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@ -347,7 +347,7 @@ void protobuf_AssignDesc_caffe_2eproto() { |
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sizeof(NormalizeBBoxParameter), |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(NormalizeBBoxParameter, _internal_metadata_)); |
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PriorBoxParameter_descriptor_ = file->message_type(5); |
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static const int PriorBoxParameter_offsets_[13] = { |
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static const int PriorBoxParameter_offsets_[14] = { |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, min_size_), |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, max_size_), |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, aspect_ratio_), |
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@ -361,6 +361,7 @@ void protobuf_AssignDesc_caffe_2eproto() { |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, step_h_), |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, step_w_), |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, offset_), |
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GOOGLE_PROTOBUF_GENERATED_MESSAGE_FIELD_OFFSET(PriorBoxParameter, additional_y_offset_), |
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}; |
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PriorBoxParameter_reflection_ = |
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::google::protobuf::internal::GeneratedMessageReflection::NewGeneratedMessageReflection( |
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@ -2130,418 +2131,419 @@ void protobuf_AddDesc_caffe_2eproto_impl() { |
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"(\r\"\226\001\n\026NormalizeBBoxParameter\022\034\n\016across_" |
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"spatial\030\001 \001(\010:\004true\022,\n\014scale_filler\030\002 \001(" |
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"\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\"\243\002\n\021" |
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"red\030\003 \001(\010:\004true\022\022\n\003eps\030\004 \001(\002:\0051e-10\"\307\002\n\021" |
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"PriorBoxParameter\022\020\n\010min_size\030\001 \001(\002\022\020\n\010m" |
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"ax_size\030\002 \001(\002\022\024\n\014aspect_ratio\030\003 \003(\002\022\022\n\004f" |
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"lip\030\004 \001(\010:\004true\022\022\n\004clip\030\005 \001(\010:\004true\022\020\n\010v" |
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"ariance\030\006 \003(\002\022\020\n\010img_size\030\007 \001(\r\022\r\n\005img_h" |
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"\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" |
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"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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"\001(\002:\0030.5\"\'\n\010CodeType\022\n\n\006CORNER\020\001\022\017\n\013CENT" |
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"ER_SIZE\020\002\"\375\002\n\030DetectionOutputParameter\022\023" |
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"\n\013num_classes\030\001 \001(\r\022\034\n\016share_location\030\002 " |
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"\001(\010:\004true\022\036\n\023background_label_id\030\003 \001(\005:\001" |
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"0\0228\n\tnms_param\030\004 \001(\0132%.caffe.NonMaximumS" |
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"uppressionParameter\0225\n\021save_output_param" |
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"\030\005 \001(\0132\032.caffe.SaveOutputParameter\022<\n\tco" |
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"de_type\030\006 \001(\0162!.caffe.PriorBoxParameter." |
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"CodeType:\006CORNER\022)\n\032variance_encoded_in_" |
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"target\030\010 \001(\010:\005false\022\026\n\nkeep_top_k\030\007 \001(\005:" |
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"\002-1\022\034\n\024confidence_threshold\030\t \001(\002\"\201\001\n\005Da" |
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"tum\022\020\n\010channels\030\001 \001(\005\022\016\n\006height\030\002 \001(\005\022\r\n" |
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"\005width\030\003 \001(\005\022\014\n\004data\030\004 \001(\014\022\r\n\005label\030\005 \001(" |
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"\005\022\022\n\nfloat_data\030\006 \003(\002\022\026\n\007encoded\030\007 \001(\010:\005" |
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"false\"\212\002\n\017FillerParameter\022\026\n\004type\030\001 \001(\t:" |
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"\010constant\022\020\n\005value\030\002 \001(\002:\0010\022\016\n\003min\030\003 \001(\002" |
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":\0010\022\016\n\003max\030\004 \001(\002:\0011\022\017\n\004mean\030\005 \001(\002:\0010\022\016\n\003" |
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"std\030\006 \001(\002:\0011\022\022\n\006sparse\030\007 \001(\005:\002-1\022B\n\rvari" |
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"ance_norm\030\010 \001(\0162#.caffe.FillerParameter." |
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"VarianceNorm:\006FAN_IN\"4\n\014VarianceNorm\022\n\n\006" |
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"FAN_IN\020\000\022\013\n\007FAN_OUT\020\001\022\013\n\007AVERAGE\020\002\"\216\002\n\014N" |
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"etParameter\022\014\n\004name\030\001 \001(\t\022\r\n\005input\030\003 \003(\t" |
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"\022%\n\013input_shape\030\010 \003(\0132\020.caffe.BlobShape\022" |
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"\021\n\tinput_dim\030\004 \003(\005\022\035\n\016force_backward\030\005 \001" |
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"(\010:\005false\022\036\n\005state\030\006 \001(\0132\017.caffe.NetStat" |
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"e\022\031\n\ndebug_info\030\007 \001(\010:\005false\022$\n\005layer\030d " |
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"\003(\0132\025.caffe.LayerParameter\022\'\n\006layers\030\002 \003" |
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"(\0132\027.caffe.V1LayerParameter\"\242\n\n\017SolverPa" |
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"rameter\022\013\n\003net\030\030 \001(\t\022&\n\tnet_param\030\031 \001(\0132" |
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"\023.caffe.NetParameter\022\021\n\ttrain_net\030\001 \001(\t\022" |
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"\020\n\010test_net\030\002 \003(\t\022,\n\017train_net_param\030\025 \001" |
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"(\0132\023.caffe.NetParameter\022+\n\016test_net_para" |
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"m\030\026 \003(\0132\023.caffe.NetParameter\022$\n\013train_st" |
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"ate\030\032 \001(\0132\017.caffe.NetState\022#\n\ntest_state" |
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"\030\033 \003(\0132\017.caffe.NetState\022\021\n\ttest_iter\030\003 \003" |
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"(\005\022\030\n\rtest_interval\030\004 \001(\005:\0010\022 \n\021test_com" |
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"pute_loss\030\023 \001(\010:\005false\022!\n\023test_initializ" |
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"ation\030 \001(\010:\004true\022\017\n\007base_lr\030\005 \001(\002\022\017\n\007di" |
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"splay\030\006 \001(\005\022\027\n\014average_loss\030! \001(\005:\0011\022\020\n\010" |
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"max_iter\030\007 \001(\005\022\024\n\titer_size\030$ \001(\005:\0011\022\021\n\t" |
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"lr_policy\030\010 \001(\t\022\r\n\005gamma\030\t \001(\002\022\r\n\005power\030" |
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"\n \001(\002\022\020\n\010momentum\030\013 \001(\002\022\024\n\014weight_decay\030" |
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"\014 \001(\002\022\037\n\023regularization_type\030\035 \001(\t:\002L2\022\020" |
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"\n\010stepsize\030\r \001(\005\022\021\n\tstepvalue\030\" \003(\005\022\032\n\016c" |
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"lip_gradients\030# \001(\002:\002-1\022\023\n\010snapshot\030\016 \001(" |
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"\005:\0010\022\027\n\017snapshot_prefix\030\017 \001(\t\022\034\n\rsnapsho" |
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"t_diff\030\020 \001(\010:\005false\022K\n\017snapshot_format\030%" |
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" \001(\0162%.caffe.SolverParameter.SnapshotFor" |
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"mat:\013BINARYPROTO\022;\n\013solver_mode\030\021 \001(\0162!." |
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"caffe.SolverParameter.SolverMode:\003GPU\022\024\n" |
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"\tdevice_id\030\022 \001(\005:\0010\022\027\n\013random_seed\030\024 \001(\003" |
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":\002-1\022\021\n\004type\030( \001(\t:\003SGD\022\024\n\005delta\030\037 \001(\002:\005" |
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"1e-08\022\030\n\tmomentum2\030\' \001(\002:\0050.999\022\027\n\trms_d" |
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"ecay\030& \001(\002:\0040.99\022\031\n\ndebug_info\030\027 \001(\010:\005fa" |
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"lse\022\"\n\024snapshot_after_train\030\034 \001(\010:\004true\022" |
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";\n\013solver_type\030\036 \001(\0162!.caffe.SolverParam" |
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"eter.SolverType:\003SGD\"+\n\016SnapshotFormat\022\010" |
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"\n\004HDF5\020\000\022\017\n\013BINARYPROTO\020\001\"\036\n\nSolverMode\022" |
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"\007\n\003CPU\020\000\022\007\n\003GPU\020\001\"U\n\nSolverType\022\007\n\003SGD\020\000" |
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"\022\014\n\010NESTEROV\020\001\022\013\n\007ADAGRAD\020\002\022\013\n\007RMSPROP\020\003" |
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"\022\014\n\010ADADELTA\020\004\022\010\n\004ADAM\020\005\"l\n\013SolverState\022" |
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"\014\n\004iter\030\001 \001(\005\022\023\n\013learned_net\030\002 \001(\t\022!\n\007hi" |
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"story\030\003 \003(\0132\020.caffe.BlobProto\022\027\n\014current" |
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"_step\030\004 \001(\005:\0010\"N\n\010NetState\022!\n\005phase\030\001 \001(" |
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"\0162\014.caffe.Phase:\004TEST\022\020\n\005level\030\002 \001(\005:\0010\022" |
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"\r\n\005stage\030\003 \003(\t\"s\n\014NetStateRule\022\033\n\005phase\030" |
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"\001 \001(\0162\014.caffe.Phase\022\021\n\tmin_level\030\002 \001(\005\022\021" |
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"\n\tmax_level\030\003 \001(\005\022\r\n\005stage\030\004 \003(\t\022\021\n\tnot_" |
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"stage\030\005 \003(\t\"\243\001\n\tParamSpec\022\014\n\004name\030\001 \001(\t\022" |
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"1\n\nshare_mode\030\002 \001(\0162\035.caffe.ParamSpec.Di" |
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"mCheckMode\022\022\n\007lr_mult\030\003 \001(\002:\0011\022\025\n\ndecay_" |
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"mult\030\004 \001(\002:\0011\"*\n\014DimCheckMode\022\n\n\006STRICT\020" |
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"\000\022\016\n\nPERMISSIVE\020\001\"\335\025\n\016LayerParameter\022\014\n\004" |
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"name\030\001 \001(\t\022\014\n\004type\030\002 \001(\t\022\016\n\006bottom\030\003 \003(\t" |
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"\022\013\n\003top\030\004 \003(\t\022\033\n\005phase\030\n \001(\0162\014.caffe.Pha" |
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"se\022\023\n\013loss_weight\030\005 \003(\002\022\037\n\005param\030\006 \003(\0132\020" |
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".caffe.ParamSpec\022\037\n\005blobs\030\007 \003(\0132\020.caffe." |
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"BlobProto\022\026\n\016propagate_down\030\013 \003(\010\022$\n\007inc" |
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"lude\030\010 \003(\0132\023.caffe.NetStateRule\022$\n\007exclu" |
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"de\030\t \003(\0132\023.caffe.NetStateRule\0227\n\017transfo" |
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"rm_param\030d \001(\0132\036.caffe.TransformationPar" |
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"ameter\022(\n\nloss_param\030e \001(\0132\024.caffe.LossP" |
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"arameter\0220\n\016accuracy_param\030f \001(\0132\030.caffe" |
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".AccuracyParameter\022,\n\014argmax_param\030g \001(\013" |
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"2\026.caffe.ArgMaxParameter\0224\n\020batch_norm_p" |
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"aram\030\213\001 \001(\0132\031.caffe.BatchNormParameter\022)" |
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"\n\nbias_param\030\215\001 \001(\0132\024.caffe.BiasParamete" |
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"r\022,\n\014concat_param\030h \001(\0132\026.caffe.ConcatPa" |
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"rameter\022\?\n\026contrastive_loss_param\030i \001(\0132" |
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"\037.caffe.ContrastiveLossParameter\0226\n\021conv" |
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"olution_param\030j \001(\0132\033.caffe.ConvolutionP" |
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"arameter\022)\n\ncrop_param\030\220\001 \001(\0132\024.caffe.Cr" |
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"opParameter\022(\n\ndata_param\030k \001(\0132\024.caffe." |
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"DataParameter\022@\n\026detection_output_param\030" |
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"\223\001 \001(\0132\037.caffe.DetectionOutputParameter\022" |
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".\n\rdropout_param\030l \001(\0132\027.caffe.DropoutPa" |
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"rameter\0223\n\020dummy_data_param\030m \001(\0132\031.caff" |
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"e.DummyDataParameter\022.\n\reltwise_param\030n " |
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"\001(\0132\027.caffe.EltwiseParameter\022\'\n\telu_para" |
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"m\030\214\001 \001(\0132\023.caffe.ELUParameter\022+\n\013embed_p" |
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"aram\030\211\001 \001(\0132\025.caffe.EmbedParameter\022&\n\tex" |
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"p_param\030o \001(\0132\023.caffe.ExpParameter\022/\n\rfl" |
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"atten_param\030\207\001 \001(\0132\027.caffe.FlattenParame" |
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"ter\0221\n\017hdf5_data_param\030p \001(\0132\030.caffe.HDF" |
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"5DataParameter\0225\n\021hdf5_output_param\030q \001(" |
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"\0132\032.caffe.HDF5OutputParameter\0223\n\020hinge_l" |
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"oss_param\030r \001(\0132\031.caffe.HingeLossParamet" |
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"er\0223\n\020image_data_param\030s \001(\0132\031.caffe.Ima" |
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"geDataParameter\0229\n\023infogain_loss_param\030t" |
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" \001(\0132\034.caffe.InfogainLossParameter\0229\n\023in" |
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"ner_product_param\030u \001(\0132\034.caffe.InnerPro" |
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"ductParameter\022+\n\013input_param\030\217\001 \001(\0132\025.ca" |
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"ffe.InputParameter\022\'\n\tlog_param\030\206\001 \001(\0132\023" |
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".caffe.LogParameter\022&\n\tlrn_param\030v \001(\0132\023" |
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".caffe.LRNParameter\0225\n\021memory_data_param" |
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"\030w \001(\0132\032.caffe.MemoryDataParameter\022&\n\tmv" |
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"n_param\030x \001(\0132\023.caffe.MVNParameter\0222\n\nno" |
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"rm_param\030\225\001 \001(\0132\035.caffe.NormalizeBBoxPar" |
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"ameter\022/\n\rpermute_param\030\224\001 \001(\0132\027.caffe.P" |
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"ermuteParameter\0223\n\017parameter_param\030\221\001 \001(" |
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"\0132\031.caffe.ParameterParameter\022.\n\rpooling_" |
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"param\030y \001(\0132\027.caffe.PoolingParameter\022*\n\013" |
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"power_param\030z \001(\0132\025.caffe.PowerParameter" |
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"\022+\n\013prelu_param\030\203\001 \001(\0132\025.caffe.PReLUPara" |
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"meter\0222\n\017prior_box_param\030\226\001 \001(\0132\030.caffe." |
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"PriorBoxParameter\022-\n\014python_param\030\202\001 \001(\013" |
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"2\026.caffe.PythonParameter\0223\n\017recurrent_pa" |
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"ram\030\222\001 \001(\0132\031.caffe.RecurrentParameter\0223\n" |
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"\017reduction_param\030\210\001 \001(\0132\031.caffe.Reductio" |
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"nParameter\022(\n\nrelu_param\030{ \001(\0132\024.caffe.R" |
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"eLUParameter\022/\n\rreshape_param\030\205\001 \001(\0132\027.c" |
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"affe.ReshapeParameter\022+\n\013scale_param\030\216\001 " |
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"\001(\0132\025.caffe.ScaleParameter\022.\n\rsigmoid_pa" |
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"ram\030| \001(\0132\027.caffe.SigmoidParameter\022.\n\rso" |
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"ftmax_param\030} \001(\0132\027.caffe.SoftmaxParamet" |
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"er\022\'\n\tspp_param\030\204\001 \001(\0132\023.caffe.SPPParame" |
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"ter\022*\n\013slice_param\030~ \001(\0132\025.caffe.SlicePa" |
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"rameter\022(\n\ntanh_param\030\177 \001(\0132\024.caffe.TanH" |
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"Parameter\0223\n\017threshold_param\030\200\001 \001(\0132\031.ca" |
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"ffe.ThresholdParameter\022)\n\ntile_param\030\212\001 " |
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"\001(\0132\024.caffe.TileParameter\0226\n\021window_data" |
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"_param\030\201\001 \001(\0132\032.caffe.WindowDataParamete" |
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"r\"\266\001\n\027TransformationParameter\022\020\n\005scale\030\001" |
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" \001(\002:\0011\022\025\n\006mirror\030\002 \001(\010:\005false\022\024\n\tcrop_s" |
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"ize\030\003 \001(\r:\0010\022\021\n\tmean_file\030\004 \001(\t\022\022\n\nmean_" |
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"value\030\005 \003(\002\022\032\n\013force_color\030\006 \001(\010:\005false\022" |
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"\031\n\nforce_gray\030\007 \001(\010:\005false\"\302\001\n\rLossParam" |
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"eter\022\024\n\014ignore_label\030\001 \001(\005\022D\n\rnormalizat" |
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"ion\030\003 \001(\0162&.caffe.LossParameter.Normaliz" |
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"ationMode:\005VALID\022\021\n\tnormalize\030\002 \001(\010\"B\n\021N" |
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"ormalizationMode\022\010\n\004FULL\020\000\022\t\n\005VALID\020\001\022\016\n" |
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"\nBATCH_SIZE\020\002\022\010\n\004NONE\020\003\"L\n\021AccuracyParam" |
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" \001(\0162\036.caffe.PoolingParameter.Engine:\007DE" |
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"\n\005CUDNN\020\002\"F\n\016PowerParameter\022\020\n\005power\030\001 \001" |
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"+\n\013bias_filler\030\003 \001(\0132\026.caffe.FillerParam" |
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"eter\022\031\n\ndebug_info\030\004 \001(\010:\005false\022\034\n\rexpos" |
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"\005:\0010\022\020\n\005coeff\030\003 \001(\002:\0011\"5\n\013ReductionOp\022\007\n" |
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"\n\rReLUParameter\022\031\n\016negative_slope\030\001 \001(\002:" |
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"\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"Z\n\020ReshapeParamete" |
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"is\030\002 \001(\005:\0010\022\024\n\010num_axes\030\003 \001(\005:\002-1\"\245\001\n\016Sc" |
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"s\030\002 \001(\005:\0011\022&\n\006filler\030\003 \001(\0132\026.caffe.Fille" |
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"r\"x\n\020SigmoidParameter\0227\n\006engine\030\001 \001(\0162\036." |
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"caffe.SigmoidParameter.Engine:\007DEFAULT\"+" |
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"\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDN" |
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"N\020\002\"L\n\016SliceParameter\022\017\n\004axis\030\003 \001(\005:\0011\022\023" |
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"\n\013slice_point\030\002 \003(\r\022\024\n\tslice_dim\030\001 \001(\r:\001" |
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"1\"\211\001\n\020SoftmaxParameter\0227\n\006engine\030\001 \001(\0162\036" |
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".caffe.SoftmaxParameter.Engine:\007DEFAULT\022" |
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"\017\n\004axis\030\002 \001(\005:\0011\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022" |
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"\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"r\n\rTanHParameter\0224" |
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"\n\006engine\030\001 \001(\0162\033.caffe.TanHParameter.Eng" |
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"ine:\007DEFAULT\"+\n\006Engine\022\013\n\007DEFAULT\020\000\022\t\n\005C" |
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"AFFE\020\001\022\t\n\005CUDNN\020\002\"/\n\rTileParameter\022\017\n\004ax" |
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"is\030\001 \001(\005:\0011\022\r\n\005tiles\030\002 \001(\005\"*\n\022ThresholdP" |
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"\002 \001(\002:\0011\022\021\n\tmean_file\030\003 \001(\t\022\022\n\nbatch_siz" |
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"\006 \001(\010:\005false\022\031\n\014fg_threshold\030\007 \001(\002:\0030.5\022" |
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"\031\n\014bg_threshold\030\010 \001(\002:\0030.5\022\031\n\013fg_fractio" |
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"n\030\t \001(\002:\0040.25\022\026\n\013context_pad\030\n \001(\r:\0010\022\027\n" |
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"\tcrop_mode\030\013 \001(\t:\004warp\022\033\n\014cache_images\030\014" |
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" \001(\010:\005false\022\025\n\013root_folder\030\r \001(\t:\000\"\353\001\n\014S" |
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"PPParameter\022\026\n\016pyramid_height\030\001 \001(\r\0221\n\004p" |
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"ool\030\002 \001(\0162\036.caffe.SPPParameter.PoolMetho" |
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"d:\003MAX\0223\n\006engine\030\006 \001(\0162\032.caffe.SPPParame" |
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"ter.Engine:\007DEFAULT\".\n\nPoolMethod\022\007\n\003MAX" |
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"\020\000\022\007\n\003AVE\020\001\022\016\n\nSTOCHASTIC\020\002\"+\n\006Engine\022\013\n" |
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"\007DEFAULT\020\000\022\t\n\005CAFFE\020\001\022\t\n\005CUDNN\020\002\"\340\023\n\020V1L" |
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"ayerParameter\022\016\n\006bottom\030\002 \003(\t\022\013\n\003top\030\003 \003" |
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"(\t\022\014\n\004name\030\004 \001(\t\022$\n\007include\030 \003(\0132\023.caff" |
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"e.NetStateRule\022$\n\007exclude\030! \003(\0132\023.caffe." |
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"NetStateRule\022/\n\004type\030\005 \001(\0162!.caffe.V1Lay" |
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"erParameter.LayerType\022\037\n\005blobs\030\006 \003(\0132\020.c" |
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"affe.BlobProto\022\016\n\005param\030\351\007 \003(\t\022>\n\017blob_s" |
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"hare_mode\030\352\007 \003(\0162$.caffe.V1LayerParamete" |
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"r.DimCheckMode\022\020\n\010blobs_lr\030\007 \003(\002\022\024\n\014weig" |
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"ccuracy_param\030\033 \001(\0132\030.caffe.AccuracyPara" |
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"meter\022,\n\014argmax_param\030\027 \001(\0132\026.caffe.ArgM" |
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"axParameter\022,\n\014concat_param\030\t \001(\0132\026.caff" |
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"e.ConcatParameter\022\?\n\026contrastive_loss_pa" |
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"ram\030( \001(\0132\037.caffe.ContrastiveLossParamet" |
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"er\0226\n\021convolution_param\030\n \001(\0132\033.caffe.Co" |
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"nvolutionParameter\022(\n\ndata_param\030\013 \001(\0132\024" |
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".caffe.DataParameter\022.\n\rdropout_param\030\014 " |
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"\001(\0132\027.caffe.DropoutParameter\0223\n\020dummy_da" |
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"ta_param\030\032 \001(\0132\031.caffe.DummyDataParamete" |
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"r\022.\n\reltwise_param\030\030 \001(\0132\027.caffe.Eltwise" |
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"Parameter\022&\n\texp_param\030) \001(\0132\023.caffe.Exp" |
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"Parameter\0221\n\017hdf5_data_param\030\r \001(\0132\030.caf" |
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"fe.HDF5DataParameter\0225\n\021hdf5_output_para" |
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"m\030\016 \001(\0132\032.caffe.HDF5OutputParameter\0223\n\020h" |
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"inge_loss_param\030\035 \001(\0132\031.caffe.HingeLossP" |
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"arameter\0223\n\020image_data_param\030\017 \001(\0132\031.caf" |
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"fe.ImageDataParameter\0229\n\023infogain_loss_p" |
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"aram\030\020 \001(\0132\034.caffe.InfogainLossParameter" |
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"\0229\n\023inner_product_param\030\021 \001(\0132\034.caffe.In" |
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"nerProductParameter\022&\n\tlrn_param\030\022 \001(\0132\023" |
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".caffe.LRNParameter\0225\n\021memory_data_param" |
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"\030\026 \001(\0132\032.caffe.MemoryDataParameter\022&\n\tmv" |
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"n_param\030\" \001(\0132\023.caffe.MVNParameter\022.\n\rpo" |
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"oling_param\030\023 \001(\0132\027.caffe.PoolingParamet" |
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"er\022*\n\013power_param\030\025 \001(\0132\025.caffe.PowerPar" |
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"ameter\022(\n\nrelu_param\030\036 \001(\0132\024.caffe.ReLUP" |
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"arameter\022.\n\rsigmoid_param\030& \001(\0132\027.caffe." |
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"SigmoidParameter\022.\n\rsoftmax_param\030\' \001(\0132" |
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"\027.caffe.SoftmaxParameter\022*\n\013slice_param\030" |
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"\037 \001(\0132\025.caffe.SliceParameter\022(\n\ntanh_par" |
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"am\030% \001(\0132\024.caffe.TanHParameter\0222\n\017thresh" |
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"old_param\030\031 \001(\0132\031.caffe.ThresholdParamet" |
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"er\0225\n\021window_data_param\030\024 \001(\0132\032.caffe.Wi" |
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"ndowDataParameter\0227\n\017transform_param\030$ \001" |
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"(\0132\036.caffe.TransformationParameter\022(\n\nlo" |
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"ss_param\030* \001(\0132\024.caffe.LossParameter\022&\n\005" |
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"layer\030\001 \001(\0132\027.caffe.V0LayerParameter\"\330\004\n" |
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"\tLayerType\022\010\n\004NONE\020\000\022\n\n\006ABSVAL\020#\022\014\n\010ACCU" |
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"RACY\020\001\022\n\n\006ARGMAX\020\036\022\010\n\004BNLL\020\002\022\n\n\006CONCAT\020\003" |
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"\022\024\n\020CONTRASTIVE_LOSS\020%\022\017\n\013CONVOLUTION\020\004\022" |
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"\010\n\004DATA\020\005\022\021\n\rDECONVOLUTION\020\'\022\013\n\007DROPOUT\020" |
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"\006\022\016\n\nDUMMY_DATA\020 \022\022\n\016EUCLIDEAN_LOSS\020\007\022\013\n" |
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"\007ELTWISE\020\031\022\007\n\003EXP\020&\022\013\n\007FLATTEN\020\010\022\r\n\tHDF5" |
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"_DATA\020\t\022\017\n\013HDF5_OUTPUT\020\n\022\016\n\nHINGE_LOSS\020\034" |
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"\022\n\n\006IM2COL\020\013\022\016\n\nIMAGE_DATA\020\014\022\021\n\rINFOGAIN" |
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"_LOSS\020\r\022\021\n\rINNER_PRODUCT\020\016\022\007\n\003LRN\020\017\022\017\n\013M" |
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"EMORY_DATA\020\035\022\035\n\031MULTINOMIAL_LOGISTIC_LOS" |
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"S\020\020\022\007\n\003MVN\020\"\022\013\n\007POOLING\020\021\022\t\n\005POWER\020\032\022\010\n\004" |
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"RELU\020\022\022\013\n\007SIGMOID\020\023\022\036\n\032SIGMOID_CROSS_ENT" |
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"ROPY_LOSS\020\033\022\013\n\007SILENCE\020$\022\013\n\007SOFTMAX\020\024\022\020\n" |
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"\014SOFTMAX_LOSS\020\025\022\t\n\005SPLIT\020\026\022\t\n\005SLICE\020!\022\010\n" |
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"\004TANH\020\027\022\017\n\013WINDOW_DATA\020\030\022\r\n\tTHRESHOLD\020\037\"" |
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"*\n\014DimCheckMode\022\n\n\006STRICT\020\000\022\016\n\nPERMISSIV" |
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"E\020\001\"\375\007\n\020V0LayerParameter\022\014\n\004name\030\001 \001(\t\022\014" |
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"\n\004type\030\002 \001(\t\022\022\n\nnum_output\030\003 \001(\r\022\026\n\010bias" |
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"term\030\004 \001(\010:\004true\022-\n\rweight_filler\030\005 \001(\0132" |
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"\026.caffe.FillerParameter\022+\n\013bias_filler\030\006" |
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" \001(\0132\026.caffe.FillerParameter\022\016\n\003pad\030\007 \001(" |
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"\r:\0010\022\022\n\nkernelsize\030\010 \001(\r\022\020\n\005group\030\t \001(\r:" |
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"\0011\022\021\n\006stride\030\n \001(\r:\0011\0225\n\004pool\030\013 \001(\0162\".ca" |
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"ffe.V0LayerParameter.PoolMethod:\003MAX\022\032\n\r" |
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"dropout_ratio\030\014 \001(\002:\0030.5\022\025\n\nlocal_size\030\r" |
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" \001(\r:\0015\022\020\n\005alpha\030\016 \001(\002:\0011\022\022\n\004beta\030\017 \001(\002:" |
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"\0040.75\022\014\n\001k\030\026 \001(\002:\0011\022\016\n\006source\030\020 \001(\t\022\020\n\005s" |
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"cale\030\021 \001(\002:\0011\022\020\n\010meanfile\030\022 \001(\t\022\021\n\tbatch" |
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"size\030\023 \001(\r\022\023\n\010cropsize\030\024 \001(\r:\0010\022\025\n\006mirro" |
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"r\030\025 \001(\010:\005false\022\037\n\005blobs\0302 \003(\0132\020.caffe.Bl" |
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"obProto\022\020\n\010blobs_lr\0303 \003(\002\022\024\n\014weight_deca" |
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"y\0304 \003(\002\022\024\n\trand_skip\0305 \001(\r:\0010\022\035\n\020det_fg_" |
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"threshold\0306 \001(\002:\0030.5\022\035\n\020det_bg_threshold" |
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"\0307 \001(\002:\0030.5\022\035\n\017det_fg_fraction\0308 \001(\002:\0040." |
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"25\022\032\n\017det_context_pad\030: \001(\r:\0010\022\033\n\rdet_cr" |
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"op_mode\030; \001(\t:\004warp\022\022\n\007new_num\030< \001(\005:\0010\022" |
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"\027\n\014new_channels\030= \001(\005:\0010\022\025\n\nnew_height\030>" |
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" \001(\005:\0010\022\024\n\tnew_width\030\? \001(\005:\0010\022\035\n\016shuffle" |
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"_images\030@ \001(\010:\005false\022\025\n\nconcat_dim\030A \001(\r" |
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":\0011\0226\n\021hdf5_output_param\030\351\007 \001(\0132\032.caffe." |
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|
"HDF5OutputParameter\".\n\nPoolMethod\022\007\n\003MAX" |
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|
"\020\000\022\007\n\003AVE\020\001\022\016\n\nSTOCHASTIC\020\002\"W\n\016PReLUPara" |
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|
|
"meter\022&\n\006filler\030\001 \001(\0132\026.caffe.FillerPara" |
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"meter\022\035\n\016channel_shared\030\002 \001(\010:\005false\"\207\001\n" |
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"\016NormalizedBBox\022\014\n\004xmin\030\001 \001(\002\022\014\n\004ymin\030\002 " |
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"\001(\002\022\014\n\004xmax\030\003 \001(\002\022\014\n\004ymax\030\004 \001(\002\022\r\n\005label" |
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"\030\005 \001(\005\022\021\n\tdifficult\030\006 \001(\010\022\r\n\005score\030\007 \001(\002" |
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"\022\014\n\004size\030\010 \001(\002*=\n\004Type\022\n\n\006DOUBLE\020\000\022\t\n\005FL" |
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"OAT\020\001\022\013\n\007FLOAT16\020\002\022\007\n\003INT\020\003\022\010\n\004UINT\020\004*\034\n" |
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|
"\005Phase\022\t\n\005TRAIN\020\000\022\010\n\004TEST\020\001", 17027); |
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|
::google::protobuf::MessageFactory::InternalRegisterGeneratedFile( |
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"caffe.proto", &protobuf_RegisterTypes); |
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::google::protobuf::internal::OnShutdown(&protobuf_ShutdownFile_caffe_2eproto); |
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@ -5141,6 +5143,7 @@ const int PriorBoxParameter::kStepFieldNumber; |
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const int PriorBoxParameter::kStepHFieldNumber; |
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const int PriorBoxParameter::kStepWFieldNumber; |
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const int PriorBoxParameter::kOffsetFieldNumber; |
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const int PriorBoxParameter::kAdditionalYOffsetFieldNumber; |
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|
#endif // !defined(_MSC_VER) || _MSC_VER >= 1900
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PriorBoxParameter::PriorBoxParameter() |
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@ -5163,8 +5166,8 @@ PriorBoxParameter::PriorBoxParameter(const PriorBoxParameter& from) |
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void PriorBoxParameter::SharedCtor() { |
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_cached_size_ = 0; |
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::memset(&min_size_, 0, reinterpret_cast<char*>(&step_w_) - |
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reinterpret_cast<char*>(&min_size_) + sizeof(step_w_)); |
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::memset(&min_size_, 0, reinterpret_cast<char*>(&additional_y_offset_) - |
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reinterpret_cast<char*>(&min_size_) + sizeof(additional_y_offset_)); |
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flip_ = true; |
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clip_ = true; |
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offset_ = 0.5f; |
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@ -5226,8 +5229,8 @@ void PriorBoxParameter::Clear() { |
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flip_ = true; |
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clip_ = true; |
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} |
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if (_has_bits_[8 / 32] & 7936u) { |
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ZR_(img_w_, step_w_); |
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if (_has_bits_[8 / 32] & 16128u) { |
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ZR_(img_w_, additional_y_offset_); |
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offset_ = 0.5f; |
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} |
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@ -5450,6 +5453,21 @@ bool PriorBoxParameter::MergePartialFromCodedStream( |
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} else { |
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goto handle_unusual; |
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} |
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if (input->ExpectTag(112)) goto parse_additional_y_offset; |
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break; |
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} |
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// optional bool additional_y_offset = 14 [default = false];
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case 14: { |
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if (tag == 112) { |
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parse_additional_y_offset: |
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set_has_additional_y_offset(); |
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DO_((::google::protobuf::internal::WireFormatLite::ReadPrimitive< |
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bool, ::google::protobuf::internal::WireFormatLite::TYPE_BOOL>( |
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|
input, &additional_y_offset_))); |
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} else { |
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goto handle_unusual; |
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} |
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if (input->ExpectAtEnd()) goto success; |
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break; |
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|
} |
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@ -5546,6 +5564,11 @@ void PriorBoxParameter::SerializeWithCachedSizes( |
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::google::protobuf::internal::WireFormatLite::WriteFloat(13, this->offset(), output); |
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} |
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// optional bool additional_y_offset = 14 [default = false];
|
|
|
|
|
if (has_additional_y_offset()) { |
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|
::google::protobuf::internal::WireFormatLite::WriteBool(14, this->additional_y_offset(), output); |
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|
} |
|
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|
|
|
|
|
|
|
if (_internal_metadata_.have_unknown_fields()) { |
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|
::google::protobuf::internal::WireFormat::SerializeUnknownFields( |
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|
unknown_fields(), output); |
|
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|
@ -5624,6 +5647,11 @@ void PriorBoxParameter::SerializeWithCachedSizes( |
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|
target = ::google::protobuf::internal::WireFormatLite::WriteFloatToArray(13, this->offset(), target); |
|
|
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|
} |
|
|
|
|
|
|
|
|
|
// 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); |
|
|
|
@ -5672,7 +5700,7 @@ size_t PriorBoxParameter::ByteSizeLong() const { |
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
} |
|
|
|
|
if (_has_bits_[8 / 32] & 7936u) { |
|
|
|
|
if (_has_bits_[8 / 32] & 16128u) { |
|
|
|
|
// optional uint32 img_w = 9;
|
|
|
|
|
if (has_img_w()) { |
|
|
|
|
total_size += 1 + |
|
|
|
@ -5700,6 +5728,11 @@ size_t PriorBoxParameter::ByteSizeLong() const { |
|
|
|
|
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;
|
|
|
|
|
{ |
|
|
|
@ -5797,6 +5830,9 @@ void PriorBoxParameter::UnsafeMergeFrom(const PriorBoxParameter& from) { |
|
|
|
|
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( |
|
|
|
@ -5841,6 +5877,7 @@ void PriorBoxParameter::InternalSwap(PriorBoxParameter* other) { |
|
|
|
|
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_); |
|
|
|
@ -6181,6 +6218,30 @@ void PriorBoxParameter::set_offset(float value) { |
|
|
|
|
// @@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(); |
|
|
|
|
} |
|
|
|
|