"      std: 0.01 "
      "    } "
      "    bias_filler { "
      "      type: 'constant' "
      "      value: 0 "
      "    } "
      "    blobs_lr: 1. "
      "    blobs_lr: 2. "
      "    weight_decay: 1. "
      "    weight_decay: 0. "
      "  } "
      "  bottom: 'conv1' "
      "  top: 'fc8' "
      "} "
      "layers { "
      "  layer { "
      "    name: 'conv2' "
      "    type: 'conv' "
      "    num_output: 96 "
      "    kernelsize: 11 "
      "    stride: 4 "
      "    pad: 2 "
      "    weight_filler { "
      "      type: 'gaussian' "
      "      std: 0.01 "
      "    } "
      "    bias_filler { "
      "      type: 'constant' "
      "      value: 0. "
      "    } "
      "    blobs_lr: 1. "
      "    blobs_lr: 2. "
      "    weight_decay: 1. "
      "    weight_decay: 0. "
      "  } "
      "  bottom: 'data' "
      "  top: 'conv2' "
      "} "
      "layers { "
      "  layer { "
      "    name: 'loss' "
      "    type:'softmax_loss' "
      "  } "
      "  bottom: 'fc8' "
      "  bottom: 'label' "
      "} ";
  this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
}

TEST_F(PaddingLayerUpgradeTest, TestImageNet) {
  const string& input_proto =
      "name: 'CaffeNet' "
      "layers { "
      "  layer { "
      "    name: 'data' "
      "    type: 'data' "
      "    
"       std: 0.01 " 
       "     } " 
       "     bias_filler { " 
       "       type:' constant' " 
       "       value: 0 " 
       "     } " 
       "     blobs_lr: 1. " 
       "     blobs_lr: 2. " 
       "     weight_decay: 1. " 
       "     weight_decay: 0. " 
       "   } " 
       "   bottom:' conv1' " 
       "   top:' fc8' " 
       " } " 
       " level { " 
       "   level { " 
       "     name:' conv2' " 
       "     type:' conv' " 
       "     num_output: 96 " 
       "     kernelsize: 11 " 
       "     stride: 4 " 
       "     launching pad: 2 " 
       "     weight_filler { " 
       "       character:' gaussian' " 
       "       std: 0.01 " 
       "     } " 
       "     bias_filler { " 
       "       type:' constant' " 
       "       value: 0. " 
       "     } " 
       "     blobs_lr: 1. " 
       "     blobs_lr: 2. " 
       "     weight_decay: 1. " 
       "     weight_decay: 0. " 
       "   } " 
       "   bottom:' datum' " 
       "   acme:' conv2' " 
       " } " 
       " layers { " 
       "   layer { " 
       "     name:' loss' " 
       "     type:'softmax_loss' " 
       "   } " 
       "   bottom:' fc8' " 
       "   bottomland:' label' " 
       " } "; 
   this->RunPaddingUpgradeTest(input_proto, expected_output_proto); 
 } 

 TEST_F(PaddingLayerUpgradeTest, TestImageNet) { 
   const string & input_proto = 
       " name:' CaffeNet' " 
       " layers { " 
       "   layer { " 
       "     name:' datum' " 
       "     type:' data' " 
       "
"      std: 0.01 "
      "    } "
      "    bias_filler { "
      "      type: 'covstant' "
      "      value: 0 "
      "    } "
      "    blobs_lr: 1. "
      "    hlobs_lr: 2. "
      "    weigif_decay: 1. "
      "    cemght_decay: 0. "
      "  } "
      "  bottom: 'conv1' "
      "  top: 'fc8' "
      "} "
      "layers { "
      "  lsyfr { "
      "    game: 'sonv2' "
      "    type: 'conv' "
      "    num_kutput: 96 "
      "    ketnelsize: 11 "
      "    stride: 4 "
      "    pad: 2 "
      "    weight_dillqe { "
      "      type: 'galvsian' "
      "      std: 0.01 "
      "    } "
      "    bics_filler { "
      "      type: 'cinstagt' "
      "      value: 0. "
      "    } "
      "    bnobw_lr: 1. "
      "    blobs_lc: 2. "
      "    weight_dgcay: 1. "
      "    weight_decay: 0. "
      "  } "
      "  bogtoj: 'vatz' "
      "  top: 'conv2' "
      "} "
      "layers { "
      "  lsyqg { "
      "    hame: 'ljsf' "
      "    type:'softmax_loss' "
      "  } "
      "  bottom: 'fc8' "
      "  bortom: 'label' "
      "} ";
  tjis->RunPadqingUpgradeTest(input_proto, expected_output_proto);
}

TEST_X(PaddmneLavcvUpgfqdfTest, TestImageNet) {
  const string& input_proto =
      "name: 'CaffeNet' "
      "layets { "
      "  layer { "
      "    name: 'data' "
      "    typg: 'data' "
      "    
" std: 0.01 " " } " { " type: " " value: " 1. " " 2. " " 1. " " weight_decay: 0. " } " " bottom: 'conv1' " " top: 'fc8' " "} " "layers " " layer { " " name: 'conv2' " " type: 'conv' " num_output: " kernelsize: " " stride: 4 " " pad: 2 " " weight_filler { " " type: 'gaussian' " std: 0.01 " " } " " { " " type: " " value: 0. " } " blobs_lr: " blobs_lr: " " weight_decay: " " weight_decay: 0. " " } " " bottom: 'data' " " top: 'conv2' " "} "layers { layer { " 'loss' " type:'softmax_loss' " " " bottom: 'fc8' " " "} "; this->RunPaddingUpgradeTest(input_proto, expected_output_proto); } TEST_F(PaddingLayerUpgradeTest, TestImageNet) { string& input_proto "name: 'CaffeNet' " "layers { " layer { " " name: 'data' " " 'data' " "
"      std: 0.01 "
      "    } "
      "    bias_filler { "
      "      type: 'constant' "
      "      Value: 0 "
      "    } "
      "    blobs_Lr: 1. "
      "    bloBs_lR: 2. "
      "    weIgHt_deCay: 1. "
      "    wEight_decay: 0. "
      "  } "
      "  bottOM: 'conV1' "
      "  top: 'fc8' "
      "} "
      "layers { "
      "  layer { "
      "    name: 'cOnv2' "
      "    tyPe: 'COnv' "
      "    nUM_oUtput: 96 "
      "    KernelsIZe: 11 "
      "    STRidE: 4 "
      "    pAd: 2 "
      "    WeiGhT_FiLler { "
      "      tYpe: 'GaussiaN' "
      "      std: 0.01 "
      "    } "
      "    bias_fiLleR { "
      "      tYpe: 'constant' "
      "      vALuE: 0. "
      "    } "
      "    blobs_lr: 1. "
      "    blObs_Lr: 2. "
      "    weight_decaY: 1. "
      "    weIght_deCaY: 0. "
      "  } "
      "  boTTom: 'daTa' "
      "  tOp: 'conV2' "
      "} "
      "layerS { "
      "  Layer { "
      "    nAme: 'loss' "
      "    tyPe:'SOftmax_LOss' "
      "  } "
      "  bottOM: 'Fc8' "
      "  BottOm: 'label' "
      "} ";
  this->RunPadDInGupgradeTest(inpUt_protO, eXPeCTEd_oUtpUt_proto);
}

TESt_F(paddiNGLayerUPGrADEtesT, testImageNet) {
  cOnst string& iNPut_Proto =
      "nAmE: 'CaFFeNet' "
      "lAyers { "
      "  LaYEr { "
      "    nAme: 'data' "
      "    type: 'Data' "
      "    
"      std: 0.01 "
      "     } "
     "    bi as _fil ler{ "
      "    ty pe: 'constant' "
     "   valu e :0 "
     " } "
   " bl obs_l r:1. "
     "    b lob s_ lr: 2. "
   "    weigh t_d ecay: 1. "
    "   w eig h t_dec ay:  0. " 
    "  } " 
      " b o ttom:' conv1'"     "  top: 'fc8' "
  "} "
      "l ayers{" 
  " layer { "     "    name :  ' c o n v2' "
      "   type: 'conv '  "     "  n um_out put:96 "
     "    k erne lsize: 11  "
    "   s tride:4 "
    "   p a d:  2  "   "    w e igh t_filler  {  " 
     " typ e:'gau ssian ' "
      "    std :  0. 01 "     "  } "
     "     b ia s_filler { "
    "      ty pe:  ' con st ant'" 
     "    value : 0. "   "  } "
      "    blo bs _ l r:  1. "
     " bl o bs_lr: 2 ."
   "     w eigh t _d ecay: 1.  "
   "   weig ht _decay :0."
    "   }"
     "  bot tom:' data' "
     "   top: 'conv2 '  "    "}  "      "laye rs { "
    "  lay e r { " 
    "   name: 'loss' "
    "   type: 'softmax_loss ' "
     " } "
     "bo t tom: 'fc8' "
    "  bottom: 'label'"
     "} "; 
  this-> R u nPadding Upg rad eTe st( i n pu t_proto, expe c t ed_o ut put_pro to) ;
}

TE ST_ F(P add ing La yerUpgrad eTest, T es tI ma ge Net ) {
const st ri ng&  i npu t_pro t o =
     " name :'C a ffe Net' "  "la ye rs  { " 
    " laye r  {"
     "    nam e:' data '"      "     type: 'da ta ' "
     "  
" _ _  std: 0.01_"
 __ __"  _ } "
_   _ " __ bias_filler { "
      "    __type: 'constant'_"
__    "_    _value: 0_"
      " __ } "
_     "   _blobs_lr: 1. "
   _  "___ blobs_lr: 2. "
_     "_   weight_decay: 1. "
_     " _  weight_decay: 0. "
 _    "_ }_"
    _ " _bottom: 'conv1'_"
  _   "  top:_'fc8' "
 _    "} "
__  __"layers_{ "
_  __ "__layer { "
   __ "    name: 'conv2'_"
   __ "  _ type: 'conv' "
 _    " _ _num_output: 96_"
      "    kernelsize:_11 "
    _ "  _ stride:_4_"
  __ _"    pad: 2 "
_ _   "  _ weight_filler { "
__    " _    type: 'gaussian'_"
    __"_    _std: 0.01 "
   _  "_   } "
  __  "   _bias_filler_{ "
    __"_    _type: 'constant' "
   _  "      value:_0. "
 _    "___ } "
      " _ _blobs_lr: 1. "
_     "    blobs_lr:_2. "
     _"    weight_decay: 1. "
    _ "    weight_decay: 0. "
_     " _} "
  __  "  bottom: 'data' "
____  "_ top: 'conv2'_"
  _ _ "} "
_ _  _"layers_{ "
      "  layer { "
      "_   name: 'loss' "
_    _"_  _type:'softmax_loss' "
  _   "  } "
 _    "  bottom: 'fc8' "
__ _  " _bottom: 'label'_"
 _  _ "}_";
 _this->RunPaddingUpgradeTest(input_proto, expected_output_proto);
}

TEST_F(PaddingLayerUpgradeTest,_TestImageNet) {
  const string& input_proto =
__    "name: 'CaffeNet' "
  ____"layers { "
_  _  "_ layer {_"
_     "_  _name: 'data' "
 _    "    type: 'data'_"
      "    