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  1. input:"data"
  2. input_dim:1
  3. input_dim:3
  4. input_dim:1200
  5. input_dim:1200
  6.  
  7. input:"dataL"
  8. input_dim:1
  9. input_dim:4
  10. input_dim:1200
  11. input_dim:1200
  12.  
  13. input:"dataD"
  14. input_dim:1
  15. input_dim:4
  16. input_dim:1200
  17. input_dim:1200
  18.  
  19. layer {
  20.   name: "conv_gA1"
  21.   type: "Convolution"
  22.   bottom: "dataL"
  23.   top: "conv_gA1"
  24.   convolution_param {
  25.     num_output: 64
  26.     kernel_size: 3
  27.     stride: 1
  28.     pad: 1    
  29.     weight_filler {
  30.       type: "msra"
  31.     }
  32.   }
  33. }
  34.  
  35. layer {
  36.   name: "relu_gA1"
  37.   type: "ReLU"
  38.   bottom: "conv_gA1"
  39.   top: "conv_gA1"
  40. }
  41.  
  42. layer {
  43.   name: "conv_gA2"
  44.   type: "Convolution"
  45.   bottom: "conv_gA1"
  46.   top: "conv_gA2"
  47.   convolution_param {
  48.     num_output: 64
  49.     kernel_size: 3
  50.     stride: 1
  51.     pad: 1    
  52.     weight_filler {
  53.       type: "msra"
  54.     }
  55.   }
  56. }
  57.  
  58. layer {
  59.   name: "relu_gA2"
  60.   type: "ReLU"
  61.   bottom: "conv_gA2"
  62.   top: "conv_gA2"
  63. }
  64.  
  65. layer {
  66.   name: "conv_gA3"
  67.   type: "Convolution"
  68.   bottom: "conv_gA2"
  69.   top: "conv_gA3"
  70.   convolution_param {
  71.     num_output: 64
  72.     kernel_size: 3
  73.     stride: 1
  74.     pad: 1    
  75.     weight_filler {
  76.       type: "msra"
  77.     }
  78.   }
  79. }
  80.  
  81. layer {
  82.   name: "relu_gA3"
  83.   type: "ReLU"
  84.   bottom: "conv_gA3"
  85.   top: "conv_gA3"
  86. }
  87.  
  88. layer {
  89.   name: "conv_gA4"
  90.   type: "Convolution"
  91.   bottom: "conv_gA3"
  92.   top: "conv_gA4"
  93.   convolution_param {
  94.     num_output: 64
  95.     kernel_size: 3
  96.     stride: 1
  97.     pad: 1    
  98.     weight_filler {
  99.       type: "msra"
  100.     }
  101.   }
  102. }
  103.  
  104. layer {
  105.   name: "relu_gA4"
  106.   type: "ReLU"
  107.   bottom: "conv_gA4"
  108.   top: "conv_gA4"
  109. }
  110.  
  111. layer {
  112.   name: "conv_gA5"
  113.   type: "Convolution"
  114.   bottom: "conv_gA4"
  115.   top: "conv_gA5"
  116.   convolution_param {
  117.     num_output: 64
  118.     kernel_size: 3
  119.     stride: 1
  120.     pad: 1    
  121.     weight_filler {
  122.       type: "msra"
  123.     }
  124.   }
  125. }
  126.  
  127. ##############################################
  128.  
  129. layer {
  130.   name: "conv_gB1"
  131.   type: "Convolution"
  132.   bottom: "dataD"
  133.   top: "conv_gB1"
  134.   convolution_param {
  135.     num_output: 64
  136.     kernel_size: 3
  137.     stride: 1
  138.     pad: 1    
  139.     weight_filler {
  140.       type: "msra"
  141.     }
  142.   }
  143. }
  144.  
  145. layer {
  146.   name: "relu_gB1"
  147.   type: "ReLU"
  148.   bottom: "conv_gB1"
  149.   top: "conv_gB1"
  150. }
  151.  
  152. layer {
  153.   name: "conv_gB2"
  154.   type: "Convolution"
  155.   bottom: "conv_gB1"
  156.   top: "conv_gB2"
  157.   convolution_param {
  158.     num_output: 64
  159.     kernel_size: 3
  160.     stride: 1
  161.     pad: 1    
  162.     weight_filler {
  163.       type: "msra"
  164.     }
  165.   }
  166. }
  167.  
  168. layer {
  169.   name: "relu_gB2"
  170.   type: "ReLU"
  171.   bottom: "conv_gB2"
  172.   top: "conv_gB2"
  173. }
  174.  
  175. layer {
  176.   name: "conv_gB3"
  177.   type: "Convolution"
  178.   bottom: "conv_gB2"
  179.   top: "conv_gB3"
  180.   convolution_param {
  181.     num_output: 64
  182.     kernel_size: 3
  183.     stride: 1
  184.     pad: 1    
  185.     weight_filler {
  186.       type: "msra"
  187.     }
  188.   }
  189. }
  190.  
  191. layer {
  192.   name: "relu_gB3"
  193.   type: "ReLU"
  194.   bottom: "conv_gB3"
  195.   top: "conv_gB3"
  196. }
  197.  
  198. layer {
  199.   name: "conv_gB4"
  200.   type: "Convolution"
  201.   bottom: "conv_gB3"
  202.   top: "conv_gB4"
  203.   convolution_param {
  204.     num_output: 64
  205.     kernel_size: 3
  206.     stride: 1
  207.     pad: 1    
  208.     weight_filler {
  209.       type: "msra"
  210.     }
  211.   }
  212. }
  213.  
  214. layer {
  215.   name: "relu_gB4"
  216.   type: "ReLU"
  217.   bottom: "conv_gB4"
  218.   top: "conv_gB4"
  219. }
  220.  
  221. layer {
  222.   name: "conv_gB5"
  223.   type: "Convolution"
  224.   bottom: "conv_gB4"
  225.   top: "conv_gB5"
  226.   convolution_param {
  227.     num_output: 64
  228.     kernel_size: 3
  229.     stride: 1
  230.     pad: 1    
  231.     weight_filler {
  232.       type: "msra"
  233.     }
  234.   }
  235. }
  236.  
  237. ##############################################
  238. layer {
  239.   name: "Concat"
  240.   type: "Concat"
  241.   bottom: "conv_gA5"
  242.   bottom: "conv_gB5"
  243.   top: "Concat"
  244. }
  245.  
  246. layer {
  247.   name: "conv_g6"
  248.   type: "Convolution"
  249.   bottom: "Concat"
  250.   top: "conv_g6"
  251.   convolution_param {
  252.     num_output: 64
  253.     kernel_size: 3
  254.     stride: 1
  255.     pad: 1    
  256.     weight_filler {
  257.       type: "msra"
  258.     }
  259.   }
  260. }
  261.  
  262. layer {
  263.   name: "conv_g7"
  264.   type: "Convolution"
  265.   bottom: "conv_g6"
  266.   top: "conv_g7"
  267.   convolution_param {
  268.     num_output: 64
  269.     kernel_size: 3
  270.     stride: 1
  271.     pad: 1    
  272.     weight_filler {
  273.       type: "msra"
  274.     }
  275.   }
  276. }
  277.  
  278. layer {
  279.   name: "relu_g7"
  280.   type: "ReLU"
  281.   bottom: "conv_g7"
  282.   top: "conv_g7"
  283. }
  284.  
  285. layer {
  286.   name: "conv_g8"
  287.   type: "Convolution"
  288.   bottom: "conv_g7"
  289.   top: "conv_g8"
  290.   convolution_param {
  291.     num_output: 64
  292.     kernel_size: 3
  293.     stride: 1
  294.     pad: 1    
  295.     weight_filler {
  296.       type: "msra"
  297.     }
  298.   }
  299. }
  300.  
  301. layer {
  302.   name: "relu_g8"
  303.   type: "ReLU"
  304.   bottom: "conv_g8"
  305.   top: "conv_g8"
  306. }
  307.  
  308. layer {
  309.   name: "conv_g9"
  310.   type: "Convolution"
  311.   bottom: "conv_g8"
  312.   top: "conv_g9"
  313.   convolution_param {
  314.     num_output: 64
  315.     kernel_size: 3
  316.     stride: 1
  317.     pad: 1    
  318.     weight_filler {
  319.       type: "msra"
  320.     }
  321.   }
  322. }
  323.  
  324. layer {
  325.   name: "relu_g9"
  326.   type: "ReLU"
  327.   bottom: "conv_g9"
  328.   top: "conv_g9"
  329. }
  330.  
  331. layer {
  332.   name: "conv_g10"
  333.   type: "Convolution"
  334.   bottom: "conv_g9"
  335.   top: "conv_g10"
  336.   convolution_param {
  337.     num_output: 64
  338.     kernel_size: 3
  339.     stride: 1
  340.     pad: 1    
  341.     weight_filler {
  342.       type: "msra"
  343.     }
  344.   }
  345. }
  346.  
  347. layer {
  348.   name: "relu_g10"
  349.   type: "ReLU"
  350.   bottom: "conv_g10"
  351.   top: "conv_g10"
  352. }
  353.  
  354. layer {
  355.   name: "conv_g11"
  356.   type: "Convolution"
  357.   bottom: "conv_g10"
  358.   top: "conv_g11"
  359.   convolution_param {
  360.     num_output: 64
  361.     kernel_size: 3
  362.     stride: 1
  363.     pad: 1    
  364.     weight_filler {
  365.       type: "msra"
  366.     }
  367.   }
  368. }
  369.  
  370. layer {
  371.   name: "relu_g11"
  372.   type: "ReLU"
  373.   bottom: "conv_g11"
  374.   top: "conv_g11"
  375. }
  376.  
  377. layer {
  378.   name: "conv_g12"
  379.   type: "Convolution"
  380.   bottom: "conv_g11"
  381.   top: "conv_g12"
  382.   convolution_param {
  383.     num_output: 3
  384.     kernel_size: 3
  385.     stride: 1
  386.     pad: 1    
  387.     weight_filler {
  388.       type: "msra"
  389.     }
  390.   }
  391. }
  392.  
  393. layer {
  394.   name: "sum"
  395.   type: "Eltwise"
  396.   bottom: "data"
  397.   bottom: "conv_g12"
  398.   top: "sum"
  399. }
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