sizeof(NormalizeBBoxParameter),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(NormalizeBBoxParameter, _internal_metadata_));
PriorBoxParameter_descriptor_ = file->message_type(5);
- static const int PriorBoxParameter_offsets_[13] = {
+ static const int PriorBoxParameter_offsets_[14] = {
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, min_size_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, max_size_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, aspect_ratio_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, step_h_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, step_w_),
GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, offset_),
+ GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, additional_y_offset_),
};
PriorBoxParameter_reflection_ =
::google::protobuf::internal::GeneratedMessageReflection::NewGeneratedMessageReflection(
"(\r\"\226\001\n\026NormalizeBBoxParameter\022\034\n\016across_"
"spatial\030\001 \001(\010:\004true\022,\n\014scale_filler\030\002 \001("
"\0132\026.caffe.FillerParameter\022\034\n\016channel_sha"
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+ "red\030\003 \001(\010:\004true\022\022\n\003eps\030\004 \001(\002:\0051e-10\"\307\002\n\021"
"PriorBoxParameter\022\020\n\010min_size\030\001 \001(\002\022\020\n\010m"
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"ariance\030\006 \003(\002\022\020\n\010img_size\030\007 \001(\r\022\r\n\005img_h"
"\030\010 \001(\r\022\r\n\005img_w\030\t \001(\r\022\014\n\004step\030\n \001(\002\022\016\n\006s"
"tep_h\030\013 \001(\002\022\016\n\006step_w\030\014 \001(\002\022\023\n\006offset\030\r "
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- "\030\005 \001(\0132\032.caffe.SaveOutputParameter\022<\n\tco"
- "de_type\030\006 \001(\0162!.caffe.PriorBoxParameter."
- "CodeType:\006CORNER\022)\n\032variance_encoded_in_"
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- "ance_norm\030\010 \001(\0162#.caffe.FillerParameter."
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- "(\010:\005false\022\036\n\005state\030\006 \001(\0132\017.caffe.NetStat"
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- "\003(\0132\025.caffe.LayerParameter\022\'\n\006layers\030\002 \003"
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+ "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"
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+ "aleParameter\022\017\n\004axis\030\001 \001(\005:\0011\022\023\n\010num_axe"
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+ "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"
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+ "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);
::google::protobuf::MessageFactory::InternalRegisterGeneratedFile(
"caffe.proto", &protobuf_RegisterTypes);
::google::protobuf::internal::OnShutdown(&protobuf_ShutdownFile_caffe_2eproto);
const int PriorBoxParameter::kStepHFieldNumber;
const int PriorBoxParameter::kStepWFieldNumber;
const int PriorBoxParameter::kOffsetFieldNumber;
+const int PriorBoxParameter::kAdditionalYOffsetFieldNumber;
#endif // !defined(_MSC_VER) || _MSC_VER >= 1900
PriorBoxParameter::PriorBoxParameter()
void PriorBoxParameter::SharedCtor() {
_cached_size_ = 0;
- ::memset(&min_size_, 0, reinterpret_cast<char*>(&step_w_) -
- reinterpret_cast<char*>(&min_size_) + sizeof(step_w_));
+ ::memset(&min_size_, 0, reinterpret_cast<char*>(&additional_y_offset_) -
+ reinterpret_cast<char*>(&min_size_) + sizeof(additional_y_offset_));
flip_ = true;
clip_ = true;
offset_ = 0.5f;
flip_ = true;
clip_ = true;
}
- if (_has_bits_[8 / 32] & 7936u) {
- ZR_(img_w_, step_w_);
+ if (_has_bits_[8 / 32] & 16128u) {
+ ZR_(img_w_, additional_y_offset_);
offset_ = 0.5f;
}
} else {
goto handle_unusual;
}
+ if (input->ExpectTag(112)) goto parse_additional_y_offset;
+ break;
+ }
+
+ // optional bool additional_y_offset = 14 [default = false];
+ case 14: {
+ if (tag == 112) {
+ parse_additional_y_offset:
+ set_has_additional_y_offset();
+ DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive<
+ bool, ::google::protobuf::internal::WireFormatLite::TYPE_BOOL>(
+ input, &additional_y_offset_)));
+ } else {
+ goto handle_unusual;
+ }
if (input->ExpectAtEnd()) goto success;
break;
}
::google::protobuf::internal::WireFormatLite::WriteFloat(13, this->offset(), output);
}
+ // optional bool additional_y_offset = 14 [default = false];
+ if (has_additional_y_offset()) {
+ ::google::protobuf::internal::WireFormatLite::WriteBool(14, this->additional_y_offset(), output);
+ }
+
if (_internal_metadata_.have_unknown_fields()) {
::google::protobuf::internal::WireFormat::SerializeUnknownFields(
unknown_fields(), output);
target = ::google::protobuf::internal::WireFormatLite::WriteFloatToArray(13, this->offset(), target);
}
+ // optional bool additional_y_offset = 14 [default = false];
+ if (has_additional_y_offset()) {
+ target = ::google::protobuf::internal::WireFormatLite::WriteBoolToArray(14, this->additional_y_offset(), target);
+ }
+
if (_internal_metadata_.have_unknown_fields()) {
target = ::google::protobuf::internal::WireFormat::SerializeUnknownFieldsToArray(
unknown_fields(), target);
}
}
- if (_has_bits_[8 / 32] & 7936u) {
+ if (_has_bits_[8 / 32] & 16128u) {
// optional uint32 img_w = 9;
if (has_img_w()) {
total_size += 1 +
total_size += 1 + 4;
}
+ // optional bool additional_y_offset = 14 [default = false];
+ if (has_additional_y_offset()) {
+ total_size += 1 + 1;
+ }
+
}
// repeated float aspect_ratio = 3;
{
if (from.has_offset()) {
set_offset(from.offset());
}
+ if (from.has_additional_y_offset()) {
+ set_additional_y_offset(from.additional_y_offset());
+ }
}
if (from._internal_metadata_.have_unknown_fields()) {
::google::protobuf::UnknownFieldSet::MergeToInternalMetdata(
std::swap(step_h_, other->step_h_);
std::swap(step_w_, other->step_w_);
std::swap(offset_, other->offset_);
+ std::swap(additional_y_offset_, other->additional_y_offset_);
std::swap(_has_bits_[0], other->_has_bits_[0]);
_internal_metadata_.Swap(&other->_internal_metadata_);
std::swap(_cached_size_, other->_cached_size_);
// @@protoc_insertion_point(field_set:caffe.PriorBoxParameter.offset)
}
+// optional bool additional_y_offset = 14 [default = false];
+bool PriorBoxParameter::has_additional_y_offset() const {
+ return (_has_bits_[0] & 0x00002000u) != 0;
+}
+void PriorBoxParameter::set_has_additional_y_offset() {
+ _has_bits_[0] |= 0x00002000u;
+}
+void PriorBoxParameter::clear_has_additional_y_offset() {
+ _has_bits_[0] &= ~0x00002000u;
+}
+void PriorBoxParameter::clear_additional_y_offset() {
+ additional_y_offset_ = false;
+ clear_has_additional_y_offset();
+}
+bool PriorBoxParameter::additional_y_offset() const {
+ // @@protoc_insertion_point(field_get:caffe.PriorBoxParameter.additional_y_offset)
+ return additional_y_offset_;
+}
+void PriorBoxParameter::set_additional_y_offset(bool value) {
+ set_has_additional_y_offset();
+ additional_y_offset_ = value;
+ // @@protoc_insertion_point(field_set:caffe.PriorBoxParameter.additional_y_offset)
+}
+
inline const PriorBoxParameter* PriorBoxParameter::internal_default_instance() {
return &PriorBoxParameter_default_instance_.get();
}