frontend_vgg_train_net.txt

layer {
  name: "data"
  type: "ImageDepthLabelData"
  top: "data"
  top: "label"
  transform_param {
    mirror: true
    crop_size: 500
  }
  image_depth_label_data_param {
    rendered_image_dir: "_MVFCN_PRendered_Images_/"
    depth_image_dir: "_MVFCN_Depth_Images_/"
	aux_image_dir: "_MVFCN_Aux_Images_/"
	label_dir: "_MVFCN_Segmentation_Images_/"
    batch_size: 4
    shuffle: true
    image_margin: 186
    padding: REFLECT
	image_mean: 110.27
	depth_mean: 127.5
	aux_mean: 127.5
	validation_mode: 0
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 64
    kernel_size: 3
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  	  
    }  
    num_output: 64
    kernel_size: 3
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 128
    kernel_size: 3
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 128
    kernel_size: 3
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  	  
    }  
    num_output: 256
    kernel_size: 3
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 256
    kernel_size: 3
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 256
    kernel_size: 3
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 512
    kernel_size: 3
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 512
    kernel_size: 3
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 512
    kernel_size: 3
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "conv4_3"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 512
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 512
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 512
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "fc6"
  type: "Convolution"
  bottom: "conv5_3"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 4096
    kernel_size: 7
    dilation: 4
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "Convolution"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }  
    num_output: 4096
    kernel_size: 1
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "final"
  type: "Convolution"
  bottom: "fc7"
  top: "final"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.001
    }
    bias_filler {
      type: "constant"
      value: 0	  
    }	
  }
}
layer {
  name: "upsample"
  type: "Deconvolution"
  bottom: "final"
  top: "upsample"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 21
    bias_term: false
    pad: 4
    kernel_size: 16
    group: 21
    stride: 8
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "upsample"
  bottom: "label"
  top: "loss"
  loss_param {
    ignore_label: 255
    normalization: VALID
  }
}