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  1. input: "image"
  2. input_dim: 1
  3. input_dim: 3
  4. input_dim: 240 # This value will be defined at runtime
  5. input_dim: 320 # This value will be defined at runtime
  6.  
  7. #########Start of MobileNet#####################
  8. layer {
  9. name: "conv1"
  10. type: "Convolution"
  11. bottom: "image"
  12. top: "conv1"
  13. param {
  14. lr_mult: 1
  15. decay_mult: 1
  16. }
  17. convolution_param {
  18. num_output: 32
  19. bias_term: false
  20. pad: 1
  21. kernel_size: 3
  22. stride: 2
  23. weight_filler {
  24. type: "msra"
  25. }
  26. }
  27. }
  28. layer {
  29. name: "conv1/bn"
  30. type: "BatchNorm"
  31. bottom: "conv1"
  32. top: "conv1"
  33. param {
  34. lr_mult: 0
  35. decay_mult: 0
  36. }
  37. param {
  38. lr_mult: 0
  39. decay_mult: 0
  40. }
  41. param {
  42. lr_mult: 0
  43. decay_mult: 0
  44. }
  45. }
  46. layer {
  47. name: "conv1/scale"
  48. type: "Scale"
  49. bottom: "conv1"
  50. top: "conv1"
  51. scale_param {
  52. filler {
  53. value: 1
  54. }
  55. bias_term: true
  56. bias_filler {
  57. value: 0
  58. }
  59. }
  60. }
  61. layer {
  62. name: "relu1"
  63. type: "ReLU"
  64. bottom: "conv1"
  65. top: "conv1"
  66. }
  67. layer {
  68. name: "conv2_1/dw"
  69. type: "Convolution"
  70. bottom: "conv1"
  71. top: "conv2_1/dw"
  72. param {
  73. lr_mult: 1
  74. decay_mult: 1
  75. }
  76. convolution_param {
  77. num_output: 32
  78. bias_term: false
  79. pad: 1
  80. kernel_size: 3
  81. group: 32
  82. #engine: CAFFE
  83. stride: 1
  84. weight_filler {
  85. type: "msra"
  86. }
  87. }
  88. }
  89. layer {
  90. name: "conv2_1/dw/bn"
  91. type: "BatchNorm"
  92. bottom: "conv2_1/dw"
  93. top: "conv2_1/dw"
  94. param {
  95. lr_mult: 0
  96. decay_mult: 0
  97. }
  98. param {
  99. lr_mult: 0
  100. decay_mult: 0
  101. }
  102. param {
  103. lr_mult: 0
  104. decay_mult: 0
  105. }
  106. }
  107. layer {
  108. name: "conv2_1/dw/scale"
  109. type: "Scale"
  110. bottom: "conv2_1/dw"
  111. top: "conv2_1/dw"
  112. scale_param {
  113. filler {
  114. value: 1
  115. }
  116. bias_term: true
  117. bias_filler {
  118. value: 0
  119. }
  120. }
  121. }
  122. layer {
  123. name: "relu2_1/dw"
  124. type: "ReLU"
  125. bottom: "conv2_1/dw"
  126. top: "conv2_1/dw"
  127. }
  128. layer {
  129. name: "conv2_1/sep"
  130. type: "Convolution"
  131. bottom: "conv2_1/dw"
  132. top: "conv2_1/sep"
  133. param {
  134. lr_mult: 1
  135. decay_mult: 1
  136. }
  137. convolution_param {
  138. num_output: 64
  139. bias_term: false
  140. pad: 0
  141. kernel_size: 1
  142. stride: 1
  143. weight_filler {
  144. type: "msra"
  145. }
  146. }
  147. }
  148. layer {
  149. name: "conv2_1/sep/bn"
  150. type: "BatchNorm"
  151. bottom: "conv2_1/sep"
  152. top: "conv2_1/sep"
  153. param {
  154. lr_mult: 0
  155. decay_mult: 0
  156. }
  157. param {
  158. lr_mult: 0
  159. decay_mult: 0
  160. }
  161. param {
  162. lr_mult: 0
  163. decay_mult: 0
  164. }
  165. }
  166. layer {
  167. name: "conv2_1/sep/scale"
  168. type: "Scale"
  169. bottom: "conv2_1/sep"
  170. top: "conv2_1/sep"
  171. scale_param {
  172. filler {
  173. value: 1
  174. }
  175. bias_term: true
  176. bias_filler {
  177. value: 0
  178. }
  179. }
  180. }
  181. layer {
  182. name: "relu2_1/sep"
  183. type: "ReLU"
  184. bottom: "conv2_1/sep"
  185. top: "conv2_1/sep"
  186. }
  187. layer {
  188. name: "conv2_2/dw"
  189. type: "Convolution"
  190. bottom: "conv2_1/sep"
  191. top: "conv2_2/dw"
  192. param {
  193. lr_mult: 1
  194. decay_mult: 1
  195. }
  196. convolution_param {
  197. num_output: 64
  198. bias_term: false
  199. pad: 1
  200. kernel_size: 3
  201. group: 64
  202. #engine: CAFFE
  203. stride: 2
  204. weight_filler {
  205. type: "msra"
  206. }
  207. }
  208. }
  209. layer {
  210. name: "conv2_2/dw/bn"
  211. type: "BatchNorm"
  212. bottom: "conv2_2/dw"
  213. top: "conv2_2/dw"
  214. param {
  215. lr_mult: 0
  216. decay_mult: 0
  217. }
  218. param {
  219. lr_mult: 0
  220. decay_mult: 0
  221. }
  222. param {
  223. lr_mult: 0
  224. decay_mult: 0
  225. }
  226. }
  227. layer {
  228. name: "conv2_2/dw/scale"
  229. type: "Scale"
  230. bottom: "conv2_2/dw"
  231. top: "conv2_2/dw"
  232. scale_param {
  233. filler {
  234. value: 1
  235. }
  236. bias_term: true
  237. bias_filler {
  238. value: 0
  239. }
  240. }
  241. }
  242. layer {
  243. name: "relu2_2/dw"
  244. type: "ReLU"
  245. bottom: "conv2_2/dw"
  246. top: "conv2_2/dw"
  247. }
  248. layer {
  249. name: "conv2_2/sep"
  250. type: "Convolution"
  251. bottom: "conv2_2/dw"
  252. top: "conv2_2/sep"
  253. param {
  254. lr_mult: 1
  255. decay_mult: 1
  256. }
  257. convolution_param {
  258. num_output: 128
  259. bias_term: false
  260. pad: 0
  261. kernel_size: 1
  262. stride: 1
  263. weight_filler {
  264. type: "msra"
  265. }
  266. }
  267. }
  268. layer {
  269. name: "conv2_2/sep/bn"
  270. type: "BatchNorm"
  271. bottom: "conv2_2/sep"
  272. top: "conv2_2/sep"
  273. param {
  274. lr_mult: 0
  275. decay_mult: 0
  276. }
  277. param {
  278. lr_mult: 0
  279. decay_mult: 0
  280. }
  281. param {
  282. lr_mult: 0
  283. decay_mult: 0
  284. }
  285. }
  286. layer {
  287. name: "conv2_2/sep/scale"
  288. type: "Scale"
  289. bottom: "conv2_2/sep"
  290. top: "conv2_2/sep"
  291. scale_param {
  292. filler {
  293. value: 1
  294. }
  295. bias_term: true
  296. bias_filler {
  297. value: 0
  298. }
  299. }
  300. }
  301. layer {
  302. name: "relu2_2/sep"
  303. type: "ReLU"
  304. bottom: "conv2_2/sep"
  305. top: "conv2_2/sep"
  306. }
  307. layer {
  308. name: "conv3_1/dw"
  309. type: "Convolution"
  310. bottom: "conv2_2/sep"
  311. top: "conv3_1/dw"
  312. param {
  313. lr_mult: 1
  314. decay_mult: 1
  315. }
  316. convolution_param {
  317. num_output: 128
  318. bias_term: false
  319. pad: 1
  320. kernel_size: 3
  321. group: 128
  322. #engine: CAFFE
  323. stride: 1
  324. weight_filler {
  325. type: "msra"
  326. }
  327. }
  328. }
  329. layer {
  330. name: "conv3_1/dw/bn"
  331. type: "BatchNorm"
  332. bottom: "conv3_1/dw"
  333. top: "conv3_1/dw"
  334. param {
  335. lr_mult: 0
  336. decay_mult: 0
  337. }
  338. param {
  339. lr_mult: 0
  340. decay_mult: 0
  341. }
  342. param {
  343. lr_mult: 0
  344. decay_mult: 0
  345. }
  346. }
  347. layer {
  348. name: "conv3_1/dw/scale"
  349. type: "Scale"
  350. bottom: "conv3_1/dw"
  351. top: "conv3_1/dw"
  352. scale_param {
  353. filler {
  354. value: 1
  355. }
  356. bias_term: true
  357. bias_filler {
  358. value: 0
  359. }
  360. }
  361. }
  362. layer {
  363. name: "relu3_1/dw"
  364. type: "ReLU"
  365. bottom: "conv3_1/dw"
  366. top: "conv3_1/dw"
  367. }
  368. layer {
  369. name: "conv3_1/sep"
  370. type: "Convolution"
  371. bottom: "conv3_1/dw"
  372. top: "conv3_1/sep"
  373. param {
  374. lr_mult: 1
  375. decay_mult: 1
  376. }
  377. convolution_param {
  378. num_output: 128
  379. bias_term: false
  380. pad: 0
  381. kernel_size: 1
  382. stride: 1
  383. weight_filler {
  384. type: "msra"
  385. }
  386. }
  387. }
  388. layer {
  389. name: "conv3_1/sep/bn"
  390. type: "BatchNorm"
  391. bottom: "conv3_1/sep"
  392. top: "conv3_1/sep"
  393. param {
  394. lr_mult: 0
  395. decay_mult: 0
  396. }
  397. param {
  398. lr_mult: 0
  399. decay_mult: 0
  400. }
  401. param {
  402. lr_mult: 0
  403. decay_mult: 0
  404. }
  405. }
  406. layer {
  407. name: "conv3_1/sep/scale"
  408. type: "Scale"
  409. bottom: "conv3_1/sep"
  410. top: "conv3_1/sep"
  411. scale_param {
  412. filler {
  413. value: 1
  414. }
  415. bias_term: true
  416. bias_filler {
  417. value: 0
  418. }
  419. }
  420. }
  421. layer {
  422. name: "relu3_1/sep"
  423. type: "ReLU"
  424. bottom: "conv3_1/sep"
  425. top: "conv3_1/sep"
  426. }
  427. layer {
  428. name: "conv3_2/dw"
  429. type: "Convolution"
  430. bottom: "conv3_1/sep"
  431. top: "conv3_2/dw"
  432. param {
  433. lr_mult: 1
  434. decay_mult: 1
  435. }
  436. convolution_param {
  437. num_output: 128
  438. bias_term: false
  439. pad: 1
  440. kernel_size: 3
  441. group: 128
  442. #engine: CAFFE
  443. stride: 2
  444. weight_filler {
  445. type: "msra"
  446. }
  447. }
  448. }
  449. layer {
  450. name: "conv3_2/dw/bn"
  451. type: "BatchNorm"
  452. bottom: "conv3_2/dw"
  453. top: "conv3_2/dw"
  454. param {
  455. lr_mult: 0
  456. decay_mult: 0
  457. }
  458. param {
  459. lr_mult: 0
  460. decay_mult: 0
  461. }
  462. param {
  463. lr_mult: 0
  464. decay_mult: 0
  465. }
  466. }
  467. layer {
  468. name: "conv3_2/dw/scale"
  469. type: "Scale"
  470. bottom: "conv3_2/dw"
  471. top: "conv3_2/dw"
  472. scale_param {
  473. filler {
  474. value: 1
  475. }
  476. bias_term: true
  477. bias_filler {
  478. value: 0
  479. }
  480. }
  481. }
  482. layer {
  483. name: "relu3_2/dw"
  484. type: "ReLU"
  485. bottom: "conv3_2/dw"
  486. top: "conv3_2/dw"
  487. }
  488. layer {
  489. name: "conv3_2/sep"
  490. type: "Convolution"
  491. bottom: "conv3_2/dw"
  492. top: "conv3_2/sep"
  493. param {
  494. lr_mult: 1
  495. decay_mult: 1
  496. }
  497. convolution_param {
  498. num_output: 256
  499. bias_term: false
  500. pad: 0
  501. kernel_size: 1
  502. stride: 1
  503. weight_filler {
  504. type: "msra"
  505. }
  506. }
  507. }
  508. layer {
  509. name: "conv3_2/sep/bn"
  510. type: "BatchNorm"
  511. bottom: "conv3_2/sep"
  512. top: "conv3_2/sep"
  513. param {
  514. lr_mult: 0
  515. decay_mult: 0
  516. }
  517. param {
  518. lr_mult: 0
  519. decay_mult: 0
  520. }
  521. param {
  522. lr_mult: 0
  523. decay_mult: 0
  524. }
  525. }
  526. layer {
  527. name: "conv3_2/sep/scale"
  528. type: "Scale"
  529. bottom: "conv3_2/sep"
  530. top: "conv3_2/sep"
  531. scale_param {
  532. filler {
  533. value: 1
  534. }
  535. bias_term: true
  536. bias_filler {
  537. value: 0
  538. }
  539. }
  540. }
  541. layer {
  542. name: "relu3_2/sep"
  543. type: "ReLU"
  544. bottom: "conv3_2/sep"
  545. top: "conv3_2/sep"
  546. }
  547.  
  548. #########End of MobileNet#######################
  549.  
  550. layer {
  551. name: "conv4_3_CPM"
  552. type: "Convolution"
  553. bottom: "conv3_2/sep"
  554. top: "conv4_3_CPM"
  555. param {
  556. lr_mult: 1.0
  557. decay_mult: 1
  558. }
  559. param {
  560. lr_mult: 2.0
  561. decay_mult: 0
  562. }
  563. convolution_param {
  564. num_output: 256
  565. pad: 1
  566. kernel_size: 3
  567. weight_filler {
  568. type: "gaussian"
  569. std: 0.01
  570. }
  571. bias_filler {
  572. type: "constant"
  573. }
  574. }
  575. }
  576. layer {
  577. name: "relu4_3_CPM"
  578. type: "ReLU"
  579. bottom: "conv4_3_CPM"
  580. top: "conv4_3_CPM"
  581. }
  582. layer {
  583. name: "conv4_4_CPM"
  584. type: "Convolution"
  585. bottom: "conv4_3_CPM"
  586. top: "conv4_4_CPM"
  587. param {
  588. lr_mult: 1.0
  589. decay_mult: 1
  590. }
  591. param {
  592. lr_mult: 2.0
  593. decay_mult: 0
  594. }
  595. convolution_param {
  596. num_output: 128
  597. pad: 1
  598. kernel_size: 3
  599. weight_filler {
  600. type: "gaussian"
  601. std: 0.01
  602. }
  603. bias_filler {
  604. type: "constant"
  605. }
  606. }
  607. }
  608. layer {
  609. name: "relu4_4_CPM"
  610. type: "ReLU"
  611. bottom: "conv4_4_CPM"
  612. top: "conv4_4_CPM"
  613. }
  614.  
  615.  
  616. #############################End of MobileNet ##########################
  617.  
  618. layer {
  619. name: "s1/hmap/conv1_dw/conv"
  620. type: "Convolution"
  621. bottom: "conv4_4_CPM"
  622. top: "s1/hmap/conv1_dw/conv"
  623. param {
  624. lr_mult: 4.0
  625. decay_mult: 1.0
  626. }
  627. convolution_param {
  628. num_output: 128
  629. bias_term: false
  630. pad: 1
  631. kernel_size: 3
  632. group: 128
  633. stride: 1
  634. weight_filler {
  635. type: "xavier"
  636. }
  637. bias_filler {
  638. type: "constant"
  639. }
  640. }
  641. }
  642. layer {
  643. name: "s1/hmap/conv1_dw/bn"
  644. type: "BatchNorm"
  645. bottom: "s1/hmap/conv1_dw/conv"
  646. top: "s1/hmap/conv1_dw/bn"
  647. param {
  648. lr_mult: 0.0
  649. decay_mult: 0.0
  650. }
  651. param {
  652. lr_mult: 0.0
  653. decay_mult: 0.0
  654. }
  655. param {
  656. lr_mult: 0.0
  657. decay_mult: 0.0
  658. }
  659. }
  660. layer {
  661. name: "s1/hmap/conv1_dw/scale"
  662. type: "Scale"
  663. bottom: "s1/hmap/conv1_dw/bn"
  664. top: "s1/hmap/conv1_dw/bn"
  665. scale_param {
  666. filler {
  667. value: 1.0
  668. }
  669. bias_term: true
  670. bias_filler {
  671. value: 0.0
  672. }
  673. }
  674. }
  675. layer {
  676. name: "s1/hmap/conv1_dw/relu"
  677. type: "ReLU"
  678. bottom: "s1/hmap/conv1_dw/bn"
  679. top: "s1/hmap/conv1_dw/bn"
  680. }
  681. layer {
  682. name: "s1/hmap/conv1_seq/conv"
  683. type: "Convolution"
  684. bottom: "s1/hmap/conv1_dw/bn"
  685. top: "s1/hmap/conv1_seq/conv"
  686. convolution_param {
  687. num_output: 128
  688. bias_term: false
  689. pad: 0
  690. kernel_size: 1
  691. stride: 1
  692. weight_filler {
  693. type: "xavier"
  694. }
  695. bias_filler {
  696. type: "constant"
  697. }
  698. }
  699. }
  700. layer {
  701. name: "s1/hmap/conv1_seq/bn"
  702. type: "BatchNorm"
  703. bottom: "s1/hmap/conv1_seq/conv"
  704. top: "s1/hmap/conv1_seq/bn"
  705. param {
  706. lr_mult: 0.0
  707. decay_mult: 0.0
  708. }
  709. param {
  710. lr_mult: 0.0
  711. decay_mult: 0.0
  712. }
  713. param {
  714. lr_mult: 0.0
  715. decay_mult: 0.0
  716. }
  717. }
  718. layer {
  719. name: "s1/hmap/conv1_seq/scale"
  720. type: "Scale"
  721. bottom: "s1/hmap/conv1_seq/bn"
  722. top: "s1/hmap/conv1_seq/bn"
  723. scale_param {
  724. filler {
  725. value: 1.0
  726. }
  727. bias_term: true
  728. bias_filler {
  729. value: 0.0
  730. }
  731. }
  732. }
  733. layer {
  734. name: "s1/hmap/conv1_seq/relu"
  735. type: "ReLU"
  736. bottom: "s1/hmap/conv1_seq/bn"
  737. top: "s1/hmap/conv1_seq/bn"
  738. }
  739. layer {
  740. name: "s1/hmap/conv2_dw/conv"
  741. type: "Convolution"
  742. bottom: "s1/hmap/conv1_seq/bn"
  743. top: "s1/hmap/conv2_dw/conv"
  744. param {
  745. lr_mult: 4.0
  746. decay_mult: 1.0
  747. }
  748. convolution_param {
  749. num_output: 128
  750. bias_term: false
  751. pad: 1
  752. kernel_size: 3
  753. group: 128
  754. stride: 1
  755. weight_filler {
  756. type: "xavier"
  757. }
  758. bias_filler {
  759. type: "constant"
  760. }
  761. }
  762. }
  763. layer {
  764. name: "s1/hmap/conv2_dw/bn"
  765. type: "BatchNorm"
  766. bottom: "s1/hmap/conv2_dw/conv"
  767. top: "s1/hmap/conv2_dw/bn"
  768. param {
  769. lr_mult: 0.0
  770. decay_mult: 0.0
  771. }
  772. param {
  773. lr_mult: 0.0
  774. decay_mult: 0.0
  775. }
  776. param {
  777. lr_mult: 0.0
  778. decay_mult: 0.0
  779. }
  780. }
  781. layer {
  782. name: "s1/hmap/conv2_dw/scale"
  783. type: "Scale"
  784. bottom: "s1/hmap/conv2_dw/bn"
  785. top: "s1/hmap/conv2_dw/bn"
  786. scale_param {
  787. filler {
  788. value: 1.0
  789. }
  790. bias_term: true
  791. bias_filler {
  792. value: 0.0
  793. }
  794. }
  795. }
  796. layer {
  797. name: "s1/hmap/conv2_dw/relu"
  798. type: "ReLU"
  799. bottom: "s1/hmap/conv2_dw/bn"
  800. top: "s1/hmap/conv2_dw/bn"
  801. }
  802. layer {
  803. name: "s1/hmap/conv2_seq/conv"
  804. type: "Convolution"
  805. bottom: "s1/hmap/conv2_dw/bn"
  806. top: "s1/hmap/conv2_seq/conv"
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  825. top: "s1/hmap/conv2_seq/bn"
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  927. top: "s1/hmap/conv3_seq/conv"
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  964. top: "s1/hmap/conv3_seq/bn"
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  979. top: "s1/hmap/conv3_seq/bn"
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  1026. top: "s1/hmap/con4_1x1/bn"
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  1112. top: "s1/paf/conv1_dw/bn"
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  1127. top: "s1/paf/conv1_dw/bn"
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  1132. bottom: "s1/paf/conv1_dw/bn"
  1133. top: "s1/paf/conv1_seq/conv"
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  1152. top: "s1/paf/conv1_seq/bn"
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  1170. top: "s1/paf/conv1_seq/bn"
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  1185. top: "s1/paf/conv1_seq/bn"
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  1215. top: "s1/paf/conv2_dw/bn"
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  1233. top: "s1/paf/conv2_dw/bn"
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  1248. top: "s1/paf/conv2_dw/bn"
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  1273. top: "s1/paf/conv2_seq/bn"
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  1291. top: "s1/paf/conv2_seq/bn"
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  1306. top: "s1/paf/conv2_seq/bn"
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  1312. top: "s1/paf/conv3_dw/conv"
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  1354. top: "s1/paf/conv3_dw/bn"
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  1368. bottom: "s1/paf/conv3_dw/bn"
  1369. top: "s1/paf/conv3_dw/bn"
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  1374. bottom: "s1/paf/conv3_dw/bn"
  1375. top: "s1/paf/conv3_seq/conv"
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  1380. kernel_size: 1
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  1394. top: "s1/paf/conv3_seq/bn"
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  1412. top: "s1/paf/conv3_seq/bn"
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  1427. top: "s1/paf/conv3_seq/bn"
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  1433. top: "s1/paf/con4_1x1/conv"
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  1436. decay_mult: 1.0
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  1456. top: "s1/paf/con4_1x1/bn"
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  1474. top: "s1/paf/con4_1x1/bn"
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  1487. type: "ReLU"
  1488. bottom: "s1/paf/con4_1x1/bn"
  1489. top: "s1/paf/con4_1x1/bn"
  1490. }
  1491. layer {
  1492. name: "s1/paf/conv5_1x1"
  1493. type: "Convolution"
  1494. bottom: "s1/paf/con4_1x1/bn"
  1495. top: "s1/paf/conv5_1x1"
  1496. param {
  1497. lr_mult: 4.0
  1498. decay_mult: 1.0
  1499. }
  1500. convolution_param {
  1501. num_output: 38
  1502. bias_term: false
  1503. pad: 0
  1504. kernel_size: 1
  1505. stride: 1
  1506. weight_filler {
  1507. type: "xavier"
  1508. }
  1509. bias_filler {
  1510. type: "constant"
  1511. }
  1512. }
  1513. }
  1514. layer {
  1515. name: "concat_stage2"
  1516. type: "Concat"
  1517. bottom: "conv4_4_CPM"
  1518. bottom: "s1/paf/conv5_1x1"
  1519. bottom: "s1/hmap/conv5_1x1"
  1520. top: "concat_stage2"
  1521. }
  1522. layer {
  1523. name: "concat_stage2_conv"
  1524. type: "Convolution"
  1525. bottom: "concat_stage2"
  1526. top: "concat_stage2_conv"
  1527. param {
  1528. lr_mult: 4.0
  1529. decay_mult: 1.0
  1530. }
  1531. convolution_param {
  1532. num_output: 128
  1533. bias_term: false
  1534. pad: 0
  1535. kernel_size: 1
  1536. stride: 1
  1537. weight_filler {
  1538. type: "xavier"
  1539. }
  1540. bias_filler {
  1541. type: "constant"
  1542. }
  1543. }
  1544. }
  1545. layer {
  1546. name: "s2/hmap/conv1_dw/conv"
  1547. type: "Convolution"
  1548. bottom: "concat_stage2_conv"
  1549. top: "s2/hmap/conv1_dw/conv"
  1550. param {
  1551. lr_mult: 4.0
  1552. decay_mult: 1.0
  1553. }
  1554. convolution_param {
  1555. num_output: 128
  1556. bias_term: false
  1557. pad: 3
  1558. kernel_size: 7
  1559. group: 128
  1560. stride: 1
  1561. weight_filler {
  1562. type: "xavier"
  1563. }
  1564. bias_filler {
  1565. type: "constant"
  1566. }
  1567. }
  1568. }
  1569. layer {
  1570. name: "s2/hmap/conv1_dw/bn"
  1571. type: "BatchNorm"
  1572. bottom: "s2/hmap/conv1_dw/conv"
  1573. top: "s2/hmap/conv1_dw/bn"
  1574. param {
  1575. lr_mult: 0.0
  1576. decay_mult: 0.0
  1577. }
  1578. param {
  1579. lr_mult: 0.0
  1580. decay_mult: 0.0
  1581. }
  1582. param {
  1583. lr_mult: 0.0
  1584. decay_mult: 0.0
  1585. }
  1586. }
  1587. layer {
  1588. name: "s2/hmap/conv1_dw/scale"
  1589. type: "Scale"
  1590. bottom: "s2/hmap/conv1_dw/bn"
  1591. top: "s2/hmap/conv1_dw/bn"
  1592. scale_param {
  1593. filler {
  1594. value: 1.0
  1595. }
  1596. bias_term: true
  1597. bias_filler {
  1598. value: 0.0
  1599. }
  1600. }
  1601. }
  1602. layer {
  1603. name: "s2/hmap/conv1_dw/relu"
  1604. type: "ReLU"
  1605. bottom: "s2/hmap/conv1_dw/bn"
  1606. top: "s2/hmap/conv1_dw/bn"
  1607. }
  1608. layer {
  1609. name: "s2/hmap/conv1_seq/conv"
  1610. type: "Convolution"
  1611. bottom: "s2/hmap/conv1_dw/bn"
  1612. top: "s2/hmap/conv1_seq/conv"
  1613. convolution_param {
  1614. num_output: 128
  1615. bias_term: false
  1616. pad: 0
  1617. kernel_size: 1
  1618. stride: 1
  1619. weight_filler {
  1620. type: "xavier"
  1621. }
  1622. bias_filler {
  1623. type: "constant"
  1624. }
  1625. }
  1626. }
  1627. layer {
  1628. name: "s2/hmap/conv1_seq/bn"
  1629. type: "BatchNorm"
  1630. bottom: "s2/hmap/conv1_seq/conv"
  1631. top: "s2/hmap/conv1_seq/bn"
  1632. param {
  1633. lr_mult: 0.0
  1634. decay_mult: 0.0
  1635. }
  1636. param {
  1637. lr_mult: 0.0
  1638. decay_mult: 0.0
  1639. }
  1640. param {
  1641. lr_mult: 0.0
  1642. decay_mult: 0.0
  1643. }
  1644. }
  1645. layer {
  1646. name: "s2/hmap/conv1_seq/scale"
  1647. type: "Scale"
  1648. bottom: "s2/hmap/conv1_seq/bn"
  1649. top: "s2/hmap/conv1_seq/bn"
  1650. scale_param {
  1651. filler {
  1652. value: 1.0
  1653. }
  1654. bias_term: true
  1655. bias_filler {
  1656. value: 0.0
  1657. }
  1658. }
  1659. }
  1660. layer {
  1661. name: "s2/hmap/conv1_seq/relu"
  1662. type: "ReLU"
  1663. bottom: "s2/hmap/conv1_seq/bn"
  1664. top: "s2/hmap/conv1_seq/bn"
  1665. }
  1666. layer {
  1667. name: "s2/hmap/conv2_dw/conv"
  1668. type: "Convolution"
  1669. bottom: "s2/hmap/conv1_seq/bn"
  1670. top: "s2/hmap/conv2_dw/conv"
  1671. param {
  1672. lr_mult: 4.0
  1673. decay_mult: 1.0
  1674. }
  1675. convolution_param {
  1676. num_output: 128
  1677. bias_term: false
  1678. pad: 3
  1679. kernel_size: 7
  1680. group: 128
  1681. stride: 1
  1682. weight_filler {
  1683. type: "xavier"
  1684. }
  1685. bias_filler {
  1686. type: "constant"
  1687. }
  1688. }
  1689. }
  1690. layer {
  1691. name: "s2/hmap/conv2_dw/bn"
  1692. type: "BatchNorm"
  1693. bottom: "s2/hmap/conv2_dw/conv"
  1694. top: "s2/hmap/conv2_dw/bn"
  1695. param {
  1696. lr_mult: 0.0
  1697. decay_mult: 0.0
  1698. }
  1699. param {
  1700. lr_mult: 0.0
  1701. decay_mult: 0.0
  1702. }
  1703. param {
  1704. lr_mult: 0.0
  1705. decay_mult: 0.0
  1706. }
  1707. }
  1708. layer {
  1709. name: "s2/hmap/conv2_dw/scale"
  1710. type: "Scale"
  1711. bottom: "s2/hmap/conv2_dw/bn"
  1712. top: "s2/hmap/conv2_dw/bn"
  1713. scale_param {
  1714. filler {
  1715. value: 1.0
  1716. }
  1717. bias_term: true
  1718. bias_filler {
  1719. value: 0.0
  1720. }
  1721. }
  1722. }
  1723. layer {
  1724. name: "s2/hmap/conv2_dw/relu"
  1725. type: "ReLU"
  1726. bottom: "s2/hmap/conv2_dw/bn"
  1727. top: "s2/hmap/conv2_dw/bn"
  1728. }
  1729. layer {
  1730. name: "s2/hmap/conv2_seq/conv"
  1731. type: "Convolution"
  1732. bottom: "s2/hmap/conv2_dw/bn"
  1733. top: "s2/hmap/conv2_seq/conv"
  1734. convolution_param {
  1735. num_output: 128
  1736. bias_term: false
  1737. pad: 0
  1738. kernel_size: 1
  1739. stride: 1
  1740. weight_filler {
  1741. type: "xavier"
  1742. }
  1743. bias_filler {
  1744. type: "constant"
  1745. }
  1746. }
  1747. }
  1748. layer {
  1749. name: "s2/hmap/conv2_seq/bn"
  1750. type: "BatchNorm"
  1751. bottom: "s2/hmap/conv2_seq/conv"
  1752. top: "s2/hmap/conv2_seq/bn"
  1753. param {
  1754. lr_mult: 0.0
  1755. decay_mult: 0.0
  1756. }
  1757. param {
  1758. lr_mult: 0.0
  1759. decay_mult: 0.0
  1760. }
  1761. param {
  1762. lr_mult: 0.0
  1763. decay_mult: 0.0
  1764. }
  1765. }
  1766. layer {
  1767. name: "s2/hmap/conv2_seq/scale"
  1768. type: "Scale"
  1769. bottom: "s2/hmap/conv2_seq/bn"
  1770. top: "s2/hmap/conv2_seq/bn"
  1771. scale_param {
  1772. filler {
  1773. value: 1.0
  1774. }
  1775. bias_term: true
  1776. bias_filler {
  1777. value: 0.0
  1778. }
  1779. }
  1780. }
  1781. layer {
  1782. name: "s2/hmap/conv2_seq/relu"
  1783. type: "ReLU"
  1784. bottom: "s2/hmap/conv2_seq/bn"
  1785. top: "s2/hmap/conv2_seq/bn"
  1786. }
  1787. layer {
  1788. name: "s2/hmap/conv3_dw/conv"
  1789. type: "Convolution"
  1790. bottom: "s2/hmap/conv2_seq/bn"
  1791. top: "s2/hmap/conv3_dw/conv"
  1792. param {
  1793. lr_mult: 4.0
  1794. decay_mult: 1.0
  1795. }
  1796. convolution_param {
  1797. num_output: 128
  1798. bias_term: false
  1799. pad: 3
  1800. kernel_size: 7
  1801. group: 128
  1802. stride: 1
  1803. weight_filler {
  1804. type: "xavier"
  1805. }
  1806. bias_filler {
  1807. type: "constant"
  1808. }
  1809. }
  1810. }
  1811. layer {
  1812. name: "s2/hmap/conv3_dw/bn"
  1813. type: "BatchNorm"
  1814. bottom: "s2/hmap/conv3_dw/conv"
  1815. top: "s2/hmap/conv3_dw/bn"
  1816. param {
  1817. lr_mult: 0.0
  1818. decay_mult: 0.0
  1819. }
  1820. param {
  1821. lr_mult: 0.0
  1822. decay_mult: 0.0
  1823. }
  1824. param {
  1825. lr_mult: 0.0
  1826. decay_mult: 0.0
  1827. }
  1828. }
  1829. layer {
  1830. name: "s2/hmap/conv3_dw/scale"
  1831. type: "Scale"
  1832. bottom: "s2/hmap/conv3_dw/bn"
  1833. top: "s2/hmap/conv3_dw/bn"
  1834. scale_param {
  1835. filler {
  1836. value: 1.0
  1837. }
  1838. bias_term: true
  1839. bias_filler {
  1840. value: 0.0
  1841. }
  1842. }
  1843. }
  1844. layer {
  1845. name: "s2/hmap/conv3_dw/relu"
  1846. type: "ReLU"
  1847. bottom: "s2/hmap/conv3_dw/bn"
  1848. top: "s2/hmap/conv3_dw/bn"
  1849. }
  1850. layer {
  1851. name: "s2/hmap/conv3_seq/conv"
  1852. type: "Convolution"
  1853. bottom: "s2/hmap/conv3_dw/bn"
  1854. top: "s2/hmap/conv3_seq/conv"
  1855. convolution_param {
  1856. num_output: 128
  1857. bias_term: false
  1858. pad: 0
  1859. kernel_size: 1
  1860. stride: 1
  1861. weight_filler {
  1862. type: "xavier"
  1863. }
  1864. bias_filler {
  1865. type: "constant"
  1866. }
  1867. }
  1868. }
  1869. layer {
  1870. name: "s2/hmap/conv3_seq/bn"
  1871. type: "BatchNorm"
  1872. bottom: "s2/hmap/conv3_seq/conv"
  1873. top: "s2/hmap/conv3_seq/bn"
  1874. param {
  1875. lr_mult: 0.0
  1876. decay_mult: 0.0
  1877. }
  1878. param {
  1879. lr_mult: 0.0
  1880. decay_mult: 0.0
  1881. }
  1882. param {
  1883. lr_mult: 0.0
  1884. decay_mult: 0.0
  1885. }
  1886. }
  1887. layer {
  1888. name: "s2/hmap/conv3_seq/scale"
  1889. type: "Scale"
  1890. bottom: "s2/hmap/conv3_seq/bn"
  1891. top: "s2/hmap/conv3_seq/bn"
  1892. scale_param {
  1893. filler {
  1894. value: 1.0
  1895. }
  1896. bias_term: true
  1897. bias_filler {
  1898. value: 0.0
  1899. }
  1900. }
  1901. }
  1902. layer {
  1903. name: "s2/hmap/conv3_seq/relu"
  1904. type: "ReLU"
  1905. bottom: "s2/hmap/conv3_seq/bn"
  1906. top: "s2/hmap/conv3_seq/bn"
  1907. }
  1908. layer {
  1909. name: "s2/hmap/con4_1x1/conv"
  1910. type: "Convolution"
  1911. bottom: "s2/hmap/conv3_seq/bn"
  1912. top: "s2/hmap/con4_1x1/conv"
  1913. param {
  1914. lr_mult: 4.0
  1915. decay_mult: 1.0
  1916. }
  1917. convolution_param {
  1918. num_output: 128
  1919. bias_term: false
  1920. pad: 3
  1921. kernel_size: 7
  1922. group: 128
  1923. stride: 1
  1924. weight_filler {
  1925. type: "xavier"
  1926. }
  1927. bias_filler {
  1928. type: "constant"
  1929. }
  1930. }
  1931. }
  1932. layer {
  1933. name: "s2/hmap/con4_1x1/bn"
  1934. type: "BatchNorm"
  1935. bottom: "s2/hmap/con4_1x1/conv"
  1936. top: "s2/hmap/con4_1x1/bn"
  1937. param {
  1938. lr_mult: 0.0
  1939. decay_mult: 0.0
  1940. }
  1941. param {
  1942. lr_mult: 0.0
  1943. decay_mult: 0.0
  1944. }
  1945. param {
  1946. lr_mult: 0.0
  1947. decay_mult: 0.0
  1948. }
  1949. }
  1950. layer {
  1951. name: "s2/hmap/con4_1x1/scale"
  1952. type: "Scale"
  1953. bottom: "s2/hmap/con4_1x1/bn"
  1954. top: "s2/hmap/con4_1x1/bn"
  1955. scale_param {
  1956. filler {
  1957. value: 1.0
  1958. }
  1959. bias_term: true
  1960. bias_filler {
  1961. value: 0.0
  1962. }
  1963. }
  1964. }
  1965. layer {
  1966. name: "s2/hmap/con4_1x1/relu"
  1967. type: "ReLU"
  1968. bottom: "s2/hmap/con4_1x1/bn"
  1969. top: "s2/hmap/con4_1x1/bn"
  1970. }
  1971. layer {
  1972. name: "s2/hmap/conv4_seq/conv"
  1973. type: "Convolution"
  1974. bottom: "s2/hmap/con4_1x1/bn"
  1975. top: "s2/hmap/conv4_seq/conv"
  1976. convolution_param {
  1977. num_output: 128
  1978. bias_term: false
  1979. pad: 0
  1980. kernel_size: 1
  1981. stride: 1
  1982. weight_filler {
  1983. type: "xavier"
  1984. }
  1985. bias_filler {
  1986. type: "constant"
  1987. }
  1988. }
  1989. }
  1990. layer {
  1991. name: "s2/hmap/conv4_seq/bn"
  1992. type: "BatchNorm"
  1993. bottom: "s2/hmap/conv4_seq/conv"
  1994. top: "s2/hmap/conv4_seq/bn"
  1995. param {
  1996. lr_mult: 0.0
  1997. decay_mult: 0.0
  1998. }
  1999. param {
  2000. lr_mult: 0.0
  2001. decay_mult: 0.0
  2002. }
  2003. param {
  2004. lr_mult: 0.0
  2005. decay_mult: 0.0
  2006. }
  2007. }
  2008. layer {
  2009. name: "s2/hmap/conv4_seq/scale"
  2010. type: "Scale"
  2011. bottom: "s2/hmap/conv4_seq/bn"
  2012. top: "s2/hmap/conv4_seq/bn"
  2013. scale_param {
  2014. filler {
  2015. value: 1.0
  2016. }
  2017. bias_term: true
  2018. bias_filler {
  2019. value: 0.0
  2020. }
  2021. }
  2022. }
  2023. layer {
  2024. name: "s2/hmap/conv4_seq/relu"
  2025. type: "ReLU"
  2026. bottom: "s2/hmap/conv4_seq/bn"
  2027. top: "s2/hmap/conv4_seq/bn"
  2028. }
  2029. layer {
  2030. name: "s2/hmap/conv5_dw/conv"
  2031. type: "Convolution"
  2032. bottom: "s2/hmap/conv4_seq/bn"
  2033. top: "s2/hmap/conv5_dw/conv"
  2034. param {
  2035. lr_mult: 4.0
  2036. decay_mult: 1.0
  2037. }
  2038. convolution_param {
  2039. num_output: 128
  2040. bias_term: false
  2041. pad: 3
  2042. kernel_size: 7
  2043. group: 128
  2044. stride: 1
  2045. weight_filler {
  2046. type: "xavier"
  2047. }
  2048. bias_filler {
  2049. type: "constant"
  2050. }
  2051. }
  2052. }
  2053. layer {
  2054. name: "s2/hmap/conv5_dw/bn"
  2055. type: "BatchNorm"
  2056. bottom: "s2/hmap/conv5_dw/conv"
  2057. top: "s2/hmap/conv5_dw/bn"
  2058. param {
  2059. lr_mult: 0.0
  2060. decay_mult: 0.0
  2061. }
  2062. param {
  2063. lr_mult: 0.0
  2064. decay_mult: 0.0
  2065. }
  2066. param {
  2067. lr_mult: 0.0
  2068. decay_mult: 0.0
  2069. }
  2070. }
  2071. layer {
  2072. name: "s2/hmap/conv5_dw/scale"
  2073. type: "Scale"
  2074. bottom: "s2/hmap/conv5_dw/bn"
  2075. top: "s2/hmap/conv5_dw/bn"
  2076. scale_param {
  2077. filler {
  2078. value: 1.0
  2079. }
  2080. bias_term: true
  2081. bias_filler {
  2082. value: 0.0
  2083. }
  2084. }
  2085. }
  2086. layer {
  2087. name: "s2/hmap/conv5_dw/relu"
  2088. type: "ReLU"
  2089. bottom: "s2/hmap/conv5_dw/bn"
  2090. top: "s2/hmap/conv5_dw/bn"
  2091. }
  2092. layer {
  2093. name: "s2/hmap/conv5_seq/conv"
  2094. type: "Convolution"
  2095. bottom: "s2/hmap/conv5_dw/bn"
  2096. top: "s2/hmap/conv5_seq/conv"
  2097. convolution_param {
  2098. num_output: 128
  2099. bias_term: false
  2100. pad: 0
  2101. kernel_size: 1
  2102. stride: 1
  2103. weight_filler {
  2104. type: "xavier"
  2105. }
  2106. bias_filler {
  2107. type: "constant"
  2108. }
  2109. }
  2110. }
  2111. layer {
  2112. name: "s2/hmap/conv5_seq/bn"
  2113. type: "BatchNorm"
  2114. bottom: "s2/hmap/conv5_seq/conv"
  2115. top: "s2/hmap/conv5_seq/bn"
  2116. param {
  2117. lr_mult: 0.0
  2118. decay_mult: 0.0
  2119. }
  2120. param {
  2121. lr_mult: 0.0
  2122. decay_mult: 0.0
  2123. }
  2124. param {
  2125. lr_mult: 0.0
  2126. decay_mult: 0.0
  2127. }
  2128. }
  2129. layer {
  2130. name: "s2/hmap/conv5_seq/scale"
  2131. type: "Scale"
  2132. bottom: "s2/hmap/conv5_seq/bn"
  2133. top: "s2/hmap/conv5_seq/bn"
  2134. scale_param {
  2135. filler {
  2136. value: 1.0
  2137. }
  2138. bias_term: true
  2139. bias_filler {
  2140. value: 0.0
  2141. }
  2142. }
  2143. }
  2144. layer {
  2145. name: "s2/hmap/conv5_seq/relu"
  2146. type: "ReLU"
  2147. bottom: "s2/hmap/conv5_seq/bn"
  2148. top: "s2/hmap/conv5_seq/bn"
  2149. }
  2150. layer {
  2151. name: "s2/hmap/con6_1x1/conv"
  2152. type: "Convolution"
  2153. bottom: "s2/hmap/conv5_seq/bn"
  2154. top: "s2/hmap/con6_1x1/conv"
  2155. param {
  2156. lr_mult: 4.0
  2157. decay_mult: 1.0
  2158. }
  2159. convolution_param {
  2160. num_output: 512
  2161. bias_term: false
  2162. pad: 0
  2163. kernel_size: 1
  2164. stride: 1
  2165. weight_filler {
  2166. type: "xavier"
  2167. }
  2168. bias_filler {
  2169. type: "constant"
  2170. }
  2171. }
  2172. }
  2173. layer {
  2174. name: "s2/hmap/con6_1x1/bn"
  2175. type: "BatchNorm"
  2176. bottom: "s2/hmap/con6_1x1/conv"
  2177. top: "s2/hmap/con6_1x1/bn"
  2178. param {
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  2180. decay_mult: 0.0
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  2182. param {
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  2184. decay_mult: 0.0
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  2186. param {
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  2188. decay_mult: 0.0
  2189. }
  2190. }
  2191. layer {
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  2193. type: "Scale"
  2194. bottom: "s2/hmap/con6_1x1/bn"
  2195. top: "s2/hmap/con6_1x1/bn"
  2196. scale_param {
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  2200. bias_term: true
  2201. bias_filler {
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  2203. }
  2204. }
  2205. }
  2206. layer {
  2207. name: "s2/hmap/con6_1x1/relu"
  2208. type: "ReLU"
  2209. bottom: "s2/hmap/con6_1x1/bn"
  2210. top: "s2/hmap/con6_1x1/bn"
  2211. }
  2212. layer {
  2213. name: "s2/hmap/conv7_1x1"
  2214. type: "Convolution"
  2215. bottom: "s2/hmap/con6_1x1/bn"
  2216. top: "s2/hmap/conv7_1x1"
  2217. param {
  2218. lr_mult: 4.0
  2219. decay_mult: 1.0
  2220. }
  2221. convolution_param {
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  2223. bias_term: false
  2224. pad: 0
  2225. kernel_size: 1
  2226. stride: 1
  2227. weight_filler {
  2228. type: "xavier"
  2229. }
  2230. bias_filler {
  2231. type: "constant"
  2232. }
  2233. }
  2234. }
  2235. layer {
  2236. name: "s2/paf/conv1_dw/conv"
  2237. type: "Convolution"
  2238. bottom: "concat_stage2_conv"
  2239. top: "s2/paf/conv1_dw/conv"
  2240. param {
  2241. lr_mult: 4.0
  2242. decay_mult: 1.0
  2243. }
  2244. convolution_param {
  2245. num_output: 128
  2246. bias_term: false
  2247. pad: 3
  2248. kernel_size: 7
  2249. group: 128
  2250. stride: 1
  2251. weight_filler {
  2252. type: "xavier"
  2253. }
  2254. bias_filler {
  2255. type: "constant"
  2256. }
  2257. }
  2258. }
  2259. layer {
  2260. name: "s2/paf/conv1_dw/bn"
  2261. type: "BatchNorm"
  2262. bottom: "s2/paf/conv1_dw/conv"
  2263. top: "s2/paf/conv1_dw/bn"
  2264. param {
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  2266. decay_mult: 0.0
  2267. }
  2268. param {
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  2270. decay_mult: 0.0
  2271. }
  2272. param {
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  2274. decay_mult: 0.0
  2275. }
  2276. }
  2277. layer {
  2278. name: "s2/paf/conv1_dw/scale"
  2279. type: "Scale"
  2280. bottom: "s2/paf/conv1_dw/bn"
  2281. top: "s2/paf/conv1_dw/bn"
  2282. scale_param {
  2283. filler {
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  2285. }
  2286. bias_term: true
  2287. bias_filler {
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  2289. }
  2290. }
  2291. }
  2292. layer {
  2293. name: "s2/paf/conv1_dw/relu"
  2294. type: "ReLU"
  2295. bottom: "s2/paf/conv1_dw/bn"
  2296. top: "s2/paf/conv1_dw/bn"
  2297. }
  2298. layer {
  2299. name: "s2/paf/conv1_seq/conv"
  2300. type: "Convolution"
  2301. bottom: "s2/paf/conv1_dw/bn"
  2302. top: "s2/paf/conv1_seq/conv"
  2303. convolution_param {
  2304. num_output: 128
  2305. bias_term: false
  2306. pad: 0
  2307. kernel_size: 1
  2308. stride: 1
  2309. weight_filler {
  2310. type: "xavier"
  2311. }
  2312. bias_filler {
  2313. type: "constant"
  2314. }
  2315. }
  2316. }
  2317. layer {
  2318. name: "s2/paf/conv1_seq/bn"
  2319. type: "BatchNorm"
  2320. bottom: "s2/paf/conv1_seq/conv"
  2321. top: "s2/paf/conv1_seq/bn"
  2322. param {
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  2324. decay_mult: 0.0
  2325. }
  2326. param {
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  2328. decay_mult: 0.0
  2329. }
  2330. param {
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  2332. decay_mult: 0.0
  2333. }
  2334. }
  2335. layer {
  2336. name: "s2/paf/conv1_seq/scale"
  2337. type: "Scale"
  2338. bottom: "s2/paf/conv1_seq/bn"
  2339. top: "s2/paf/conv1_seq/bn"
  2340. scale_param {
  2341. filler {
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  2343. }
  2344. bias_term: true
  2345. bias_filler {
  2346. value: 0.0
  2347. }
  2348. }
  2349. }
  2350. layer {
  2351. name: "s2/paf/conv1_seq/relu"
  2352. type: "ReLU"
  2353. bottom: "s2/paf/conv1_seq/bn"
  2354. top: "s2/paf/conv1_seq/bn"
  2355. }
  2356. layer {
  2357. name: "s2/paf/conv2_dw/conv"
  2358. type: "Convolution"
  2359. bottom: "s2/paf/conv1_seq/bn"
  2360. top: "s2/paf/conv2_dw/conv"
  2361. param {
  2362. lr_mult: 4.0
  2363. decay_mult: 1.0
  2364. }
  2365. convolution_param {
  2366. num_output: 128
  2367. bias_term: false
  2368. pad: 3
  2369. kernel_size: 7
  2370. group: 128
  2371. stride: 1
  2372. weight_filler {
  2373. type: "xavier"
  2374. }
  2375. bias_filler {
  2376. type: "constant"
  2377. }
  2378. }
  2379. }
  2380. layer {
  2381. name: "s2/paf/conv2_dw/bn"
  2382. type: "BatchNorm"
  2383. bottom: "s2/paf/conv2_dw/conv"
  2384. top: "s2/paf/conv2_dw/bn"
  2385. param {
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  2387. decay_mult: 0.0
  2388. }
  2389. param {
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  2391. decay_mult: 0.0
  2392. }
  2393. param {
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  2395. decay_mult: 0.0
  2396. }
  2397. }
  2398. layer {
  2399. name: "s2/paf/conv2_dw/scale"
  2400. type: "Scale"
  2401. bottom: "s2/paf/conv2_dw/bn"
  2402. top: "s2/paf/conv2_dw/bn"
  2403. scale_param {
  2404. filler {
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  2406. }
  2407. bias_term: true
  2408. bias_filler {
  2409. value: 0.0
  2410. }
  2411. }
  2412. }
  2413. layer {
  2414. name: "s2/paf/conv2_dw/relu"
  2415. type: "ReLU"
  2416. bottom: "s2/paf/conv2_dw/bn"
  2417. top: "s2/paf/conv2_dw/bn"
  2418. }
  2419. layer {
  2420. name: "s2/paf/conv2_seq/conv"
  2421. type: "Convolution"
  2422. bottom: "s2/paf/conv2_dw/bn"
  2423. top: "s2/paf/conv2_seq/conv"
  2424. convolution_param {
  2425. num_output: 128
  2426. bias_term: false
  2427. pad: 0
  2428. kernel_size: 1
  2429. stride: 1
  2430. weight_filler {
  2431. type: "xavier"
  2432. }
  2433. bias_filler {
  2434. type: "constant"
  2435. }
  2436. }
  2437. }
  2438. layer {
  2439. name: "s2/paf/conv2_seq/bn"
  2440. type: "BatchNorm"
  2441. bottom: "s2/paf/conv2_seq/conv"
  2442. top: "s2/paf/conv2_seq/bn"
  2443. param {
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  2445. decay_mult: 0.0
  2446. }
  2447. param {
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  2449. decay_mult: 0.0
  2450. }
  2451. param {
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  2453. decay_mult: 0.0
  2454. }
  2455. }
  2456. layer {
  2457. name: "s2/paf/conv2_seq/scale"
  2458. type: "Scale"
  2459. bottom: "s2/paf/conv2_seq/bn"
  2460. top: "s2/paf/conv2_seq/bn"
  2461. scale_param {
  2462. filler {
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  2464. }
  2465. bias_term: true
  2466. bias_filler {
  2467. value: 0.0
  2468. }
  2469. }
  2470. }
  2471. layer {
  2472. name: "s2/paf/conv2_seq/relu"
  2473. type: "ReLU"
  2474. bottom: "s2/paf/conv2_seq/bn"
  2475. top: "s2/paf/conv2_seq/bn"
  2476. }
  2477. layer {
  2478. name: "s2/paf/conv3_dw/conv"
  2479. type: "Convolution"
  2480. bottom: "s2/paf/conv2_seq/bn"
  2481. top: "s2/paf/conv3_dw/conv"
  2482. param {
  2483. lr_mult: 4.0
  2484. decay_mult: 1.0
  2485. }
  2486. convolution_param {
  2487. num_output: 128
  2488. bias_term: false
  2489. pad: 3
  2490. kernel_size: 7
  2491. group: 128
  2492. stride: 1
  2493. weight_filler {
  2494. type: "xavier"
  2495. }
  2496. bias_filler {
  2497. type: "constant"
  2498. }
  2499. }
  2500. }
  2501. layer {
  2502. name: "s2/paf/conv3_dw/bn"
  2503. type: "BatchNorm"
  2504. bottom: "s2/paf/conv3_dw/conv"
  2505. top: "s2/paf/conv3_dw/bn"
  2506. param {
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  2508. decay_mult: 0.0
  2509. }
  2510. param {
  2511. lr_mult: 0.0
  2512. decay_mult: 0.0
  2513. }
  2514. param {
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  2516. decay_mult: 0.0
  2517. }
  2518. }
  2519. layer {
  2520. name: "s2/paf/conv3_dw/scale"
  2521. type: "Scale"
  2522. bottom: "s2/paf/conv3_dw/bn"
  2523. top: "s2/paf/conv3_dw/bn"
  2524. scale_param {
  2525. filler {
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  2527. }
  2528. bias_term: true
  2529. bias_filler {
  2530. value: 0.0
  2531. }
  2532. }
  2533. }
  2534. layer {
  2535. name: "s2/paf/conv3_dw/relu"
  2536. type: "ReLU"
  2537. bottom: "s2/paf/conv3_dw/bn"
  2538. top: "s2/paf/conv3_dw/bn"
  2539. }
  2540. layer {
  2541. name: "s2/paf/conv3_seq/conv"
  2542. type: "Convolution"
  2543. bottom: "s2/paf/conv3_dw/bn"
  2544. top: "s2/paf/conv3_seq/conv"
  2545. convolution_param {
  2546. num_output: 128
  2547. bias_term: false
  2548. pad: 0
  2549. kernel_size: 1
  2550. stride: 1
  2551. weight_filler {
  2552. type: "xavier"
  2553. }
  2554. bias_filler {
  2555. type: "constant"
  2556. }
  2557. }
  2558. }
  2559. layer {
  2560. name: "s2/paf/conv3_seq/bn"
  2561. type: "BatchNorm"
  2562. bottom: "s2/paf/conv3_seq/conv"
  2563. top: "s2/paf/conv3_seq/bn"
  2564. param {
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  2566. decay_mult: 0.0
  2567. }
  2568. param {
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  2570. decay_mult: 0.0
  2571. }
  2572. param {
  2573. lr_mult: 0.0
  2574. decay_mult: 0.0
  2575. }
  2576. }
  2577. layer {
  2578. name: "s2/paf/conv3_seq/scale"
  2579. type: "Scale"
  2580. bottom: "s2/paf/conv3_seq/bn"
  2581. top: "s2/paf/conv3_seq/bn"
  2582. scale_param {
  2583. filler {
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  2585. }
  2586. bias_term: true
  2587. bias_filler {
  2588. value: 0.0
  2589. }
  2590. }
  2591. }
  2592. layer {
  2593. name: "s2/paf/conv3_seq/relu"
  2594. type: "ReLU"
  2595. bottom: "s2/paf/conv3_seq/bn"
  2596. top: "s2/paf/conv3_seq/bn"
  2597. }
  2598. layer {
  2599. name: "s2/paf/con4_1x1/conv"
  2600. type: "Convolution"
  2601. bottom: "s2/paf/conv3_seq/bn"
  2602. top: "s2/paf/con4_1x1/conv"
  2603. param {
  2604. lr_mult: 4.0
  2605. decay_mult: 1.0
  2606. }
  2607. convolution_param {
  2608. num_output: 128
  2609. bias_term: false
  2610. pad: 3
  2611. kernel_size: 7
  2612. group: 128
  2613. stride: 1
  2614. weight_filler {
  2615. type: "xavier"
  2616. }
  2617. bias_filler {
  2618. type: "constant"
  2619. }
  2620. }
  2621. }
  2622. layer {
  2623. name: "s2/paf/con4_1x1/bn"
  2624. type: "BatchNorm"
  2625. bottom: "s2/paf/con4_1x1/conv"
  2626. top: "s2/paf/con4_1x1/bn"
  2627. param {
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  2629. decay_mult: 0.0
  2630. }
  2631. param {
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  2633. decay_mult: 0.0
  2634. }
  2635. param {
  2636. lr_mult: 0.0
  2637. decay_mult: 0.0
  2638. }
  2639. }
  2640. layer {
  2641. name: "s2/paf/con4_1x1/scale"
  2642. type: "Scale"
  2643. bottom: "s2/paf/con4_1x1/bn"
  2644. top: "s2/paf/con4_1x1/bn"
  2645. scale_param {
  2646. filler {
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  2648. }
  2649. bias_term: true
  2650. bias_filler {
  2651. value: 0.0
  2652. }
  2653. }
  2654. }
  2655. layer {
  2656. name: "s2/paf/con4_1x1/relu"
  2657. type: "ReLU"
  2658. bottom: "s2/paf/con4_1x1/bn"
  2659. top: "s2/paf/con4_1x1/bn"
  2660. }
  2661. layer {
  2662. name: "s2/paf/conv4_seq/conv"
  2663. type: "Convolution"
  2664. bottom: "s2/paf/con4_1x1/bn"
  2665. top: "s2/paf/conv4_seq/conv"
  2666. convolution_param {
  2667. num_output: 128
  2668. bias_term: false
  2669. pad: 0
  2670. kernel_size: 1
  2671. stride: 1
  2672. weight_filler {
  2673. type: "xavier"
  2674. }
  2675. bias_filler {
  2676. type: "constant"
  2677. }
  2678. }
  2679. }
  2680. layer {
  2681. name: "s2/paf/conv4_seq/bn"
  2682. type: "BatchNorm"
  2683. bottom: "s2/paf/conv4_seq/conv"
  2684. top: "s2/paf/conv4_seq/bn"
  2685. param {
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  2687. decay_mult: 0.0
  2688. }
  2689. param {
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  2691. decay_mult: 0.0
  2692. }
  2693. param {
  2694. lr_mult: 0.0
  2695. decay_mult: 0.0
  2696. }
  2697. }
  2698. layer {
  2699. name: "s2/paf/conv4_seq/scale"
  2700. type: "Scale"
  2701. bottom: "s2/paf/conv4_seq/bn"
  2702. top: "s2/paf/conv4_seq/bn"
  2703. scale_param {
  2704. filler {
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  2706. }
  2707. bias_term: true
  2708. bias_filler {
  2709. value: 0.0
  2710. }
  2711. }
  2712. }
  2713. layer {
  2714. name: "s2/paf/conv4_seq/relu"
  2715. type: "ReLU"
  2716. bottom: "s2/paf/conv4_seq/bn"
  2717. top: "s2/paf/conv4_seq/bn"
  2718. }
  2719. layer {
  2720. name: "s2/paf/conv5_dw/conv"
  2721. type: "Convolution"
  2722. bottom: "s2/paf/conv4_seq/bn"
  2723. top: "s2/paf/conv5_dw/conv"
  2724. param {
  2725. lr_mult: 4.0
  2726. decay_mult: 1.0
  2727. }
  2728. convolution_param {
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  2730. bias_term: false
  2731. pad: 3
  2732. kernel_size: 7
  2733. group: 128
  2734. stride: 1
  2735. weight_filler {
  2736. type: "xavier"
  2737. }
  2738. bias_filler {
  2739. type: "constant"
  2740. }
  2741. }
  2742. }
  2743. layer {
  2744. name: "s2/paf/conv5_dw/bn"
  2745. type: "BatchNorm"
  2746. bottom: "s2/paf/conv5_dw/conv"
  2747. top: "s2/paf/conv5_dw/bn"
  2748. param {
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  2750. decay_mult: 0.0
  2751. }
  2752. param {
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  2754. decay_mult: 0.0
  2755. }
  2756. param {
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  2758. decay_mult: 0.0
  2759. }
  2760. }
  2761. layer {
  2762. name: "s2/paf/conv5_dw/scale"
  2763. type: "Scale"
  2764. bottom: "s2/paf/conv5_dw/bn"
  2765. top: "s2/paf/conv5_dw/bn"
  2766. scale_param {
  2767. filler {
  2768. value: 1.0
  2769. }
  2770. bias_term: true
  2771. bias_filler {
  2772. value: 0.0
  2773. }
  2774. }
  2775. }
  2776. layer {
  2777. name: "s2/paf/conv5_dw/relu"
  2778. type: "ReLU"
  2779. bottom: "s2/paf/conv5_dw/bn"
  2780. top: "s2/paf/conv5_dw/bn"
  2781. }
  2782. layer {
  2783. name: "s2/paf/conv5_seq/conv"
  2784. type: "Convolution"
  2785. bottom: "s2/paf/conv5_dw/bn"
  2786. top: "s2/paf/conv5_seq/conv"
  2787. convolution_param {
  2788. num_output: 128
  2789. bias_term: false
  2790. pad: 0
  2791. kernel_size: 1
  2792. stride: 1
  2793. weight_filler {
  2794. type: "xavier"
  2795. }
  2796. bias_filler {
  2797. type: "constant"
  2798. }
  2799. }
  2800. }
  2801. layer {
  2802. name: "s2/paf/conv5_seq/bn"
  2803. type: "BatchNorm"
  2804. bottom: "s2/paf/conv5_seq/conv"
  2805. top: "s2/paf/conv5_seq/bn"
  2806. param {
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  2808. decay_mult: 0.0
  2809. }
  2810. param {
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  2812. decay_mult: 0.0
  2813. }
  2814. param {
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  2816. decay_mult: 0.0
  2817. }
  2818. }
  2819. layer {
  2820. name: "s2/paf/conv5_seq/scale"
  2821. type: "Scale"
  2822. bottom: "s2/paf/conv5_seq/bn"
  2823. top: "s2/paf/conv5_seq/bn"
  2824. scale_param {
  2825. filler {
  2826. value: 1.0
  2827. }
  2828. bias_term: true
  2829. bias_filler {
  2830. value: 0.0
  2831. }
  2832. }
  2833. }
  2834. layer {
  2835. name: "s2/paf/conv5_seq/relu"
  2836. type: "ReLU"
  2837. bottom: "s2/paf/conv5_seq/bn"
  2838. top: "s2/paf/conv5_seq/bn"
  2839. }
  2840. layer {
  2841. name: "s2/paf/con6_1x1/conv"
  2842. type: "Convolution"
  2843. bottom: "s2/paf/conv5_seq/bn"
  2844. top: "s2/paf/con6_1x1/conv"
  2845. param {
  2846. lr_mult: 4.0
  2847. decay_mult: 1.0
  2848. }
  2849. convolution_param {
  2850. num_output: 512
  2851. bias_term: false
  2852. pad: 0
  2853. kernel_size: 1
  2854. stride: 1
  2855. weight_filler {
  2856. type: "xavier"
  2857. }
  2858. bias_filler {
  2859. type: "constant"
  2860. }
  2861. }
  2862. }
  2863. layer {
  2864. name: "s2/paf/con6_1x1/bn"
  2865. type: "BatchNorm"
  2866. bottom: "s2/paf/con6_1x1/conv"
  2867. top: "s2/paf/con6_1x1/bn"
  2868. param {
  2869. lr_mult: 0.0
  2870. decay_mult: 0.0
  2871. }
  2872. param {
  2873. lr_mult: 0.0
  2874. decay_mult: 0.0
  2875. }
  2876. param {
  2877. lr_mult: 0.0
  2878. decay_mult: 0.0
  2879. }
  2880. }
  2881. layer {
  2882. name: "s2/paf/con6_1x1/scale"
  2883. type: "Scale"
  2884. bottom: "s2/paf/con6_1x1/bn"
  2885. top: "s2/paf/con6_1x1/bn"
  2886. scale_param {
  2887. filler {
  2888. value: 1.0
  2889. }
  2890. bias_term: true
  2891. bias_filler {
  2892. value: 0.0
  2893. }
  2894. }
  2895. }
  2896. layer {
  2897. name: "s2/paf/con6_1x1/relu"
  2898. type: "ReLU"
  2899. bottom: "s2/paf/con6_1x1/bn"
  2900. top: "s2/paf/con6_1x1/bn"
  2901. }
  2902. layer {
  2903. name: "s2/paf/conv7_1x1"
  2904. type: "Convolution"
  2905. bottom: "s2/paf/con6_1x1/bn"
  2906. top: "s2/paf/conv7_1x1"
  2907. param {
  2908. lr_mult: 4.0
  2909. decay_mult: 1.0
  2910. }
  2911. convolution_param {
  2912. num_output: 38
  2913. bias_term: false
  2914. pad: 0
  2915. kernel_size: 1
  2916. stride: 1
  2917. weight_filler {
  2918. type: "xavier"
  2919. }
  2920. bias_filler {
  2921. type: "constant"
  2922. }
  2923. }
  2924. }
  2925. layer {
  2926. name: "output"
  2927. type: "Concat"
  2928. bottom: "s2/hmap/conv7_1x1"
  2929. bottom: "s2/paf/conv7_1x1"
  2930. top: "output"
  2931. }
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