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  1. name: "FCN"
  2. layer {
  3. name: "data"
  4. type: "Data"
  5. top: "data"
  6. include {
  7. phase: TRAIN
  8. }
  9. transform_param {
  10. mirror: false
  11. crop_size: 0
  12. mean_value: 77
  13. }
  14. data_param {
  15. source: "./train_images_lmdb"
  16. batch_size: 1
  17. backend: LMDB
  18. }
  19. }
  20. layer {
  21. name: "label"
  22. type: "Data"
  23. top: "label"
  24. include {
  25. phase: TRAIN
  26. }
  27. data_param {
  28. source: "./train_labels_lmdb"
  29. batch_size: 1
  30. backend: LMDB
  31. }
  32. }
  33. layer {
  34. name: "data"
  35. type: "Data"
  36. top: "data"
  37. include {
  38. phase: TEST
  39. }
  40. transform_param {
  41. mirror: false
  42. crop_size: 0
  43. mean_value: 77
  44. }
  45. data_param {
  46. source: "./val_images_lmdb"
  47. batch_size: 1
  48. backend: LMDB
  49. }
  50. }
  51. layer {
  52. name: "label"
  53. type: "Data"
  54. top: "label"
  55. include {
  56. phase: TEST
  57. }
  58. data_param {
  59. source: "./val_labels_lmdb"
  60. batch_size: 1
  61. backend: LMDB
  62. }
  63. }
  64. layer {
  65. name: "conv1_1"
  66. type: "Convolution"
  67. bottom: "data"
  68. top: "conv1_1"
  69. param {
  70. lr_mult: 1
  71. decay_mult: 1
  72. }
  73. param {
  74. lr_mult: 2
  75. decay_mult: 0
  76. }
  77. convolution_param {
  78. num_output: 64
  79. pad: 100
  80. kernel_size: 3
  81. engine: CAFFE
  82. }
  83. }
  84. layer {
  85. name: "relu1_1"
  86. type: "ReLU"
  87. bottom: "conv1_1"
  88. top: "conv1_1"
  89. }
  90. layer {
  91. name: "conv1_2"
  92. type: "Convolution"
  93. bottom: "conv1_1"
  94. top: "conv1_2"
  95. param {
  96. lr_mult: 1
  97. decay_mult: 1
  98. }
  99. param {
  100. lr_mult: 2
  101. decay_mult: 0
  102. }
  103. convolution_param {
  104. num_output: 64
  105. pad: 1
  106. kernel_size: 3
  107. engine: CAFFE
  108. }
  109. }
  110. layer {
  111. name: "relu1_2"
  112. type: "ReLU"
  113. bottom: "conv1_2"
  114. top: "conv1_2"
  115. }
  116. layer {
  117. name: "pool1"
  118. type: "Pooling"
  119. bottom: "conv1_2"
  120. top: "pool1"
  121. pooling_param {
  122. pool: MAX
  123. kernel_size: 2
  124. stride: 2
  125. }
  126. }
  127. layer {
  128. name: "conv2_1"
  129. type: "Convolution"
  130. bottom: "pool1"
  131. top: "conv2_1"
  132. param {
  133. lr_mult: 1
  134. decay_mult: 1
  135. }
  136. param {
  137. lr_mult: 2
  138. decay_mult: 0
  139. }
  140. convolution_param {
  141. num_output: 128
  142. pad: 1
  143. kernel_size: 3
  144. engine: CAFFE
  145. }
  146. }
  147. layer {
  148. name: "relu2_1"
  149. type: "ReLU"
  150. bottom: "conv2_1"
  151. top: "conv2_1"
  152. }
  153. layer {
  154. name: "conv2_2"
  155. type: "Convolution"
  156. bottom: "conv2_1"
  157. top: "conv2_2"
  158. param {
  159. lr_mult: 1
  160. decay_mult: 1
  161. }
  162. param {
  163. lr_mult: 2
  164. decay_mult: 0
  165. }
  166. convolution_param {
  167. num_output: 128
  168. pad: 1
  169. kernel_size: 3
  170. engine: CAFFE
  171. }
  172. }
  173. layer {
  174. name: "relu2_2"
  175. type: "ReLU"
  176. bottom: "conv2_2"
  177. top: "conv2_2"
  178. }
  179. layer {
  180. name: "pool2"
  181. type: "Pooling"
  182. bottom: "conv2_2"
  183. top: "pool2"
  184. pooling_param {
  185. pool: MAX
  186. kernel_size: 2
  187. stride: 2
  188. }
  189. }
  190. layer {
  191. name: "conv3_1"
  192. type: "Convolution"
  193. bottom: "pool2"
  194. top: "conv3_1"
  195. param {
  196. lr_mult: 1
  197. decay_mult: 1
  198. }
  199. param {
  200. lr_mult: 2
  201. decay_mult: 0
  202. }
  203. convolution_param {
  204. num_output: 256
  205. pad: 1
  206. kernel_size: 3
  207. engine: CAFFE
  208. }
  209. }
  210. layer {
  211. name: "relu3_1"
  212. type: "ReLU"
  213. bottom: "conv3_1"
  214. top: "conv3_1"
  215. }
  216. layer {
  217. name: "conv3_2"
  218. type: "Convolution"
  219. bottom: "conv3_1"
  220. top: "conv3_2"
  221. param {
  222. lr_mult: 1
  223. decay_mult: 1
  224. }
  225. param {
  226. lr_mult: 2
  227. decay_mult: 0
  228. }
  229. convolution_param {
  230. num_output: 256
  231. pad: 1
  232. kernel_size: 3
  233. engine: CAFFE
  234. }
  235. }
  236. layer {
  237. name: "relu3_2"
  238. type: "ReLU"
  239. bottom: "conv3_2"
  240. top: "conv3_2"
  241. }
  242. layer {
  243. name: "conv3_3"
  244. type: "Convolution"
  245. bottom: "conv3_2"
  246. top: "conv3_3"
  247. param {
  248. lr_mult: 1
  249. decay_mult: 1
  250. }
  251. param {
  252. lr_mult: 2
  253. decay_mult: 0
  254. }
  255. convolution_param {
  256. num_output: 256
  257. pad: 1
  258. kernel_size: 3
  259. engine: CAFFE
  260. }
  261. }
  262. layer {
  263. name: "relu3_3"
  264. type: "ReLU"
  265. bottom: "conv3_3"
  266. top: "conv3_3"
  267. }
  268. layer {
  269. name: "pool3"
  270. type: "Pooling"
  271. bottom: "conv3_3"
  272. top: "pool3"
  273. pooling_param {
  274. pool: MAX
  275. kernel_size: 2
  276. stride: 2
  277. }
  278. }
  279. layer {
  280. name: "conv4_1"
  281. type: "Convolution"
  282. bottom: "pool3"
  283. top: "conv4_1"
  284. param {
  285. lr_mult: 1
  286. decay_mult: 1
  287. }
  288. param {
  289. lr_mult: 2
  290. decay_mult: 0
  291. }
  292. convolution_param {
  293. num_output: 512
  294. pad: 1
  295. kernel_size: 3
  296. engine: CAFFE
  297. }
  298. }
  299. layer {
  300. name: "relu4_1"
  301. type: "ReLU"
  302. bottom: "conv4_1"
  303. top: "conv4_1"
  304. }
  305. layer {
  306. name: "conv4_2"
  307. type: "Convolution"
  308. bottom: "conv4_1"
  309. top: "conv4_2"
  310. param {
  311. lr_mult: 1
  312. decay_mult: 1
  313. }
  314. param {
  315. lr_mult: 2
  316. decay_mult: 0
  317. }
  318. convolution_param {
  319. num_output: 512
  320. pad: 1
  321. kernel_size: 3
  322. engine: CAFFE
  323. }
  324. }
  325. layer {
  326. name: "relu4_2"
  327. type: "ReLU"
  328. bottom: "conv4_2"
  329. top: "conv4_2"
  330. }
  331. layer {
  332. name: "conv4_3"
  333. type: "Convolution"
  334. bottom: "conv4_2"
  335. top: "conv4_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. pad: 1
  347. kernel_size: 3
  348. engine: CAFFE
  349. }
  350. }
  351. layer {
  352. name: "relu4_3"
  353. type: "ReLU"
  354. bottom: "conv4_3"
  355. top: "conv4_3"
  356. }
  357. layer {
  358. name: "pool4"
  359. type: "Pooling"
  360. bottom: "conv4_3"
  361. top: "pool4"
  362. pooling_param {
  363. pool: MAX
  364. kernel_size: 2
  365. stride: 2
  366. }
  367. }
  368. layer {
  369. name: "conv5_1"
  370. type: "Convolution"
  371. bottom: "pool4"
  372. top: "conv5_1"
  373. param {
  374. lr_mult: 1
  375. decay_mult: 1
  376. }
  377. param {
  378. lr_mult: 2
  379. decay_mult: 0
  380. }
  381. convolution_param {
  382. num_output: 512
  383. pad: 1
  384. kernel_size: 3
  385. engine: CAFFE
  386. }
  387. }
  388. layer {
  389. name: "relu5_1"
  390. type: "ReLU"
  391. bottom: "conv5_1"
  392. top: "conv5_1"
  393. }
  394. layer {
  395. name: "conv5_2"
  396. type: "Convolution"
  397. bottom: "conv5_1"
  398. top: "conv5_2"
  399. param {
  400. lr_mult: 1
  401. decay_mult: 1
  402. }
  403. param {
  404. lr_mult: 2
  405. decay_mult: 0
  406. }
  407. convolution_param {
  408. num_output: 512
  409. pad: 1
  410. kernel_size: 3
  411. engine: CAFFE
  412. }
  413. }
  414. layer {
  415. name: "relu5_2"
  416. type: "ReLU"
  417. bottom: "conv5_2"
  418. top: "conv5_2"
  419. }
  420. layer {
  421. name: "conv5_3"
  422. type: "Convolution"
  423. bottom: "conv5_2"
  424. top: "conv5_3"
  425. param {
  426. lr_mult: 1
  427. decay_mult: 1
  428. }
  429. param {
  430. lr_mult: 2
  431. decay_mult: 0
  432. }
  433. convolution_param {
  434. num_output: 512
  435. pad: 1
  436. kernel_size: 3
  437. engine: CAFFE
  438. }
  439. }
  440. layer {
  441. name: "relu5_3"
  442. type: "ReLU"
  443. bottom: "conv5_3"
  444. top: "conv5_3"
  445. }
  446. layer {
  447. name: "pool5"
  448. type: "Pooling"
  449. bottom: "conv5_3"
  450. top: "pool5"
  451. pooling_param {
  452. pool: MAX
  453. kernel_size: 2
  454. stride: 2
  455. }
  456. }
  457. layer {
  458. name: "fc6"
  459. type: "Convolution"
  460. bottom: "pool5"
  461. top: "fc6"
  462. param {
  463. lr_mult: 1
  464. decay_mult: 1
  465. }
  466. param {
  467. lr_mult: 2
  468. decay_mult: 0
  469. }
  470. convolution_param {
  471. num_output: 4096
  472. kernel_size: 7
  473. engine: CAFFE
  474. }
  475. }
  476. layer {
  477. name: "relu6"
  478. type: "ReLU"
  479. bottom: "fc6"
  480. top: "fc6"
  481. }
  482. layer {
  483. name: "drop6"
  484. type: "Dropout"
  485. bottom: "fc6"
  486. top: "fc6"
  487. dropout_param {
  488. dropout_ratio: 0.5
  489. }
  490. }
  491. layer {
  492. name: "fc7"
  493. type: "Convolution"
  494. bottom: "fc6"
  495. top: "fc7"
  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: 4096
  506. kernel_size: 1
  507. engine: CAFFE
  508. }
  509. }
  510. layer {
  511. name: "relu7"
  512. type: "ReLU"
  513. bottom: "fc7"
  514. top: "fc7"
  515. }
  516. layer {
  517. name: "drop7"
  518. type: "Dropout"
  519. bottom: "fc7"
  520. top: "fc7"
  521. dropout_param {
  522. dropout_ratio: 0.5
  523. }
  524. }
  525. layer {
  526. name: "score59"
  527. type: "Convolution"
  528. bottom: "fc7"
  529. top: "score59"
  530. param {
  531. lr_mult: 1
  532. decay_mult: 1
  533. }
  534. param {
  535. lr_mult: 2
  536. decay_mult: 0
  537. }
  538. convolution_param {
  539. num_output: 60
  540. kernel_size: 1
  541. engine: CAFFE
  542. }
  543. }
  544. layer {
  545. name: "upscore2"
  546. type: "Deconvolution"
  547. bottom: "score59"
  548. top: "upscore2"
  549. param {
  550. lr_mult: 1
  551. decay_mult: 1
  552. }
  553. convolution_param {
  554. num_output: 60
  555. bias_term: false
  556. kernel_size: 4
  557. stride: 2
  558. }
  559. }
  560. layer {
  561. name: "score-pool4"
  562. type: "Convolution"
  563. bottom: "pool4"
  564. top: "score-pool4"
  565. param {
  566. lr_mult: 1
  567. decay_mult: 1
  568. }
  569. param {
  570. lr_mult: 2
  571. decay_mult: 0
  572. }
  573. convolution_param {
  574. num_output: 60
  575. kernel_size: 1
  576. engine: CAFFE
  577. }
  578. }
  579. layer { type: 'Crop' name: 'crop' bottom: 'score-pool4' bottom: 'upscore2'
  580. top: 'score-pool4c' }
  581. layer {
  582. name: "fuse"
  583. type: "Eltwise"
  584. bottom: "upscore2"
  585. bottom: "score-pool4c"
  586. top: "score-fused"
  587. eltwise_param {
  588. operation: SUM
  589. }
  590. }
  591. layer {
  592. name: "upsample-fused-16"
  593. type: "Deconvolution"
  594. bottom: "score-fused"
  595. top: "score4"
  596. param {
  597. lr_mult: 1
  598. decay_mult: 1
  599. }
  600. convolution_param {
  601. num_output: 60
  602. bias_term: false
  603. kernel_size: 4
  604. stride: 2
  605. }
  606. }
  607. layer {
  608. name: "score-pool3"
  609. type: "Convolution"
  610. bottom: "pool3"
  611. top: "score-pool3"
  612. param {
  613. lr_mult: 1
  614. decay_mult: 1
  615. }
  616. param {
  617. lr_mult: 2
  618. decay_mult: 0
  619. }
  620. convolution_param {
  621. num_output: 60
  622. kernel_size: 1
  623. engine: CAFFE
  624. }
  625. }
  626. layer { type: 'Crop' name: 'crop' bottom: 'score-pool3' bottom: 'score4'
  627. top: 'score-pool3c' }
  628. layer {
  629. name: "fuse"
  630. type: "Eltwise"
  631. bottom: "score4"
  632. bottom: "score-pool3c"
  633. top: "score-final"
  634. eltwise_param {
  635. operation: SUM
  636. }
  637. }
  638. layer {
  639. name: "upsample"
  640. type: "Deconvolution"
  641. bottom: "score-final"
  642. top: "bigscore"
  643. param {
  644. lr_mult: 0
  645. }
  646. convolution_param {
  647. num_output: 60
  648. bias_term: false
  649. kernel_size: 16
  650. stride: 8
  651. }
  652. }
  653. layer { type: 'Crop' name: 'crop' bottom: 'bigscore' bottom: 'data' top: 'score' }
  654. layer {
  655. name: "loss"
  656. type: "SoftmaxWithLoss"
  657. bottom: "score"
  658. bottom: "label"
  659. top: "loss"
  660. loss_param {
  661. normalize: false
  662. }
  663. }
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