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  1. force_backward: true
  2.  
  3. name: "GroovyNet"
  4. layer {
  5.   name: "data"
  6.   type: "Input"
  7.   top: "data"
  8.   input_param { shape: { dim: 1 dim: 3 dim: 227 dim: 227 } }
  9. }
  10.  
  11. layer {
  12.   name: "conv1_a"
  13.   type: "Convolution"
  14.   bottom: "data"
  15.   top: "conv1_a"
  16.   param {
  17.     lr_mult: 1
  18.     decay_mult: 1
  19.   }
  20.   param {
  21.     lr_mult: 2
  22.     decay_mult: 0
  23.   }
  24.   convolution_param {
  25.     num_output: 16
  26.     pad: 2
  27.     kernel_size: 5
  28.     weight_filler {
  29.       type: "msra"
  30.     }
  31.     bias_filler {
  32.       type: "constant"
  33.       value: 0
  34.     }
  35.   }
  36. }
  37. layer {
  38.   name: "relu1_a"
  39.   type: "ELU"
  40.   bottom: "conv1_a"
  41.   top: "conv1_a"
  42. }
  43. layer {
  44.   name: "conv2_a"
  45.   type: "Convolution"
  46.   bottom: "conv1_a"
  47.   top: "conv2_a"
  48.   param {
  49.     lr_mult: 1
  50.     decay_mult: 1
  51.   }
  52.   param {
  53.     lr_mult: 2
  54.     decay_mult: 0
  55.   }
  56.   convolution_param {
  57.     num_output: 16
  58.     pad: 2
  59.     kernel_size: 5
  60.     weight_filler {
  61.       type: "msra"
  62.     }
  63.     bias_filler {
  64.       type: "constant"
  65.       value: 0
  66.     }
  67.   }
  68. }
  69. layer {
  70.   name: "relu2_a"
  71.   type: "ELU"
  72.   bottom: "conv2_a"
  73.   top: "conv2_a"
  74. }
  75. layer {
  76.   name: "conv3_a"
  77.   type: "Convolution"
  78.   bottom: "conv2_a"
  79.   top: "conv3_a"
  80.   param {
  81.     lr_mult: 1
  82.     decay_mult: 1
  83.   }
  84.   param {
  85.     lr_mult: 2
  86.     decay_mult: 0
  87.   }
  88.   convolution_param {
  89.     num_output: 16
  90.     pad: 1
  91.     kernel_size: 3
  92.     weight_filler {
  93.       type: "msra"
  94.     }
  95.     bias_filler {
  96.       type: "constant"
  97.       value: 0
  98.     }
  99.   }
  100. }
  101. layer {
  102.   name: "relu3_a"
  103.   type: "ELU"
  104.   bottom: "conv3_a"
  105.   top: "conv3_a"
  106. }
  107. layer {
  108.   name: "pool3_a"
  109.   type: "Pooling"
  110.   bottom: "conv3_a"
  111.   top: "pool3_a"
  112.   pooling_param {
  113.     pool: MAX
  114.     kernel_size: 3
  115.     stride: 2
  116.   }
  117. }
  118. layer {
  119.   name: "conv1_b"
  120.   type: "Convolution"
  121.   bottom: "pool3_a"
  122.   top: "conv1_b"
  123.   param {
  124.     lr_mult: 1
  125.     decay_mult: 1
  126.   }
  127.   param {
  128.     lr_mult: 2
  129.     decay_mult: 0
  130.   }
  131.   convolution_param {
  132.     num_output: 32
  133.     pad: 2
  134.     kernel_size: 5
  135.     weight_filler {
  136.       type: "msra"
  137.     }
  138.     bias_filler {
  139.       type: "constant"
  140.       value: 0
  141.     }
  142.   }
  143. }
  144. layer {
  145.   name: "relu1_b"
  146.   type: "ELU"
  147.   bottom: "conv1_b"
  148.   top: "conv1_b"
  149. }
  150. layer {
  151.   name: "conv2_b"
  152.   type: "Convolution"
  153.   bottom: "conv1_b"
  154.   top: "conv2_b"
  155.   param {
  156.     lr_mult: 1
  157.     decay_mult: 1
  158.   }
  159.   param {
  160.     lr_mult: 2
  161.     decay_mult: 0
  162.   }
  163.   convolution_param {
  164.     num_output: 32
  165.     pad: 2
  166.     kernel_size: 5
  167.     weight_filler {
  168.       type: "msra"
  169.     }
  170.     bias_filler {
  171.       type: "constant"
  172.       value: 0
  173.     }
  174.   }
  175. }
  176. layer {
  177.   name: "relu2_b"
  178.   type: "ELU"
  179.   bottom: "conv2_b"
  180.   top: "conv2_b"
  181. }
  182. layer {
  183.   name: "conv3_b"
  184.   type: "Convolution"
  185.   bottom: "conv2_b"
  186.   top: "conv3_b"
  187.   param {
  188.     lr_mult: 1
  189.     decay_mult: 1
  190.   }
  191.   param {
  192.     lr_mult: 2
  193.     decay_mult: 0
  194.   }
  195.   convolution_param {
  196.     num_output: 32
  197.     pad: 1
  198.     kernel_size: 3
  199.     weight_filler {
  200.       type: "msra"
  201.     }
  202.     bias_filler {
  203.       type: "constant"
  204.       value: 0
  205.     }
  206.   }
  207. }
  208. layer {
  209.   name: "relu3_b"
  210.   type: "ELU"
  211.   bottom: "conv3_b"
  212.   top: "conv3_b"
  213. }
  214. layer {
  215.   name: "pool3_b"
  216.   type: "Pooling"
  217.   bottom: "conv3_b"
  218.   top: "pool3_b"
  219.   pooling_param {
  220.     pool: MAX
  221.     kernel_size: 3
  222.     stride: 2
  223.   }
  224. }
  225. layer {
  226.   name: "conv1_c"
  227.   type: "Convolution"
  228.   bottom: "pool3_b"
  229.   top: "conv1_c"
  230.   param {
  231.     lr_mult: 1
  232.     decay_mult: 1
  233.   }
  234.   param {
  235.     lr_mult: 2
  236.     decay_mult: 0
  237.   }
  238.   convolution_param {
  239.     num_output: 64
  240.     pad: 2
  241.     kernel_size: 5
  242.     weight_filler {
  243.       type: "msra"
  244.     }
  245.     bias_filler {
  246.       type: "constant"
  247.       value: 0
  248.     }
  249.   }
  250. }
  251. layer {
  252.   name: "relu1_c"
  253.   type: "ELU"
  254.   bottom: "conv1_c"
  255.   top: "conv1_c"
  256. }
  257. layer {
  258.   name: "conv2_c"
  259.   type: "Convolution"
  260.   bottom: "conv1_c"
  261.   top: "conv2_c"
  262.   param {
  263.     lr_mult: 1
  264.     decay_mult: 1
  265.   }
  266.   param {
  267.     lr_mult: 2
  268.     decay_mult: 0
  269.   }
  270.   convolution_param {
  271.     num_output: 64
  272.     pad: 2
  273.     kernel_size: 5
  274.     weight_filler {
  275.       type: "msra"
  276.     }
  277.     bias_filler {
  278.       type: "constant"
  279.       value: 0
  280.     }
  281.   }
  282. }
  283. layer {
  284.   name: "relu2_c"
  285.   type: "ELU"
  286.   bottom: "conv2_c"
  287.   top: "conv2_c"
  288. }
  289. layer {
  290.   name: "conv3_c"
  291.   type: "Convolution"
  292.   bottom: "conv2_c"
  293.   top: "conv3_c"
  294.   param {
  295.     lr_mult: 1
  296.     decay_mult: 1
  297.   }
  298.   param {
  299.     lr_mult: 2
  300.     decay_mult: 0
  301.   }
  302.   convolution_param {
  303.     num_output: 64
  304.     pad: 1
  305.     kernel_size: 3
  306.     weight_filler {
  307.       type: "msra"
  308.     }
  309.     bias_filler {
  310.       type: "constant"
  311.       value: 0
  312.     }
  313.   }
  314. }
  315. layer {
  316.   name: "relu3_c"
  317.   type: "ELU"
  318.   bottom: "conv3_c"
  319.   top: "conv3_c"
  320. }
  321. layer {
  322.   name: "pool3_c"
  323.   type: "Pooling"
  324.   bottom: "conv3_c"
  325.   top: "pool3_c"
  326.   pooling_param {
  327.     pool: MAX
  328.     kernel_size: 3
  329.     stride: 2
  330.   }
  331. }
  332. layer {
  333.   name: "conv1_d"
  334.   type: "Convolution"
  335.   bottom: "pool3_c"
  336.   top: "conv1_d"
  337.   param {
  338.     lr_mult: 1
  339.     decay_mult: 1
  340.   }
  341.   param {
  342.     lr_mult: 2
  343.     decay_mult: 0
  344.   }
  345.   convolution_param {
  346.     num_output: 96
  347.     pad: 2
  348.     kernel_size: 5
  349.     weight_filler {
  350.       type: "msra"
  351.     }
  352.     bias_filler {
  353.       type: "constant"
  354.       value: 0
  355.     }
  356.   }
  357. }
  358. layer {
  359.   name: "relu1_d"
  360.   type: "ELU"
  361.   bottom: "conv1_d"
  362.   top: "conv1_d"
  363. }
  364. layer {
  365.   name: "conv2_d"
  366.   type: "Convolution"
  367.   bottom: "conv1_d"
  368.   top: "conv2_d"
  369.   param {
  370.     lr_mult: 1
  371.     decay_mult: 1
  372.   }
  373.   param {
  374.     lr_mult: 2
  375.     decay_mult: 0
  376.   }
  377.   convolution_param {
  378.     num_output: 96
  379.     pad: 2
  380.     kernel_size: 5
  381.     weight_filler {
  382.       type: "msra"
  383.     }
  384.     bias_filler {
  385.       type: "constant"
  386.       value: 0
  387.     }
  388.   }
  389. }
  390. layer {
  391.   name: "relu2_d"
  392.   type: "ELU"
  393.   bottom: "conv2_d"
  394.   top: "conv2_d"
  395. }
  396. layer {
  397.   name: "conv3_d"
  398.   type: "Convolution"
  399.   bottom: "conv2_d"
  400.   top: "conv3_d"
  401.   param {
  402.     lr_mult: 1
  403.     decay_mult: 1
  404.   }
  405.   param {
  406.     lr_mult: 2
  407.     decay_mult: 0
  408.   }
  409.   convolution_param {
  410.     num_output: 96
  411.     pad: 1
  412.     kernel_size: 3
  413.     weight_filler {
  414.       type: "msra"
  415.     }
  416.     bias_filler {
  417.       type: "constant"
  418.       value: 0
  419.     }
  420.   }
  421. }
  422. layer {
  423.   name: "relu3_d"
  424.   type: "ELU"
  425.   bottom: "conv3_d"
  426.   top: "conv3_d"
  427. }
  428. layer {
  429.   name: "pool3_d"
  430.   type: "Pooling"
  431.   bottom: "conv3_d"
  432.   top: "pool3_d"
  433.   pooling_param {
  434.     pool: MAX
  435.     kernel_size: 3
  436.     stride: 2
  437.   }
  438. }
  439. layer {
  440.   name: "conv1_e"
  441.   type: "Convolution"
  442.   bottom: "pool3_d"
  443.   top: "conv1_e"
  444.   param {
  445.     lr_mult: 1
  446.     decay_mult: 1
  447.   }
  448.   param {
  449.     lr_mult: 2
  450.     decay_mult: 0
  451.   }
  452.   convolution_param {
  453.     num_output: 64
  454.     pad: 2
  455.     kernel_size: 5
  456.     weight_filler {
  457.       type: "msra"
  458.     }
  459.     bias_filler {
  460.       type: "constant"
  461.       value: 0
  462.     }
  463.   }
  464. }
  465. layer {
  466.   name: "relu1_e"
  467.   type: "ELU"
  468.   bottom: "conv1_e"
  469.   top: "conv1_e"
  470. }
  471. layer {
  472.   name: "conv2_e"
  473.   type: "Convolution"
  474.   bottom: "conv1_e"
  475.   top: "conv2_e"
  476.   param {
  477.     lr_mult: 1
  478.     decay_mult: 1
  479.   }
  480.   param {
  481.     lr_mult: 2
  482.     decay_mult: 0
  483.   }
  484.   convolution_param {
  485.     num_output: 64
  486.     pad: 2
  487.     kernel_size: 5
  488.     weight_filler {
  489.       type: "msra"
  490.     }
  491.     bias_filler {
  492.       type: "constant"
  493.       value: 0
  494.     }
  495.   }
  496. }
  497. layer {
  498.   name: "relu2_e"
  499.   type: "ELU"
  500.   bottom: "conv2_e"
  501.   top: "conv2_e"
  502. }
  503. layer {
  504.   name: "conv3_e"
  505.   type: "Convolution"
  506.   bottom: "conv2_e"
  507.   top: "conv3_e"
  508.   param {
  509.     lr_mult: 1
  510.     decay_mult: 1
  511.   }
  512.   param {
  513.     lr_mult: 2
  514.     decay_mult: 0
  515.   }
  516.   convolution_param {
  517.     num_output: 64
  518.     pad: 1
  519.     kernel_size: 3
  520.     weight_filler {
  521.       type: "msra"
  522.     }
  523.     bias_filler {
  524.       type: "constant"
  525.       value: 0
  526.     }
  527.   }
  528. }
  529. layer {
  530.   name: "relu3_e"
  531.   type: "ELU"
  532.   bottom: "conv3_e"
  533.   top: "conv3_e"
  534. }
  535. layer {
  536.   name: "deconv_fc1"
  537.   type: "InnerProduct"
  538.   bottom: "conv3_e"
  539.   top: "deconv_fc1"
  540.   param {
  541.     lr_mult: 1
  542.     decay_mult: 0
  543.   }
  544.   param {
  545.     lr_mult: 2
  546.     decay_mult: 0
  547.   }
  548.   inner_product_param {
  549.     num_output: 512
  550.     weight_filler {
  551.       type: "msra"
  552.     }
  553.   }
  554. }
  555. layer {
  556.   name: "relu_deconv_fc1"
  557.   type: "ELU"
  558.   bottom: "deconv_fc1"
  559.   top: "deconv_fc1"
  560. }
  561. layer {
  562.   name: "deconv_fc2"
  563.   type: "InnerProduct"
  564.   bottom: "deconv_fc1"
  565.   top: "deconv_fc2"
  566.   param {
  567.     lr_mult: 1
  568.     decay_mult: 0
  569.   }
  570.   param {
  571.     lr_mult: 2
  572.     decay_mult: 0
  573.   }
  574.   inner_product_param {
  575.     num_output: 1024
  576.     weight_filler {
  577.       type: "msra"
  578.     }
  579.   }
  580. }
  581. layer {
  582.   name: "relu_deconv_fc2"
  583.   type: "ELU"
  584.   bottom: "deconv_fc2"
  585.   top: "deconv_fc2"
  586. }
  587. layer {
  588.   name: "deconv_fc3_"
  589.   type: "InnerProduct"
  590.   bottom: "deconv_fc2"
  591.   top: "deconv_fc3_"
  592.   param {
  593.     lr_mult: 1
  594.     decay_mult: 0
  595.   }
  596.   param {
  597.     lr_mult: 2
  598.     decay_mult: 0
  599.   }
  600.   inner_product_param {
  601.     num_output: 12544
  602.     weight_filler {
  603.       type: "msra"
  604.     }
  605.   }
  606. }
  607. layer {
  608.   name: "relu_deconv_fc3_"
  609.   type: "ELU"
  610.   bottom: "deconv_fc3_"
  611.   top: "deconv_fc3_"
  612. }
  613. layer {
  614.   name: "reshape_fc"
  615.   type: "Reshape"
  616.   bottom: "deconv_fc3_"
  617.   top: "reshape_fc"
  618.   reshape_param {
  619.     shape {
  620.       dim: 1
  621.       dim: -1
  622.       dim: 14
  623.       dim: 14
  624.     }
  625.   }
  626. }
  627. layer {
  628.   name: "conv1_e_t"
  629.   type: "Deconvolution"
  630.   bottom: "reshape_fc"
  631.   top: "conv1_e_t"
  632.   param {
  633.     lr_mult: 1
  634.     decay_mult: 1
  635.   }
  636.   param {
  637.     lr_mult: 0
  638.     decay_mult: 0
  639.   }
  640.   convolution_param {
  641.     num_output: 96
  642.     pad: 1
  643.     kernel_size: 3
  644.     weight_filler {
  645.       type: "msra"
  646.     }
  647.     bias_filler {
  648.       type: "constant"
  649.       value: 0
  650.     }
  651.   }
  652. }
  653. layer {
  654.   name: "relu1_e_t"
  655.   type: "ELU"
  656.   bottom: "conv1_e_t"
  657.   top: "conv1_e_t"
  658. }
  659. layer {
  660.   name: "pool3_d_t"
  661.   type: "Resize"
  662.   bottom: "conv1_e_t"
  663.   top: "pool3_d_t"
  664.   resize_param {
  665.     interpolation: LINEAR
  666.     height: 28
  667.     width: 28
  668.   }
  669. }
  670. layer {
  671.   name: "conv1_d_t"
  672.   type: "Deconvolution"
  673.   bottom: "pool3_d_t"
  674.   top: "conv1_d_t"
  675.   param {
  676.     lr_mult: 1
  677.     decay_mult: 1
  678.   }
  679.   param {
  680.     lr_mult: 0
  681.     decay_mult: 0
  682.   }
  683.   convolution_param {
  684.     num_output: 64
  685.     pad: 1
  686.     kernel_size: 3
  687.     weight_filler {
  688.       type: "msra"
  689.     }
  690.     bias_filler {
  691.       type: "constant"
  692.       value: 0
  693.     }
  694.   }
  695. }
  696. layer {
  697.   name: "relu1_d_t"
  698.   type: "ELU"
  699.   bottom: "conv1_d_t"
  700.   top: "conv1_d_t"
  701. }
  702. layer {
  703.   name: "pool3_c_t"
  704.   type: "Resize"
  705.   bottom: "conv1_d_t"
  706.   top: "pool3_c_t"
  707.   resize_param {
  708.     interpolation: LINEAR
  709.     height: 56
  710.     width: 56
  711.   }
  712. }
  713. layer {
  714.   name: "conv1_c_t"
  715.   type: "Deconvolution"
  716.   bottom: "pool3_c_t"
  717.   top: "conv1_c_t"
  718.   param {
  719.     lr_mult: 1
  720.     decay_mult: 1
  721.   }
  722.   param {
  723.     lr_mult: 0
  724.     decay_mult: 0
  725.   }
  726.   convolution_param {
  727.     num_output: 32
  728.     pad: 1
  729.     kernel_size: 3
  730.     weight_filler {
  731.       type: "msra"
  732.     }
  733.     bias_filler {
  734.       type: "constant"
  735.       value: 0
  736.     }
  737.   }
  738. }
  739. layer {
  740.   name: "relu1_c_t"
  741.   type: "ELU"
  742.   bottom: "conv1_c_t"
  743.   top: "conv1_c_t"
  744. }
  745. layer {
  746.   name: "pool3_b_t"
  747.   type: "Resize"
  748.   bottom: "conv1_c_t"
  749.   top: "pool3_b_t"
  750.   resize_param {
  751.     interpolation: LINEAR
  752.     height: 113
  753.     width: 113
  754.   }
  755. }
  756. layer {
  757.   name: "conv1_b_t"
  758.   type: "Deconvolution"
  759.   bottom: "pool3_b_t"
  760.   top: "conv1_b_t"
  761.   param {
  762.     lr_mult: 1
  763.     decay_mult: 1
  764.   }
  765.   param {
  766.     lr_mult: 0
  767.     decay_mult: 0
  768.   }
  769.   convolution_param {
  770.     num_output: 16
  771.     pad: 1
  772.     kernel_size: 3
  773.     weight_filler {
  774.       type: "msra"
  775.     }
  776.     bias_filler {
  777.       type: "constant"
  778.       value: 0
  779.     }
  780.   }
  781. }
  782. layer {
  783.   name: "relu1_b_t"
  784.   type: "ELU"
  785.   bottom: "conv1_b_t"
  786.   top: "conv1_b_t"
  787. }
  788. layer {
  789.   name: "pool3_a_t"
  790.   type: "Resize"
  791.   bottom: "conv1_b_t"
  792.   top: "pool3_a_t"
  793.   resize_param {
  794.     interpolation: LINEAR
  795.     height: 227
  796.     width: 227
  797.   }
  798. }
  799. layer {
  800.   name: "conv1_a_t"
  801.   type: "Deconvolution"
  802.   bottom: "pool3_a_t"
  803.   top: "conv1_a_t"
  804.   param {
  805.     lr_mult: 1
  806.     decay_mult: 1
  807.   }
  808.   param {
  809.     lr_mult: 0
  810.     decay_mult: 0
  811.   }
  812.   convolution_param {
  813.     num_output: 3
  814.     pad: 1
  815.     kernel_size: 3
  816.     weight_filler {
  817.       type: "msra"
  818.     }
  819.     bias_filler {
  820.       type: "constant"
  821.       value: 0
  822.     }
  823.   }
  824. }
  825. layer {
  826.   name: "relu1_a_t"
  827.   type: "ELU"
  828.   bottom: "conv1_a_t"
  829.   top: "conv1_a_t"
  830. }
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