net_train.prototxt

name: "refining_network_norm_abs_train"
#INPUTS START HERE
#COLOR
layer {
      name: "train_color"
      type: "Data"
      top: "X"
      data_param {
        source: "train_raw2_lmdb/train_raw2_color_298x218.lmdb"
        backend: LMDB
        batch_size: 16
      }
      transform_param {
        mean_value: 127
      }
    include {
      phase: TRAIN
      }
    }
    layer {
      name: "train_depth"
      type: "Data"
      top: "gt"
      data_param {
        source: "train_raw2_lmdb/train_raw2_depth_74x54.lmdb"
        backend: LMDB
        batch_size: 16
      }
    transform_param {
        scale: 0.00390625
      }

    include {
      phase: TRAIN
      }
    }
    layer {
      name: "test_color"
      type: "Data"
      top: "X"
      data_param {
        source: "test_lmdb/test_color_298x218.lmdb"
        backend: LMDB
        batch_size: 16
      }
      transform_param {
          mean_value: 127
      }
    include {
      phase: TEST
      }
    }
    layer {
      name: "test_depth"
      type: "Data"
      top: "gt"
      data_param {
        source: "test_lmdb/test_depth_74x54.lmdb"
        backend: LMDB
        batch_size: 16
      }
    transform_param {
        scale: 0.00390625
      }

    include {
      phase: TEST
      }
    }
#INPUTS END HERE
#GLOBAL NETWORK STARS HERE

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "X"
  top: "conv1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

# MAIN

layer {
  name: "fc-main"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc-main"
  param {
    decay_mult: 0
    lr_mult: 0
  }
  param {
    decay_mult: 0
    lr_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "xavier"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc-main"
  top: "fc-main"
}
layer {
  name: "fc-depth"
  type: "InnerProduct"
  bottom: "fc-main"
  top: "fc-depth"
  param {
    decay_mult: 0
    lr_mult:  0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  inner_product_param {
    num_output: 999
    weight_filler {
      type: "gaussian"
      std: 0.001
    }
    bias_filler {
      type: "constant"
      value: 0.5
    }
  }
}
layer {
    name: "reshape"
    type: "Reshape"
    bottom: "fc-depth"
    top: "depth"
    reshape_param {
      shape {
        dim: 0  # copy the dimension from below
        dim: 1
        dim: 27
        dim: 37 # infer it from the other dimensions
      }
    }
  }

layer {
   name: "mvnDepth-global"
   type: "MVN"
   bottom: "depth"
   top: "depthMVN"
}

#GRADIENT NETWORK STARTS HERE

layer {
  name: "conv1-grad"
  type: "Convolution"
  bottom: "X"
  top: "conv1-grad"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1-grad"
  type: "ReLU"
  bottom: "conv1-grad"
  top: "conv1-grad"
}
layer {
  name: "norm1-grad"
  type: "LRN"
  bottom: "conv1-grad"
  top: "norm1-grad"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1-grad"
  type: "Pooling"
  bottom: "norm1-grad"
  top: "pool1-grad"
  pooling_param {
    pool: MAX
    kernel_size: 4
    stride: 2
  }
}
layer {
  name: "conv2-grad"
  type: "Convolution"
  bottom: "pool1-grad"
  top: "conv2-grad"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu2-grad"
  type: "ReLU"
  bottom: "conv2-grad"
  top: "conv2-grad"
}
layer {
  name: "conv3-grad"
  type: "Convolution"
  bottom: "conv2-grad"
  top: "conv3-grad"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3-grad"
  type: "ReLU"
  bottom: "conv3-grad"
  top: "conv3-grad"
}
layer {
  name: "conv4-grad"
  type: "Convolution"
  bottom: "conv3-grad"
  top: "conv4-grad"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 2
    kernel_size: 5
    group: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4-grad"
  type: "ReLU"
  bottom: "conv4-grad"
  top: "conv4-grad"
}

layer {
  name: "conv5-grad"
  type: "Convolution"
  bottom: "conv4-grad"
  top: "grad_out"
  param {
    lr_mult:  0
    decay_mult: 0
  }
  param {
    lr_mult:  0
    decay_mult: 0
  }
  convolution_param {
    num_output: 2
    pad: 2
    kernel_size: 5
    group: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}

layer {
   name: "mvnGrad"
   type: "MVN"
   bottom: "grad_out"
   top: "gradMVN"
}

#GRADIENT NETWORK ENDS HERE


#PREPROCESSING FOR THE REFINE
layer {
    name: "upsample"
    type: "Deconvolution"
    bottom: "gradMVN"
    top: "grad-upsample"
 param {
    lr_mult:  0
    decay_mult: 0
  }
    convolution_param { 
        kernel_size: 3
        stride: 1
        num_output: 2
        group: 2 
        pad: 0
        weight_filler: {
           type: "bilinear" 
        } 
        bias_term: false 
    }
}
layer {
  name: "concat-global"
  bottom: "grad-upsample"
  bottom: "depthMVN"
  top: "global-output"
  type: "Concat"
  concat_param {
    axis: 1
  }
}
layer {
    name: "upsample-global"
    type: "Deconvolution"
    bottom: "global-output"
    top: "est"
  param {
    lr_mult:  0
    decay_mult: 0
  }
    convolution_param { 
        kernel_size: 2
        stride: 2
        num_output: 3
        group: 3
        pad: 0
        weight_filler: {
           type: "bilinear" 
        } 
        bias_term: false 
    }
}

#GLOBAL NETWORK ENDS HERE
#REFINE NETWORK HERE

layer {
  name: "conv1-refine"
  type: "Convolution"
  bottom: "X"
  top: "conv1-refine"
  param {
    lr_mult: 0.001
    decay_mult: 1
  }
  param {
    lr_mult:  0.001
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 2
    pad: 2
    weight_filler {
      type: "constant"
#      std: 0.001
	value: 0 
   }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1-refine"
  type: "ReLU"
  bottom: "conv1-refine"
  top: "conv1-refine"
}
layer {
  name: "norm1-refine"
  type: "LRN"
  bottom: "conv1-refine"
  top: "norm1-refine"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1-refine"
  type: "Pooling"
  bottom: "norm1-refine"
  top: "pool1-refine"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat"
  bottom: "pool1-refine"
  bottom: "est"
  top: "input-refine"
  type: "Concat"
  concat_param {
    axis: 1
  }
}

layer {
  name: "conv2-refine"
  type: "Convolution"
  bottom: "input-refine"
  top: "conv2-refine"
  param {
    lr_mult:  1
    decay_mult: 1
  }
  param {
    lr_mult:  1
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    group: 1
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu2-refine"
  type: "ReLU"
  bottom: "conv2-refine"
  top: "conv2-refine"
}
layer {
  name: "conv3-refine"
  type: "Convolution"
  bottom: "conv2-refine"
  top: "conv3-refine"
  param {
    lr_mult:  1
    decay_mult: 1
  }
  param {
    lr_mult:  1
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3-refine"
  type: "ReLU"
  bottom: "conv3-refine"
  top: "conv3-refine"
}
layer {
  name: "conv4-refine"
  type: "Convolution"
  bottom: "conv3-refine"
  top: "conv4-refine"
  param {
    lr_mult:  1
    decay_mult: 1
  }
  param {
    lr_mult:  1
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "xavier"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4-refine"
  type: "ReLU"
  bottom: "conv4-refine"
  top: "conv4-refine"
}

layer {
  name: "conv5-refine"
  type: "Convolution"
  bottom: "conv4-refine"
  top: "depth-refine_"
  param {
    lr_mult:  0.01
    decay_mult: 1
  }
  param {
    lr_mult:  0.01
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    group: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.5
    }
  }
}
layer {
  name: "power-refine"
  type: "Power"
  bottom: "depth-refine_"
  top: "depth-refine"
  power_param {
    power: 1
    scale: 0.01
    shift: 0
  }
}






#LOSSES
layer {
   name: "mvnDepthRefine"
   type: "MVN"
   bottom: "depth-refine"
   top: "depthMVN-refine"
}
layer {
   name: "mvnGT"
   type: "MVN"
   bottom: "gt"
   top: "gtMVN"
}

layer {
  name: "lossMVNDepth"
  type: "EuclideanLoss"
  bottom: "depthMVN-refine"
  bottom: "gtMVN"
  top: "lossMVNDepth"
  loss_weight: 0.5
}

layer {
  name: "lossABSDepth"
  type: "EuclideanLoss"
  bottom: "depth-refine"
  bottom: "gt"
  top: "lossABSDepth"
  loss_weight: 0.5
}