train.prototxt

layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "score_map"
  top: "geo_map"
  python_param {
    module: "pylayerUtils"
    layer: "DataLayer"
    param_str: "{\'data_dir\': \'/home1/zjy/datasets/\', \'dataset\': \'invoice\', \'patch_size\': 512, \'batch_size\': 4, \'seed\': 123, \'mean\': (103.939, 116.779, 123.68)}"
  }
}
# layer {
  # name: "data"
  # type: "Input"
  # top: "data"
  # input_param {
    # shape {
      # dim: 1
      # dim: 3
      # dim: 512
      # dim: 512
    # }
  # }
# }
layer {
  name: "Conv"
  type: "Convolution"
  bottom: "data"
  top: "Conv"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "Conv/bn"
  type: "BatchNorm"
  bottom: "Conv"
  top: "Conv"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Conv/scale"
  type: "Scale"
  bottom: "Conv"
  top: "Conv"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "Conv/relu"
  type: "ReLU6"
  bottom: "Conv"
  top: "Conv"
}
layer {
  name: "conv/depthwise"
  type: "Convolution"
  bottom: "Conv"
  top: "conv/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv/depthwise"
  top: "conv/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv/depthwise/scale"
  type: "Scale"
  bottom: "conv/depthwise"
  top: "conv/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv/depthwise/relu"
  type: "ReLU6"
  bottom: "conv/depthwise"
  top: "conv/depthwise"
}
layer {
  name: "conv/project"
  type: "Convolution"
  bottom: "conv/depthwise"
  top: "conv/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 16
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv/project/bn"
  type: "BatchNorm"
  bottom: "conv/project"
  top: "conv/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv/project/scale"
  type: "Scale"
  bottom: "conv/project"
  top: "conv/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_1/expand"
  type: "Convolution"
  bottom: "conv/project"
  top: "conv_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_1/expand/bn"
  type: "BatchNorm"
  bottom: "conv_1/expand"
  top: "conv_1/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_1/expand/scale"
  type: "Scale"
  bottom: "conv_1/expand"
  top: "conv_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_1/expand/relu"
  type: "ReLU6"
  bottom: "conv_1/expand"
  top: "conv_1/expand"
}
layer {
  name: "conv_1/depthwise"
  type: "Convolution"
  bottom: "conv_1/expand"
  top: "conv_1/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 96
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_1/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_1/depthwise"
  top: "conv_1/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_1/depthwise/scale"
  type: "Scale"
  bottom: "conv_1/depthwise"
  top: "conv_1/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_1/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_1/depthwise"
  top: "conv_1/depthwise"
}
layer {
  name: "conv_1/project"
  type: "Convolution"
  bottom: "conv_1/depthwise"
  top: "conv_1/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 24
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_1/project/bn"
  type: "BatchNorm"
  bottom: "conv_1/project"
  top: "conv_1/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_1/project/scale"
  type: "Scale"
  bottom: "conv_1/project"
  top: "conv_1/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_2/expand"
  type: "Convolution"
  bottom: "conv_1/project"
  top: "conv_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_2/expand/bn"
  type: "BatchNorm"
  bottom: "conv_2/expand"
  top: "conv_2/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_2/expand/scale"
  type: "Scale"
  bottom: "conv_2/expand"
  top: "conv_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_2/expand/relu"
  type: "ReLU6"
  bottom: "conv_2/expand"
  top: "conv_2/expand"
}
layer {
  name: "conv_2/depthwise"
  type: "Convolution"
  bottom: "conv_2/expand"
  top: "conv_2/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 144
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_2/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_2/depthwise"
  top: "conv_2/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_2/depthwise/scale"
  type: "Scale"
  bottom: "conv_2/depthwise"
  top: "conv_2/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_2/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_2/depthwise"
  top: "conv_2/depthwise"
}
layer {
  name: "conv_2/project"
  type: "Convolution"
  bottom: "conv_2/depthwise"
  top: "conv_2/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 24
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_2/project/bn"
  type: "BatchNorm"
  bottom: "conv_2/project"
  top: "conv_2/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_2/project/scale"
  type: "Scale"
  bottom: "conv_2/project"
  top: "conv_2/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_2/sum"
  type: "Eltwise"
  bottom: "conv_1/project"
  bottom: "conv_2/project"
  top: "conv_2"
}
layer {
  name: "conv_3/expand"
  type: "Convolution"
  bottom: "conv_2"
  top: "conv_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_3/expand/bn"
  type: "BatchNorm"
  bottom: "conv_3/expand"
  top: "conv_3/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_3/expand/scale"
  type: "Scale"
  bottom: "conv_3/expand"
  top: "conv_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_3/expand/relu"
  type: "ReLU6"
  bottom: "conv_3/expand"
  top: "conv_3/expand"
}
layer {
  name: "conv_3/depthwise"
  type: "Convolution"
  bottom: "conv_3/expand"
  top: "conv_3/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 144
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    group: 144
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_3/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_3/depthwise"
  top: "conv_3/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_3/depthwise/scale"
  type: "Scale"
  bottom: "conv_3/depthwise"
  top: "conv_3/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_3/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_3/depthwise"
  top: "conv_3/depthwise"
}
layer {
  name: "conv_3/project"
  type: "Convolution"
  bottom: "conv_3/depthwise"
  top: "conv_3/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_3/project/bn"
  type: "BatchNorm"
  bottom: "conv_3/project"
  top: "conv_3/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_3/project/scale"
  type: "Scale"
  bottom: "conv_3/project"
  top: "conv_3/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_4/expand"
  type: "Convolution"
  bottom: "conv_3/project"
  top: "conv_4/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_4/expand/bn"
  type: "BatchNorm"
  bottom: "conv_4/expand"
  top: "conv_4/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_4/expand/scale"
  type: "Scale"
  bottom: "conv_4/expand"
  top: "conv_4/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_4/expand/relu"
  type: "ReLU6"
  bottom: "conv_4/expand"
  top: "conv_4/expand"
}
layer {
  name: "conv_4/depthwise"
  type: "Convolution"
  bottom: "conv_4/expand"
  top: "conv_4/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 192
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_4/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_4/depthwise"
  top: "conv_4/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_4/depthwise/scale"
  type: "Scale"
  bottom: "conv_4/depthwise"
  top: "conv_4/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_4/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_4/depthwise"
  top: "conv_4/depthwise"
}
layer {
  name: "conv_4/project"
  type: "Convolution"
  bottom: "conv_4/depthwise"
  top: "conv_4/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_4/project/bn"
  type: "BatchNorm"
  bottom: "conv_4/project"
  top: "conv_4/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_4/project/scale"
  type: "Scale"
  bottom: "conv_4/project"
  top: "conv_4/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_4/sum"
  type: "Eltwise"
  bottom: "conv_3/project"
  bottom: "conv_4/project"
  top: "conv_4"
}
layer {
  name: "conv_5/expand"
  type: "Convolution"
  bottom: "conv_4"
  top: "conv_5/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_5/expand/bn"
  type: "BatchNorm"
  bottom: "conv_5/expand"
  top: "conv_5/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_5/expand/scale"
  type: "Scale"
  bottom: "conv_5/expand"
  top: "conv_5/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_5/expand/relu"
  type: "ReLU6"
  bottom: "conv_5/expand"
  top: "conv_5/expand"
}
layer {
  name: "conv_5/depthwise"
  type: "Convolution"
  bottom: "conv_5/expand"
  top: "conv_5/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 192
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_5/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_5/depthwise"
  top: "conv_5/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_5/depthwise/scale"
  type: "Scale"
  bottom: "conv_5/depthwise"
  top: "conv_5/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_5/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_5/depthwise"
  top: "conv_5/depthwise"
}
layer {
  name: "conv_5/project"
  type: "Convolution"
  bottom: "conv_5/depthwise"
  top: "conv_5/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_5/project/bn"
  type: "BatchNorm"
  bottom: "conv_5/project"
  top: "conv_5/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_5/project/scale"
  type: "Scale"
  bottom: "conv_5/project"
  top: "conv_5/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_5/sum"
  type: "Eltwise"
  bottom: "conv_4"
  bottom: "conv_5/project"
  top: "conv_5"
}
layer {
  name: "conv_6/expand"
  type: "Convolution"
  bottom: "conv_5"
  top: "conv_6/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_6/expand/bn"
  type: "BatchNorm"
  bottom: "conv_6/expand"
  top: "conv_6/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_6/expand/scale"
  type: "Scale"
  bottom: "conv_6/expand"
  top: "conv_6/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_6/expand/relu"
  type: "ReLU6"
  bottom: "conv_6/expand"
  top: "conv_6/expand"
}
layer {
  name: "conv_6/depthwise"
  type: "Convolution"
  bottom: "conv_6/expand"
  top: "conv_6/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 192
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_6/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_6/depthwise"
  top: "conv_6/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_6/depthwise/scale"
  type: "Scale"
  bottom: "conv_6/depthwise"
  top: "conv_6/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_6/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_6/depthwise"
  top: "conv_6/depthwise"
}
layer {
  name: "conv_6/project"
  type: "Convolution"
  bottom: "conv_6/depthwise"
  top: "conv_6/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_6/project/bn"
  type: "BatchNorm"
  bottom: "conv_6/project"
  top: "conv_6/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_6/project/scale"
  type: "Scale"
  bottom: "conv_6/project"
  top: "conv_6/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_7/expand"
  type: "Convolution"
  bottom: "conv_6/project"
  top: "conv_7/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_7/expand/bn"
  type: "BatchNorm"
  bottom: "conv_7/expand"
  top: "conv_7/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_7/expand/scale"
  type: "Scale"
  bottom: "conv_7/expand"
  top: "conv_7/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_7/expand/relu"
  type: "ReLU6"
  bottom: "conv_7/expand"
  top: "conv_7/expand"
}
layer {
  name: "conv_7/depthwise"
  type: "Convolution"
  bottom: "conv_7/expand"
  top: "conv_7/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_7/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_7/depthwise"
  top: "conv_7/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_7/depthwise/scale"
  type: "Scale"
  bottom: "conv_7/depthwise"
  top: "conv_7/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_7/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_7/depthwise"
  top: "conv_7/depthwise"
}
layer {
  name: "conv_7/project"
  type: "Convolution"
  bottom: "conv_7/depthwise"
  top: "conv_7/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_7/project/bn"
  type: "BatchNorm"
  bottom: "conv_7/project"
  top: "conv_7/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_7/project/scale"
  type: "Scale"
  bottom: "conv_7/project"
  top: "conv_7/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_7/sum"
  type: "Eltwise"
  bottom: "conv_6/project"
  bottom: "conv_7/project"
  top: "conv_7"
}
layer {
  name: "conv_8/expand"
  type: "Convolution"
  bottom: "conv_7"
  top: "conv_8/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_8/expand/bn"
  type: "BatchNorm"
  bottom: "conv_8/expand"
  top: "conv_8/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_8/expand/scale"
  type: "Scale"
  bottom: "conv_8/expand"
  top: "conv_8/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_8/expand/relu"
  type: "ReLU6"
  bottom: "conv_8/expand"
  top: "conv_8/expand"
}
layer {
  name: "conv_8/depthwise"
  type: "Convolution"
  bottom: "conv_8/expand"
  top: "conv_8/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_8/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_8/depthwise"
  top: "conv_8/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_8/depthwise/scale"
  type: "Scale"
  bottom: "conv_8/depthwise"
  top: "conv_8/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_8/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_8/depthwise"
  top: "conv_8/depthwise"
}
layer {
  name: "conv_8/project"
  type: "Convolution"
  bottom: "conv_8/depthwise"
  top: "conv_8/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_8/project/bn"
  type: "BatchNorm"
  bottom: "conv_8/project"
  top: "conv_8/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_8/project/scale"
  type: "Scale"
  bottom: "conv_8/project"
  top: "conv_8/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_8/sum"
  type: "Eltwise"
  bottom: "conv_7"
  bottom: "conv_8/project"
  top: "conv_8"
}
layer {
  name: "conv_9/expand"
  type: "Convolution"
  bottom: "conv_8"
  top: "conv_9/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_9/expand/bn"
  type: "BatchNorm"
  bottom: "conv_9/expand"
  top: "conv_9/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_9/expand/scale"
  type: "Scale"
  bottom: "conv_9/expand"
  top: "conv_9/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_9/expand/relu"
  type: "ReLU6"
  bottom: "conv_9/expand"
  top: "conv_9/expand"
}
layer {
  name: "conv_9/depthwise"
  type: "Convolution"
  bottom: "conv_9/expand"
  top: "conv_9/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 384
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_9/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_9/depthwise"
  top: "conv_9/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_9/depthwise/scale"
  type: "Scale"
  bottom: "conv_9/depthwise"
  top: "conv_9/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_9/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_9/depthwise"
  top: "conv_9/depthwise"
}
layer {
  name: "conv_9/project"
  type: "Convolution"
  bottom: "conv_9/depthwise"
  top: "conv_9/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_9/project/bn"
  type: "BatchNorm"
  bottom: "conv_9/project"
  top: "conv_9/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_9/project/scale"
  type: "Scale"
  bottom: "conv_9/project"
  top: "conv_9/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_9/sum"
  type: "Eltwise"
  bottom: "conv_8"
  bottom: "conv_9/project"
  top: "conv_9"
}
layer {
  name: "conv_10/expand"
  type: "Convolution"
  bottom: "conv_9"
  top: "conv_10/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_10/expand/bn"
  type: "BatchNorm"
  bottom: "conv_10/expand"
  top: "conv_10/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_10/expand/scale"
  type: "Scale"
  bottom: "conv_10/expand"
  top: "conv_10/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_10/expand/relu"
  type: "ReLU6"
  bottom: "conv_10/expand"
  top: "conv_10/expand"
}
layer {
  name: "conv_10/depthwise"
  type: "Convolution"
  bottom: "conv_10/expand"
  top: "conv_10/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    pad: 1
    kernel_size: 3
    stride:2
    group: 384
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_10/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_10/depthwise"
  top: "conv_10/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_10/depthwise/scale"
  type: "Scale"
  bottom: "conv_10/depthwise"
  top: "conv_10/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_10/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_10/depthwise"
  top: "conv_10/depthwise"
}
layer {
  name: "conv_10/project"
  type: "Convolution"
  bottom: "conv_10/depthwise"
  top: "conv_10/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_10/project/bn"
  type: "BatchNorm"
  bottom: "conv_10/project"
  top: "conv_10/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_10/project/scale"
  type: "Scale"
  bottom: "conv_10/project"
  top: "conv_10/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_11/expand"
  type: "Convolution"
  bottom: "conv_10/project"
  top: "conv_11/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_11/expand/bn"
  type: "BatchNorm"
  bottom: "conv_11/expand"
  top: "conv_11/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_11/expand/scale"
  type: "Scale"
  bottom: "conv_11/expand"
  top: "conv_11/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_11/expand/relu"
  type: "ReLU6"
  bottom: "conv_11/expand"
  top: "conv_11/expand"
}
layer {
  name: "conv_11/depthwise"
  type: "Convolution"
  bottom: "conv_11/expand"
  top: "conv_11/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_11/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_11/depthwise"
  top: "conv_11/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_11/depthwise/scale"
  type: "Scale"
  bottom: "conv_11/depthwise"
  top: "conv_11/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_11/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_11/depthwise"
  top: "conv_11/depthwise"
}
layer {
  name: "conv_11/project"
  type: "Convolution"
  bottom: "conv_11/depthwise"
  top: "conv_11/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_11/project/bn"
  type: "BatchNorm"
  bottom: "conv_11/project"
  top: "conv_11/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_11/project/scale"
  type: "Scale"
  bottom: "conv_11/project"
  top: "conv_11/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_11/sum"
  type: "Eltwise"
  bottom: "conv_10/project"
  bottom: "conv_11/project"
  top: "conv_11"
}
layer {
  name: "conv_12/expand"
  type: "Convolution"
  bottom: "conv_11"
  top: "conv_12/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_12/expand/bn"
  type: "BatchNorm"
  bottom: "conv_12/expand"
  top: "conv_12/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_12/expand/scale"
  type: "Scale"
  bottom: "conv_12/expand"
  top: "conv_12/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_12/expand/relu"
  type: "ReLU6"
  bottom: "conv_12/expand"
  top: "conv_12/expand"
}
layer {
  name: "conv_12/depthwise"
  type: "Convolution"
  bottom: "conv_12/expand"
  top: "conv_12/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_12/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_12/depthwise"
  top: "conv_12/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_12/depthwise/scale"
  type: "Scale"
  bottom: "conv_12/depthwise"
  top: "conv_12/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_12/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_12/depthwise"
  top: "conv_12/depthwise"
}
layer {
  name: "conv_12/project"
  type: "Convolution"
  bottom: "conv_12/depthwise"
  top: "conv_12/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 96
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_12/project/bn"
  type: "BatchNorm"
  bottom: "conv_12/project"
  top: "conv_12/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_12/project/scale"
  type: "Scale"
  bottom: "conv_12/project"
  top: "conv_12/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_12/sum"
  type: "Eltwise"
  bottom: "conv_11"
  bottom: "conv_12/project"
  top: "conv_12"
}
layer {
  name: "conv_13/expand"
  type: "Convolution"
  bottom: "conv_12"
  top: "conv_13/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_13/expand/bn"
  type: "BatchNorm"
  bottom: "conv_13/expand"
  top: "conv_13/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_13/expand/scale"
  type: "Scale"
  bottom: "conv_13/expand"
  top: "conv_13/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_13/expand/relu"
  type: "ReLU6"
  bottom: "conv_13/expand"
  top: "conv_13/expand"
}
layer {
  name: "conv_13/depthwise"
  type: "Convolution"
  bottom: "conv_13/expand"
  top: "conv_13/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 576
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_13/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_13/depthwise"
  top: "conv_13/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_13/depthwise/scale"
  type: "Scale"
  bottom: "conv_13/depthwise"
  top: "conv_13/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_13/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_13/depthwise"
  top: "conv_13/depthwise"
}
layer {
  name: "conv_13/project"
  type: "Convolution"
  bottom: "conv_13/depthwise"
  top: "conv_13/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_13/project/bn"
  type: "BatchNorm"
  bottom: "conv_13/project"
  top: "conv_13/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_13/project/scale"
  type: "Scale"
  bottom: "conv_13/project"
  top: "conv_13/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_14/expand"
  type: "Convolution"
  bottom: "conv_13/project"
  top: "conv_14/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 768
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_14/expand/bn"
  type: "BatchNorm"
  bottom: "conv_14/expand"
  top: "conv_14/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_14/expand/scale"
  type: "Scale"
  bottom: "conv_14/expand"
  top: "conv_14/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_14/expand/relu"
  type: "ReLU6"
  bottom: "conv_14/expand"
  top: "conv_14/expand"
}
layer {
  name: "conv_14/depthwise"
  type: "Convolution"
  bottom: "conv_14/expand"
  top: "conv_14/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 768
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 768
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_14/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_14/depthwise"
  top: "conv_14/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_14/depthwise/scale"
  type: "Scale"
  bottom: "conv_14/depthwise"
  top: "conv_14/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_14/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_14/depthwise"
  top: "conv_14/depthwise"
}
layer {
  name: "conv_14/project"
  type: "Convolution"
  bottom: "conv_14/depthwise"
  top: "conv_14/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_14/project/bn"
  type: "BatchNorm"
  bottom: "conv_14/project"
  top: "conv_14/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_14/project/scale"
  type: "Scale"
  bottom: "conv_14/project"
  top: "conv_14/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_14/sum"
  type: "Eltwise"
  bottom: "conv_13/project"
  bottom: "conv_14/project"
  top: "conv_14"
}
layer {
  name: "conv_15/expand"
  type: "Convolution"
  bottom: "conv_14"
  top: "conv_15/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 768
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_15/expand/bn"
  type: "BatchNorm"
  bottom: "conv_15/expand"
  top: "conv_15/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_15/expand/scale"
  type: "Scale"
  bottom: "conv_15/expand"
  top: "conv_15/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_15/expand/relu"
  type: "ReLU6"
  bottom: "conv_15/expand"
  top: "conv_15/expand"
}
layer {
  name: "conv_15/depthwise"
  type: "Convolution"
  bottom: "conv_15/expand"
  top: "conv_15/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 768
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 768
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_15/depthwise/bn"
  type: "BatchNorm"
  bottom: "conv_15/depthwise"
  top: "conv_15/depthwise"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_15/depthwise/scale"
  type: "Scale"
  bottom: "conv_15/depthwise"
  top: "conv_15/depthwise"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_15/depthwise/relu"
  type: "ReLU6"
  bottom: "conv_15/depthwise"
  top: "conv_15/depthwise"
}
layer {
  name: "conv_15/project"
  type: "Convolution"
  bottom: "conv_15/depthwise"
  top: "conv_15/project"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv_15/project/bn"
  type: "BatchNorm"
  bottom: "conv_15/project"
  top: "conv_15/project"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv_15/project/scale"
  type: "Scale"
  bottom: "conv_15/project"
  top: "conv_15/project"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_15/sum"
  type: "Eltwise"
  bottom: "conv_14"
  bottom: "conv_15/project"
  top: "conv_15"
}

## mbv2 part end 

layer {
  name: "sconv_1/expand"
  type: "Convolution"
  bottom: "conv_15"
  top: "sconv_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 768
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "sconv_1/expand/bn"
  type: "BatchNorm"
  bottom: "sconv_1/expand"
  top: "sconv_1/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "sconv_1/expand/scale"
  type: "Scale"
  bottom: "sconv_1/expand"
  top: "sconv_1/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "sconv_1/expand/relu"
  type: "ReLU6"
  bottom: "sconv_1/expand"
  top: "sconv_1/expand"
}


layer {
  name: "upconv_1_1"
  type: "Deconvolution"
  bottom: "sconv_1/expand"
  top: "upconv_1_1"
    param { lr_mult: 0 decay_mult: 0}
    convolution_param {
        num_output: 768 kernel_size: 4 pad: 1 stride: 2 group: 768
        weight_filler: {type: "bilinear" } 
        bias_term: false
    }  
}

layer {
  name: "sconcat_1"
  type: "Concat"
  bottom: "upconv_1_1"
  bottom: "conv_13/expand"
  top: "sconcat_1"
  concat_param {
    concat_dim: 1
  }
}

layer {
  name: "sconv_2/expand"
  type: "Convolution"
  bottom: "sconcat_1"
  top: "sconv_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "sconv_2/expand/bn"
  type: "BatchNorm"
  bottom: "sconv_2/expand"
  top: "sconv_2/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "sconv_2/expand/scale"
  type: "Scale"
  bottom: "sconv_2/expand"
  top: "sconv_2/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "sconv_2/expand/relu"
  type: "ReLU6"
  bottom: "sconv_2/expand"
  top: "sconv_2/expand"
}

layer {
  name: "upconv_2_1"
  type: "Deconvolution"
  bottom: "sconv_2/expand"
  top: "upconv_2_1"
    param { lr_mult: 0 decay_mult: 0}
    convolution_param {
        num_output: 576 
        kernel_size: 4 
        pad: 1 
        stride: 2 
        group: 576
        weight_filler: {type: "bilinear" } 
        bias_term: false
    }  
}

layer {
  name: "sconcat_2"
  type: "Concat"
  bottom: "upconv_2_1"
  bottom: "conv_10/expand"
  top: "sconcat_2"
  concat_param {
    concat_dim: 1
  }
}

layer {
  name: "sconv_3/expand"
  type: "Convolution"
  bottom: "sconcat_2"
  top: "sconv_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 384
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "sconv_3/expand/bn"
  type: "BatchNorm"
  bottom: "sconv_3/expand"
  top: "sconv_3/expand"
  batch_norm_param {
    eps: 0.001
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "sconv_3/expand/scale"
  type: "Scale"
  bottom: "sconv_3/expand"
  top: "sconv_3/expand"
  param {
    lr_mult: 1.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "sconv_3/expand/relu"
  type: "ReLU6"
  bottom: "sconv_3/expand"
  top: "sconv_3/expand"
}

layer {
  name: "upconv_3_1"
  type: "Deconvolution"
  bottom: "sconv_3/expand"
  top: "upconv_3_1"

  param { lr_mult: 0 decay_mult: 0}
  convolution_param {
    num_output: 384 
    kernel_size: 4 
    pad: 1 
    stride: 2 
    group: 384
    weight_filler: {
        type: "bilinear" 
        } 
    bias_term: false
    }  
}

layer {
  name: "sconcat_3"
  type: "Concat"
  bottom: "upconv_3_1"
  bottom: "conv_6/expand"
  top: "sconcat_3"
  concat_param {
    concat_dim: 1
  }
}

### 输出结果前的再次融合
layer {
  name: "fconv1"
  type: "Convolution"
  bottom: "sconcat_3"
  top: "fconv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "frelu1"
  type: "ReLU"
  bottom: "fconv1"
  top: "fconv1"
}
layer {
  name: "fconv2_new"
  type: "Convolution"
  bottom: "fconv1"
  top: "fconv2_new"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "frelu2"
  type: "ReLU"
  bottom: "fconv2_new"
  top: "fconv2_new"
}

## 生成scoremap
layer {
  name: "fconv3_score_new"
  type: "Convolution"
  bottom: "fconv2_new"
  top: "fconv3_score_new"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "F_score"
  bottom: "fconv3_score_new"
  top: "fconv3_score_new"
  type: "Sigmoid"
}

## 生成geomap
layer {
  name: "conv20_new"
  type: "Convolution"
  bottom: "fconv2_new"
  top: "conv20_new"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 4
 #   bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "geomap_sigmoid"
  bottom: "conv20_new"
  top: "conv20_new"
  type: "Sigmoid"
}
layer {
  name: "geomap"
  bottom: "conv20_new"
  top: "geomap"  # 1*4*128*128
  type: "Power"
  ## f(x)= (shift + scale * x) ^ power
  power_param {
    power: 1
    ## * FLAGS.text_scale
    scale: 512  
    shift: 0
  }
}
## 生成anglemap
layer {
  name: "conv21_new"
  type: "Convolution"
  bottom: "fconv2_new"
  top: "conv21_new"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
  #  bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "anglemap_sigmoid"
  bottom: "conv21_new"
  top: "conv21_new"
  type: "Sigmoid"
}
layer {
  name: "anglemap_tmp"
  bottom: "conv21_new"
  top: "anglemap_tmp"
  type: "Power"
  ## f(x)= (shift + scale * x) ^ power
  power_param {
    power: 1
    scale: 1
    shift: -0.5
  }
}
layer {
  name: "anglemap"
  bottom: "anglemap_tmp"
  top: "anglemap" # 1*1*128*128
  type: "Power"
  ## f(x)= (shift + scale * x) ^ power
  power_param {
    power: 1
    scale: 1.57
    shift: 0
  }
}
layer {
  name: "F_geometry"
  type: "Concat"
  bottom: "geomap"
  bottom: "anglemap"
  top: "concat4"  # 1*5*128*128 (4+1)
  concat_param {
    concat_dim: 1
  }
}

## 计算loss
layer {
  name: "Loss_rbox"
  type: "Python"
  bottom: "concat4"  # 1*5*128*128 (4+1)
  bottom: "geo_map"  # 1*5*128*128 (4+1)
  bottom: "score_map" # scoremap的ture_gt 1*1*128*128
  top: "Loss_rbox"
  loss_weight: 1
  python_param {
    module: "pylayerUtils"
    layer: "RBoxLossLayer"
  }
}
# layer {
  # name: "Loss_score"
  # type: "DiceCoefLoss"
  # bottom: "fconv3_score_new"
  # bottom: "score_map"
  # top: "Loss_score"
  # loss_weight: 0.01
# }
layer {
  name: "Loss_score"
  type: "Python"
  bottom: "fconv3_score_new"
  bottom: "score_map"
  top: "loss_score"
  loss_weight: 0.01
  python_param {
    module: "pylayerUtils"
    layer: "DiceCoefLossLayer"
  }
}