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  1. I0314 16:51:19.210767 13053 caffe.cpp:187] Using GPUs 0
  2. I0314 16:51:19.211489 13053 caffe.cpp:192] GPU 0: GRID K520
  3. I0314 16:51:19.549685 13053 solver.cpp:48] Initializing solver from parameters:
  4. test_iter: 1
  5. test_interval: 10000000
  6. base_lr: 3.16e-05
  7. display: 1
  8. max_iter: 500000
  9. lr_policy: "step"
  10. gamma: 0.316
  11. momentum: 0.9
  12. weight_decay: 0.001
  13. stepsize: 215000
  14. snapshot: 1000
  15. snapshot_prefix: "./train/models/colornet"
  16. solver_mode: GPU
  17. device_id: 0
  18. net: "./models/colorization_train_val_v2.prototxt"
  19. test_initialization: false
  20. average_loss: 1000
  21. momentum2: 0.99
  22. type: "Adam"
  23. I0314 16:51:19.549935 13053 solver.cpp:91] Creating training net from net file: ./models/colorization_train_val_v2.prototxt
  24. I0314 16:51:19.550474 13053 net.cpp:313] The NetState phase (0) differed from the phase (1) specified by a rule in layer data
  25. I0314 16:51:19.550799 13053 net.cpp:49] Initializing net from parameters:
  26. name: "LtoAB"
  27. state {
  28. phase: TRAIN
  29. }
  30. layer {
  31. name: "data"
  32. type: "Data"
  33. top: "data"
  34. include {
  35. phase: TRAIN
  36. }
  37. transform_param {
  38. mirror: true
  39. crop_size: 30
  40. }
  41. data_param {
  42. source: "/home/ubuntu/work/data/manga_lmdb"
  43. batch_size: 40
  44. backend: LMDB
  45. }
  46. }
  47. layer {
  48. name: "img_lab"
  49. type: "Python"
  50. bottom: "data"
  51. top: "img_lab"
  52. python_param {
  53. module: "caffe_traininglayers"
  54. layer: "BGR2LabLayer"
  55. }
  56. }
  57. layer {
  58. name: "img_slice"
  59. type: "Slice"
  60. bottom: "img_lab"
  61. top: "img_l"
  62. top: "data_ab"
  63. propagate_down: false
  64. slice_param {
  65. slice_point: 1
  66. axis: 1
  67. }
  68. }
  69. layer {
  70. name: "data_l_meansub"
  71. type: "Scale"
  72. bottom: "img_l"
  73. top: "data_l"
  74. param {
  75. lr_mult: 0
  76. decay_mult: 0
  77. }
  78. param {
  79. lr_mult: 0
  80. decay_mult: 0
  81. }
  82. propagate_down: false
  83. scale_param {
  84. filler {
  85. type: "constant"
  86. value: 1
  87. }
  88. bias_term: true
  89. bias_filler {
  90. type: "constant"
  91. value: -50
  92. }
  93. }
  94. }
  95. layer {
  96. name: "data_ab_ss"
  97. type: "Convolution"
  98. bottom: "data_ab"
  99. top: "data_ab_ss"
  100. param {
  101. lr_mult: 0
  102. decay_mult: 0
  103. }
  104. param {
  105. lr_mult: 0
  106. decay_mult: 0
  107. }
  108. convolution_param {
  109. num_output: 2
  110. kernel_size: 1
  111. group: 2
  112. stride: 4
  113. weight_filler {
  114. type: "constant"
  115. value: 1
  116. }
  117. }
  118. }
  119. layer {
  120. name: "ab_enc"
  121. type: "Python"
  122. bottom: "data_ab_ss"
  123. top: "gt_ab_313"
  124. python_param {
  125. module: "caffe_traininglayers"
  126. layer: "NNEncLayer"
  127. }
  128. }
  129. layer {
  130. name: "nongray_mask"
  131. type: "Python"
  132. bottom: "data_ab_ss"
  133. top: "nongray_mask"
  134. python_param {
  135. module: "caffe_traininglayers"
  136. layer: "NonGrayMaskLayer"
  137. }
  138. }
  139. layer {
  140. name: "prior_boost"
  141. type: "Python"
  142. bottom: "gt_ab_313"
  143. top: "prior_boost"
  144. python_param {
  145. module: "caffe_traininglayers"
  146. layer: "PriorBoostLayer"
  147. }
  148. }
  149. layer {
  150. name: "prior_boost_nongray"
  151. type: "Eltwise"
  152. bottom: "prior_boost"
  153. bottom: "nongray_mask"
  154. top: "prior_boost_nongray"
  155. eltwise_param {
  156. operation: PROD
  157. }
  158. }
  159. layer {
  160. name: "bw_conv1_1"
  161. type: "Convolution"
  162. bottom: "data_l"
  163. top: "conv1_1"
  164. convolution_param {
  165. num_output: 64
  166. pad: 1
  167. kernel_size: 3
  168. }
  169. }
  170. layer {
  171. name: "relu1_1"
  172. type: "ReLU"
  173. bottom: "conv1_1"
  174. top: "conv1_1"
  175. }
  176. layer {
  177. name: "conv1_2"
  178. type: "Convolution"
  179. bottom: "conv1_1"
  180. top: "conv1_2"
  181. convolution_param {
  182. num_output: 64
  183. pad: 1
  184. kernel_size: 3
  185. stride: 2
  186. }
  187. }
  188. layer {
  189. name: "relu1_2"
  190. type: "ReLU"
  191. bottom: "conv1_2"
  192. top: "conv1_2"
  193. }
  194. layer {
  195. name: "conv1_2norm"
  196. type: "BatchNorm"
  197. bottom: "conv1_2"
  198. top: "conv1_2norm"
  199. param {
  200. lr_mult: 0
  201. decay_mult: 0
  202. }
  203. param {
  204. lr_mult: 0
  205. decay_mult: 0
  206. }
  207. param {
  208. lr_mult: 0
  209. decay_mult: 0
  210. }
  211. batch_norm_param {
  212. }
  213. }
  214. layer {
  215. name: "conv2_1"
  216. type: "Convolution"
  217. bottom: "conv1_2norm"
  218. top: "conv2_1"
  219. convolution_param {
  220. num_output: 128
  221. pad: 1
  222. kernel_size: 3
  223. }
  224. }
  225. layer {
  226. name: "relu2_1"
  227. type: "ReLU"
  228. bottom: "conv2_1"
  229. top: "conv2_1"
  230. }
  231. layer {
  232. name: "conv2_2"
  233. type: "Convolution"
  234. bottom: "conv2_1"
  235. top: "conv2_2"
  236. convolution_param {
  237. num_output: 128
  238. pad: 1
  239. kernel_size: 3
  240. stride: 2
  241. }
  242. }
  243. layer {
  244. name: "relu2_2"
  245. type: "ReLU"
  246. bottom: "conv2_2"
  247. top: "conv2_2"
  248. }
  249. layer {
  250. name: "conv2_2norm"
  251. type: "BatchNorm"
  252. bottom: "conv2_2"
  253. top: "conv2_2norm"
  254. param {
  255. lr_mult: 0
  256. decay_mult: 0
  257. }
  258. param {
  259. lr_mult: 0
  260. decay_mult: 0
  261. }
  262. param {
  263. lr_mult: 0
  264. decay_mult: 0
  265. }
  266. batch_norm_param {
  267. }
  268. }
  269. layer {
  270. name: "conv3_1"
  271. type: "Convolution"
  272. bottom: "conv2_2norm"
  273. top: "conv3_1"
  274. convolution_param {
  275. num_output: 256
  276. pad: 1
  277. kernel_size: 3
  278. }
  279. }
  280. layer {
  281. name: "relu3_1"
  282. type: "ReLU"
  283. bottom: "conv3_1"
  284. top: "conv3_1"
  285. }
  286. layer {
  287. name: "conv3_2"
  288. type: "Convolution"
  289. bottom: "conv3_1"
  290. top: "conv3_2"
  291. convolution_param {
  292. num_output: 256
  293. pad: 1
  294. kernel_size: 3
  295. }
  296. }
  297. layer {
  298. name: "relu3_2"
  299. type: "ReLU"
  300. bottom: "conv3_2"
  301. top: "conv3_2"
  302. }
  303. layer {
  304. name: "conv3_3"
  305. type: "Convolution"
  306. bottom: "conv3_2"
  307. top: "conv3_3"
  308. convolution_param {
  309. num_output: 256
  310. pad: 1
  311. kernel_size: 3
  312. stride: 2
  313. }
  314. }
  315. layer {
  316. name: "relu3_3"
  317. type: "ReLU"
  318. bottom: "conv3_3"
  319. top: "conv3_3"
  320. }
  321. layer {
  322. name: "conv3_3norm"
  323. type: "BatchNorm"
  324. bottom: "conv3_3"
  325. top: "conv3_3norm"
  326. param {
  327. lr_mult: 0
  328. decay_mult: 0
  329. }
  330. param {
  331. lr_mult: 0
  332. decay_mult: 0
  333. }
  334. param {
  335. lr_mult: 0
  336. decay_mult: 0
  337. }
  338. batch_norm_param {
  339. }
  340. }
  341. layer {
  342. name: "conv4_1"
  343. type: "Convolution"
  344. bottom: "conv3_3norm"
  345. top: "conv4_1"
  346. convolution_param {
  347. num_output: 512
  348. pad: 1
  349. kernel_size: 3
  350. stride: 1
  351. dilation: 1
  352. }
  353. }
  354. layer {
  355. name: "relu4_1"
  356. type: "ReLU"
  357. bottom: "conv4_1"
  358. top: "conv4_1"
  359. }
  360. layer {
  361. name: "conv4_2"
  362. type: "Convolution"
  363. bottom: "conv4_1"
  364. top: "conv4_2"
  365. convolution_param {
  366. num_output: 512
  367. pad: 1
  368. kernel_size: 3
  369. stride: 1
  370. dilation: 1
  371. }
  372. }
  373. layer {
  374. name: "relu4_2"
  375. type: "ReLU"
  376. bottom: "conv4_2"
  377. top: "conv4_2"
  378. }
  379. layer {
  380. name: "conv4_3"
  381. type: "Convolution"
  382. bottom: "conv4_2"
  383. top: "conv4_3"
  384. convolution_param {
  385. num_output: 512
  386. pad: 1
  387. kernel_size: 3
  388. stride: 1
  389. dilation: 1
  390. }
  391. }
  392. layer {
  393. name: "relu4_3"
  394. type: "ReLU"
  395. bottom: "conv4_3"
  396. top: "conv4_3"
  397. }
  398. layer {
  399. name: "conv4_3norm"
  400. type: "BatchNorm"
  401. bottom: "conv4_3"
  402. top: "conv4_3norm"
  403. param {
  404. lr_mult: 0
  405. decay_mult: 0
  406. }
  407. param {
  408. lr_mult: 0
  409. decay_mult: 0
  410. }
  411. param {
  412. lr_mult: 0
  413. decay_mult: 0
  414. }
  415. batch_norm_param {
  416. }
  417. }
  418. layer {
  419. name: "conv5_1"
  420. type: "Convolution"
  421. bottom: "conv4_3norm"
  422. top: "conv5_1"
  423. convolution_param {
  424. num_output: 512
  425. pad: 2
  426. kernel_size: 3
  427. stride: 1
  428. dilation: 2
  429. }
  430. }
  431. layer {
  432. name: "relu5_1"
  433. type: "ReLU"
  434. bottom: "conv5_1"
  435. top: "conv5_1"
  436. }
  437. layer {
  438. name: "conv5_2"
  439. type: "Convolution"
  440. bottom: "conv5_1"
  441. top: "conv5_2"
  442. convolution_param {
  443. num_output: 512
  444. pad: 2
  445. kernel_size: 3
  446. stride: 1
  447. dilation: 2
  448. }
  449. }
  450. layer {
  451. name: "relu5_2"
  452. type: "ReLU"
  453. bottom: "conv5_2"
  454. top: "conv5_2"
  455. }
  456. layer {
  457. name: "conv5_3"
  458. type: "Convolution"
  459. bottom: "conv5_2"
  460. top: "conv5_3"
  461. convolution_param {
  462. num_output: 512
  463. pad: 2
  464. kernel_size: 3
  465. stride: 1
  466. dilation: 2
  467. }
  468. }
  469. layer {
  470. name: "relu5_3"
  471. type: "ReLU"
  472. bottom: "conv5_3"
  473. top: "conv5_3"
  474. }
  475. layer {
  476. name: "conv5_3norm"
  477. type: "BatchNorm"
  478. bottom: "conv5_3"
  479. top: "conv5_3norm"
  480. param {
  481. lr_mult: 0
  482. decay_mult: 0
  483. }
  484. param {
  485. lr_mult: 0
  486. decay_mult: 0
  487. }
  488. param {
  489. lr_mult: 0
  490. decay_mult: 0
  491. }
  492. batch_norm_param {
  493. }
  494. }
  495. layer {
  496. name: "conv6_1"
  497. type: "Convolution"
  498. bottom: "conv5_3norm"
  499. top: "conv6_1"
  500. convolution_param {
  501. num_output: 512
  502. pad: 2
  503. kernel_size: 3
  504. dilation: 2
  505. }
  506. }
  507. layer {
  508. name: "relu6_1"
  509. type: "ReLU"
  510. bottom: "conv6_1"
  511. top: "conv6_1"
  512. }
  513. layer {
  514. name: "conv6_2"
  515. type: "Convolution"
  516. bottom: "conv6_1"
  517. top: "conv6_2"
  518. convolution_param {
  519. num_output: 512
  520. pad: 2
  521. kernel_size: 3
  522. dilation: 2
  523. }
  524. }
  525. layer {
  526. name: "relu6_2"
  527. type: "ReLU"
  528. bottom: "conv6_2"
  529. top: "conv6_2"
  530. }
  531. layer {
  532. name: "conv6_3"
  533. type: "Convolution"
  534. bottom: "conv6_2"
  535. top: "conv6_3"
  536. convolution_param {
  537. num_output: 512
  538. pad: 2
  539. kernel_size: 3
  540. dilation: 2
  541. }
  542. }
  543. layer {
  544. name: "relu6_3"
  545. type: "ReLU"
  546. bottom: "conv6_3"
  547. top: "conv6_3"
  548. }
  549. layer {
  550. name: "conv6_3norm"
  551. type: "BatchNorm"
  552. bottom: "conv6_3"
  553. top: "conv6_3norm"
  554. param {
  555. lr_mult: 0
  556. decay_mult: 0
  557. }
  558. param {
  559. lr_mult: 0
  560. decay_mult: 0
  561. }
  562. param {
  563. lr_mult: 0
  564. decay_mult: 0
  565. }
  566. batch_norm_param {
  567. }
  568. }
  569. layer {
  570. name: "conv7_1"
  571. type: "Convolution"
  572. bottom: "conv6_3norm"
  573. top: "conv7_1"
  574. convolution_param {
  575. num_output: 512
  576. pad: 1
  577. kernel_size: 3
  578. dilation: 1
  579. }
  580. }
  581. layer {
  582. name: "relu7_1"
  583. type: "ReLU"
  584. bottom: "conv7_1"
  585. top: "conv7_1"
  586. }
  587. layer {
  588. name: "conv7_2"
  589. type: "Convolution"
  590. bottom: "conv7_1"
  591. top: "conv7_2"
  592. convolution_param {
  593. num_output: 512
  594. pad: 1
  595. kernel_size: 3
  596. dilation: 1
  597. }
  598. }
  599. layer {
  600. name: "relu7_2"
  601. type: "ReLU"
  602. bottom: "conv7_2"
  603. top: "conv7_2"
  604. }
  605. layer {
  606. name: "conv7_3"
  607. type: "Convolution"
  608. bottom: "conv7_2"
  609. top: "conv7_3"
  610. convolution_param {
  611. num_output: 512
  612. pad: 1
  613. kernel_size: 3
  614. dilation: 1
  615. }
  616. }
  617. layer {
  618. name: "relu7_3"
  619. type: "ReLU"
  620. bottom: "conv7_3"
  621. top: "conv7_3"
  622. }
  623. layer {
  624. name: "conv7_3norm"
  625. type: "BatchNorm"
  626. bottom: "conv7_3"
  627. top: "conv7_3norm"
  628. param {
  629. lr_mult: 0
  630. decay_mult: 0
  631. }
  632. param {
  633. lr_mult: 0
  634. decay_mult: 0
  635. }
  636. param {
  637. lr_mult: 0
  638. decay_mult: 0
  639. }
  640. batch_norm_param {
  641. }
  642. }
  643. layer {
  644. name: "conv8_1"
  645. type: "Deconvolution"
  646. bottom: "conv7_3norm"
  647. top: "conv8_1"
  648. convolution_param {
  649. num_output: 256
  650. pad: 1
  651. kernel_size: 4
  652. stride: 2
  653. dilation: 1
  654. }
  655. }
  656. layer {
  657. name: "relu8_1"
  658. type: "ReLU"
  659. bottom: "conv8_1"
  660. top: "conv8_1"
  661. }
  662. layer {
  663. name: "conv8_2"
  664. type: "Convolution"
  665. bottom: "conv8_1"
  666. top: "conv8_2"
  667. convolution_param {
  668. num_output: 256
  669. pad: 1
  670. kernel_size: 3
  671. dilation: 1
  672. }
  673. }
  674. layer {
  675. name: "relu8_2"
  676. type: "ReLU"
  677. bottom: "conv8_2"
  678. top: "conv8_2"
  679. }
  680. layer {
  681. name: "conv8_3"
  682. type: "Convolution"
  683. bottom: "conv8_2"
  684. top: "conv8_3"
  685. convolution_param {
  686. num_output: 256
  687. pad: 1
  688. kernel_size: 3
  689. dilation: 1
  690. }
  691. }
  692. layer {
  693. name: "relu8_3"
  694. type: "ReLU"
  695. bottom: "conv8_3"
  696. top: "conv8_3"
  697. }
  698. layer {
  699. name: "conv8_313"
  700. type: "Convolution"
  701. bottom: "conv8_3"
  702. top: "conv8_313"
  703. convolution_param {
  704. num_output: 313
  705. kernel_size: 1
  706. stride: 1
  707. dilation: 1
  708. }
  709. }
  710. layer {
  711. name: "conv8_313_boost"
  712. type: "Python"
  713. bottom: "conv8_313"
  714. bottom: "prior_boost_nongray"
  715. top: "conv8_313_boost"
  716. python_param {
  717. module: "caffe_traininglayers"
  718. layer: "ClassRebalanceMultLayer"
  719. }
  720. }
  721. layer {
  722. name: "loss8_313"
  723. type: "SoftmaxCrossEntropyLoss"
  724. bottom: "conv8_313_boost"
  725. bottom: "gt_ab_313"
  726. top: "loss8_313"
  727. loss_weight: 1
  728. }
  729. I0314 16:51:19.551455 13053 layer_factory.hpp:77] Creating layer data
  730. I0314 16:51:19.551903 13053 net.cpp:91] Creating Layer data
  731. I0314 16:51:19.551926 13053 net.cpp:399] data -> data
  732. I0314 16:51:19.553302 13088 db_lmdb.cpp:35] Opened lmdb /home/ubuntu/work/data/manga_lmdb
  733. I0314 16:51:19.554257 13053 data_layer.cpp:41] output data size: 40,3,30,30
  734. I0314 16:51:19.556293 13053 net.cpp:141] Setting up data
  735. I0314 16:51:19.556324 13053 net.cpp:148] Top shape: 40 3 30 30 (108000)
  736. I0314 16:51:19.556342 13053 net.cpp:156] Memory required for data: 432000
  737. I0314 16:51:19.556361 13053 layer_factory.hpp:77] Creating layer img_lab
  738. I0314 16:51:19.558543 13089 blocking_queue.cpp:52] Waiting for data
  739. [libprotobuf FATAL google/protobuf/stubs/common.cc:61] This program requires version 3.2.0 of the Protocol Buffer runtime library, but the installed version is 2.6.1. Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "google/protobuf/descriptor.pb.cc".)
  740. terminate called after throwing an instance of 'google::protobuf::FatalException'
  741. what(): This program requires version 3.2.0 of the Protocol Buffer runtime library, but the installed version is 2.6.1. Please update your library. If you compiled the program yourself, make sure that your headers are from the same version of Protocol Buffers as your link-time library. (Version verification failed in "google/protobuf/descriptor.pb.cc".)
  742. *** Aborted at 1489510280 (unix time) try "date -d @1489510280" if you are using GNU date ***
  743. PC: @ 0x7f06e52e5428 gsignal
  744. *** SIGABRT (@0x3e8000032fd) received by PID 13053 (TID 0x7f06e7ff4740) from PID 13053; stack trace: ***
  745. @ 0x7f06e52e54b0 (unknown)
  746. @ 0x7f06e52e5428 gsignal
  747. @ 0x7f06e52e702a abort
  748. @ 0x7f06e60f884d __gnu_cxx::__verbose_terminate_handler()
  749. @ 0x7f06e60f66b6 (unknown)
  750. @ 0x7f06e60f6701 std::terminate()
  751. @ 0x7f06e60f6919 __cxa_throw
  752. @ 0x7f06de5d1647 google::protobuf::internal::LogMessage::Finish()
  753. @ 0x7f06de5d187d google::protobuf::internal::VerifyVersion()
  754. @ 0x7f06d5a1f0d4 google::protobuf::protobuf_google_2fprotobuf_2fdescriptor_2eproto::TableStruct::InitDefaultsImpl()
  755. @ 0x7f06de5d1f75 google::protobuf::GoogleOnceInitImpl()
  756. @ 0x7f06d5a19d85 google::protobuf::protobuf_google_2fprotobuf_2fdescriptor_2eproto::InitDefaults()
  757. @ 0x7f06d5a19db9 google::protobuf::protobuf_google_2fprotobuf_2fdescriptor_2eproto::AddDescriptorsImpl()
  758. @ 0x7f06de5d1f75 google::protobuf::GoogleOnceInitImpl()
  759. @ 0x7f06d5a19e35 google::protobuf::protobuf_google_2fprotobuf_2fdescriptor_2eproto::AddDescriptors()
  760. @ 0x7f06e7e0d4ea (unknown)
  761. @ 0x7f06e7e0d5fb (unknown)
  762. @ 0x7f06e7e12712 (unknown)
  763. @ 0x7f06e7e0d394 (unknown)
  764. @ 0x7f06e7e11bd9 (unknown)
  765. @ 0x7f06d750ef09 (unknown)
  766. @ 0x7f06e7e0d394 (unknown)
  767. @ 0x7f06d750f571 (unknown)
  768. @ 0x7f06d750efa1 dlopen
  769. @ 0x7f06e596c88d _PyImport_GetDynLoadFunc
  770. @ 0x7f06e59db4be _PyImport_LoadDynamicModule
  771. @ 0x7f06e59dc300 (unknown)
  772. @ 0x7f06e59dc5c8 (unknown)
  773. @ 0x7f06e59dd6db PyImport_ImportModuleLevel
  774. @ 0x7f06e5954698 (unknown)
  775. @ 0x7f06e59a01e3 PyObject_Call
  776. @ 0x7f06e5a76447 PyEval_CallObjectWithKeywords
  777. Aborted (core dumped)
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