resnet_14l.prototxt 7.8 KB

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  1. name: "resnet_14l"
  2. layer {
  3. name: "input"
  4. type: "Input"
  5. top: "input"
  6. input_param { shape: { dim: 1 dim: 3 dim: 156 dim: 156 } }
  7. }
  8. layer {
  9. name: "Convolution1"
  10. type: "Convolution"
  11. bottom: "input"
  12. top: "Convolution1"
  13. convolution_param {
  14. num_output: 32
  15. bias_term: true
  16. pad: 0
  17. kernel_size: 3
  18. stride: 1
  19. weight_filler {
  20. type: "msra"
  21. }
  22. }
  23. }
  24. layer {
  25. name: "ReLU1"
  26. type: "ReLU"
  27. bottom: "Convolution1"
  28. top: "Convolution1"
  29. relu_param {
  30. negative_slope: 0.1
  31. }
  32. }
  33. layer {
  34. name: "Convolution2"
  35. type: "Convolution"
  36. bottom: "Convolution1"
  37. top: "Convolution2"
  38. convolution_param {
  39. num_output: 64
  40. bias_term: true
  41. pad: 0
  42. kernel_size: 3
  43. stride: 1
  44. weight_filler {
  45. type: "msra"
  46. }
  47. }
  48. }
  49. layer {
  50. name: "ReLU2"
  51. type: "ReLU"
  52. bottom: "Convolution2"
  53. top: "Convolution2"
  54. relu_param {
  55. negative_slope: 0.1
  56. }
  57. }
  58. layer {
  59. name: "Convolution3"
  60. type: "Convolution"
  61. bottom: "Convolution2"
  62. top: "Convolution3"
  63. convolution_param {
  64. num_output: 64
  65. bias_term: true
  66. pad: 0
  67. kernel_size: 3
  68. stride: 1
  69. weight_filler {
  70. type: "msra"
  71. }
  72. }
  73. }
  74. layer {
  75. name: "ReLU3"
  76. type: "ReLU"
  77. bottom: "Convolution3"
  78. top: "Convolution3"
  79. relu_param {
  80. negative_slope: 0.1
  81. }
  82. }
  83. layer {
  84. name: "Convolution4"
  85. type: "Convolution"
  86. bottom: "Convolution1"
  87. top: "Convolution4"
  88. convolution_param {
  89. num_output: 64
  90. bias_term: true
  91. pad: 0
  92. kernel_size: 1
  93. stride: 1
  94. weight_filler {
  95. type: "msra"
  96. }
  97. }
  98. }
  99. layer {
  100. name: "Crop1"
  101. type: "Crop"
  102. bottom: "Convolution4"
  103. bottom: "Convolution3"
  104. top: "Crop1"
  105. crop_param {
  106. axis: 2
  107. offset: 2
  108. offset: 2
  109. }
  110. }
  111. layer {
  112. name: "Eltwise1"
  113. type: "Eltwise"
  114. bottom: "Convolution3"
  115. bottom: "Crop1"
  116. top: "Eltwise1"
  117. eltwise_param {
  118. operation: SUM
  119. }
  120. }
  121. layer {
  122. name: "Convolution5"
  123. type: "Convolution"
  124. bottom: "Eltwise1"
  125. top: "Convolution5"
  126. convolution_param {
  127. num_output: 64
  128. bias_term: true
  129. pad: 0
  130. kernel_size: 3
  131. stride: 1
  132. weight_filler {
  133. type: "msra"
  134. }
  135. }
  136. }
  137. layer {
  138. name: "ReLU4"
  139. type: "ReLU"
  140. bottom: "Convolution5"
  141. top: "Convolution5"
  142. relu_param {
  143. negative_slope: 0.1
  144. }
  145. }
  146. layer {
  147. name: "Convolution6"
  148. type: "Convolution"
  149. bottom: "Convolution5"
  150. top: "Convolution6"
  151. convolution_param {
  152. num_output: 64
  153. bias_term: true
  154. pad: 0
  155. kernel_size: 3
  156. stride: 1
  157. weight_filler {
  158. type: "msra"
  159. }
  160. }
  161. }
  162. layer {
  163. name: "ReLU5"
  164. type: "ReLU"
  165. bottom: "Convolution6"
  166. top: "Convolution6"
  167. relu_param {
  168. negative_slope: 0.1
  169. }
  170. }
  171. layer {
  172. name: "Crop2"
  173. type: "Crop"
  174. bottom: "Eltwise1"
  175. bottom: "Convolution6"
  176. top: "Crop2"
  177. crop_param {
  178. axis: 2
  179. offset: 2
  180. offset: 2
  181. }
  182. }
  183. layer {
  184. name: "Eltwise2"
  185. type: "Eltwise"
  186. bottom: "Convolution6"
  187. bottom: "Crop2"
  188. top: "Eltwise2"
  189. eltwise_param {
  190. operation: SUM
  191. }
  192. }
  193. layer {
  194. name: "Convolution7"
  195. type: "Convolution"
  196. bottom: "Eltwise2"
  197. top: "Convolution7"
  198. convolution_param {
  199. num_output: 128
  200. bias_term: true
  201. pad: 0
  202. kernel_size: 3
  203. stride: 1
  204. weight_filler {
  205. type: "msra"
  206. }
  207. }
  208. }
  209. layer {
  210. name: "ReLU6"
  211. type: "ReLU"
  212. bottom: "Convolution7"
  213. top: "Convolution7"
  214. relu_param {
  215. negative_slope: 0.1
  216. }
  217. }
  218. layer {
  219. name: "Convolution8"
  220. type: "Convolution"
  221. bottom: "Convolution7"
  222. top: "Convolution8"
  223. convolution_param {
  224. num_output: 128
  225. bias_term: true
  226. pad: 0
  227. kernel_size: 3
  228. stride: 1
  229. weight_filler {
  230. type: "msra"
  231. }
  232. }
  233. }
  234. layer {
  235. name: "ReLU7"
  236. type: "ReLU"
  237. bottom: "Convolution8"
  238. top: "Convolution8"
  239. relu_param {
  240. negative_slope: 0.1
  241. }
  242. }
  243. layer {
  244. name: "Convolution9"
  245. type: "Convolution"
  246. bottom: "Eltwise2"
  247. top: "Convolution9"
  248. convolution_param {
  249. num_output: 128
  250. bias_term: true
  251. pad: 0
  252. kernel_size: 1
  253. stride: 1
  254. weight_filler {
  255. type: "msra"
  256. }
  257. }
  258. }
  259. layer {
  260. name: "Crop3"
  261. type: "Crop"
  262. bottom: "Convolution9"
  263. bottom: "Convolution8"
  264. top: "Crop3"
  265. crop_param {
  266. axis: 2
  267. offset: 2
  268. offset: 2
  269. }
  270. }
  271. layer {
  272. name: "Eltwise3"
  273. type: "Eltwise"
  274. bottom: "Convolution8"
  275. bottom: "Crop3"
  276. top: "Eltwise3"
  277. eltwise_param {
  278. operation: SUM
  279. }
  280. }
  281. layer {
  282. name: "Convolution10"
  283. type: "Convolution"
  284. bottom: "Eltwise3"
  285. top: "Convolution10"
  286. convolution_param {
  287. num_output: 128
  288. bias_term: true
  289. pad: 0
  290. kernel_size: 3
  291. stride: 1
  292. weight_filler {
  293. type: "msra"
  294. }
  295. }
  296. }
  297. layer {
  298. name: "ReLU8"
  299. type: "ReLU"
  300. bottom: "Convolution10"
  301. top: "Convolution10"
  302. relu_param {
  303. negative_slope: 0.1
  304. }
  305. }
  306. layer {
  307. name: "Convolution11"
  308. type: "Convolution"
  309. bottom: "Convolution10"
  310. top: "Convolution11"
  311. convolution_param {
  312. num_output: 128
  313. bias_term: true
  314. pad: 0
  315. kernel_size: 3
  316. stride: 1
  317. weight_filler {
  318. type: "msra"
  319. }
  320. }
  321. }
  322. layer {
  323. name: "ReLU9"
  324. type: "ReLU"
  325. bottom: "Convolution11"
  326. top: "Convolution11"
  327. relu_param {
  328. negative_slope: 0.1
  329. }
  330. }
  331. layer {
  332. name: "Crop4"
  333. type: "Crop"
  334. bottom: "Eltwise3"
  335. bottom: "Convolution11"
  336. top: "Crop4"
  337. crop_param {
  338. axis: 2
  339. offset: 2
  340. offset: 2
  341. }
  342. }
  343. layer {
  344. name: "Eltwise4"
  345. type: "Eltwise"
  346. bottom: "Convolution11"
  347. bottom: "Crop4"
  348. top: "Eltwise4"
  349. eltwise_param {
  350. operation: SUM
  351. }
  352. }
  353. layer {
  354. name: "Convolution12"
  355. type: "Convolution"
  356. bottom: "Eltwise4"
  357. top: "Convolution12"
  358. convolution_param {
  359. num_output: 256
  360. bias_term: true
  361. pad: 0
  362. kernel_size: 3
  363. stride: 1
  364. weight_filler {
  365. type: "msra"
  366. }
  367. }
  368. }
  369. layer {
  370. name: "ReLU10"
  371. type: "ReLU"
  372. bottom: "Convolution12"
  373. top: "Convolution12"
  374. relu_param {
  375. negative_slope: 0.1
  376. }
  377. }
  378. layer {
  379. name: "Convolution13"
  380. type: "Convolution"
  381. bottom: "Convolution12"
  382. top: "Convolution13"
  383. convolution_param {
  384. num_output: 256
  385. bias_term: true
  386. pad: 0
  387. kernel_size: 3
  388. stride: 1
  389. weight_filler {
  390. type: "msra"
  391. }
  392. }
  393. }
  394. layer {
  395. name: "ReLU11"
  396. type: "ReLU"
  397. bottom: "Convolution13"
  398. top: "Convolution13"
  399. relu_param {
  400. negative_slope: 0.1
  401. }
  402. }
  403. layer {
  404. name: "Convolution14"
  405. type: "Convolution"
  406. bottom: "Eltwise4"
  407. top: "Convolution14"
  408. convolution_param {
  409. num_output: 256
  410. bias_term: true
  411. pad: 0
  412. kernel_size: 1
  413. stride: 1
  414. weight_filler {
  415. type: "msra"
  416. }
  417. }
  418. }
  419. layer {
  420. name: "Crop5"
  421. type: "Crop"
  422. bottom: "Convolution14"
  423. bottom: "Convolution13"
  424. top: "Crop5"
  425. crop_param {
  426. axis: 2
  427. offset: 2
  428. offset: 2
  429. }
  430. }
  431. layer {
  432. name: "Eltwise5"
  433. type: "Eltwise"
  434. bottom: "Convolution13"
  435. bottom: "Crop5"
  436. top: "Eltwise5"
  437. eltwise_param {
  438. operation: SUM
  439. }
  440. }
  441. layer {
  442. name: "Convolution15"
  443. type: "Convolution"
  444. bottom: "Eltwise5"
  445. top: "Convolution15"
  446. convolution_param {
  447. num_output: 256
  448. bias_term: true
  449. pad: 0
  450. kernel_size: 3
  451. stride: 1
  452. weight_filler {
  453. type: "msra"
  454. }
  455. }
  456. }
  457. layer {
  458. name: "ReLU12"
  459. type: "ReLU"
  460. bottom: "Convolution15"
  461. top: "Convolution15"
  462. relu_param {
  463. negative_slope: 0.1
  464. }
  465. }
  466. layer {
  467. name: "Convolution16"
  468. type: "Convolution"
  469. bottom: "Convolution15"
  470. top: "Convolution16"
  471. convolution_param {
  472. num_output: 256
  473. bias_term: true
  474. pad: 0
  475. kernel_size: 3
  476. stride: 1
  477. weight_filler {
  478. type: "msra"
  479. }
  480. }
  481. }
  482. layer {
  483. name: "ReLU13"
  484. type: "ReLU"
  485. bottom: "Convolution16"
  486. top: "Convolution16"
  487. relu_param {
  488. negative_slope: 0.1
  489. }
  490. }
  491. layer {
  492. name: "Crop6"
  493. type: "Crop"
  494. bottom: "Eltwise5"
  495. bottom: "Convolution16"
  496. top: "Crop6"
  497. crop_param {
  498. axis: 2
  499. offset: 2
  500. offset: 2
  501. }
  502. }
  503. layer {
  504. name: "Eltwise6"
  505. type: "Eltwise"
  506. bottom: "Convolution16"
  507. bottom: "Crop6"
  508. top: "Eltwise6"
  509. eltwise_param {
  510. operation: SUM
  511. }
  512. }
  513. layer {
  514. name: "Deconvolution1"
  515. type: "Deconvolution"
  516. bottom: "Eltwise6"
  517. top: "Deconvolution1"
  518. convolution_param {
  519. num_output: 3
  520. pad: 3
  521. kernel_size: 4
  522. stride: 2
  523. }
  524. }