train_val.prototxt

name: "LIAONet"
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "unuse"
  transform_param {
    mean_file: "train_mean.binaryproto"
  }
  image_data_param {
    source: "list"
    batch_size: 1
    root_folder: "image/"
  }
}
# convolutional layer 1 
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization 1
layer {
  name: "conv1_bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 1
layer {
  name: "conv1_prelu"
  type: "PReLU"
  bottom: "conv1_bn"
  top: "conv1_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 1
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_bn"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# convolution layer 2
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 8
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
# batch normalization 2
layer {
  name: "conv2_bn"
  type: "BatchNorm"
  bottom: "conv2"
  top: "conv2_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 2
layer {
  name: "conv2_prelu"
  type: "PReLU"
  bottom: "conv2_bn"
  top: "conv2_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 2
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_bn"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# convolution layer 3
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization 3
layer {
  name: "conv3_bn"
  type: "BatchNorm"
  bottom: "conv3"
  top: "conv3_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 3
layer {
  name: "conv3_prelu"
  type: "PReLU"
  bottom: "conv3_bn"
  top: "conv3_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 3
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_bn"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# convolution layer 4
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization 4
layer {
  name: "conv4_bn"
  type: "BatchNorm"
  bottom: "conv4"
  top: "conv4_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 4
layer {
  name: "conv4_prelu"
  type: "PReLU"
  bottom: "conv4_bn"
  top: "conv4_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 4
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_bn"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# convolution layer 5
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization 5
layer {
  name: "conv5_bn"
  type: "BatchNorm"
  bottom: "conv5"
  top: "conv5_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 5
layer {
  name: "conv5_prelu"
  type: "PReLU"
  bottom: "conv5_bn"
  top: "conv5_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 5
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_bn"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# convolution layer 6
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization 6
layer {
  name: "conv6_bn"
  type: "BatchNorm"
  bottom: "conv6"
  top: "conv6_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 6
layer {
  name: "conv6_prelu"
  type: "PReLU"
  bottom: "conv6_bn"
  top: "conv6_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 6
layer {
  name: "pool6"
  type: "Pooling"
  bottom: "conv6_bn"
  top: "pool6"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# convolution layer 7
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "pool6"
  top: "conv7"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization 7
layer {
  name: "conv7_bn"
  type: "BatchNorm"
  bottom: "conv7"
  top: "conv7_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer 7
layer {
  name: "conv7_prelu"
  type: "PReLU"
  bottom: "conv7_bn"
  top: "conv7_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# pooling layer 7
layer {
  name: "pool7"
  type: "Pooling"
  bottom: "conv7_bn"
  top: "pool7"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
# fc
layer {
  name: "fc"
  type: "Convolution"
  bottom: "pool7"
  top: "fc"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1024
    kernel_size: 4
    stride: 4
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
# batch normalization fc
layer {
  name: "fc_bn"
  type: "BatchNorm"
  bottom: "fc"
  top: "fc_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer fc
layer {
  name: "fc_prelu"
  type: "PReLU"
  bottom: "fc_bn"
  top: "fc_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# drop out
layer {
  name: "drop"
  type: "Dropout"
  bottom: "fc_bn"
  top: "fc_bn"
  dropout_param {
    dropout_ratio: 0.1
  }
}
# deconvolution fc
layer {
  name: "deconv_fc"
  type: "Deconvolution"
  bottom: "fc_bn"
  top: "deconv_fc"
  convolution_param {
    num_output: 256
    kernel_size: 8 # (2 * factor - factor % 2) 
    stride: 4 # (factor)
    pad: 2 # {{ceil((factor - 1) / 2.)}}
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv-fc
layer {
  name: "deconv_fc_bn"
  type: "BatchNorm"
  bottom: "deconv_fc"
  top: "deconv_fc_bn" 
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  }
}
# prelu layer deconv-fc
layer {
  name: "deconv_fc_prelu"
  type: "PReLU"
  bottom: "deconv_fc_bn"
  top: "deconv_fc_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 7
layer {
  name: "deconv7"
  type: "Deconvolution"
  bottom: "deconv_fc_bn"
  top: "deconv7"
  convolution_param {
    num_output: 128
    kernel_size: 4 # (2 * factor - factor % 2) 
    stride: 2 # (factor)
    pad: 1 # {{ceil((factor - 1) / 2.)}}
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv7
layer {
  name: "deconv7_bn"
  type: "BatchNorm"
  bottom: "deconv7"
  top: "deconv7_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv7
layer {
  name: "deconv7_prelu"
  type: "PReLU"
  bottom: "deconv7_bn"
  top: "deconv7_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 6
layer {
  name: "deconv6"
  type: "Deconvolution"
  bottom: "deconv7_bn"
  top: "deconv6"
  convolution_param {
    num_output: 64
    kernel_size: 4 
    stride: 2 
    pad: 1 
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv6
layer {
  name: "deconv6_bn"
  type: "BatchNorm"
  bottom: "deconv6"
  top: "deconv6_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv6
layer {
  name: "deconv6_prelu"
  type: "PReLU"
  bottom: "deconv6_bn"
  top: "deconv6_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 5
layer {
  name: "deconv5"
  type: "Deconvolution"
  bottom: "deconv6_bn"
  top: "deconv5"
  convolution_param {
    num_output: 32
    kernel_size: 4 
    stride: 2 
    pad: 1 
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv5
layer {
  name: "deconv5_bn"
  type: "BatchNorm"
  bottom: "deconv5"
  top: "deconv5_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv5
layer {
  name: "deconv5_prelu"
  type: "PReLU"
  bottom: "deconv5_bn"
  top: "deconv5_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 4
layer {
  name: "deconv4"
  type: "Deconvolution"
  bottom: "deconv5_bn"
  top: "deconv4"
  convolution_param {
    num_output: 16
    kernel_size: 4 
    stride: 2 
    pad: 1 
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv4
layer {
  name: "deconv4_bn"
  type: "BatchNorm"
  bottom: "deconv4"
  top: "deconv4_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv4
layer {
  name: "deconv4_prelu"
  type: "PReLU"
  bottom: "deconv4_bn"
  top: "deconv4_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 3
layer {
  name: "deconv3"
  type: "Deconvolution"
  bottom: "deconv4_bn"
  top: "deconv3"
  convolution_param {
    num_output: 8
    kernel_size: 4 
    stride: 2 
    pad: 1 
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv3
layer {
  name: "deconv3_bn"
  type: "BatchNorm"
  bottom: "deconv3"
  top: "deconv3_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv3
layer {
  name: "deconv3_prelu"
  type: "PReLU"
  bottom: "deconv3_bn"
  top: "deconv3_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 2
layer {
  name: "deconv2"
  type: "Deconvolution"
  bottom: "deconv3_bn"
  top: "deconv2"
  convolution_param {
    num_output: 4
    kernel_size: 4 
    stride: 2 
    pad: 1 
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv2
layer {
  name: "deconv2_bn"
  type: "BatchNorm"
  bottom: "deconv2"
  top: "deconv2_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv2
layer {
  name: "deconv2_prelu"
  type: "PReLU"
  bottom: "deconv2_bn"
  top: "deconv2_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# deconvolution layer 1
layer {
  name: "deconv1"
  type: "Deconvolution"
  bottom: "deconv2_bn"
  top: "deconv1"
  convolution_param {
    num_output: 2
    kernel_size: 4
    stride: 2 
    pad: 1 
    weight_filler: { 
      type: "bilinear" 
    } 
    bias_term: false
  }
}
# batch normalization deconv1
layer {
  name: "deconv1_bn"
  type: "BatchNorm"
  bottom: "deconv1"
  top: "deconv1_bn"
  param {
    decay_mult: 0
  }
  param {
    decay_mult: 0
  } 
}
# prelu layer deconv1
layer {
  name: "deconv1_prelu"
  type: "PReLU"
  bottom: "deconv1_bn"
  top: "deconv1_bn"
  param {
    decay_mult: 0
  }
  prelu_param {
    filler {
      value: 0.1
      type: "constant"
    }
  }
}
# output layer
layer {
  name: "output"
  type: "Convolution"
  bottom: "deconv1_bn"
  top: "output"
  param { # weight
    lr_mult: 1
    decay_mult: 1
  }
  param { # bias
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 2
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
      variance_norm: FAN_IN
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "loss"
  type: "Softmax"
  bottom: "output"
  top: "prob"
}