input: "data"
input_shape {
  dim: 1
  dim: 6
  dim: 512
  dim: 512
}

layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "data"
  top: "Convolution1"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm1"
  type: "BatchNorm"
  bottom: "Convolution1"
  top: "BatchNorm1"
}
layer {
  name: "Scale1"
  type: "Scale"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_s1"
  type: "ReLU"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
}
layer {
  name: "pool_s1"
  type: "Pooling"
  bottom: "BatchNorm1"
  top: "pool_s1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "pool_s1"
  top: "Convolution2"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm2"
  type: "BatchNorm"
  bottom: "Convolution2"
  top: "BatchNorm2"
}
layer {
  name: "Scale2"
  type: "Scale"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s2"
  type: "ReLU"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
}
layer {
  name: "concat_s2"
  type: "Concat"
  bottom: "pool_s1"
  bottom: "BatchNorm2"
  top: "concat_s2"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s2"
  type: "Pooling"
  bottom: "concat_s2"
  top: "pool_s2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "pool_s2"
  top: "Convolution3"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm3"
  type: "BatchNorm"
  bottom: "Convolution3"
  top: "BatchNorm3"
}
layer {
  name: "Scale3"
  type: "Scale"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s3"
  type: "ReLU"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
}
layer {
  name: "concat_s3"
  type: "Concat"
  bottom: "pool_s2"
  bottom: "BatchNorm3"
  top: "concat_s3"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s3"
  type: "Pooling"
  bottom: "concat_s3"
  top: "pool_s3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution4"
  type: "Convolution"
  bottom: "pool_s3"
  top: "Convolution4"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm4"
  type: "BatchNorm"
  bottom: "Convolution4"
  top: "BatchNorm4"
}
layer {
  name: "Scale4"
  type: "Scale"
  bottom: "BatchNorm4"
  top: "BatchNorm4"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s4"
  type: "ReLU"
  bottom: "BatchNorm4"
  top: "BatchNorm4"
}
layer {
  name: "concat_s4"
  type: "Concat"
  bottom: "pool_s3"
  bottom: "BatchNorm4"
  top: "concat_s4"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s4"
  type: "Pooling"
  bottom: "concat_s4"
  top: "pool_s4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution5"
  type: "Convolution"
  bottom: "pool_s4"
  top: "Convolution5"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm5"
  type: "BatchNorm"
  bottom: "Convolution5"
  top: "BatchNorm5"
}
layer {
  name: "Scale5"
  type: "Scale"
  bottom: "BatchNorm5"
  top: "BatchNorm5"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s5"
  type: "ReLU"
  bottom: "BatchNorm5"
  top: "BatchNorm5"
}
layer {
  name: "concat_s5"
  type: "Concat"
  bottom: "pool_s4"
  bottom: "BatchNorm5"
  top: "concat_s5"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s5"
  type: "Pooling"
  bottom: "concat_s5"
  top: "pool_s5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution6"
  type: "Convolution"
  bottom: "pool_s5"
  top: "Convolution6"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm6"
  type: "BatchNorm"
  bottom: "Convolution6"
  top: "BatchNorm6"
}
layer {
  name: "Scale6"
  type: "Scale"
  bottom: "BatchNorm6"
  top: "BatchNorm6"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s6"
  type: "ReLU"
  bottom: "BatchNorm6"
  top: "BatchNorm6"
}
layer {
  name: "concat_s6"
  type: "Concat"
  bottom: "pool_s5"
  bottom: "BatchNorm6"
  top: "concat_s6"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s6"
  type: "Pooling"
  bottom: "concat_s6"
  top: "pool_s6"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution7"
  type: "Convolution"
  bottom: "pool_s6"
  top: "Convolution7"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm7"
  type: "BatchNorm"
  bottom: "Convolution7"
  top: "BatchNorm7"
}
layer {
  name: "Scale7"
  type: "Scale"
  bottom: "BatchNorm7"
  top: "BatchNorm7"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s7"
  type: "ReLU"
  bottom: "BatchNorm7"
  top: "BatchNorm7"
}
layer {
  name: "concat_s7"
  type: "Concat"
  bottom: "pool_s6"
  bottom: "BatchNorm7"
  top: "concat_s7"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s7"
  type: "Pooling"
  bottom: "concat_s7"
  top: "pool_s7"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution8"
  type: "Convolution"
  bottom: "pool_s7"
  top: "Convolution8"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm8"
  type: "BatchNorm"
  bottom: "Convolution8"
  top: "BatchNorm8"
}
layer {
  name: "Scale8"
  type: "Scale"
  bottom: "BatchNorm8"
  top: "BatchNorm8"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s8"
  type: "ReLU"
  bottom: "BatchNorm8"
  top: "BatchNorm8"
}
layer {
  name: "concat_s8"
  type: "Concat"
  bottom: "pool_s7"
  bottom: "BatchNorm8"
  top: "concat_s8"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s8"
  type: "Pooling"
  bottom: "concat_s8"
  top: "pool_s8"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution9"
  type: "Convolution"
  bottom: "pool_s8"
  top: "Convolution9"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm9"
  type: "BatchNorm"
  bottom: "Convolution9"
  top: "BatchNorm9"
}
layer {
  name: "Scale9"
  type: "Scale"
  bottom: "BatchNorm9"
  top: "BatchNorm9"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s9"
  type: "ReLU"
  bottom: "BatchNorm9"
  top: "BatchNorm9"
}
layer {
  name: "concat_s9"
  type: "Concat"
  bottom: "pool_s8"
  bottom: "BatchNorm9"
  top: "concat_s9"
  concat_param {
    axis: 1
  }
}
layer {
  name: "pool_s9"
  type: "Pooling"
  bottom: "concat_s9"
  top: "pool_s9"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "Convolution10"
  type: "Convolution"
  bottom: "pool_s9"
  top: "Convolution10"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm10"
  type: "BatchNorm"
  bottom: "Convolution10"
  top: "BatchNorm10"
}
layer {
  name: "Scale10"
  type: "Scale"
  bottom: "BatchNorm10"
  top: "BatchNorm10"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s10"
  type: "ReLU"
  bottom: "BatchNorm10"
  top: "BatchNorm10"
}
layer {
  name: "upsample_s10"
  type: "Deconvolution"
  bottom: "BatchNorm10"
  top: "upsample_s10"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s9d"
  type: "Concat"
  bottom: "upsample_s10"
  bottom: "concat_s9"
  top: "concat_s9d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution11"
  type: "Convolution"
  bottom: "concat_s9d"
  top: "Convolution11"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm11"
  type: "BatchNorm"
  bottom: "Convolution11"
  top: "BatchNorm11"
}
layer {
  name: "Scale11"
  type: "Scale"
  bottom: "BatchNorm11"
  top: "BatchNorm11"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s9d"
  type: "ReLU"
  bottom: "BatchNorm11"
  top: "BatchNorm11"
}
layer {
  name: "upsample_s9"
  type: "Deconvolution"
  bottom: "BatchNorm11"
  top: "upsample_s9"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s8d"
  type: "Concat"
  bottom: "upsample_s9"
  bottom: "concat_s8"
  top: "concat_s8d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution12"
  type: "Convolution"
  bottom: "concat_s8d"
  top: "Convolution12"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm12"
  type: "BatchNorm"
  bottom: "Convolution12"
  top: "BatchNorm12"
}
layer {
  name: "Scale12"
  type: "Scale"
  bottom: "BatchNorm12"
  top: "BatchNorm12"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s8d"
  type: "ReLU"
  bottom: "BatchNorm12"
  top: "BatchNorm12"
}
layer {
  name: "upsample_s8"
  type: "Deconvolution"
  bottom: "BatchNorm12"
  top: "upsample_s8"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s7d"
  type: "Concat"
  bottom: "upsample_s8"
  bottom: "concat_s7"
  top: "concat_s7d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution13"
  type: "Convolution"
  bottom: "concat_s7d"
  top: "Convolution13"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm13"
  type: "BatchNorm"
  bottom: "Convolution13"
  top: "BatchNorm13"
}
layer {
  name: "Scale13"
  type: "Scale"
  bottom: "BatchNorm13"
  top: "BatchNorm13"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s7d"
  type: "ReLU"
  bottom: "BatchNorm13"
  top: "BatchNorm13"
}
layer {
  name: "upsample_s7"
  type: "Deconvolution"
  bottom: "BatchNorm13"
  top: "upsample_s7"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s6d"
  type: "Concat"
  bottom: "upsample_s7"
  bottom: "concat_s6"
  top: "concat_s6d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution14"
  type: "Convolution"
  bottom: "concat_s6d"
  top: "Convolution14"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm14"
  type: "BatchNorm"
  bottom: "Convolution14"
  top: "BatchNorm14"
}
layer {
  name: "Scale14"
  type: "Scale"
  bottom: "BatchNorm14"
  top: "BatchNorm14"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s6d"
  type: "ReLU"
  bottom: "BatchNorm14"
  top: "BatchNorm14"
}
layer {
  name: "upsample_s6"
  type: "Deconvolution"
  bottom: "BatchNorm14"
  top: "upsample_s6"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s5d"
  type: "Concat"
  bottom: "upsample_s6"
  bottom: "concat_s5"
  top: "concat_s5d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution15"
  type: "Convolution"
  bottom: "concat_s5d"
  top: "Convolution15"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm15"
  type: "BatchNorm"
  bottom: "Convolution15"
  top: "BatchNorm15"
}
layer {
  name: "Scale15"
  type: "Scale"
  bottom: "BatchNorm15"
  top: "BatchNorm15"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s5d"
  type: "ReLU"
  bottom: "BatchNorm15"
  top: "BatchNorm15"
}
layer {
  name: "upsample_s5"
  type: "Deconvolution"
  bottom: "BatchNorm15"
  top: "upsample_s5"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s4d"
  type: "Concat"
  bottom: "upsample_s5"
  bottom: "concat_s4"
  top: "concat_s4d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution16"
  type: "Convolution"
  bottom: "concat_s4d"
  top: "Convolution16"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm16"
  type: "BatchNorm"
  bottom: "Convolution16"
  top: "BatchNorm16"
}
layer {
  name: "Scale16"
  type: "Scale"
  bottom: "BatchNorm16"
  top: "BatchNorm16"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s4d"
  type: "ReLU"
  bottom: "BatchNorm16"
  top: "BatchNorm16"
}
layer {
  name: "upsample_s4"
  type: "Deconvolution"
  bottom: "BatchNorm16"
  top: "upsample_s4"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s3d"
  type: "Concat"
  bottom: "upsample_s4"
  bottom: "concat_s3"
  top: "concat_s3d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution17"
  type: "Convolution"
  bottom: "concat_s3d"
  top: "Convolution17"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm17"
  type: "BatchNorm"
  bottom: "Convolution17"
  top: "BatchNorm17"
}
layer {
  name: "Scale17"
  type: "Scale"
  bottom: "BatchNorm17"
  top: "BatchNorm17"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s3d"
  type: "ReLU"
  bottom: "BatchNorm17"
  top: "BatchNorm17"
}
layer {
  name: "upsample_s3"
  type: "Deconvolution"
  bottom: "BatchNorm17"
  top: "upsample_s3"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s2d"
  type: "Concat"
  bottom: "upsample_s3"
  bottom: "concat_s2"
  top: "concat_s2d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution18"
  type: "Convolution"
  bottom: "concat_s2d"
  top: "Convolution18"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm18"
  type: "BatchNorm"
  bottom: "Convolution18"
  top: "BatchNorm18"
}
layer {
  name: "Scale18"
  type: "Scale"
  bottom: "BatchNorm18"
  top: "BatchNorm18"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "subnet_s2d"
  type: "ReLU"
  bottom: "BatchNorm18"
  top: "BatchNorm18"
}
layer {
  name: "upsample_s2"
  type: "Deconvolution"
  bottom: "BatchNorm18"
  top: "upsample_s2"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 4
    group: 16
    stride: 2
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "concat_s1d"
  type: "Concat"
  bottom: "upsample_s2"
  bottom: "BatchNorm1"
  top: "concat_s1d"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Convolution19"
  type: "Convolution"
  bottom: "concat_s1d"
  top: "Convolution19"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "BatchNorm19"
  type: "BatchNorm"
  bottom: "Convolution19"
  top: "BatchNorm19"
}
layer {
  name: "Scale19"
  type: "Scale"
  bottom: "BatchNorm19"
  top: "BatchNorm19"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv_s1d"
  type: "ReLU"
  bottom: "BatchNorm19"
  top: "BatchNorm19"
}
layer {
  name: "score"
  type: "Convolution"
  bottom: "BatchNorm19"
  top: "score"
  convolution_param {
    num_output: 5
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "prob"
  type: "Softmax"
  bottom: "score"
  top: "prob"
}