Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Ceil mode from experimental version of Caffe, https://github.com/BVLC/caffe/pull/3057

Browse Source
pull/9880/head
Dmitry Kurtaev 7 years ago
parent 1563300197
commit b903ff8992
4 changed files with 275 additions and 156 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 377
      modules/dnn/misc/caffe/caffe.pb.cc
  2. 34
      modules/dnn/misc/caffe/caffe.pb.h
  3. 3
      modules/dnn/src/caffe/caffe.proto
  4. 17
      modules/dnn/test/test_caffe_importer.cpp

377
modules/dnn/misc/caffe/caffe.pb.cc
Unescape View File

@ -1212,7 +1212,7 @@ void protobuf_AssignDesc_caffe_2eproto() {
sizeof(ParameterParameter),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(ParameterParameter, _internal_metadata_));
PoolingParameter_descriptor_ = file->message_type(48);
static const int PoolingParameter_offsets_[12] = {
static const int PoolingParameter_offsets_[13] = {
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, pool_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, pad_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, pad_h_),
@ -1225,6 +1225,7 @@ void protobuf_AssignDesc_caffe_2eproto() {
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, stride_w_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, engine_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, global_pooling_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PoolingParameter, ceil_mode_),
};
PoolingParameter_reflection_ =
::google::protobuf::internal::GeneratedMessageReflection::NewGeneratedMessageReflection(
@ -2384,7 +2385,7 @@ void protobuf_AddDesc_caffe_2eproto_impl() {
"rameter\022 \n\022normalize_variance\030\001 \001(\010:\004tru"
"e\022\036\n\017across_channels\030\002 \001(\010:\005false\022\022\n\003eps"
"\030\003 \001(\002:\0051e-09\"5\n\022ParameterParameter\022\037\n\005s"
"hape\030\001 \001(\0132\020.caffe.BlobShape\"\242\003\n\020Pooling"
"hape\030\001 \001(\0132\020.caffe.BlobShape\"\273\003\n\020Pooling"
"Parameter\0225\n\004pool\030\001 \001(\0162\".caffe.PoolingP"
"arameter.PoolMethod:\003MAX\022\016\n\003pad\030\004 \001(\r:\0010"
"\022\020\n\005pad_h\030\t \001(\r:\0010\022\020\n\005pad_w\030\n \001(\r:\0010\022\023\n\013"
@ -2392,158 +2393,159 @@ void protobuf_AddDesc_caffe_2eproto_impl() {
"ernel_w\030\006 \001(\r\022\021\n\006stride\030\003 \001(\r:\0011\022\020\n\010stri"
"de_h\030\007 \001(\r\022\020\n\010stride_w\030\010 \001(\r\0227\n\006engine\030\013"
" \001(\0162\036.caffe.PoolingParameter.Engine:\007DE"
"FAULT\022\035\n\016global_pooling\030\014 \001(\010:\005false\".\n\n"
"PoolMethod\022\007\n\003MAX\020\000\022\007\n\003AVE\020\001\022\016\n\nSTOCHAST"
"IC\020\002\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t"
"\n\005CUDNN\020\002\"F\n\016PowerParameter\022\020\n\005power\030\001 \001"
"(\002:\0011\022\020\n\005scale\030\002 \001(\002:\0011\022\020\n\005shift\030\003 \001(\002:\001"
"0\"g\n\017PythonParameter\022\016\n\006module\030\001 \001(\t\022\r\n\005"
"layer\030\002 \001(\t\022\023\n\tparam_str\030\003 \001(\t:\000\022 \n\021shar"
"e_in_parallel\030\004 \001(\010:\005false\"\300\001\n\022Recurrent"
"Parameter\022\025\n\nnum_output\030\001 \001(\r:\0010\022-\n\rweig"
"ht_filler\030\002 \001(\0132\026.caffe.FillerParameter\022"
"+\n\013bias_filler\030\003 \001(\0132\026.caffe.FillerParam"
"eter\022\031\n\ndebug_info\030\004 \001(\010:\005false\022\034\n\rexpos"
"e_hidden\030\005 \001(\010:\005false\"\255\001\n\022ReductionParam"
"eter\022=\n\toperation\030\001 \001(\0162%.caffe.Reductio"
"nParameter.ReductionOp:\003SUM\022\017\n\004axis\030\002 \001("
"\005:\0010\022\020\n\005coeff\030\003 \001(\002:\0011\"5\n\013ReductionOp\022\007\n"
"\003SUM\020\001\022\010\n\004ASUM\020\002\022\t\n\005SUMSQ\020\003\022\010\n\004MEAN\020\004\"\215\001"
"\n\rReLUParameter\022\031\n\016negative_slope\030\001 \001(\002:"
"\0010\0224\n\006engine\030\002 \001(\0162\033.caffe.ReLUParameter"
".Engine:\007DEFAULT\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022"
"\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"Z\n\020ReshapeParamete"
"r\022\037\n\005shape\030\001 \001(\0132\020.caffe.BlobShape\022\017\n\004ax"
"is\030\002 \001(\005:\0010\022\024\n\010num_axes\030\003 \001(\005:\002-1\"\245\001\n\016Sc"
"aleParameter\022\017\n\004axis\030\001 \001(\005:\0011\022\023\n\010num_axe"
"s\030\002 \001(\005:\0011\022&\n\006filler\030\003 \001(\0132\026.caffe.Fille"
"rParameter\022\030\n\tbias_term\030\004 \001(\010:\005false\022+\n\013"
"bias_filler\030\005 \001(\0132\026.caffe.FillerParamete"
"r\"x\n\020SigmoidParameter\0227\n\006engine\030\001 \001(\0162\036."
"caffe.SigmoidParameter.Engine:\007DEFAULT\"+"
"\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDN"
"N\020\002\"L\n\016SliceParameter\022\017\n\004axis\030\003 \001(\005:\0011\022\023"
"\n\013slice_point\030\002 \003(\r\022\024\n\tslice_dim\030\001 \001(\r:\001"
"1\"\211\001\n\020SoftmaxParameter\0227\n\006engine\030\001 \001(\0162\036"
".caffe.SoftmaxParameter.Engine:\007DEFAULT\022"
"\017\n\004axis\030\002 \001(\005:\0011\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022"
"\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"r\n\rTanHParameter\0224"
"\n\006engine\030\001 \001(\0162\033.caffe.TanHParameter.Eng"
"ine:\007DEFAULT\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005C"
"AFFE\020\001\022\t\n\005CUDNN\020\002\"/\n\rTileParameter\022\017\n\004ax"
"is\030\001 \001(\005:\0011\022\r\n\005tiles\030\002 \001(\005\"*\n\022ThresholdP"
"arameter\022\024\n\tthreshold\030\001 \001(\002:\0010\"\301\002\n\023Windo"
"wDataParameter\022\016\n\006source\030\001 \001(\t\022\020\n\005scale\030"
"\002 \001(\002:\0011\022\021\n\tmean_file\030\003 \001(\t\022\022\n\nbatch_siz"
"e\030\004 \001(\r\022\024\n\tcrop_size\030\005 \001(\r:\0010\022\025\n\006mirror\030"
"\006 \001(\010:\005false\022\031\n\014fg_threshold\030\007 \001(\002:\0030.5\022"
"\031\n\014bg_threshold\030\010 \001(\002:\0030.5\022\031\n\013fg_fractio"
"n\030\t \001(\002:\0040.25\022\026\n\013context_pad\030\n \001(\r:\0010\022\027\n"
"\tcrop_mode\030\013 \001(\t:\004warp\022\033\n\014cache_images\030\014"
" \001(\010:\005false\022\025\n\013root_folder\030\r \001(\t:\000\"\353\001\n\014S"
"PPParameter\022\026\n\016pyramid_height\030\001 \001(\r\0221\n\004p"
"ool\030\002 \001(\0162\036.caffe.SPPParameter.PoolMetho"
"d:\003MAX\0223\n\006engine\030\006 \001(\0162\032.caffe.SPPParame"
"ter.Engine:\007DEFAULT\".\n\nPoolMethod\022\007\n\003MAX"
"\020\000\022\007\n\003AVE\020\001\022\016\n\nSTOCHASTIC\020\002\"+\n\006Engine\022\013\n"
"\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"\340\023\n\020V1L"
"ayerParameter\022\016\n\006bottom\030\002 \003(\t\022\013\n\003top\030\003 \003"
"(\t\022\014\n\004name\030\004 \001(\t\022$\n\007include\030 \003(\0132\023.caff"
"e.NetStateRule\022$\n\007exclude\030! \003(\0132\023.caffe."
"NetStateRule\022/\n\004type\030\005 \001(\0162!.caffe.V1Lay"
"erParameter.LayerType\022\037\n\005blobs\030\006 \003(\0132\020.c"
"affe.BlobProto\022\016\n\005param\030\351\007 \003(\t\022>\n\017blob_s"
"hare_mode\030\352\007 \003(\0162$.caffe.V1LayerParamete"
"r.DimCheckMode\022\020\n\010blobs_lr\030\007 \003(\002\022\024\n\014weig"
"ht_decay\030\010 \003(\002\022\023\n\013loss_weight\030# \003(\002\0220\n\016a"
"ccuracy_param\030\033 \001(\0132\030.caffe.AccuracyPara"
"meter\022,\n\014argmax_param\030\027 \001(\0132\026.caffe.ArgM"
"axParameter\022,\n\014concat_param\030\t \001(\0132\026.caff"
"e.ConcatParameter\022\?\n\026contrastive_loss_pa"
"ram\030( \001(\0132\037.caffe.ContrastiveLossParamet"
"er\0226\n\021convolution_param\030\n \001(\0132\033.caffe.Co"
"nvolutionParameter\022(\n\ndata_param\030\013 \001(\0132\024"
".caffe.DataParameter\022.\n\rdropout_param\030\014 "
"\001(\0132\027.caffe.DropoutParameter\0223\n\020dummy_da"
"ta_param\030\032 \001(\0132\031.caffe.DummyDataParamete"
"r\022.\n\reltwise_param\030\030 \001(\0132\027.caffe.Eltwise"
"Parameter\022&\n\texp_param\030) \001(\0132\023.caffe.Exp"
"Parameter\0221\n\017hdf5_data_param\030\r \001(\0132\030.caf"
"fe.HDF5DataParameter\0225\n\021hdf5_output_para"
"m\030\016 \001(\0132\032.caffe.HDF5OutputParameter\0223\n\020h"
"inge_loss_param\030\035 \001(\0132\031.caffe.HingeLossP"
"arameter\0223\n\020image_data_param\030\017 \001(\0132\031.caf"
"fe.ImageDataParameter\0229\n\023infogain_loss_p"
"aram\030\020 \001(\0132\034.caffe.InfogainLossParameter"
"\0229\n\023inner_product_param\030\021 \001(\0132\034.caffe.In"
"nerProductParameter\022&\n\tlrn_param\030\022 \001(\0132\023"
".caffe.LRNParameter\0225\n\021memory_data_param"
"\030\026 \001(\0132\032.caffe.MemoryDataParameter\022&\n\tmv"
"n_param\030\" \001(\0132\023.caffe.MVNParameter\022.\n\rpo"
"oling_param\030\023 \001(\0132\027.caffe.PoolingParamet"
"er\022*\n\013power_param\030\025 \001(\0132\025.caffe.PowerPar"
"ameter\022(\n\nrelu_param\030\036 \001(\0132\024.caffe.ReLUP"
"arameter\022.\n\rsigmoid_param\030& \001(\0132\027.caffe."
"SigmoidParameter\022.\n\rsoftmax_param\030\' \001(\0132"
"\027.caffe.SoftmaxParameter\022*\n\013slice_param\030"
"\037 \001(\0132\025.caffe.SliceParameter\022(\n\ntanh_par"
"am\030% \001(\0132\024.caffe.TanHParameter\0222\n\017thresh"
"old_param\030\031 \001(\0132\031.caffe.ThresholdParamet"
"er\0225\n\021window_data_param\030\024 \001(\0132\032.caffe.Wi"
"ndowDataParameter\0227\n\017transform_param\030$ \001"
"(\0132\036.caffe.TransformationParameter\022(\n\nlo"
"ss_param\030* \001(\0132\024.caffe.LossParameter\022&\n\005"
"layer\030\001 \001(\0132\027.caffe.V0LayerParameter\"\330\004\n"
"\tLayerType\022\010\n\004NONE\020\000\022\n\n\006ABSVAL\020#\022\014\n\010ACCU"
"RACY\020\001\022\n\n\006ARGMAX\020\036\022\010\n\004BNLL\020\002\022\n\n\006CONCAT\020\003"
"\022\024\n\020CONTRASTIVE_LOSS\020%\022\017\n\013CONVOLUTION\020\004\022"
"\010\n\004DATA\020\005\022\021\n\rDECONVOLUTION\020\'\022\013\n\007DROPOUT\020"
"\006\022\016\n\nDUMMY_DATA\020 \022\022\n\016EUCLIDEAN_LOSS\020\007\022\013\n"
"\007ELTWISE\020\031\022\007\n\003EXP\020&\022\013\n\007FLATTEN\020\010\022\r\n\tHDF5"
"_DATA\020\t\022\017\n\013HDF5_OUTPUT\020\n\022\016\n\nHINGE_LOSS\020\034"
"\022\n\n\006IM2COL\020\013\022\016\n\nIMAGE_DATA\020\014\022\021\n\rINFOGAIN"
"_LOSS\020\r\022\021\n\rINNER_PRODUCT\020\016\022\007\n\003LRN\020\017\022\017\n\013M"
"EMORY_DATA\020\035\022\035\n\031MULTINOMIAL_LOGISTIC_LOS"
"S\020\020\022\007\n\003MVN\020\"\022\013\n\007POOLING\020\021\022\t\n\005POWER\020\032\022\010\n\004"
"RELU\020\022\022\013\n\007SIGMOID\020\023\022\036\n\032SIGMOID_CROSS_ENT"
"ROPY_LOSS\020\033\022\013\n\007SILENCE\020$\022\013\n\007SOFTMAX\020\024\022\020\n"
"\014SOFTMAX_LOSS\020\025\022\t\n\005SPLIT\020\026\022\t\n\005SLICE\020!\022\010\n"
"\004TANH\020\027\022\017\n\013WINDOW_DATA\020\030\022\r\n\tTHRESHOLD\020\037\""
"*\n\014DimCheckMode\022\n\n\006STRICT\020\000\022\016\n\nPERMISSIV"
"E\020\001\"\375\007\n\020V0LayerParameter\022\014\n\004name\030\001 \001(\t\022\014"
"\n\004type\030\002 \001(\t\022\022\n\nnum_output\030\003 \001(\r\022\026\n\010bias"
"term\030\004 \001(\010:\004true\022-\n\rweight_filler\030\005 \001(\0132"
"\026.caffe.FillerParameter\022+\n\013bias_filler\030\006"
" \001(\0132\026.caffe.FillerParameter\022\016\n\003pad\030\007 \001("
"\r:\0010\022\022\n\nkernelsize\030\010 \001(\r\022\020\n\005group\030\t \001(\r:"
"\0011\022\021\n\006stride\030\n \001(\r:\0011\0225\n\004pool\030\013 \001(\0162\".ca"
"ffe.V0LayerParameter.PoolMethod:\003MAX\022\032\n\r"
"dropout_ratio\030\014 \001(\002:\0030.5\022\025\n\nlocal_size\030\r"
" \001(\r:\0015\022\020\n\005alpha\030\016 \001(\002:\0011\022\022\n\004beta\030\017 \001(\002:"
"\0040.75\022\014\n\001k\030\026 \001(\002:\0011\022\016\n\006source\030\020 \001(\t\022\020\n\005s"
"cale\030\021 \001(\002:\0011\022\020\n\010meanfile\030\022 \001(\t\022\021\n\tbatch"
"size\030\023 \001(\r\022\023\n\010cropsize\030\024 \001(\r:\0010\022\025\n\006mirro"
"r\030\025 \001(\010:\005false\022\037\n\005blobs\0302 \003(\0132\020.caffe.Bl"
"obProto\022\020\n\010blobs_lr\0303 \003(\002\022\024\n\014weight_deca"
"y\0304 \003(\002\022\024\n\trand_skip\0305 \001(\r:\0010\022\035\n\020det_fg_"
"threshold\0306 \001(\002:\0030.5\022\035\n\020det_bg_threshold"
"\0307 \001(\002:\0030.5\022\035\n\017det_fg_fraction\0308 \001(\002:\0040."
"25\022\032\n\017det_context_pad\030: \001(\r:\0010\022\033\n\rdet_cr"
"op_mode\030; \001(\t:\004warp\022\022\n\007new_num\030< \001(\005:\0010\022"
"\027\n\014new_channels\030= \001(\005:\0010\022\025\n\nnew_height\030>"
" \001(\005:\0010\022\024\n\tnew_width\030\? \001(\005:\0010\022\035\n\016shuffle"
"_images\030@ \001(\010:\005false\022\025\n\nconcat_dim\030A \001(\r"
":\0011\0226\n\021hdf5_output_param\030\351\007 \001(\0132\032.caffe."
"HDF5OutputParameter\".\n\nPoolMethod\022\007\n\003MAX"
"\020\000\022\007\n\003AVE\020\001\022\016\n\nSTOCHASTIC\020\002\"W\n\016PReLUPara"
"meter\022&\n\006filler\030\001 \001(\0132\026.caffe.FillerPara"
"meter\022\035\n\016channel_shared\030\002 \001(\010:\005false\"\207\001\n"
"\016NormalizedBBox\022\014\n\004xmin\030\001 \001(\002\022\014\n\004ymin\030\002 "
"\001(\002\022\014\n\004xmax\030\003 \001(\002\022\014\n\004ymax\030\004 \001(\002\022\r\n\005label"
"\030\005 \001(\005\022\021\n\tdifficult\030\006 \001(\010\022\r\n\005score\030\007 \001(\002"
"\022\014\n\004size\030\010 \001(\002*=\n\004Type\022\n\n\006DOUBLE\020\000\022\t\n\005FL"
"OAT\020\001\022\013\n\007FLOAT16\020\002\022\007\n\003INT\020\003\022\010\n\004UINT\020\004*\034\n"
"\005Phase\022\t\n\005TRAIN\020\000\022\010\n\004TEST\020\001", 17027);
"FAULT\022\035\n\016global_pooling\030\014 \001(\010:\005false\022\027\n\t"
"ceil_mode\030\r \001(\010:\004true\".\n\nPoolMethod\022\007\n\003M"
"AX\020\000\022\007\n\003AVE\020\001\022\016\n\nSTOCHASTIC\020\002\"+\n\006Engine\022"
"\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"F\n\016Po"
"werParameter\022\020\n\005power\030\001 \001(\002:\0011\022\020\n\005scale\030"
"\002 \001(\002:\0011\022\020\n\005shift\030\003 \001(\002:\0010\"g\n\017PythonPara"
"meter\022\016\n\006module\030\001 \001(\t\022\r\n\005layer\030\002 \001(\t\022\023\n\t"
"param_str\030\003 \001(\t:\000\022 \n\021share_in_parallel\030\004"
" \001(\010:\005false\"\300\001\n\022RecurrentParameter\022\025\n\nnu"
"m_output\030\001 \001(\r:\0010\022-\n\rweight_filler\030\002 \001(\013"
"2\026.caffe.FillerParameter\022+\n\013bias_filler\030"
"\003 \001(\0132\026.caffe.FillerParameter\022\031\n\ndebug_i"
"nfo\030\004 \001(\010:\005false\022\034\n\rexpose_hidden\030\005 \001(\010:"
"\005false\"\255\001\n\022ReductionParameter\022=\n\toperati"
"on\030\001 \001(\0162%.caffe.ReductionParameter.Redu"
"ctionOp:\003SUM\022\017\n\004axis\030\002 \001(\005:\0010\022\020\n\005coeff\030\003"
" \001(\002:\0011\"5\n\013ReductionOp\022\007\n\003SUM\020\001\022\010\n\004ASUM\020"
"\002\022\t\n\005SUMSQ\020\003\022\010\n\004MEAN\020\004\"\215\001\n\rReLUParameter"
"\022\031\n\016negative_slope\030\001 \001(\002:\0010\0224\n\006engine\030\002 "
"\001(\0162\033.caffe.ReLUParameter.Engine:\007DEFAUL"
"T\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005C"
"UDNN\020\002\"Z\n\020ReshapeParameter\022\037\n\005shape\030\001 \001("
"\0132\020.caffe.BlobShape\022\017\n\004axis\030\002 \001(\005:\0010\022\024\n\010"
"num_axes\030\003 \001(\005:\002-1\"\245\001\n\016ScaleParameter\022\017\n"
"\004axis\030\001 \001(\005:\0011\022\023\n\010num_axes\030\002 \001(\005:\0011\022&\n\006f"
"iller\030\003 \001(\0132\026.caffe.FillerParameter\022\030\n\tb"
"ias_term\030\004 \001(\010:\005false\022+\n\013bias_filler\030\005 \001"
"(\0132\026.caffe.FillerParameter\"x\n\020SigmoidPar"
"ameter\0227\n\006engine\030\001 \001(\0162\036.caffe.SigmoidPa"
"rameter.Engine:\007DEFAULT\"+\n\006Engine\022\013\n\007DEF"
"AULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"L\n\016SlicePar"
"ameter\022\017\n\004axis\030\003 \001(\005:\0011\022\023\n\013slice_point\030\002"
" \003(\r\022\024\n\tslice_dim\030\001 \001(\r:\0011\"\211\001\n\020SoftmaxPa"
"rameter\0227\n\006engine\030\001 \001(\0162\036.caffe.SoftmaxP"
"arameter.Engine:\007DEFAULT\022\017\n\004axis\030\002 \001(\005:\001"
"1\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005C"
"UDNN\020\002\"r\n\rTanHParameter\0224\n\006engine\030\001 \001(\0162"
"\033.caffe.TanHParameter.Engine:\007DEFAULT\"+\n"
"\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDNN"
"\020\002\"/\n\rTileParameter\022\017\n\004axis\030\001 \001(\005:\0011\022\r\n\005"
"tiles\030\002 \001(\005\"*\n\022ThresholdParameter\022\024\n\tthr"
"eshold\030\001 \001(\002:\0010\"\301\002\n\023WindowDataParameter\022"
"\016\n\006source\030\001 \001(\t\022\020\n\005scale\030\002 \001(\002:\0011\022\021\n\tmea"
"n_file\030\003 \001(\t\022\022\n\nbatch_size\030\004 \001(\r\022\024\n\tcrop"
"_size\030\005 \001(\r:\0010\022\025\n\006mirror\030\006 \001(\010:\005false\022\031\n"
"\014fg_threshold\030\007 \001(\002:\0030.5\022\031\n\014bg_threshold"
"\030\010 \001(\002:\0030.5\022\031\n\013fg_fraction\030\t \001(\002:\0040.25\022\026"
"\n\013context_pad\030\n \001(\r:\0010\022\027\n\tcrop_mode\030\013 \001("
"\t:\004warp\022\033\n\014cache_images\030\014 \001(\010:\005false\022\025\n\013"
"root_folder\030\r \001(\t:\000\"\353\001\n\014SPPParameter\022\026\n\016"
"pyramid_height\030\001 \001(\r\0221\n\004pool\030\002 \001(\0162\036.caf"
"fe.SPPParameter.PoolMethod:\003MAX\0223\n\006engin"
"e\030\006 \001(\0162\032.caffe.SPPParameter.Engine:\007DEF"
"AULT\".\n\nPoolMethod\022\007\n\003MAX\020\000\022\007\n\003AVE\020\001\022\016\n\n"
"STOCHASTIC\020\002\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005C"
"AFFE\020\001\022\t\n\005CUDNN\020\002\"\340\023\n\020V1LayerParameter\022\016"
"\n\006bottom\030\002 \003(\t\022\013\n\003top\030\003 \003(\t\022\014\n\004name\030\004 \001("
"\t\022$\n\007include\030 \003(\0132\023.caffe.NetStateRule\022"
"$\n\007exclude\030! \003(\0132\023.caffe.NetStateRule\022/\n"
"\004type\030\005 \001(\0162!.caffe.V1LayerParameter.Lay"
"erType\022\037\n\005blobs\030\006 \003(\0132\020.caffe.BlobProto\022"
"\016\n\005param\030\351\007 \003(\t\022>\n\017blob_share_mode\030\352\007 \003("
"\0162$.caffe.V1LayerParameter.DimCheckMode\022"
"\020\n\010blobs_lr\030\007 \003(\002\022\024\n\014weight_decay\030\010 \003(\002\022"
"\023\n\013loss_weight\030# \003(\002\0220\n\016accuracy_param\030\033"
" \001(\0132\030.caffe.AccuracyParameter\022,\n\014argmax"
"_param\030\027 \001(\0132\026.caffe.ArgMaxParameter\022,\n\014"
"concat_param\030\t \001(\0132\026.caffe.ConcatParamet"
"er\022\?\n\026contrastive_loss_param\030( \001(\0132\037.caf"
"fe.ContrastiveLossParameter\0226\n\021convoluti"
"on_param\030\n \001(\0132\033.caffe.ConvolutionParame"
"ter\022(\n\ndata_param\030\013 \001(\0132\024.caffe.DataPara"
"meter\022.\n\rdropout_param\030\014 \001(\0132\027.caffe.Dro"
"poutParameter\0223\n\020dummy_data_param\030\032 \001(\0132"
"\031.caffe.DummyDataParameter\022.\n\reltwise_pa"
"ram\030\030 \001(\0132\027.caffe.EltwiseParameter\022&\n\tex"
"p_param\030) \001(\0132\023.caffe.ExpParameter\0221\n\017hd"
"f5_data_param\030\r \001(\0132\030.caffe.HDF5DataPara"
"meter\0225\n\021hdf5_output_param\030\016 \001(\0132\032.caffe"
".HDF5OutputParameter\0223\n\020hinge_loss_param"
"\030\035 \001(\0132\031.caffe.HingeLossParameter\0223\n\020ima"
"ge_data_param\030\017 \001(\0132\031.caffe.ImageDataPar"
"ameter\0229\n\023infogain_loss_param\030\020 \001(\0132\034.ca"
"ffe.InfogainLossParameter\0229\n\023inner_produ"
"ct_param\030\021 \001(\0132\034.caffe.InnerProductParam"
"eter\022&\n\tlrn_param\030\022 \001(\0132\023.caffe.LRNParam"
"eter\0225\n\021memory_data_param\030\026 \001(\0132\032.caffe."
"MemoryDataParameter\022&\n\tmvn_param\030\" \001(\0132\023"
".caffe.MVNParameter\022.\n\rpooling_param\030\023 \001"
"(\0132\027.caffe.PoolingParameter\022*\n\013power_par"
"am\030\025 \001(\0132\025.caffe.PowerParameter\022(\n\nrelu_"
"param\030\036 \001(\0132\024.caffe.ReLUParameter\022.\n\rsig"
"moid_param\030& \001(\0132\027.caffe.SigmoidParamete"
"r\022.\n\rsoftmax_param\030\' \001(\0132\027.caffe.Softmax"
"Parameter\022*\n\013slice_param\030\037 \001(\0132\025.caffe.S"
"liceParameter\022(\n\ntanh_param\030% \001(\0132\024.caff"
"e.TanHParameter\0222\n\017threshold_param\030\031 \001(\013"
"2\031.caffe.ThresholdParameter\0225\n\021window_da"
"ta_param\030\024 \001(\0132\032.caffe.WindowDataParamet"
"er\0227\n\017transform_param\030$ \001(\0132\036.caffe.Tran"
"sformationParameter\022(\n\nloss_param\030* \001(\0132"
"\024.caffe.LossParameter\022&\n\005layer\030\001 \001(\0132\027.c"
"affe.V0LayerParameter\"\330\004\n\tLayerType\022\010\n\004N"
"ONE\020\000\022\n\n\006ABSVAL\020#\022\014\n\010ACCURACY\020\001\022\n\n\006ARGMA"
"X\020\036\022\010\n\004BNLL\020\002\022\n\n\006CONCAT\020\003\022\024\n\020CONTRASTIVE"
"_LOSS\020%\022\017\n\013CONVOLUTION\020\004\022\010\n\004DATA\020\005\022\021\n\rDE"
"CONVOLUTION\020\'\022\013\n\007DROPOUT\020\006\022\016\n\nDUMMY_DATA"
"\020 \022\022\n\016EUCLIDEAN_LOSS\020\007\022\013\n\007ELTWISE\020\031\022\007\n\003E"
"XP\020&\022\013\n\007FLATTEN\020\010\022\r\n\tHDF5_DATA\020\t\022\017\n\013HDF5"
"_OUTPUT\020\n\022\016\n\nHINGE_LOSS\020\034\022\n\n\006IM2COL\020\013\022\016\n"
"\nIMAGE_DATA\020\014\022\021\n\rINFOGAIN_LOSS\020\r\022\021\n\rINNE"
"R_PRODUCT\020\016\022\007\n\003LRN\020\017\022\017\n\013MEMORY_DATA\020\035\022\035\n"
"\031MULTINOMIAL_LOGISTIC_LOSS\020\020\022\007\n\003MVN\020\"\022\013\n"
"\007POOLING\020\021\022\t\n\005POWER\020\032\022\010\n\004RELU\020\022\022\013\n\007SIGMO"
"ID\020\023\022\036\n\032SIGMOID_CROSS_ENTROPY_LOSS\020\033\022\013\n\007"
"SILENCE\020$\022\013\n\007SOFTMAX\020\024\022\020\n\014SOFTMAX_LOSS\020\025"
"\022\t\n\005SPLIT\020\026\022\t\n\005SLICE\020!\022\010\n\004TANH\020\027\022\017\n\013WIND"
"OW_DATA\020\030\022\r\n\tTHRESHOLD\020\037\"*\n\014DimCheckMode"
"\022\n\n\006STRICT\020\000\022\016\n\nPERMISSIVE\020\001\"\375\007\n\020V0Layer"
"Parameter\022\014\n\004name\030\001 \001(\t\022\014\n\004type\030\002 \001(\t\022\022\n"
"\nnum_output\030\003 \001(\r\022\026\n\010biasterm\030\004 \001(\010:\004tru"
"e\022-\n\rweight_filler\030\005 \001(\0132\026.caffe.FillerP"
"arameter\022+\n\013bias_filler\030\006 \001(\0132\026.caffe.Fi"
"llerParameter\022\016\n\003pad\030\007 \001(\r:\0010\022\022\n\nkernels"
"ize\030\010 \001(\r\022\020\n\005group\030\t \001(\r:\0011\022\021\n\006stride\030\n "
"\001(\r:\0011\0225\n\004pool\030\013 \001(\0162\".caffe.V0LayerPara"
"meter.PoolMethod:\003MAX\022\032\n\rdropout_ratio\030\014"
" \001(\002:\0030.5\022\025\n\nlocal_size\030\r \001(\r:\0015\022\020\n\005alph"
"a\030\016 \001(\002:\0011\022\022\n\004beta\030\017 \001(\002:\0040.75\022\014\n\001k\030\026 \001("
"\002:\0011\022\016\n\006source\030\020 \001(\t\022\020\n\005scale\030\021 \001(\002:\0011\022\020"
"\n\010meanfile\030\022 \001(\t\022\021\n\tbatchsize\030\023 \001(\r\022\023\n\010c"
"ropsize\030\024 \001(\r:\0010\022\025\n\006mirror\030\025 \001(\010:\005false\022"
"\037\n\005blobs\0302 \003(\0132\020.caffe.BlobProto\022\020\n\010blob"
"s_lr\0303 \003(\002\022\024\n\014weight_decay\0304 \003(\002\022\024\n\trand"
"_skip\0305 \001(\r:\0010\022\035\n\020det_fg_threshold\0306 \001(\002"
":\0030.5\022\035\n\020det_bg_threshold\0307 \001(\002:\0030.5\022\035\n\017"
"det_fg_fraction\0308 \001(\002:\0040.25\022\032\n\017det_conte"
"xt_pad\030: \001(\r:\0010\022\033\n\rdet_crop_mode\030; \001(\t:\004"
"warp\022\022\n\007new_num\030< \001(\005:\0010\022\027\n\014new_channels"
"\030= \001(\005:\0010\022\025\n\nnew_height\030> \001(\005:\0010\022\024\n\tnew_"
"width\030\? \001(\005:\0010\022\035\n\016shuffle_images\030@ \001(\010:\005"
"false\022\025\n\nconcat_dim\030A \001(\r:\0011\0226\n\021hdf5_out"
"put_param\030\351\007 \001(\0132\032.caffe.HDF5OutputParam"
"eter\".\n\nPoolMethod\022\007\n\003MAX\020\000\022\007\n\003AVE\020\001\022\016\n\n"
"STOCHASTIC\020\002\"W\n\016PReLUParameter\022&\n\006filler"
"\030\001 \001(\0132\026.caffe.FillerParameter\022\035\n\016channe"
"l_shared\030\002 \001(\010:\005false\"\207\001\n\016NormalizedBBox"
"\022\014\n\004xmin\030\001 \001(\002\022\014\n\004ymin\030\002 \001(\002\022\014\n\004xmax\030\003 \001"
"(\002\022\014\n\004ymax\030\004 \001(\002\022\r\n\005label\030\005 \001(\005\022\021\n\tdiffi"
"cult\030\006 \001(\010\022\r\n\005score\030\007 \001(\002\022\014\n\004size\030\010 \001(\002*"
"=\n\004Type\022\n\n\006DOUBLE\020\000\022\t\n\005FLOAT\020\001\022\013\n\007FLOAT1"
"6\020\002\022\007\n\003INT\020\003\022\010\n\004UINT\020\004*\034\n\005Phase\022\t\n\005TRAIN"
"\020\000\022\010\n\004TEST\020\001", 17052);
::google::protobuf::MessageFactory::InternalRegisterGeneratedFile(
"caffe.proto", &protobuf_RegisterTypes);
::google::protobuf::internal::OnShutdown(&protobuf_ShutdownFile_caffe_2eproto);
@ -37422,6 +37424,7 @@ const int PoolingParameter::kStrideHFieldNumber;
const int PoolingParameter::kStrideWFieldNumber;
const int PoolingParameter::kEngineFieldNumber;
const int PoolingParameter::kGlobalPoolingFieldNumber;
const int PoolingParameter::kCeilModeFieldNumber;
#endif // !defined(_MSC_VER) || _MSC_VER >= 1900
PoolingParameter::PoolingParameter()
@ -37447,6 +37450,7 @@ void PoolingParameter::SharedCtor() {
::memset(&pool_, 0, reinterpret_cast<char*>(&global_pooling_) -
reinterpret_cast<char*>(&pool_) + sizeof(global_pooling_));
stride_ = 1u;
ceil_mode_ = true;
}
PoolingParameter::~PoolingParameter() {
@ -37504,7 +37508,10 @@ void PoolingParameter::Clear() {
ZR_(pool_, kernel_w_);
stride_ = 1u;
}
ZR_(stride_h_, global_pooling_);
if (_has_bits_[8 / 32] & 7936u) {
ZR_(stride_h_, global_pooling_);
ceil_mode_ = true;
}
#undef ZR_HELPER_
#undef ZR_
@ -37710,6 +37717,21 @@ bool PoolingParameter::MergePartialFromCodedStream(
} else {
goto handle_unusual;
}
if (input->ExpectTag(104)) goto parse_ceil_mode;
break;
}
// optional bool ceil_mode = 13 [default = true];
case 13: {
if (tag == 104) {
parse_ceil_mode:
set_has_ceil_mode();
DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
bool, ::google::protobuf::internal::WireFormatLite::TYPE_BOOL>(
input, &ceil_mode_)));
} else {
goto handle_unusual;
}
if (input->ExpectAtEnd()) goto success;
break;
}
@ -37801,6 +37823,11 @@ void PoolingParameter::SerializeWithCachedSizes(
::google::protobuf::internal::WireFormatLite::WriteBool(12, this->global_pooling(), output);
}
// optional bool ceil_mode = 13 [default = true];
if (has_ceil_mode()) {
::google::protobuf::internal::WireFormatLite::WriteBool(13, this->ceil_mode(), output);
}
if (_internal_metadata_.have_unknown_fields()) {
::google::protobuf::internal::WireFormat::SerializeUnknownFields(
unknown_fields(), output);
@ -37874,6 +37901,11 @@ void PoolingParameter::SerializeWithCachedSizes(
target = ::google::protobuf::internal::WireFormatLite::WriteBoolToArray(12, this->global_pooling(), target);
}
// optional bool ceil_mode = 13 [default = true];
if (has_ceil_mode()) {
target = ::google::protobuf::internal::WireFormatLite::WriteBoolToArray(13, this->ceil_mode(), target);
}
if (_internal_metadata_.have_unknown_fields()) {
target = ::google::protobuf::internal::WireFormat::SerializeUnknownFieldsToArray(
unknown_fields(), target);
@ -37943,7 +37975,7 @@ size_t PoolingParameter::ByteSizeLong() const {
}
}
if (_has_bits_[8 / 32] & 3840u) {
if (_has_bits_[8 / 32] & 7936u) {
// optional uint32 stride_h = 7;
if (has_stride_h()) {
total_size += 1 +
@ -37969,6 +38001,11 @@ size_t PoolingParameter::ByteSizeLong() const {
total_size += 1 + 1;
}
// optional bool ceil_mode = 13 [default = true];
if (has_ceil_mode()) {
total_size += 1 + 1;
}
}
if (_internal_metadata_.have_unknown_fields()) {
total_size +=
@ -38047,6 +38084,9 @@ void PoolingParameter::UnsafeMergeFrom(const PoolingParameter& from) {
if (from.has_global_pooling()) {
set_global_pooling(from.global_pooling());
}
if (from.has_ceil_mode()) {
set_ceil_mode(from.ceil_mode());
}
}
if (from._internal_metadata_.have_unknown_fields()) {
::google::protobuf::UnknownFieldSet::MergeToInternalMetdata(
@ -38090,6 +38130,7 @@ void PoolingParameter::InternalSwap(PoolingParameter* other) {
std::swap(stride_w_, other->stride_w_);
std::swap(engine_, other->engine_);
std::swap(global_pooling_, other->global_pooling_);
std::swap(ceil_mode_, other->ceil_mode_);
std::swap(_has_bits_[0], other->_has_bits_[0]);
_internal_metadata_.Swap(&other->_internal_metadata_);
std::swap(_cached_size_, other->_cached_size_);
@ -38396,6 +38437,30 @@ void PoolingParameter::set_global_pooling(bool value) {
// @@protoc_insertion_point(field_set:caffe.PoolingParameter.global_pooling)
}
// optional bool ceil_mode = 13 [default = true];
bool PoolingParameter::has_ceil_mode() const {
return (_has_bits_[0] & 0x00001000u) != 0;
}
void PoolingParameter::set_has_ceil_mode() {
_has_bits_[0] |= 0x00001000u;
}
void PoolingParameter::clear_has_ceil_mode() {
_has_bits_[0] &= ~0x00001000u;
}
void PoolingParameter::clear_ceil_mode() {
ceil_mode_ = true;
clear_has_ceil_mode();
}
bool PoolingParameter::ceil_mode() const {
// @@protoc_insertion_point(field_get:caffe.PoolingParameter.ceil_mode)
return ceil_mode_;
}
void PoolingParameter::set_ceil_mode(bool value) {
set_has_ceil_mode();
ceil_mode_ = value;
// @@protoc_insertion_point(field_set:caffe.PoolingParameter.ceil_mode)
}
inline const PoolingParameter* PoolingParameter::internal_default_instance() {
return &PoolingParameter_default_instance_.get();
}

34
modules/dnn/misc/caffe/caffe.pb.h
Unescape View File

@ -9035,6 +9035,13 @@ class PoolingParameter : public ::google::protobuf::Message /* @@protoc_insertio
bool global_pooling() const;
void set_global_pooling(bool value);
// optional bool ceil_mode = 13 [default = true];
bool has_ceil_mode() const;
void clear_ceil_mode();
static const int kCeilModeFieldNumber = 13;
bool ceil_mode() const;
void set_ceil_mode(bool value);
// @@protoc_insertion_point(class_scope:caffe.PoolingParameter)
private:
inline void set_has_pool();
@ -9061,6 +9068,8 @@ class PoolingParameter : public ::google::protobuf::Message /* @@protoc_insertio
inline void clear_has_engine();
inline void set_has_global_pooling();
inline void clear_has_global_pooling();
inline void set_has_ceil_mode();
inline void clear_has_ceil_mode();
::google::protobuf::internal::InternalMetadataWithArena _internal_metadata_;
::google::protobuf::internal::HasBits<1> _has_bits_;
@ -9077,6 +9086,7 @@ class PoolingParameter : public ::google::protobuf::Message /* @@protoc_insertio
int engine_;
bool global_pooling_;
::google::protobuf::uint32 stride_;
bool ceil_mode_;
friend void protobuf_InitDefaults_caffe_2eproto_impl();
friend void protobuf_AddDesc_caffe_2eproto_impl();
friend void protobuf_AssignDesc_caffe_2eproto();
@ -23544,6 +23554,30 @@ inline void PoolingParameter::set_global_pooling(bool value) {
// @@protoc_insertion_point(field_set:caffe.PoolingParameter.global_pooling)
}
// optional bool ceil_mode = 13 [default = true];
inline bool PoolingParameter::has_ceil_mode() const {
return (_has_bits_[0] & 0x00001000u) != 0;
}
inline void PoolingParameter::set_has_ceil_mode() {
_has_bits_[0] |= 0x00001000u;
}
inline void PoolingParameter::clear_has_ceil_mode() {
_has_bits_[0] &= ~0x00001000u;
}
inline void PoolingParameter::clear_ceil_mode() {
ceil_mode_ = true;
clear_has_ceil_mode();
}
inline bool PoolingParameter::ceil_mode() const {
// @@protoc_insertion_point(field_get:caffe.PoolingParameter.ceil_mode)
return ceil_mode_;
}
inline void PoolingParameter::set_ceil_mode(bool value) {
set_has_ceil_mode();
ceil_mode_ = value;
// @@protoc_insertion_point(field_set:caffe.PoolingParameter.ceil_mode)
}
inline const PoolingParameter* PoolingParameter::internal_default_instance() {
return &PoolingParameter_default_instance_.get();
}

3
modules/dnn/src/caffe/caffe.proto
Unescape View File

@ -1093,6 +1093,9 @@ message PoolingParameter {
// If global_pooling then it will pool over the size of the bottom by doing
// kernel_h = bottom->height and kernel_w = bottom->width
optional bool global_pooling = 12 [default = false];
// Specify floor/ceil mode
// source: https://github.com/BVLC/caffe/pull/3057
optional bool ceil_mode = 13 [default = true];
}
message PowerParameter {

17
modules/dnn/test/test_caffe_importer.cpp
Unescape View File

@ -234,4 +234,21 @@ TEST(Reproducibility_Colorization, Accuracy)
normAssert(out, ref, "", l1, lInf);
}
TEST(Reproducibility_DenseNet_121, Accuracy)
{
const string proto = findDataFile("dnn/DenseNet_121.prototxt", false);
const string model = findDataFile("dnn/DenseNet_121.caffemodel", false);
Mat inp = imread(_tf("dog416.png"));
inp = blobFromImage(inp, 1.0 / 255, Size(224, 224));
Mat ref = blobFromNPY(_tf("densenet_121_output.npy"));
Net net = readNetFromCaffe(proto, model);
net.setInput(inp);
Mat out = net.forward();
normAssert(out, ref);
}
}

Write Preview
Loading…
Cancel
Save