dani_fcn.prototxt

name: "alexNet_FCN"

layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "label"
  python_param {
    module: "LSPdataLayer"
    layer: "LSPDataLayer"
    param_str: "{\'dir\': \'/home/vauser2/data/tmp/train\', \'input\': \'../train.txt\', \'label\': \'../../hpe/lsp/dani/val/val_gt.txt\', \'mean\': (114.89033, 113.26829, 96.56158), \'randomize\': True, \'seed\': None}"
  }
  include { phase:TRAIN }
}
layer {
  name: "data"
  type: "Python"
  top: "data"
  top: "label"
  python_param {
    module: "LSPdataLayer"
    layer: "LSPDataLayer"
    param_str: "{\'dir\': \'/home/vauser2/data/tmp/val\', \'input\': \'../val.txt\', \'label\': \'../../hpe/lsp/dani/val/val_gt.txt\', \'mean\': (114.50071, 113.42002, 96.82453), \'randomize\': True, \'seed\': None}"
  }
  include { phase:TEST }
}
layer {
  name: "reshape_1"
  type: "Reshape"
  bottom: "data"
  top: "datareshape"
  reshape_param {
    shape {
      dim: 5
      dim: 10
      dim: 451
      dim: 451
    }
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "datareshape"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
  }
}
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: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
  }
}
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: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    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: 384
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
  }
}
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
  }
}
layer {
  name: "fc6-conv"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6-conv"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    kernel_size: 6
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6-conv"
  top: "fc6-conv"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6-conv"
  top: "fc6-conv"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7-conv"
  type: "Convolution"
  bottom: "fc6-conv"
  top: "fc7-conv"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
 convolution_param {
    num_output: 4096
    kernel_size: 1
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7-conv"
  top: "fc7-conv"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7-conv"
  top: "fc7-conv"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "reshape_2-conv"
  type: "Reshape"
  bottom: "fc7-conv"
  top: "fc7reshape-conv"
  reshape_param {
    shape {
      dim: 1
      dim: 20480
      dim: 8
      dim: 8
    }
  }
}
layer {
  name: "fc8-conv"
  type: "Convolution"
  bottom: "fc7reshape-conv"
  top: "fc8-conv"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 7
    kernel_size: 1
  }
}
layer {
  name: "deconv"
  type: "Deconvolution"
  bottom: "fc8-conv"
  top: "deconv"
  param {
    lr_mult: 10
  }
  convolution_param {
    num_output: 7
    kernel_size: 221 #63
    stride: 33 # 32
    bias_term: false
    weight_filler {
      type: "bilinear"
    }
  }
}
layer {
  name: "score_"
  type: "Crop"
  bottom: "deconv"
  bottom: "data"
  top: "score_"
  crop_param {
    axis: 2
    offset: 1
  }
}
layer {
  name: "accuracy_"
  type: "Accuracy"
  bottom: "score_"
  bottom: "label"
  top: "loss_"
  include { phase:TEST }
}
layer {
  name: "loss_"
  type: "SoftmaxWithLoss"
  bottom: "score_"
  bottom: "label"
  top: "loss__"
}