {
  46. num_output: 64
  47. kernel_size: 3
  48. }
  49. }
  50. layer {
  51. name: "relu1_2"
  52. type: "ReLU"
  53. bottom: "conv1_2"
  54. top: "conv1_2"
  55. }
  56. layer {
  57. name: "pool1"
  58. type: "Pooling"
  59. bottom: "conv1_2"
  60. top: "pool1"
  61. pooling_param {
  62. pool: MAX
  63. kernel_size: 2
  64. stride: 2
  65. }
  66. }
  67. layer {
  68. name: "conv2_1"
  69. type: "Convolution"
  70. bottom: "pool1"
  71. top: "conv2_1"
  72. param {
  73. lr_mult: 1
  74. decay_mult: 1
  75. }
  76. param {
  77. lr_mult: 2
  78. decay_mult: 0
  79. }
  80. convolution_param {
  81. num_output: 128
  82. kernel_size: 3
  83. }
  84. }
  85. layer {
  86. name: "relu2_1"
  87. type: "ReLU"
  88. bottom: "conv2_1"
  89. top: "conv2_1"
  90. }
  91. layer {
  92. name: "conv2_2"
  93. type: "Convolution"
  94. bottom: "conv2_1"
  95. top: "conv2_2"
  96. param {
  97. lr_mult: 1
  98. decay_mult: 1
  99. }
  100. param {
  101. lr_mult: 2
  102. decay_mult: 0
  103. }
  104. convolution_param {
  105. num_output: 128
  106. kernel_size: 3
  107. }
  108. }
  109. layer {
  110. name: "relu2_2"
  111. type: "ReLU"
  112. bottom: "conv2_2"
  113. top: "conv2_2"
  114. }
  115. layer {
  116. name: "pool2"
  117. type: "Pooling"
  118. bottom: "conv2_2"
  119. top: "pool2"
  120. pooling_param {
  121. pool: MAX
  122. kernel_size: 2
  123. stride: 2
  124. }
  125. }
  126. layer {
  127. name: "conv3_1"
  128. type: "Convolution"
  129. bottom: "pool2"
  130. top: "conv3_1"
  131. param {
  132. lr_mult: 1
  133. decay_mult: 1
  134. }
  135. param {
  136. lr_mult: 2
  137. decay_mult: 0
  138. }
  139. convolution_param {
  140. num_output: 256
  141. kernel_size: 3
  142. }
  143. }
  144. layer {
  145. name: "relu3_1"
  146. type: "ReLU"
  147. bottom: "conv3_1"
  148. top: "conv3_1"
  149. }
  150. layer {
  151. name: "conv3_2"
  152. type: "Convolution"
  153. bottom: "conv3_1"
  154. top: "conv3_2"
  155. param {
  156. lr_mult: 1
  157. decay_mult: 1
  158. }
  159. param {
  160. lr_mult: 2
  161. decay_mult: 0
  162. }
  163. convolution_param {
  164. num_output: 256
  165. kernel_size: 3
  166. }
  167. }
  168. layer {
  169. name: "relu3_2"
  170. type: "ReLU"
  171. bottom: "conv3_2"
  172. top: "conv3_2"
  173. }
  174. layer {
  175. name: "conv3_3"
  176. type: "Convolution"
  177. bottom: "conv3_2"
  178. top: "conv3_3"
  179. param {
  180. lr_mult: 1
  181. decay_mult: 1
  182. }
  183. param {
  184. lr_mult: 2
  185. decay_mult: 0
  186. }
  187. convolution_param {
  188. num_output: 256
  189. kernel_size: 3
  190. }
  191. }
  192. layer {
  193. name: "relu3_3"
  194. type: "ReLU"
  195. bottom: "conv3_3"
  196. top: "conv3_3"
  197. }
  198. layer {
  199. name: "pool3"
  200. type: "Pooling"
  201. bottom: "conv3_3"
  202. top: "pool3"
  203. pooling_param {
  204. pool: MAX
  205. kernel_size: 2
  206. stride: 2
  207. }
  208. }
  209. layer {
  210. name: "conv4_1"
  211. type: "Convolution"
  212. bottom: "pool3"
  213. top: "conv4_1"
  214. param {
  215. lr_mult: 1
  216. decay_mult: 1
  217. }
  218. param {
  219. lr_mult: 2
  220. decay_mult: 0
  221. }
  222. convolution_param {
  223. num_output: 512
  224. kernel_size: 3
  225. }
  226. }
  227. layer {
  228. name: "relu4_1"
  229. type: "ReLU"
  230. bottom: "conv4_1"
  231. top: "conv4_1"
  232. }
  233. layer {
  234. name: "conv4_2"
  235. type: "Convolution"
  236. bottom: "conv4_1"
  237. top: "conv4_2"
  238. param {
  239. lr_mult: 1
  240. decay_mult: 1
  241. }
  242. param {
  243. lr_mult: 2
  244. decay_mult: 0
  245. }
  246. convolution_param {
  247. num_output: 512
  248. kernel_size: 3
  249. }
  250. }
  251. layer {
  252. name: "relu4_2"
  253. type: "ReLU"
  254. bottom: "conv4_2"
  255. top: "conv4_2"
  256. }
  257. layer {
  258. name: "conv4_3"
  259. type: "Convolution"
  260. bottom: "conv4_2"
  261. top: "conv4_3"
  262. param {
  263. lr_mult: 1
  264. decay_mult: 1
  265. }
  266. param {
  267. lr_mult: 2
  268. decay_mult: 0
  269. }
  270. convolution_param {
  271. num_output: 512
  272. kernel_size: 3
  273. }
  274. }
  275. layer {
  276. name: "relu4_3"
  277. type: "ReLU"
  278. bottom: "conv4_3"
  279. top: "conv4_3"
  280. }
  281. layer {
  282. name: "conv5_1"
  283. type: "Convolution"
  284. bottom: "conv4_3"
  285. top: "conv5_1"
  286. param {
  287. lr_mult: 1
  288. decay_mult: 1
  289. }
  290. param {
  291. lr_mult: 2
  292. decay_mult: 0
  293. }
  294. convolution_param {
  295. num_output: 512
  296. kernel_size: 3
  297. dilation: 2
  298. }
  299. }
  300. layer {
  301. name: "relu5_1"
  302. type: "ReLU"
  303. bottom: "conv5_1"
  304. top: "conv5_1"
  305. }
  306. layer {
  307. name: "conv5_2"
  308. type: "Convolution"
  309. bottom: "conv5_1"
  310. top: "conv5_2"
  311. param {
  312. lr_mult: 1
  313. decay_mult: 1
  314. }
  315. param {
  316. lr_mult: 2
  317. decay_mult: 0
  318. }
  319. convolution_param {
  320. num_output: 512
  321. kernel_size: 3
  322. dilation: 2
  323. }
  324. }
  325. layer {
  326. name: "relu5_2"
  327. type: "ReLU"
  328. bottom: "conv5_2"
  329. top: "conv5_2"
  330. }
  331. layer {
  332. name: "conv5_3"
  333. type: "Convolution"
  334. bottom: "conv5_2"
  335. top: "conv5_3"
  336. param {
  337. lr_mult: 1
  338. decay_mult: 1
  339. }
  340. param {
  341. lr_mult: 2
  342. decay_mult: 0
  343. }
  344. convolution_param {
  345. num_output: 512
  346. kernel_size: 3
  347. dilation: 2
  348. }
  349. }
  350. layer {
  351. name: "relu5_3"
  352. type: "ReLU"
  353. bottom: "conv5_3"
  354. top: "conv5_3"
  355. }
  356. layer {
  357. name: "fc6"
  358. type: "Convolution"
  359. bottom: "conv5_3"
  360. top: "fc6"
  361. param {
  362. lr_mult: 1
  363. decay_mult: 1
  364. }
  365. param {
  366. lr_mult: 2
  367. decay_mult: 0
  368. }
  369. convolution_param {
  370. num_output: 4096
  371. kernel_size: 7
  372. dilation: 4
  373. }
  374. }
  375. layer {
  376. name: "relu6"
  377. type: "ReLU"
  378. bottom: "fc6"
  379. top: "fc6"
  380. }
  381. layer {
  382. name: "drop6"
  383. type: "Dropout"
  384. bottom: "fc6"
  385. top: "fc6"
  386. dropout_param {
  387. dropout_ratio: 0.5
  388. }
  389. }
  390. layer {
  391. name: "fc7"
  392. type: "Convolution"
  393. bottom: "fc6"
  394. top: "fc7"
  395. param {
  396. lr_mult: 1
  397. decay_mult: 1
  398. }
  399. param {
  400. lr_mult: 2
  401. decay_mult: 0
  402. }
  403. convolution_param {
  404. num_output: 4096
  405. kernel_size: 1
  406. }
  407. }
  408. layer {
  409. name: "relu7"
  410. type: "ReLU"
  411. bottom: "fc7"
  412. top: "fc7"
  413. }
  414. layer {
  415. name: "drop7"
  416. type: "Dropout"
  417. bottom: "fc7"
  418. top: "fc7"
  419. dropout_param {
  420. dropout_ratio: 0.5
  421. }
  422. }
  423. layer {
  424. name: "final"
  425. type: "Convolution"
  426. bottom: "fc7"
  427. top: "final"
  428. param {
  429. lr_mult: 1
  430. decay_mult: 1
  431. }
  432. param {
  433. lr_mult: 2
  434. decay_mult: 0
  435. }
  436. convolution_param {
  437. num_output: 11
  438. kernel_size: 1
  439. }
  440. }
  441. layer {
  442. name: "ctx_conv1_1"
  443. type: "Convolution"
  444. bottom: "final"
  445. top: "ctx_conv1_1"
  446. param {
  447. lr_mult: 1
  448. decay_mult: 1
  449. }
  450. param {
  451. lr_mult: 2
  452. decay_mult: 0
  453. }
  454. convolution_param {
  455. num_output: 11
  456. pad: 1
  457. kernel_size: 3
  458. }
  459. }
  460. layer {
  461. name: "ctx_relu1_1"
  462. type: "ReLU"
  463. bottom: "ctx_conv1_1"
  464. top: "ctx_conv1_1"
  465. }
  466. layer {
  467. name: "ctx_conv1_2"
  468. type: "Convolution"
  469. bottom: "ctx_conv1_1"
  470. top: "ctx_conv1_2"
  471. param {
  472. lr_mult: 1
  473. decay_mult: 1
  474. }
  475. param {
  476. lr_mult: 2
  477. decay_mult: 0
  478. }
  479. convolution_param {
  480. num_output: 11
  481. pad: 1
  482. kernel_size: 3
  483. }
  484. }
  485. layer {
  486. name: "ctx_relu1_2"
  487. type: "ReLU"
  488. bottom: "ctx_conv1_2"
  489. top: "ctx_conv1_2"
  490. }
  491. layer {
  492. name: "ctx_conv2_1"
  493. type: "Convolution"
  494. bottom: "ctx_conv1_2"
  495. top: "ctx_conv2_1"
  496. param {
  497. lr_mult: 1
  498. decay_mult: 1
  499. }
  500. param {
  501. lr_mult: 2
  502. decay_mult: 0
  503. }
  504. convolution_param {
  505. num_output: 11
  506. pad: 2
  507. kernel_size: 3
  508. dilation: 2
  509. }
  510. }
  511. layer {
  512. name: "ctx_relu2_1"
  513. type: "ReLU"
  514. bottom: "ctx_conv2_1"
  515. top: "ctx_conv2_1"
  516. }
  517. layer {
  518. name: "ctx_conv3_1"
  519. type: "Convolution"
  520. bottom: "ctx_conv2_1"
  521. top: "ctx_conv3_1"
  522. param {
  523. lr_mult: 1
  524. decay_mult: 1
  525. }
  526. param {
  527. lr_mult: 2
  528. decay_mult: 0
  529. }
  530. convolution_param {
  531. num_output: 11
  532. pad: 4
  533. kernel_size: 3
  534. dilation: 4
  535. }
  536. }
  537. layer {
  538. name: "ctx_relu3_1"
  539. type: "ReLU"
  540. bottom: "ctx_conv3_1"
  541. top: "ctx_conv3_1"
  542. }
  543. layer {
  544. name: "ctx_conv4_1"
  545. type: "Convolution"
  546. bottom: "ctx_conv3_1"
  547. top: "ctx_conv4_1"
  548. param {
  549. lr_mult: 1
  550. decay_mult: 1
  551. }
  552. param {
  553. lr_mult: 2
  554. decay_mult: 0
  555. }
  556. convolution_param {
  557. num_output: 11
  558. pad: 8
  559. kernel_size: 3
  560. dilation: 8
  561. }
  562. }
  563. layer {
  564. name: "ctx_relu4_1"
  565. type: "ReLU"
  566. bottom: "ctx_conv4_1"
  567. top: "ctx_conv4_1"
  568. }
  569. layer {
  570. name: "ctx_fc1"
  571. type: "Convolution"
  572. bottom: "ctx_conv4_1"
  573. top: "ctx_fc1"
  574. param {
  575. lr_mult: 1
  576. decay_mult: 1
  577. }
  578. param {
  579. lr_mult: 2
  580. decay_mult: 0
  581. }
  582. convolution_param {
  583. num_output: 11
  584. pad: 1
  585. kernel_size: 3
  586. }
  587. }
  588. layer {
  589. name: "ctx_fc1_relu"
  590. type: "ReLU"
  591. bottom: "ctx_fc1"
  592. top: "ctx_fc1"
  593. }
  594. layer {
  595. name: "ctx_final"
  596. type: "Convolution"
  597. bottom: "ctx_fc1"
  598. top: "ctx_final"
  599. param {
  600. lr_mult: 1
  601. decay_mult: 1
  602. }
  603. param {
  604. lr_mult: 2
  605. decay_mult: 0
  606. }
  607. convolution_param {
  608. num_output: 11
  609. kernel_size: 1
  610. }
  611. }
  612. layer {
  613. name: "prob"
  614. type: "Softmax"
  615. bottom: "ctx_final"
  616. top: "prob"
  617. }
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