infant_train_test_unet_bn.prototxt

# Simple single-layer network to showcase editing model parameters.
layer {
  name: "data"
  type: "HDF5Data"
  top: "dataT1"
  top: "dataT2"
  top: "dataSeg"
  include {
    phase: TRAIN 
  }
  hdf5_data_param {
    source: "./trainInfant_list.txt"
    batch_size: 10
    shuffle: true 
  }
}
layer {
  name: "data"
  type: "HDF5Data"
  top: "dataT1"
  top: "dataT2"
  top: "dataSeg"
  include {
    phase: TEST
  }
  hdf5_data_param {
    source: "./testInfant_list.txt"
    batch_size: 10
    shuffle: true
  }
}
layer {
  name: "concat"
  type: "Concat" 
  bottom: "dataT1"
  bottom: "dataT2"
  top: "data"
  #concat_param {
  #concat_dim:1
  #}
}
#-------------layer group 1---------------
layer {
  name: "conv1a"
  type: "Convolution"
  bottom: "data"
  top: "conv1a"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
## BN
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "conv1a"
  top: "conv1a_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu1a"
  type: "ReLU" 
  bottom: "conv1a_bn"
  top: "conv1a_bn"
}
layer {
  name: "conv1b"
  type: "Convolution"
  bottom: "conv1a_bn"
  top: "conv1b"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
## BN
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "conv1b"
  top: "conv1b_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu1b"
  type: "ReLU"
  bottom: "conv1b_bn"
  top: "conv1b_bn"
}
layer {
  name: "conv1c"
  type: "Convolution"
  bottom: "conv1b_bn"
  top: "conv1c"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
## BN
layer {
  name: "bn3"
  type: "BatchNorm"
  bottom: "conv1c"
  top: "conv1c_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu1c"
  type: "ReLU"
  bottom: "conv1c_bn"
  top: "conv1c_bn"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1c_bn"
  top: "pool1"
  pooling_param {
    #pool: AVE 
    pool: MAX 
    kernel_size: 3
    stride: 2
    engine: CUDNN
  }
}
#-------------layer group 2---------------
layer {
  name: "conv2a"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2a"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
## BN
layer {
  name: "bn4"
  type: "BatchNorm"
  bottom: "conv2a"
  top: "conv2a_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu2a"
  type: "ReLU"
  bottom: "conv2a_bn"
  top: "conv2a_bn"
}
layer {
  name: "conv2b"
  type: "Convolution"
  bottom: "conv2a_bn"
  top: "conv2b"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
## BN
layer {
  name: "bn5"
  type: "BatchNorm"
  bottom: "conv2b"
  top: "conv2b_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu2b"
  type: "ReLU"
  bottom: "conv2b_bn"
  top: "conv2b_bn"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2b_bn"
  top: "pool2"
  pooling_param {
    #pool: AVE
    pool: MAX 
    kernel_size: 3
    stride: 2
    engine: CUDNN
  }
}
#-------------layer group 3---------------
layer {
  name: "conv3a"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3a"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 64 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
## BN
layer {
  name: "bn6"
  type: "BatchNorm"
  bottom: "conv3a"
  top: "conv3a_bn"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu3a"
  type: "ReLU"
  bottom: "conv3a_bn"
  top: "conv3a_bn"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3a_bn"
  top: "pool3"
  pooling_param {
    #pool: AVE
    pool: MAX 
    kernel_size: 3
    stride: 2
    engine: CUDNN
  }
}
#------------layer group 4-------------
layer {
  name: "deconv4"
  type: "Deconvolution"
  bottom: "pool3" #size is 4*4*4
  top: "deconv4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32 
    #bias_term: false
    engine: CUDNN
    kernel_size: 4
    pad: 1
    stride: 2
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
  }
}
#layer {
#  name: "relu4"
#  type: "ReLU"
#  bottom: "deconv4"
#  top: "deconv4"
#}
layer {
  name: "concat8"
  type: "Concat" 
  bottom: "conv3a"
  bottom: "deconv4"
  top: "concat8"
  concat_param {
  concat_dim:1
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "concat8"
  top: "conv4"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4a"
  type: "ReLU" 
  bottom: "conv4"
  top: "conv4"
}
#------------layer group 5-------------
layer {
  name: "deconv5"
  type: "Deconvolution"
  bottom: "conv4"
  top: "deconv5"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    #bias_term: false
    engine: CUDNN
    kernel_size: 4
    pad: 1
    stride: 2
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
  }
}
#layer {
#  name: "relu5"
#  type: "ReLU"
#  bottom: "deconv5"
#  top: "deconv5"
#}
layer {
  name: "concat16"
  type: "Concat" 
  bottom: "conv2b"
  bottom: "deconv5"
  top: "concat16"
  concat_param {
  concat_dim:1
  }
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "concat16"
  top: "conv5"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5a"
  type: "ReLU" 
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5"
  top: "conv5_2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5a_2"
  type: "ReLU" 
  bottom: "conv5_2"
  top: "conv5_2"
}
#------------layer group 6-------------
layer {
  name: "deconv6"
  type: "Deconvolution"
  bottom: "conv5_2"
  top: "deconv6"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32 
    #bias_term: false
    engine: CUDNN
    kernel_size: 4
    pad: 1
    stride: 2
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
  }
}
#layer {
#  name: "relu6"
#  type: "ReLU"
#  bottom: "deconv6"
#  top: "deconv6"
#}
layer {
  name: "concat32"
  type: "Concat" 
  bottom: "conv1c"
  bottom: "deconv6"
  top: "concat32"
  concat_param {
  concat_dim:1
  }
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "concat32"
  top: "conv7"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU" 
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv7_2"
  type: "Convolution"
  bottom: "conv7"
  top: "conv7_2"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7_2"
  type: "ReLU" 
  bottom: "conv7_2"
  top: "conv7_2"
}
layer {
  name: "conv7_3"
  type: "Convolution"
  bottom: "conv7_2"
  top: "conv7_3"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  convolution_param {
    num_output: 4 
    kernel_size: 3
    pad: 1
    stride: 1
    engine: CUDNN
    weight_filler {
      type: "xavier"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7_3"
  type: "ReLU" 
  bottom: "conv7_3"
  top: "conv7_3"
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "conv7_3"
  bottom: "dataSeg"
  top: "loss"
  loss_param {
    ignore_label: -1
  }
  softmax_param {
    axis: 1
  }
  include: { phase: TRAIN }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "conv7_3"
  bottom: "dataSeg"
  top: "loss"
  loss_param {
    ignore_label: -1
  }
  softmax_param {
    axis: 1
  }
  include: { phase: TEST }
}
#or you can use the following weight loss ##
#############################################
layer {
  name: "loss"
  type: "MultiTaskSoftmaxWithLoss"
  bottom: "conv6_3"
  bottom: "dataSeg"
  top: "loss"
  multi_task_softmax_loss_param {
    weight_labels: 0
    weight_labels: 1
    weight_labels: 2 
    weight_labels: 3
    labels_weight: 0.1
    labels_weight: 2 
    labels_weight: 4 
    labels_weight: 3
  }
  loss_param {
    ignore_label: -1
  }
  softmax_param {
    axis: 1
  }
  #include: { phase: TEST }
}
##################################
#layer {
#  name: "frscore"
#  type: "Crop"
#  bottom: "upscore"
#  bottom: "data"
#  top: "score"
#  crop_param {
#    axis: 2
#    offset: 19
#  }
#}