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  1. layer {
  2. name: "data"
  3. type: "Python"
  4. top: "data"
  5. top: "label"
  6. python_param {
  7. module: "layers"
  8. layer: "InputLayer"
  9. param_str: "{\'seed\': 1337, \'split\': \'train\', \'work_dir\': \'../work\'}"
  10. }
  11. }
  12. layer {
  13. name: "conv1_1_1"
  14. type: "Convolution"
  15. bottom: "data"
  16. top: "conv1_1_1"
  17. param {
  18. lr_mult: 1
  19. decay_mult: 1
  20. }
  21. param {
  22. lr_mult: 2
  23. decay_mult: 0
  24. }
  25. convolution_param {
  26. num_output: 32
  27. pad: 100
  28. kernel_size: 3
  29. stride: 1
  30. weight_filler {
  31. type: "xavier"
  32. }
  33. bias_filler {
  34. type: "constant"
  35. }
  36. }
  37. }
  38. layer {
  39. name: "relu1_1_1"
  40. type: "ReLU"
  41. bottom: "conv1_1_1"
  42. top: "conv1_1_1"
  43. }
  44. layer {
  45. name: "conv1_2_1"
  46. type: "Convolution"
  47. bottom: "conv1_1_1"
  48. top: "conv1_2_1"
  49. param {
  50. lr_mult: 1
  51. decay_mult: 1
  52. }
  53. param {
  54. lr_mult: 2
  55. decay_mult: 0
  56. }
  57. convolution_param {
  58. num_output: 32
  59. pad: 1
  60. kernel_size: 3
  61. stride: 1
  62. weight_filler {
  63. type: "xavier"
  64. }
  65. bias_filler {
  66. type: "constant"
  67. }
  68. }
  69. }
  70. layer {
  71. name: "relu1_2_1"
  72. type: "ReLU"
  73. bottom: "conv1_2_1"
  74. top: "conv1_2_1"
  75. }
  76. layer {
  77. name: "pool1"
  78. type: "Pooling"
  79. bottom: "conv1_2_1"
  80. top: "pool1"
  81. pooling_param {
  82. pool: MAX
  83. kernel_size: 2
  84. stride: 2
  85. }
  86. }
  87. layer {
  88. name: "conv2_1_1"
  89. type: "Convolution"
  90. bottom: "pool1"
  91. top: "conv2_1_1"
  92. param {
  93. lr_mult: 1
  94. decay_mult: 1
  95. }
  96. param {
  97. lr_mult: 2
  98. decay_mult: 0
  99. }
  100. convolution_param {
  101. num_output: 64
  102. pad: 1
  103. kernel_size: 3
  104. stride: 1
  105. weight_filler {
  106. type: "xavier"
  107. }
  108. bias_filler {
  109. type: "constant"
  110. }
  111. }
  112. }
  113. layer {
  114. name: "relu2_1_1"
  115. type: "ReLU"
  116. bottom: "conv2_1_1"
  117. top: "conv2_1_1"
  118. }
  119. layer {
  120. name: "conv2_2_1"
  121. type: "Convolution"
  122. bottom: "conv2_1_1"
  123. top: "conv2_2_1"
  124. param {
  125. lr_mult: 1
  126. decay_mult: 1
  127. }
  128. param {
  129. lr_mult: 2
  130. decay_mult: 0
  131. }
  132. convolution_param {
  133. num_output: 64
  134. pad: 1
  135. kernel_size: 3
  136. stride: 1
  137. weight_filler {
  138. type: "xavier"
  139. }
  140. bias_filler {
  141. type: "constant"
  142. }
  143. }
  144. }
  145. layer {
  146. name: "relu2_2_1"
  147. type: "ReLU"
  148. bottom: "conv2_2_1"
  149. top: "conv2_2_1"
  150. }
  151. layer {
  152. name: "pool2"
  153. type: "Pooling"
  154. bottom: "conv2_2_1"
  155. top: "pool2"
  156. pooling_param {
  157. pool: MAX
  158. kernel_size: 2
  159. stride: 2
  160. }
  161. }
  162. layer {
  163. name: "conv3_1_1"
  164. type: "Convolution"
  165. bottom: "pool2"
  166. top: "conv3_1_1"
  167. param {
  168. lr_mult: 1
  169. decay_mult: 1
  170. }
  171. param {
  172. lr_mult: 2
  173. decay_mult: 0
  174. }
  175. convolution_param {
  176. num_output: 128
  177. pad: 1
  178. kernel_size: 3
  179. stride: 1
  180. weight_filler {
  181. type: "xavier"
  182. }
  183. bias_filler {
  184. type: "constant"
  185. }
  186. }
  187. }
  188. layer {
  189. name: "relu3_1_1"
  190. type: "ReLU"
  191. bottom: "conv3_1_1"
  192. top: "conv3_1_1"
  193. }
  194. layer {
  195. name: "conv3_2_1"
  196. type: "Convolution"
  197. bottom: "conv3_1_1"
  198. top: "conv3_2_1"
  199. param {
  200. lr_mult: 1
  201. decay_mult: 1
  202. }
  203. param {
  204. lr_mult: 2
  205. decay_mult: 0
  206. }
  207. convolution_param {
  208. num_output: 128
  209. pad: 1
  210. kernel_size: 3
  211. stride: 1
  212. weight_filler {
  213. type: "xavier"
  214. }
  215. bias_filler {
  216. type: "constant"
  217. }
  218. }
  219. }
  220. layer {
  221. name: "relu3_2_1"
  222. type: "ReLU"
  223. bottom: "conv3_2_1"
  224. top: "conv3_2_1"
  225. }
  226. layer {
  227. name: "conv3_3_1"
  228. type: "Convolution"
  229. bottom: "conv3_2_1"
  230. top: "conv3_3_1"
  231. param {
  232. lr_mult: 1
  233. decay_mult: 1
  234. }
  235. param {
  236. lr_mult: 2
  237. decay_mult: 0
  238. }
  239. convolution_param {
  240. num_output: 256
  241. pad: 1
  242. kernel_size: 3
  243. stride: 1
  244. weight_filler {
  245. type: "xavier"
  246. }
  247. bias_filler {
  248. type: "constant"
  249. }
  250. }
  251. }
  252. layer {
  253. name: "relu3_3_1"
  254. type: "ReLU"
  255. bottom: "conv3_3_1"
  256. top: "conv3_3_1"
  257. }
  258. layer {
  259. name: "pool3"
  260. type: "Pooling"
  261. bottom: "conv3_3_1"
  262. top: "pool3"
  263. pooling_param {
  264. pool: MAX
  265. kernel_size: 2
  266. stride: 2
  267. }
  268. }
  269. layer {
  270. name: "conv4_1_1"
  271. type: "Convolution"
  272. bottom: "pool3"
  273. top: "conv4_1_1"
  274. param {
  275. lr_mult: 1
  276. decay_mult: 1
  277. }
  278. param {
  279. lr_mult: 2
  280. decay_mult: 0
  281. }
  282. convolution_param {
  283. num_output: 256
  284. pad: 1
  285. kernel_size: 3
  286. stride: 1
  287. weight_filler {
  288. type: "xavier"
  289. }
  290. bias_filler {
  291. type: "constant"
  292. }
  293. }
  294. }
  295. layer {
  296. name: "relu4_1_1"
  297. type: "ReLU"
  298. bottom: "conv4_1_1"
  299. top: "conv4_1_1"
  300. }
  301. layer {
  302. name: "conv4_2_1"
  303. type: "Convolution"
  304. bottom: "conv4_1_1"
  305. top: "conv4_2_1"
  306. param {
  307. lr_mult: 1
  308. decay_mult: 1
  309. }
  310. param {
  311. lr_mult: 2
  312. decay_mult: 0
  313. }
  314. convolution_param {
  315. num_output: 256
  316. pad: 1
  317. kernel_size: 3
  318. stride: 1
  319. weight_filler {
  320. type: "xavier"
  321. }
  322. bias_filler {
  323. type: "constant"
  324. }
  325. }
  326. }
  327. layer {
  328. name: "relu4_2_1"
  329. type: "ReLU"
  330. bottom: "conv4_2_1"
  331. top: "conv4_2_1"
  332. }
  333. layer {
  334. name: "conv4_3_1"
  335. type: "Convolution"
  336. bottom: "conv4_2_1"
  337. top: "conv4_3_1"
  338. param {
  339. lr_mult: 1
  340. decay_mult: 1
  341. }
  342. param {
  343. lr_mult: 2
  344. decay_mult: 0
  345. }
  346. convolution_param {
  347. num_output: 512
  348. pad: 1
  349. kernel_size: 3
  350. stride: 1
  351. weight_filler {
  352. type: "xavier"
  353. }
  354. bias_filler {
  355. type: "constant"
  356. }
  357. }
  358. }
  359. layer {
  360. name: "relu4_3_1"
  361. type: "ReLU"
  362. bottom: "conv4_3_1"
  363. top: "conv4_3_1"
  364. }
  365. layer {
  366. name: "pool4"
  367. type: "Pooling"
  368. bottom: "conv4_3_1"
  369. top: "pool4"
  370. pooling_param {
  371. pool: MAX
  372. kernel_size: 2
  373. stride: 2
  374. }
  375. }
  376. layer {
  377. name: "conv5_1_1"
  378. type: "Convolution"
  379. bottom: "pool4"
  380. top: "conv5_1_1"
  381. param {
  382. lr_mult: 1
  383. decay_mult: 1
  384. }
  385. param {
  386. lr_mult: 2
  387. decay_mult: 0
  388. }
  389. convolution_param {
  390. num_output: 256
  391. pad: 1
  392. kernel_size: 3
  393. stride: 1
  394. weight_filler {
  395. type: "xavier"
  396. }
  397. bias_filler {
  398. type: "constant"
  399. }
  400. }
  401. }
  402. layer {
  403. name: "relu5_1_1"
  404. type: "ReLU"
  405. bottom: "conv5_1_1"
  406. top: "conv5_1_1"
  407. }
  408. layer {
  409. name: "conv5_2_1"
  410. type: "Convolution"
  411. bottom: "conv5_1_1"
  412. top: "conv5_2_1"
  413. param {
  414. lr_mult: 1
  415. decay_mult: 1
  416. }
  417. param {
  418. lr_mult: 2
  419. decay_mult: 0
  420. }
  421. convolution_param {
  422. num_output: 256
  423. pad: 1
  424. kernel_size: 3
  425. stride: 1
  426. weight_filler {
  427. type: "xavier"
  428. }
  429. bias_filler {
  430. type: "constant"
  431. }
  432. }
  433. }
  434. layer {
  435. name: "relu5_2_1"
  436. type: "ReLU"
  437. bottom: "conv5_2_1"
  438. top: "conv5_2_1"
  439. }
  440. layer {
  441. name: "conv5_3_1"
  442. type: "Convolution"
  443. bottom: "conv5_2_1"
  444. top: "conv5_3_1"
  445. param {
  446. lr_mult: 1
  447. decay_mult: 1
  448. }
  449. param {
  450. lr_mult: 2
  451. decay_mult: 0
  452. }
  453. convolution_param {
  454. num_output: 256
  455. pad: 1
  456. kernel_size: 3
  457. stride: 1
  458. weight_filler {
  459. type: "xavier"
  460. }
  461. bias_filler {
  462. type: "constant"
  463. }
  464. }
  465. }
  466. layer {
  467. name: "relu5_3_1"
  468. type: "ReLU"
  469. bottom: "conv5_3_1"
  470. top: "conv5_3_1"
  471. }
  472. layer {
  473. name: "pool5"
  474. type: "Pooling"
  475. bottom: "conv5_3_1"
  476. top: "pool5"
  477. pooling_param {
  478. pool: MAX
  479. kernel_size: 2
  480. stride: 2
  481. }
  482. }
  483. layer {
  484. name: "fc6_1"
  485. type: "Convolution"
  486. bottom: "pool5"
  487. top: "fc6_1"
  488. param {
  489. lr_mult: 1
  490. decay_mult: 1
  491. }
  492. param {
  493. lr_mult: 2
  494. decay_mult: 0
  495. }
  496. convolution_param {
  497. num_output: 4096
  498. pad: 0
  499. kernel_size: 7
  500. stride: 1
  501. weight_filler {
  502. type: "xavier"
  503. }
  504. bias_filler {
  505. type: "constant"
  506. }
  507. }
  508. }
  509. layer {
  510. name: "relu6_1"
  511. type: "ReLU"
  512. bottom: "fc6_1"
  513. top: "fc6_1"
  514. }
  515. layer {
  516. name: "drop6"
  517. type: "Dropout"
  518. bottom: "fc6_1"
  519. top: "fc6_1"
  520. dropout_param {
  521. dropout_ratio: 0.5
  522. }
  523. }
  524. layer {
  525. name: "fc7"
  526. type: "Convolution"
  527. bottom: "fc6_1"
  528. top: "fc7"
  529. param {
  530. lr_mult: 1
  531. decay_mult: 1
  532. }
  533. param {
  534. lr_mult: 2
  535. decay_mult: 0
  536. }
  537. convolution_param {
  538. num_output: 4096
  539. pad: 0
  540. kernel_size: 1
  541. stride: 1
  542. weight_filler {
  543. type: "xavier"
  544. }
  545. bias_filler {
  546. type: "constant"
  547. }
  548. }
  549. }
  550. layer {
  551. name: "relu7"
  552. type: "ReLU"
  553. bottom: "fc7"
  554. top: "fc7"
  555. }
  556. layer {
  557. name: "drop7"
  558. type: "Dropout"
  559. bottom: "fc7"
  560. top: "fc7"
  561. dropout_param {
  562. dropout_ratio: 0.5
  563. }
  564. }
  565. layer {
  566. name: "score_fr_1"
  567. type: "Convolution"
  568. bottom: "fc7"
  569. top: "score_fr_1"
  570. param {
  571. lr_mult: 1
  572. decay_mult: 1
  573. }
  574. param {
  575. lr_mult: 2
  576. decay_mult: 0
  577. }
  578. convolution_param {
  579. num_output: 6
  580. pad: 0
  581. kernel_size: 1
  582. weight_filler {
  583. type: "xavier"
  584. }
  585. }
  586. }
  587. layer {
  588. name: "upscore2_1"
  589. type: "Deconvolution"
  590. bottom: "score_fr_1"
  591. top: "upscore2_1"
  592. param {
  593. lr_mult: 0
  594. }
  595. convolution_param {
  596. num_output: 6
  597. bias_term: false
  598. kernel_size: 4
  599. stride: 2
  600. }
  601. }
  602. layer {
  603. name: "score_pool4_1"
  604. type: "Convolution"
  605. bottom: "pool4"
  606. top: "score_pool4_1"
  607. param {
  608. lr_mult: 1
  609. decay_mult: 1
  610. }
  611. param {
  612. lr_mult: 2
  613. decay_mult: 0
  614. }
  615. convolution_param {
  616. num_output: 6
  617. pad: 0
  618. kernel_size: 1
  619. weight_filler {
  620. type: "xavier"
  621. }
  622. }
  623. }
  624. layer {
  625. name: "score_pool4c"
  626. type: "Crop"
  627. bottom: "score_pool4_1"
  628. bottom: "upscore2_1"
  629. top: "score_pool4c"
  630. crop_param {
  631. axis: 2
  632. offset: 5
  633. }
  634. }
  635. layer {
  636. name: "fuse_pool4"
  637. type: "Eltwise"
  638. bottom: "upscore2_1"
  639. bottom: "score_pool4c"
  640. top: "fuse_pool4"
  641. eltwise_param {
  642. operation: SUM
  643. }
  644. }
  645. layer {
  646. name: "upscore_pool4_1"
  647. type: "Deconvolution"
  648. bottom: "fuse_pool4"
  649. top: "upscore_pool4_1"
  650. param {
  651. lr_mult: 0
  652. }
  653. convolution_param {
  654. num_output: 6
  655. bias_term: false
  656. kernel_size: 4
  657. stride: 2
  658. }
  659. }
  660. layer {
  661. name: "score_pool3_1"
  662. type: "Convolution"
  663. bottom: "pool3"
  664. top: "score_pool3_1"
  665. param {
  666. lr_mult: 1
  667. decay_mult: 1
  668. }
  669. param {
  670. lr_mult: 2
  671. decay_mult: 0
  672. }
  673. convolution_param {
  674. num_output: 6
  675. pad: 0
  676. kernel_size: 1
  677. weight_filler {
  678. type: "xavier"
  679. }
  680. }
  681. }
  682. layer {
  683. name: "score_pool3c_1"
  684. type: "Crop"
  685. bottom: "score_pool3_1"
  686. bottom: "upscore_pool4_1"
  687. top: "score_pool3c_1"
  688. crop_param {
  689. axis: 2
  690. offset: 9
  691. }
  692. }
  693. layer {
  694. name: "fuse_pool3"
  695. type: "Eltwise"
  696. bottom: "upscore_pool4_1"
  697. bottom: "score_pool3c_1"
  698. top: "fuse_pool3"
  699. eltwise_param {
  700. operation: SUM
  701. }
  702. }
  703. layer {
  704. name: "upscore8_1"
  705. type: "Deconvolution"
  706. bottom: "fuse_pool3"
  707. top: "upscore8_1"
  708. param {
  709. lr_mult: 0
  710. }
  711. convolution_param {
  712. num_output: 6
  713. bias_term: false
  714. kernel_size: 16
  715. stride: 8
  716. }
  717. }
  718. layer {
  719. name: "score"
  720. type: "Crop"
  721. bottom: "upscore8_1"
  722. bottom: "data"
  723. top: "score"
  724. crop_param {
  725. axis: 2
  726. offset: 31
  727. }
  728. }
  729. layer {
  730. name: "loss"
  731. type: "SoftmaxWithLoss"
  732. bottom: "score"
  733. bottom: "label"
  734. top: "loss"
  735. loss_param {
  736. ignore_label: 255
  737. normalize: false
  738. }
  739. }
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