API tools faq
paste
Advertisement
Guest User

Untitled

a guest
Jun 23rd, 2020
275
0
Never
Add comment
Not a member of Pastebin yet? Sign Up, it unlocks many cool features!
text 219.32 KB | None | 0 0
raw download clone embed print report
  1. layer {
  2. name: "Placeholder"
  3. type: "Input"
  4. top: "Placeholder"
  5. input_param {
  6. shape {
  7. dim: 1
  8. dim: 3
  9. dim: 299
  10. dim: 299
  11. }
  12. }
  13. }
  14. layer {
  15. name: "resnet_v2_152_conv1_Conv2D"
  16. type: "Convolution"
  17. bottom: "Placeholder"
  18. top: "resnet_v2_152_conv1_Conv2D"
  19. convolution_param {
  20. num_output: 64
  21. bias_term: true
  22. group: 1
  23. stride: 2
  24. pad_h: 3
  25. pad_w: 3
  26. kernel_h: 7
  27. kernel_w: 7
  28. }
  29. }
  30. layer {
  31. name: "DummyData1"
  32. type: "Input"
  33. top: "DummyData1"
  34. dummy_data_param {
  35. shape {
  36. dim: 1
  37. dim: 64
  38. dim: 150
  39. dim: 150
  40. }
  41. }
  42. }
  43. layer {
  44. name: "resnet_v2_152_conv1_Conv2D_crop"
  45. type: "Crop"
  46. bottom: "resnet_v2_152_conv1_Conv2D"
  47. bottom: "DummyData1"
  48. top: "resnet_v2_152_conv1_Conv2D_crop"
  49. crop_param {
  50. offset: 0
  51. offset: 0
  52. }
  53. }
  54. layer {
  55. name: "resnet_v2_152_pool1_MaxPool"
  56. type: "Pooling"
  57. bottom: "resnet_v2_152_conv1_Conv2D_crop"
  58. top: "resnet_v2_152_pool1_MaxPool"
  59. pooling_param {
  60. pool: MAX
  61. kernel_size: 3
  62. stride: 2
  63. pad_h: 0
  64. pad_w: 0
  65. }
  66. }
  67. layer {
  68. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  69. type: "BatchNorm"
  70. bottom: "resnet_v2_152_pool1_MaxPool"
  71. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  72. batch_norm_param {
  73. use_global_stats: true
  74. eps: 1.0009999641624745e-05
  75. }
  76. }
  77. layer {
  78. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm_scale"
  79. type: "Scale"
  80. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  81. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  82. scale_param {
  83. bias_term: true
  84. }
  85. }
  86. layer {
  87. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_Relu"
  88. type: "ReLU"
  89. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  90. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  91. }
  92. layer {
  93. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_shortcut_Conv2D"
  94. type: "Convolution"
  95. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  96. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_shortcut_Conv2D"
  97. convolution_param {
  98. num_output: 256
  99. bias_term: true
  100. group: 1
  101. stride: 1
  102. pad_h: 0
  103. pad_w: 0
  104. kernel_h: 1
  105. kernel_w: 1
  106. }
  107. }
  108. layer {
  109. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_Conv2D"
  110. type: "Convolution"
  111. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  112. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_Conv2D"
  113. convolution_param {
  114. num_output: 64
  115. bias_term: false
  116. group: 1
  117. stride: 1
  118. pad_h: 0
  119. pad_w: 0
  120. kernel_h: 1
  121. kernel_w: 1
  122. }
  123. }
  124. layer {
  125. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  126. type: "BatchNorm"
  127. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_Conv2D"
  128. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  129. batch_norm_param {
  130. use_global_stats: true
  131. eps: 1.0009999641624745e-05
  132. }
  133. }
  134. layer {
  135. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  136. type: "Scale"
  137. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  138. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  139. scale_param {
  140. bias_term: true
  141. }
  142. }
  143. layer {
  144. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_Relu"
  145. type: "ReLU"
  146. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  147. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  148. }
  149. layer {
  150. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_Conv2D"
  151. type: "Convolution"
  152. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  153. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_Conv2D"
  154. convolution_param {
  155. num_output: 64
  156. bias_term: false
  157. group: 1
  158. stride: 1
  159. pad_h: 1
  160. pad_w: 1
  161. kernel_h: 3
  162. kernel_w: 3
  163. }
  164. }
  165. layer {
  166. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  167. type: "BatchNorm"
  168. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_Conv2D"
  169. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  170. batch_norm_param {
  171. use_global_stats: true
  172. eps: 1.0009999641624745e-05
  173. }
  174. }
  175. layer {
  176. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  177. type: "Scale"
  178. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  179. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  180. scale_param {
  181. bias_term: true
  182. }
  183. }
  184. layer {
  185. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_Relu"
  186. type: "ReLU"
  187. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  188. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  189. }
  190. layer {
  191. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv3_Conv2D"
  192. type: "Convolution"
  193. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  194. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv3_Conv2D"
  195. convolution_param {
  196. num_output: 256
  197. bias_term: true
  198. group: 1
  199. stride: 1
  200. pad_h: 0
  201. pad_w: 0
  202. kernel_h: 1
  203. kernel_w: 1
  204. }
  205. }
  206. layer {
  207. name: "resnet_v2_152_block1_unit_1_bottleneck_v2_add"
  208. type: "Eltwise"
  209. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_shortcut_Conv2D"
  210. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_conv3_Conv2D"
  211. top: "resnet_v2_152_block1_unit_1_bottleneck_v2_add"
  212. eltwise_param {
  213. operation: SUM
  214. }
  215. }
  216. layer {
  217. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  218. type: "BatchNorm"
  219. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_add"
  220. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  221. batch_norm_param {
  222. use_global_stats: true
  223. eps: 1.0009999641624745e-05
  224. }
  225. }
  226. layer {
  227. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm_scale"
  228. type: "Scale"
  229. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  230. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  231. scale_param {
  232. bias_term: true
  233. }
  234. }
  235. layer {
  236. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_Relu"
  237. type: "ReLU"
  238. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  239. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  240. }
  241. layer {
  242. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_Conv2D"
  243. type: "Convolution"
  244. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  245. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_Conv2D"
  246. convolution_param {
  247. num_output: 64
  248. bias_term: false
  249. group: 1
  250. stride: 1
  251. pad_h: 0
  252. pad_w: 0
  253. kernel_h: 1
  254. kernel_w: 1
  255. }
  256. }
  257. layer {
  258. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  259. type: "BatchNorm"
  260. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_Conv2D"
  261. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  262. batch_norm_param {
  263. use_global_stats: true
  264. eps: 1.0009999641624745e-05
  265. }
  266. }
  267. layer {
  268. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  269. type: "Scale"
  270. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  271. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  272. scale_param {
  273. bias_term: true
  274. }
  275. }
  276. layer {
  277. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_Relu"
  278. type: "ReLU"
  279. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  280. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  281. }
  282. layer {
  283. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_Conv2D"
  284. type: "Convolution"
  285. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  286. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_Conv2D"
  287. convolution_param {
  288. num_output: 64
  289. bias_term: false
  290. group: 1
  291. stride: 1
  292. pad_h: 1
  293. pad_w: 1
  294. kernel_h: 3
  295. kernel_w: 3
  296. }
  297. }
  298. layer {
  299. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  300. type: "BatchNorm"
  301. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_Conv2D"
  302. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  303. batch_norm_param {
  304. use_global_stats: true
  305. eps: 1.0009999641624745e-05
  306. }
  307. }
  308. layer {
  309. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  310. type: "Scale"
  311. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  312. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  313. scale_param {
  314. bias_term: true
  315. }
  316. }
  317. layer {
  318. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_Relu"
  319. type: "ReLU"
  320. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  321. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  322. }
  323. layer {
  324. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv3_Conv2D"
  325. type: "Convolution"
  326. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  327. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv3_Conv2D"
  328. convolution_param {
  329. num_output: 256
  330. bias_term: true
  331. group: 1
  332. stride: 1
  333. pad_h: 0
  334. pad_w: 0
  335. kernel_h: 1
  336. kernel_w: 1
  337. }
  338. }
  339. layer {
  340. name: "resnet_v2_152_block1_unit_2_bottleneck_v2_add"
  341. type: "Eltwise"
  342. bottom: "resnet_v2_152_block1_unit_1_bottleneck_v2_add"
  343. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_conv3_Conv2D"
  344. top: "resnet_v2_152_block1_unit_2_bottleneck_v2_add"
  345. eltwise_param {
  346. operation: SUM
  347. }
  348. }
  349. layer {
  350. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  351. type: "BatchNorm"
  352. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_add"
  353. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  354. batch_norm_param {
  355. use_global_stats: true
  356. eps: 1.0009999641624745e-05
  357. }
  358. }
  359. layer {
  360. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm_scale"
  361. type: "Scale"
  362. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  363. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  364. scale_param {
  365. bias_term: true
  366. }
  367. }
  368. layer {
  369. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_shortcut_MaxPool"
  370. type: "Pooling"
  371. bottom: "resnet_v2_152_block1_unit_2_bottleneck_v2_add"
  372. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_shortcut_MaxPool"
  373. pooling_param {
  374. pool: MAX
  375. kernel_size: 1
  376. stride: 2
  377. pad_h: 0
  378. pad_w: 0
  379. }
  380. }
  381. layer {
  382. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_Relu"
  383. type: "ReLU"
  384. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  385. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  386. }
  387. layer {
  388. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_Conv2D"
  389. type: "Convolution"
  390. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  391. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_Conv2D"
  392. convolution_param {
  393. num_output: 64
  394. bias_term: false
  395. group: 1
  396. stride: 1
  397. pad_h: 0
  398. pad_w: 0
  399. kernel_h: 1
  400. kernel_w: 1
  401. }
  402. }
  403. layer {
  404. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  405. type: "BatchNorm"
  406. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_Conv2D"
  407. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  408. batch_norm_param {
  409. use_global_stats: true
  410. eps: 1.0009999641624745e-05
  411. }
  412. }
  413. layer {
  414. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  415. type: "Scale"
  416. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  417. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  418. scale_param {
  419. bias_term: true
  420. }
  421. }
  422. layer {
  423. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_Relu"
  424. type: "ReLU"
  425. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  426. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  427. }
  428. layer {
  429. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Conv2D"
  430. type: "Convolution"
  431. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  432. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Conv2D"
  433. convolution_param {
  434. num_output: 64
  435. bias_term: false
  436. group: 1
  437. stride: 2
  438. pad_h: 1
  439. pad_w: 1
  440. kernel_h: 3
  441. kernel_w: 3
  442. }
  443. }
  444. layer {
  445. name: "DummyData2"
  446. type: "DummyData"
  447. top: "DummyData2"
  448. dummy_data_param {
  449. shape {
  450. dim: 1
  451. dim: 64
  452. dim: 38
  453. dim: 38
  454. }
  455. }
  456. }
  457. layer {
  458. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Conv2D_crop"
  459. type: "Crop"
  460. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Conv2D"
  461. bottom: "DummyData2"
  462. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Conv2D_crop"
  463. crop_param {
  464. offset: 0
  465. offset: 0
  466. }
  467. }
  468. layer {
  469. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  470. type: "BatchNorm"
  471. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Conv2D_crop"
  472. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  473. batch_norm_param {
  474. use_global_stats: true
  475. eps: 1.0009999641624745e-05
  476. }
  477. }
  478. layer {
  479. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  480. type: "Scale"
  481. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  482. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  483. scale_param {
  484. bias_term: true
  485. }
  486. }
  487. layer {
  488. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_Relu"
  489. type: "ReLU"
  490. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  491. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  492. }
  493. layer {
  494. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv3_Conv2D"
  495. type: "Convolution"
  496. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  497. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv3_Conv2D"
  498. convolution_param {
  499. num_output: 256
  500. bias_term: true
  501. group: 1
  502. stride: 1
  503. pad_h: 0
  504. pad_w: 0
  505. kernel_h: 1
  506. kernel_w: 1
  507. }
  508. }
  509. layer {
  510. name: "resnet_v2_152_block1_unit_3_bottleneck_v2_add"
  511. type: "Eltwise"
  512. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_shortcut_MaxPool"
  513. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_conv3_Conv2D"
  514. top: "resnet_v2_152_block1_unit_3_bottleneck_v2_add"
  515. eltwise_param {
  516. operation: SUM
  517. }
  518. }
  519. layer {
  520. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  521. type: "BatchNorm"
  522. bottom: "resnet_v2_152_block1_unit_3_bottleneck_v2_add"
  523. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  524. batch_norm_param {
  525. use_global_stats: true
  526. eps: 1.0009999641624745e-05
  527. }
  528. }
  529. layer {
  530. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm_scale"
  531. type: "Scale"
  532. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  533. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  534. scale_param {
  535. bias_term: true
  536. }
  537. }
  538. layer {
  539. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_Relu"
  540. type: "ReLU"
  541. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  542. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  543. }
  544. layer {
  545. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_shortcut_Conv2D"
  546. type: "Convolution"
  547. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  548. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_shortcut_Conv2D"
  549. convolution_param {
  550. num_output: 512
  551. bias_term: true
  552. group: 1
  553. stride: 1
  554. pad_h: 0
  555. pad_w: 0
  556. kernel_h: 1
  557. kernel_w: 1
  558. }
  559. }
  560. layer {
  561. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_Conv2D"
  562. type: "Convolution"
  563. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  564. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_Conv2D"
  565. convolution_param {
  566. num_output: 128
  567. bias_term: false
  568. group: 1
  569. stride: 1
  570. pad_h: 0
  571. pad_w: 0
  572. kernel_h: 1
  573. kernel_w: 1
  574. }
  575. }
  576. layer {
  577. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  578. type: "BatchNorm"
  579. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_Conv2D"
  580. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  581. batch_norm_param {
  582. use_global_stats: true
  583. eps: 1.0009999641624745e-05
  584. }
  585. }
  586. layer {
  587. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  588. type: "Scale"
  589. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  590. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  591. scale_param {
  592. bias_term: true
  593. }
  594. }
  595. layer {
  596. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_Relu"
  597. type: "ReLU"
  598. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  599. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  600. }
  601. layer {
  602. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_Conv2D"
  603. type: "Convolution"
  604. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  605. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_Conv2D"
  606. convolution_param {
  607. num_output: 128
  608. bias_term: false
  609. group: 1
  610. stride: 1
  611. pad_h: 1
  612. pad_w: 1
  613. kernel_h: 3
  614. kernel_w: 3
  615. }
  616. }
  617. layer {
  618. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  619. type: "BatchNorm"
  620. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_Conv2D"
  621. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  622. batch_norm_param {
  623. use_global_stats: true
  624. eps: 1.0009999641624745e-05
  625. }
  626. }
  627. layer {
  628. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  629. type: "Scale"
  630. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  631. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  632. scale_param {
  633. bias_term: true
  634. }
  635. }
  636. layer {
  637. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_Relu"
  638. type: "ReLU"
  639. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  640. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  641. }
  642. layer {
  643. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv3_Conv2D"
  644. type: "Convolution"
  645. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  646. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv3_Conv2D"
  647. convolution_param {
  648. num_output: 512
  649. bias_term: true
  650. group: 1
  651. stride: 1
  652. pad_h: 0
  653. pad_w: 0
  654. kernel_h: 1
  655. kernel_w: 1
  656. }
  657. }
  658. layer {
  659. name: "resnet_v2_152_block2_unit_1_bottleneck_v2_add"
  660. type: "Eltwise"
  661. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_shortcut_Conv2D"
  662. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_conv3_Conv2D"
  663. top: "resnet_v2_152_block2_unit_1_bottleneck_v2_add"
  664. eltwise_param {
  665. operation: SUM
  666. }
  667. }
  668. layer {
  669. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  670. type: "BatchNorm"
  671. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_add"
  672. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  673. batch_norm_param {
  674. use_global_stats: true
  675. eps: 1.0009999641624745e-05
  676. }
  677. }
  678. layer {
  679. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm_scale"
  680. type: "Scale"
  681. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  682. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  683. scale_param {
  684. bias_term: true
  685. }
  686. }
  687. layer {
  688. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_Relu"
  689. type: "ReLU"
  690. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  691. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  692. }
  693. layer {
  694. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_Conv2D"
  695. type: "Convolution"
  696. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  697. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_Conv2D"
  698. convolution_param {
  699. num_output: 128
  700. bias_term: false
  701. group: 1
  702. stride: 1
  703. pad_h: 0
  704. pad_w: 0
  705. kernel_h: 1
  706. kernel_w: 1
  707. }
  708. }
  709. layer {
  710. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  711. type: "BatchNorm"
  712. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_Conv2D"
  713. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  714. batch_norm_param {
  715. use_global_stats: true
  716. eps: 1.0009999641624745e-05
  717. }
  718. }
  719. layer {
  720. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  721. type: "Scale"
  722. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  723. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  724. scale_param {
  725. bias_term: true
  726. }
  727. }
  728. layer {
  729. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_Relu"
  730. type: "ReLU"
  731. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  732. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  733. }
  734. layer {
  735. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_Conv2D"
  736. type: "Convolution"
  737. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  738. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_Conv2D"
  739. convolution_param {
  740. num_output: 128
  741. bias_term: false
  742. group: 1
  743. stride: 1
  744. pad_h: 1
  745. pad_w: 1
  746. kernel_h: 3
  747. kernel_w: 3
  748. }
  749. }
  750. layer {
  751. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  752. type: "BatchNorm"
  753. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_Conv2D"
  754. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  755. batch_norm_param {
  756. use_global_stats: true
  757. eps: 1.0009999641624745e-05
  758. }
  759. }
  760. layer {
  761. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  762. type: "Scale"
  763. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  764. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  765. scale_param {
  766. bias_term: true
  767. }
  768. }
  769. layer {
  770. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_Relu"
  771. type: "ReLU"
  772. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  773. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  774. }
  775. layer {
  776. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv3_Conv2D"
  777. type: "Convolution"
  778. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  779. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv3_Conv2D"
  780. convolution_param {
  781. num_output: 512
  782. bias_term: true
  783. group: 1
  784. stride: 1
  785. pad_h: 0
  786. pad_w: 0
  787. kernel_h: 1
  788. kernel_w: 1
  789. }
  790. }
  791. layer {
  792. name: "resnet_v2_152_block2_unit_2_bottleneck_v2_add"
  793. type: "Eltwise"
  794. bottom: "resnet_v2_152_block2_unit_1_bottleneck_v2_add"
  795. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_conv3_Conv2D"
  796. top: "resnet_v2_152_block2_unit_2_bottleneck_v2_add"
  797. eltwise_param {
  798. operation: SUM
  799. }
  800. }
  801. layer {
  802. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  803. type: "BatchNorm"
  804. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_add"
  805. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  806. batch_norm_param {
  807. use_global_stats: true
  808. eps: 1.0009999641624745e-05
  809. }
  810. }
  811. layer {
  812. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm_scale"
  813. type: "Scale"
  814. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  815. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  816. scale_param {
  817. bias_term: true
  818. }
  819. }
  820. layer {
  821. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_Relu"
  822. type: "ReLU"
  823. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  824. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  825. }
  826. layer {
  827. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_Conv2D"
  828. type: "Convolution"
  829. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  830. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_Conv2D"
  831. convolution_param {
  832. num_output: 128
  833. bias_term: false
  834. group: 1
  835. stride: 1
  836. pad_h: 0
  837. pad_w: 0
  838. kernel_h: 1
  839. kernel_w: 1
  840. }
  841. }
  842. layer {
  843. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  844. type: "BatchNorm"
  845. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_Conv2D"
  846. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  847. batch_norm_param {
  848. use_global_stats: true
  849. eps: 1.0009999641624745e-05
  850. }
  851. }
  852. layer {
  853. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  854. type: "Scale"
  855. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  856. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  857. scale_param {
  858. bias_term: true
  859. }
  860. }
  861. layer {
  862. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_Relu"
  863. type: "ReLU"
  864. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  865. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  866. }
  867. layer {
  868. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_Conv2D"
  869. type: "Convolution"
  870. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  871. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_Conv2D"
  872. convolution_param {
  873. num_output: 128
  874. bias_term: false
  875. group: 1
  876. stride: 1
  877. pad_h: 1
  878. pad_w: 1
  879. kernel_h: 3
  880. kernel_w: 3
  881. }
  882. }
  883. layer {
  884. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  885. type: "BatchNorm"
  886. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_Conv2D"
  887. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  888. batch_norm_param {
  889. use_global_stats: true
  890. eps: 1.0009999641624745e-05
  891. }
  892. }
  893. layer {
  894. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  895. type: "Scale"
  896. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  897. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  898. scale_param {
  899. bias_term: true
  900. }
  901. }
  902. layer {
  903. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_Relu"
  904. type: "ReLU"
  905. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  906. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  907. }
  908. layer {
  909. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv3_Conv2D"
  910. type: "Convolution"
  911. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  912. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv3_Conv2D"
  913. convolution_param {
  914. num_output: 512
  915. bias_term: true
  916. group: 1
  917. stride: 1
  918. pad_h: 0
  919. pad_w: 0
  920. kernel_h: 1
  921. kernel_w: 1
  922. }
  923. }
  924. layer {
  925. name: "resnet_v2_152_block2_unit_3_bottleneck_v2_add"
  926. type: "Eltwise"
  927. bottom: "resnet_v2_152_block2_unit_2_bottleneck_v2_add"
  928. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_conv3_Conv2D"
  929. top: "resnet_v2_152_block2_unit_3_bottleneck_v2_add"
  930. eltwise_param {
  931. operation: SUM
  932. }
  933. }
  934. layer {
  935. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  936. type: "BatchNorm"
  937. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_add"
  938. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  939. batch_norm_param {
  940. use_global_stats: true
  941. eps: 1.0009999641624745e-05
  942. }
  943. }
  944. layer {
  945. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm_scale"
  946. type: "Scale"
  947. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  948. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  949. scale_param {
  950. bias_term: true
  951. }
  952. }
  953. layer {
  954. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_Relu"
  955. type: "ReLU"
  956. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  957. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  958. }
  959. layer {
  960. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_Conv2D"
  961. type: "Convolution"
  962. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  963. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_Conv2D"
  964. convolution_param {
  965. num_output: 128
  966. bias_term: false
  967. group: 1
  968. stride: 1
  969. pad_h: 0
  970. pad_w: 0
  971. kernel_h: 1
  972. kernel_w: 1
  973. }
  974. }
  975. layer {
  976. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  977. type: "BatchNorm"
  978. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_Conv2D"
  979. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  980. batch_norm_param {
  981. use_global_stats: true
  982. eps: 1.0009999641624745e-05
  983. }
  984. }
  985. layer {
  986. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  987. type: "Scale"
  988. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  989. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  990. scale_param {
  991. bias_term: true
  992. }
  993. }
  994. layer {
  995. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_Relu"
  996. type: "ReLU"
  997. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  998. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  999. }
  1000. layer {
  1001. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_Conv2D"
  1002. type: "Convolution"
  1003. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1004. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_Conv2D"
  1005. convolution_param {
  1006. num_output: 128
  1007. bias_term: false
  1008. group: 1
  1009. stride: 1
  1010. pad_h: 1
  1011. pad_w: 1
  1012. kernel_h: 3
  1013. kernel_w: 3
  1014. }
  1015. }
  1016. layer {
  1017. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1018. type: "BatchNorm"
  1019. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_Conv2D"
  1020. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1021. batch_norm_param {
  1022. use_global_stats: true
  1023. eps: 1.0009999641624745e-05
  1024. }
  1025. }
  1026. layer {
  1027. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1028. type: "Scale"
  1029. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1030. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1031. scale_param {
  1032. bias_term: true
  1033. }
  1034. }
  1035. layer {
  1036. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_Relu"
  1037. type: "ReLU"
  1038. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1039. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1040. }
  1041. layer {
  1042. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv3_Conv2D"
  1043. type: "Convolution"
  1044. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1045. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv3_Conv2D"
  1046. convolution_param {
  1047. num_output: 512
  1048. bias_term: true
  1049. group: 1
  1050. stride: 1
  1051. pad_h: 0
  1052. pad_w: 0
  1053. kernel_h: 1
  1054. kernel_w: 1
  1055. }
  1056. }
  1057. layer {
  1058. name: "resnet_v2_152_block2_unit_4_bottleneck_v2_add"
  1059. type: "Eltwise"
  1060. bottom: "resnet_v2_152_block2_unit_3_bottleneck_v2_add"
  1061. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_conv3_Conv2D"
  1062. top: "resnet_v2_152_block2_unit_4_bottleneck_v2_add"
  1063. eltwise_param {
  1064. operation: SUM
  1065. }
  1066. }
  1067. layer {
  1068. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1069. type: "BatchNorm"
  1070. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_add"
  1071. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1072. batch_norm_param {
  1073. use_global_stats: true
  1074. eps: 1.0009999641624745e-05
  1075. }
  1076. }
  1077. layer {
  1078. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm_scale"
  1079. type: "Scale"
  1080. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1081. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1082. scale_param {
  1083. bias_term: true
  1084. }
  1085. }
  1086. layer {
  1087. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_Relu"
  1088. type: "ReLU"
  1089. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1090. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1091. }
  1092. layer {
  1093. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_Conv2D"
  1094. type: "Convolution"
  1095. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  1096. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_Conv2D"
  1097. convolution_param {
  1098. num_output: 128
  1099. bias_term: false
  1100. group: 1
  1101. stride: 1
  1102. pad_h: 0
  1103. pad_w: 0
  1104. kernel_h: 1
  1105. kernel_w: 1
  1106. }
  1107. }
  1108. layer {
  1109. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1110. type: "BatchNorm"
  1111. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_Conv2D"
  1112. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1113. batch_norm_param {
  1114. use_global_stats: true
  1115. eps: 1.0009999641624745e-05
  1116. }
  1117. }
  1118. layer {
  1119. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1120. type: "Scale"
  1121. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1122. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1123. scale_param {
  1124. bias_term: true
  1125. }
  1126. }
  1127. layer {
  1128. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_Relu"
  1129. type: "ReLU"
  1130. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1131. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1132. }
  1133. layer {
  1134. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_Conv2D"
  1135. type: "Convolution"
  1136. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1137. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_Conv2D"
  1138. convolution_param {
  1139. num_output: 128
  1140. bias_term: false
  1141. group: 1
  1142. stride: 1
  1143. pad_h: 1
  1144. pad_w: 1
  1145. kernel_h: 3
  1146. kernel_w: 3
  1147. }
  1148. }
  1149. layer {
  1150. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1151. type: "BatchNorm"
  1152. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_Conv2D"
  1153. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1154. batch_norm_param {
  1155. use_global_stats: true
  1156. eps: 1.0009999641624745e-05
  1157. }
  1158. }
  1159. layer {
  1160. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1161. type: "Scale"
  1162. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1163. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1164. scale_param {
  1165. bias_term: true
  1166. }
  1167. }
  1168. layer {
  1169. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_Relu"
  1170. type: "ReLU"
  1171. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1172. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1173. }
  1174. layer {
  1175. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv3_Conv2D"
  1176. type: "Convolution"
  1177. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1178. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv3_Conv2D"
  1179. convolution_param {
  1180. num_output: 512
  1181. bias_term: true
  1182. group: 1
  1183. stride: 1
  1184. pad_h: 0
  1185. pad_w: 0
  1186. kernel_h: 1
  1187. kernel_w: 1
  1188. }
  1189. }
  1190. layer {
  1191. name: "resnet_v2_152_block2_unit_5_bottleneck_v2_add"
  1192. type: "Eltwise"
  1193. bottom: "resnet_v2_152_block2_unit_4_bottleneck_v2_add"
  1194. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_conv3_Conv2D"
  1195. top: "resnet_v2_152_block2_unit_5_bottleneck_v2_add"
  1196. eltwise_param {
  1197. operation: SUM
  1198. }
  1199. }
  1200. layer {
  1201. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1202. type: "BatchNorm"
  1203. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_add"
  1204. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1205. batch_norm_param {
  1206. use_global_stats: true
  1207. eps: 1.0009999641624745e-05
  1208. }
  1209. }
  1210. layer {
  1211. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm_scale"
  1212. type: "Scale"
  1213. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1214. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1215. scale_param {
  1216. bias_term: true
  1217. }
  1218. }
  1219. layer {
  1220. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_Relu"
  1221. type: "ReLU"
  1222. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1223. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1224. }
  1225. layer {
  1226. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_Conv2D"
  1227. type: "Convolution"
  1228. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  1229. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_Conv2D"
  1230. convolution_param {
  1231. num_output: 128
  1232. bias_term: false
  1233. group: 1
  1234. stride: 1
  1235. pad_h: 0
  1236. pad_w: 0
  1237. kernel_h: 1
  1238. kernel_w: 1
  1239. }
  1240. }
  1241. layer {
  1242. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1243. type: "BatchNorm"
  1244. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_Conv2D"
  1245. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1246. batch_norm_param {
  1247. use_global_stats: true
  1248. eps: 1.0009999641624745e-05
  1249. }
  1250. }
  1251. layer {
  1252. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1253. type: "Scale"
  1254. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1255. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1256. scale_param {
  1257. bias_term: true
  1258. }
  1259. }
  1260. layer {
  1261. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_Relu"
  1262. type: "ReLU"
  1263. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1264. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1265. }
  1266. layer {
  1267. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_Conv2D"
  1268. type: "Convolution"
  1269. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1270. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_Conv2D"
  1271. convolution_param {
  1272. num_output: 128
  1273. bias_term: false
  1274. group: 1
  1275. stride: 1
  1276. pad_h: 1
  1277. pad_w: 1
  1278. kernel_h: 3
  1279. kernel_w: 3
  1280. }
  1281. }
  1282. layer {
  1283. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1284. type: "BatchNorm"
  1285. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_Conv2D"
  1286. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1287. batch_norm_param {
  1288. use_global_stats: true
  1289. eps: 1.0009999641624745e-05
  1290. }
  1291. }
  1292. layer {
  1293. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1294. type: "Scale"
  1295. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1296. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1297. scale_param {
  1298. bias_term: true
  1299. }
  1300. }
  1301. layer {
  1302. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_Relu"
  1303. type: "ReLU"
  1304. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1305. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1306. }
  1307. layer {
  1308. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv3_Conv2D"
  1309. type: "Convolution"
  1310. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1311. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv3_Conv2D"
  1312. convolution_param {
  1313. num_output: 512
  1314. bias_term: true
  1315. group: 1
  1316. stride: 1
  1317. pad_h: 0
  1318. pad_w: 0
  1319. kernel_h: 1
  1320. kernel_w: 1
  1321. }
  1322. }
  1323. layer {
  1324. name: "resnet_v2_152_block2_unit_6_bottleneck_v2_add"
  1325. type: "Eltwise"
  1326. bottom: "resnet_v2_152_block2_unit_5_bottleneck_v2_add"
  1327. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_conv3_Conv2D"
  1328. top: "resnet_v2_152_block2_unit_6_bottleneck_v2_add"
  1329. eltwise_param {
  1330. operation: SUM
  1331. }
  1332. }
  1333. layer {
  1334. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1335. type: "BatchNorm"
  1336. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_add"
  1337. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1338. batch_norm_param {
  1339. use_global_stats: true
  1340. eps: 1.0009999641624745e-05
  1341. }
  1342. }
  1343. layer {
  1344. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm_scale"
  1345. type: "Scale"
  1346. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1347. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1348. scale_param {
  1349. bias_term: true
  1350. }
  1351. }
  1352. layer {
  1353. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_Relu"
  1354. type: "ReLU"
  1355. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1356. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1357. }
  1358. layer {
  1359. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_Conv2D"
  1360. type: "Convolution"
  1361. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  1362. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_Conv2D"
  1363. convolution_param {
  1364. num_output: 128
  1365. bias_term: false
  1366. group: 1
  1367. stride: 1
  1368. pad_h: 0
  1369. pad_w: 0
  1370. kernel_h: 1
  1371. kernel_w: 1
  1372. }
  1373. }
  1374. layer {
  1375. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1376. type: "BatchNorm"
  1377. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_Conv2D"
  1378. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1379. batch_norm_param {
  1380. use_global_stats: true
  1381. eps: 1.0009999641624745e-05
  1382. }
  1383. }
  1384. layer {
  1385. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1386. type: "Scale"
  1387. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1388. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1389. scale_param {
  1390. bias_term: true
  1391. }
  1392. }
  1393. layer {
  1394. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_Relu"
  1395. type: "ReLU"
  1396. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1397. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1398. }
  1399. layer {
  1400. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_Conv2D"
  1401. type: "Convolution"
  1402. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1403. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_Conv2D"
  1404. convolution_param {
  1405. num_output: 128
  1406. bias_term: false
  1407. group: 1
  1408. stride: 1
  1409. pad_h: 1
  1410. pad_w: 1
  1411. kernel_h: 3
  1412. kernel_w: 3
  1413. }
  1414. }
  1415. layer {
  1416. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1417. type: "BatchNorm"
  1418. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_Conv2D"
  1419. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1420. batch_norm_param {
  1421. use_global_stats: true
  1422. eps: 1.0009999641624745e-05
  1423. }
  1424. }
  1425. layer {
  1426. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1427. type: "Scale"
  1428. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1429. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1430. scale_param {
  1431. bias_term: true
  1432. }
  1433. }
  1434. layer {
  1435. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_Relu"
  1436. type: "ReLU"
  1437. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1438. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1439. }
  1440. layer {
  1441. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv3_Conv2D"
  1442. type: "Convolution"
  1443. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1444. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv3_Conv2D"
  1445. convolution_param {
  1446. num_output: 512
  1447. bias_term: true
  1448. group: 1
  1449. stride: 1
  1450. pad_h: 0
  1451. pad_w: 0
  1452. kernel_h: 1
  1453. kernel_w: 1
  1454. }
  1455. }
  1456. layer {
  1457. name: "resnet_v2_152_block2_unit_7_bottleneck_v2_add"
  1458. type: "Eltwise"
  1459. bottom: "resnet_v2_152_block2_unit_6_bottleneck_v2_add"
  1460. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_conv3_Conv2D"
  1461. top: "resnet_v2_152_block2_unit_7_bottleneck_v2_add"
  1462. eltwise_param {
  1463. operation: SUM
  1464. }
  1465. }
  1466. layer {
  1467. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1468. type: "BatchNorm"
  1469. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_add"
  1470. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1471. batch_norm_param {
  1472. use_global_stats: true
  1473. eps: 1.0009999641624745e-05
  1474. }
  1475. }
  1476. layer {
  1477. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm_scale"
  1478. type: "Scale"
  1479. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1480. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1481. scale_param {
  1482. bias_term: true
  1483. }
  1484. }
  1485. layer {
  1486. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_shortcut_MaxPool"
  1487. type: "Pooling"
  1488. bottom: "resnet_v2_152_block2_unit_7_bottleneck_v2_add"
  1489. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_shortcut_MaxPool"
  1490. pooling_param {
  1491. pool: MAX
  1492. kernel_size: 1
  1493. stride: 2
  1494. pad_h: 0
  1495. pad_w: 0
  1496. }
  1497. }
  1498. layer {
  1499. name: "DummyData3"
  1500. type: "DummyData"
  1501. top: "DummyData3"
  1502. dummy_data_param {
  1503. shape {
  1504. dim: 1
  1505. dim: 512
  1506. dim: 19
  1507. dim: 19
  1508. }
  1509. }
  1510. }
  1511. layer {
  1512. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_shortcut_MaxPool_crop"
  1513. type: "Crop"
  1514. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_shortcut_MaxPool"
  1515. bottom: "DummyData3"
  1516. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_shortcut_MaxPool_crop"
  1517. crop_param {
  1518. offset: 0
  1519. offset: 0
  1520. }
  1521. }
  1522. layer {
  1523. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_Relu"
  1524. type: "ReLU"
  1525. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1526. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1527. }
  1528. layer {
  1529. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_Conv2D"
  1530. type: "Convolution"
  1531. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  1532. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_Conv2D"
  1533. convolution_param {
  1534. num_output: 128
  1535. bias_term: false
  1536. group: 1
  1537. stride: 1
  1538. pad_h: 0
  1539. pad_w: 0
  1540. kernel_h: 1
  1541. kernel_w: 1
  1542. }
  1543. }
  1544. layer {
  1545. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1546. type: "BatchNorm"
  1547. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_Conv2D"
  1548. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1549. batch_norm_param {
  1550. use_global_stats: true
  1551. eps: 1.0009999641624745e-05
  1552. }
  1553. }
  1554. layer {
  1555. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1556. type: "Scale"
  1557. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1558. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1559. scale_param {
  1560. bias_term: true
  1561. }
  1562. }
  1563. layer {
  1564. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_Relu"
  1565. type: "ReLU"
  1566. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1567. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1568. }
  1569. layer {
  1570. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Conv2D"
  1571. type: "Convolution"
  1572. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1573. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Conv2D"
  1574. convolution_param {
  1575. num_output: 128
  1576. bias_term: false
  1577. group: 1
  1578. stride: 2
  1579. pad_h: 1
  1580. pad_w: 1
  1581. kernel_h: 3
  1582. kernel_w: 3
  1583. }
  1584. }
  1585. layer {
  1586. name: "DummyData4"
  1587. type: "DummyData"
  1588. top: "DummyData4"
  1589. dummy_data_param {
  1590. shape {
  1591. dim: 1
  1592. dim: 128
  1593. dim: 19
  1594. dim: 19
  1595. }
  1596. }
  1597. }
  1598. layer {
  1599. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Conv2D_crop"
  1600. type: "Crop"
  1601. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Conv2D"
  1602. bottom: "DummyData4"
  1603. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Conv2D_crop"
  1604. crop_param {
  1605. offset: 0
  1606. offset: 0
  1607. }
  1608. }
  1609. layer {
  1610. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1611. type: "BatchNorm"
  1612. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Conv2D_crop"
  1613. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1614. batch_norm_param {
  1615. use_global_stats: true
  1616. eps: 1.0009999641624745e-05
  1617. }
  1618. }
  1619. layer {
  1620. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1621. type: "Scale"
  1622. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1623. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1624. scale_param {
  1625. bias_term: true
  1626. }
  1627. }
  1628. layer {
  1629. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_Relu"
  1630. type: "ReLU"
  1631. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1632. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1633. }
  1634. layer {
  1635. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv3_Conv2D"
  1636. type: "Convolution"
  1637. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1638. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv3_Conv2D"
  1639. convolution_param {
  1640. num_output: 512
  1641. bias_term: true
  1642. group: 1
  1643. stride: 1
  1644. pad_h: 0
  1645. pad_w: 0
  1646. kernel_h: 1
  1647. kernel_w: 1
  1648. }
  1649. }
  1650. layer {
  1651. name: "resnet_v2_152_block2_unit_8_bottleneck_v2_add"
  1652. type: "Eltwise"
  1653. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_shortcut_MaxPool_crop"
  1654. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_conv3_Conv2D"
  1655. top: "resnet_v2_152_block2_unit_8_bottleneck_v2_add"
  1656. eltwise_param {
  1657. operation: SUM
  1658. }
  1659. }
  1660. layer {
  1661. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1662. type: "BatchNorm"
  1663. bottom: "resnet_v2_152_block2_unit_8_bottleneck_v2_add"
  1664. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1665. batch_norm_param {
  1666. use_global_stats: true
  1667. eps: 1.0009999641624745e-05
  1668. }
  1669. }
  1670. layer {
  1671. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm_scale"
  1672. type: "Scale"
  1673. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1674. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1675. scale_param {
  1676. bias_term: true
  1677. }
  1678. }
  1679. layer {
  1680. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_Relu"
  1681. type: "ReLU"
  1682. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1683. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1684. }
  1685. layer {
  1686. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_shortcut_Conv2D"
  1687. type: "Convolution"
  1688. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1689. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_shortcut_Conv2D"
  1690. convolution_param {
  1691. num_output: 1024
  1692. bias_term: true
  1693. group: 1
  1694. stride: 1
  1695. pad_h: 0
  1696. pad_w: 0
  1697. kernel_h: 1
  1698. kernel_w: 1
  1699. }
  1700. }
  1701. layer {
  1702. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_Conv2D"
  1703. type: "Convolution"
  1704. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  1705. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_Conv2D"
  1706. convolution_param {
  1707. num_output: 256
  1708. bias_term: false
  1709. group: 1
  1710. stride: 1
  1711. pad_h: 0
  1712. pad_w: 0
  1713. kernel_h: 1
  1714. kernel_w: 1
  1715. }
  1716. }
  1717. layer {
  1718. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1719. type: "BatchNorm"
  1720. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_Conv2D"
  1721. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1722. batch_norm_param {
  1723. use_global_stats: true
  1724. eps: 1.0009999641624745e-05
  1725. }
  1726. }
  1727. layer {
  1728. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1729. type: "Scale"
  1730. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1731. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1732. scale_param {
  1733. bias_term: true
  1734. }
  1735. }
  1736. layer {
  1737. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_Relu"
  1738. type: "ReLU"
  1739. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1740. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1741. }
  1742. layer {
  1743. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_Conv2D"
  1744. type: "Convolution"
  1745. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1746. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_Conv2D"
  1747. convolution_param {
  1748. num_output: 256
  1749. bias_term: false
  1750. group: 1
  1751. stride: 1
  1752. pad_h: 1
  1753. pad_w: 1
  1754. kernel_h: 3
  1755. kernel_w: 3
  1756. }
  1757. }
  1758. layer {
  1759. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1760. type: "BatchNorm"
  1761. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_Conv2D"
  1762. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1763. batch_norm_param {
  1764. use_global_stats: true
  1765. eps: 1.0009999641624745e-05
  1766. }
  1767. }
  1768. layer {
  1769. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1770. type: "Scale"
  1771. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1772. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1773. scale_param {
  1774. bias_term: true
  1775. }
  1776. }
  1777. layer {
  1778. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_Relu"
  1779. type: "ReLU"
  1780. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1781. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1782. }
  1783. layer {
  1784. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv3_Conv2D"
  1785. type: "Convolution"
  1786. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1787. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv3_Conv2D"
  1788. convolution_param {
  1789. num_output: 1024
  1790. bias_term: true
  1791. group: 1
  1792. stride: 1
  1793. pad_h: 0
  1794. pad_w: 0
  1795. kernel_h: 1
  1796. kernel_w: 1
  1797. }
  1798. }
  1799. layer {
  1800. name: "resnet_v2_152_block3_unit_1_bottleneck_v2_add"
  1801. type: "Eltwise"
  1802. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_shortcut_Conv2D"
  1803. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_conv3_Conv2D"
  1804. top: "resnet_v2_152_block3_unit_1_bottleneck_v2_add"
  1805. eltwise_param {
  1806. operation: SUM
  1807. }
  1808. }
  1809. layer {
  1810. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1811. type: "BatchNorm"
  1812. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_add"
  1813. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1814. batch_norm_param {
  1815. use_global_stats: true
  1816. eps: 1.0009999641624745e-05
  1817. }
  1818. }
  1819. layer {
  1820. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm_scale"
  1821. type: "Scale"
  1822. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1823. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1824. scale_param {
  1825. bias_term: true
  1826. }
  1827. }
  1828. layer {
  1829. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_Relu"
  1830. type: "ReLU"
  1831. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1832. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1833. }
  1834. layer {
  1835. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_Conv2D"
  1836. type: "Convolution"
  1837. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  1838. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_Conv2D"
  1839. convolution_param {
  1840. num_output: 256
  1841. bias_term: false
  1842. group: 1
  1843. stride: 1
  1844. pad_h: 0
  1845. pad_w: 0
  1846. kernel_h: 1
  1847. kernel_w: 1
  1848. }
  1849. }
  1850. layer {
  1851. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1852. type: "BatchNorm"
  1853. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_Conv2D"
  1854. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1855. batch_norm_param {
  1856. use_global_stats: true
  1857. eps: 1.0009999641624745e-05
  1858. }
  1859. }
  1860. layer {
  1861. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1862. type: "Scale"
  1863. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1864. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1865. scale_param {
  1866. bias_term: true
  1867. }
  1868. }
  1869. layer {
  1870. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_Relu"
  1871. type: "ReLU"
  1872. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1873. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1874. }
  1875. layer {
  1876. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_Conv2D"
  1877. type: "Convolution"
  1878. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1879. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_Conv2D"
  1880. convolution_param {
  1881. num_output: 256
  1882. bias_term: false
  1883. group: 1
  1884. stride: 1
  1885. pad_h: 1
  1886. pad_w: 1
  1887. kernel_h: 3
  1888. kernel_w: 3
  1889. }
  1890. }
  1891. layer {
  1892. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1893. type: "BatchNorm"
  1894. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_Conv2D"
  1895. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1896. batch_norm_param {
  1897. use_global_stats: true
  1898. eps: 1.0009999641624745e-05
  1899. }
  1900. }
  1901. layer {
  1902. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  1903. type: "Scale"
  1904. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1905. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1906. scale_param {
  1907. bias_term: true
  1908. }
  1909. }
  1910. layer {
  1911. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_Relu"
  1912. type: "ReLU"
  1913. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1914. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1915. }
  1916. layer {
  1917. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv3_Conv2D"
  1918. type: "Convolution"
  1919. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  1920. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv3_Conv2D"
  1921. convolution_param {
  1922. num_output: 1024
  1923. bias_term: true
  1924. group: 1
  1925. stride: 1
  1926. pad_h: 0
  1927. pad_w: 0
  1928. kernel_h: 1
  1929. kernel_w: 1
  1930. }
  1931. }
  1932. layer {
  1933. name: "resnet_v2_152_block3_unit_2_bottleneck_v2_add"
  1934. type: "Eltwise"
  1935. bottom: "resnet_v2_152_block3_unit_1_bottleneck_v2_add"
  1936. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_conv3_Conv2D"
  1937. top: "resnet_v2_152_block3_unit_2_bottleneck_v2_add"
  1938. eltwise_param {
  1939. operation: SUM
  1940. }
  1941. }
  1942. layer {
  1943. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1944. type: "BatchNorm"
  1945. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_add"
  1946. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1947. batch_norm_param {
  1948. use_global_stats: true
  1949. eps: 1.0009999641624745e-05
  1950. }
  1951. }
  1952. layer {
  1953. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm_scale"
  1954. type: "Scale"
  1955. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1956. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1957. scale_param {
  1958. bias_term: true
  1959. }
  1960. }
  1961. layer {
  1962. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_Relu"
  1963. type: "ReLU"
  1964. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1965. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1966. }
  1967. layer {
  1968. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_Conv2D"
  1969. type: "Convolution"
  1970. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  1971. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_Conv2D"
  1972. convolution_param {
  1973. num_output: 256
  1974. bias_term: false
  1975. group: 1
  1976. stride: 1
  1977. pad_h: 0
  1978. pad_w: 0
  1979. kernel_h: 1
  1980. kernel_w: 1
  1981. }
  1982. }
  1983. layer {
  1984. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1985. type: "BatchNorm"
  1986. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_Conv2D"
  1987. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1988. batch_norm_param {
  1989. use_global_stats: true
  1990. eps: 1.0009999641624745e-05
  1991. }
  1992. }
  1993. layer {
  1994. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  1995. type: "Scale"
  1996. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1997. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  1998. scale_param {
  1999. bias_term: true
  2000. }
  2001. }
  2002. layer {
  2003. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_Relu"
  2004. type: "ReLU"
  2005. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2006. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2007. }
  2008. layer {
  2009. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_Conv2D"
  2010. type: "Convolution"
  2011. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2012. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_Conv2D"
  2013. convolution_param {
  2014. num_output: 256
  2015. bias_term: false
  2016. group: 1
  2017. stride: 1
  2018. pad_h: 1
  2019. pad_w: 1
  2020. kernel_h: 3
  2021. kernel_w: 3
  2022. }
  2023. }
  2024. layer {
  2025. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2026. type: "BatchNorm"
  2027. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_Conv2D"
  2028. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2029. batch_norm_param {
  2030. use_global_stats: true
  2031. eps: 1.0009999641624745e-05
  2032. }
  2033. }
  2034. layer {
  2035. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2036. type: "Scale"
  2037. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2038. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2039. scale_param {
  2040. bias_term: true
  2041. }
  2042. }
  2043. layer {
  2044. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_Relu"
  2045. type: "ReLU"
  2046. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2047. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2048. }
  2049. layer {
  2050. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv3_Conv2D"
  2051. type: "Convolution"
  2052. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2053. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv3_Conv2D"
  2054. convolution_param {
  2055. num_output: 1024
  2056. bias_term: true
  2057. group: 1
  2058. stride: 1
  2059. pad_h: 0
  2060. pad_w: 0
  2061. kernel_h: 1
  2062. kernel_w: 1
  2063. }
  2064. }
  2065. layer {
  2066. name: "resnet_v2_152_block3_unit_3_bottleneck_v2_add"
  2067. type: "Eltwise"
  2068. bottom: "resnet_v2_152_block3_unit_2_bottleneck_v2_add"
  2069. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_conv3_Conv2D"
  2070. top: "resnet_v2_152_block3_unit_3_bottleneck_v2_add"
  2071. eltwise_param {
  2072. operation: SUM
  2073. }
  2074. }
  2075. layer {
  2076. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2077. type: "BatchNorm"
  2078. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_add"
  2079. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2080. batch_norm_param {
  2081. use_global_stats: true
  2082. eps: 1.0009999641624745e-05
  2083. }
  2084. }
  2085. layer {
  2086. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm_scale"
  2087. type: "Scale"
  2088. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2089. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2090. scale_param {
  2091. bias_term: true
  2092. }
  2093. }
  2094. layer {
  2095. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_Relu"
  2096. type: "ReLU"
  2097. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2098. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2099. }
  2100. layer {
  2101. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_Conv2D"
  2102. type: "Convolution"
  2103. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm"
  2104. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_Conv2D"
  2105. convolution_param {
  2106. num_output: 256
  2107. bias_term: false
  2108. group: 1
  2109. stride: 1
  2110. pad_h: 0
  2111. pad_w: 0
  2112. kernel_h: 1
  2113. kernel_w: 1
  2114. }
  2115. }
  2116. layer {
  2117. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2118. type: "BatchNorm"
  2119. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_Conv2D"
  2120. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2121. batch_norm_param {
  2122. use_global_stats: true
  2123. eps: 1.0009999641624745e-05
  2124. }
  2125. }
  2126. layer {
  2127. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2128. type: "Scale"
  2129. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2130. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2131. scale_param {
  2132. bias_term: true
  2133. }
  2134. }
  2135. layer {
  2136. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_Relu"
  2137. type: "ReLU"
  2138. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2139. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2140. }
  2141. layer {
  2142. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_Conv2D"
  2143. type: "Convolution"
  2144. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2145. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_Conv2D"
  2146. convolution_param {
  2147. num_output: 256
  2148. bias_term: false
  2149. group: 1
  2150. stride: 1
  2151. pad_h: 1
  2152. pad_w: 1
  2153. kernel_h: 3
  2154. kernel_w: 3
  2155. }
  2156. }
  2157. layer {
  2158. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2159. type: "BatchNorm"
  2160. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_Conv2D"
  2161. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2162. batch_norm_param {
  2163. use_global_stats: true
  2164. eps: 1.0009999641624745e-05
  2165. }
  2166. }
  2167. layer {
  2168. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2169. type: "Scale"
  2170. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2171. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2172. scale_param {
  2173. bias_term: true
  2174. }
  2175. }
  2176. layer {
  2177. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_Relu"
  2178. type: "ReLU"
  2179. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2180. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2181. }
  2182. layer {
  2183. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv3_Conv2D"
  2184. type: "Convolution"
  2185. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2186. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv3_Conv2D"
  2187. convolution_param {
  2188. num_output: 1024
  2189. bias_term: true
  2190. group: 1
  2191. stride: 1
  2192. pad_h: 0
  2193. pad_w: 0
  2194. kernel_h: 1
  2195. kernel_w: 1
  2196. }
  2197. }
  2198. layer {
  2199. name: "resnet_v2_152_block3_unit_4_bottleneck_v2_add"
  2200. type: "Eltwise"
  2201. bottom: "resnet_v2_152_block3_unit_3_bottleneck_v2_add"
  2202. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_conv3_Conv2D"
  2203. top: "resnet_v2_152_block3_unit_4_bottleneck_v2_add"
  2204. eltwise_param {
  2205. operation: SUM
  2206. }
  2207. }
  2208. layer {
  2209. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2210. type: "BatchNorm"
  2211. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_add"
  2212. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2213. batch_norm_param {
  2214. use_global_stats: true
  2215. eps: 1.0009999641624745e-05
  2216. }
  2217. }
  2218. layer {
  2219. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm_scale"
  2220. type: "Scale"
  2221. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2222. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2223. scale_param {
  2224. bias_term: true
  2225. }
  2226. }
  2227. layer {
  2228. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_Relu"
  2229. type: "ReLU"
  2230. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2231. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2232. }
  2233. layer {
  2234. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_Conv2D"
  2235. type: "Convolution"
  2236. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm"
  2237. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_Conv2D"
  2238. convolution_param {
  2239. num_output: 256
  2240. bias_term: false
  2241. group: 1
  2242. stride: 1
  2243. pad_h: 0
  2244. pad_w: 0
  2245. kernel_h: 1
  2246. kernel_w: 1
  2247. }
  2248. }
  2249. layer {
  2250. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2251. type: "BatchNorm"
  2252. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_Conv2D"
  2253. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2254. batch_norm_param {
  2255. use_global_stats: true
  2256. eps: 1.0009999641624745e-05
  2257. }
  2258. }
  2259. layer {
  2260. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2261. type: "Scale"
  2262. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2263. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2264. scale_param {
  2265. bias_term: true
  2266. }
  2267. }
  2268. layer {
  2269. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_Relu"
  2270. type: "ReLU"
  2271. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2272. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2273. }
  2274. layer {
  2275. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_Conv2D"
  2276. type: "Convolution"
  2277. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2278. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_Conv2D"
  2279. convolution_param {
  2280. num_output: 256
  2281. bias_term: false
  2282. group: 1
  2283. stride: 1
  2284. pad_h: 1
  2285. pad_w: 1
  2286. kernel_h: 3
  2287. kernel_w: 3
  2288. }
  2289. }
  2290. layer {
  2291. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2292. type: "BatchNorm"
  2293. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_Conv2D"
  2294. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2295. batch_norm_param {
  2296. use_global_stats: true
  2297. eps: 1.0009999641624745e-05
  2298. }
  2299. }
  2300. layer {
  2301. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2302. type: "Scale"
  2303. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2304. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2305. scale_param {
  2306. bias_term: true
  2307. }
  2308. }
  2309. layer {
  2310. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_Relu"
  2311. type: "ReLU"
  2312. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2313. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2314. }
  2315. layer {
  2316. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv3_Conv2D"
  2317. type: "Convolution"
  2318. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2319. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv3_Conv2D"
  2320. convolution_param {
  2321. num_output: 1024
  2322. bias_term: true
  2323. group: 1
  2324. stride: 1
  2325. pad_h: 0
  2326. pad_w: 0
  2327. kernel_h: 1
  2328. kernel_w: 1
  2329. }
  2330. }
  2331. layer {
  2332. name: "resnet_v2_152_block3_unit_5_bottleneck_v2_add"
  2333. type: "Eltwise"
  2334. bottom: "resnet_v2_152_block3_unit_4_bottleneck_v2_add"
  2335. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_conv3_Conv2D"
  2336. top: "resnet_v2_152_block3_unit_5_bottleneck_v2_add"
  2337. eltwise_param {
  2338. operation: SUM
  2339. }
  2340. }
  2341. layer {
  2342. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2343. type: "BatchNorm"
  2344. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_add"
  2345. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2346. batch_norm_param {
  2347. use_global_stats: true
  2348. eps: 1.0009999641624745e-05
  2349. }
  2350. }
  2351. layer {
  2352. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm_scale"
  2353. type: "Scale"
  2354. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2355. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2356. scale_param {
  2357. bias_term: true
  2358. }
  2359. }
  2360. layer {
  2361. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_Relu"
  2362. type: "ReLU"
  2363. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2364. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2365. }
  2366. layer {
  2367. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_Conv2D"
  2368. type: "Convolution"
  2369. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm"
  2370. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_Conv2D"
  2371. convolution_param {
  2372. num_output: 256
  2373. bias_term: false
  2374. group: 1
  2375. stride: 1
  2376. pad_h: 0
  2377. pad_w: 0
  2378. kernel_h: 1
  2379. kernel_w: 1
  2380. }
  2381. }
  2382. layer {
  2383. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2384. type: "BatchNorm"
  2385. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_Conv2D"
  2386. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2387. batch_norm_param {
  2388. use_global_stats: true
  2389. eps: 1.0009999641624745e-05
  2390. }
  2391. }
  2392. layer {
  2393. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2394. type: "Scale"
  2395. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2396. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2397. scale_param {
  2398. bias_term: true
  2399. }
  2400. }
  2401. layer {
  2402. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_Relu"
  2403. type: "ReLU"
  2404. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2405. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2406. }
  2407. layer {
  2408. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_Conv2D"
  2409. type: "Convolution"
  2410. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2411. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_Conv2D"
  2412. convolution_param {
  2413. num_output: 256
  2414. bias_term: false
  2415. group: 1
  2416. stride: 1
  2417. pad_h: 1
  2418. pad_w: 1
  2419. kernel_h: 3
  2420. kernel_w: 3
  2421. }
  2422. }
  2423. layer {
  2424. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2425. type: "BatchNorm"
  2426. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_Conv2D"
  2427. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2428. batch_norm_param {
  2429. use_global_stats: true
  2430. eps: 1.0009999641624745e-05
  2431. }
  2432. }
  2433. layer {
  2434. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2435. type: "Scale"
  2436. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2437. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2438. scale_param {
  2439. bias_term: true
  2440. }
  2441. }
  2442. layer {
  2443. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_Relu"
  2444. type: "ReLU"
  2445. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2446. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2447. }
  2448. layer {
  2449. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv3_Conv2D"
  2450. type: "Convolution"
  2451. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2452. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv3_Conv2D"
  2453. convolution_param {
  2454. num_output: 1024
  2455. bias_term: true
  2456. group: 1
  2457. stride: 1
  2458. pad_h: 0
  2459. pad_w: 0
  2460. kernel_h: 1
  2461. kernel_w: 1
  2462. }
  2463. }
  2464. layer {
  2465. name: "resnet_v2_152_block3_unit_6_bottleneck_v2_add"
  2466. type: "Eltwise"
  2467. bottom: "resnet_v2_152_block3_unit_5_bottleneck_v2_add"
  2468. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_conv3_Conv2D"
  2469. top: "resnet_v2_152_block3_unit_6_bottleneck_v2_add"
  2470. eltwise_param {
  2471. operation: SUM
  2472. }
  2473. }
  2474. layer {
  2475. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2476. type: "BatchNorm"
  2477. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_add"
  2478. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2479. batch_norm_param {
  2480. use_global_stats: true
  2481. eps: 1.0009999641624745e-05
  2482. }
  2483. }
  2484. layer {
  2485. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm_scale"
  2486. type: "Scale"
  2487. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2488. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2489. scale_param {
  2490. bias_term: true
  2491. }
  2492. }
  2493. layer {
  2494. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_Relu"
  2495. type: "ReLU"
  2496. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2497. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2498. }
  2499. layer {
  2500. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_Conv2D"
  2501. type: "Convolution"
  2502. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_preact_FusedBatchNorm"
  2503. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_Conv2D"
  2504. convolution_param {
  2505. num_output: 256
  2506. bias_term: false
  2507. group: 1
  2508. stride: 1
  2509. pad_h: 0
  2510. pad_w: 0
  2511. kernel_h: 1
  2512. kernel_w: 1
  2513. }
  2514. }
  2515. layer {
  2516. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2517. type: "BatchNorm"
  2518. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_Conv2D"
  2519. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2520. batch_norm_param {
  2521. use_global_stats: true
  2522. eps: 1.0009999641624745e-05
  2523. }
  2524. }
  2525. layer {
  2526. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2527. type: "Scale"
  2528. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2529. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2530. scale_param {
  2531. bias_term: true
  2532. }
  2533. }
  2534. layer {
  2535. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_Relu"
  2536. type: "ReLU"
  2537. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2538. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2539. }
  2540. layer {
  2541. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_Conv2D"
  2542. type: "Convolution"
  2543. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2544. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_Conv2D"
  2545. convolution_param {
  2546. num_output: 256
  2547. bias_term: false
  2548. group: 1
  2549. stride: 1
  2550. pad_h: 1
  2551. pad_w: 1
  2552. kernel_h: 3
  2553. kernel_w: 3
  2554. }
  2555. }
  2556. layer {
  2557. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2558. type: "BatchNorm"
  2559. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_Conv2D"
  2560. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2561. batch_norm_param {
  2562. use_global_stats: true
  2563. eps: 1.0009999641624745e-05
  2564. }
  2565. }
  2566. layer {
  2567. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2568. type: "Scale"
  2569. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2570. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2571. scale_param {
  2572. bias_term: true
  2573. }
  2574. }
  2575. layer {
  2576. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_Relu"
  2577. type: "ReLU"
  2578. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2579. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2580. }
  2581. layer {
  2582. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv3_Conv2D"
  2583. type: "Convolution"
  2584. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2585. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv3_Conv2D"
  2586. convolution_param {
  2587. num_output: 1024
  2588. bias_term: true
  2589. group: 1
  2590. stride: 1
  2591. pad_h: 0
  2592. pad_w: 0
  2593. kernel_h: 1
  2594. kernel_w: 1
  2595. }
  2596. }
  2597. layer {
  2598. name: "resnet_v2_152_block3_unit_7_bottleneck_v2_add"
  2599. type: "Eltwise"
  2600. bottom: "resnet_v2_152_block3_unit_6_bottleneck_v2_add"
  2601. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_conv3_Conv2D"
  2602. top: "resnet_v2_152_block3_unit_7_bottleneck_v2_add"
  2603. eltwise_param {
  2604. operation: SUM
  2605. }
  2606. }
  2607. layer {
  2608. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2609. type: "BatchNorm"
  2610. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_add"
  2611. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2612. batch_norm_param {
  2613. use_global_stats: true
  2614. eps: 1.0009999641624745e-05
  2615. }
  2616. }
  2617. layer {
  2618. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm_scale"
  2619. type: "Scale"
  2620. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2621. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2622. scale_param {
  2623. bias_term: true
  2624. }
  2625. }
  2626. layer {
  2627. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_Relu"
  2628. type: "ReLU"
  2629. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2630. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2631. }
  2632. layer {
  2633. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_Conv2D"
  2634. type: "Convolution"
  2635. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_preact_FusedBatchNorm"
  2636. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_Conv2D"
  2637. convolution_param {
  2638. num_output: 256
  2639. bias_term: false
  2640. group: 1
  2641. stride: 1
  2642. pad_h: 0
  2643. pad_w: 0
  2644. kernel_h: 1
  2645. kernel_w: 1
  2646. }
  2647. }
  2648. layer {
  2649. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2650. type: "BatchNorm"
  2651. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_Conv2D"
  2652. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2653. batch_norm_param {
  2654. use_global_stats: true
  2655. eps: 1.0009999641624745e-05
  2656. }
  2657. }
  2658. layer {
  2659. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2660. type: "Scale"
  2661. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2662. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2663. scale_param {
  2664. bias_term: true
  2665. }
  2666. }
  2667. layer {
  2668. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_Relu"
  2669. type: "ReLU"
  2670. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2671. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2672. }
  2673. layer {
  2674. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_Conv2D"
  2675. type: "Convolution"
  2676. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2677. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_Conv2D"
  2678. convolution_param {
  2679. num_output: 256
  2680. bias_term: false
  2681. group: 1
  2682. stride: 1
  2683. pad_h: 1
  2684. pad_w: 1
  2685. kernel_h: 3
  2686. kernel_w: 3
  2687. }
  2688. }
  2689. layer {
  2690. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2691. type: "BatchNorm"
  2692. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_Conv2D"
  2693. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2694. batch_norm_param {
  2695. use_global_stats: true
  2696. eps: 1.0009999641624745e-05
  2697. }
  2698. }
  2699. layer {
  2700. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2701. type: "Scale"
  2702. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2703. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2704. scale_param {
  2705. bias_term: true
  2706. }
  2707. }
  2708. layer {
  2709. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_Relu"
  2710. type: "ReLU"
  2711. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2712. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2713. }
  2714. layer {
  2715. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv3_Conv2D"
  2716. type: "Convolution"
  2717. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2718. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv3_Conv2D"
  2719. convolution_param {
  2720. num_output: 1024
  2721. bias_term: true
  2722. group: 1
  2723. stride: 1
  2724. pad_h: 0
  2725. pad_w: 0
  2726. kernel_h: 1
  2727. kernel_w: 1
  2728. }
  2729. }
  2730. layer {
  2731. name: "resnet_v2_152_block3_unit_8_bottleneck_v2_add"
  2732. type: "Eltwise"
  2733. bottom: "resnet_v2_152_block3_unit_7_bottleneck_v2_add"
  2734. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_conv3_Conv2D"
  2735. top: "resnet_v2_152_block3_unit_8_bottleneck_v2_add"
  2736. eltwise_param {
  2737. operation: SUM
  2738. }
  2739. }
  2740. layer {
  2741. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2742. type: "BatchNorm"
  2743. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_add"
  2744. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2745. batch_norm_param {
  2746. use_global_stats: true
  2747. eps: 1.0009999641624745e-05
  2748. }
  2749. }
  2750. layer {
  2751. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm_scale"
  2752. type: "Scale"
  2753. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2754. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2755. scale_param {
  2756. bias_term: true
  2757. }
  2758. }
  2759. layer {
  2760. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_Relu"
  2761. type: "ReLU"
  2762. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2763. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2764. }
  2765. layer {
  2766. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_Conv2D"
  2767. type: "Convolution"
  2768. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_preact_FusedBatchNorm"
  2769. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_Conv2D"
  2770. convolution_param {
  2771. num_output: 256
  2772. bias_term: false
  2773. group: 1
  2774. stride: 1
  2775. pad_h: 0
  2776. pad_w: 0
  2777. kernel_h: 1
  2778. kernel_w: 1
  2779. }
  2780. }
  2781. layer {
  2782. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2783. type: "BatchNorm"
  2784. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_Conv2D"
  2785. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2786. batch_norm_param {
  2787. use_global_stats: true
  2788. eps: 1.0009999641624745e-05
  2789. }
  2790. }
  2791. layer {
  2792. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2793. type: "Scale"
  2794. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2795. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2796. scale_param {
  2797. bias_term: true
  2798. }
  2799. }
  2800. layer {
  2801. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_Relu"
  2802. type: "ReLU"
  2803. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2804. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2805. }
  2806. layer {
  2807. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_Conv2D"
  2808. type: "Convolution"
  2809. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2810. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_Conv2D"
  2811. convolution_param {
  2812. num_output: 256
  2813. bias_term: false
  2814. group: 1
  2815. stride: 1
  2816. pad_h: 1
  2817. pad_w: 1
  2818. kernel_h: 3
  2819. kernel_w: 3
  2820. }
  2821. }
  2822. layer {
  2823. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2824. type: "BatchNorm"
  2825. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_Conv2D"
  2826. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2827. batch_norm_param {
  2828. use_global_stats: true
  2829. eps: 1.0009999641624745e-05
  2830. }
  2831. }
  2832. layer {
  2833. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2834. type: "Scale"
  2835. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2836. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2837. scale_param {
  2838. bias_term: true
  2839. }
  2840. }
  2841. layer {
  2842. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_Relu"
  2843. type: "ReLU"
  2844. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2845. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2846. }
  2847. layer {
  2848. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv3_Conv2D"
  2849. type: "Convolution"
  2850. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2851. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv3_Conv2D"
  2852. convolution_param {
  2853. num_output: 1024
  2854. bias_term: true
  2855. group: 1
  2856. stride: 1
  2857. pad_h: 0
  2858. pad_w: 0
  2859. kernel_h: 1
  2860. kernel_w: 1
  2861. }
  2862. }
  2863. layer {
  2864. name: "resnet_v2_152_block3_unit_9_bottleneck_v2_add"
  2865. type: "Eltwise"
  2866. bottom: "resnet_v2_152_block3_unit_8_bottleneck_v2_add"
  2867. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_conv3_Conv2D"
  2868. top: "resnet_v2_152_block3_unit_9_bottleneck_v2_add"
  2869. eltwise_param {
  2870. operation: SUM
  2871. }
  2872. }
  2873. layer {
  2874. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2875. type: "BatchNorm"
  2876. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_add"
  2877. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2878. batch_norm_param {
  2879. use_global_stats: true
  2880. eps: 1.0009999641624745e-05
  2881. }
  2882. }
  2883. layer {
  2884. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm_scale"
  2885. type: "Scale"
  2886. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2887. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2888. scale_param {
  2889. bias_term: true
  2890. }
  2891. }
  2892. layer {
  2893. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_Relu"
  2894. type: "ReLU"
  2895. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2896. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2897. }
  2898. layer {
  2899. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_Conv2D"
  2900. type: "Convolution"
  2901. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_preact_FusedBatchNorm"
  2902. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_Conv2D"
  2903. convolution_param {
  2904. num_output: 256
  2905. bias_term: false
  2906. group: 1
  2907. stride: 1
  2908. pad_h: 0
  2909. pad_w: 0
  2910. kernel_h: 1
  2911. kernel_w: 1
  2912. }
  2913. }
  2914. layer {
  2915. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2916. type: "BatchNorm"
  2917. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_Conv2D"
  2918. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2919. batch_norm_param {
  2920. use_global_stats: true
  2921. eps: 1.0009999641624745e-05
  2922. }
  2923. }
  2924. layer {
  2925. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  2926. type: "Scale"
  2927. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2928. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2929. scale_param {
  2930. bias_term: true
  2931. }
  2932. }
  2933. layer {
  2934. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_Relu"
  2935. type: "ReLU"
  2936. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2937. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2938. }
  2939. layer {
  2940. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_Conv2D"
  2941. type: "Convolution"
  2942. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  2943. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_Conv2D"
  2944. convolution_param {
  2945. num_output: 256
  2946. bias_term: false
  2947. group: 1
  2948. stride: 1
  2949. pad_h: 1
  2950. pad_w: 1
  2951. kernel_h: 3
  2952. kernel_w: 3
  2953. }
  2954. }
  2955. layer {
  2956. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2957. type: "BatchNorm"
  2958. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_Conv2D"
  2959. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2960. batch_norm_param {
  2961. use_global_stats: true
  2962. eps: 1.0009999641624745e-05
  2963. }
  2964. }
  2965. layer {
  2966. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  2967. type: "Scale"
  2968. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2969. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2970. scale_param {
  2971. bias_term: true
  2972. }
  2973. }
  2974. layer {
  2975. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_Relu"
  2976. type: "ReLU"
  2977. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2978. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2979. }
  2980. layer {
  2981. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv3_Conv2D"
  2982. type: "Convolution"
  2983. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  2984. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv3_Conv2D"
  2985. convolution_param {
  2986. num_output: 1024
  2987. bias_term: true
  2988. group: 1
  2989. stride: 1
  2990. pad_h: 0
  2991. pad_w: 0
  2992. kernel_h: 1
  2993. kernel_w: 1
  2994. }
  2995. }
  2996. layer {
  2997. name: "resnet_v2_152_block3_unit_10_bottleneck_v2_add"
  2998. type: "Eltwise"
  2999. bottom: "resnet_v2_152_block3_unit_9_bottleneck_v2_add"
  3000. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_conv3_Conv2D"
  3001. top: "resnet_v2_152_block3_unit_10_bottleneck_v2_add"
  3002. eltwise_param {
  3003. operation: SUM
  3004. }
  3005. }
  3006. layer {
  3007. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3008. type: "BatchNorm"
  3009. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_add"
  3010. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3011. batch_norm_param {
  3012. use_global_stats: true
  3013. eps: 1.0009999641624745e-05
  3014. }
  3015. }
  3016. layer {
  3017. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm_scale"
  3018. type: "Scale"
  3019. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3020. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3021. scale_param {
  3022. bias_term: true
  3023. }
  3024. }
  3025. layer {
  3026. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_Relu"
  3027. type: "ReLU"
  3028. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3029. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3030. }
  3031. layer {
  3032. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_Conv2D"
  3033. type: "Convolution"
  3034. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_preact_FusedBatchNorm"
  3035. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_Conv2D"
  3036. convolution_param {
  3037. num_output: 256
  3038. bias_term: false
  3039. group: 1
  3040. stride: 1
  3041. pad_h: 0
  3042. pad_w: 0
  3043. kernel_h: 1
  3044. kernel_w: 1
  3045. }
  3046. }
  3047. layer {
  3048. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3049. type: "BatchNorm"
  3050. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_Conv2D"
  3051. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3052. batch_norm_param {
  3053. use_global_stats: true
  3054. eps: 1.0009999641624745e-05
  3055. }
  3056. }
  3057. layer {
  3058. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3059. type: "Scale"
  3060. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3061. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3062. scale_param {
  3063. bias_term: true
  3064. }
  3065. }
  3066. layer {
  3067. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_Relu"
  3068. type: "ReLU"
  3069. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3070. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3071. }
  3072. layer {
  3073. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_Conv2D"
  3074. type: "Convolution"
  3075. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3076. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_Conv2D"
  3077. convolution_param {
  3078. num_output: 256
  3079. bias_term: false
  3080. group: 1
  3081. stride: 1
  3082. pad_h: 1
  3083. pad_w: 1
  3084. kernel_h: 3
  3085. kernel_w: 3
  3086. }
  3087. }
  3088. layer {
  3089. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3090. type: "BatchNorm"
  3091. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_Conv2D"
  3092. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3093. batch_norm_param {
  3094. use_global_stats: true
  3095. eps: 1.0009999641624745e-05
  3096. }
  3097. }
  3098. layer {
  3099. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3100. type: "Scale"
  3101. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3102. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3103. scale_param {
  3104. bias_term: true
  3105. }
  3106. }
  3107. layer {
  3108. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_Relu"
  3109. type: "ReLU"
  3110. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3111. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3112. }
  3113. layer {
  3114. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv3_Conv2D"
  3115. type: "Convolution"
  3116. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3117. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv3_Conv2D"
  3118. convolution_param {
  3119. num_output: 1024
  3120. bias_term: true
  3121. group: 1
  3122. stride: 1
  3123. pad_h: 0
  3124. pad_w: 0
  3125. kernel_h: 1
  3126. kernel_w: 1
  3127. }
  3128. }
  3129. layer {
  3130. name: "resnet_v2_152_block3_unit_11_bottleneck_v2_add"
  3131. type: "Eltwise"
  3132. bottom: "resnet_v2_152_block3_unit_10_bottleneck_v2_add"
  3133. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_conv3_Conv2D"
  3134. top: "resnet_v2_152_block3_unit_11_bottleneck_v2_add"
  3135. eltwise_param {
  3136. operation: SUM
  3137. }
  3138. }
  3139. layer {
  3140. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3141. type: "BatchNorm"
  3142. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_add"
  3143. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3144. batch_norm_param {
  3145. use_global_stats: true
  3146. eps: 1.0009999641624745e-05
  3147. }
  3148. }
  3149. layer {
  3150. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm_scale"
  3151. type: "Scale"
  3152. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3153. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3154. scale_param {
  3155. bias_term: true
  3156. }
  3157. }
  3158. layer {
  3159. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_Relu"
  3160. type: "ReLU"
  3161. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3162. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3163. }
  3164. layer {
  3165. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_Conv2D"
  3166. type: "Convolution"
  3167. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_preact_FusedBatchNorm"
  3168. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_Conv2D"
  3169. convolution_param {
  3170. num_output: 256
  3171. bias_term: false
  3172. group: 1
  3173. stride: 1
  3174. pad_h: 0
  3175. pad_w: 0
  3176. kernel_h: 1
  3177. kernel_w: 1
  3178. }
  3179. }
  3180. layer {
  3181. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3182. type: "BatchNorm"
  3183. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_Conv2D"
  3184. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3185. batch_norm_param {
  3186. use_global_stats: true
  3187. eps: 1.0009999641624745e-05
  3188. }
  3189. }
  3190. layer {
  3191. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3192. type: "Scale"
  3193. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3194. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3195. scale_param {
  3196. bias_term: true
  3197. }
  3198. }
  3199. layer {
  3200. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_Relu"
  3201. type: "ReLU"
  3202. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3203. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3204. }
  3205. layer {
  3206. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_Conv2D"
  3207. type: "Convolution"
  3208. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3209. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_Conv2D"
  3210. convolution_param {
  3211. num_output: 256
  3212. bias_term: false
  3213. group: 1
  3214. stride: 1
  3215. pad_h: 1
  3216. pad_w: 1
  3217. kernel_h: 3
  3218. kernel_w: 3
  3219. }
  3220. }
  3221. layer {
  3222. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3223. type: "BatchNorm"
  3224. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_Conv2D"
  3225. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3226. batch_norm_param {
  3227. use_global_stats: true
  3228. eps: 1.0009999641624745e-05
  3229. }
  3230. }
  3231. layer {
  3232. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3233. type: "Scale"
  3234. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3235. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3236. scale_param {
  3237. bias_term: true
  3238. }
  3239. }
  3240. layer {
  3241. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_Relu"
  3242. type: "ReLU"
  3243. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3244. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3245. }
  3246. layer {
  3247. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv3_Conv2D"
  3248. type: "Convolution"
  3249. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3250. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv3_Conv2D"
  3251. convolution_param {
  3252. num_output: 1024
  3253. bias_term: true
  3254. group: 1
  3255. stride: 1
  3256. pad_h: 0
  3257. pad_w: 0
  3258. kernel_h: 1
  3259. kernel_w: 1
  3260. }
  3261. }
  3262. layer {
  3263. name: "resnet_v2_152_block3_unit_12_bottleneck_v2_add"
  3264. type: "Eltwise"
  3265. bottom: "resnet_v2_152_block3_unit_11_bottleneck_v2_add"
  3266. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_conv3_Conv2D"
  3267. top: "resnet_v2_152_block3_unit_12_bottleneck_v2_add"
  3268. eltwise_param {
  3269. operation: SUM
  3270. }
  3271. }
  3272. layer {
  3273. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3274. type: "BatchNorm"
  3275. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_add"
  3276. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3277. batch_norm_param {
  3278. use_global_stats: true
  3279. eps: 1.0009999641624745e-05
  3280. }
  3281. }
  3282. layer {
  3283. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm_scale"
  3284. type: "Scale"
  3285. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3286. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3287. scale_param {
  3288. bias_term: true
  3289. }
  3290. }
  3291. layer {
  3292. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_Relu"
  3293. type: "ReLU"
  3294. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3295. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3296. }
  3297. layer {
  3298. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_Conv2D"
  3299. type: "Convolution"
  3300. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_preact_FusedBatchNorm"
  3301. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_Conv2D"
  3302. convolution_param {
  3303. num_output: 256
  3304. bias_term: false
  3305. group: 1
  3306. stride: 1
  3307. pad_h: 0
  3308. pad_w: 0
  3309. kernel_h: 1
  3310. kernel_w: 1
  3311. }
  3312. }
  3313. layer {
  3314. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3315. type: "BatchNorm"
  3316. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_Conv2D"
  3317. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3318. batch_norm_param {
  3319. use_global_stats: true
  3320. eps: 1.0009999641624745e-05
  3321. }
  3322. }
  3323. layer {
  3324. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3325. type: "Scale"
  3326. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3327. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3328. scale_param {
  3329. bias_term: true
  3330. }
  3331. }
  3332. layer {
  3333. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_Relu"
  3334. type: "ReLU"
  3335. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3336. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3337. }
  3338. layer {
  3339. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_Conv2D"
  3340. type: "Convolution"
  3341. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3342. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_Conv2D"
  3343. convolution_param {
  3344. num_output: 256
  3345. bias_term: false
  3346. group: 1
  3347. stride: 1
  3348. pad_h: 1
  3349. pad_w: 1
  3350. kernel_h: 3
  3351. kernel_w: 3
  3352. }
  3353. }
  3354. layer {
  3355. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3356. type: "BatchNorm"
  3357. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_Conv2D"
  3358. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3359. batch_norm_param {
  3360. use_global_stats: true
  3361. eps: 1.0009999641624745e-05
  3362. }
  3363. }
  3364. layer {
  3365. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3366. type: "Scale"
  3367. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3368. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3369. scale_param {
  3370. bias_term: true
  3371. }
  3372. }
  3373. layer {
  3374. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_Relu"
  3375. type: "ReLU"
  3376. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3377. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3378. }
  3379. layer {
  3380. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv3_Conv2D"
  3381. type: "Convolution"
  3382. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3383. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv3_Conv2D"
  3384. convolution_param {
  3385. num_output: 1024
  3386. bias_term: true
  3387. group: 1
  3388. stride: 1
  3389. pad_h: 0
  3390. pad_w: 0
  3391. kernel_h: 1
  3392. kernel_w: 1
  3393. }
  3394. }
  3395. layer {
  3396. name: "resnet_v2_152_block3_unit_13_bottleneck_v2_add"
  3397. type: "Eltwise"
  3398. bottom: "resnet_v2_152_block3_unit_12_bottleneck_v2_add"
  3399. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_conv3_Conv2D"
  3400. top: "resnet_v2_152_block3_unit_13_bottleneck_v2_add"
  3401. eltwise_param {
  3402. operation: SUM
  3403. }
  3404. }
  3405. layer {
  3406. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3407. type: "BatchNorm"
  3408. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_add"
  3409. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3410. batch_norm_param {
  3411. use_global_stats: true
  3412. eps: 1.0009999641624745e-05
  3413. }
  3414. }
  3415. layer {
  3416. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm_scale"
  3417. type: "Scale"
  3418. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3419. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3420. scale_param {
  3421. bias_term: true
  3422. }
  3423. }
  3424. layer {
  3425. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_Relu"
  3426. type: "ReLU"
  3427. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3428. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3429. }
  3430. layer {
  3431. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_Conv2D"
  3432. type: "Convolution"
  3433. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_preact_FusedBatchNorm"
  3434. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_Conv2D"
  3435. convolution_param {
  3436. num_output: 256
  3437. bias_term: false
  3438. group: 1
  3439. stride: 1
  3440. pad_h: 0
  3441. pad_w: 0
  3442. kernel_h: 1
  3443. kernel_w: 1
  3444. }
  3445. }
  3446. layer {
  3447. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3448. type: "BatchNorm"
  3449. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_Conv2D"
  3450. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3451. batch_norm_param {
  3452. use_global_stats: true
  3453. eps: 1.0009999641624745e-05
  3454. }
  3455. }
  3456. layer {
  3457. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3458. type: "Scale"
  3459. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3460. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3461. scale_param {
  3462. bias_term: true
  3463. }
  3464. }
  3465. layer {
  3466. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_Relu"
  3467. type: "ReLU"
  3468. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3469. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3470. }
  3471. layer {
  3472. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_Conv2D"
  3473. type: "Convolution"
  3474. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3475. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_Conv2D"
  3476. convolution_param {
  3477. num_output: 256
  3478. bias_term: false
  3479. group: 1
  3480. stride: 1
  3481. pad_h: 1
  3482. pad_w: 1
  3483. kernel_h: 3
  3484. kernel_w: 3
  3485. }
  3486. }
  3487. layer {
  3488. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3489. type: "BatchNorm"
  3490. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_Conv2D"
  3491. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3492. batch_norm_param {
  3493. use_global_stats: true
  3494. eps: 1.0009999641624745e-05
  3495. }
  3496. }
  3497. layer {
  3498. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3499. type: "Scale"
  3500. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3501. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3502. scale_param {
  3503. bias_term: true
  3504. }
  3505. }
  3506. layer {
  3507. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_Relu"
  3508. type: "ReLU"
  3509. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3510. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3511. }
  3512. layer {
  3513. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv3_Conv2D"
  3514. type: "Convolution"
  3515. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3516. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv3_Conv2D"
  3517. convolution_param {
  3518. num_output: 1024
  3519. bias_term: true
  3520. group: 1
  3521. stride: 1
  3522. pad_h: 0
  3523. pad_w: 0
  3524. kernel_h: 1
  3525. kernel_w: 1
  3526. }
  3527. }
  3528. layer {
  3529. name: "resnet_v2_152_block3_unit_14_bottleneck_v2_add"
  3530. type: "Eltwise"
  3531. bottom: "resnet_v2_152_block3_unit_13_bottleneck_v2_add"
  3532. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_conv3_Conv2D"
  3533. top: "resnet_v2_152_block3_unit_14_bottleneck_v2_add"
  3534. eltwise_param {
  3535. operation: SUM
  3536. }
  3537. }
  3538. layer {
  3539. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3540. type: "BatchNorm"
  3541. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_add"
  3542. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3543. batch_norm_param {
  3544. use_global_stats: true
  3545. eps: 1.0009999641624745e-05
  3546. }
  3547. }
  3548. layer {
  3549. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm_scale"
  3550. type: "Scale"
  3551. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3552. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3553. scale_param {
  3554. bias_term: true
  3555. }
  3556. }
  3557. layer {
  3558. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_Relu"
  3559. type: "ReLU"
  3560. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3561. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3562. }
  3563. layer {
  3564. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_Conv2D"
  3565. type: "Convolution"
  3566. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_preact_FusedBatchNorm"
  3567. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_Conv2D"
  3568. convolution_param {
  3569. num_output: 256
  3570. bias_term: false
  3571. group: 1
  3572. stride: 1
  3573. pad_h: 0
  3574. pad_w: 0
  3575. kernel_h: 1
  3576. kernel_w: 1
  3577. }
  3578. }
  3579. layer {
  3580. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3581. type: "BatchNorm"
  3582. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_Conv2D"
  3583. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3584. batch_norm_param {
  3585. use_global_stats: true
  3586. eps: 1.0009999641624745e-05
  3587. }
  3588. }
  3589. layer {
  3590. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3591. type: "Scale"
  3592. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3593. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3594. scale_param {
  3595. bias_term: true
  3596. }
  3597. }
  3598. layer {
  3599. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_Relu"
  3600. type: "ReLU"
  3601. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3602. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3603. }
  3604. layer {
  3605. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_Conv2D"
  3606. type: "Convolution"
  3607. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3608. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_Conv2D"
  3609. convolution_param {
  3610. num_output: 256
  3611. bias_term: false
  3612. group: 1
  3613. stride: 1
  3614. pad_h: 1
  3615. pad_w: 1
  3616. kernel_h: 3
  3617. kernel_w: 3
  3618. }
  3619. }
  3620. layer {
  3621. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3622. type: "BatchNorm"
  3623. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_Conv2D"
  3624. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3625. batch_norm_param {
  3626. use_global_stats: true
  3627. eps: 1.0009999641624745e-05
  3628. }
  3629. }
  3630. layer {
  3631. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3632. type: "Scale"
  3633. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3634. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3635. scale_param {
  3636. bias_term: true
  3637. }
  3638. }
  3639. layer {
  3640. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_Relu"
  3641. type: "ReLU"
  3642. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3643. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3644. }
  3645. layer {
  3646. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv3_Conv2D"
  3647. type: "Convolution"
  3648. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3649. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv3_Conv2D"
  3650. convolution_param {
  3651. num_output: 1024
  3652. bias_term: true
  3653. group: 1
  3654. stride: 1
  3655. pad_h: 0
  3656. pad_w: 0
  3657. kernel_h: 1
  3658. kernel_w: 1
  3659. }
  3660. }
  3661. layer {
  3662. name: "resnet_v2_152_block3_unit_15_bottleneck_v2_add"
  3663. type: "Eltwise"
  3664. bottom: "resnet_v2_152_block3_unit_14_bottleneck_v2_add"
  3665. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_conv3_Conv2D"
  3666. top: "resnet_v2_152_block3_unit_15_bottleneck_v2_add"
  3667. eltwise_param {
  3668. operation: SUM
  3669. }
  3670. }
  3671. layer {
  3672. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3673. type: "BatchNorm"
  3674. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_add"
  3675. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3676. batch_norm_param {
  3677. use_global_stats: true
  3678. eps: 1.0009999641624745e-05
  3679. }
  3680. }
  3681. layer {
  3682. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm_scale"
  3683. type: "Scale"
  3684. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3685. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3686. scale_param {
  3687. bias_term: true
  3688. }
  3689. }
  3690. layer {
  3691. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_Relu"
  3692. type: "ReLU"
  3693. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3694. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3695. }
  3696. layer {
  3697. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_Conv2D"
  3698. type: "Convolution"
  3699. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_preact_FusedBatchNorm"
  3700. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_Conv2D"
  3701. convolution_param {
  3702. num_output: 256
  3703. bias_term: false
  3704. group: 1
  3705. stride: 1
  3706. pad_h: 0
  3707. pad_w: 0
  3708. kernel_h: 1
  3709. kernel_w: 1
  3710. }
  3711. }
  3712. layer {
  3713. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3714. type: "BatchNorm"
  3715. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_Conv2D"
  3716. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3717. batch_norm_param {
  3718. use_global_stats: true
  3719. eps: 1.0009999641624745e-05
  3720. }
  3721. }
  3722. layer {
  3723. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3724. type: "Scale"
  3725. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3726. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3727. scale_param {
  3728. bias_term: true
  3729. }
  3730. }
  3731. layer {
  3732. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_Relu"
  3733. type: "ReLU"
  3734. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3735. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3736. }
  3737. layer {
  3738. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_Conv2D"
  3739. type: "Convolution"
  3740. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3741. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_Conv2D"
  3742. convolution_param {
  3743. num_output: 256
  3744. bias_term: false
  3745. group: 1
  3746. stride: 1
  3747. pad_h: 1
  3748. pad_w: 1
  3749. kernel_h: 3
  3750. kernel_w: 3
  3751. }
  3752. }
  3753. layer {
  3754. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3755. type: "BatchNorm"
  3756. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_Conv2D"
  3757. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3758. batch_norm_param {
  3759. use_global_stats: true
  3760. eps: 1.0009999641624745e-05
  3761. }
  3762. }
  3763. layer {
  3764. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3765. type: "Scale"
  3766. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3767. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3768. scale_param {
  3769. bias_term: true
  3770. }
  3771. }
  3772. layer {
  3773. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_Relu"
  3774. type: "ReLU"
  3775. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3776. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3777. }
  3778. layer {
  3779. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv3_Conv2D"
  3780. type: "Convolution"
  3781. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3782. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv3_Conv2D"
  3783. convolution_param {
  3784. num_output: 1024
  3785. bias_term: true
  3786. group: 1
  3787. stride: 1
  3788. pad_h: 0
  3789. pad_w: 0
  3790. kernel_h: 1
  3791. kernel_w: 1
  3792. }
  3793. }
  3794. layer {
  3795. name: "resnet_v2_152_block3_unit_16_bottleneck_v2_add"
  3796. type: "Eltwise"
  3797. bottom: "resnet_v2_152_block3_unit_15_bottleneck_v2_add"
  3798. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_conv3_Conv2D"
  3799. top: "resnet_v2_152_block3_unit_16_bottleneck_v2_add"
  3800. eltwise_param {
  3801. operation: SUM
  3802. }
  3803. }
  3804. layer {
  3805. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3806. type: "BatchNorm"
  3807. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_add"
  3808. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3809. batch_norm_param {
  3810. use_global_stats: true
  3811. eps: 1.0009999641624745e-05
  3812. }
  3813. }
  3814. layer {
  3815. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm_scale"
  3816. type: "Scale"
  3817. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3818. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3819. scale_param {
  3820. bias_term: true
  3821. }
  3822. }
  3823. layer {
  3824. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_Relu"
  3825. type: "ReLU"
  3826. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3827. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3828. }
  3829. layer {
  3830. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_Conv2D"
  3831. type: "Convolution"
  3832. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_preact_FusedBatchNorm"
  3833. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_Conv2D"
  3834. convolution_param {
  3835. num_output: 256
  3836. bias_term: false
  3837. group: 1
  3838. stride: 1
  3839. pad_h: 0
  3840. pad_w: 0
  3841. kernel_h: 1
  3842. kernel_w: 1
  3843. }
  3844. }
  3845. layer {
  3846. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3847. type: "BatchNorm"
  3848. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_Conv2D"
  3849. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3850. batch_norm_param {
  3851. use_global_stats: true
  3852. eps: 1.0009999641624745e-05
  3853. }
  3854. }
  3855. layer {
  3856. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3857. type: "Scale"
  3858. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3859. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3860. scale_param {
  3861. bias_term: true
  3862. }
  3863. }
  3864. layer {
  3865. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_Relu"
  3866. type: "ReLU"
  3867. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3868. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3869. }
  3870. layer {
  3871. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_Conv2D"
  3872. type: "Convolution"
  3873. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3874. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_Conv2D"
  3875. convolution_param {
  3876. num_output: 256
  3877. bias_term: false
  3878. group: 1
  3879. stride: 1
  3880. pad_h: 1
  3881. pad_w: 1
  3882. kernel_h: 3
  3883. kernel_w: 3
  3884. }
  3885. }
  3886. layer {
  3887. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3888. type: "BatchNorm"
  3889. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_Conv2D"
  3890. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3891. batch_norm_param {
  3892. use_global_stats: true
  3893. eps: 1.0009999641624745e-05
  3894. }
  3895. }
  3896. layer {
  3897. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  3898. type: "Scale"
  3899. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3900. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3901. scale_param {
  3902. bias_term: true
  3903. }
  3904. }
  3905. layer {
  3906. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_Relu"
  3907. type: "ReLU"
  3908. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3909. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3910. }
  3911. layer {
  3912. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv3_Conv2D"
  3913. type: "Convolution"
  3914. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  3915. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv3_Conv2D"
  3916. convolution_param {
  3917. num_output: 1024
  3918. bias_term: true
  3919. group: 1
  3920. stride: 1
  3921. pad_h: 0
  3922. pad_w: 0
  3923. kernel_h: 1
  3924. kernel_w: 1
  3925. }
  3926. }
  3927. layer {
  3928. name: "resnet_v2_152_block3_unit_17_bottleneck_v2_add"
  3929. type: "Eltwise"
  3930. bottom: "resnet_v2_152_block3_unit_16_bottleneck_v2_add"
  3931. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_conv3_Conv2D"
  3932. top: "resnet_v2_152_block3_unit_17_bottleneck_v2_add"
  3933. eltwise_param {
  3934. operation: SUM
  3935. }
  3936. }
  3937. layer {
  3938. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3939. type: "BatchNorm"
  3940. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_add"
  3941. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3942. batch_norm_param {
  3943. use_global_stats: true
  3944. eps: 1.0009999641624745e-05
  3945. }
  3946. }
  3947. layer {
  3948. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm_scale"
  3949. type: "Scale"
  3950. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3951. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3952. scale_param {
  3953. bias_term: true
  3954. }
  3955. }
  3956. layer {
  3957. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_Relu"
  3958. type: "ReLU"
  3959. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3960. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3961. }
  3962. layer {
  3963. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_Conv2D"
  3964. type: "Convolution"
  3965. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_preact_FusedBatchNorm"
  3966. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_Conv2D"
  3967. convolution_param {
  3968. num_output: 256
  3969. bias_term: false
  3970. group: 1
  3971. stride: 1
  3972. pad_h: 0
  3973. pad_w: 0
  3974. kernel_h: 1
  3975. kernel_w: 1
  3976. }
  3977. }
  3978. layer {
  3979. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3980. type: "BatchNorm"
  3981. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_Conv2D"
  3982. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3983. batch_norm_param {
  3984. use_global_stats: true
  3985. eps: 1.0009999641624745e-05
  3986. }
  3987. }
  3988. layer {
  3989. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  3990. type: "Scale"
  3991. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3992. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  3993. scale_param {
  3994. bias_term: true
  3995. }
  3996. }
  3997. layer {
  3998. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_Relu"
  3999. type: "ReLU"
  4000. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4001. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4002. }
  4003. layer {
  4004. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_Conv2D"
  4005. type: "Convolution"
  4006. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4007. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_Conv2D"
  4008. convolution_param {
  4009. num_output: 256
  4010. bias_term: false
  4011. group: 1
  4012. stride: 1
  4013. pad_h: 1
  4014. pad_w: 1
  4015. kernel_h: 3
  4016. kernel_w: 3
  4017. }
  4018. }
  4019. layer {
  4020. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4021. type: "BatchNorm"
  4022. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_Conv2D"
  4023. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4024. batch_norm_param {
  4025. use_global_stats: true
  4026. eps: 1.0009999641624745e-05
  4027. }
  4028. }
  4029. layer {
  4030. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4031. type: "Scale"
  4032. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4033. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4034. scale_param {
  4035. bias_term: true
  4036. }
  4037. }
  4038. layer {
  4039. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_Relu"
  4040. type: "ReLU"
  4041. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4042. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4043. }
  4044. layer {
  4045. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv3_Conv2D"
  4046. type: "Convolution"
  4047. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4048. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv3_Conv2D"
  4049. convolution_param {
  4050. num_output: 1024
  4051. bias_term: true
  4052. group: 1
  4053. stride: 1
  4054. pad_h: 0
  4055. pad_w: 0
  4056. kernel_h: 1
  4057. kernel_w: 1
  4058. }
  4059. }
  4060. layer {
  4061. name: "resnet_v2_152_block3_unit_18_bottleneck_v2_add"
  4062. type: "Eltwise"
  4063. bottom: "resnet_v2_152_block3_unit_17_bottleneck_v2_add"
  4064. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_conv3_Conv2D"
  4065. top: "resnet_v2_152_block3_unit_18_bottleneck_v2_add"
  4066. eltwise_param {
  4067. operation: SUM
  4068. }
  4069. }
  4070. layer {
  4071. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4072. type: "BatchNorm"
  4073. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_add"
  4074. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4075. batch_norm_param {
  4076. use_global_stats: true
  4077. eps: 1.0009999641624745e-05
  4078. }
  4079. }
  4080. layer {
  4081. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm_scale"
  4082. type: "Scale"
  4083. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4084. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4085. scale_param {
  4086. bias_term: true
  4087. }
  4088. }
  4089. layer {
  4090. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_Relu"
  4091. type: "ReLU"
  4092. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4093. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4094. }
  4095. layer {
  4096. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_Conv2D"
  4097. type: "Convolution"
  4098. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_preact_FusedBatchNorm"
  4099. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_Conv2D"
  4100. convolution_param {
  4101. num_output: 256
  4102. bias_term: false
  4103. group: 1
  4104. stride: 1
  4105. pad_h: 0
  4106. pad_w: 0
  4107. kernel_h: 1
  4108. kernel_w: 1
  4109. }
  4110. }
  4111. layer {
  4112. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4113. type: "BatchNorm"
  4114. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_Conv2D"
  4115. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4116. batch_norm_param {
  4117. use_global_stats: true
  4118. eps: 1.0009999641624745e-05
  4119. }
  4120. }
  4121. layer {
  4122. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4123. type: "Scale"
  4124. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4125. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4126. scale_param {
  4127. bias_term: true
  4128. }
  4129. }
  4130. layer {
  4131. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_Relu"
  4132. type: "ReLU"
  4133. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4134. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4135. }
  4136. layer {
  4137. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_Conv2D"
  4138. type: "Convolution"
  4139. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4140. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_Conv2D"
  4141. convolution_param {
  4142. num_output: 256
  4143. bias_term: false
  4144. group: 1
  4145. stride: 1
  4146. pad_h: 1
  4147. pad_w: 1
  4148. kernel_h: 3
  4149. kernel_w: 3
  4150. }
  4151. }
  4152. layer {
  4153. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4154. type: "BatchNorm"
  4155. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_Conv2D"
  4156. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4157. batch_norm_param {
  4158. use_global_stats: true
  4159. eps: 1.0009999641624745e-05
  4160. }
  4161. }
  4162. layer {
  4163. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4164. type: "Scale"
  4165. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4166. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4167. scale_param {
  4168. bias_term: true
  4169. }
  4170. }
  4171. layer {
  4172. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_Relu"
  4173. type: "ReLU"
  4174. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4175. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4176. }
  4177. layer {
  4178. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv3_Conv2D"
  4179. type: "Convolution"
  4180. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4181. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv3_Conv2D"
  4182. convolution_param {
  4183. num_output: 1024
  4184. bias_term: true
  4185. group: 1
  4186. stride: 1
  4187. pad_h: 0
  4188. pad_w: 0
  4189. kernel_h: 1
  4190. kernel_w: 1
  4191. }
  4192. }
  4193. layer {
  4194. name: "resnet_v2_152_block3_unit_19_bottleneck_v2_add"
  4195. type: "Eltwise"
  4196. bottom: "resnet_v2_152_block3_unit_18_bottleneck_v2_add"
  4197. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_conv3_Conv2D"
  4198. top: "resnet_v2_152_block3_unit_19_bottleneck_v2_add"
  4199. eltwise_param {
  4200. operation: SUM
  4201. }
  4202. }
  4203. layer {
  4204. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4205. type: "BatchNorm"
  4206. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_add"
  4207. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4208. batch_norm_param {
  4209. use_global_stats: true
  4210. eps: 1.0009999641624745e-05
  4211. }
  4212. }
  4213. layer {
  4214. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm_scale"
  4215. type: "Scale"
  4216. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4217. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4218. scale_param {
  4219. bias_term: true
  4220. }
  4221. }
  4222. layer {
  4223. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_Relu"
  4224. type: "ReLU"
  4225. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4226. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4227. }
  4228. layer {
  4229. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_Conv2D"
  4230. type: "Convolution"
  4231. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_preact_FusedBatchNorm"
  4232. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_Conv2D"
  4233. convolution_param {
  4234. num_output: 256
  4235. bias_term: false
  4236. group: 1
  4237. stride: 1
  4238. pad_h: 0
  4239. pad_w: 0
  4240. kernel_h: 1
  4241. kernel_w: 1
  4242. }
  4243. }
  4244. layer {
  4245. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4246. type: "BatchNorm"
  4247. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_Conv2D"
  4248. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4249. batch_norm_param {
  4250. use_global_stats: true
  4251. eps: 1.0009999641624745e-05
  4252. }
  4253. }
  4254. layer {
  4255. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4256. type: "Scale"
  4257. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4258. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4259. scale_param {
  4260. bias_term: true
  4261. }
  4262. }
  4263. layer {
  4264. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_Relu"
  4265. type: "ReLU"
  4266. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4267. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4268. }
  4269. layer {
  4270. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_Conv2D"
  4271. type: "Convolution"
  4272. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4273. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_Conv2D"
  4274. convolution_param {
  4275. num_output: 256
  4276. bias_term: false
  4277. group: 1
  4278. stride: 1
  4279. pad_h: 1
  4280. pad_w: 1
  4281. kernel_h: 3
  4282. kernel_w: 3
  4283. }
  4284. }
  4285. layer {
  4286. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4287. type: "BatchNorm"
  4288. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_Conv2D"
  4289. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4290. batch_norm_param {
  4291. use_global_stats: true
  4292. eps: 1.0009999641624745e-05
  4293. }
  4294. }
  4295. layer {
  4296. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4297. type: "Scale"
  4298. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4299. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4300. scale_param {
  4301. bias_term: true
  4302. }
  4303. }
  4304. layer {
  4305. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_Relu"
  4306. type: "ReLU"
  4307. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4308. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4309. }
  4310. layer {
  4311. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv3_Conv2D"
  4312. type: "Convolution"
  4313. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4314. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv3_Conv2D"
  4315. convolution_param {
  4316. num_output: 1024
  4317. bias_term: true
  4318. group: 1
  4319. stride: 1
  4320. pad_h: 0
  4321. pad_w: 0
  4322. kernel_h: 1
  4323. kernel_w: 1
  4324. }
  4325. }
  4326. layer {
  4327. name: "resnet_v2_152_block3_unit_20_bottleneck_v2_add"
  4328. type: "Eltwise"
  4329. bottom: "resnet_v2_152_block3_unit_19_bottleneck_v2_add"
  4330. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_conv3_Conv2D"
  4331. top: "resnet_v2_152_block3_unit_20_bottleneck_v2_add"
  4332. eltwise_param {
  4333. operation: SUM
  4334. }
  4335. }
  4336. layer {
  4337. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4338. type: "BatchNorm"
  4339. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_add"
  4340. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4341. batch_norm_param {
  4342. use_global_stats: true
  4343. eps: 1.0009999641624745e-05
  4344. }
  4345. }
  4346. layer {
  4347. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm_scale"
  4348. type: "Scale"
  4349. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4350. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4351. scale_param {
  4352. bias_term: true
  4353. }
  4354. }
  4355. layer {
  4356. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_Relu"
  4357. type: "ReLU"
  4358. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4359. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4360. }
  4361. layer {
  4362. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_Conv2D"
  4363. type: "Convolution"
  4364. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_preact_FusedBatchNorm"
  4365. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_Conv2D"
  4366. convolution_param {
  4367. num_output: 256
  4368. bias_term: false
  4369. group: 1
  4370. stride: 1
  4371. pad_h: 0
  4372. pad_w: 0
  4373. kernel_h: 1
  4374. kernel_w: 1
  4375. }
  4376. }
  4377. layer {
  4378. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4379. type: "BatchNorm"
  4380. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_Conv2D"
  4381. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4382. batch_norm_param {
  4383. use_global_stats: true
  4384. eps: 1.0009999641624745e-05
  4385. }
  4386. }
  4387. layer {
  4388. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4389. type: "Scale"
  4390. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4391. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4392. scale_param {
  4393. bias_term: true
  4394. }
  4395. }
  4396. layer {
  4397. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_Relu"
  4398. type: "ReLU"
  4399. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4400. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4401. }
  4402. layer {
  4403. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_Conv2D"
  4404. type: "Convolution"
  4405. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4406. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_Conv2D"
  4407. convolution_param {
  4408. num_output: 256
  4409. bias_term: false
  4410. group: 1
  4411. stride: 1
  4412. pad_h: 1
  4413. pad_w: 1
  4414. kernel_h: 3
  4415. kernel_w: 3
  4416. }
  4417. }
  4418. layer {
  4419. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4420. type: "BatchNorm"
  4421. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_Conv2D"
  4422. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4423. batch_norm_param {
  4424. use_global_stats: true
  4425. eps: 1.0009999641624745e-05
  4426. }
  4427. }
  4428. layer {
  4429. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4430. type: "Scale"
  4431. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4432. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4433. scale_param {
  4434. bias_term: true
  4435. }
  4436. }
  4437. layer {
  4438. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_Relu"
  4439. type: "ReLU"
  4440. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4441. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4442. }
  4443. layer {
  4444. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv3_Conv2D"
  4445. type: "Convolution"
  4446. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4447. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv3_Conv2D"
  4448. convolution_param {
  4449. num_output: 1024
  4450. bias_term: true
  4451. group: 1
  4452. stride: 1
  4453. pad_h: 0
  4454. pad_w: 0
  4455. kernel_h: 1
  4456. kernel_w: 1
  4457. }
  4458. }
  4459. layer {
  4460. name: "resnet_v2_152_block3_unit_21_bottleneck_v2_add"
  4461. type: "Eltwise"
  4462. bottom: "resnet_v2_152_block3_unit_20_bottleneck_v2_add"
  4463. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_conv3_Conv2D"
  4464. top: "resnet_v2_152_block3_unit_21_bottleneck_v2_add"
  4465. eltwise_param {
  4466. operation: SUM
  4467. }
  4468. }
  4469. layer {
  4470. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4471. type: "BatchNorm"
  4472. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_add"
  4473. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4474. batch_norm_param {
  4475. use_global_stats: true
  4476. eps: 1.0009999641624745e-05
  4477. }
  4478. }
  4479. layer {
  4480. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm_scale"
  4481. type: "Scale"
  4482. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4483. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4484. scale_param {
  4485. bias_term: true
  4486. }
  4487. }
  4488. layer {
  4489. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_Relu"
  4490. type: "ReLU"
  4491. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4492. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4493. }
  4494. layer {
  4495. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_Conv2D"
  4496. type: "Convolution"
  4497. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_preact_FusedBatchNorm"
  4498. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_Conv2D"
  4499. convolution_param {
  4500. num_output: 256
  4501. bias_term: false
  4502. group: 1
  4503. stride: 1
  4504. pad_h: 0
  4505. pad_w: 0
  4506. kernel_h: 1
  4507. kernel_w: 1
  4508. }
  4509. }
  4510. layer {
  4511. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4512. type: "BatchNorm"
  4513. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_Conv2D"
  4514. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4515. batch_norm_param {
  4516. use_global_stats: true
  4517. eps: 1.0009999641624745e-05
  4518. }
  4519. }
  4520. layer {
  4521. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4522. type: "Scale"
  4523. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4524. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4525. scale_param {
  4526. bias_term: true
  4527. }
  4528. }
  4529. layer {
  4530. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_Relu"
  4531. type: "ReLU"
  4532. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4533. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4534. }
  4535. layer {
  4536. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_Conv2D"
  4537. type: "Convolution"
  4538. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4539. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_Conv2D"
  4540. convolution_param {
  4541. num_output: 256
  4542. bias_term: false
  4543. group: 1
  4544. stride: 1
  4545. pad_h: 1
  4546. pad_w: 1
  4547. kernel_h: 3
  4548. kernel_w: 3
  4549. }
  4550. }
  4551. layer {
  4552. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4553. type: "BatchNorm"
  4554. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_Conv2D"
  4555. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4556. batch_norm_param {
  4557. use_global_stats: true
  4558. eps: 1.0009999641624745e-05
  4559. }
  4560. }
  4561. layer {
  4562. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4563. type: "Scale"
  4564. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4565. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4566. scale_param {
  4567. bias_term: true
  4568. }
  4569. }
  4570. layer {
  4571. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_Relu"
  4572. type: "ReLU"
  4573. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4574. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4575. }
  4576. layer {
  4577. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv3_Conv2D"
  4578. type: "Convolution"
  4579. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4580. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv3_Conv2D"
  4581. convolution_param {
  4582. num_output: 1024
  4583. bias_term: true
  4584. group: 1
  4585. stride: 1
  4586. pad_h: 0
  4587. pad_w: 0
  4588. kernel_h: 1
  4589. kernel_w: 1
  4590. }
  4591. }
  4592. layer {
  4593. name: "resnet_v2_152_block3_unit_22_bottleneck_v2_add"
  4594. type: "Eltwise"
  4595. bottom: "resnet_v2_152_block3_unit_21_bottleneck_v2_add"
  4596. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_conv3_Conv2D"
  4597. top: "resnet_v2_152_block3_unit_22_bottleneck_v2_add"
  4598. eltwise_param {
  4599. operation: SUM
  4600. }
  4601. }
  4602. layer {
  4603. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4604. type: "BatchNorm"
  4605. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_add"
  4606. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4607. batch_norm_param {
  4608. use_global_stats: true
  4609. eps: 1.0009999641624745e-05
  4610. }
  4611. }
  4612. layer {
  4613. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm_scale"
  4614. type: "Scale"
  4615. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4616. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4617. scale_param {
  4618. bias_term: true
  4619. }
  4620. }
  4621. layer {
  4622. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_Relu"
  4623. type: "ReLU"
  4624. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4625. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4626. }
  4627. layer {
  4628. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_Conv2D"
  4629. type: "Convolution"
  4630. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_preact_FusedBatchNorm"
  4631. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_Conv2D"
  4632. convolution_param {
  4633. num_output: 256
  4634. bias_term: false
  4635. group: 1
  4636. stride: 1
  4637. pad_h: 0
  4638. pad_w: 0
  4639. kernel_h: 1
  4640. kernel_w: 1
  4641. }
  4642. }
  4643. layer {
  4644. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4645. type: "BatchNorm"
  4646. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_Conv2D"
  4647. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4648. batch_norm_param {
  4649. use_global_stats: true
  4650. eps: 1.0009999641624745e-05
  4651. }
  4652. }
  4653. layer {
  4654. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4655. type: "Scale"
  4656. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4657. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4658. scale_param {
  4659. bias_term: true
  4660. }
  4661. }
  4662. layer {
  4663. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_Relu"
  4664. type: "ReLU"
  4665. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4666. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4667. }
  4668. layer {
  4669. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_Conv2D"
  4670. type: "Convolution"
  4671. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4672. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_Conv2D"
  4673. convolution_param {
  4674. num_output: 256
  4675. bias_term: false
  4676. group: 1
  4677. stride: 1
  4678. pad_h: 1
  4679. pad_w: 1
  4680. kernel_h: 3
  4681. kernel_w: 3
  4682. }
  4683. }
  4684. layer {
  4685. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4686. type: "BatchNorm"
  4687. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_Conv2D"
  4688. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4689. batch_norm_param {
  4690. use_global_stats: true
  4691. eps: 1.0009999641624745e-05
  4692. }
  4693. }
  4694. layer {
  4695. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4696. type: "Scale"
  4697. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4698. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4699. scale_param {
  4700. bias_term: true
  4701. }
  4702. }
  4703. layer {
  4704. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_Relu"
  4705. type: "ReLU"
  4706. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4707. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4708. }
  4709. layer {
  4710. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv3_Conv2D"
  4711. type: "Convolution"
  4712. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4713. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv3_Conv2D"
  4714. convolution_param {
  4715. num_output: 1024
  4716. bias_term: true
  4717. group: 1
  4718. stride: 1
  4719. pad_h: 0
  4720. pad_w: 0
  4721. kernel_h: 1
  4722. kernel_w: 1
  4723. }
  4724. }
  4725. layer {
  4726. name: "resnet_v2_152_block3_unit_23_bottleneck_v2_add"
  4727. type: "Eltwise"
  4728. bottom: "resnet_v2_152_block3_unit_22_bottleneck_v2_add"
  4729. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_conv3_Conv2D"
  4730. top: "resnet_v2_152_block3_unit_23_bottleneck_v2_add"
  4731. eltwise_param {
  4732. operation: SUM
  4733. }
  4734. }
  4735. layer {
  4736. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4737. type: "BatchNorm"
  4738. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_add"
  4739. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4740. batch_norm_param {
  4741. use_global_stats: true
  4742. eps: 1.0009999641624745e-05
  4743. }
  4744. }
  4745. layer {
  4746. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm_scale"
  4747. type: "Scale"
  4748. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4749. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4750. scale_param {
  4751. bias_term: true
  4752. }
  4753. }
  4754. layer {
  4755. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_Relu"
  4756. type: "ReLU"
  4757. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4758. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4759. }
  4760. layer {
  4761. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_Conv2D"
  4762. type: "Convolution"
  4763. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_preact_FusedBatchNorm"
  4764. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_Conv2D"
  4765. convolution_param {
  4766. num_output: 256
  4767. bias_term: false
  4768. group: 1
  4769. stride: 1
  4770. pad_h: 0
  4771. pad_w: 0
  4772. kernel_h: 1
  4773. kernel_w: 1
  4774. }
  4775. }
  4776. layer {
  4777. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4778. type: "BatchNorm"
  4779. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_Conv2D"
  4780. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4781. batch_norm_param {
  4782. use_global_stats: true
  4783. eps: 1.0009999641624745e-05
  4784. }
  4785. }
  4786. layer {
  4787. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4788. type: "Scale"
  4789. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4790. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4791. scale_param {
  4792. bias_term: true
  4793. }
  4794. }
  4795. layer {
  4796. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_Relu"
  4797. type: "ReLU"
  4798. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4799. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4800. }
  4801. layer {
  4802. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_Conv2D"
  4803. type: "Convolution"
  4804. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4805. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_Conv2D"
  4806. convolution_param {
  4807. num_output: 256
  4808. bias_term: false
  4809. group: 1
  4810. stride: 1
  4811. pad_h: 1
  4812. pad_w: 1
  4813. kernel_h: 3
  4814. kernel_w: 3
  4815. }
  4816. }
  4817. layer {
  4818. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4819. type: "BatchNorm"
  4820. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_Conv2D"
  4821. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4822. batch_norm_param {
  4823. use_global_stats: true
  4824. eps: 1.0009999641624745e-05
  4825. }
  4826. }
  4827. layer {
  4828. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4829. type: "Scale"
  4830. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4831. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4832. scale_param {
  4833. bias_term: true
  4834. }
  4835. }
  4836. layer {
  4837. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_Relu"
  4838. type: "ReLU"
  4839. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4840. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4841. }
  4842. layer {
  4843. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv3_Conv2D"
  4844. type: "Convolution"
  4845. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4846. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv3_Conv2D"
  4847. convolution_param {
  4848. num_output: 1024
  4849. bias_term: true
  4850. group: 1
  4851. stride: 1
  4852. pad_h: 0
  4853. pad_w: 0
  4854. kernel_h: 1
  4855. kernel_w: 1
  4856. }
  4857. }
  4858. layer {
  4859. name: "resnet_v2_152_block3_unit_24_bottleneck_v2_add"
  4860. type: "Eltwise"
  4861. bottom: "resnet_v2_152_block3_unit_23_bottleneck_v2_add"
  4862. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_conv3_Conv2D"
  4863. top: "resnet_v2_152_block3_unit_24_bottleneck_v2_add"
  4864. eltwise_param {
  4865. operation: SUM
  4866. }
  4867. }
  4868. layer {
  4869. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4870. type: "BatchNorm"
  4871. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_add"
  4872. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4873. batch_norm_param {
  4874. use_global_stats: true
  4875. eps: 1.0009999641624745e-05
  4876. }
  4877. }
  4878. layer {
  4879. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm_scale"
  4880. type: "Scale"
  4881. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4882. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4883. scale_param {
  4884. bias_term: true
  4885. }
  4886. }
  4887. layer {
  4888. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_Relu"
  4889. type: "ReLU"
  4890. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4891. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4892. }
  4893. layer {
  4894. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_Conv2D"
  4895. type: "Convolution"
  4896. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_preact_FusedBatchNorm"
  4897. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_Conv2D"
  4898. convolution_param {
  4899. num_output: 256
  4900. bias_term: false
  4901. group: 1
  4902. stride: 1
  4903. pad_h: 0
  4904. pad_w: 0
  4905. kernel_h: 1
  4906. kernel_w: 1
  4907. }
  4908. }
  4909. layer {
  4910. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4911. type: "BatchNorm"
  4912. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_Conv2D"
  4913. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4914. batch_norm_param {
  4915. use_global_stats: true
  4916. eps: 1.0009999641624745e-05
  4917. }
  4918. }
  4919. layer {
  4920. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  4921. type: "Scale"
  4922. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4923. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4924. scale_param {
  4925. bias_term: true
  4926. }
  4927. }
  4928. layer {
  4929. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_Relu"
  4930. type: "ReLU"
  4931. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4932. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4933. }
  4934. layer {
  4935. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_Conv2D"
  4936. type: "Convolution"
  4937. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  4938. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_Conv2D"
  4939. convolution_param {
  4940. num_output: 256
  4941. bias_term: false
  4942. group: 1
  4943. stride: 1
  4944. pad_h: 1
  4945. pad_w: 1
  4946. kernel_h: 3
  4947. kernel_w: 3
  4948. }
  4949. }
  4950. layer {
  4951. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4952. type: "BatchNorm"
  4953. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_Conv2D"
  4954. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4955. batch_norm_param {
  4956. use_global_stats: true
  4957. eps: 1.0009999641624745e-05
  4958. }
  4959. }
  4960. layer {
  4961. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  4962. type: "Scale"
  4963. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4964. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4965. scale_param {
  4966. bias_term: true
  4967. }
  4968. }
  4969. layer {
  4970. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_Relu"
  4971. type: "ReLU"
  4972. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4973. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4974. }
  4975. layer {
  4976. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv3_Conv2D"
  4977. type: "Convolution"
  4978. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  4979. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv3_Conv2D"
  4980. convolution_param {
  4981. num_output: 1024
  4982. bias_term: true
  4983. group: 1
  4984. stride: 1
  4985. pad_h: 0
  4986. pad_w: 0
  4987. kernel_h: 1
  4988. kernel_w: 1
  4989. }
  4990. }
  4991. layer {
  4992. name: "resnet_v2_152_block3_unit_25_bottleneck_v2_add"
  4993. type: "Eltwise"
  4994. bottom: "resnet_v2_152_block3_unit_24_bottleneck_v2_add"
  4995. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_conv3_Conv2D"
  4996. top: "resnet_v2_152_block3_unit_25_bottleneck_v2_add"
  4997. eltwise_param {
  4998. operation: SUM
  4999. }
  5000. }
  5001. layer {
  5002. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5003. type: "BatchNorm"
  5004. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_add"
  5005. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5006. batch_norm_param {
  5007. use_global_stats: true
  5008. eps: 1.0009999641624745e-05
  5009. }
  5010. }
  5011. layer {
  5012. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm_scale"
  5013. type: "Scale"
  5014. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5015. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5016. scale_param {
  5017. bias_term: true
  5018. }
  5019. }
  5020. layer {
  5021. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_Relu"
  5022. type: "ReLU"
  5023. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5024. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5025. }
  5026. layer {
  5027. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_Conv2D"
  5028. type: "Convolution"
  5029. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_preact_FusedBatchNorm"
  5030. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_Conv2D"
  5031. convolution_param {
  5032. num_output: 256
  5033. bias_term: false
  5034. group: 1
  5035. stride: 1
  5036. pad_h: 0
  5037. pad_w: 0
  5038. kernel_h: 1
  5039. kernel_w: 1
  5040. }
  5041. }
  5042. layer {
  5043. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5044. type: "BatchNorm"
  5045. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_Conv2D"
  5046. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5047. batch_norm_param {
  5048. use_global_stats: true
  5049. eps: 1.0009999641624745e-05
  5050. }
  5051. }
  5052. layer {
  5053. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5054. type: "Scale"
  5055. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5056. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5057. scale_param {
  5058. bias_term: true
  5059. }
  5060. }
  5061. layer {
  5062. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_Relu"
  5063. type: "ReLU"
  5064. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5065. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5066. }
  5067. layer {
  5068. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_Conv2D"
  5069. type: "Convolution"
  5070. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5071. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_Conv2D"
  5072. convolution_param {
  5073. num_output: 256
  5074. bias_term: false
  5075. group: 1
  5076. stride: 1
  5077. pad_h: 1
  5078. pad_w: 1
  5079. kernel_h: 3
  5080. kernel_w: 3
  5081. }
  5082. }
  5083. layer {
  5084. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5085. type: "BatchNorm"
  5086. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_Conv2D"
  5087. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5088. batch_norm_param {
  5089. use_global_stats: true
  5090. eps: 1.0009999641624745e-05
  5091. }
  5092. }
  5093. layer {
  5094. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5095. type: "Scale"
  5096. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5097. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5098. scale_param {
  5099. bias_term: true
  5100. }
  5101. }
  5102. layer {
  5103. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_Relu"
  5104. type: "ReLU"
  5105. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5106. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5107. }
  5108. layer {
  5109. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv3_Conv2D"
  5110. type: "Convolution"
  5111. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5112. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv3_Conv2D"
  5113. convolution_param {
  5114. num_output: 1024
  5115. bias_term: true
  5116. group: 1
  5117. stride: 1
  5118. pad_h: 0
  5119. pad_w: 0
  5120. kernel_h: 1
  5121. kernel_w: 1
  5122. }
  5123. }
  5124. layer {
  5125. name: "resnet_v2_152_block3_unit_26_bottleneck_v2_add"
  5126. type: "Eltwise"
  5127. bottom: "resnet_v2_152_block3_unit_25_bottleneck_v2_add"
  5128. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_conv3_Conv2D"
  5129. top: "resnet_v2_152_block3_unit_26_bottleneck_v2_add"
  5130. eltwise_param {
  5131. operation: SUM
  5132. }
  5133. }
  5134. layer {
  5135. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5136. type: "BatchNorm"
  5137. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_add"
  5138. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5139. batch_norm_param {
  5140. use_global_stats: true
  5141. eps: 1.0009999641624745e-05
  5142. }
  5143. }
  5144. layer {
  5145. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm_scale"
  5146. type: "Scale"
  5147. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5148. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5149. scale_param {
  5150. bias_term: true
  5151. }
  5152. }
  5153. layer {
  5154. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_Relu"
  5155. type: "ReLU"
  5156. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5157. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5158. }
  5159. layer {
  5160. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_Conv2D"
  5161. type: "Convolution"
  5162. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_preact_FusedBatchNorm"
  5163. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_Conv2D"
  5164. convolution_param {
  5165. num_output: 256
  5166. bias_term: false
  5167. group: 1
  5168. stride: 1
  5169. pad_h: 0
  5170. pad_w: 0
  5171. kernel_h: 1
  5172. kernel_w: 1
  5173. }
  5174. }
  5175. layer {
  5176. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5177. type: "BatchNorm"
  5178. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_Conv2D"
  5179. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5180. batch_norm_param {
  5181. use_global_stats: true
  5182. eps: 1.0009999641624745e-05
  5183. }
  5184. }
  5185. layer {
  5186. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5187. type: "Scale"
  5188. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5189. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5190. scale_param {
  5191. bias_term: true
  5192. }
  5193. }
  5194. layer {
  5195. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_Relu"
  5196. type: "ReLU"
  5197. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5198. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5199. }
  5200. layer {
  5201. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_Conv2D"
  5202. type: "Convolution"
  5203. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5204. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_Conv2D"
  5205. convolution_param {
  5206. num_output: 256
  5207. bias_term: false
  5208. group: 1
  5209. stride: 1
  5210. pad_h: 1
  5211. pad_w: 1
  5212. kernel_h: 3
  5213. kernel_w: 3
  5214. }
  5215. }
  5216. layer {
  5217. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5218. type: "BatchNorm"
  5219. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_Conv2D"
  5220. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5221. batch_norm_param {
  5222. use_global_stats: true
  5223. eps: 1.0009999641624745e-05
  5224. }
  5225. }
  5226. layer {
  5227. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5228. type: "Scale"
  5229. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5230. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5231. scale_param {
  5232. bias_term: true
  5233. }
  5234. }
  5235. layer {
  5236. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_Relu"
  5237. type: "ReLU"
  5238. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5239. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5240. }
  5241. layer {
  5242. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv3_Conv2D"
  5243. type: "Convolution"
  5244. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5245. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv3_Conv2D"
  5246. convolution_param {
  5247. num_output: 1024
  5248. bias_term: true
  5249. group: 1
  5250. stride: 1
  5251. pad_h: 0
  5252. pad_w: 0
  5253. kernel_h: 1
  5254. kernel_w: 1
  5255. }
  5256. }
  5257. layer {
  5258. name: "resnet_v2_152_block3_unit_27_bottleneck_v2_add"
  5259. type: "Eltwise"
  5260. bottom: "resnet_v2_152_block3_unit_26_bottleneck_v2_add"
  5261. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_conv3_Conv2D"
  5262. top: "resnet_v2_152_block3_unit_27_bottleneck_v2_add"
  5263. eltwise_param {
  5264. operation: SUM
  5265. }
  5266. }
  5267. layer {
  5268. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5269. type: "BatchNorm"
  5270. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_add"
  5271. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5272. batch_norm_param {
  5273. use_global_stats: true
  5274. eps: 1.0009999641624745e-05
  5275. }
  5276. }
  5277. layer {
  5278. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm_scale"
  5279. type: "Scale"
  5280. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5281. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5282. scale_param {
  5283. bias_term: true
  5284. }
  5285. }
  5286. layer {
  5287. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_Relu"
  5288. type: "ReLU"
  5289. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5290. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5291. }
  5292. layer {
  5293. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_Conv2D"
  5294. type: "Convolution"
  5295. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_preact_FusedBatchNorm"
  5296. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_Conv2D"
  5297. convolution_param {
  5298. num_output: 256
  5299. bias_term: false
  5300. group: 1
  5301. stride: 1
  5302. pad_h: 0
  5303. pad_w: 0
  5304. kernel_h: 1
  5305. kernel_w: 1
  5306. }
  5307. }
  5308. layer {
  5309. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5310. type: "BatchNorm"
  5311. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_Conv2D"
  5312. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5313. batch_norm_param {
  5314. use_global_stats: true
  5315. eps: 1.0009999641624745e-05
  5316. }
  5317. }
  5318. layer {
  5319. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5320. type: "Scale"
  5321. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5322. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5323. scale_param {
  5324. bias_term: true
  5325. }
  5326. }
  5327. layer {
  5328. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_Relu"
  5329. type: "ReLU"
  5330. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5331. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5332. }
  5333. layer {
  5334. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_Conv2D"
  5335. type: "Convolution"
  5336. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5337. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_Conv2D"
  5338. convolution_param {
  5339. num_output: 256
  5340. bias_term: false
  5341. group: 1
  5342. stride: 1
  5343. pad_h: 1
  5344. pad_w: 1
  5345. kernel_h: 3
  5346. kernel_w: 3
  5347. }
  5348. }
  5349. layer {
  5350. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5351. type: "BatchNorm"
  5352. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_Conv2D"
  5353. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5354. batch_norm_param {
  5355. use_global_stats: true
  5356. eps: 1.0009999641624745e-05
  5357. }
  5358. }
  5359. layer {
  5360. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5361. type: "Scale"
  5362. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5363. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5364. scale_param {
  5365. bias_term: true
  5366. }
  5367. }
  5368. layer {
  5369. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_Relu"
  5370. type: "ReLU"
  5371. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5372. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5373. }
  5374. layer {
  5375. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv3_Conv2D"
  5376. type: "Convolution"
  5377. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5378. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv3_Conv2D"
  5379. convolution_param {
  5380. num_output: 1024
  5381. bias_term: true
  5382. group: 1
  5383. stride: 1
  5384. pad_h: 0
  5385. pad_w: 0
  5386. kernel_h: 1
  5387. kernel_w: 1
  5388. }
  5389. }
  5390. layer {
  5391. name: "resnet_v2_152_block3_unit_28_bottleneck_v2_add"
  5392. type: "Eltwise"
  5393. bottom: "resnet_v2_152_block3_unit_27_bottleneck_v2_add"
  5394. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_conv3_Conv2D"
  5395. top: "resnet_v2_152_block3_unit_28_bottleneck_v2_add"
  5396. eltwise_param {
  5397. operation: SUM
  5398. }
  5399. }
  5400. layer {
  5401. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5402. type: "BatchNorm"
  5403. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_add"
  5404. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5405. batch_norm_param {
  5406. use_global_stats: true
  5407. eps: 1.0009999641624745e-05
  5408. }
  5409. }
  5410. layer {
  5411. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm_scale"
  5412. type: "Scale"
  5413. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5414. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5415. scale_param {
  5416. bias_term: true
  5417. }
  5418. }
  5419. layer {
  5420. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_Relu"
  5421. type: "ReLU"
  5422. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5423. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5424. }
  5425. layer {
  5426. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_Conv2D"
  5427. type: "Convolution"
  5428. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_preact_FusedBatchNorm"
  5429. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_Conv2D"
  5430. convolution_param {
  5431. num_output: 256
  5432. bias_term: false
  5433. group: 1
  5434. stride: 1
  5435. pad_h: 0
  5436. pad_w: 0
  5437. kernel_h: 1
  5438. kernel_w: 1
  5439. }
  5440. }
  5441. layer {
  5442. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5443. type: "BatchNorm"
  5444. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_Conv2D"
  5445. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5446. batch_norm_param {
  5447. use_global_stats: true
  5448. eps: 1.0009999641624745e-05
  5449. }
  5450. }
  5451. layer {
  5452. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5453. type: "Scale"
  5454. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5455. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5456. scale_param {
  5457. bias_term: true
  5458. }
  5459. }
  5460. layer {
  5461. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_Relu"
  5462. type: "ReLU"
  5463. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5464. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5465. }
  5466. layer {
  5467. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_Conv2D"
  5468. type: "Convolution"
  5469. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5470. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_Conv2D"
  5471. convolution_param {
  5472. num_output: 256
  5473. bias_term: false
  5474. group: 1
  5475. stride: 1
  5476. pad_h: 1
  5477. pad_w: 1
  5478. kernel_h: 3
  5479. kernel_w: 3
  5480. }
  5481. }
  5482. layer {
  5483. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5484. type: "BatchNorm"
  5485. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_Conv2D"
  5486. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5487. batch_norm_param {
  5488. use_global_stats: true
  5489. eps: 1.0009999641624745e-05
  5490. }
  5491. }
  5492. layer {
  5493. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5494. type: "Scale"
  5495. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5496. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5497. scale_param {
  5498. bias_term: true
  5499. }
  5500. }
  5501. layer {
  5502. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_Relu"
  5503. type: "ReLU"
  5504. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5505. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5506. }
  5507. layer {
  5508. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv3_Conv2D"
  5509. type: "Convolution"
  5510. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5511. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv3_Conv2D"
  5512. convolution_param {
  5513. num_output: 1024
  5514. bias_term: true
  5515. group: 1
  5516. stride: 1
  5517. pad_h: 0
  5518. pad_w: 0
  5519. kernel_h: 1
  5520. kernel_w: 1
  5521. }
  5522. }
  5523. layer {
  5524. name: "resnet_v2_152_block3_unit_29_bottleneck_v2_add"
  5525. type: "Eltwise"
  5526. bottom: "resnet_v2_152_block3_unit_28_bottleneck_v2_add"
  5527. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_conv3_Conv2D"
  5528. top: "resnet_v2_152_block3_unit_29_bottleneck_v2_add"
  5529. eltwise_param {
  5530. operation: SUM
  5531. }
  5532. }
  5533. layer {
  5534. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5535. type: "BatchNorm"
  5536. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_add"
  5537. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5538. batch_norm_param {
  5539. use_global_stats: true
  5540. eps: 1.0009999641624745e-05
  5541. }
  5542. }
  5543. layer {
  5544. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm_scale"
  5545. type: "Scale"
  5546. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5547. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5548. scale_param {
  5549. bias_term: true
  5550. }
  5551. }
  5552. layer {
  5553. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_Relu"
  5554. type: "ReLU"
  5555. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5556. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5557. }
  5558. layer {
  5559. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_Conv2D"
  5560. type: "Convolution"
  5561. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_preact_FusedBatchNorm"
  5562. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_Conv2D"
  5563. convolution_param {
  5564. num_output: 256
  5565. bias_term: false
  5566. group: 1
  5567. stride: 1
  5568. pad_h: 0
  5569. pad_w: 0
  5570. kernel_h: 1
  5571. kernel_w: 1
  5572. }
  5573. }
  5574. layer {
  5575. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5576. type: "BatchNorm"
  5577. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_Conv2D"
  5578. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5579. batch_norm_param {
  5580. use_global_stats: true
  5581. eps: 1.0009999641624745e-05
  5582. }
  5583. }
  5584. layer {
  5585. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5586. type: "Scale"
  5587. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5588. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5589. scale_param {
  5590. bias_term: true
  5591. }
  5592. }
  5593. layer {
  5594. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_Relu"
  5595. type: "ReLU"
  5596. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5597. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5598. }
  5599. layer {
  5600. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_Conv2D"
  5601. type: "Convolution"
  5602. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5603. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_Conv2D"
  5604. convolution_param {
  5605. num_output: 256
  5606. bias_term: false
  5607. group: 1
  5608. stride: 1
  5609. pad_h: 1
  5610. pad_w: 1
  5611. kernel_h: 3
  5612. kernel_w: 3
  5613. }
  5614. }
  5615. layer {
  5616. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5617. type: "BatchNorm"
  5618. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_Conv2D"
  5619. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5620. batch_norm_param {
  5621. use_global_stats: true
  5622. eps: 1.0009999641624745e-05
  5623. }
  5624. }
  5625. layer {
  5626. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5627. type: "Scale"
  5628. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5629. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5630. scale_param {
  5631. bias_term: true
  5632. }
  5633. }
  5634. layer {
  5635. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_Relu"
  5636. type: "ReLU"
  5637. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5638. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5639. }
  5640. layer {
  5641. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv3_Conv2D"
  5642. type: "Convolution"
  5643. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5644. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv3_Conv2D"
  5645. convolution_param {
  5646. num_output: 1024
  5647. bias_term: true
  5648. group: 1
  5649. stride: 1
  5650. pad_h: 0
  5651. pad_w: 0
  5652. kernel_h: 1
  5653. kernel_w: 1
  5654. }
  5655. }
  5656. layer {
  5657. name: "resnet_v2_152_block3_unit_30_bottleneck_v2_add"
  5658. type: "Eltwise"
  5659. bottom: "resnet_v2_152_block3_unit_29_bottleneck_v2_add"
  5660. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_conv3_Conv2D"
  5661. top: "resnet_v2_152_block3_unit_30_bottleneck_v2_add"
  5662. eltwise_param {
  5663. operation: SUM
  5664. }
  5665. }
  5666. layer {
  5667. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5668. type: "BatchNorm"
  5669. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_add"
  5670. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5671. batch_norm_param {
  5672. use_global_stats: true
  5673. eps: 1.0009999641624745e-05
  5674. }
  5675. }
  5676. layer {
  5677. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm_scale"
  5678. type: "Scale"
  5679. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5680. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5681. scale_param {
  5682. bias_term: true
  5683. }
  5684. }
  5685. layer {
  5686. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_Relu"
  5687. type: "ReLU"
  5688. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5689. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5690. }
  5691. layer {
  5692. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_Conv2D"
  5693. type: "Convolution"
  5694. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_preact_FusedBatchNorm"
  5695. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_Conv2D"
  5696. convolution_param {
  5697. num_output: 256
  5698. bias_term: false
  5699. group: 1
  5700. stride: 1
  5701. pad_h: 0
  5702. pad_w: 0
  5703. kernel_h: 1
  5704. kernel_w: 1
  5705. }
  5706. }
  5707. layer {
  5708. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5709. type: "BatchNorm"
  5710. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_Conv2D"
  5711. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5712. batch_norm_param {
  5713. use_global_stats: true
  5714. eps: 1.0009999641624745e-05
  5715. }
  5716. }
  5717. layer {
  5718. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5719. type: "Scale"
  5720. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5721. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5722. scale_param {
  5723. bias_term: true
  5724. }
  5725. }
  5726. layer {
  5727. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_Relu"
  5728. type: "ReLU"
  5729. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5730. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5731. }
  5732. layer {
  5733. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_Conv2D"
  5734. type: "Convolution"
  5735. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5736. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_Conv2D"
  5737. convolution_param {
  5738. num_output: 256
  5739. bias_term: false
  5740. group: 1
  5741. stride: 1
  5742. pad_h: 1
  5743. pad_w: 1
  5744. kernel_h: 3
  5745. kernel_w: 3
  5746. }
  5747. }
  5748. layer {
  5749. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5750. type: "BatchNorm"
  5751. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_Conv2D"
  5752. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5753. batch_norm_param {
  5754. use_global_stats: true
  5755. eps: 1.0009999641624745e-05
  5756. }
  5757. }
  5758. layer {
  5759. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5760. type: "Scale"
  5761. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5762. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5763. scale_param {
  5764. bias_term: true
  5765. }
  5766. }
  5767. layer {
  5768. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_Relu"
  5769. type: "ReLU"
  5770. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5771. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5772. }
  5773. layer {
  5774. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv3_Conv2D"
  5775. type: "Convolution"
  5776. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5777. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv3_Conv2D"
  5778. convolution_param {
  5779. num_output: 1024
  5780. bias_term: true
  5781. group: 1
  5782. stride: 1
  5783. pad_h: 0
  5784. pad_w: 0
  5785. kernel_h: 1
  5786. kernel_w: 1
  5787. }
  5788. }
  5789. layer {
  5790. name: "resnet_v2_152_block3_unit_31_bottleneck_v2_add"
  5791. type: "Eltwise"
  5792. bottom: "resnet_v2_152_block3_unit_30_bottleneck_v2_add"
  5793. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_conv3_Conv2D"
  5794. top: "resnet_v2_152_block3_unit_31_bottleneck_v2_add"
  5795. eltwise_param {
  5796. operation: SUM
  5797. }
  5798. }
  5799. layer {
  5800. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5801. type: "BatchNorm"
  5802. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_add"
  5803. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5804. batch_norm_param {
  5805. use_global_stats: true
  5806. eps: 1.0009999641624745e-05
  5807. }
  5808. }
  5809. layer {
  5810. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm_scale"
  5811. type: "Scale"
  5812. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5813. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5814. scale_param {
  5815. bias_term: true
  5816. }
  5817. }
  5818. layer {
  5819. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_Relu"
  5820. type: "ReLU"
  5821. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5822. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5823. }
  5824. layer {
  5825. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_Conv2D"
  5826. type: "Convolution"
  5827. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_preact_FusedBatchNorm"
  5828. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_Conv2D"
  5829. convolution_param {
  5830. num_output: 256
  5831. bias_term: false
  5832. group: 1
  5833. stride: 1
  5834. pad_h: 0
  5835. pad_w: 0
  5836. kernel_h: 1
  5837. kernel_w: 1
  5838. }
  5839. }
  5840. layer {
  5841. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5842. type: "BatchNorm"
  5843. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_Conv2D"
  5844. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5845. batch_norm_param {
  5846. use_global_stats: true
  5847. eps: 1.0009999641624745e-05
  5848. }
  5849. }
  5850. layer {
  5851. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5852. type: "Scale"
  5853. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5854. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5855. scale_param {
  5856. bias_term: true
  5857. }
  5858. }
  5859. layer {
  5860. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_Relu"
  5861. type: "ReLU"
  5862. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5863. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5864. }
  5865. layer {
  5866. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_Conv2D"
  5867. type: "Convolution"
  5868. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5869. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_Conv2D"
  5870. convolution_param {
  5871. num_output: 256
  5872. bias_term: false
  5873. group: 1
  5874. stride: 1
  5875. pad_h: 1
  5876. pad_w: 1
  5877. kernel_h: 3
  5878. kernel_w: 3
  5879. }
  5880. }
  5881. layer {
  5882. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5883. type: "BatchNorm"
  5884. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_Conv2D"
  5885. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5886. batch_norm_param {
  5887. use_global_stats: true
  5888. eps: 1.0009999641624745e-05
  5889. }
  5890. }
  5891. layer {
  5892. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  5893. type: "Scale"
  5894. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5895. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5896. scale_param {
  5897. bias_term: true
  5898. }
  5899. }
  5900. layer {
  5901. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_Relu"
  5902. type: "ReLU"
  5903. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5904. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5905. }
  5906. layer {
  5907. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv3_Conv2D"
  5908. type: "Convolution"
  5909. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  5910. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv3_Conv2D"
  5911. convolution_param {
  5912. num_output: 1024
  5913. bias_term: true
  5914. group: 1
  5915. stride: 1
  5916. pad_h: 0
  5917. pad_w: 0
  5918. kernel_h: 1
  5919. kernel_w: 1
  5920. }
  5921. }
  5922. layer {
  5923. name: "resnet_v2_152_block3_unit_32_bottleneck_v2_add"
  5924. type: "Eltwise"
  5925. bottom: "resnet_v2_152_block3_unit_31_bottleneck_v2_add"
  5926. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_conv3_Conv2D"
  5927. top: "resnet_v2_152_block3_unit_32_bottleneck_v2_add"
  5928. eltwise_param {
  5929. operation: SUM
  5930. }
  5931. }
  5932. layer {
  5933. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5934. type: "BatchNorm"
  5935. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_add"
  5936. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5937. batch_norm_param {
  5938. use_global_stats: true
  5939. eps: 1.0009999641624745e-05
  5940. }
  5941. }
  5942. layer {
  5943. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm_scale"
  5944. type: "Scale"
  5945. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5946. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5947. scale_param {
  5948. bias_term: true
  5949. }
  5950. }
  5951. layer {
  5952. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_Relu"
  5953. type: "ReLU"
  5954. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5955. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5956. }
  5957. layer {
  5958. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_Conv2D"
  5959. type: "Convolution"
  5960. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_preact_FusedBatchNorm"
  5961. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_Conv2D"
  5962. convolution_param {
  5963. num_output: 256
  5964. bias_term: false
  5965. group: 1
  5966. stride: 1
  5967. pad_h: 0
  5968. pad_w: 0
  5969. kernel_h: 1
  5970. kernel_w: 1
  5971. }
  5972. }
  5973. layer {
  5974. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5975. type: "BatchNorm"
  5976. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_Conv2D"
  5977. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5978. batch_norm_param {
  5979. use_global_stats: true
  5980. eps: 1.0009999641624745e-05
  5981. }
  5982. }
  5983. layer {
  5984. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  5985. type: "Scale"
  5986. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5987. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5988. scale_param {
  5989. bias_term: true
  5990. }
  5991. }
  5992. layer {
  5993. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_Relu"
  5994. type: "ReLU"
  5995. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5996. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  5997. }
  5998. layer {
  5999. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_Conv2D"
  6000. type: "Convolution"
  6001. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6002. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_Conv2D"
  6003. convolution_param {
  6004. num_output: 256
  6005. bias_term: false
  6006. group: 1
  6007. stride: 1
  6008. pad_h: 1
  6009. pad_w: 1
  6010. kernel_h: 3
  6011. kernel_w: 3
  6012. }
  6013. }
  6014. layer {
  6015. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6016. type: "BatchNorm"
  6017. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_Conv2D"
  6018. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6019. batch_norm_param {
  6020. use_global_stats: true
  6021. eps: 1.0009999641624745e-05
  6022. }
  6023. }
  6024. layer {
  6025. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6026. type: "Scale"
  6027. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6028. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6029. scale_param {
  6030. bias_term: true
  6031. }
  6032. }
  6033. layer {
  6034. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_Relu"
  6035. type: "ReLU"
  6036. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6037. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6038. }
  6039. layer {
  6040. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv3_Conv2D"
  6041. type: "Convolution"
  6042. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6043. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv3_Conv2D"
  6044. convolution_param {
  6045. num_output: 1024
  6046. bias_term: true
  6047. group: 1
  6048. stride: 1
  6049. pad_h: 0
  6050. pad_w: 0
  6051. kernel_h: 1
  6052. kernel_w: 1
  6053. }
  6054. }
  6055. layer {
  6056. name: "resnet_v2_152_block3_unit_33_bottleneck_v2_add"
  6057. type: "Eltwise"
  6058. bottom: "resnet_v2_152_block3_unit_32_bottleneck_v2_add"
  6059. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_conv3_Conv2D"
  6060. top: "resnet_v2_152_block3_unit_33_bottleneck_v2_add"
  6061. eltwise_param {
  6062. operation: SUM
  6063. }
  6064. }
  6065. layer {
  6066. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6067. type: "BatchNorm"
  6068. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_add"
  6069. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6070. batch_norm_param {
  6071. use_global_stats: true
  6072. eps: 1.0009999641624745e-05
  6073. }
  6074. }
  6075. layer {
  6076. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm_scale"
  6077. type: "Scale"
  6078. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6079. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6080. scale_param {
  6081. bias_term: true
  6082. }
  6083. }
  6084. layer {
  6085. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_Relu"
  6086. type: "ReLU"
  6087. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6088. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6089. }
  6090. layer {
  6091. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_Conv2D"
  6092. type: "Convolution"
  6093. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_preact_FusedBatchNorm"
  6094. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_Conv2D"
  6095. convolution_param {
  6096. num_output: 256
  6097. bias_term: false
  6098. group: 1
  6099. stride: 1
  6100. pad_h: 0
  6101. pad_w: 0
  6102. kernel_h: 1
  6103. kernel_w: 1
  6104. }
  6105. }
  6106. layer {
  6107. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6108. type: "BatchNorm"
  6109. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_Conv2D"
  6110. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6111. batch_norm_param {
  6112. use_global_stats: true
  6113. eps: 1.0009999641624745e-05
  6114. }
  6115. }
  6116. layer {
  6117. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  6118. type: "Scale"
  6119. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6120. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6121. scale_param {
  6122. bias_term: true
  6123. }
  6124. }
  6125. layer {
  6126. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_Relu"
  6127. type: "ReLU"
  6128. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6129. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6130. }
  6131. layer {
  6132. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_Conv2D"
  6133. type: "Convolution"
  6134. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6135. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_Conv2D"
  6136. convolution_param {
  6137. num_output: 256
  6138. bias_term: false
  6139. group: 1
  6140. stride: 1
  6141. pad_h: 1
  6142. pad_w: 1
  6143. kernel_h: 3
  6144. kernel_w: 3
  6145. }
  6146. }
  6147. layer {
  6148. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6149. type: "BatchNorm"
  6150. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_Conv2D"
  6151. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6152. batch_norm_param {
  6153. use_global_stats: true
  6154. eps: 1.0009999641624745e-05
  6155. }
  6156. }
  6157. layer {
  6158. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6159. type: "Scale"
  6160. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6161. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6162. scale_param {
  6163. bias_term: true
  6164. }
  6165. }
  6166. layer {
  6167. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_Relu"
  6168. type: "ReLU"
  6169. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6170. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6171. }
  6172. layer {
  6173. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv3_Conv2D"
  6174. type: "Convolution"
  6175. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6176. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv3_Conv2D"
  6177. convolution_param {
  6178. num_output: 1024
  6179. bias_term: true
  6180. group: 1
  6181. stride: 1
  6182. pad_h: 0
  6183. pad_w: 0
  6184. kernel_h: 1
  6185. kernel_w: 1
  6186. }
  6187. }
  6188. layer {
  6189. name: "resnet_v2_152_block3_unit_34_bottleneck_v2_add"
  6190. type: "Eltwise"
  6191. bottom: "resnet_v2_152_block3_unit_33_bottleneck_v2_add"
  6192. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_conv3_Conv2D"
  6193. top: "resnet_v2_152_block3_unit_34_bottleneck_v2_add"
  6194. eltwise_param {
  6195. operation: SUM
  6196. }
  6197. }
  6198. layer {
  6199. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6200. type: "BatchNorm"
  6201. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_add"
  6202. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6203. batch_norm_param {
  6204. use_global_stats: true
  6205. eps: 1.0009999641624745e-05
  6206. }
  6207. }
  6208. layer {
  6209. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm_scale"
  6210. type: "Scale"
  6211. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6212. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6213. scale_param {
  6214. bias_term: true
  6215. }
  6216. }
  6217. layer {
  6218. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_Relu"
  6219. type: "ReLU"
  6220. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6221. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6222. }
  6223. layer {
  6224. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_Conv2D"
  6225. type: "Convolution"
  6226. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_preact_FusedBatchNorm"
  6227. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_Conv2D"
  6228. convolution_param {
  6229. num_output: 256
  6230. bias_term: false
  6231. group: 1
  6232. stride: 1
  6233. pad_h: 0
  6234. pad_w: 0
  6235. kernel_h: 1
  6236. kernel_w: 1
  6237. }
  6238. }
  6239. layer {
  6240. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6241. type: "BatchNorm"
  6242. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_Conv2D"
  6243. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6244. batch_norm_param {
  6245. use_global_stats: true
  6246. eps: 1.0009999641624745e-05
  6247. }
  6248. }
  6249. layer {
  6250. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  6251. type: "Scale"
  6252. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6253. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6254. scale_param {
  6255. bias_term: true
  6256. }
  6257. }
  6258. layer {
  6259. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_Relu"
  6260. type: "ReLU"
  6261. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6262. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6263. }
  6264. layer {
  6265. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_Conv2D"
  6266. type: "Convolution"
  6267. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6268. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_Conv2D"
  6269. convolution_param {
  6270. num_output: 256
  6271. bias_term: false
  6272. group: 1
  6273. stride: 1
  6274. pad_h: 1
  6275. pad_w: 1
  6276. kernel_h: 3
  6277. kernel_w: 3
  6278. }
  6279. }
  6280. layer {
  6281. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6282. type: "BatchNorm"
  6283. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_Conv2D"
  6284. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6285. batch_norm_param {
  6286. use_global_stats: true
  6287. eps: 1.0009999641624745e-05
  6288. }
  6289. }
  6290. layer {
  6291. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6292. type: "Scale"
  6293. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6294. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6295. scale_param {
  6296. bias_term: true
  6297. }
  6298. }
  6299. layer {
  6300. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_Relu"
  6301. type: "ReLU"
  6302. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6303. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6304. }
  6305. layer {
  6306. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv3_Conv2D"
  6307. type: "Convolution"
  6308. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6309. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv3_Conv2D"
  6310. convolution_param {
  6311. num_output: 1024
  6312. bias_term: true
  6313. group: 1
  6314. stride: 1
  6315. pad_h: 0
  6316. pad_w: 0
  6317. kernel_h: 1
  6318. kernel_w: 1
  6319. }
  6320. }
  6321. layer {
  6322. name: "resnet_v2_152_block3_unit_35_bottleneck_v2_add"
  6323. type: "Eltwise"
  6324. bottom: "resnet_v2_152_block3_unit_34_bottleneck_v2_add"
  6325. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_conv3_Conv2D"
  6326. top: "resnet_v2_152_block3_unit_35_bottleneck_v2_add"
  6327. eltwise_param {
  6328. operation: SUM
  6329. }
  6330. }
  6331. layer {
  6332. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6333. type: "BatchNorm"
  6334. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_add"
  6335. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6336. batch_norm_param {
  6337. use_global_stats: true
  6338. eps: 1.0009999641624745e-05
  6339. }
  6340. }
  6341. layer {
  6342. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm_scale"
  6343. type: "Scale"
  6344. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6345. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6346. scale_param {
  6347. bias_term: true
  6348. }
  6349. }
  6350. layer {
  6351. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_shortcut_MaxPool"
  6352. type: "Pooling"
  6353. bottom: "resnet_v2_152_block3_unit_35_bottleneck_v2_add"
  6354. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_shortcut_MaxPool"
  6355. pooling_param {
  6356. pool: MAX
  6357. kernel_size: 1
  6358. stride: 2
  6359. pad_h: 0
  6360. pad_w: 0
  6361. }
  6362. }
  6363. layer {
  6364. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_Relu"
  6365. type: "ReLU"
  6366. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6367. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6368. }
  6369. layer {
  6370. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_Conv2D"
  6371. type: "Convolution"
  6372. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_preact_FusedBatchNorm"
  6373. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_Conv2D"
  6374. convolution_param {
  6375. num_output: 256
  6376. bias_term: false
  6377. group: 1
  6378. stride: 1
  6379. pad_h: 0
  6380. pad_w: 0
  6381. kernel_h: 1
  6382. kernel_w: 1
  6383. }
  6384. }
  6385. layer {
  6386. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6387. type: "BatchNorm"
  6388. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_Conv2D"
  6389. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6390. batch_norm_param {
  6391. use_global_stats: true
  6392. eps: 1.0009999641624745e-05
  6393. }
  6394. }
  6395. layer {
  6396. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  6397. type: "Scale"
  6398. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6399. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6400. scale_param {
  6401. bias_term: true
  6402. }
  6403. }
  6404. layer {
  6405. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_Relu"
  6406. type: "ReLU"
  6407. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6408. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6409. }
  6410. layer {
  6411. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Conv2D"
  6412. type: "Convolution"
  6413. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6414. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Conv2D"
  6415. convolution_param {
  6416. num_output: 256
  6417. bias_term: false
  6418. group: 1
  6419. stride: 2
  6420. pad_h: 1
  6421. pad_w: 1
  6422. kernel_h: 3
  6423. kernel_w: 3
  6424. }
  6425. }
  6426. layer {
  6427. name: "DummyData5"
  6428. type: "DummyData"
  6429. top: "DummyData5"
  6430. dummy_data_param {
  6431. shape {
  6432. dim: 1
  6433. dim: 256
  6434. dim: 10
  6435. dim: 10
  6436. }
  6437. }
  6438. }
  6439. layer {
  6440. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Conv2D_crop"
  6441. type: "Crop"
  6442. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Conv2D"
  6443. bottom: "DummyData5"
  6444. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Conv2D_crop"
  6445. crop_param {
  6446. offset: 0
  6447. offset: 0
  6448. }
  6449. }
  6450. layer {
  6451. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6452. type: "BatchNorm"
  6453. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Conv2D_crop"
  6454. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6455. batch_norm_param {
  6456. use_global_stats: true
  6457. eps: 1.0009999641624745e-05
  6458. }
  6459. }
  6460. layer {
  6461. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6462. type: "Scale"
  6463. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6464. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6465. scale_param {
  6466. bias_term: true
  6467. }
  6468. }
  6469. layer {
  6470. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_Relu"
  6471. type: "ReLU"
  6472. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6473. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6474. }
  6475. layer {
  6476. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv3_Conv2D"
  6477. type: "Convolution"
  6478. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6479. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv3_Conv2D"
  6480. convolution_param {
  6481. num_output: 1024
  6482. bias_term: true
  6483. group: 1
  6484. stride: 1
  6485. pad_h: 0
  6486. pad_w: 0
  6487. kernel_h: 1
  6488. kernel_w: 1
  6489. }
  6490. }
  6491. layer {
  6492. name: "resnet_v2_152_block3_unit_36_bottleneck_v2_add"
  6493. type: "Eltwise"
  6494. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_shortcut_MaxPool"
  6495. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_conv3_Conv2D"
  6496. top: "resnet_v2_152_block3_unit_36_bottleneck_v2_add"
  6497. eltwise_param {
  6498. operation: SUM
  6499. }
  6500. }
  6501. layer {
  6502. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6503. type: "BatchNorm"
  6504. bottom: "resnet_v2_152_block3_unit_36_bottleneck_v2_add"
  6505. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6506. batch_norm_param {
  6507. use_global_stats: true
  6508. eps: 1.0009999641624745e-05
  6509. }
  6510. }
  6511. layer {
  6512. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm_scale"
  6513. type: "Scale"
  6514. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6515. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6516. scale_param {
  6517. bias_term: true
  6518. }
  6519. }
  6520. layer {
  6521. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_Relu"
  6522. type: "ReLU"
  6523. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6524. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6525. }
  6526. layer {
  6527. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_shortcut_Conv2D"
  6528. type: "Convolution"
  6529. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6530. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_shortcut_Conv2D"
  6531. convolution_param {
  6532. num_output: 2048
  6533. bias_term: true
  6534. group: 1
  6535. stride: 1
  6536. pad_h: 0
  6537. pad_w: 0
  6538. kernel_h: 1
  6539. kernel_w: 1
  6540. }
  6541. }
  6542. layer {
  6543. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_Conv2D"
  6544. type: "Convolution"
  6545. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm"
  6546. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_Conv2D"
  6547. convolution_param {
  6548. num_output: 512
  6549. bias_term: false
  6550. group: 1
  6551. stride: 1
  6552. pad_h: 0
  6553. pad_w: 0
  6554. kernel_h: 1
  6555. kernel_w: 1
  6556. }
  6557. }
  6558. layer {
  6559. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6560. type: "BatchNorm"
  6561. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_Conv2D"
  6562. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6563. batch_norm_param {
  6564. use_global_stats: true
  6565. eps: 1.0009999641624745e-05
  6566. }
  6567. }
  6568. layer {
  6569. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  6570. type: "Scale"
  6571. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6572. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6573. scale_param {
  6574. bias_term: true
  6575. }
  6576. }
  6577. layer {
  6578. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_Relu"
  6579. type: "ReLU"
  6580. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6581. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6582. }
  6583. layer {
  6584. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_Conv2D"
  6585. type: "Convolution"
  6586. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6587. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_Conv2D"
  6588. convolution_param {
  6589. num_output: 512
  6590. bias_term: false
  6591. group: 1
  6592. stride: 1
  6593. pad_h: 1
  6594. pad_w: 1
  6595. kernel_h: 3
  6596. kernel_w: 3
  6597. }
  6598. }
  6599. layer {
  6600. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6601. type: "BatchNorm"
  6602. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_Conv2D"
  6603. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6604. batch_norm_param {
  6605. use_global_stats: true
  6606. eps: 1.0009999641624745e-05
  6607. }
  6608. }
  6609. layer {
  6610. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6611. type: "Scale"
  6612. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6613. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6614. scale_param {
  6615. bias_term: true
  6616. }
  6617. }
  6618. layer {
  6619. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_Relu"
  6620. type: "ReLU"
  6621. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6622. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6623. }
  6624. layer {
  6625. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv3_Conv2D"
  6626. type: "Convolution"
  6627. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6628. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv3_Conv2D"
  6629. convolution_param {
  6630. num_output: 2048
  6631. bias_term: true
  6632. group: 1
  6633. stride: 1
  6634. pad_h: 0
  6635. pad_w: 0
  6636. kernel_h: 1
  6637. kernel_w: 1
  6638. }
  6639. }
  6640. layer {
  6641. name: "resnet_v2_152_block4_unit_1_bottleneck_v2_add"
  6642. type: "Eltwise"
  6643. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_shortcut_Conv2D"
  6644. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_conv3_Conv2D"
  6645. top: "resnet_v2_152_block4_unit_1_bottleneck_v2_add"
  6646. eltwise_param {
  6647. operation: SUM
  6648. }
  6649. }
  6650. layer {
  6651. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6652. type: "BatchNorm"
  6653. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_add"
  6654. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6655. batch_norm_param {
  6656. use_global_stats: true
  6657. eps: 1.0009999641624745e-05
  6658. }
  6659. }
  6660. layer {
  6661. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm_scale"
  6662. type: "Scale"
  6663. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6664. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6665. scale_param {
  6666. bias_term: true
  6667. }
  6668. }
  6669. layer {
  6670. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_Relu"
  6671. type: "ReLU"
  6672. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6673. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6674. }
  6675. layer {
  6676. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_Conv2D"
  6677. type: "Convolution"
  6678. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm"
  6679. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_Conv2D"
  6680. convolution_param {
  6681. num_output: 512
  6682. bias_term: false
  6683. group: 1
  6684. stride: 1
  6685. pad_h: 0
  6686. pad_w: 0
  6687. kernel_h: 1
  6688. kernel_w: 1
  6689. }
  6690. }
  6691. layer {
  6692. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6693. type: "BatchNorm"
  6694. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_Conv2D"
  6695. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6696. batch_norm_param {
  6697. use_global_stats: true
  6698. eps: 1.0009999641624745e-05
  6699. }
  6700. }
  6701. layer {
  6702. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  6703. type: "Scale"
  6704. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6705. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6706. scale_param {
  6707. bias_term: true
  6708. }
  6709. }
  6710. layer {
  6711. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_Relu"
  6712. type: "ReLU"
  6713. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6714. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6715. }
  6716. layer {
  6717. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_Conv2D"
  6718. type: "Convolution"
  6719. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6720. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_Conv2D"
  6721. convolution_param {
  6722. num_output: 512
  6723. bias_term: false
  6724. group: 1
  6725. stride: 1
  6726. pad_h: 1
  6727. pad_w: 1
  6728. kernel_h: 3
  6729. kernel_w: 3
  6730. }
  6731. }
  6732. layer {
  6733. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6734. type: "BatchNorm"
  6735. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_Conv2D"
  6736. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6737. batch_norm_param {
  6738. use_global_stats: true
  6739. eps: 1.0009999641624745e-05
  6740. }
  6741. }
  6742. layer {
  6743. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6744. type: "Scale"
  6745. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6746. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6747. scale_param {
  6748. bias_term: true
  6749. }
  6750. }
  6751. layer {
  6752. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_Relu"
  6753. type: "ReLU"
  6754. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6755. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6756. }
  6757. layer {
  6758. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv3_Conv2D"
  6759. type: "Convolution"
  6760. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6761. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv3_Conv2D"
  6762. convolution_param {
  6763. num_output: 2048
  6764. bias_term: true
  6765. group: 1
  6766. stride: 1
  6767. pad_h: 0
  6768. pad_w: 0
  6769. kernel_h: 1
  6770. kernel_w: 1
  6771. }
  6772. }
  6773. layer {
  6774. name: "resnet_v2_152_block4_unit_2_bottleneck_v2_add"
  6775. type: "Eltwise"
  6776. bottom: "resnet_v2_152_block4_unit_1_bottleneck_v2_add"
  6777. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_conv3_Conv2D"
  6778. top: "resnet_v2_152_block4_unit_2_bottleneck_v2_add"
  6779. eltwise_param {
  6780. operation: SUM
  6781. }
  6782. }
  6783. layer {
  6784. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6785. type: "BatchNorm"
  6786. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_add"
  6787. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6788. batch_norm_param {
  6789. use_global_stats: true
  6790. eps: 1.0009999641624745e-05
  6791. }
  6792. }
  6793. layer {
  6794. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm_scale"
  6795. type: "Scale"
  6796. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6797. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6798. scale_param {
  6799. bias_term: true
  6800. }
  6801. }
  6802. layer {
  6803. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_Relu"
  6804. type: "ReLU"
  6805. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6806. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6807. }
  6808. layer {
  6809. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_Conv2D"
  6810. type: "Convolution"
  6811. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm"
  6812. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_Conv2D"
  6813. convolution_param {
  6814. num_output: 512
  6815. bias_term: false
  6816. group: 1
  6817. stride: 1
  6818. pad_h: 0
  6819. pad_w: 0
  6820. kernel_h: 1
  6821. kernel_w: 1
  6822. }
  6823. }
  6824. layer {
  6825. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6826. type: "BatchNorm"
  6827. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_Conv2D"
  6828. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6829. batch_norm_param {
  6830. use_global_stats: true
  6831. eps: 1.0009999641624745e-05
  6832. }
  6833. }
  6834. layer {
  6835. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm_scale"
  6836. type: "Scale"
  6837. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6838. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6839. scale_param {
  6840. bias_term: true
  6841. }
  6842. }
  6843. layer {
  6844. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_Relu"
  6845. type: "ReLU"
  6846. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6847. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6848. }
  6849. layer {
  6850. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_Conv2D"
  6851. type: "Convolution"
  6852. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm"
  6853. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_Conv2D"
  6854. convolution_param {
  6855. num_output: 512
  6856. bias_term: false
  6857. group: 1
  6858. stride: 1
  6859. pad_h: 1
  6860. pad_w: 1
  6861. kernel_h: 3
  6862. kernel_w: 3
  6863. }
  6864. }
  6865. layer {
  6866. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6867. type: "BatchNorm"
  6868. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_Conv2D"
  6869. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6870. batch_norm_param {
  6871. use_global_stats: true
  6872. eps: 1.0009999641624745e-05
  6873. }
  6874. }
  6875. layer {
  6876. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm_scale"
  6877. type: "Scale"
  6878. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6879. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6880. scale_param {
  6881. bias_term: true
  6882. }
  6883. }
  6884. layer {
  6885. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_Relu"
  6886. type: "ReLU"
  6887. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6888. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6889. }
  6890. layer {
  6891. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv3_Conv2D"
  6892. type: "Convolution"
  6893. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm"
  6894. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv3_Conv2D"
  6895. convolution_param {
  6896. num_output: 2048
  6897. bias_term: true
  6898. group: 1
  6899. stride: 1
  6900. pad_h: 0
  6901. pad_w: 0
  6902. kernel_h: 1
  6903. kernel_w: 1
  6904. }
  6905. }
  6906. layer {
  6907. name: "resnet_v2_152_block4_unit_3_bottleneck_v2_add"
  6908. type: "Eltwise"
  6909. bottom: "resnet_v2_152_block4_unit_2_bottleneck_v2_add"
  6910. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_conv3_Conv2D"
  6911. top: "resnet_v2_152_block4_unit_3_bottleneck_v2_add"
  6912. eltwise_param {
  6913. operation: SUM
  6914. }
  6915. }
  6916. layer {
  6917. name: "resnet_v2_152_postnorm_FusedBatchNorm"
  6918. type: "BatchNorm"
  6919. bottom: "resnet_v2_152_block4_unit_3_bottleneck_v2_add"
  6920. top: "resnet_v2_152_postnorm_FusedBatchNorm"
  6921. batch_norm_param {
  6922. use_global_stats: true
  6923. eps: 1.0009999641624745e-05
  6924. }
  6925. }
  6926. layer {
  6927. name: "resnet_v2_152_postnorm_FusedBatchNorm_scale"
  6928. type: "Scale"
  6929. bottom: "resnet_v2_152_postnorm_FusedBatchNorm"
  6930. top: "resnet_v2_152_postnorm_FusedBatchNorm"
  6931. scale_param {
  6932. bias_term: true
  6933. }
  6934. }
  6935. layer {
  6936. name: "resnet_v2_152_postnorm_Relu"
  6937. type: "ReLU"
  6938. bottom: "resnet_v2_152_postnorm_FusedBatchNorm"
  6939. top: "resnet_v2_152_postnorm_FusedBatchNorm"
  6940. }
  6941. layer {
  6942. name: "resnet_v2_152_pool5"
  6943. type: "Reduction"
  6944. bottom: "resnet_v2_152_postnorm_FusedBatchNorm"
  6945. top: "resnet_v2_152_pool5"
  6946. reduction_param {
  6947. operation: MEAN
  6948. axis: 2
  6949. }
  6950. }
  6951. layer {
  6952. name: "resnet_v2_152_pool5_reshape"
  6953. type: "Reshape"
  6954. bottom: "resnet_v2_152_pool5"
  6955. top: "resnet_v2_152_pool5_reshape"
  6956. reshape_param {
  6957. shape {
  6958. dim: 1
  6959. dim: 2048
  6960. dim: 1
  6961. dim: 1
  6962. }
  6963. }
  6964. }
  6965. layer {
  6966. name: "resnet_v2_152_logits_Conv2D"
  6967. type: "Convolution"
  6968. bottom: "resnet_v2_152_pool5_reshape"
  6969. top: "resnet_v2_152_logits_Conv2D"
  6970. convolution_param {
  6971. num_output: 1001
  6972. bias_term: true
  6973. group: 1
  6974. stride: 1
  6975. pad_h: 0
  6976. pad_w: 0
  6977. kernel_h: 1
  6978. kernel_w: 1
  6979. }
  6980. }
  6981. layer {
  6982. name: "MMdnn_Output"
  6983. type: "Reshape"
  6984. bottom: "resnet_v2_152_logits_Conv2D"
  6985. top: "MMdnn_Output"
  6986. reshape_param {
  6987. shape {
  6988. dim: 1001
  6989. }
  6990. }
  6991. }
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement
Public Pastes
Advertisement
We use cookies for various purposes including analytics. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy.  OK, I Understand
Not a member of Pastebin yet?
Sign Up, it unlocks many cool features!