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  1. name: "Fisher Yu's Context Train Net @ PASCAL VOC 2012"
  2. layer {
  3. name: "data"
  4. type: "BinLabelData"
  5. top: "data"
  6. top: "label"
  7. bin_label_data_param {
  8. bin_list_path: "PASCAL_VOC2012"
  9. label_list_path: "PASCAL_VOC2012"
  10. batch_size: 14
  11. shuffle: true
  12. label_slice {
  13. dim: 66
  14. dim: 66
  15. stride: 8
  16. stride: 8
  17. }
  18. }
  19. }
  20. layer {
  21. name: "ctx_conv1_1"
  22. type: "Convolution"
  23. bottom: "data"
  24. top: "ctx_conv1_1"
  25. param {
  26. lr_mult: 1
  27. decay_mult: 1
  28. }
  29. param {
  30. lr_mult: 2
  31. decay_mult: 0
  32. }
  33. convolution_param {
  34. num_output: 21
  35. pad: 1
  36. kernel_size: 3
  37. weight_filler {
  38. type: "identity"
  39. std: 0.01
  40. num_groups: 21
  41. }
  42. bias_filler {
  43. type: "constant"
  44. value: 0
  45. }
  46. }
  47. }
  48. layer {
  49. name: "ctx_relu1_1"
  50. type: "ReLU"
  51. bottom: "ctx_conv1_1"
  52. top: "ctx_conv1_1"
  53. }
  54. layer {
  55. name: "ctx_conv1_2"
  56. type: "Convolution"
  57. bottom: "ctx_conv1_1"
  58. top: "ctx_conv1_2"
  59. param {
  60. lr_mult: 1
  61. decay_mult: 1
  62. }
  63. param {
  64. lr_mult: 2
  65. decay_mult: 0
  66. }
  67. convolution_param {
  68. num_output: 21
  69. pad: 1
  70. kernel_size: 3
  71. weight_filler {
  72. type: "identity"
  73. std: 0.01
  74. num_groups: 21
  75. }
  76. bias_filler {
  77. type: "constant"
  78. value: 0
  79. }
  80. }
  81. }
  82. layer {
  83. name: "ctx_relu1_2"
  84. type: "ReLU"
  85. bottom: "ctx_conv1_2"
  86. top: "ctx_conv1_2"
  87. }
  88. layer {
  89. name: "ctx_conv2_1"
  90. type: "Convolution"
  91. bottom: "ctx_conv1_2"
  92. top: "ctx_conv2_1"
  93. param {
  94. lr_mult: 1
  95. decay_mult: 1
  96. }
  97. param {
  98. lr_mult: 2
  99. decay_mult: 0
  100. }
  101. convolution_param {
  102. num_output: 21
  103. pad: 2
  104. kernel_size: 3
  105. weight_filler {
  106. type: "identity"
  107. std: 0.01
  108. num_groups: 21
  109. }
  110. bias_filler {
  111. type: "constant"
  112. value: 0
  113. }
  114. dilation: 2
  115. }
  116. }
  117. layer {
  118. name: "ctx_relu2_1"
  119. type: "ReLU"
  120. bottom: "ctx_conv2_1"
  121. top: "ctx_conv2_1"
  122. }
  123. layer {
  124. name: "ctx_conv3_1"
  125. type: "Convolution"
  126. bottom: "ctx_conv2_1"
  127. top: "ctx_conv3_1"
  128. param {
  129. lr_mult: 1
  130. decay_mult: 1
  131. }
  132. param {
  133. lr_mult: 2
  134. decay_mult: 0
  135. }
  136. convolution_param {
  137. num_output: 21
  138. pad: 4
  139. kernel_size: 3
  140. weight_filler {
  141. type: "identity"
  142. std: 0.01
  143. num_groups: 21
  144. }
  145. bias_filler {
  146. type: "constant"
  147. value: 0
  148. }
  149. dilation: 4
  150. }
  151. }
  152. layer {
  153. name: "ctx_relu3_1"
  154. type: "ReLU"
  155. bottom: "ctx_conv3_1"
  156. top: "ctx_conv3_1"
  157. }
  158. layer {
  159. name: "ctx_conv4_1"
  160. type: "Convolution"
  161. bottom: "ctx_conv3_1"
  162. top: "ctx_conv4_1"
  163. param {
  164. lr_mult: 1
  165. decay_mult: 1
  166. }
  167. param {
  168. lr_mult: 2
  169. decay_mult: 0
  170. }
  171. convolution_param {
  172. num_output: 21
  173. pad: 8
  174. kernel_size: 3
  175. weight_filler {
  176. type: "identity"
  177. std: 0.01
  178. num_groups: 21
  179. }
  180. bias_filler {
  181. type: "constant"
  182. value: 0
  183. }
  184. dilation: 8
  185. }
  186. }
  187. layer {
  188. name: "ctx_relu4_1"
  189. type: "ReLU"
  190. bottom: "ctx_conv4_1"
  191. top: "ctx_conv4_1"
  192. }
  193. layer {
  194. name: "ctx_conv5_1"
  195. type: "Convolution"
  196. bottom: "ctx_conv4_1"
  197. top: "ctx_conv5_1"
  198. param {
  199. lr_mult: 1
  200. decay_mult: 1
  201. }
  202. param {
  203. lr_mult: 2
  204. decay_mult: 0
  205. }
  206. convolution_param {
  207. num_output: 21
  208. pad: 16
  209. kernel_size: 3
  210. weight_filler {
  211. type: "identity"
  212. std: 0.01
  213. num_groups: 21
  214. }
  215. bias_filler {
  216. type: "constant"
  217. value: 0
  218. }
  219. dilation: 16
  220. }
  221. }
  222. layer {
  223. name: "ctx_relu5_1"
  224. type: "ReLU"
  225. bottom: "ctx_conv5_1"
  226. top: "ctx_conv5_1"
  227. }
  228. layer {
  229. name: "ctx_fc1"
  230. type: "Convolution"
  231. bottom: "ctx_conv5_1"
  232. top: "ctx_fc1"
  233. param {
  234. lr_mult: 1
  235. decay_mult: 1
  236. }
  237. param {
  238. lr_mult: 2
  239. decay_mult: 0
  240. }
  241. convolution_param {
  242. num_output: 21
  243. pad: 1
  244. kernel_size: 3
  245. weight_filler {
  246. type: "identity"
  247. std: 0.01
  248. num_groups: 21
  249. }
  250. bias_filler {
  251. type: "constant"
  252. value: 0
  253. }
  254. }
  255. }
  256. layer {
  257. name: "ctx_fc1_relu"
  258. type: "ReLU"
  259. bottom: "ctx_fc1"
  260. top: "ctx_fc1"
  261. }
  262. layer {
  263. name: "ctx_final"
  264. type: "Convolution"
  265. bottom: "ctx_fc1"
  266. top: "ctx_final"
  267. param {
  268. lr_mult: 1
  269. decay_mult: 1
  270. }
  271. param {
  272. lr_mult: 2
  273. decay_mult: 0
  274. }
  275. convolution_param {
  276. num_output: 21
  277. kernel_size: 1
  278. weight_filler {
  279. type: "identity"
  280. std: 0.01
  281. num_groups: 21
  282. }
  283. bias_filler {
  284. type: "constant"
  285. value: 0
  286. }
  287. }
  288. }
  289. layer {
  290. name: "loss"
  291. type: "SoftmaxWithLoss"
  292. bottom: "ctx_final"
  293. bottom: "label"
  294. top: "loss"
  295. loss_param {
  296. ignore_label: 255
  297. normalization: VALID
  298. }
  299. }
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