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  1. name: "C3D-UCF101Net"
  2. layer {
  3. name: "data"
  4. type: "VideoData"
  5. top: "data"
  6. top: "label"
  7. include {
  8. phase: TRAIN
  9. }
  10. transform_param {
  11. crop_size: 112
  12. mean_value: 128
  13. mean_value: 128
  14. mean_value: 128
  15. mirror: true
  16. }
  17. video_data_param {
  18. source: "train_01.lst"
  19. root_folder: "/data/users/trandu/datasets/ucf101/frm/"
  20. new_length: 16
  21. new_height: 128
  22. new_width: 171
  23. use_image: true
  24. show_data: false
  25. batch_size: 10
  26. shuffle: true
  27. }
  28. }
  29. layer {
  30. name: "data"
  31. type: "VideoData"
  32. top: "data"
  33. top: "label"
  34. include {
  35. phase: TEST
  36. stage: "test-on-train"
  37. }
  38. transform_param {
  39. crop_size: 112
  40. mean_value: 128
  41. mean_value: 128
  42. mean_value: 128
  43. mirror: false
  44. }
  45. video_data_param {
  46. source: "train_01.lst"
  47. root_folder: "/data/users/trandu/datasets/ucf101/frm/"
  48. new_length: 16
  49. new_height: 128
  50. new_width: 171
  51. use_image: true
  52. show_data: false
  53. batch_size: 30
  54. shuffle: true
  55. }
  56. }
  57. layer {
  58. name: "data"
  59. type: "VideoData"
  60. top: "data"
  61. top: "label"
  62. include {
  63. phase: TEST
  64. stage: "test-on-val"
  65. }
  66. transform_param {
  67. crop_size: 112
  68. mean_value: 128
  69. mean_value: 128
  70. mean_value: 128
  71. mirror: false
  72. }
  73. video_data_param {
  74. source: "test_01.lst"
  75. root_folder: "/data/users/trandu/datasets/ucf101/frm/"
  76. new_length: 16
  77. new_height: 128
  78. new_width: 171
  79. use_image: true
  80. show_data: false
  81. batch_size: 30
  82. shuffle: true
  83. }
  84. }
  85. # ----- 1st group -----
  86. layer {
  87. name: "conv1a"
  88. type: "Convolution3D"
  89. bottom: "data"
  90. top: "conv1a"
  91. param {
  92. lr_mult: 1
  93. decay_mult: 1
  94. }
  95. param {
  96. lr_mult: 2
  97. decay_mult: 0
  98. }
  99. convolution3d_param {
  100. num_output: 64
  101. kernel_size: 3
  102. kernel_depth: 3
  103. stride: 1
  104. temporal_stride: 1
  105. pad: 1
  106. temporal_pad: 1
  107. weight_filler {
  108. type: "gaussian"
  109. std: 0.01
  110. }
  111. bias_filler {
  112. type: "constant"
  113. value: 0
  114. }
  115. }
  116. }
  117. layer {
  118. name: "relu1a"
  119. type: "ReLU"
  120. bottom: "conv1a"
  121. top: "conv1a"
  122. }
  123. layer {
  124. name: "pool1"
  125. type: "Pooling3D"
  126. bottom: "conv1a"
  127. top: "pool1"
  128. pooling3d_param {
  129. pool: MAX
  130. kernel_size: 2
  131. kernel_depth: 1
  132. stride: 2
  133. temporal_stride: 1
  134. }
  135. }
  136.  
  137. # ----- 2nd group -----
  138. layer {
  139. name: "conv2a"
  140. type: "Convolution3D"
  141. bottom: "pool1"
  142. top: "conv2a"
  143. param {
  144. lr_mult: 1
  145. decay_mult: 1
  146. }
  147. param {
  148. lr_mult: 2
  149. decay_mult: 0
  150. }
  151. convolution3d_param {
  152. num_output: 128
  153. kernel_size:3
  154. kernel_depth: 3
  155. stride: 1
  156. temporal_stride: 1
  157. pad: 1
  158. temporal_pad: 1
  159. weight_filler {
  160. type: "gaussian"
  161. std: 0.01
  162. }
  163. bias_filler {
  164. type: "constant"
  165. value: 1
  166. }
  167. }
  168. }
  169. layer {
  170. name: "relu2a"
  171. type: "ReLU"
  172. bottom: "conv2a"
  173. top: "conv2a"
  174. }
  175. layer {
  176. name: "pool2"
  177. type: "Pooling3D"
  178. bottom: "conv2a"
  179. top: "pool2"
  180. pooling3d_param {
  181. pool: MAX
  182. kernel_size: 2
  183. kernel_depth: 2
  184. stride: 2
  185. temporal_stride: 2
  186. }
  187. }
  188.  
  189. # ----- 3rd group -----
  190. layer {
  191. name: "conv3a"
  192. type: "Convolution3D"
  193. bottom: "pool2"
  194. top: "conv3a"
  195. param {
  196. lr_mult: 1
  197. decay_mult: 1
  198. }
  199. param {
  200. lr_mult: 2
  201. decay_mult: 0
  202. }
  203. convolution3d_param {
  204. num_output: 256
  205. kernel_size: 3
  206. kernel_depth: 3
  207. stride: 1
  208. temporal_stride: 1
  209. pad: 1
  210. temporal_pad: 1
  211. weight_filler {
  212. type: "gaussian"
  213. std: 0.01
  214. }
  215. bias_filler {
  216. type: "constant"
  217. value: 1
  218. }
  219. }
  220. }
  221. layer {
  222. name: "relu3a"
  223. type: "ReLU"
  224. bottom: "conv3a"
  225. top: "conv3a"
  226. }
  227. layer {
  228. name: "pool3"
  229. type: "Pooling3D"
  230. bottom: "conv3a"
  231. top: "pool3"
  232. pooling3d_param {
  233. pool: MAX
  234. kernel_size: 2
  235. kernel_depth: 2
  236. stride: 2
  237. temporal_stride: 2
  238. }
  239. }
  240.  
  241. # ----- 4th group -----
  242. layer {
  243. name: "conv4a"
  244. type: "Convolution3D"
  245. bottom: "pool3"
  246. top: "conv4a"
  247. param {
  248. lr_mult: 1
  249. decay_mult: 1
  250. }
  251. param {
  252. lr_mult: 2
  253. decay_mult: 0
  254. }
  255. convolution3d_param {
  256. num_output: 256
  257. kernel_size: 3
  258. kernel_depth: 3
  259. stride: 1
  260. temporal_stride: 1
  261. pad: 1
  262. temporal_pad: 1
  263. weight_filler {
  264. type: "gaussian"
  265. std: 0.01
  266. }
  267. bias_filler {
  268. type: "constant"
  269. value: 1
  270. }
  271. }
  272. }
  273. layer {
  274. name: "relu4a"
  275. type: "ReLU"
  276. bottom: "conv4a"
  277. top: "conv4a"
  278. }
  279. layer {
  280. name: "pool4"
  281. type: "Pooling3D"
  282. bottom: "conv4a"
  283. top: "pool4"
  284. pooling3d_param {
  285. pool: MAX
  286. kernel_size: 2
  287. kernel_depth: 2
  288. stride: 2
  289. temporal_stride: 2
  290. }
  291. }
  292.  
  293. # ----- 5th group -----
  294. layer {
  295. name: "conv5a"
  296. type: "Convolution3D"
  297. bottom: "pool4"
  298. top: "conv5a"
  299. param {
  300. lr_mult: 1
  301. decay_mult: 1
  302. }
  303. param {
  304. lr_mult: 2
  305. decay_mult: 0
  306. }
  307. convolution3d_param {
  308. num_output: 256
  309. kernel_size: 3
  310. kernel_depth: 3
  311. stride: 1
  312. temporal_stride: 1
  313. pad: 1
  314. temporal_pad: 1
  315. weight_filler {
  316. type: "gaussian"
  317. std: 0.01
  318. }
  319. bias_filler {
  320. type: "constant"
  321. value: 1
  322. }
  323. }
  324. }
  325. layer {
  326. name: "relu5a"
  327. type: "ReLU"
  328. bottom: "conv5a"
  329. top: "conv5a"
  330. }
  331. layer {
  332. name: "pool5"
  333. type: "Pooling3D"
  334. bottom: "conv5a"
  335. top: "pool5"
  336. pooling3d_param {
  337. pool: MAX
  338. kernel_size: 2
  339. kernel_depth: 2
  340. stride: 2
  341. temporal_stride: 2
  342. }
  343. }
  344.  
  345. # ----- 1st fc group -----
  346. layer {
  347. name: "fc6"
  348. type: "InnerProduct"
  349. bottom: "pool5"
  350. top: "fc6"
  351. param {
  352. lr_mult: 1
  353. decay_mult: 1
  354. }
  355. param {
  356. lr_mult: 2
  357. decay_mult: 0
  358. }
  359. inner_product_param {
  360. num_output: 2048
  361. weight_filler {
  362. type: "gaussian"
  363. std: 0.005
  364. }
  365. bias_filler {
  366. type: "constant"
  367. value: 1
  368. }
  369. }
  370. }
  371. layer {
  372. name: "relu6"
  373. type: "ReLU"
  374. bottom: "fc6"
  375. top: "fc6"
  376. }
  377. layer {
  378. name: "drop6"
  379. type: "Dropout"
  380. bottom: "fc6"
  381. top: "fc6"
  382. dropout_param {
  383. dropout_ratio: 0.5
  384. }
  385. }
  386.  
  387. # ----- 2nd fc group -----
  388. layer {
  389. name: "fc7"
  390. type: "InnerProduct"
  391. bottom: "fc6"
  392. top: "fc7"
  393. param {
  394. lr_mult: 1
  395. decay_mult: 1
  396. }
  397. param {
  398. lr_mult: 2
  399. decay_mult: 0
  400. }
  401. inner_product_param {
  402. num_output: 2048
  403. weight_filler {
  404. type: "gaussian"
  405. std: 0.005
  406. }
  407. bias_filler {
  408. type: "constant"
  409. value: 1
  410. }
  411. }
  412. }
  413. layer {
  414. name: "relu7"
  415. type: "ReLU"
  416. bottom: "fc7"
  417. top: "fc7"
  418. }
  419. layer {
  420. name: "drop7"
  421. type: "Dropout"
  422. bottom: "fc7"
  423. top: "fc7"
  424. dropout_param {
  425. dropout_ratio: 0.5
  426. }
  427. }
  428.  
  429. # ----- 3rd fc group -----
  430. layer {
  431. name: "fc8"
  432. type: "InnerProduct"
  433. bottom: "fc7"
  434. top: "fc8"
  435. param {
  436. lr_mult: 1
  437. decay_mult: 1
  438. }
  439. param {
  440. lr_mult: 2
  441. decay_mult: 0
  442. }
  443. inner_product_param {
  444. num_output: 101
  445. weight_filler {
  446. type: "gaussian"
  447. std: 0.01
  448. }
  449. bias_filler {
  450. type: "constant"
  451. value: 0
  452. }
  453. }
  454. }
  455. layer {
  456. name: "prob"
  457. type: "Softmax"
  458. bottom: "fc8"
  459. top: "prob"
  460. include {
  461. phase: TEST
  462. }
  463. }
  464. layer {
  465. name: "accuracy"
  466. type: "Accuracy"
  467. bottom: "prob"
  468. bottom: "label"
  469. top: "accuracy/top-1"
  470. include {
  471. phase: TEST
  472. }
  473. }
  474. layer {
  475. name: "accuracy"
  476. type: "Accuracy"
  477. bottom: "prob"
  478. bottom: "label"
  479. top: "accuracy/top-5"
  480. include {
  481. phase: TEST
  482. }
  483. accuracy_param {
  484. top_k: 5
  485. }
  486. }
  487. layer {
  488. name: "loss"
  489. type: "SoftmaxWithLoss"
  490. bottom: "fc8"
  491. bottom: "label"
  492. top: "loss"
  493. }
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