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  1. input: "data"

  2. input_dim: 1

  3. 

  4. # change to input_dim: 1 if not using color inputs

  5. input_dim: 3

  6. 

  7. input_dim: 256

  8. input_dim: 256

  9. 

  10. input: "gt"

  11. input_dim: 1

  12. input_dim: 1

  13. input_dim: 256

  14. input_dim: 256

  15. 

  16. layer {

  17.   name: "Convolution1"

  18.   type: "Convolution"

  19.   bottom: "data"

  20.   top: "Convolution1"

  21.   param {

  22.     lr_mult: 1

  23.   }

  24.   param {

  25.     lr_mult: 2

  26.   }

  27.   convolution_param {

  28.     num_output: 32

  29.     pad: 3

  30.     kernel_size: 7

  31.     stride: 1

  32.     weight_filler {

  33.       type: "msra"

  34.     }

  35.   }

  36. }

  37. layer {

  38.   name: "conv_0_0"

  39.   type: "ReLU"

  40.   bottom: "Convolution1"

  41.   top: "Convolution1"

  42. }

  43. layer {

  44.   name: "Convolution2"

  45.   type: "Convolution"

  46.   bottom: "Convolution1"

  47.   top: "Convolution2"

  48.   param {

  49.     lr_mult: 1

  50.   }

  51.   param {

  52.     lr_mult: 2

  53.   }

  54.   convolution_param {

  55.     num_output: 32

  56.     pad: 3

  57.     kernel_size: 7

  58.     stride: 1

  59.     weight_filler {

  60.       type: "msra"

  61.     }

  62.   }

  63. }

  64. layer {

  65.   name: "conv_0_1"

  66.   type: "ReLU"

  67.   bottom: "Convolution2"

  68.   top: "Convolution2"

  69. }

  70. layer {

  71.   name: "Convolution3"

  72.   type: "Convolution"

  73.   bottom: "Convolution2"

  74.   top: "Convolution3"

  75.   param {

  76.     lr_mult: 1

  77.   }

  78.   param {

  79.     lr_mult: 2

  80.   }

  81.   convolution_param {

  82.     num_output: 32

  83.     pad: 3

  84.     kernel_size: 7

  85.     stride: 1

  86.     weight_filler {

  87.       type: "msra"

  88.     }

  89.   }

  90. }

  91. layer {

  92.   name: "conv_0_2"

  93.   type: "ReLU"

  94.   bottom: "Convolution3"

  95.   top: "Convolution3"

  96. }

  97. layer {

  98.   name: "Convolution4"

  99.   type: "Convolution"

  100.   bottom: "Convolution3"

  101.   top: "Convolution4"

  102.   param {

  103.     lr_mult: 1

  104.   }

  105.   param {

  106.     lr_mult: 2

  107.   }

  108.   convolution_param {

  109.     num_output: 32

  110.     pad: 3

  111.     kernel_size: 7

  112.     stride: 1

  113.     weight_filler {

  114.       type: "msra"

  115.     }

  116.   }

  117. }

  118. layer {

  119.   name: "conv_0_3"

  120.   type: "ReLU"

  121.   bottom: "Convolution4"

  122.   top: "Convolution4"

  123. }

  124. layer {

  125.   name: "Convolution5"

  126.   type: "Convolution"

  127.   bottom: "Convolution4"

  128.   top: "Convolution5"

  129.   param {

  130.     lr_mult: 1

  131.   }

  132.   param {

  133.     lr_mult: 2

  134.   }

  135.   convolution_param {

  136.     num_output: 32

  137.     pad: 3

  138.     kernel_size: 7

  139.     stride: 1

  140.     weight_filler {

  141.       type: "msra"

  142.     }

  143.   }

  144. }

  145. layer {

  146.   name: "conv_0_4"

  147.   type: "ReLU"

  148.   bottom: "Convolution5"

  149.   top: "Convolution5"

  150. }

  151. layer {

  152.   name: "Convolution6"

  153.   type: "Convolution"

  154.   bottom: "Convolution5"

  155.   top: "Convolution6"

  156.   param {

  157.     lr_mult: 1

  158.   }

  159.   param {

  160.     lr_mult: 2

  161.   }

  162.   convolution_param {

  163.     num_output: 32

  164.     pad: 3

  165.     kernel_size: 7

  166.     stride: 1

  167.     weight_filler {

  168.       type: "msra"

  169.     }

  170.   }

  171. }

  172. layer {

  173.   name: "conv_0_5"

  174.   type: "ReLU"

  175.   bottom: "Convolution6"

  176.   top: "Convolution6"

  177. }

  178. layer {

  179.   name: "Convolution7"

  180.   type: "Convolution"

  181.   bottom: "Convolution6"

  182.   top: "Convolution7"

  183.   param {

  184.     lr_mult: 1

  185.   }

  186.   param {

  187.     lr_mult: 2

  188.   }

  189.   convolution_param {

  190.     num_output: 32

  191.     pad: 3

  192.     kernel_size: 7

  193.     stride: 1

  194.     weight_filler {

  195.       type: "msra"

  196.     }

  197.   }

  198. }

  199. layer {

  200.   name: "conv_0_6"

  201.   type: "ReLU"

  202.   bottom: "Convolution7"

  203.   top: "Convolution7"

  204. }

  205. layer {

  206.   name: "Pooling1"

  207.   type: "Pooling"

  208.   bottom: "Convolution1"

  209.   top: "Pooling1"

  210.   pooling_param {

  211.     pool: AVE

  212.     kernel_size: 2

  213.     stride: 2

  214.   }

  215. }

  216. layer {

  217.   name: "Convolution8"

  218.   type: "Convolution"

  219.   bottom: "Pooling1"

  220.   top: "Convolution8"

  221.   param {

  222.     lr_mult: 1

  223.   }

  224.   param {

  225.     lr_mult: 2

  226.   }

  227.   convolution_param {

  228.     num_output: 32

  229.     pad: 3

  230.     kernel_size: 7

  231.     stride: 1

  232.     weight_filler {

  233.       type: "msra"

  234.     }

  235.   }

  236. }

  237. layer {

  238.   name: "conv_1_0"

  239.   type: "ReLU"

  240.   bottom: "Convolution8"

  241.   top: "Convolution8"

  242. }

  243. layer {

  244.   name: "Convolution9"

  245.   type: "Convolution"

  246.   bottom: "Convolution8"

  247.   top: "Convolution9"

  248.   param {

  249.     lr_mult: 1

  250.   }

  251.   param {

  252.     lr_mult: 2

  253.   }

  254.   convolution_param {

  255.     num_output: 32

  256.     pad: 3

  257.     kernel_size: 7

  258.     stride: 1

  259.     weight_filler {

  260.       type: "msra"

  261.     }

  262.   }

  263. }

  264. layer {

  265.   name: "conv_1_1"

  266.   type: "ReLU"

  267.   bottom: "Convolution9"

  268.   top: "Convolution9"

  269. }

  270. layer {

  271.   name: "Convolution10"

  272.   type: "Convolution"

  273.   bottom: "Convolution9"

  274.   top: "Convolution10"

  275.   param {

  276.     lr_mult: 1

  277.   }

  278.   param {

  279.     lr_mult: 2

  280.   }

  281.   convolution_param {

  282.     num_output: 32

  283.     pad: 3

  284.     kernel_size: 7

  285.     stride: 1

  286.     weight_filler {

  287.       type: "msra"

  288.     }

  289.   }

  290. }

  291. layer {

  292.   name: "conv_1_2"

  293.   type: "ReLU"

  294.   bottom: "Convolution10"

  295.   top: "Convolution10"

  296. }

  297. layer {

  298.   name: "Convolution11"

  299.   type: "Convolution"

  300.   bottom: "Convolution10"

  301.   top: "Convolution11"

  302.   param {

  303.     lr_mult: 1

  304.   }

  305.   param {

  306.     lr_mult: 2

  307.   }

  308.   convolution_param {

  309.     num_output: 32

  310.     pad: 3

  311.     kernel_size: 7

  312.     stride: 1

  313.     weight_filler {

  314.       type: "msra"

  315.     }

  316.   }

  317. }

  318. layer {

  319.   name: "conv_1_3"

  320.   type: "ReLU"

  321.   bottom: "Convolution11"

  322.   top: "Convolution11"

  323. }

  324. layer {

  325.   name: "Convolution12"

  326.   type: "Convolution"

  327.   bottom: "Convolution11"

  328.   top: "Convolution12"

  329.   param {

  330.     lr_mult: 1

  331.   }

  332.   param {

  333.     lr_mult: 2

  334.   }

  335.   convolution_param {

  336.     num_output: 32

  337.     pad: 3

  338.     kernel_size: 7

  339.     stride: 1

  340.     weight_filler {

  341.       type: "msra"

  342.     }

  343.   }

  344. }

  345. layer {

  346.   name: "conv_1_4"

  347.   type: "ReLU"

  348.   bottom: "Convolution12"

  349.   top: "Convolution12"

  350. }

  351. layer {

  352.   name: "Convolution13"

  353.   type: "Convolution"

  354.   bottom: "Convolution12"

  355.   top: "Convolution13"

  356.   param {

  357.     lr_mult: 1

  358.   }

  359.   param {

  360.     lr_mult: 2

  361.   }

  362.   convolution_param {

  363.     num_output: 32

  364.     pad: 3

  365.     kernel_size: 7

  366.     stride: 1

  367.     weight_filler {

  368.       type: "msra"

  369.     }

  370.   }

  371. }

  372. layer {

  373.   name: "conv_1_5"

  374.   type: "ReLU"

  375.   bottom: "Convolution13"

  376.   top: "Convolution13"

  377. }

  378. layer {

  379.   name: "Pooling2"

  380.   type: "Pooling"

  381.   bottom: "Convolution8"

  382.   top: "Pooling2"

  383.   pooling_param {

  384.     pool: AVE

  385.     kernel_size: 2

  386.     stride: 2

  387.   }

  388. }

  389. layer {

  390.   name: "Convolution14"

  391.   type: "Convolution"

  392.   bottom: "Pooling2"

  393.   top: "Convolution14"

  394.   param {

  395.     lr_mult: 1

  396.   }

  397.   param {

  398.     lr_mult: 2

  399.   }

  400.   convolution_param {

  401.     num_output: 32

  402.     pad: 2

  403.     kernel_size: 5

  404.     stride: 1

  405.     weight_filler {

  406.       type: "msra"

  407.     }

  408.   }

  409. }

  410. layer {

  411.   name: "conv_2_0"

  412.   type: "ReLU"

  413.   bottom: "Convolution14"

  414.   top: "Convolution14"

  415. }

  416. layer {

  417.   name: "Convolution15"

  418.   type: "Convolution"

  419.   bottom: "Convolution14"

  420.   top: "Convolution15"

  421.   param {

  422.     lr_mult: 1

  423.   }

  424.   param {

  425.     lr_mult: 2

  426.   }

  427.   convolution_param {

  428.     num_output: 32

  429.     pad: 2

  430.     kernel_size: 5

  431.     stride: 1

  432.     weight_filler {

  433.       type: "msra"

  434.     }

  435.   }

  436. }

  437. layer {

  438.   name: "conv_2_1"

  439.   type: "ReLU"

  440.   bottom: "Convolution15"

  441.   top: "Convolution15"

  442. }

  443. layer {

  444.   name: "Convolution16"

  445.   type: "Convolution"

  446.   bottom: "Convolution15"

  447.   top: "Convolution16"

  448.   param {

  449.     lr_mult: 1

  450.   }

  451.   param {

  452.     lr_mult: 2

  453.   }

  454.   convolution_param {

  455.     num_output: 32

  456.     pad: 2

  457.     kernel_size: 5

  458.     stride: 1

  459.     weight_filler {

  460.       type: "msra"

  461.     }

  462.   }

  463. }

  464. layer {

  465.   name: "conv_2_2"

  466.   type: "ReLU"

  467.   bottom: "Convolution16"

  468.   top: "Convolution16"

  469. }

  470. layer {

  471.   name: "Convolution17"

  472.   type: "Convolution"

  473.   bottom: "Convolution16"

  474.   top: "Convolution17"

  475.   param {

  476.     lr_mult: 1

  477.   }

  478.   param {

  479.     lr_mult: 2

  480.   }

  481.   convolution_param {

  482.     num_output: 32

  483.     pad: 2

  484.     kernel_size: 5

  485.     stride: 1

  486.     weight_filler {

  487.       type: "msra"

  488.     }

  489.   }

  490. }

  491. layer {

  492.   name: "conv_2_3"

  493.   type: "ReLU"

  494.   bottom: "Convolution17"

  495.   top: "Convolution17"

  496. }

  497. layer {

  498.   name: "Convolution18"

  499.   type: "Convolution"

  500.   bottom: "Convolution17"

  501.   top: "Convolution18"

  502.   param {

  503.     lr_mult: 1

  504.   }

  505.   param {

  506.     lr_mult: 2

  507.   }

  508.   convolution_param {

  509.     num_output: 32

  510.     pad: 2

  511.     kernel_size: 5

  512.     stride: 1

  513.     weight_filler {

  514.       type: "msra"

  515.     }

  516.   }

  517. }

  518. layer {

  519.   name: "conv_2_4"

  520.   type: "ReLU"

  521.   bottom: "Convolution18"

  522.   top: "Convolution18"

  523. }

  524. layer {

  525.   name: "Pooling3"

  526.   type: "Pooling"

  527.   bottom: "Convolution14"

  528.   top: "Pooling3"

  529.   pooling_param {

  530.     pool: AVE

  531.     kernel_size: 2

  532.     stride: 2

  533.   }

  534. }

  535. layer {

  536.   name: "Convolution19"

  537.   type: "Convolution"

  538.   bottom: "Pooling3"

  539.   top: "Convolution19"

  540.   param {

  541.     lr_mult: 1

  542.   }

  543.   param {

  544.     lr_mult: 2

  545.   }

  546.   convolution_param {

  547.     num_output: 32

  548.     pad: 2

  549.     kernel_size: 5

  550.     stride: 1

  551.     weight_filler {

  552.       type: "msra"

  553.     }

  554.   }

  555. }

  556. layer {

  557.   name: "conv_3_0"

  558.   type: "ReLU"

  559.   bottom: "Convolution19"

  560.   top: "Convolution19"

  561. }

  562. layer {

  563.   name: "Convolution20"

  564.   type: "Convolution"

  565.   bottom: "Convolution19"

  566.   top: "Convolution20"

  567.   param {

  568.     lr_mult: 1

  569.   }

  570.   param {

  571.     lr_mult: 2

  572.   }

  573.   convolution_param {

  574.     num_output: 32

  575.     pad: 2

  576.     kernel_size: 5

  577.     stride: 1

  578.     weight_filler {

  579.       type: "msra"

  580.     }

  581.   }

  582. }

  583. layer {

  584.   name: "conv_3_1"

  585.   type: "ReLU"

  586.   bottom: "Convolution20"

  587.   top: "Convolution20"

  588. }

  589. layer {

  590.   name: "Convolution21"

  591.   type: "Convolution"

  592.   bottom: "Convolution20"

  593.   top: "Convolution21"

  594.   param {

  595.     lr_mult: 1

  596.   }

  597.   param {

  598.     lr_mult: 2

  599.   }

  600.   convolution_param {

  601.     num_output: 32

  602.     pad: 2

  603.     kernel_size: 5

  604.     stride: 1

  605.     weight_filler {

  606.       type: "msra"

  607.     }

  608.   }

  609. }

  610. layer {

  611.   name: "conv_3_2"

  612.   type: "ReLU"

  613.   bottom: "Convolution21"

  614.   top: "Convolution21"

  615. }

  616. layer {

  617.   name: "Convolution22"

  618.   type: "Convolution"

  619.   bottom: "Convolution21"

  620.   top: "Convolution22"

  621.   param {

  622.     lr_mult: 1

  623.   }

  624.   param {

  625.     lr_mult: 2

  626.   }

  627.   convolution_param {

  628.     num_output: 32

  629.     pad: 2

  630.     kernel_size: 5

  631.     stride: 1

  632.     weight_filler {

  633.       type: "msra"

  634.     }

  635.   }

  636. }

  637. layer {

  638.   name: "conv_3_3"

  639.   type: "ReLU"

  640.   bottom: "Convolution22"

  641.   top: "Convolution22"

  642. }

  643. layer {

  644.   name: "upsample_1"

  645.   type: "Deconvolution"

  646.   bottom: "Convolution13"

  647.   top: "upsample_1"

  648.   param {

  649.     lr_mult: 0

  650. 	decay_mult: 0

  651.   }

  652.   convolution_param {

  653.     kernel_size: 4

  654. 	num_output: 32

  655. 	group: 32

  656. 	stride: 2

  657. 	pad: 1

  658. 	weight_filler: { type: 'bilinear' }

  659. 	bias_term: false

  660.   }

  661. }

  662. layer {

  663.   name: "upsample_2"

  664.   type: "Deconvolution"

  665.   bottom: "Convolution18"

  666.   top: "upsample_2"

  667.   param {

  668.     lr_mult: 0

  669. 	decay_mult: 0

  670.   }

  671.   convolution_param {

  672.     kernel_size: 8

  673. 	num_output: 32

  674. 	group: 32

  675. 	stride: 4

  676. 	pad: 2

  677. 	weight_filler: { type: 'bilinear' }

  678. 	bias_term: false

  679.   }

  680. }

  681. layer {

  682.   name: "upsample_3"

  683.   type: "Deconvolution"

  684.   bottom: "Convolution22"

  685.   top: "upsample_3"

  686.   param {

  687.     lr_mult: 0

  688. 	decay_mult: 0

  689.   }

  690.   convolution_param {

  691.     kernel_size: 16

  692. 	num_output: 32

  693. 	group: 32

  694. 	stride: 8

  695. 	pad: 4

  696. 	weight_filler: { type: 'bilinear' }

  697. 	bias_term: false

  698.   }

  699. }

  700. layer {

  701.   name: "merged"

  702.   type: "Concat"

  703.   bottom: "Convolution7"

  704.   bottom: "upsample_1"

  705.   bottom: "upsample_2"

  706.   bottom: "upsample_3"

  707.   top: "merged"

  708. }

  709. layer {

  710.   name: "Convolution26"

  711.   type: "Convolution"

  712.   bottom: "merged"

  713.   top: "Convolution26"

  714.   param {

  715.     lr_mult: 1

  716.   }

  717.   param {

  718.     lr_mult: 2

  719.   }

  720.   convolution_param {

  721.     num_output: 32

  722.     pad: 3

  723.     kernel_size: 7

  724.     stride: 1

  725.     weight_filler {

  726.       type: "msra"

  727.     }

  728.   }

  729. }

  730. layer {

  731.   name: "merged_conv"

  732.   type: "ReLU"

  733.   bottom: "Convolution26"

  734.   top: "Convolution26"

  735. }

  736. layer {

  737.   name: "Convolution27"

  738.   type: "Convolution"

  739.   bottom: "Convolution26"

  740.   top: "Convolution27"

  741.   param {

  742.     lr_mult: 1

  743.   }

  744.   param {

  745.     lr_mult: 2

  746.   }

  747.   convolution_param {

  748.     num_output: 32

  749.     pad: 2

  750.     kernel_size: 5

  751.     stride: 1

  752.     weight_filler {

  753.       type: "msra"

  754.     }

  755.   }

  756. }

  757. layer {

  758.   name: "ReLU1"

  759.   type: "ReLU"

  760.   bottom: "Convolution27"

  761.   top: "Convolution27"

  762. }

  763. layer {

  764.   name: "Convolution28"

  765.   type: "Convolution"

  766.   bottom: "Convolution27"

  767.   top: "Convolution28"

  768.   param {

  769.     lr_mult: 1

  770.   }

  771.   param {

  772.     lr_mult: 2

  773.   }

  774.   convolution_param {

  775.     num_output: 32

  776.     pad: 2

  777.     kernel_size: 5

  778.     stride: 1

  779.     weight_filler {

  780.       type: "msra"

  781.     }

  782.   }

  783. }

  784. layer {

  785.   name: "rep_layer_0"

  786.   type: "ReLU"

  787.   bottom: "Convolution28"

  788.   top: "Convolution28"

  789. }

  790. layer {

  791.   name: "Convolution29"

  792.   type: "Convolution"

  793.   bottom: "Convolution28"

  794.   top: "Convolution29"

  795.   param {

  796.     lr_mult: 1

  797.   }

  798.   param {

  799.     lr_mult: 2

  800.   }

  801.   convolution_param {

  802.     num_output: 32

  803.     pad: 2

  804.     kernel_size: 5

  805.     stride: 1

  806.     weight_filler {

  807.       type: "msra"

  808.     }

  809.   }

  810. }

  811. layer {

  812.   name: "ReLU2"

  813.   type: "ReLU"

  814.   bottom: "Convolution29"

  815.   top: "Convolution29"

  816. }

  817. layer {

  818.   name: "Convolution30"

  819.   type: "Convolution"

  820.   bottom: "Convolution29"

  821.   top: "out"

  822.   param {

  823.     lr_mult: 1

  824.   }

  825.   param {

  826.     lr_mult: 2

  827.   }

  828.   convolution_param {

  829.     num_output: 2

  830.     pad: 0

  831.     kernel_size: 1

  832.     stride: 1

  833.     weight_filler {

  834.       type: "msra"

  835.     }

  836.   }

  837. }

  838. layer {

  839.   name: "baselines_7_loss_0"

  840.   type: "SoftmaxWithLoss"

  841.   bottom: "out"

  842.   bottom: "gt"

  843.   top: "loss"

  844.   loss_weight: 1.0

  845. }