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  1. name: "MOBILENET_V2"
  2. #  transform_mobilenet_v2.prototxtparam {
  3. #    scale: 0.017
  4. #    mirror: false
  5. #    crop_size: 224
  6. #    mean_value: [103.94,116.78,123.68]
  7. #  }
  8. input: "data"
  9. input_dim: 1
  10. input_dim: 3
  11. input_dim: 224
  12. input_dim: 224
  13. layer {
  14.   name: "conv1"
  15.   type: "Convolution"
  16.   bottom: "data"
  17.   top: "conv1"
  18.   param {
  19.     lr_mult: 1
  20.     decay_mult: 1
  21.   }
  22.   convolution_param {
  23.     num_output: 32
  24.     bias_term: false
  25.     pad: 1
  26.     kernel_size: 3
  27.     stride: 2
  28.     weight_filler {
  29.       type: "msra"
  30.     }
  31.   }
  32. }
  33. layer {
  34.   name: "conv1/bn"
  35.   type: "BatchNorm"
  36.   bottom: "conv1"
  37.   top: "conv1/bn"
  38.   param {
  39.     lr_mult: 0
  40.     decay_mult: 0
  41.   }
  42.   param {
  43.     lr_mult: 0
  44.     decay_mult: 0
  45.   }
  46.   param {
  47.     lr_mult: 0
  48.     decay_mult: 0
  49.   }
  50.   batch_norm_param {
  51.     use_global_stats: true
  52.     eps: 1e-5
  53.   }
  54. }
  55. layer {
  56.   name: "conv1/scale"
  57.   type: "Scale"
  58.   bottom: "conv1/bn"
  59.   top: "conv1/bn"
  60.   param {
  61.     lr_mult: 1
  62.     decay_mult: 0
  63.   }
  64.   param {
  65.     lr_mult: 1
  66.     decay_mult: 0
  67.   }
  68.   scale_param {
  69.     bias_term: true
  70.   }
  71. }
  72. layer {
  73.   name: "relu1"
  74.   type: "ReLU"
  75.   bottom: "conv1/bn"
  76.   top: "conv1/bn"
  77. }
  78. layer {
  79.   name: "conv2_1/expand"
  80.   type: "Convolution"
  81.   bottom: "conv1/bn"
  82.   top: "conv2_1/expand"
  83.   param {
  84.     lr_mult: 1
  85.     decay_mult: 1
  86.   }
  87.   convolution_param {
  88.     num_output: 32
  89.     bias_term: false
  90.     kernel_size: 1
  91.     weight_filler {
  92.       type: "msra"
  93.     }
  94.   }
  95. }
  96. layer {
  97.   name: "conv2_1/expand/bn"
  98.   type: "BatchNorm"
  99.   bottom: "conv2_1/expand"
  100.   top: "conv2_1/expand/bn"
  101.   param {
  102.     lr_mult: 0
  103.     decay_mult: 0
  104.   }
  105.   param {
  106.     lr_mult: 0
  107.     decay_mult: 0
  108.   }
  109.   param {
  110.     lr_mult: 0
  111.     decay_mult: 0
  112.   }
  113.   batch_norm_param {
  114.     use_global_stats: true
  115.     eps: 1e-5
  116.   }
  117. }
  118. layer {
  119.   name: "conv2_1/expand/scale"
  120.   type: "Scale"
  121.   bottom: "conv2_1/expand/bn"
  122.   top: "conv2_1/expand/bn"
  123.   param {
  124.     lr_mult: 1
  125.     decay_mult: 0
  126.   }
  127.   param {
  128.     lr_mult: 1
  129.     decay_mult: 0
  130.   }
  131.   scale_param {
  132.     bias_term: true
  133.   }
  134. }
  135. layer {
  136.   name: "relu2_1/expand"
  137.   type: "ReLU"
  138.   bottom: "conv2_1/expand/bn"
  139.   top: "conv2_1/expand/bn"
  140. }
  141. layer {
  142.   name: "conv2_1/dwise"
  143.   type: "Convolution"
  144.   bottom: "conv2_1/expand/bn"
  145.   top: "conv2_1/dwise"
  146.   param {
  147.     lr_mult: 1
  148.     decay_mult: 1
  149.   }
  150.   convolution_param {
  151.     num_output: 32
  152.     bias_term: false
  153.     pad: 1
  154.     kernel_size: 3
  155.     group: 32
  156.     weight_filler {
  157.       type: "msra"
  158.     }
  159.     engine: CAFFE
  160.   }
  161. }
  162. layer {
  163.   name: "conv2_1/dwise/bn"
  164.   type: "BatchNorm"
  165.   bottom: "conv2_1/dwise"
  166.   top: "conv2_1/dwise/bn"
  167.   param {
  168.     lr_mult: 0
  169.     decay_mult: 0
  170.   }
  171.   param {
  172.     lr_mult: 0
  173.     decay_mult: 0
  174.   }
  175.   param {
  176.     lr_mult: 0
  177.     decay_mult: 0
  178.   }
  179.   batch_norm_param {
  180.     use_global_stats: true
  181.     eps: 1e-5
  182.   }
  183. }
  184. layer {
  185.   name: "conv2_1/dwise/scale"
  186.   type: "Scale"
  187.   bottom: "conv2_1/dwise/bn"
  188.   top: "conv2_1/dwise/bn"
  189.   param {
  190.     lr_mult: 1
  191.     decay_mult: 0
  192.   }
  193.   param {
  194.     lr_mult: 1
  195.     decay_mult: 0
  196.   }
  197.   scale_param {
  198.     bias_term: true
  199.   }
  200. }
  201. layer {
  202.   name: "relu2_1/dwise"
  203.   type: "ReLU"
  204.   bottom: "conv2_1/dwise/bn"
  205.   top: "conv2_1/dwise/bn"
  206. }
  207. layer {
  208.   name: "conv2_1/linear"
  209.   type: "Convolution"
  210.   bottom: "conv2_1/dwise/bn"
  211.   top: "conv2_1/linear"
  212.   param {
  213.     lr_mult: 1
  214.     decay_mult: 1
  215.   }
  216.   convolution_param {
  217.     num_output: 16
  218.     bias_term: false
  219.     kernel_size: 1
  220.     weight_filler {
  221.       type: "msra"
  222.     }
  223.   }
  224. }
  225. layer {
  226.   name: "conv2_1/linear/bn"
  227.   type: "BatchNorm"
  228.   bottom: "conv2_1/linear"
  229.   top: "conv2_1/linear/bn"
  230.   param {
  231.     lr_mult: 0
  232.     decay_mult: 0
  233.   }
  234.   param {
  235.     lr_mult: 0
  236.     decay_mult: 0
  237.   }
  238.   param {
  239.     lr_mult: 0
  240.     decay_mult: 0
  241.   }
  242.   batch_norm_param {
  243.     use_global_stats: true
  244.     eps: 1e-5
  245.   }
  246. }
  247. layer {
  248.   name: "conv2_1/linear/scale"
  249.   type: "Scale"
  250.   bottom: "conv2_1/linear/bn"
  251.   top: "conv2_1/linear/bn"
  252.   param {
  253.     lr_mult: 1
  254.     decay_mult: 0
  255.   }
  256.   param {
  257.     lr_mult: 1
  258.     decay_mult: 0
  259.   }
  260.   scale_param {
  261.     bias_term: true
  262.   }
  263. }
  264. layer {
  265.   name: "conv2_2/expand"
  266.   type: "Convolution"
  267.   bottom: "conv2_1/linear/bn"
  268.   top: "conv2_2/expand"
  269.   param {
  270.     lr_mult: 1
  271.     decay_mult: 1
  272.   }
  273.   convolution_param {
  274.     num_output: 96
  275.     bias_term: false
  276.     kernel_size: 1
  277.     weight_filler {
  278.       type: "msra"
  279.     }
  280.   }
  281. }
  282. layer {
  283.   name: "conv2_2/expand/bn"
  284.   type: "BatchNorm"
  285.   bottom: "conv2_2/expand"
  286.   top: "conv2_2/expand/bn"
  287.   param {
  288.     lr_mult: 0
  289.     decay_mult: 0
  290.   }
  291.   param {
  292.     lr_mult: 0
  293.     decay_mult: 0
  294.   }
  295.   param {
  296.     lr_mult: 0
  297.     decay_mult: 0
  298.   }
  299.   batch_norm_param {
  300.     use_global_stats: true
  301.     eps: 1e-5
  302.   }
  303. }
  304. layer {
  305.   name: "conv2_2/expand/scale"
  306.   type: "Scale"
  307.   bottom: "conv2_2/expand/bn"
  308.   top: "conv2_2/expand/bn"
  309.   param {
  310.     lr_mult: 1
  311.     decay_mult: 0
  312.   }
  313.   param {
  314.     lr_mult: 1
  315.     decay_mult: 0
  316.   }
  317.   scale_param {
  318.     bias_term: true
  319.   }
  320. }
  321. layer {
  322.   name: "relu2_2/expand"
  323.   type: "ReLU"
  324.   bottom: "conv2_2/expand/bn"
  325.   top: "conv2_2/expand/bn"
  326. }
  327. layer {
  328.   name: "conv2_2/dwise"
  329.   type: "Convolution"
  330.   bottom: "conv2_2/expand/bn"
  331.   top: "conv2_2/dwise"
  332.   param {
  333.     lr_mult: 1
  334.     decay_mult: 1
  335.   }
  336.   convolution_param {
  337.     num_output: 96
  338.     bias_term: false
  339.     pad: 1
  340.     kernel_size: 3
  341.     group: 96
  342.     stride: 2
  343.     weight_filler {
  344.       type: "msra"
  345.     }
  346.     engine: CAFFE
  347.   }
  348. }
  349. layer {
  350.   name: "conv2_2/dwise/bn"
  351.   type: "BatchNorm"
  352.   bottom: "conv2_2/dwise"
  353.   top: "conv2_2/dwise/bn"
  354.   param {
  355.     lr_mult: 0
  356.     decay_mult: 0
  357.   }
  358.   param {
  359.     lr_mult: 0
  360.     decay_mult: 0
  361.   }
  362.   param {
  363.     lr_mult: 0
  364.     decay_mult: 0
  365.   }
  366.   batch_norm_param {
  367.     use_global_stats: true
  368.     eps: 1e-5
  369.   }
  370. }
  371. layer {
  372.   name: "conv2_2/dwise/scale"
  373.   type: "Scale"
  374.   bottom: "conv2_2/dwise/bn"
  375.   top: "conv2_2/dwise/bn"
  376.   param {
  377.     lr_mult: 1
  378.     decay_mult: 0
  379.   }
  380.   param {
  381.     lr_mult: 1
  382.     decay_mult: 0
  383.   }
  384.   scale_param {
  385.     bias_term: true
  386.   }
  387. }
  388. layer {
  389.   name: "relu2_2/dwise"
  390.   type: "ReLU"
  391.   bottom: "conv2_2/dwise/bn"
  392.   top: "conv2_2/dwise/bn"
  393. }
  394. layer {
  395.   name: "conv2_2/linear"
  396.   type: "Convolution"
  397.   bottom: "conv2_2/dwise/bn"
  398.   top: "conv2_2/linear"
  399.   param {
  400.     lr_mult: 1
  401.     decay_mult: 1
  402.   }
  403.   convolution_param {
  404.     num_output: 24
  405.     bias_term: false
  406.     kernel_size: 1
  407.     weight_filler {
  408.       type: "msra"
  409.     }
  410.   }
  411. }
  412. layer {
  413.   name: "conv2_2/linear/bn"
  414.   type: "BatchNorm"
  415.   bottom: "conv2_2/linear"
  416.   top: "conv2_2/linear/bn"
  417.   param {
  418.     lr_mult: 0
  419.     decay_mult: 0
  420.   }
  421.   param {
  422.     lr_mult: 0
  423.     decay_mult: 0
  424.   }
  425.   param {
  426.     lr_mult: 0
  427.     decay_mult: 0
  428.   }
  429.   batch_norm_param {
  430.     use_global_stats: true
  431.     eps: 1e-5
  432.   }
  433. }
  434. layer {
  435.   name: "conv2_2/linear/scale"
  436.   type: "Scale"
  437.   bottom: "conv2_2/linear/bn"
  438.   top: "conv2_2/linear/bn"
  439.   param {
  440.     lr_mult: 1
  441.     decay_mult: 0
  442.   }
  443.   param {
  444.     lr_mult: 1
  445.     decay_mult: 0
  446.   }
  447.   scale_param {
  448.     bias_term: true
  449.   }
  450. }
  451. layer {
  452.   name: "conv3_1/expand"
  453.   type: "Convolution"
  454.   bottom: "conv2_2/linear/bn"
  455.   top: "conv3_1/expand"
  456.   param {
  457.     lr_mult: 1
  458.     decay_mult: 1
  459.   }
  460.   convolution_param {
  461.     num_output: 144
  462.     bias_term: false
  463.     kernel_size: 1
  464.     weight_filler {
  465.       type: "msra"
  466.     }
  467.   }
  468. }
  469. layer {
  470.   name: "conv3_1/expand/bn"
  471.   type: "BatchNorm"
  472.   bottom: "conv3_1/expand"
  473.   top: "conv3_1/expand/bn"
  474.   param {
  475.     lr_mult: 0
  476.     decay_mult: 0
  477.   }
  478.   param {
  479.     lr_mult: 0
  480.     decay_mult: 0
  481.   }
  482.   param {
  483.     lr_mult: 0
  484.     decay_mult: 0
  485.   }
  486.   batch_norm_param {
  487.     use_global_stats: true
  488.     eps: 1e-5
  489.   }
  490. }
  491. layer {
  492.   name: "conv3_1/expand/scale"
  493.   type: "Scale"
  494.   bottom: "conv3_1/expand/bn"
  495.   top: "conv3_1/expand/bn"
  496.   param {
  497.     lr_mult: 1
  498.     decay_mult: 0
  499.   }
  500.   param {
  501.     lr_mult: 1
  502.     decay_mult: 0
  503.   }
  504.   scale_param {
  505.     bias_term: true
  506.   }
  507. }
  508. layer {
  509.   name: "relu3_1/expand"
  510.   type: "ReLU"
  511.   bottom: "conv3_1/expand/bn"
  512.   top: "conv3_1/expand/bn"
  513. }
  514. layer {
  515.   name: "conv3_1/dwise"
  516.   type: "Convolution"
  517.   bottom: "conv3_1/expand/bn"
  518.   top: "conv3_1/dwise"
  519.   param {
  520.     lr_mult: 1
  521.     decay_mult: 1
  522.   }
  523.   convolution_param {
  524.     num_output: 144
  525.     bias_term: false
  526.     pad: 1
  527.     kernel_size: 3
  528.     group: 144
  529.     weight_filler {
  530.       type: "msra"
  531.     }
  532.     engine: CAFFE
  533.   }
  534. }
  535. layer {
  536.   name: "conv3_1/dwise/bn"
  537.   type: "BatchNorm"
  538.   bottom: "conv3_1/dwise"
  539.   top: "conv3_1/dwise/bn"
  540.   param {
  541.     lr_mult: 0
  542.     decay_mult: 0
  543.   }
  544.   param {
  545.     lr_mult: 0
  546.     decay_mult: 0
  547.   }
  548.   param {
  549.     lr_mult: 0
  550.     decay_mult: 0
  551.   }
  552.   batch_norm_param {
  553.     use_global_stats: true
  554.     eps: 1e-5
  555.   }
  556. }
  557. layer {
  558.   name: "conv3_1/dwise/scale"
  559.   type: "Scale"
  560.   bottom: "conv3_1/dwise/bn"
  561.   top: "conv3_1/dwise/bn"
  562.   param {
  563.     lr_mult: 1
  564.     decay_mult: 0
  565.   }
  566.   param {
  567.     lr_mult: 1
  568.     decay_mult: 0
  569.   }
  570.   scale_param {
  571.     bias_term: true
  572.   }
  573. }
  574. layer {
  575.   name: "relu3_1/dwise"
  576.   type: "ReLU"
  577.   bottom: "conv3_1/dwise/bn"
  578.   top: "conv3_1/dwise/bn"
  579. }
  580. layer {
  581.   name: "conv3_1/linear"
  582.   type: "Convolution"
  583.   bottom: "conv3_1/dwise/bn"
  584.   top: "conv3_1/linear"
  585.   param {
  586.     lr_mult: 1
  587.     decay_mult: 1
  588.   }
  589.   convolution_param {
  590.     num_output: 24
  591.     bias_term: false
  592.     kernel_size: 1
  593.     weight_filler {
  594.       type: "msra"
  595.     }
  596.   }
  597. }
  598. layer {
  599.   name: "conv3_1/linear/bn"
  600.   type: "BatchNorm"
  601.   bottom: "conv3_1/linear"
  602.   top: "conv3_1/linear/bn"
  603.   param {
  604.     lr_mult: 0
  605.     decay_mult: 0
  606.   }
  607.   param {
  608.     lr_mult: 0
  609.     decay_mult: 0
  610.   }
  611.   param {
  612.     lr_mult: 0
  613.     decay_mult: 0
  614.   }
  615.   batch_norm_param {
  616.     use_global_stats: true
  617.     eps: 1e-5
  618.   }
  619. }
  620. layer {
  621.   name: "conv3_1/linear/scale"
  622.   type: "Scale"
  623.   bottom: "conv3_1/linear/bn"
  624.   top: "conv3_1/linear/bn"
  625.   param {
  626.     lr_mult: 1
  627.     decay_mult: 0
  628.   }
  629.   param {
  630.     lr_mult: 1
  631.     decay_mult: 0
  632.   }
  633.   scale_param {
  634.     bias_term: true
  635.   }
  636. }
  637. layer {
  638.   name: "block_3_1"
  639.   type: "Eltwise"
  640.   bottom: "conv2_2/linear/bn"
  641.   bottom: "conv3_1/linear/bn"
  642.   top: "block_3_1"
  643. }
  644. layer {
  645.   name: "conv3_2/expand"
  646.   type: "Convolution"
  647.   bottom: "block_3_1"
  648.   top: "conv3_2/expand"
  649.   param {
  650.     lr_mult: 1
  651.     decay_mult: 1
  652.   }
  653.   convolution_param {
  654.     num_output: 144
  655.     bias_term: false
  656.     kernel_size: 1
  657.     weight_filler {
  658.       type: "msra"
  659.     }
  660.   }
  661. }
  662. layer {
  663.   name: "conv3_2/expand/bn"
  664.   type: "BatchNorm"
  665.   bottom: "conv3_2/expand"
  666.   top: "conv3_2/expand/bn"
  667.   param {
  668.     lr_mult: 0
  669.     decay_mult: 0
  670.   }
  671.   param {
  672.     lr_mult: 0
  673.     decay_mult: 0
  674.   }
  675.   param {
  676.     lr_mult: 0
  677.     decay_mult: 0
  678.   }
  679.   batch_norm_param {
  680.     use_global_stats: true
  681.     eps: 1e-5
  682.   }
  683. }
  684. layer {
  685.   name: "conv3_2/expand/scale"
  686.   type: "Scale"
  687.   bottom: "conv3_2/expand/bn"
  688.   top: "conv3_2/expand/bn"
  689.   param {
  690.     lr_mult: 1
  691.     decay_mult: 0
  692.   }
  693.   param {
  694.     lr_mult: 1
  695.     decay_mult: 0
  696.   }
  697.   scale_param {
  698.     bias_term: true
  699.   }
  700. }
  701. layer {
  702.   name: "relu3_2/expand"
  703.   type: "ReLU"
  704.   bottom: "conv3_2/expand/bn"
  705.   top: "conv3_2/expand/bn"
  706. }
  707. layer {
  708.   name: "conv3_2/dwise"
  709.   type: "Convolution"
  710.   bottom: "conv3_2/expand/bn"
  711.   top: "conv3_2/dwise"
  712.   param {
  713.     lr_mult: 1
  714.     decay_mult: 1
  715.   }
  716.   convolution_param {
  717.     num_output: 144
  718.     bias_term: false
  719.     pad: 1
  720.     kernel_size: 3
  721.     group: 144
  722.     stride: 2
  723.     weight_filler {
  724.       type: "msra"
  725.     }
  726.     engine: CAFFE
  727.   }
  728. }
  729. layer {
  730.   name: "conv3_2/dwise/bn"
  731.   type: "BatchNorm"
  732.   bottom: "conv3_2/dwise"
  733.   top: "conv3_2/dwise/bn"
  734.   param {
  735.     lr_mult: 0
  736.     decay_mult: 0
  737.   }
  738.   param {
  739.     lr_mult: 0
  740.     decay_mult: 0
  741.   }
  742.   param {
  743.     lr_mult: 0
  744.     decay_mult: 0
  745.   }
  746.   batch_norm_param {
  747.     use_global_stats: true
  748.     eps: 1e-5
  749.   }
  750. }
  751. layer {
  752.   name: "conv3_2/dwise/scale"
  753.   type: "Scale"
  754.   bottom: "conv3_2/dwise/bn"
  755.   top: "conv3_2/dwise/bn"
  756.   param {
  757.     lr_mult: 1
  758.     decay_mult: 0
  759.   }
  760.   param {
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  762.     decay_mult: 0
  763.   }
  764.   scale_param {
  765.     bias_term: true
  766.   }
  767. }
  768. layer {
  769.   name: "relu3_2/dwise"
  770.   type: "ReLU"
  771.   bottom: "conv3_2/dwise/bn"
  772.   top: "conv3_2/dwise/bn"
  773. }
  774. layer {
  775.   name: "conv3_2/linear"
  776.   type: "Convolution"
  777.   bottom: "conv3_2/dwise/bn"
  778.   top: "conv3_2/linear"
  779.   param {
  780.     lr_mult: 1
  781.     decay_mult: 1
  782.   }
  783.   convolution_param {
  784.     num_output: 32
  785.     bias_term: false
  786.     kernel_size: 1
  787.     weight_filler {
  788.       type: "msra"
  789.     }
  790.   }
  791. }
  792. layer {
  793.   name: "conv3_2/linear/bn"
  794.   type: "BatchNorm"
  795.   bottom: "conv3_2/linear"
  796.   top: "conv3_2/linear/bn"
  797.   param {
  798.     lr_mult: 0
  799.     decay_mult: 0
  800.   }
  801.   param {
  802.     lr_mult: 0
  803.     decay_mult: 0
  804.   }
  805.   param {
  806.     lr_mult: 0
  807.     decay_mult: 0
  808.   }
  809.   batch_norm_param {
  810.     use_global_stats: true
  811.     eps: 1e-5
  812.   }
  813. }
  814. layer {
  815.   name: "conv3_2/linear/scale"
  816.   type: "Scale"
  817.   bottom: "conv3_2/linear/bn"
  818.   top: "conv3_2/linear/bn"
  819.   param {
  820.     lr_mult: 1
  821.     decay_mult: 0
  822.   }
  823.   param {
  824.     lr_mult: 1
  825.     decay_mult: 0
  826.   }
  827.   scale_param {
  828.     bias_term: true
  829.   }
  830. }
  831. layer {
  832.   name: "conv4_1/expand"
  833.   type: "Convolution"
  834.   bottom: "conv3_2/linear/bn"
  835.   top: "conv4_1/expand"
  836.   param {
  837.     lr_mult: 1
  838.     decay_mult: 1
  839.   }
  840.   convolution_param {
  841.     num_output: 192
  842.     bias_term: false
  843.     kernel_size: 1
  844.     weight_filler {
  845.       type: "msra"
  846.     }
  847.   }
  848. }
  849. layer {
  850.   name: "conv4_1/expand/bn"
  851.   type: "BatchNorm"
  852.   bottom: "conv4_1/expand"
  853.   top: "conv4_1/expand/bn"
  854.   param {
  855.     lr_mult: 0
  856.     decay_mult: 0
  857.   }
  858.   param {
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  860.     decay_mult: 0
  861.   }
  862.   param {
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  864.     decay_mult: 0
  865.   }
  866.   batch_norm_param {
  867.     use_global_stats: true
  868.     eps: 1e-5
  869.   }
  870. }
  871. layer {
  872.   name: "conv4_1/expand/scale"
  873.   type: "Scale"
  874.   bottom: "conv4_1/expand/bn"
  875.   top: "conv4_1/expand/bn"
  876.   param {
  877.     lr_mult: 1
  878.     decay_mult: 0
  879.   }
  880.   param {
  881.     lr_mult: 1
  882.     decay_mult: 0
  883.   }
  884.   scale_param {
  885.     bias_term: true
  886.   }
  887. }
  888. layer {
  889.   name: "relu4_1/expand"
  890.   type: "ReLU"
  891.   bottom: "conv4_1/expand/bn"
  892.   top: "conv4_1/expand/bn"
  893. }
  894. layer {
  895.   name: "conv4_1/dwise"
  896.   type: "Convolution"
  897.   bottom: "conv4_1/expand/bn"
  898.   top: "conv4_1/dwise"
  899.   param {
  900.     lr_mult: 1
  901.     decay_mult: 1
  902.   }
  903.   convolution_param {
  904.     num_output: 192
  905.     bias_term: false
  906.     pad: 1
  907.     kernel_size: 3
  908.     group: 192
  909.     weight_filler {
  910.       type: "msra"
  911.     }
  912.     engine: CAFFE
  913.   }
  914. }
  915. layer {
  916.   name: "conv4_1/dwise/bn"
  917.   type: "BatchNorm"
  918.   bottom: "conv4_1/dwise"
  919.   top: "conv4_1/dwise/bn"
  920.   param {
  921.     lr_mult: 0
  922.     decay_mult: 0
  923.   }
  924.   param {
  925.     lr_mult: 0
  926.     decay_mult: 0
  927.   }
  928.   param {
  929.     lr_mult: 0
  930.     decay_mult: 0
  931.   }
  932.   batch_norm_param {
  933.     use_global_stats: true
  934.     eps: 1e-5
  935.   }
  936. }
  937. layer {
  938.   name: "conv4_1/dwise/scale"
  939.   type: "Scale"
  940.   bottom: "conv4_1/dwise/bn"
  941.   top: "conv4_1/dwise/bn"
  942.   param {
  943.     lr_mult: 1
  944.     decay_mult: 0
  945.   }
  946.   param {
  947.     lr_mult: 1
  948.     decay_mult: 0
  949.   }
  950.   scale_param {
  951.     bias_term: true
  952.   }
  953. }
  954. layer {
  955.   name: "relu4_1/dwise"
  956.   type: "ReLU"
  957.   bottom: "conv4_1/dwise/bn"
  958.   top: "conv4_1/dwise/bn"
  959. }
  960. layer {
  961.   name: "conv4_1/linear"
  962.   type: "Convolution"
  963.   bottom: "conv4_1/dwise/bn"
  964.   top: "conv4_1/linear"
  965.   param {
  966.     lr_mult: 1
  967.     decay_mult: 1
  968.   }
  969.   convolution_param {
  970.     num_output: 32
  971.     bias_term: false
  972.     kernel_size: 1
  973.     weight_filler {
  974.       type: "msra"
  975.     }
  976.   }
  977. }
  978. layer {
  979.   name: "conv4_1/linear/bn"
  980.   type: "BatchNorm"
  981.   bottom: "conv4_1/linear"
  982.   top: "conv4_1/linear/bn"
  983.   param {
  984.     lr_mult: 0
  985.     decay_mult: 0
  986.   }
  987.   param {
  988.     lr_mult: 0
  989.     decay_mult: 0
  990.   }
  991.   param {
  992.     lr_mult: 0
  993.     decay_mult: 0
  994.   }
  995.   batch_norm_param {
  996.     use_global_stats: true
  997.     eps: 1e-5
  998.   }
  999. }
  1000. layer {
  1001.   name: "conv4_1/linear/scale"
  1002.   type: "Scale"
  1003.   bottom: "conv4_1/linear/bn"
  1004.   top: "conv4_1/linear/bn"
  1005.   param {
  1006.     lr_mult: 1
  1007.     decay_mult: 0
  1008.   }
  1009.   param {
  1010.     lr_mult: 1
  1011.     decay_mult: 0
  1012.   }
  1013.   scale_param {
  1014.     bias_term: true
  1015.   }
  1016. }
  1017. layer {
  1018.   name: "block_4_1"
  1019.   type: "Eltwise"
  1020.   bottom: "conv3_2/linear/bn"
  1021.   bottom: "conv4_1/linear/bn"
  1022.   top: "block_4_1"
  1023. }
  1024. layer {
  1025.   name: "conv4_2/expand"
  1026.   type: "Convolution"
  1027.   bottom: "block_4_1"
  1028.   top: "conv4_2/expand"
  1029.   param {
  1030.     lr_mult: 1
  1031.     decay_mult: 1
  1032.   }
  1033.   convolution_param {
  1034.     num_output: 192
  1035.     bias_term: false
  1036.     kernel_size: 1
  1037.     weight_filler {
  1038.       type: "msra"
  1039.     }
  1040.   }
  1041. }
  1042. layer {
  1043.   name: "conv4_2/expand/bn"
  1044.   type: "BatchNorm"
  1045.   bottom: "conv4_2/expand"
  1046.   top: "conv4_2/expand/bn"
  1047.   param {
  1048.     lr_mult: 0
  1049.     decay_mult: 0
  1050.   }
  1051.   param {
  1052.     lr_mult: 0
  1053.     decay_mult: 0
  1054.   }
  1055.   param {
  1056.     lr_mult: 0
  1057.     decay_mult: 0
  1058.   }
  1059.   batch_norm_param {
  1060.     use_global_stats: true
  1061.     eps: 1e-5
  1062.   }
  1063. }
  1064. layer {
  1065.   name: "conv4_2/expand/scale"
  1066.   type: "Scale"
  1067.   bottom: "conv4_2/expand/bn"
  1068.   top: "conv4_2/expand/bn"
  1069.   param {
  1070.     lr_mult: 1
  1071.     decay_mult: 0
  1072.   }
  1073.   param {
  1074.     lr_mult: 1
  1075.     decay_mult: 0
  1076.   }
  1077.   scale_param {
  1078.     bias_term: true
  1079.   }
  1080. }
  1081. layer {
  1082.   name: "relu4_2/expand"
  1083.   type: "ReLU"
  1084.   bottom: "conv4_2/expand/bn"
  1085.   top: "conv4_2/expand/bn"
  1086. }
  1087. layer {
  1088.   name: "conv4_2/dwise"
  1089.   type: "Convolution"
  1090.   bottom: "conv4_2/expand/bn"
  1091.   top: "conv4_2/dwise"
  1092.   param {
  1093.     lr_mult: 1
  1094.     decay_mult: 1
  1095.   }
  1096.   convolution_param {
  1097.     num_output: 192
  1098.     bias_term: false
  1099.     pad: 1
  1100.     kernel_size: 3
  1101.     group: 192
  1102.     weight_filler {
  1103.       type: "msra"
  1104.     }
  1105.     engine: CAFFE
  1106.   }
  1107. }
  1108. layer {
  1109.   name: "conv4_2/dwise/bn"
  1110.   type: "BatchNorm"
  1111.   bottom: "conv4_2/dwise"
  1112.   top: "conv4_2/dwise/bn"
  1113.   param {
  1114.     lr_mult: 0
  1115.     decay_mult: 0
  1116.   }
  1117.   param {
  1118.     lr_mult: 0
  1119.     decay_mult: 0
  1120.   }
  1121.   param {
  1122.     lr_mult: 0
  1123.     decay_mult: 0
  1124.   }
  1125.   batch_norm_param {
  1126.     use_global_stats: true
  1127.     eps: 1e-5
  1128.   }
  1129. }
  1130. layer {
  1131.   name: "conv4_2/dwise/scale"
  1132.   type: "Scale"
  1133.   bottom: "conv4_2/dwise/bn"
  1134.   top: "conv4_2/dwise/bn"
  1135.   param {
  1136.     lr_mult: 1
  1137.     decay_mult: 0
  1138.   }
  1139.   param {
  1140.     lr_mult: 1
  1141.     decay_mult: 0
  1142.   }
  1143.   scale_param {
  1144.     bias_term: true
  1145.   }
  1146. }
  1147. layer {
  1148.   name: "relu4_2/dwise"
  1149.   type: "ReLU"
  1150.   bottom: "conv4_2/dwise/bn"
  1151.   top: "conv4_2/dwise/bn"
  1152. }
  1153. layer {
  1154.   name: "conv4_2/linear"
  1155.   type: "Convolution"
  1156.   bottom: "conv4_2/dwise/bn"
  1157.   top: "conv4_2/linear"
  1158.   param {
  1159.     lr_mult: 1
  1160.     decay_mult: 1
  1161.   }
  1162.   convolution_param {
  1163.     num_output: 32
  1164.     bias_term: false
  1165.     kernel_size: 1
  1166.     weight_filler {
  1167.       type: "msra"
  1168.     }
  1169.   }
  1170. }
  1171. layer {
  1172.   name: "conv4_2/linear/bn"
  1173.   type: "BatchNorm"
  1174.   bottom: "conv4_2/linear"
  1175.   top: "conv4_2/linear/bn"
  1176.   param {
  1177.     lr_mult: 0
  1178.     decay_mult: 0
  1179.   }
  1180.   param {
  1181.     lr_mult: 0
  1182.     decay_mult: 0
  1183.   }
  1184.   param {
  1185.     lr_mult: 0
  1186.     decay_mult: 0
  1187.   }
  1188.   batch_norm_param {
  1189.     use_global_stats: true
  1190.     eps: 1e-5
  1191.   }
  1192. }
  1193. layer {
  1194.   name: "conv4_2/linear/scale"
  1195.   type: "Scale"
  1196.   bottom: "conv4_2/linear/bn"
  1197.   top: "conv4_2/linear/bn"
  1198.   param {
  1199.     lr_mult: 1
  1200.     decay_mult: 0
  1201.   }
  1202.   param {
  1203.     lr_mult: 1
  1204.     decay_mult: 0
  1205.   }
  1206.   scale_param {
  1207.     bias_term: true
  1208.   }
  1209. }
  1210. layer {
  1211.   name: "block_4_2"
  1212.   type: "Eltwise"
  1213.   bottom: "block_4_1"
  1214.   bottom: "conv4_2/linear/bn"
  1215.   top: "block_4_2"
  1216. }
  1217. layer {
  1218.   name: "conv4_3/expand"
  1219.   type: "Convolution"
  1220.   bottom: "block_4_2"
  1221.   top: "conv4_3/expand"
  1222.   param {
  1223.     lr_mult: 1
  1224.     decay_mult: 1
  1225.   }
  1226.   convolution_param {
  1227.     num_output: 192
  1228.     bias_term: false
  1229.     kernel_size: 1
  1230.     weight_filler {
  1231.       type: "msra"
  1232.     }
  1233.   }
  1234. }
  1235. layer {
  1236.   name: "conv4_3/expand/bn"
  1237.   type: "BatchNorm"
  1238.   bottom: "conv4_3/expand"
  1239.   top: "conv4_3/expand/bn"
  1240.   param {
  1241.     lr_mult: 0
  1242.     decay_mult: 0
  1243.   }
  1244.   param {
  1245.     lr_mult: 0
  1246.     decay_mult: 0
  1247.   }
  1248.   param {
  1249.     lr_mult: 0
  1250.     decay_mult: 0
  1251.   }
  1252.   batch_norm_param {
  1253.     use_global_stats: true
  1254.     eps: 1e-5
  1255.   }
  1256. }
  1257. layer {
  1258.   name: "conv4_3/expand/scale"
  1259.   type: "Scale"
  1260.   bottom: "conv4_3/expand/bn"
  1261.   top: "conv4_3/expand/bn"
  1262.   param {
  1263.     lr_mult: 1
  1264.     decay_mult: 0
  1265.   }
  1266.   param {
  1267.     lr_mult: 1
  1268.     decay_mult: 0
  1269.   }
  1270.   scale_param {
  1271.     bias_term: true
  1272.   }
  1273. }
  1274. layer {
  1275.   name: "relu4_3/expand"
  1276.   type: "ReLU"
  1277.   bottom: "conv4_3/expand/bn"
  1278.   top: "conv4_3/expand/bn"
  1279. }
  1280. layer {
  1281.   name: "conv4_3/dwise"
  1282.   type: "Convolution"
  1283.   bottom: "conv4_3/expand/bn"
  1284.   top: "conv4_3/dwise"
  1285.   param {
  1286.     lr_mult: 1
  1287.     decay_mult: 1
  1288.   }
  1289.   convolution_param {
  1290.     num_output: 192
  1291.     bias_term: false
  1292.     pad: 1
  1293.     kernel_size: 3
  1294.     group: 192
  1295.     weight_filler {
  1296.       type: "msra"
  1297.     }
  1298.     engine: CAFFE
  1299.   }
  1300. }
  1301. layer {
  1302.   name: "conv4_3/dwise/bn"
  1303.   type: "BatchNorm"
  1304.   bottom: "conv4_3/dwise"
  1305.   top: "conv4_3/dwise/bn"
  1306.   param {
  1307.     lr_mult: 0
  1308.     decay_mult: 0
  1309.   }
  1310.   param {
  1311.     lr_mult: 0
  1312.     decay_mult: 0
  1313.   }
  1314.   param {
  1315.     lr_mult: 0
  1316.     decay_mult: 0
  1317.   }
  1318.   batch_norm_param {
  1319.     use_global_stats: true
  1320.     eps: 1e-5
  1321.   }
  1322. }
  1323. layer {
  1324.   name: "conv4_3/dwise/scale"
  1325.   type: "Scale"
  1326.   bottom: "conv4_3/dwise/bn"
  1327.   top: "conv4_3/dwise/bn"
  1328.   param {
  1329.     lr_mult: 1
  1330.     decay_mult: 0
  1331.   }
  1332.   param {
  1333.     lr_mult: 1
  1334.     decay_mult: 0
  1335.   }
  1336.   scale_param {
  1337.     bias_term: true
  1338.   }
  1339. }
  1340. layer {
  1341.   name: "relu4_3/dwise"
  1342.   type: "ReLU"
  1343.   bottom: "conv4_3/dwise/bn"
  1344.   top: "conv4_3/dwise/bn"
  1345. }
  1346. layer {
  1347.   name: "conv4_3/linear"
  1348.   type: "Convolution"
  1349.   bottom: "conv4_3/dwise/bn"
  1350.   top: "conv4_3/linear"
  1351.   param {
  1352.     lr_mult: 1
  1353.     decay_mult: 1
  1354.   }
  1355.   convolution_param {
  1356.     num_output: 64
  1357.     bias_term: false
  1358.     kernel_size: 1
  1359.     weight_filler {
  1360.       type: "msra"
  1361.     }
  1362.   }
  1363. }
  1364. layer {
  1365.   name: "conv4_3/linear/bn"
  1366.   type: "BatchNorm"
  1367.   bottom: "conv4_3/linear"
  1368.   top: "conv4_3/linear/bn"
  1369.   param {
  1370.     lr_mult: 0
  1371.     decay_mult: 0
  1372.   }
  1373.   param {
  1374.     lr_mult: 0
  1375.     decay_mult: 0
  1376.   }
  1377.   param {
  1378.     lr_mult: 0
  1379.     decay_mult: 0
  1380.   }
  1381.   batch_norm_param {
  1382.     use_global_stats: true
  1383.     eps: 1e-5
  1384.   }
  1385. }
  1386. layer {
  1387.   name: "conv4_3/linear/scale"
  1388.   type: "Scale"
  1389.   bottom: "conv4_3/linear/bn"
  1390.   top: "conv4_3/linear/bn"
  1391.   param {
  1392.     lr_mult: 1
  1393.     decay_mult: 0
  1394.   }
  1395.   param {
  1396.     lr_mult: 1
  1397.     decay_mult: 0
  1398.   }
  1399.   scale_param {
  1400.     bias_term: true
  1401.   }
  1402. }
  1403. layer {
  1404.   name: "conv4_4/expand"
  1405.   type: "Convolution"
  1406.   bottom: "conv4_3/linear/bn"
  1407.   top: "conv4_4/expand"
  1408.   param {
  1409.     lr_mult: 1
  1410.     decay_mult: 1
  1411.   }
  1412.   convolution_param {
  1413.     num_output: 384
  1414.     bias_term: false
  1415.     kernel_size: 1
  1416.     weight_filler {
  1417.       type: "msra"
  1418.     }
  1419.   }
  1420. }
  1421. layer {
  1422.   name: "conv4_4/expand/bn"
  1423.   type: "BatchNorm"
  1424.   bottom: "conv4_4/expand"
  1425.   top: "conv4_4/expand/bn"
  1426.   param {
  1427.     lr_mult: 0
  1428.     decay_mult: 0
  1429.   }
  1430.   param {
  1431.     lr_mult: 0
  1432.     decay_mult: 0
  1433.   }
  1434.   param {
  1435.     lr_mult: 0
  1436.     decay_mult: 0
  1437.   }
  1438.   batch_norm_param {
  1439.     use_global_stats: true
  1440.     eps: 1e-5
  1441.   }
  1442. }
  1443. layer {
  1444.   name: "conv4_4/expand/scale"
  1445.   type: "Scale"
  1446.   bottom: "conv4_4/expand/bn"
  1447.   top: "conv4_4/expand/bn"
  1448.   param {
  1449.     lr_mult: 1
  1450.     decay_mult: 0
  1451.   }
  1452.   param {
  1453.     lr_mult: 1
  1454.     decay_mult: 0
  1455.   }
  1456.   scale_param {
  1457.     bias_term: true
  1458.   }
  1459. }
  1460. layer {
  1461.   name: "relu4_4/expand"
  1462.   type: "ReLU"
  1463.   bottom: "conv4_4/expand/bn"
  1464.   top: "conv4_4/expand/bn"
  1465. }
  1466. layer {
  1467.   name: "conv4_4/dwise"
  1468.   type: "Convolution"
  1469.   bottom: "conv4_4/expand/bn"
  1470.   top: "conv4_4/dwise"
  1471.   param {
  1472.     lr_mult: 1
  1473.     decay_mult: 1
  1474.   }
  1475.   convolution_param {
  1476.     num_output: 384
  1477.     bias_term: false
  1478.     pad: 1
  1479.     kernel_size: 3
  1480.     group: 384
  1481.     weight_filler {
  1482.       type: "msra"
  1483.     }
  1484.     engine: CAFFE
  1485.   }
  1486. }
  1487. layer {
  1488.   name: "conv4_4/dwise/bn"
  1489.   type: "BatchNorm"
  1490.   bottom: "conv4_4/dwise"
  1491.   top: "conv4_4/dwise/bn"
  1492.   param {
  1493.     lr_mult: 0
  1494.     decay_mult: 0
  1495.   }
  1496.   param {
  1497.     lr_mult: 0
  1498.     decay_mult: 0
  1499.   }
  1500.   param {
  1501.     lr_mult: 0
  1502.     decay_mult: 0
  1503.   }
  1504.   batch_norm_param {
  1505.     use_global_stats: true
  1506.     eps: 1e-5
  1507.   }
  1508. }
  1509. layer {
  1510.   name: "conv4_4/dwise/scale"
  1511.   type: "Scale"
  1512.   bottom: "conv4_4/dwise/bn"
  1513.   top: "conv4_4/dwise/bn"
  1514.   param {
  1515.     lr_mult: 1
  1516.     decay_mult: 0
  1517.   }
  1518.   param {
  1519.     lr_mult: 1
  1520.     decay_mult: 0
  1521.   }
  1522.   scale_param {
  1523.     bias_term: true
  1524.   }
  1525. }
  1526. layer {
  1527.   name: "relu4_4/dwise"
  1528.   type: "ReLU"
  1529.   bottom: "conv4_4/dwise/bn"
  1530.   top: "conv4_4/dwise/bn"
  1531. }
  1532. layer {
  1533.   name: "conv4_4/linear"
  1534.   type: "Convolution"
  1535.   bottom: "conv4_4/dwise/bn"
  1536.   top: "conv4_4/linear"
  1537.   param {
  1538.     lr_mult: 1
  1539.     decay_mult: 1
  1540.   }
  1541.   convolution_param {
  1542.     num_output: 64
  1543.     bias_term: false
  1544.     kernel_size: 1
  1545.     weight_filler {
  1546.       type: "msra"
  1547.     }
  1548.   }
  1549. }
  1550. layer {
  1551.   name: "conv4_4/linear/bn"
  1552.   type: "BatchNorm"
  1553.   bottom: "conv4_4/linear"
  1554.   top: "conv4_4/linear/bn"
  1555.   param {
  1556.     lr_mult: 0
  1557.     decay_mult: 0
  1558.   }
  1559.   param {
  1560.     lr_mult: 0
  1561.     decay_mult: 0
  1562.   }
  1563.   param {
  1564.     lr_mult: 0
  1565.     decay_mult: 0
  1566.   }
  1567.   batch_norm_param {
  1568.     use_global_stats: true
  1569.     eps: 1e-5
  1570.   }
  1571. }
  1572. layer {
  1573.   name: "conv4_4/linear/scale"
  1574.   type: "Scale"
  1575.   bottom: "conv4_4/linear/bn"
  1576.   top: "conv4_4/linear/bn"
  1577.   param {
  1578.     lr_mult: 1
  1579.     decay_mult: 0
  1580.   }
  1581.   param {
  1582.     lr_mult: 1
  1583.     decay_mult: 0
  1584.   }
  1585.   scale_param {
  1586.     bias_term: true
  1587.   }
  1588. }
  1589. layer {
  1590.   name: "block_4_4"
  1591.   type: "Eltwise"
  1592.   bottom: "conv4_3/linear/bn"
  1593.   bottom: "conv4_4/linear/bn"
  1594.   top: "block_4_4"
  1595. }
  1596. layer {
  1597.   name: "conv4_5/expand"
  1598.   type: "Convolution"
  1599.   bottom: "block_4_4"
  1600.   top: "conv4_5/expand"
  1601.   param {
  1602.     lr_mult: 1
  1603.     decay_mult: 1
  1604.   }
  1605.   convolution_param {
  1606.     num_output: 384
  1607.     bias_term: false
  1608.     kernel_size: 1
  1609.     weight_filler {
  1610.       type: "msra"
  1611.     }
  1612.   }
  1613. }
  1614. layer {
  1615.   name: "conv4_5/expand/bn"
  1616.   type: "BatchNorm"
  1617.   bottom: "conv4_5/expand"
  1618.   top: "conv4_5/expand/bn"
  1619.   param {
  1620.     lr_mult: 0
  1621.     decay_mult: 0
  1622.   }
  1623.   param {
  1624.     lr_mult: 0
  1625.     decay_mult: 0
  1626.   }
  1627.   param {
  1628.     lr_mult: 0
  1629.     decay_mult: 0
  1630.   }
  1631.   batch_norm_param {
  1632.     use_global_stats: true
  1633.     eps: 1e-5
  1634.   }
  1635. }
  1636. layer {
  1637.   name: "conv4_5/expand/scale"
  1638.   type: "Scale"
  1639.   bottom: "conv4_5/expand/bn"
  1640.   top: "conv4_5/expand/bn"
  1641.   param {
  1642.     lr_mult: 1
  1643.     decay_mult: 0
  1644.   }
  1645.   param {
  1646.     lr_mult: 1
  1647.     decay_mult: 0
  1648.   }
  1649.   scale_param {
  1650.     bias_term: true
  1651.   }
  1652. }
  1653. layer {
  1654.   name: "relu4_5/expand"
  1655.   type: "ReLU"
  1656.   bottom: "conv4_5/expand/bn"
  1657.   top: "conv4_5/expand/bn"
  1658. }
  1659. layer {
  1660.   name: "conv4_5/dwise"
  1661.   type: "Convolution"
  1662.   bottom: "conv4_5/expand/bn"
  1663.   top: "conv4_5/dwise"
  1664.   param {
  1665.     lr_mult: 1
  1666.     decay_mult: 1
  1667.   }
  1668.   convolution_param {
  1669.     num_output: 384
  1670.     bias_term: false
  1671.     pad: 1
  1672.     kernel_size: 3
  1673.     group: 384
  1674.     weight_filler {
  1675.       type: "msra"
  1676.     }
  1677.     engine: CAFFE
  1678.   }
  1679. }
  1680. layer {
  1681.   name: "conv4_5/dwise/bn"
  1682.   type: "BatchNorm"
  1683.   bottom: "conv4_5/dwise"
  1684.   top: "conv4_5/dwise/bn"
  1685.   param {
  1686.     lr_mult: 0
  1687.     decay_mult: 0
  1688.   }
  1689.   param {
  1690.     lr_mult: 0
  1691.     decay_mult: 0
  1692.   }
  1693.   param {
  1694.     lr_mult: 0
  1695.     decay_mult: 0
  1696.   }
  1697.   batch_norm_param {
  1698.     use_global_stats: true
  1699.     eps: 1e-5
  1700.   }
  1701. }
  1702. layer {
  1703.   name: "conv4_5/dwise/scale"
  1704.   type: "Scale"
  1705.   bottom: "conv4_5/dwise/bn"
  1706.   top: "conv4_5/dwise/bn"
  1707.   param {
  1708.     lr_mult: 1
  1709.     decay_mult: 0
  1710.   }
  1711.   param {
  1712.     lr_mult: 1
  1713.     decay_mult: 0
  1714.   }
  1715.   scale_param {
  1716.     bias_term: true
  1717.   }
  1718. }
  1719. layer {
  1720.   name: "relu4_5/dwise"
  1721.   type: "ReLU"
  1722.   bottom: "conv4_5/dwise/bn"
  1723.   top: "conv4_5/dwise/bn"
  1724. }
  1725. layer {
  1726.   name: "conv4_5/linear"
  1727.   type: "Convolution"
  1728.   bottom: "conv4_5/dwise/bn"
  1729.   top: "conv4_5/linear"
  1730.   param {
  1731.     lr_mult: 1
  1732.     decay_mult: 1
  1733.   }
  1734.   convolution_param {
  1735.     num_output: 64
  1736.     bias_term: false
  1737.     kernel_size: 1
  1738.     weight_filler {
  1739.       type: "msra"
  1740.     }
  1741.   }
  1742. }
  1743. layer {
  1744.   name: "conv4_5/linear/bn"
  1745.   type: "BatchNorm"
  1746.   bottom: "conv4_5/linear"
  1747.   top: "conv4_5/linear/bn"
  1748.   param {
  1749.     lr_mult: 0
  1750.     decay_mult: 0
  1751.   }
  1752.   param {
  1753.     lr_mult: 0
  1754.     decay_mult: 0
  1755.   }
  1756.   param {
  1757.     lr_mult: 0
  1758.     decay_mult: 0
  1759.   }
  1760.   batch_norm_param {
  1761.     use_global_stats: true
  1762.     eps: 1e-5
  1763.   }
  1764. }
  1765. layer {
  1766.   name: "conv4_5/linear/scale"
  1767.   type: "Scale"
  1768.   bottom: "conv4_5/linear/bn"
  1769.   top: "conv4_5/linear/bn"
  1770.   param {
  1771.     lr_mult: 1
  1772.     decay_mult: 0
  1773.   }
  1774.   param {
  1775.     lr_mult: 1
  1776.     decay_mult: 0
  1777.   }
  1778.   scale_param {
  1779.     bias_term: true
  1780.   }
  1781. }
  1782. layer {
  1783.   name: "block_4_5"
  1784.   type: "Eltwise"
  1785.   bottom: "block_4_4"
  1786.   bottom: "conv4_5/linear/bn"
  1787.   top: "block_4_5"
  1788. }
  1789. layer {
  1790.   name: "conv4_6/expand"
  1791.   type: "Convolution"
  1792.   bottom: "block_4_5"
  1793.   top: "conv4_6/expand"
  1794.   param {
  1795.     lr_mult: 1
  1796.     decay_mult: 1
  1797.   }
  1798.   convolution_param {
  1799.     num_output: 384
  1800.     bias_term: false
  1801.     kernel_size: 1
  1802.     weight_filler {
  1803.       type: "msra"
  1804.     }
  1805.   }
  1806. }
  1807. layer {
  1808.   name: "conv4_6/expand/bn"
  1809.   type: "BatchNorm"
  1810.   bottom: "conv4_6/expand"
  1811.   top: "conv4_6/expand/bn"
  1812.   param {
  1813.     lr_mult: 0
  1814.     decay_mult: 0
  1815.   }
  1816.   param {
  1817.     lr_mult: 0
  1818.     decay_mult: 0
  1819.   }
  1820.   param {
  1821.     lr_mult: 0
  1822.     decay_mult: 0
  1823.   }
  1824.   batch_norm_param {
  1825.     use_global_stats: true
  1826.     eps: 1e-5
  1827.   }
  1828. }
  1829. layer {
  1830.   name: "conv4_6/expand/scale"
  1831.   type: "Scale"
  1832.   bottom: "conv4_6/expand/bn"
  1833.   top: "conv4_6/expand/bn"
  1834.   param {
  1835.     lr_mult: 1
  1836.     decay_mult: 0
  1837.   }
  1838.   param {
  1839.     lr_mult: 1
  1840.     decay_mult: 0
  1841.   }
  1842.   scale_param {
  1843.     bias_term: true
  1844.   }
  1845. }
  1846. layer {
  1847.   name: "relu4_6/expand"
  1848.   type: "ReLU"
  1849.   bottom: "conv4_6/expand/bn"
  1850.   top: "conv4_6/expand/bn"
  1851. }
  1852. layer {
  1853.   name: "conv4_6/dwise"
  1854.   type: "Convolution"
  1855.   bottom: "conv4_6/expand/bn"
  1856.   top: "conv4_6/dwise"
  1857.   param {
  1858.     lr_mult: 1
  1859.     decay_mult: 1
  1860.   }
  1861.   convolution_param {
  1862.     num_output: 384
  1863.     bias_term: false
  1864.     pad: 1
  1865.     kernel_size: 3
  1866.     group: 384
  1867.     weight_filler {
  1868.       type: "msra"
  1869.     }
  1870.     engine: CAFFE
  1871.   }
  1872. }
  1873. layer {
  1874.   name: "conv4_6/dwise/bn"
  1875.   type: "BatchNorm"
  1876.   bottom: "conv4_6/dwise"
  1877.   top: "conv4_6/dwise/bn"
  1878.   param {
  1879.     lr_mult: 0
  1880.     decay_mult: 0
  1881.   }
  1882.   param {
  1883.     lr_mult: 0
  1884.     decay_mult: 0
  1885.   }
  1886.   param {
  1887.     lr_mult: 0
  1888.     decay_mult: 0
  1889.   }
  1890.   batch_norm_param {
  1891.     use_global_stats: true
  1892.     eps: 1e-5
  1893.   }
  1894. }
  1895. layer {
  1896.   name: "conv4_6/dwise/scale"
  1897.   type: "Scale"
  1898.   bottom: "conv4_6/dwise/bn"
  1899.   top: "conv4_6/dwise/bn"
  1900.   param {
  1901.     lr_mult: 1
  1902.     decay_mult: 0
  1903.   }
  1904.   param {
  1905.     lr_mult: 1
  1906.     decay_mult: 0
  1907.   }
  1908.   scale_param {
  1909.     bias_term: true
  1910.   }
  1911. }
  1912. layer {
  1913.   name: "relu4_6/dwise"
  1914.   type: "ReLU"
  1915.   bottom: "conv4_6/dwise/bn"
  1916.   top: "conv4_6/dwise/bn"
  1917. }
  1918. layer {
  1919.   name: "conv4_6/linear"
  1920.   type: "Convolution"
  1921.   bottom: "conv4_6/dwise/bn"
  1922.   top: "conv4_6/linear"
  1923.   param {
  1924.     lr_mult: 1
  1925.     decay_mult: 1
  1926.   }
  1927.   convolution_param {
  1928.     num_output: 64
  1929.     bias_term: false
  1930.     kernel_size: 1
  1931.     weight_filler {
  1932.       type: "msra"
  1933.     }
  1934.   }
  1935. }
  1936. layer {
  1937.   name: "conv4_6/linear/bn"
  1938.   type: "BatchNorm"
  1939.   bottom: "conv4_6/linear"
  1940.   top: "conv4_6/linear/bn"
  1941.   param {
  1942.     lr_mult: 0
  1943.     decay_mult: 0
  1944.   }
  1945.   param {
  1946.     lr_mult: 0
  1947.     decay_mult: 0
  1948.   }
  1949.   param {
  1950.     lr_mult: 0
  1951.     decay_mult: 0
  1952.   }
  1953.   batch_norm_param {
  1954.     use_global_stats: true
  1955.     eps: 1e-5
  1956.   }
  1957. }
  1958. layer {
  1959.   name: "conv4_6/linear/scale"
  1960.   type: "Scale"
  1961.   bottom: "conv4_6/linear/bn"
  1962.   top: "conv4_6/linear/bn"
  1963.   param {
  1964.     lr_mult: 1
  1965.     decay_mult: 0
  1966.   }
  1967.   param {
  1968.     lr_mult: 1
  1969.     decay_mult: 0
  1970.   }
  1971.   scale_param {
  1972.     bias_term: true
  1973.   }
  1974. }
  1975. layer {
  1976.   name: "block_4_6"
  1977.   type: "Eltwise"
  1978.   bottom: "block_4_5"
  1979.   bottom: "conv4_6/linear/bn"
  1980.   top: "block_4_6"
  1981. }
  1982. layer {
  1983.   name: "conv4_7/expand"
  1984.   type: "Convolution"
  1985.   bottom: "block_4_6"
  1986.   top: "conv4_7/expand"
  1987.   param {
  1988.     lr_mult: 1
  1989.     decay_mult: 1
  1990.   }
  1991.   convolution_param {
  1992.     num_output: 384
  1993.     bias_term: false
  1994.     kernel_size: 1
  1995.     weight_filler {
  1996.       type: "msra"
  1997.     }
  1998.   }
  1999. }
  2000. layer {
  2001.   name: "conv4_7/expand/bn"
  2002.   type: "BatchNorm"
  2003.   bottom: "conv4_7/expand"
  2004.   top: "conv4_7/expand/bn"
  2005.   param {
  2006.     lr_mult: 0
  2007.     decay_mult: 0
  2008.   }
  2009.   param {
  2010.     lr_mult: 0
  2011.     decay_mult: 0
  2012.   }
  2013.   param {
  2014.     lr_mult: 0
  2015.     decay_mult: 0
  2016.   }
  2017.   batch_norm_param {
  2018.     use_global_stats: true
  2019.     eps: 1e-5
  2020.   }
  2021. }
  2022. layer {
  2023.   name: "conv4_7/expand/scale"
  2024.   type: "Scale"
  2025.   bottom: "conv4_7/expand/bn"
  2026.   top: "conv4_7/expand/bn"
  2027.   param {
  2028.     lr_mult: 1
  2029.     decay_mult: 0
  2030.   }
  2031.   param {
  2032.     lr_mult: 1
  2033.     decay_mult: 0
  2034.   }
  2035.   scale_param {
  2036.     bias_term: true
  2037.   }
  2038. }
  2039. layer {
  2040.   name: "relu4_7/expand"
  2041.   type: "ReLU"
  2042.   bottom: "conv4_7/expand/bn"
  2043.   top: "conv4_7/expand/bn"
  2044. }
  2045. layer {
  2046.   name: "conv4_7/dwise"
  2047.   type: "Convolution"
  2048.   bottom: "conv4_7/expand/bn"
  2049.   top: "conv4_7/dwise"
  2050.   param {
  2051.     lr_mult: 1
  2052.     decay_mult: 1
  2053.   }
  2054.   convolution_param {
  2055.     num_output: 384
  2056.     bias_term: false
  2057.     pad: 1
  2058.     kernel_size: 3
  2059.     group: 384
  2060.     stride: 2
  2061.     weight_filler {
  2062.       type: "msra"
  2063.     }
  2064.     engine: CAFFE
  2065.   }
  2066. }
  2067. layer {
  2068.   name: "conv4_7/dwise/bn"
  2069.   type: "BatchNorm"
  2070.   bottom: "conv4_7/dwise"
  2071.   top: "conv4_7/dwise/bn"
  2072.   param {
  2073.     lr_mult: 0
  2074.     decay_mult: 0
  2075.   }
  2076.   param {
  2077.     lr_mult: 0
  2078.     decay_mult: 0
  2079.   }
  2080.   param {
  2081.     lr_mult: 0
  2082.     decay_mult: 0
  2083.   }
  2084.   batch_norm_param {
  2085.     use_global_stats: true
  2086.     eps: 1e-5
  2087.   }
  2088. }
  2089. layer {
  2090.   name: "conv4_7/dwise/scale"
  2091.   type: "Scale"
  2092.   bottom: "conv4_7/dwise/bn"
  2093.   top: "conv4_7/dwise/bn"
  2094.   param {
  2095.     lr_mult: 1
  2096.     decay_mult: 0
  2097.   }
  2098.   param {
  2099.     lr_mult: 1
  2100.     decay_mult: 0
  2101.   }
  2102.   scale_param {
  2103.     bias_term: true
  2104.   }
  2105. }
  2106. layer {
  2107.   name: "relu4_7/dwise"
  2108.   type: "ReLU"
  2109.   bottom: "conv4_7/dwise/bn"
  2110.   top: "conv4_7/dwise/bn"
  2111. }
  2112. layer {
  2113.   name: "conv4_7/linear"
  2114.   type: "Convolution"
  2115.   bottom: "conv4_7/dwise/bn"
  2116.   top: "conv4_7/linear"
  2117.   param {
  2118.     lr_mult: 1
  2119.     decay_mult: 1
  2120.   }
  2121.   convolution_param {
  2122.     num_output: 96
  2123.     bias_term: false
  2124.     kernel_size: 1
  2125.     weight_filler {
  2126.       type: "msra"
  2127.     }
  2128.   }
  2129. }
  2130. layer {
  2131.   name: "conv4_7/linear/bn"
  2132.   type: "BatchNorm"
  2133.   bottom: "conv4_7/linear"
  2134.   top: "conv4_7/linear/bn"
  2135.   param {
  2136.     lr_mult: 0
  2137.     decay_mult: 0
  2138.   }
  2139.   param {
  2140.     lr_mult: 0
  2141.     decay_mult: 0
  2142.   }
  2143.   param {
  2144.     lr_mult: 0
  2145.     decay_mult: 0
  2146.   }
  2147.   batch_norm_param {
  2148.     use_global_stats: true
  2149.     eps: 1e-5
  2150.   }
  2151. }
  2152. layer {
  2153.   name: "conv4_7/linear/scale"
  2154.   type: "Scale"
  2155.   bottom: "conv4_7/linear/bn"
  2156.   top: "conv4_7/linear/bn"
  2157.   param {
  2158.     lr_mult: 1
  2159.     decay_mult: 0
  2160.   }
  2161.   param {
  2162.     lr_mult: 1
  2163.     decay_mult: 0
  2164.   }
  2165.   scale_param {
  2166.     bias_term: true
  2167.   }
  2168. }
  2169. layer {
  2170.   name: "conv5_1/expand"
  2171.   type: "Convolution"
  2172.   bottom: "conv4_7/linear/bn"
  2173.   top: "conv5_1/expand"
  2174.   param {
  2175.     lr_mult: 1
  2176.     decay_mult: 1
  2177.   }
  2178.   convolution_param {
  2179.     num_output: 576
  2180.     bias_term: false
  2181.     kernel_size: 1
  2182.     weight_filler {
  2183.       type: "msra"
  2184.     }
  2185.   }
  2186. }
  2187. layer {
  2188.   name: "conv5_1/expand/bn"
  2189.   type: "BatchNorm"
  2190.   bottom: "conv5_1/expand"
  2191.   top: "conv5_1/expand/bn"
  2192.   param {
  2193.     lr_mult: 0
  2194.     decay_mult: 0
  2195.   }
  2196.   param {
  2197.     lr_mult: 0
  2198.     decay_mult: 0
  2199.   }
  2200.   param {
  2201.     lr_mult: 0
  2202.     decay_mult: 0
  2203.   }
  2204.   batch_norm_param {
  2205.     use_global_stats: true
  2206.     eps: 1e-5
  2207.   }
  2208. }
  2209. layer {
  2210.   name: "conv5_1/expand/scale"
  2211.   type: "Scale"
  2212.   bottom: "conv5_1/expand/bn"
  2213.   top: "conv5_1/expand/bn"
  2214.   param {
  2215.     lr_mult: 1
  2216.     decay_mult: 0
  2217.   }
  2218.   param {
  2219.     lr_mult: 1
  2220.     decay_mult: 0
  2221.   }
  2222.   scale_param {
  2223.     bias_term: true
  2224.   }
  2225. }
  2226. layer {
  2227.   name: "relu5_1/expand"
  2228.   type: "ReLU"
  2229.   bottom: "conv5_1/expand/bn"
  2230.   top: "conv5_1/expand/bn"
  2231. }
  2232. layer {
  2233.   name: "conv5_1/dwise"
  2234.   type: "Convolution"
  2235.   bottom: "conv5_1/expand/bn"
  2236.   top: "conv5_1/dwise"
  2237.   param {
  2238.     lr_mult: 1
  2239.     decay_mult: 1
  2240.   }
  2241.   convolution_param {
  2242.     num_output: 576
  2243.     bias_term: false
  2244.     pad: 1
  2245.     kernel_size: 3
  2246.     group: 576
  2247.     weight_filler {
  2248.       type: "msra"
  2249.     }
  2250.     engine: CAFFE
  2251.   }
  2252. }
  2253. layer {
  2254.   name: "conv5_1/dwise/bn"
  2255.   type: "BatchNorm"
  2256.   bottom: "conv5_1/dwise"
  2257.   top: "conv5_1/dwise/bn"
  2258.   param {
  2259.     lr_mult: 0
  2260.     decay_mult: 0
  2261.   }
  2262.   param {
  2263.     lr_mult: 0
  2264.     decay_mult: 0
  2265.   }
  2266.   param {
  2267.     lr_mult: 0
  2268.     decay_mult: 0
  2269.   }
  2270.   batch_norm_param {
  2271.     use_global_stats: true
  2272.     eps: 1e-5
  2273.   }
  2274. }
  2275. layer {
  2276.   name: "conv5_1/dwise/scale"
  2277.   type: "Scale"
  2278.   bottom: "conv5_1/dwise/bn"
  2279.   top: "conv5_1/dwise/bn"
  2280.   param {
  2281.     lr_mult: 1
  2282.     decay_mult: 0
  2283.   }
  2284.   param {
  2285.     lr_mult: 1
  2286.     decay_mult: 0
  2287.   }
  2288.   scale_param {
  2289.     bias_term: true
  2290.   }
  2291. }
  2292. layer {
  2293.   name: "relu5_1/dwise"
  2294.   type: "ReLU"
  2295.   bottom: "conv5_1/dwise/bn"
  2296.   top: "conv5_1/dwise/bn"
  2297. }
  2298. layer {
  2299.   name: "conv5_1/linear"
  2300.   type: "Convolution"
  2301.   bottom: "conv5_1/dwise/bn"
  2302.   top: "conv5_1/linear"
  2303.   param {
  2304.     lr_mult: 1
  2305.     decay_mult: 1
  2306.   }
  2307.   convolution_param {
  2308.     num_output: 96
  2309.     bias_term: false
  2310.     kernel_size: 1
  2311.     weight_filler {
  2312.       type: "msra"
  2313.     }
  2314.   }
  2315. }
  2316. layer {
  2317.   name: "conv5_1/linear/bn"
  2318.   type: "BatchNorm"
  2319.   bottom: "conv5_1/linear"
  2320.   top: "conv5_1/linear/bn"
  2321.   param {
  2322.     lr_mult: 0
  2323.     decay_mult: 0
  2324.   }
  2325.   param {
  2326.     lr_mult: 0
  2327.     decay_mult: 0
  2328.   }
  2329.   param {
  2330.     lr_mult: 0
  2331.     decay_mult: 0
  2332.   }
  2333.   batch_norm_param {
  2334.     use_global_stats: true
  2335.     eps: 1e-5
  2336.   }
  2337. }
  2338. layer {
  2339.   name: "conv5_1/linear/scale"
  2340.   type: "Scale"
  2341.   bottom: "conv5_1/linear/bn"
  2342.   top: "conv5_1/linear/bn"
  2343.   param {
  2344.     lr_mult: 1
  2345.     decay_mult: 0
  2346.   }
  2347.   param {
  2348.     lr_mult: 1
  2349.     decay_mult: 0
  2350.   }
  2351.   scale_param {
  2352.     bias_term: true
  2353.   }
  2354. }
  2355. layer {
  2356.   name: "block_5_1"
  2357.   type: "Eltwise"
  2358.   bottom: "conv4_7/linear/bn"
  2359.   bottom: "conv5_1/linear/bn"
  2360.   top: "block_5_1"
  2361. }
  2362. layer {
  2363.   name: "conv5_2/expand"
  2364.   type: "Convolution"
  2365.   bottom: "block_5_1"
  2366.   top: "conv5_2/expand"
  2367.   param {
  2368.     lr_mult: 1
  2369.     decay_mult: 1
  2370.   }
  2371.   convolution_param {
  2372.     num_output: 576
  2373.     bias_term: false
  2374.     kernel_size: 1
  2375.     weight_filler {
  2376.       type: "msra"
  2377.     }
  2378.   }
  2379. }
  2380. layer {
  2381.   name: "conv5_2/expand/bn"
  2382.   type: "BatchNorm"
  2383.   bottom: "conv5_2/expand"
  2384.   top: "conv5_2/expand/bn"
  2385.   param {
  2386.     lr_mult: 0
  2387.     decay_mult: 0
  2388.   }
  2389.   param {
  2390.     lr_mult: 0
  2391.     decay_mult: 0
  2392.   }
  2393.   param {
  2394.     lr_mult: 0
  2395.     decay_mult: 0
  2396.   }
  2397.   batch_norm_param {
  2398.     use_global_stats: true
  2399.     eps: 1e-5
  2400.   }
  2401. }
  2402. layer {
  2403.   name: "conv5_2/expand/scale"
  2404.   type: "Scale"
  2405.   bottom: "conv5_2/expand/bn"
  2406.   top: "conv5_2/expand/bn"
  2407.   param {
  2408.     lr_mult: 1
  2409.     decay_mult: 0
  2410.   }
  2411.   param {
  2412.     lr_mult: 1
  2413.     decay_mult: 0
  2414.   }
  2415.   scale_param {
  2416.     bias_term: true
  2417.   }
  2418. }
  2419. layer {
  2420.   name: "relu5_2/expand"
  2421.   type: "ReLU"
  2422.   bottom: "conv5_2/expand/bn"
  2423.   top: "conv5_2/expand/bn"
  2424. }
  2425. layer {
  2426.   name: "conv5_2/dwise"
  2427.   type: "Convolution"
  2428.   bottom: "conv5_2/expand/bn"
  2429.   top: "conv5_2/dwise"
  2430.   param {
  2431.     lr_mult: 1
  2432.     decay_mult: 1
  2433.   }
  2434.   convolution_param {
  2435.     num_output: 576
  2436.     bias_term: false
  2437.     pad: 1
  2438.     kernel_size: 3
  2439.     group: 576
  2440.     weight_filler {
  2441.       type: "msra"
  2442.     }
  2443.     engine: CAFFE
  2444.   }
  2445. }
  2446. layer {
  2447.   name: "conv5_2/dwise/bn"
  2448.   type: "BatchNorm"
  2449.   bottom: "conv5_2/dwise"
  2450.   top: "conv5_2/dwise/bn"
  2451.   param {
  2452.     lr_mult: 0
  2453.     decay_mult: 0
  2454.   }
  2455.   param {
  2456.     lr_mult: 0
  2457.     decay_mult: 0
  2458.   }
  2459.   param {
  2460.     lr_mult: 0
  2461.     decay_mult: 0
  2462.   }
  2463.   batch_norm_param {
  2464.     use_global_stats: true
  2465.     eps: 1e-5
  2466.   }
  2467. }
  2468. layer {
  2469.   name: "conv5_2/dwise/scale"
  2470.   type: "Scale"
  2471.   bottom: "conv5_2/dwise/bn"
  2472.   top: "conv5_2/dwise/bn"
  2473.   param {
  2474.     lr_mult: 1
  2475.     decay_mult: 0
  2476.   }
  2477.   param {
  2478.     lr_mult: 1
  2479.     decay_mult: 0
  2480.   }
  2481.   scale_param {
  2482.     bias_term: true
  2483.   }
  2484. }
  2485. layer {
  2486.   name: "relu5_2/dwise"
  2487.   type: "ReLU"
  2488.   bottom: "conv5_2/dwise/bn"
  2489.   top: "conv5_2/dwise/bn"
  2490. }
  2491. layer {
  2492.   name: "conv5_2/linear"
  2493.   type: "Convolution"
  2494.   bottom: "conv5_2/dwise/bn"
  2495.   top: "conv5_2/linear"
  2496.   param {
  2497.     lr_mult: 1
  2498.     decay_mult: 1
  2499.   }
  2500.   convolution_param {
  2501.     num_output: 96
  2502.     bias_term: false
  2503.     kernel_size: 1
  2504.     weight_filler {
  2505.       type: "msra"
  2506.     }
  2507.   }
  2508. }
  2509. layer {
  2510.   name: "conv5_2/linear/bn"
  2511.   type: "BatchNorm"
  2512.   bottom: "conv5_2/linear"
  2513.   top: "conv5_2/linear/bn"
  2514.   param {
  2515.     lr_mult: 0
  2516.     decay_mult: 0
  2517.   }
  2518.   param {
  2519.     lr_mult: 0
  2520.     decay_mult: 0
  2521.   }
  2522.   param {
  2523.     lr_mult: 0
  2524.     decay_mult: 0
  2525.   }
  2526.   batch_norm_param {
  2527.     use_global_stats: true
  2528.     eps: 1e-5
  2529.   }
  2530. }
  2531. layer {
  2532.   name: "conv5_2/linear/scale"
  2533.   type: "Scale"
  2534.   bottom: "conv5_2/linear/bn"
  2535.   top: "conv5_2/linear/bn"
  2536.   param {
  2537.     lr_mult: 1
  2538.     decay_mult: 0
  2539.   }
  2540.   param {
  2541.     lr_mult: 1
  2542.     decay_mult: 0
  2543.   }
  2544.   scale_param {
  2545.     bias_term: true
  2546.   }
  2547. }
  2548. layer {
  2549.   name: "block_5_2"
  2550.   type: "Eltwise"
  2551.   bottom: "block_5_1"
  2552.   bottom: "conv5_2/linear/bn"
  2553.   top: "block_5_2"
  2554. }
  2555. layer {
  2556.   name: "conv5_3/expand"
  2557.   type: "Convolution"
  2558.   bottom: "block_5_2"
  2559.   top: "conv5_3/expand"
  2560.   param {
  2561.     lr_mult: 1
  2562.     decay_mult: 1
  2563.   }
  2564.   convolution_param {
  2565.     num_output: 576
  2566.     bias_term: false
  2567.     kernel_size: 1
  2568.     weight_filler {
  2569.       type: "msra"
  2570.     }
  2571.   }
  2572. }
  2573. layer {
  2574.   name: "conv5_3/expand/bn"
  2575.   type: "BatchNorm"
  2576.   bottom: "conv5_3/expand"
  2577.   top: "conv5_3/expand/bn"
  2578.   param {
  2579.     lr_mult: 0
  2580.     decay_mult: 0
  2581.   }
  2582.   param {
  2583.     lr_mult: 0
  2584.     decay_mult: 0
  2585.   }
  2586.   param {
  2587.     lr_mult: 0
  2588.     decay_mult: 0
  2589.   }
  2590.   batch_norm_param {
  2591.     use_global_stats: true
  2592.     eps: 1e-5
  2593.   }
  2594. }
  2595. layer {
  2596.   name: "conv5_3/expand/scale"
  2597.   type: "Scale"
  2598.   bottom: "conv5_3/expand/bn"
  2599.   top: "conv5_3/expand/bn"
  2600.   param {
  2601.     lr_mult: 1
  2602.     decay_mult: 0
  2603.   }
  2604.   param {
  2605.     lr_mult: 1
  2606.     decay_mult: 0
  2607.   }
  2608.   scale_param {
  2609.     bias_term: true
  2610.   }
  2611. }
  2612. layer {
  2613.   name: "relu5_3/expand"
  2614.   type: "ReLU"
  2615.   bottom: "conv5_3/expand/bn"
  2616.   top: "conv5_3/expand/bn"
  2617. }
  2618. layer {
  2619.   name: "conv5_3/dwise"
  2620.   type: "Convolution"
  2621.   bottom: "conv5_3/expand/bn"
  2622.   top: "conv5_3/dwise"
  2623.   param {
  2624.     lr_mult: 1
  2625.     decay_mult: 1
  2626.   }
  2627.   convolution_param {
  2628.     num_output: 576
  2629.     bias_term: false
  2630.     pad: 1
  2631.     kernel_size: 3
  2632.     group: 576
  2633.     stride: 2
  2634.     weight_filler {
  2635.       type: "msra"
  2636.     }
  2637.     engine: CAFFE
  2638.   }
  2639. }
  2640. layer {
  2641.   name: "conv5_3/dwise/bn"
  2642.   type: "BatchNorm"
  2643.   bottom: "conv5_3/dwise"
  2644.   top: "conv5_3/dwise/bn"
  2645.   param {
  2646.     lr_mult: 0
  2647.     decay_mult: 0
  2648.   }
  2649.   param {
  2650.     lr_mult: 0
  2651.     decay_mult: 0
  2652.   }
  2653.   param {
  2654.     lr_mult: 0
  2655.     decay_mult: 0
  2656.   }
  2657.   batch_norm_param {
  2658.     use_global_stats: true
  2659.     eps: 1e-5
  2660.   }
  2661. }
  2662. layer {
  2663.   name: "conv5_3/dwise/scale"
  2664.   type: "Scale"
  2665.   bottom: "conv5_3/dwise/bn"
  2666.   top: "conv5_3/dwise/bn"
  2667.   param {
  2668.     lr_mult: 1
  2669.     decay_mult: 0
  2670.   }
  2671.   param {
  2672.     lr_mult: 1
  2673.     decay_mult: 0
  2674.   }
  2675.   scale_param {
  2676.     bias_term: true
  2677.   }
  2678. }
  2679. layer {
  2680.   name: "relu5_3/dwise"
  2681.   type: "ReLU"
  2682.   bottom: "conv5_3/dwise/bn"
  2683.   top: "conv5_3/dwise/bn"
  2684. }
  2685. layer {
  2686.   name: "conv5_3/linear"
  2687.   type: "Convolution"
  2688.   bottom: "conv5_3/dwise/bn"
  2689.   top: "conv5_3/linear"
  2690.   param {
  2691.     lr_mult: 1
  2692.     decay_mult: 1
  2693.   }
  2694.   convolution_param {
  2695.     num_output: 160
  2696.     bias_term: false
  2697.     kernel_size: 1
  2698.     weight_filler {
  2699.       type: "msra"
  2700.     }
  2701.   }
  2702. }
  2703. layer {
  2704.   name: "conv5_3/linear/bn"
  2705.   type: "BatchNorm"
  2706.   bottom: "conv5_3/linear"
  2707.   top: "conv5_3/linear/bn"
  2708.   param {
  2709.     lr_mult: 0
  2710.     decay_mult: 0
  2711.   }
  2712.   param {
  2713.     lr_mult: 0
  2714.     decay_mult: 0
  2715.   }
  2716.   param {
  2717.     lr_mult: 0
  2718.     decay_mult: 0
  2719.   }
  2720.   batch_norm_param {
  2721.     use_global_stats: true
  2722.     eps: 1e-5
  2723.   }
  2724. }
  2725. layer {
  2726.   name: "conv5_3/linear/scale"
  2727.   type: "Scale"
  2728.   bottom: "conv5_3/linear/bn"
  2729.   top: "conv5_3/linear/bn"
  2730.   param {
  2731.     lr_mult: 1
  2732.     decay_mult: 0
  2733.   }
  2734.   param {
  2735.     lr_mult: 1
  2736.     decay_mult: 0
  2737.   }
  2738.   scale_param {
  2739.     bias_term: true
  2740.   }
  2741. }
  2742. layer {
  2743.   name: "conv6_1/expand"
  2744.   type: "Convolution"
  2745.   bottom: "conv5_3/linear/bn"
  2746.   top: "conv6_1/expand"
  2747.   param {
  2748.     lr_mult: 1
  2749.     decay_mult: 1
  2750.   }
  2751.   convolution_param {
  2752.     num_output: 960
  2753.     bias_term: false
  2754.     kernel_size: 1
  2755.     weight_filler {
  2756.       type: "msra"
  2757.     }
  2758.   }
  2759. }
  2760. layer {
  2761.   name: "conv6_1/expand/bn"
  2762.   type: "BatchNorm"
  2763.   bottom: "conv6_1/expand"
  2764.   top: "conv6_1/expand/bn"
  2765.   param {
  2766.     lr_mult: 0
  2767.     decay_mult: 0
  2768.   }
  2769.   param {
  2770.     lr_mult: 0
  2771.     decay_mult: 0
  2772.   }
  2773.   param {
  2774.     lr_mult: 0
  2775.     decay_mult: 0
  2776.   }
  2777.   batch_norm_param {
  2778.     use_global_stats: true
  2779.     eps: 1e-5
  2780.   }
  2781. }
  2782. layer {
  2783.   name: "conv6_1/expand/scale"
  2784.   type: "Scale"
  2785.   bottom: "conv6_1/expand/bn"
  2786.   top: "conv6_1/expand/bn"
  2787.   param {
  2788.     lr_mult: 1
  2789.     decay_mult: 0
  2790.   }
  2791.   param {
  2792.     lr_mult: 1
  2793.     decay_mult: 0
  2794.   }
  2795.   scale_param {
  2796.     bias_term: true
  2797.   }
  2798. }
  2799. layer {
  2800.   name: "relu6_1/expand"
  2801.   type: "ReLU"
  2802.   bottom: "conv6_1/expand/bn"
  2803.   top: "conv6_1/expand/bn"
  2804. }
  2805. layer {
  2806.   name: "conv6_1/dwise"
  2807.   type: "Convolution"
  2808.   bottom: "conv6_1/expand/bn"
  2809.   top: "conv6_1/dwise"
  2810.   param {
  2811.     lr_mult: 1
  2812.     decay_mult: 1
  2813.   }
  2814.   convolution_param {
  2815.     num_output: 960
  2816.     bias_term: false
  2817.     pad: 1
  2818.     kernel_size: 3
  2819.     group: 960
  2820.     weight_filler {
  2821.       type: "msra"
  2822.     }
  2823.     engine: CAFFE
  2824.   }
  2825. }
  2826. layer {
  2827.   name: "conv6_1/dwise/bn"
  2828.   type: "BatchNorm"
  2829.   bottom: "conv6_1/dwise"
  2830.   top: "conv6_1/dwise/bn"
  2831.   param {
  2832.     lr_mult: 0
  2833.     decay_mult: 0
  2834.   }
  2835.   param {
  2836.     lr_mult: 0
  2837.     decay_mult: 0
  2838.   }
  2839.   param {
  2840.     lr_mult: 0
  2841.     decay_mult: 0
  2842.   }
  2843.   batch_norm_param {
  2844.     use_global_stats: true
  2845.     eps: 1e-5
  2846.   }
  2847. }
  2848. layer {
  2849.   name: "conv6_1/dwise/scale"
  2850.   type: "Scale"
  2851.   bottom: "conv6_1/dwise/bn"
  2852.   top: "conv6_1/dwise/bn"
  2853.   param {
  2854.     lr_mult: 1
  2855.     decay_mult: 0
  2856.   }
  2857.   param {
  2858.     lr_mult: 1
  2859.     decay_mult: 0
  2860.   }
  2861.   scale_param {
  2862.     bias_term: true
  2863.   }
  2864. }
  2865. layer {
  2866.   name: "relu6_1/dwise"
  2867.   type: "ReLU"
  2868.   bottom: "conv6_1/dwise/bn"
  2869.   top: "conv6_1/dwise/bn"
  2870. }
  2871. layer {
  2872.   name: "conv6_1/linear"
  2873.   type: "Convolution"
  2874.   bottom: "conv6_1/dwise/bn"
  2875.   top: "conv6_1/linear"
  2876.   param {
  2877.     lr_mult: 1
  2878.     decay_mult: 1
  2879.   }
  2880.   convolution_param {
  2881.     num_output: 160
  2882.     bias_term: false
  2883.     kernel_size: 1
  2884.     weight_filler {
  2885.       type: "msra"
  2886.     }
  2887.   }
  2888. }
  2889. layer {
  2890.   name: "conv6_1/linear/bn"
  2891.   type: "BatchNorm"
  2892.   bottom: "conv6_1/linear"
  2893.   top: "conv6_1/linear/bn"
  2894.   param {
  2895.     lr_mult: 0
  2896.     decay_mult: 0
  2897.   }
  2898.   param {
  2899.     lr_mult: 0
  2900.     decay_mult: 0
  2901.   }
  2902.   param {
  2903.     lr_mult: 0
  2904.     decay_mult: 0
  2905.   }
  2906.   batch_norm_param {
  2907.     use_global_stats: true
  2908.     eps: 1e-5
  2909.   }
  2910. }
  2911. layer {
  2912.   name: "conv6_1/linear/scale"
  2913.   type: "Scale"
  2914.   bottom: "conv6_1/linear/bn"
  2915.   top: "conv6_1/linear/bn"
  2916.   param {
  2917.     lr_mult: 1
  2918.     decay_mult: 0
  2919.   }
  2920.   param {
  2921.     lr_mult: 1
  2922.     decay_mult: 0
  2923.   }
  2924.   scale_param {
  2925.     bias_term: true
  2926.   }
  2927. }
  2928. layer {
  2929.   name: "block_6_1"
  2930.   type: "Eltwise"
  2931.   bottom: "conv5_3/linear/bn"
  2932.   bottom: "conv6_1/linear/bn"
  2933.   top: "block_6_1"
  2934. }
  2935. layer {
  2936.   name: "conv6_2/expand"
  2937.   type: "Convolution"
  2938.   bottom: "block_6_1"
  2939.   top: "conv6_2/expand"
  2940.   param {
  2941.     lr_mult: 1
  2942.     decay_mult: 1
  2943.   }
  2944.   convolution_param {
  2945.     num_output: 960
  2946.     bias_term: false
  2947.     kernel_size: 1
  2948.     weight_filler {
  2949.       type: "msra"
  2950.     }
  2951.   }
  2952. }
  2953. layer {
  2954.   name: "conv6_2/expand/bn"
  2955.   type: "BatchNorm"
  2956.   bottom: "conv6_2/expand"
  2957.   top: "conv6_2/expand/bn"
  2958.   param {
  2959.     lr_mult: 0
  2960.     decay_mult: 0
  2961.   }
  2962.   param {
  2963.     lr_mult: 0
  2964.     decay_mult: 0
  2965.   }
  2966.   param {
  2967.     lr_mult: 0
  2968.     decay_mult: 0
  2969.   }
  2970.   batch_norm_param {
  2971.     use_global_stats: true
  2972.     eps: 1e-5
  2973.   }
  2974. }
  2975. layer {
  2976.   name: "conv6_2/expand/scale"
  2977.   type: "Scale"
  2978.   bottom: "conv6_2/expand/bn"
  2979.   top: "conv6_2/expand/bn"
  2980.   param {
  2981.     lr_mult: 1
  2982.     decay_mult: 0
  2983.   }
  2984.   param {
  2985.     lr_mult: 1
  2986.     decay_mult: 0
  2987.   }
  2988.   scale_param {
  2989.     bias_term: true
  2990.   }
  2991. }
  2992. layer {
  2993.   name: "relu6_2/expand"
  2994.   type: "ReLU"
  2995.   bottom: "conv6_2/expand/bn"
  2996.   top: "conv6_2/expand/bn"
  2997. }
  2998. layer {
  2999.   name: "conv6_2/dwise"
  3000.   type: "Convolution"
  3001.   bottom: "conv6_2/expand/bn"
  3002.   top: "conv6_2/dwise"
  3003.   param {
  3004.     lr_mult: 1
  3005.     decay_mult: 1
  3006.   }
  3007.   convolution_param {
  3008.     num_output: 960
  3009.     bias_term: false
  3010.     pad: 1
  3011.     kernel_size: 3
  3012.     group: 960
  3013.     weight_filler {
  3014.       type: "msra"
  3015.     }
  3016.     engine: CAFFE
  3017.   }
  3018. }
  3019. layer {
  3020.   name: "conv6_2/dwise/bn"
  3021.   type: "BatchNorm"
  3022.   bottom: "conv6_2/dwise"
  3023.   top: "conv6_2/dwise/bn"
  3024.   param {
  3025.     lr_mult: 0
  3026.     decay_mult: 0
  3027.   }
  3028.   param {
  3029.     lr_mult: 0
  3030.     decay_mult: 0
  3031.   }
  3032.   param {
  3033.     lr_mult: 0
  3034.     decay_mult: 0
  3035.   }
  3036.   batch_norm_param {
  3037.     use_global_stats: true
  3038.     eps: 1e-5
  3039.   }
  3040. }
  3041. layer {
  3042.   name: "conv6_2/dwise/scale"
  3043.   type: "Scale"
  3044.   bottom: "conv6_2/dwise/bn"
  3045.   top: "conv6_2/dwise/bn"
  3046.   param {
  3047.     lr_mult: 1
  3048.     decay_mult: 0
  3049.   }
  3050.   param {
  3051.     lr_mult: 1
  3052.     decay_mult: 0
  3053.   }
  3054.   scale_param {
  3055.     bias_term: true
  3056.   }
  3057. }
  3058. layer {
  3059.   name: "relu6_2/dwise"
  3060.   type: "ReLU"
  3061.   bottom: "conv6_2/dwise/bn"
  3062.   top: "conv6_2/dwise/bn"
  3063. }
  3064. layer {
  3065.   name: "conv6_2/linear"
  3066.   type: "Convolution"
  3067.   bottom: "conv6_2/dwise/bn"
  3068.   top: "conv6_2/linear"
  3069.   param {
  3070.     lr_mult: 1
  3071.     decay_mult: 1
  3072.   }
  3073.   convolution_param {
  3074.     num_output: 160
  3075.     bias_term: false
  3076.     kernel_size: 1
  3077.     weight_filler {
  3078.       type: "msra"
  3079.     }
  3080.   }
  3081. }
  3082. layer {
  3083.   name: "conv6_2/linear/bn"
  3084.   type: "BatchNorm"
  3085.   bottom: "conv6_2/linear"
  3086.   top: "conv6_2/linear/bn"
  3087.   param {
  3088.     lr_mult: 0
  3089.     decay_mult: 0
  3090.   }
  3091.   param {
  3092.     lr_mult: 0
  3093.     decay_mult: 0
  3094.   }
  3095.   param {
  3096.     lr_mult: 0
  3097.     decay_mult: 0
  3098.   }
  3099.   batch_norm_param {
  3100.     use_global_stats: true
  3101.     eps: 1e-5
  3102.   }
  3103. }
  3104. layer {
  3105.   name: "conv6_2/linear/scale"
  3106.   type: "Scale"
  3107.   bottom: "conv6_2/linear/bn"
  3108.   top: "conv6_2/linear/bn"
  3109.   param {
  3110.     lr_mult: 1
  3111.     decay_mult: 0
  3112.   }
  3113.   param {
  3114.     lr_mult: 1
  3115.     decay_mult: 0
  3116.   }
  3117.   scale_param {
  3118.     bias_term: true
  3119.   }
  3120. }
  3121. layer {
  3122.   name: "block_6_2"
  3123.   type: "Eltwise"
  3124.   bottom: "block_6_1"
  3125.   bottom: "conv6_2/linear/bn"
  3126.   top: "block_6_2"
  3127. }
  3128. layer {
  3129.   name: "conv6_3/expand"
  3130.   type: "Convolution"
  3131.   bottom: "block_6_2"
  3132.   top: "conv6_3/expand"
  3133.   param {
  3134.     lr_mult: 1
  3135.     decay_mult: 1
  3136.   }
  3137.   convolution_param {
  3138.     num_output: 960
  3139.     bias_term: false
  3140.     kernel_size: 1
  3141.     weight_filler {
  3142.       type: "msra"
  3143.     }
  3144.   }
  3145. }
  3146. layer {
  3147.   name: "conv6_3/expand/bn"
  3148.   type: "BatchNorm"
  3149.   bottom: "conv6_3/expand"
  3150.   top: "conv6_3/expand/bn"
  3151.   param {
  3152.     lr_mult: 0
  3153.     decay_mult: 0
  3154.   }
  3155.   param {
  3156.     lr_mult: 0
  3157.     decay_mult: 0
  3158.   }
  3159.   param {
  3160.     lr_mult: 0
  3161.     decay_mult: 0
  3162.   }
  3163.   batch_norm_param {
  3164.     use_global_stats: true
  3165.     eps: 1e-5
  3166.   }
  3167. }
  3168. layer {
  3169.   name: "conv6_3/expand/scale"
  3170.   type: "Scale"
  3171.   bottom: "conv6_3/expand/bn"
  3172.   top: "conv6_3/expand/bn"
  3173.   param {
  3174.     lr_mult: 1
  3175.     decay_mult: 0
  3176.   }
  3177.   param {
  3178.     lr_mult: 1
  3179.     decay_mult: 0
  3180.   }
  3181.   scale_param {
  3182.     bias_term: true
  3183.   }
  3184. }
  3185. layer {
  3186.   name: "relu6_3/expand"
  3187.   type: "ReLU"
  3188.   bottom: "conv6_3/expand/bn"
  3189.   top: "conv6_3/expand/bn"
  3190. }
  3191. layer {
  3192.   name: "conv6_3/dwise"
  3193.   type: "Convolution"
  3194.   bottom: "conv6_3/expand/bn"
  3195.   top: "conv6_3/dwise"
  3196.   param {
  3197.     lr_mult: 1
  3198.     decay_mult: 1
  3199.   }
  3200.   convolution_param {
  3201.     num_output: 960
  3202.     bias_term: false
  3203.     pad: 1
  3204.     kernel_size: 3
  3205.     group: 960
  3206.     weight_filler {
  3207.       type: "msra"
  3208.     }
  3209.     engine: CAFFE
  3210.   }
  3211. }
  3212. layer {
  3213.   name: "conv6_3/dwise/bn"
  3214.   type: "BatchNorm"
  3215.   bottom: "conv6_3/dwise"
  3216.   top: "conv6_3/dwise/bn"
  3217.   param {
  3218.     lr_mult: 0
  3219.     decay_mult: 0
  3220.   }
  3221.   param {
  3222.     lr_mult: 0
  3223.     decay_mult: 0
  3224.   }
  3225.   param {
  3226.     lr_mult: 0
  3227.     decay_mult: 0
  3228.   }
  3229.   batch_norm_param {
  3230.     use_global_stats: true
  3231.     eps: 1e-5
  3232.   }
  3233. }
  3234. layer {
  3235.   name: "conv6_3/dwise/scale"
  3236.   type: "Scale"
  3237.   bottom: "conv6_3/dwise/bn"
  3238.   top: "conv6_3/dwise/bn"
  3239.   param {
  3240.     lr_mult: 1
  3241.     decay_mult: 0
  3242.   }
  3243.   param {
  3244.     lr_mult: 1
  3245.     decay_mult: 0
  3246.   }
  3247.   scale_param {
  3248.     bias_term: true
  3249.   }
  3250. }
  3251. layer {
  3252.   name: "relu6_3/dwise"
  3253.   type: "ReLU"
  3254.   bottom: "conv6_3/dwise/bn"
  3255.   top: "conv6_3/dwise/bn"
  3256. }
  3257. layer {
  3258.   name: "conv6_3/linear"
  3259.   type: "Convolution"
  3260.   bottom: "conv6_3/dwise/bn"
  3261.   top: "conv6_3/linear"
  3262.   param {
  3263.     lr_mult: 1
  3264.     decay_mult: 1
  3265.   }
  3266.   convolution_param {
  3267.     num_output: 320
  3268.     bias_term: false
  3269.     kernel_size: 1
  3270.     weight_filler {
  3271.       type: "msra"
  3272.     }
  3273.   }
  3274. }
  3275. layer {
  3276.   name: "conv6_3/linear/bn"
  3277.   type: "BatchNorm"
  3278.   bottom: "conv6_3/linear"
  3279.   top: "conv6_3/linear/bn"
  3280.   param {
  3281.     lr_mult: 0
  3282.     decay_mult: 0
  3283.   }
  3284.   param {
  3285.     lr_mult: 0
  3286.     decay_mult: 0
  3287.   }
  3288.   param {
  3289.     lr_mult: 0
  3290.     decay_mult: 0
  3291.   }
  3292.   batch_norm_param {
  3293.     use_global_stats: true
  3294.     eps: 1e-5
  3295.   }
  3296. }
  3297. layer {
  3298.   name: "conv6_3/linear/scale"
  3299.   type: "Scale"
  3300.   bottom: "conv6_3/linear/bn"
  3301.   top: "conv6_3/linear/bn"
  3302.   param {
  3303.     lr_mult: 1
  3304.     decay_mult: 0
  3305.   }
  3306.   param {
  3307.     lr_mult: 1
  3308.     decay_mult: 0
  3309.   }
  3310.   scale_param {
  3311.     bias_term: true
  3312.   }
  3313. }
  3314. layer {
  3315.   name: "conv6_4"
  3316.   type: "Convolution"
  3317.   bottom: "conv6_3/linear/bn"
  3318.   top: "conv6_4"
  3319.   param {
  3320.     lr_mult: 1
  3321.     decay_mult: 1
  3322.   }
  3323.   convolution_param {
  3324.     num_output: 1280
  3325.     bias_term: false
  3326.     kernel_size: 1
  3327.     weight_filler {
  3328.       type: "msra"
  3329.     }
  3330.   }
  3331. }
  3332. layer {
  3333.   name: "conv6_4/bn"
  3334.   type: "BatchNorm"
  3335.   bottom: "conv6_4"
  3336.   top: "conv6_4/bn"
  3337.   param {
  3338.     lr_mult: 0
  3339.     decay_mult: 0
  3340.   }
  3341.   param {
  3342.     lr_mult: 0
  3343.     decay_mult: 0
  3344.   }
  3345.   param {
  3346.     lr_mult: 0
  3347.     decay_mult: 0
  3348.   }
  3349.   batch_norm_param {
  3350.     use_global_stats: true
  3351.     eps: 1e-5
  3352.   }
  3353. }
  3354. layer {
  3355.   name: "conv6_4/scale"
  3356.   type: "Scale"
  3357.   bottom: "conv6_4/bn"
  3358.   top: "conv6_4/bn"
  3359.   param {
  3360.     lr_mult: 1
  3361.     decay_mult: 0
  3362.   }
  3363.   param {
  3364.     lr_mult: 1
  3365.     decay_mult: 0
  3366.   }
  3367.   scale_param {
  3368.     bias_term: true
  3369.   }
  3370. }
  3371. layer {
  3372.   name: "relu6_4"
  3373.   type: "ReLU"
  3374.   bottom: "conv6_4/bn"
  3375.   top: "conv6_4/bn"
  3376. }
  3377. layer {
  3378.   name: "pool6"
  3379.   type: "Pooling"
  3380.   bottom: "conv6_4/bn"
  3381.   top: "pool6"
  3382.   pooling_param {
  3383.     pool: AVE
  3384.     global_pooling: true
  3385.   }
  3386. }
  3387. layer {
  3388.   name: "fc7"
  3389.   type: "Convolution"
  3390.   bottom: "pool6"
  3391.   top: "fc7"
  3392.   param {
  3393.     lr_mult: 1
  3394.     decay_mult: 1
  3395.   }
  3396.   param {
  3397.     lr_mult: 2
  3398.     decay_mult: 0
  3399.   }
  3400.   convolution_param {
  3401.     num_output: 1000
  3402.     kernel_size: 1
  3403.     weight_filler {
  3404.       type: "msra"
  3405.     }
  3406.     bias_filler {
  3407.       type: "constant"
  3408.       value: 0
  3409.     }
  3410.   }
  3411. }
  3412. layer {
  3413.   name: "prob"
  3414.   type: "Softmax"
  3415.   bottom: "fc7"
  3416.   top: "prob"
  3417. }
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