input:"data"
input_dim:1
input_dim:3
input_dim:1200
input_dim:1200

input:"dataL"
input_dim:1
input_dim:4
input_dim:1200
input_dim:1200

input:"dataD"
input_dim:1
input_dim:4
input_dim:1200
input_dim:1200

layer {
  name: "conv_gA1"
  type: "Convolution"
  bottom: "dataL"
  top: "conv_gA1"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gA1"
  type: "ReLU"
  bottom: "conv_gA1"
  top: "conv_gA1"
}

layer {
  name: "conv_gA2"
  type: "Convolution"
  bottom: "conv_gA1"
  top: "conv_gA2"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gA2"
  type: "ReLU"
  bottom: "conv_gA2"
  top: "conv_gA2"
}

layer {
  name: "conv_gA3"
  type: "Convolution"
  bottom: "conv_gA2"
  top: "conv_gA3"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gA3"
  type: "ReLU"
  bottom: "conv_gA3"
  top: "conv_gA3"
}

layer {
  name: "conv_gA4"
  type: "Convolution"
  bottom: "conv_gA3"
  top: "conv_gA4"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gA4"
  type: "ReLU"
  bottom: "conv_gA4"
  top: "conv_gA4"
}

layer {
  name: "conv_gA5"
  type: "Convolution"
  bottom: "conv_gA4"
  top: "conv_gA5"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

##############################################

layer {
  name: "conv_gB1"
  type: "Convolution"
  bottom: "dataD"
  top: "conv_gB1"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gB1"
  type: "ReLU"
  bottom: "conv_gB1"
  top: "conv_gB1"
}

layer {
  name: "conv_gB2"
  type: "Convolution"
  bottom: "conv_gB1"
  top: "conv_gB2"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gB2"
  type: "ReLU"
  bottom: "conv_gB2"
  top: "conv_gB2"
}

layer {
  name: "conv_gB3"
  type: "Convolution"
  bottom: "conv_gB2"
  top: "conv_gB3"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gB3"
  type: "ReLU"
  bottom: "conv_gB3"
  top: "conv_gB3"
}

layer {
  name: "conv_gB4"
  type: "Convolution"
  bottom: "conv_gB3"
  top: "conv_gB4"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_gB4"
  type: "ReLU"
  bottom: "conv_gB4"
  top: "conv_gB4"
}

layer {
  name: "conv_gB5"
  type: "Convolution"
  bottom: "conv_gB4"
  top: "conv_gB5"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

##############################################
layer {
  name: "Concat"
  type: "Concat"
  bottom: "conv_gA5"
  bottom: "conv_gB5"
  top: "Concat"
}

layer {
  name: "conv_g6"
  type: "Convolution"
  bottom: "Concat"
  top: "conv_g6"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "conv_g7"
  type: "Convolution"
  bottom: "conv_g6"
  top: "conv_g7"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_g7"
  type: "ReLU"
  bottom: "conv_g7"
  top: "conv_g7"
}

layer {
  name: "conv_g8"
  type: "Convolution"
  bottom: "conv_g7"
  top: "conv_g8"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_g8"
  type: "ReLU"
  bottom: "conv_g8"
  top: "conv_g8"
}

layer {
  name: "conv_g9"
  type: "Convolution"
  bottom: "conv_g8"
  top: "conv_g9"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_g9"
  type: "ReLU"
  bottom: "conv_g9"
  top: "conv_g9"
}

layer {
  name: "conv_g10"
  type: "Convolution"
  bottom: "conv_g9"
  top: "conv_g10"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_g10"
  type: "ReLU"
  bottom: "conv_g10"
  top: "conv_g10"
}

layer {
  name: "conv_g11"
  type: "Convolution"
  bottom: "conv_g10"
  top: "conv_g11"
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "relu_g11"
  type: "ReLU"
  bottom: "conv_g11"
  top: "conv_g11"
}

layer {
  name: "conv_g12"
  type: "Convolution"
  bottom: "conv_g11"
  top: "conv_g12"
  convolution_param {
    num_output: 3
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
  }
}

layer {
  name: "sum"
  type: "Eltwise"
  bottom: "data"
  bottom: "conv_g12"
  top: "sum"
}