rivanet.prototxt

name: "RivaNet"

layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TRAIN
  }
  transform_param {
    crop_size: 640
    mean_value: 80
    mean_value: 66
    mean_value: 68
    mean_value: 69
    mean_value: 74
    mean_value: 53
    mean_value: 0
  }
  data_param {
    source: "/run/media/leo/Leo_DB/train/data/"
    batch_size: 1
    backend: LMDB
  }
}

layer {
  name: "data"
  type: "Data"
  top: "data"
  include {
    phase: TEST
  }
  transform_param {
    crop_size: 640
    mean_value: 80
    mean_value: 66
    mean_value: 68
    mean_value: 69
    mean_value: 74
    mean_value: 53
    mean_value: 0
  }
  data_param {
    source: "/run/media/leo/Leo_DB/test/data/"
    batch_size: 1
    backend: LMDB
  }
}

# Slice ground truth
layer {
  name: "slicer"
  type: "Slice"
  bottom: "data"
  top: "landsat_scenes"
  top: "ground_truth"
  slice_param {
    axis: 1
    slice_point: 6
  }
}


# Convolutional and Pooling Layers (Encoder)

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "landsat_scenes"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    pad: 0
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu1"
  type: "RELU"
  bottom: "conv1"
  top: "conv1"
}

layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu2"
  type: "RELU"
  bottom: "conv2"
  top: "conv2"
}

layer {
  name: "concat2"
  type: "Concat"
  bottom: "pool1"
  bottom: "conv2"
  top: "concat2"
  concat_param {
    axis: 1
  }
}

layer {
  name: "pool2"
  type: "Pooling"
  bottom: "concat2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu3"
  type: "RELU"
  bottom: "conv3"
  top: "conv3"
}

layer {
  name: "concat3"
  type: "Concat"
  bottom: "pool2"
  bottom: "conv3"
  top: "concat3"
  concat_param {
    axis: 1
  }
}

layer {
  name: "pool3"
  type: "Pooling"
  bottom: "concat3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer {
  name: "conv4"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu4"
  type: "RELU"
  bottom: "conv4"
  top: "conv4"
}

layer {
  name: "concat4"
  type: "Concat"
  bottom: "pool3"
  bottom: "conv4"
  top: "concat4"
  concat_param {
    axis: 1
  }
}

layer {
  name: "pool4"
  type: "Pooling"
  bottom: "concat4"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "conv5"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu5"
  type: "RELU"
  bottom: "conv5"
  top: "conv5"
}

layer {
  name: "concat5"
  type: "Concat"
  bottom: "pool4"
  bottom: "conv5"
  top: "concat5"
  concat_param {
    axis: 1
  }
}

layer {
  name: "pool5"
  type: "Pooling"
  bottom: "concat5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer {
  name: "conv6"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu6"
  type: "RELU"
  bottom: "conv6"
  top: "conv6"
}

layer {
  name: "concat6"
  type: "Concat"
  bottom: "pool5"
  bottom: "conv6"
  top: "concat6"
  concat_param {
    axis: 1
  }
}

layer {
  name: "pool6"
  type: "Pooling"
  bottom: "concat6"
  top: "pool6"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer {
  name: "conv7"
  type: "Convolution"
  bottom: "pool6"
  top: "conv7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu7"
  type: "RELU"
  bottom: "conv7"
  top: "conv7"
}


# Upsampling Layers (Decoder)

layer {
  name: "upsample7"
  type: "Deconvolution"
  bottom: "conv7"
  top: "upsample7"
  param { lr_mult: 0 }
  convolution_param {
    num_output: 16
    group: 16
    weight_filler: { type: "bilinear" }
    bias_term: false
    kernel_size: 4
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat6d"
  type: "Concat"
  bottom: "upsample7"
  bottom: "concat6"
  top: "concat6d"
  concat_param {
    axis: 1
  }
}

layer {
  name: "conv6d"
  type: "Convolution"
  bottom: "concat6d"
  top: "conv6d"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu6d"
  type: "RELU"
  bottom: "conv6d"
  top: "conv6d"
}

layer {
  name: "upsample6"
  type: "Deconvolution"
  bottom: "conv6d"
  top: "upsample6"
  param { lr_mult: 0 }
  convolution_param {
    num_output: 16
    group: 16
    weight_filler: { type: "bilinear" }
    bias_term: false
    kernel_size: 4
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat5d"
  type: "Concat"
  bottom: "upsample6"
  bottom: "concat5"
  top: "concat5d"
  concat_param {
    axis: 1
  }
}


layer {
  name: "conv5d"
  type: "Convolution"
  bottom: "concat5d"
  top: "conv5d"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu5d"
  type: "RELU"
  bottom: "conv5d"
  top: "conv5d"
}

layer {
  name: "upsample5"
  type: "Deconvolution"
  bottom: "conv5d"
  top: "upsample5"
  param { lr_mult: 0 }
  convolution_param {
    num_output: 16
    group: 16
    weight_filler: { type: "bilinear" }
    bias_term: false
    kernel_size: 4
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat4d"
  type: "Concat"
  bottom: "upsample5"
  bottom: "concat4"
  top: "concat4d"
  concat_param {
    axis: 1
  }
}


layer {
  name: "conv4d"
  type: "Convolution"
  bottom: "concat4d"
  top: "conv4d"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu4d"
  type: "RELU"
  bottom: "conv4d"
  top: "conv4d"
}

layer {
  name: "upsample4"
  type: "Deconvolution"
  bottom: "conv4d"
  top: "upsample4"
  param { lr_mult: 0 }
  convolution_param {
    num_output: 16
    group: 16
    weight_filler: { type: "bilinear" }
    bias_term: false
    kernel_size: 4
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat3d"
  type: "Concat"
  bottom: "upsample4"
  bottom: "concat3"
  top: "concat3d"
  concat_param {
    axis: 1
  }
}


layer {
  name: "conv3d"
  type: "Convolution"
  bottom: "concat3d"
  top: "conv3d"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu3d"
  type: "RELU"
  bottom: "conv3d"
  top: "conv3d"
}

layer {
  name: "upsample3"
  type: "Deconvolution"
  bottom: "conv3d"
  top: "upsample3"
  param { lr_mult: 0 }
  convolution_param {
    num_output: 16
    group: 16
    weight_filler: { type: "bilinear" }
    bias_term: false
    kernel_size: 4
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat2d"
  type: "Concat"
  bottom: "upsample3"
  bottom: "concat2"
  top: "concat2d"
  concat_param {
    axis: 1
  }
}


layer {
  name: "conv2d"
  type: "Convolution"
  bottom: "concat2d"
  top: "conv2d"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    pad: 1
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu2d"
  type: "RELU"
  bottom: "conv2d"
  top: "conv2d"
}

layer {
  name: "upsample2"
  type: "Deconvolution"
  bottom: "conv2d"
  top: "upsample2"
  param { lr_mult: 0 }
  convolution_param {
    num_output: 16
    group: 16
    weight_filler: { type: "bilinear" }
    bias_term: false
    kernel_size: 4
    stride: 2
    pad: 1
  }
}

layer {
  name: "concat1d"
  type: "Concat"
  bottom: "upsample2"
  bottom: "conv1"
  top: "concat1d"
  concat_param {
    axis: 1
  }
}


layer {
  name: "conv1d"
  type: "Convolution"
  bottom: "concat1d"
  top: "conv1d"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    pad: 0
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "elu1d"
  type: "RELU"
  bottom: "conv1d"
  top: "conv1d"
}

# Score and Loss Layers

layer {
  name: "score"
  type: "Convolution"
  bottom: "conv1d"
  top: "score"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 5
    kernel_size: 1
    pad: 0
    weight_filler { 
      type: "msra" 
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}


layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "score"
  bottom: "ground_truth"
  top: "loss"
  loss_param: {
    weight_by_label_freqs: true
    class_weighting: 0.061
    class_weighting: 0.2
    class_weighting: 2.0
    class_weighting: 1.9
    class_weighting: 1
  }
}

layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "score"
  bottom: "ground_truth"
  top: "accuracy"
  top: "per_class_accuracy"
  include {
    phase: TEST
  }
}