How to modify batch normalization layers (DeconvNe

Question

I wanted to run the Deconvnet on my data, however it seemd it has been written for another version of caffe. Does anyone know how to change batch_params?

The one that is in Deconvnet

layers { bottom: 'conv1_1' top: 'conv1_1' name: 'bn1_1' type: BN
  bn_param { scale_filler { type: 'constant' value: 1 }
             shift_filler { type: 'constant' value: 0.001 }
             bn_mode: INFERENCE } }

And the one that Caffe provides for cifar10 example:

layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "pool1"
  top: "bn1"
  batch_norm_param {
    use_global_stats: true
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TEST
  }
}

Once I wanted to run it show me the following error first:

I1029 13:46:47.156885 11601 solver.cpp:87] Creating training net from net file: train_val.prototxt
[libprotobuf ERROR google/protobuf/text_format.cc:299] Error parsing text-format caffe.NetParameter: 59:3: Unknown enumeration value of "BN" for field "type".
F1029 13:46:47.157971 11601 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param)

and after changing BN into BatchNorm, it shows new error about parameters:

I1029 14:03:38.497725 12097 solver.cpp:87] Creating training net from net file: train_val.prototxt
[libprotobuf ERROR google/protobuf/text_format.cc:299] Error parsing text-format caffe.NetParameter: 59:3: Unknown enumeration value of "BatchNorm" for field "type".
F1029 14:03:38.503345 12097 upgrade_proto.cpp:88] Check failed: ReadProtoFromTextFile(param_file, param)

Has anyone tried to train Deconvnet? if yes could you please guide me? Thanks

Answer 1

Could you please let me know if this is correct to change like this?

layer {
  name: "bn1_1"
  type: "BatchNorm"
  bottom: "conv1_1"
  top: "conv1_1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  include {
    phase: TEST
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "scale_conv1_1"
  type: "Scale"
  bottom: "conv1_1"
  top: "conv1_1"
  scale_param {
    bias_term: true
    bias_filler {
      type: "constant"
      value: 0.001
    }
  }
}