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  1. I0122 12:08:31.571379 27277 caffe.cpp:204] Using GPUs 2
  2. I0122 12:08:32.534587 27277 caffe.cpp:209] GPU 2: GeForce GTX 1080 Ti
  3. I0122 12:08:32.928421 27277 solver.cpp:45] Initializing solver from parameters:
  4. base_lr: 0.0005
  5. display: 20
  6. max_iter: 100
  7. lr_policy: "multistep"
  8. gamma: 0.5
  9. momentum: 0.9
  10. weight_decay: 0.0005
  11. snapshot: 50000
  12. snapshot_prefix: "/home/rguesdon/Networks/openpose_train/training_results/pose/model/pose"
  13. solver_mode: GPU
  14. device_id: 2
  15. net: "pose_training.prototxt"
  16. train_state {
  17. level: 0
  18. stage: ""
  19. }
  20. stepvalue: 200000
  21. stepvalue: 300000
  22. stepvalue: 360000
  23. stepvalue: 420000
  24. stepvalue: 480000
  25. stepvalue: 540000
  26. stepvalue: 600000
  27. stepvalue: 700000
  28. stepvalue: 800000
  29. stepvalue: 900000
  30. momentum2: 0.999
  31. type: "Adam"
  32. weights: "/home/rguesdon/Networks/openpose_train/dataset/vgg/VGG_ILSVRC_19_layers.caffemodel"
  33. I0122 12:08:32.928637 27277 solver.cpp:102] Creating training net from net file: pose_training.prototxt
  34. I0122 12:08:32.932651 27277 net.cpp:53] Initializing net from parameters:
  35. state {
  36. phase: TRAIN
  37. level: 0
  38. stage: ""
  39. }
  40. layer {
  41. name: "image"
  42. type: "OPData"
  43. top: "image"
  44. top: "label"
  45. data_param {
  46. batch_size: 1
  47. backend: LMDB
  48. }
  49. op_transform_param {
  50. stride: 8
  51. max_degree_rotations: "45"
  52. crop_size_x: 368
  53. crop_size_y: 368
  54. center_perterb_max: 40
  55. center_swap_prob: 0
  56. scale_prob: 1
  57. scale_mins: "0.333333333333"
  58. scale_maxs: "1.5"
  59. target_dist: 0.6
  60. number_max_occlusions: "2"
  61. sigmas: "7.0"
  62. models: "MPII_25B_16"
  63. sources: "/home/rguesdon/Networks/openpose_train/dataset/lmdb_twingo"
  64. probabilities: "0.98"
  65. source_background: "/home/rguesdon/Networks/openpose_train/dataset/lmdb_background"
  66. prob_only_background: 0.02
  67. normalization: 0
  68. add_distance: false
  69. }
  70. }
  71. layer {
  72. name: "vec_weight"
  73. type: "Slice"
  74. bottom: "label"
  75. top: "vec_weight"
  76. top: "heat_weight"
  77. top: "vec_temp"
  78. top: "heat_temp"
  79. slice_param {
  80. slice_point: 72
  81. slice_point: 97
  82. slice_point: 169
  83. axis: 1
  84. }
  85. }
  86. layer {
  87. name: "label_heat"
  88. type: "Eltwise"
  89. bottom: "heat_weight"
  90. bottom: "heat_temp"
  91. top: "label_heat"
  92. eltwise_param {
  93. operation: PROD
  94. }
  95. }
  96. layer {
  97. name: "label_vec"
  98. type: "Eltwise"
  99. bottom: "vec_weight"
  100. bottom: "vec_temp"
  101. top: "label_vec"
  102. eltwise_param {
  103. operation: PROD
  104. }
  105. }
  106. layer {
  107. name: "conv1_1"
  108. type: "Convolution"
  109. bottom: "image"
  110. top: "conv1_1"
  111. param {
  112. lr_mult: 1
  113. decay_mult: 1
  114. }
  115. param {
  116. lr_mult: 2
  117. decay_mult: 0
  118. }
  119. convolution_param {
  120. num_output: 64
  121. pad: 1
  122. kernel_size: 3
  123. weight_filler {
  124. type: "gaussian"
  125. std: 0.01
  126. }
  127. bias_filler {
  128. type: "constant"
  129. }
  130. }
  131. }
  132. layer {
  133. name: "relu1_1"
  134. type: "ReLU"
  135. bottom: "conv1_1"
  136. top: "conv1_1"
  137. }
  138. layer {
  139. name: "conv1_2"
  140. type: "Convolution"
  141. bottom: "conv1_1"
  142. top: "conv1_2"
  143. param {
  144. lr_mult: 1
  145. decay_mult: 1
  146. }
  147. param {
  148. lr_mult: 2
  149. decay_mult: 0
  150. }
  151. convolution_param {
  152. num_output: 64
  153. pad: 1
  154. kernel_size: 3
  155. weight_filler {
  156. type: "gaussian"
  157. std: 0.01
  158. }
  159. bias_filler {
  160. type: "constant"
  161. }
  162. }
  163. }
  164. layer {
  165. name: "relu1_2"
  166. type: "ReLU"
  167. bottom: "conv1_2"
  168. top: "conv1_2"
  169. }
  170. layer {
  171. name: "pool1_stage1"
  172. type: "Pooling"
  173. bottom: "conv1_2"
  174. top: "pool1_stage1"
  175. pooling_param {
  176. pool: MAX
  177. kernel_size: 2
  178. stride: 2
  179. }
  180. }
  181. layer {
  182. name: "conv2_1"
  183. type: "Convolution"
  184. bottom: "pool1_stage1"
  185. top: "conv2_1"
  186. param {
  187. lr_mult: 1
  188. decay_mult: 1
  189. }
  190. param {
  191. lr_mult: 2
  192. decay_mult: 0
  193. }
  194. convolution_param {
  195. num_output: 128
  196. pad: 1
  197. kernel_size: 3
  198. weight_filler {
  199. type: "gaussian"
  200. std: 0.01
  201. }
  202. bias_filler {
  203. type: "constant"
  204. }
  205. }
  206. }
  207. layer {
  208. name: "relu2_1"
  209. type: "ReLU"
  210. bottom: "conv2_1"
  211. top: "conv2_1"
  212. }
  213. layer {
  214. name: "conv2_2"
  215. type: "Convolution"
  216. bottom: "conv2_1"
  217. top: "conv2_2"
  218. param {
  219. lr_mult: 1
  220. decay_mult: 1
  221. }
  222. param {
  223. lr_mult: 2
  224. decay_mult: 0
  225. }
  226. convolution_param {
  227. num_output: 128
  228. pad: 1
  229. kernel_size: 3
  230. weight_filler {
  231. type: "gaussian"
  232. std: 0.01
  233. }
  234. bias_filler {
  235. type: "constant"
  236. }
  237. }
  238. }
  239. layer {
  240. name: "relu2_2"
  241. type: "ReLU"
  242. bottom: "conv2_2"
  243. top: "conv2_2"
  244. }
  245. layer {
  246. name: "pool2_stage1"
  247. type: "Pooling"
  248. bottom: "conv2_2"
  249. top: "pool2_stage1"
  250. pooling_param {
  251. pool: MAX
  252. kernel_size: 2
  253. stride: 2
  254. }
  255. }
  256. layer {
  257. name: "conv3_1"
  258. type: "Convolution"
  259. bottom: "pool2_stage1"
  260. top: "conv3_1"
  261. param {
  262. lr_mult: 1
  263. decay_mult: 1
  264. }
  265. param {
  266. lr_mult: 2
  267. decay_mult: 0
  268. }
  269. convolution_param {
  270. num_output: 256
  271. pad: 1
  272. kernel_size: 3
  273. weight_filler {
  274. type: "gaussian"
  275. std: 0.01
  276. }
  277. bias_filler {
  278. type: "constant"
  279. }
  280. }
  281. }
  282. layer {
  283. name: "relu3_1"
  284. type: "ReLU"
  285. bottom: "conv3_1"
  286. top: "conv3_1"
  287. }
  288. layer {
  289. name: "conv3_2"
  290. type: "Convolution"
  291. bottom: "conv3_1"
  292. top: "conv3_2"
  293. param {
  294. lr_mult: 1
  295. decay_mult: 1
  296. }
  297. param {
  298. lr_mult: 2
  299. decay_mult: 0
  300. }
  301. convolution_param {
  302. num_output: 256
  303. pad: 1
  304. kernel_size: 3
  305. weight_filler {
  306. type: "gaussian"
  307. std: 0.01
  308. }
  309. bias_filler {
  310. type: "constant"
  311. }
  312. }
  313. }
  314. layer {
  315. name: "relu3_2"
  316. type: "ReLU"
  317. bottom: "conv3_2"
  318. top: "conv3_2"
  319. }
  320. layer {
  321. name: "conv3_3"
  322. type: "Convolution"
  323. bottom: "conv3_2"
  324. top: "conv3_3"
  325. param {
  326. lr_mult: 1
  327. decay_mult: 1
  328. }
  329. param {
  330. lr_mult: 2
  331. decay_mult: 0
  332. }
  333. convolution_param {
  334. num_output: 256
  335. pad: 1
  336. kernel_size: 3
  337. weight_filler {
  338. type: "gaussian"
  339. std: 0.01
  340. }
  341. bias_filler {
  342. type: "constant"
  343. }
  344. }
  345. }
  346. layer {
  347. name: "relu3_3"
  348. type: "ReLU"
  349. bottom: "conv3_3"
  350. top: "conv3_3"
  351. }
  352. layer {
  353. name: "conv3_4"
  354. type: "Convolution"
  355. bottom: "conv3_3"
  356. top: "conv3_4"
  357. param {
  358. lr_mult: 1
  359. decay_mult: 1
  360. }
  361. param {
  362. lr_mult: 2
  363. decay_mult: 0
  364. }
  365. convolution_param {
  366. num_output: 256
  367. pad: 1
  368. kernel_size: 3
  369. weight_filler {
  370. type: "gaussian"
  371. std: 0.01
  372. }
  373. bias_filler {
  374. type: "constant"
  375. }
  376. }
  377. }
  378. layer {
  379. name: "relu3_4"
  380. type: "ReLU"
  381. bottom: "conv3_4"
  382. top: "conv3_4"
  383. }
  384. layer {
  385. name: "pool3_stage1"
  386. type: "Pooling"
  387. bottom: "conv3_4"
  388. top: "pool3_stage1"
  389. pooling_param {
  390. pool: MAX
  391. kernel_size: 2
  392. stride: 2
  393. }
  394. }
  395. layer {
  396. name: "conv4_1"
  397. type: "Convolution"
  398. bottom: "pool3_stage1"
  399. top: "conv4_1"
  400. param {
  401. lr_mult: 1
  402. decay_mult: 1
  403. }
  404. param {
  405. lr_mult: 2
  406. decay_mult: 0
  407. }
  408. convolution_param {
  409. num_output: 512
  410. pad: 1
  411. kernel_size: 3
  412. weight_filler {
  413. type: "gaussian"
  414. std: 0.01
  415. }
  416. bias_filler {
  417. type: "constant"
  418. }
  419. }
  420. }
  421. layer {
  422. name: "relu4_1"
  423. type: "ReLU"
  424. bottom: "conv4_1"
  425. top: "conv4_1"
  426. }
  427. layer {
  428. name: "conv4_2"
  429. type: "Convolution"
  430. bottom: "conv4_1"
  431. top: "conv4_2"
  432. param {
  433. lr_mult: 1
  434. decay_mult: 1
  435. }
  436. param {
  437. lr_mult: 2
  438. decay_mult: 0
  439. }
  440. convolution_param {
  441. num_output: 512
  442. pad: 1
  443. kernel_size: 3
  444. weight_filler {
  445. type: "gaussian"
  446. std: 0.01
  447. }
  448. bias_filler {
  449. type: "constant"
  450. }
  451. }
  452. }
  453. layer {
  454. name: "prelu4_2"
  455. type: "PReLU"
  456. bottom: "conv4_2"
  457. top: "conv4_2"
  458. param {
  459. lr_mult: 1
  460. decay_mult: 1
  461. }
  462. prelu_param {
  463. channel_shared: false
  464. }
  465. }
  466. layer {
  467. name: "conv4_3_CPM"
  468. type: "Convolution"
  469. bottom: "conv4_2"
  470. top: "conv4_3_CPM"
  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: 256
  481. pad: 1
  482. kernel_size: 3
  483. weight_filler {
  484. type: "xavier"
  485. }
  486. bias_filler {
  487. type: "constant"
  488. }
  489. }
  490. }
  491. layer {
  492. name: "prelu4_3_CPM"
  493. type: "PReLU"
  494. bottom: "conv4_3_CPM"
  495. top: "conv4_3_CPM"
  496. param {
  497. lr_mult: 1
  498. decay_mult: 1
  499. }
  500. prelu_param {
  501. channel_shared: false
  502. }
  503. }
  504. layer {
  505. name: "conv4_4_CPM"
  506. type: "Convolution"
  507. bottom: "conv4_3_CPM"
  508. top: "conv4_4_CPM"
  509. param {
  510. lr_mult: 1
  511. decay_mult: 1
  512. }
  513. param {
  514. lr_mult: 2
  515. decay_mult: 0
  516. }
  517. convolution_param {
  518. num_output: 128
  519. pad: 1
  520. kernel_size: 3
  521. weight_filler {
  522. type: "xavier"
  523. }
  524. bias_filler {
  525. type: "constant"
  526. }
  527. }
  528. }
  529. layer {
  530. name: "prelu4_4_CPM"
  531. type: "PReLU"
  532. bottom: "conv4_4_CPM"
  533. top: "conv4_4_CPM"
  534. param {
  535. lr_mult: 1
  536. decay_mult: 1
  537. }
  538. prelu_param {
  539. channel_shared: false
  540. }
  541. }
  542. layer {
  543. name: "Mconv1_stage0_L2_0"
  544. type: "Convolution"
  545. bottom: "conv4_4_CPM"
  546. top: "Mconv1_stage0_L2_0"
  547. param {
  548. lr_mult: 4
  549. decay_mult: 1
  550. }
  551. param {
  552. lr_mult: 8
  553. decay_mult: 0
  554. }
  555. convolution_param {
  556. num_output: 64
  557. pad: 1
  558. kernel_size: 3
  559. weight_filler {
  560. type: "xavier"
  561. }
  562. bias_filler {
  563. type: "constant"
  564. }
  565. }
  566. }
  567. layer {
  568. name: "Mprelu1_stage0_L2_0"
  569. type: "PReLU"
  570. bottom: "Mconv1_stage0_L2_0"
  571. top: "Mconv1_stage0_L2_0"
  572. param {
  573. lr_mult: 4
  574. decay_mult: 1
  575. }
  576. prelu_param {
  577. channel_shared: false
  578. }
  579. }
  580. layer {
  581. name: "Mconv1_stage0_L2_1"
  582. type: "Convolution"
  583. bottom: "Mconv1_stage0_L2_0"
  584. top: "Mconv1_stage0_L2_1"
  585. param {
  586. lr_mult: 4
  587. decay_mult: 1
  588. }
  589. param {
  590. lr_mult: 8
  591. decay_mult: 0
  592. }
  593. convolution_param {
  594. num_output: 64
  595. pad: 1
  596. kernel_size: 3
  597. weight_filler {
  598. type: "xavier"
  599. }
  600. bias_filler {
  601. type: "constant"
  602. }
  603. }
  604. }
  605. layer {
  606. name: "Mprelu1_stage0_L2_1"
  607. type: "PReLU"
  608. bottom: "Mconv1_stage0_L2_1"
  609. top: "Mconv1_stage0_L2_1"
  610. param {
  611. lr_mult: 4
  612. decay_mult: 1
  613. }
  614. prelu_param {
  615. channel_shared: false
  616. }
  617. }
  618. layer {
  619. name: "Mconv1_stage0_L2_2"
  620. type: "Convolution"
  621. bottom: "Mconv1_stage0_L2_1"
  622. top: "Mconv1_stage0_L2_2"
  623. param {
  624. lr_mult: 4
  625. decay_mult: 1
  626. }
  627. param {
  628. lr_mult: 8
  629. decay_mult: 0
  630. }
  631. convolution_param {
  632. num_output: 64
  633. pad: 1
  634. kernel_size: 3
  635. weight_filler {
  636. type: "xavier"
  637. }
  638. bias_filler {
  639. type: "constant"
  640. }
  641. }
  642. }
  643. layer {
  644. name: "Mprelu1_stage0_L2_2"
  645. type: "PReLU"
  646. bottom: "Mconv1_stage0_L2_2"
  647. top: "Mconv1_stage0_L2_2"
  648. param {
  649. lr_mult: 4
  650. decay_mult: 1
  651. }
  652. prelu_param {
  653. channel_shared: false
  654. }
  655. }
  656. layer {
  657. name: "Mconv1_stage0_L2_concat"
  658. type: "Concat"
  659. bottom: "Mconv1_stage0_L2_0"
  660. bottom: "Mconv1_stage0_L2_1"
  661. bottom: "Mconv1_stage0_L2_2"
  662. top: "Mconv1_stage0_L2_concat"
  663. concat_param {
  664. axis: 1
  665. }
  666. }
  667. layer {
  668. name: "Mconv2_stage0_L2_0"
  669. type: "Convolution"
  670. bottom: "Mconv1_stage0_L2_concat"
  671. top: "Mconv2_stage0_L2_0"
  672. param {
  673. lr_mult: 4
  674. decay_mult: 1
  675. }
  676. param {
  677. lr_mult: 8
  678. decay_mult: 0
  679. }
  680. convolution_param {
  681. num_output: 64
  682. pad: 1
  683. kernel_size: 3
  684. weight_filler {
  685. type: "xavier"
  686. }
  687. bias_filler {
  688. type: "constant"
  689. }
  690. }
  691. }
  692. layer {
  693. name: "Mprelu2_stage0_L2_0"
  694. type: "PReLU"
  695. bottom: "Mconv2_stage0_L2_0"
  696. top: "Mconv2_stage0_L2_0"
  697. param {
  698. lr_mult: 4
  699. decay_mult: 1
  700. }
  701. prelu_param {
  702. channel_shared: false
  703. }
  704. }
  705. layer {
  706. name: "Mconv2_stage0_L2_1"
  707. type: "Convolution"
  708. bottom: "Mconv2_stage0_L2_0"
  709. top: "Mconv2_stage0_L2_1"
  710. param {
  711. lr_mult: 4
  712. decay_mult: 1
  713. }
  714. param {
  715. lr_mult: 8
  716. decay_mult: 0
  717. }
  718. convolution_param {
  719. num_output: 64
  720. pad: 1
  721. kernel_size: 3
  722. weight_filler {
  723. type: "xavier"
  724. }
  725. bias_filler {
  726. type: "constant"
  727. }
  728. }
  729. }
  730. layer {
  731. name: "Mprelu2_stage0_L2_1"
  732. type: "PReLU"
  733. bottom: "Mconv2_stage0_L2_1"
  734. top: "Mconv2_stage0_L2_1"
  735. param {
  736. lr_mult: 4
  737. decay_mult: 1
  738. }
  739. prelu_param {
  740. channel_shared: false
  741. }
  742. }
  743. layer {
  744. name: "Mconv2_stage0_L2_2"
  745. type: "Convolution"
  746. bottom: "Mconv2_stage0_L2_1"
  747. top: "Mconv2_stage0_L2_2"
  748. param {
  749. lr_mult: 4
  750. decay_mult: 1
  751. }
  752. param {
  753. lr_mult: 8
  754. decay_mult: 0
  755. }
  756. convolution_param {
  757. num_output: 64
  758. pad: 1
  759. kernel_size: 3
  760. weight_filler {
  761. type: "xavier"
  762. }
  763. bias_filler {
  764. type: "constant"
  765. }
  766. }
  767. }
  768. layer {
  769. name: "Mprelu2_stage0_L2_2"
  770. type: "PReLU"
  771. bottom: "Mconv2_stage0_L2_2"
  772. top: "Mconv2_stage0_L2_2"
  773. param {
  774. lr_mult: 4
  775. decay_mult: 1
  776. }
  777. prelu_param {
  778. channel_shared: false
  779. }
  780. }
  781. layer {
  782. name: "Mconv2_stage0_L2_concat"
  783. type: "Concat"
  784. bottom: "Mconv2_stage0_L2_0"
  785. bottom: "Mconv2_stage0_L2_1"
  786. bottom: "Mconv2_stage0_L2_2"
  787. top: "Mconv2_stage0_L2_concat"
  788. concat_param {
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  834. top: "Mconv3_stage0_L2_1"
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  853. }
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  866. }
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  939. }
  940. }
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  1013. bias_filler {
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  1015. }
  1016. }
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  1020. type: "PReLU"
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  1022. top: "Mconv4_stage0_L2_2"
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  1025. decay_mult: 1
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  1033. type: "Concat"
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  1035. bottom: "Mconv4_stage0_L2_1"
  1036. bottom: "Mconv4_stage0_L2_2"
  1037. top: "Mconv4_stage0_L2_concat"
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  1046. top: "Mconv5_stage0_L2_0"
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  1058. kernel_size: 3
  1059. weight_filler {
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  1061. }
  1062. bias_filler {
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  1064. }
  1065. }
  1066. }
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  1069. type: "PReLU"
  1070. bottom: "Mconv5_stage0_L2_0"
  1071. top: "Mconv5_stage0_L2_0"
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  1074. decay_mult: 1
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  1076. prelu_param {
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  1082. type: "Convolution"
  1083. bottom: "Mconv5_stage0_L2_0"
  1084. top: "Mconv5_stage0_L2_1"
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  1099. }
  1100. bias_filler {
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  1102. }
  1103. }
  1104. }
  1105. layer {
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  1109. top: "Mconv5_stage0_L2_1"
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  1112. decay_mult: 1
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  1114. prelu_param {
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  1118. layer {
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  1120. type: "Convolution"
  1121. bottom: "Mconv5_stage0_L2_1"
  1122. top: "Mconv5_stage0_L2_2"
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  1125. decay_mult: 1
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  1127. param {
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  1131. convolution_param {
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  1133. pad: 1
  1134. kernel_size: 3
  1135. weight_filler {
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  1137. }
  1138. bias_filler {
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  1140. }
  1141. }
  1142. }
  1143. layer {
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  1145. type: "PReLU"
  1146. bottom: "Mconv5_stage0_L2_2"
  1147. top: "Mconv5_stage0_L2_2"
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  1150. decay_mult: 1
  1151. }
  1152. prelu_param {
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  1154. }
  1155. }
  1156. layer {
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  1158. type: "Concat"
  1159. bottom: "Mconv5_stage0_L2_0"
  1160. bottom: "Mconv5_stage0_L2_1"
  1161. bottom: "Mconv5_stage0_L2_2"
  1162. top: "Mconv5_stage0_L2_concat"
  1163. concat_param {
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  1165. }
  1166. }
  1167. layer {
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  1169. type: "Convolution"
  1170. bottom: "Mconv5_stage0_L2_concat"
  1171. top: "Mconv6_stage0_L2"
  1172. param {
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  1174. decay_mult: 1
  1175. }
  1176. param {
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  1180. convolution_param {
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  1182. pad: 0
  1183. kernel_size: 1
  1184. weight_filler {
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  1186. }
  1187. bias_filler {
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  1189. }
  1190. }
  1191. }
  1192. layer {
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  1194. type: "PReLU"
  1195. bottom: "Mconv6_stage0_L2"
  1196. top: "Mconv6_stage0_L2"
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  1199. decay_mult: 1
  1200. }
  1201. prelu_param {
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  1203. }
  1204. }
  1205. layer {
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  1207. type: "Convolution"
  1208. bottom: "Mconv6_stage0_L2"
  1209. top: "Mconv7_stage0_L2"
  1210. param {
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  1212. decay_mult: 1
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  1214. param {
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  1218. convolution_param {
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  1220. pad: 0
  1221. kernel_size: 1
  1222. weight_filler {
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  1224. }
  1225. bias_filler {
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  1227. }
  1228. }
  1229. }
  1230. layer {
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  1232. type: "Eltwise"
  1233. bottom: "Mconv7_stage0_L2"
  1234. bottom: "vec_weight"
  1235. top: "weight_stage0_L2"
  1236. eltwise_param {
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  1238. }
  1239. }
  1240. layer {
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  1242. type: "EuclideanLoss"
  1243. bottom: "weight_stage0_L2"
  1244. bottom: "label_vec"
  1245. top: "loss_stage0_L2"
  1246. loss_weight: 1
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  1248. layer {
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  1250. type: "Concat"
  1251. bottom: "conv4_4_CPM"
  1252. bottom: "Mconv7_stage0_L2"
  1253. top: "concat_stage1_L2"
  1254. concat_param {
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  1256. }
  1257. }
  1258. layer {
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  1260. type: "Convolution"
  1261. bottom: "concat_stage1_L2"
  1262. top: "Mconv1_stage1_L2_0"
  1263. param {
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  1265. decay_mult: 1
  1266. }
  1267. param {
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  1271. convolution_param {
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  1273. pad: 1
  1274. kernel_size: 3
  1275. weight_filler {
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  1277. }
  1278. bias_filler {
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  1280. }
  1281. }
  1282. }
  1283. layer {
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  1285. type: "PReLU"
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  1287. top: "Mconv1_stage1_L2_0"
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  1290. decay_mult: 1
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  1292. prelu_param {
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  1295. }
  1296. layer {
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  1298. type: "Convolution"
  1299. bottom: "Mconv1_stage1_L2_0"
  1300. top: "Mconv1_stage1_L2_1"
  1301. param {
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  1303. decay_mult: 1
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  1305. param {
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  1307. decay_mult: 0
  1308. }
  1309. convolution_param {
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  1311. pad: 1
  1312. kernel_size: 3
  1313. weight_filler {
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  1315. }
  1316. bias_filler {
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  1318. }
  1319. }
  1320. }
  1321. layer {
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  1323. type: "PReLU"
  1324. bottom: "Mconv1_stage1_L2_1"
  1325. top: "Mconv1_stage1_L2_1"
  1326. param {
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  1328. decay_mult: 1
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  1330. prelu_param {
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  1332. }
  1333. }
  1334. layer {
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  1336. type: "Convolution"
  1337. bottom: "Mconv1_stage1_L2_1"
  1338. top: "Mconv1_stage1_L2_2"
  1339. param {
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  1341. decay_mult: 1
  1342. }
  1343. param {
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  1345. decay_mult: 0
  1346. }
  1347. convolution_param {
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  1349. pad: 1
  1350. kernel_size: 3
  1351. weight_filler {
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  1353. }
  1354. bias_filler {
  1355. type: "constant"
  1356. }
  1357. }
  1358. }
  1359. layer {
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  1361. type: "PReLU"
  1362. bottom: "Mconv1_stage1_L2_2"
  1363. top: "Mconv1_stage1_L2_2"
  1364. param {
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  1366. decay_mult: 1
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  1368. prelu_param {
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  1370. }
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  1372. layer {
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  1374. type: "Concat"
  1375. bottom: "Mconv1_stage1_L2_0"
  1376. bottom: "Mconv1_stage1_L2_1"
  1377. bottom: "Mconv1_stage1_L2_2"
  1378. top: "Mconv1_stage1_L2_concat"
  1379. concat_param {
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  1382. }
  1383. layer {
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  1385. type: "Convolution"
  1386. bottom: "Mconv1_stage1_L2_concat"
  1387. top: "Mconv2_stage1_L2_0"
  1388. param {
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  1390. decay_mult: 1
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  1392. param {
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  1396. convolution_param {
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  1398. pad: 1
  1399. kernel_size: 3
  1400. weight_filler {
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  1402. }
  1403. bias_filler {
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  1405. }
  1406. }
  1407. }
  1408. layer {
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  1410. type: "PReLU"
  1411. bottom: "Mconv2_stage1_L2_0"
  1412. top: "Mconv2_stage1_L2_0"
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  1415. decay_mult: 1
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  1417. prelu_param {
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  1419. }
  1420. }
  1421. layer {
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  1423. type: "Convolution"
  1424. bottom: "Mconv2_stage1_L2_0"
  1425. top: "Mconv2_stage1_L2_1"
  1426. param {
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  1428. decay_mult: 1
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  1430. param {
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  1432. decay_mult: 0
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  1434. convolution_param {
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  1436. pad: 1
  1437. kernel_size: 3
  1438. weight_filler {
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  1440. }
  1441. bias_filler {
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  1443. }
  1444. }
  1445. }
  1446. layer {
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  1448. type: "PReLU"
  1449. bottom: "Mconv2_stage1_L2_1"
  1450. top: "Mconv2_stage1_L2_1"
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  1453. decay_mult: 1
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  1455. prelu_param {
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  1457. }
  1458. }
  1459. layer {
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  1461. type: "Convolution"
  1462. bottom: "Mconv2_stage1_L2_1"
  1463. top: "Mconv2_stage1_L2_2"
  1464. param {
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  1466. decay_mult: 1
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  1468. param {
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  1470. decay_mult: 0
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  1472. convolution_param {
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  1474. pad: 1
  1475. kernel_size: 3
  1476. weight_filler {
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  1478. }
  1479. bias_filler {
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  1481. }
  1482. }
  1483. }
  1484. layer {
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  1486. type: "PReLU"
  1487. bottom: "Mconv2_stage1_L2_2"
  1488. top: "Mconv2_stage1_L2_2"
  1489. param {
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  1491. decay_mult: 1
  1492. }
  1493. prelu_param {
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  1495. }
  1496. }
  1497. layer {
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  1499. type: "Concat"
  1500. bottom: "Mconv2_stage1_L2_0"
  1501. bottom: "Mconv2_stage1_L2_1"
  1502. bottom: "Mconv2_stage1_L2_2"
  1503. top: "Mconv2_stage1_L2_concat"
  1504. concat_param {
  1505. axis: 1
  1506. }
  1507. }
  1508. layer {
  1509. name: "Mconv3_stage1_L2_0"
  1510. type: "Convolution"
  1511. bottom: "Mconv2_stage1_L2_concat"
  1512. top: "Mconv3_stage1_L2_0"
  1513. param {
  1514. lr_mult: 4
  1515. decay_mult: 1
  1516. }
  1517. param {
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  1519. decay_mult: 0
  1520. }
  1521. convolution_param {
  1522. num_output: 128
  1523. pad: 1
  1524. kernel_size: 3
  1525. weight_filler {
  1526. type: "xavier"
  1527. }
  1528. bias_filler {
  1529. type: "constant"
  1530. }
  1531. }
  1532. }
  1533. layer {
  1534. name: "Mprelu3_stage1_L2_0"
  1535. type: "PReLU"
  1536. bottom: "Mconv3_stage1_L2_0"
  1537. top: "Mconv3_stage1_L2_0"
  1538. param {
  1539. lr_mult: 4
  1540. decay_mult: 1
  1541. }
  1542. prelu_param {
  1543. channel_shared: false
  1544. }
  1545. }
  1546. layer {
  1547. name: "Mconv3_stage1_L2_1"
  1548. type: "Convolution"
  1549. bottom: "Mconv3_stage1_L2_0"
  1550. top: "Mconv3_stage1_L2_1"
  1551. param {
  1552. lr_mult: 4
  1553. decay_mult: 1
  1554. }
  1555. param {
  1556. lr_mult: 8
  1557. decay_mult: 0
  1558. }
  1559. convolution_param {
  1560. num_output: 128
  1561. pad: 1
  1562. kernel_size: 3
  1563. weight_filler {
  1564. type: "xavier"
  1565. }
  1566. bias_filler {
  1567. type: "constant"
  1568. }
  1569. }
  1570. }
  1571. layer {
  1572. name: "Mprelu3_stage1_L2_1"
  1573. type: "PReLU"
  1574. bottom: "Mconv3_stage1_L2_1"
  1575. top: "Mconv3_stage1_L2_1"
  1576. param {
  1577. lr_mult: 4
  1578. decay_mult: 1
  1579. }
  1580. prelu_param {
  1581. channel_shared: false
  1582. }
  1583. }
  1584. layer {
  1585. name: "Mconv3_stage1_L2_2"
  1586. type: "Convolution"
  1587. bottom: "Mconv3_stage1_L2_1"
  1588. top: "Mconv3_stage1_L2_2"
  1589. param {
  1590. lr_mult: 4
  1591. decay_mult: 1
  1592. }
  1593. param {
  1594. lr_mult: 8
  1595. decay_mult: 0
  1596. }
  1597. convolution_param {
  1598. num_output: 128
  1599. pad: 1
  1600. kernel_size: 3
  1601. weight_filler {
  1602. type: "xavier"
  1603. }
  1604. bias_filler {
  1605. type: "constant"
  1606. }
  1607. }
  1608. }
  1609. layer {
  1610. name: "Mprelu3_stage1_L2_2"
  1611. type: "PReLU"
  1612. bottom: "Mconv3_stage1_L2_2"
  1613. top: "Mconv3_stage1_L2_2"
  1614. param {
  1615. lr_mult: 4
  1616. decay_mult: 1
  1617. }
  1618. prelu_param {
  1619. channel_shared: false
  1620. }
  1621. }
  1622. layer {
  1623. name: "Mconv3_stage1_L2_concat"
  1624. type: "Concat"
  1625. bottom: "Mconv3_stage1_L2_0"
  1626. bottom: "Mconv3_stage1_L2_1"
  1627. bottom: "Mconv3_stage1_L2_2"
  1628. top: "Mconv3_stage1_L2_concat"
  1629. concat_param {
  1630. axis: 1
  1631. }
  1632. }
  1633. layer {
  1634. name: "Mconv4_stage1_L2_0"
  1635. type: "Convolution"
  1636. bottom: "Mconv3_stage1_L2_concat"
  1637. top: "Mconv4_stage1_L2_0"
  1638. param {
  1639. lr_mult: 4
  1640. decay_mult: 1
  1641. }
  1642. param {
  1643. lr_mult: 8
  1644. decay_mult: 0
  1645. }
  1646. convolution_param {
  1647. num_output: 128
  1648. pad: 1
  1649. kernel_size: 3
  1650. weight_filler {
  1651. type: "xavier"
  1652. }
  1653. bias_filler {
  1654. type: "constant"
  1655. }
  1656. }
  1657. }
  1658. layer {
  1659. name: "Mprelu4_stage1_L2_0"
  1660. type: "PReLU"
  1661. bottom: "Mconv4_stage1_L2_0"
  1662. top: "Mconv4_stage1_L2_0"
  1663. param {
  1664. lr_mult: 4
  1665. decay_mult: 1
  1666. }
  1667. prelu_param {
  1668. channel_shared: false
  1669. }
  1670. }
  1671. layer {
  1672. name: "Mconv4_stage1_L2_1"
  1673. type: "Convolution"
  1674. bottom: "Mconv4_stage1_L2_0"
  1675. top: "Mconv4_stage1_L2_1"
  1676. param {
  1677. lr_mult: 4
  1678. decay_mult: 1
  1679. }
  1680. param {
  1681. lr_mult: 8
  1682. decay_mult: 0
  1683. }
  1684. convolution_param {
  1685. num_output: 128
  1686. pad: 1
  1687. kernel_size: 3
  1688. weight_filler {
  1689. type: "xavier"
  1690. }
  1691. bias_filler {
  1692. type: "constant"
  1693. }
  1694. }
  1695. }
  1696. layer {
  1697. name: "Mprelu4_stage1_L2_1"
  1698. type: "PReLU"
  1699. bottom: "Mconv4_stage1_L2_1"
  1700. top: "Mconv4_stage1_L2_1"
  1701. param {
  1702. lr_mult: 4
  1703. decay_mult: 1
  1704. }
  1705. prelu_param {
  1706. channel_shared: false
  1707. }
  1708. }
  1709. layer {
  1710. name: "Mconv4_stage1_L2_2"
  1711. type: "Convolution"
  1712. bottom: "Mconv4_stage1_L2_1"
  1713. top: "Mconv4_stage1_L2_2"
  1714. param {
  1715. lr_mult: 4
  1716. decay_mult: 1
  1717. }
  1718. param {
  1719. lr_mult: 8
  1720. decay_mult: 0
  1721. }
  1722. convolution_param {
  1723. num_output: 128
  1724. pad: 1
  1725. kernel_size: 3
  1726. weight_filler {
  1727. type: "xavier"
  1728. }
  1729. bias_filler {
  1730. type: "constant"
  1731. }
  1732. }
  1733. }
  1734. layer {
  1735. name: "Mprelu4_stage1_L2_2"
  1736. type: "PReLU"
  1737. bottom: "Mconv4_stage1_L2_2"
  1738. top: "Mconv4_stage1_L2_2"
  1739. param {
  1740. lr_mult: 4
  1741. decay_mult: 1
  1742. }
  1743. prelu_param {
  1744. channel_shared: false
  1745. }
  1746. }
  1747. layer {
  1748. name: "Mconv4_stage1_L2_concat"
  1749. type: "Concat"
  1750. bottom: "Mconv4_stage1_L2_0"
  1751. bottom: "Mconv4_stage1_L2_1"
  1752. bottom: "Mconv4_stage1_L2_2"
  1753. top: "Mconv4_stage1_L2_concat"
  1754. concat_param {
  1755. axis: 1
  1756. }
  1757. }
  1758. layer {
  1759. name: "Mconv5_stage1_L2_0"
  1760. type: "Convolution"
  1761. bottom: "Mconv4_stage1_L2_concat"
  1762. top: "Mconv5_stage1_L2_0"
  1763. param {
  1764. lr_mult: 4
  1765. decay_mult: 1
  1766. }
  1767. param {
  1768. lr_mult: 8
  1769. decay_mult: 0
  1770. }
  1771. convolution_param {
  1772. num_output: 128
  1773. pad: 1
  1774. kernel_size: 3
  1775. weight_filler {
  1776. type: "xavier"
  1777. }
  1778. bias_filler {
  1779. type: "constant"
  1780. }
  1781. }
  1782. }
  1783. layer {
  1784. name: "Mprelu5_stage1_L2_0"
  1785. type: "PReLU"
  1786. bottom: "Mconv5_stage1_L2_0"
  1787. top: "Mconv5_stage1_L2_0"
  1788. param {
  1789. lr_mult: 4
  1790. decay_mult: 1
  1791. }
  1792. prelu_param {
  1793. channel_shared: false
  1794. }
  1795. }
  1796. layer {
  1797. name: "Mconv5_stage1_L2_1"
  1798. type: "Convolution"
  1799. bottom: "Mconv5_stage1_L2_0"
  1800. top: "Mconv5_stage1_L2_1"
  1801. param {
  1802. lr_mult: 4
  1803. decay_mult: 1
  1804. }
  1805. param {
  1806. lr_mult: 8
  1807. decay_mult: 0
  1808. }
  1809. convolution_param {
  1810. num_output: 128
  1811. pad: 1
  1812. kernel_size: 3
  1813. weight_filler {
  1814. type: "xavier"
  1815. }
  1816. bias_filler {
  1817. type: "constant"
  1818. }
  1819. }
  1820. }
  1821. layer {
  1822. name: "Mprelu5_stage1_L2_1"
  1823. type: "PReLU"
  1824. bottom: "Mconv5_stage1_L2_1"
  1825. top: "Mconv5_stage1_L2_1"
  1826. param {
  1827. lr_mult: 4
  1828. decay_mult: 1
  1829. }
  1830. prelu_param {
  1831. channel_shared: false
  1832. }
  1833. }
  1834. layer {
  1835. name: "Mconv5_stage1_L2_2"
  1836. type: "Convolution"
  1837. bottom: "Mconv5_stage1_L2_1"
  1838. top: "Mconv5_stage1_L2_2"
  1839. param {
  1840. lr_mult: 4
  1841. decay_mult: 1
  1842. }
  1843. param {
  1844. lr_mult: 8
  1845. decay_mult: 0
  1846. }
  1847. convolution_param {
  1848. num_output: 128
  1849. pad: 1
  1850. kernel_size: 3
  1851. weight_filler {
  1852. type: "xavier"
  1853. }
  1854. bias_filler {
  1855. type: "constant"
  1856. }
  1857. }
  1858. }
  1859. layer {
  1860. name: "Mprelu5_stage1_L2_2"
  1861. type: "PReLU"
  1862. bottom: "Mconv5_stage1_L2_2"
  1863. top: "Mconv5_stage1_L2_2"
  1864. param {
  1865. lr_mult: 4
  1866. decay_mult: 1
  1867. }
  1868. prelu_param {
  1869. channel_shared: false
  1870. }
  1871. }
  1872. layer {
  1873. name: "Mconv5_stage1_L2_concat"
  1874. type: "Concat"
  1875. bottom: "Mconv5_stage1_L2_0"
  1876. bottom: "Mconv5_stage1_L2_1"
  1877. bottom: "Mconv5_stage1_L2_2"
  1878. top: "Mconv5_stage1_L2_concat"
  1879. concat_param {
  1880. axis: 1
  1881. }
  1882. }
  1883. layer {
  1884. name: "Mconv6_stage1_L2"
  1885. type: "Convolution"
  1886. bottom: "Mconv5_stage1_L2_concat"
  1887. top: "Mconv6_stage1_L2"
  1888. param {
  1889. lr_mult: 4
  1890. decay_mult: 1
  1891. }
  1892. param {
  1893. lr_mult: 8
  1894. decay_mult: 0
  1895. }
  1896. convolution_param {
  1897. num_output: 256
  1898. pad: 0
  1899. kernel_size: 1
  1900. weight_filler {
  1901. type: "xavier"
  1902. }
  1903. bias_filler {
  1904. type: "constant"
  1905. }
  1906. }
  1907. }
  1908. layer {
  1909. name: "Mprelu6_stage1_L2"
  1910. type: "PReLU"
  1911. bottom: "Mconv6_stage1_L2"
  1912. top: "Mconv6_stage1_L2"
  1913. param {
  1914. lr_mult: 4
  1915. decay_mult: 1
  1916. }
  1917. prelu_param {
  1918. channel_shared: false
  1919. }
  1920. }
  1921. layer {
  1922. name: "Mconv7_stage1_L2"
  1923. type: "Convolution"
  1924. bottom: "Mconv6_stage1_L2"
  1925. top: "Mconv7_stage1_L2"
  1926. param {
  1927. lr_mult: 4
  1928. decay_mult: 1
  1929. }
  1930. param {
  1931. lr_mult: 8
  1932. decay_mult: 0
  1933. }
  1934. convolution_param {
  1935. num_output: 72
  1936. pad: 0
  1937. kernel_size: 1
  1938. weight_filler {
  1939. type: "xavier"
  1940. }
  1941. bias_filler {
  1942. type: "constant"
  1943. }
  1944. }
  1945. }
  1946. layer {
  1947. name: "weight_stage
  1948. I0122 12:08:32.936003 27277 layer_factory.hpp:77] Creating layer image
  1949. I0122 12:08:32.936283 27277 db_lmdb.cpp:35] Opened lmdb /home/rguesdon/Networks/openpose_train/dataset/lmdb_twingo
  1950. I0122 12:08:32.936394 27277 db_lmdb.cpp:35] Opened lmdb /home/rguesdon/Networks/openpose_train/dataset/lmdb_background
  1951. 0.98 0.02
  1952. I0122 12:08:32.936452 27277 net.cpp:86] Creating Layer image
  1953. I0122 12:08:32.936478 27277 net.cpp:382] image -> image
  1954. I0122 12:08:32.936607 27277 net.cpp:382] image -> label
  1955. *** Aborted at 1579691312 (unix time) try "date -d @1579691312" if you are using GNU date ***
  1956. PC: @ 0x7ff6122e7b3e (unknown)
  1957. *** SIGSEGV (@0x17) received by PID 27277 (TID 0x7ff61437e840) from PID 23; stack trace: ***
  1958. @ 0x7ff612197f20 (unknown)
  1959. @ 0x7ff6122e7b3e (unknown)
  1960. @ 0x7ff6128893c7 std::__cxx11::basic_string<>::_M_construct<>()
  1961. @ 0x7ff613bd9c7e _ZN5caffe2db10LMDBCursor5valueB5cxx11Ev
  1962. @ 0x7ff613a87132 caffe::OPDataLayer<>::DataLayerSetUp()
  1963. @ 0x7ff613b6276b caffe::BaseDataLayer<>::LayerSetUp()
  1964. @ 0x7ff613b62bc1 caffe::BasePrefetchingDataLayer<>::LayerSetUp()
  1965. @ 0x7ff613addfc6 caffe::Layer<>::SetUp()
  1966. @ 0x7ff613ac955c caffe::Net<>::Init()
  1967. @ 0x7ff613ac7fb2 caffe::Net<>::Net()
  1968. @ 0x7ff6139efd8f caffe::Solver<>::InitTrainNet()
  1969. @ 0x7ff6139ef5bf caffe::Solver<>::Init()
  1970. @ 0x7ff6139ef0df caffe::Solver<>::Solver()
  1971. @ 0x7ff613bb4584 caffe::SGDSolver<>::SGDSolver()
  1972. @ 0x7ff613bb9de4 caffe::AdamSolver<>::AdamSolver()
  1973. @ 0x7ff613bbad18 caffe::Creator_AdamSolver<>()
  1974. @ 0x435da3 caffe::SolverRegistry<>::CreateSolver()
  1975. @ 0x4314a0 train()
  1976. @ 0x4339a5 main
  1977. @ 0x7ff61217ab97 __libc_start_main
  1978. @ 0x43024a _start
  1979. @ 0x0 (unknown)
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