reference.prototxt

name: "LtoAB"
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {    phase: TRAIN  }
  transform_param {
   mirror: true
   crop_size: 176
  }
  data_param {
    # source: "/PATH/TO/DATA/ilsvrc_lmdb/caffe-train-lmdb"
    source: "/data/big/rzhang/dataset/ilsvrc_lmdb/caffe-train-lmdb" # [[REPLACE WITH YOUR PATH]]
    batch_size: 40
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  include {    phase: TEST  }
  transform_param {
   mirror: false
   crop_size: 256
  }
  data_param {
    # source: "/PATH/TO/DATA/ilsvrc_lmdb/caffe-val-lmdb"
    source: "/data/big/rzhang/dataset/ilsvrc_lmdb/caffe-val-lmdb" # [[REPLACE WITH YOUR PATH]]
    batch_size: 10
    backend: LMDB
  }
}
# ****************************
# ***** Color Conversion *****
# ****************************
layer { # color conversion
  type: 'Python'
  name: 'img_lab'
  bottom: "data"
  top: "img_lab" # image in Lab space
  python_param {
    module: 'caffe_traininglayers'
    layer: 'BGR2LabLayer'
  }
}
layer {
  name: "img_slice"
  type: "Slice"
  bottom: "img_lab"
  top: "img_l" # [0,100]
  top: "data_ab" # [-110,110]
  propagate_down: false
  slice_param {
    axis: 1
    slice_point: 1
  }
}
layer { # 0-center data_l channel
  name: "data_l_meansub"
  type: "Scale"
  bottom: "img_l"
  top: "data_l" # [-50,50]
  propagate_down: false
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  scale_param {
    bias_term: True
    filler {      type: 'constant'      value: 1    }
    bias_filler {      type: 'constant'      value: -50    }
  }
}
# ****************************
# ***** PROCESS LABELS *******
# ****************************
layer { # subsample ab
  name: 'data_ab_ss'
  type: 'Convolution'
  bottom: "data_ab"
  top: "data_ab_ss" # subsampled colors
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  convolution_param {
    num_output: 2
    kernel_size: 1
    stride: 4
    group: 2
    weight_filler { type: 'constant' value: 1 }
  }
}
layer { # encode
  type: 'Python'
  name: 'ab_enc'
  bottom: "data_ab_ss"
  top: "gt_ab_313" # quantized gt colors
  python_param {
    module: 'caffe_traininglayers'
    layer: 'NNEncLayer'
  }
}
layer { # compute gray mask
  type: 'Python'
  name: 'nongray_mask'
  bottom: "data_ab_ss"
  top: "nongray_mask" # mask out grayscale images
  python_param {
    module: 'caffe_traininglayers'
    layer: 'NonGrayMaskLayer'
  }
}
layer { # compute prior boost
  type: 'Python'
  name: 'prior_boost'
  bottom: "gt_ab_313"
  top: "prior_boost" # gradient boosting factors
  python_param {
    module: 'caffe_traininglayers'
    layer: 'PriorBoostLayer'
  }
}
layer { # multiply nongray mask and prior boost
  type: 'Eltwise'
  name: 'prior_boost_nongray'
  bottom: "prior_boost"
  bottom: "nongray_mask"
  top: "prior_boost_nongray"
  eltwise_param {
    operation: 0
  }
}

# *****************
# ***** conv1 *****
# *****************
layer {
  name: "bw_conv1_1"
  type: "Convolution"
  bottom: "data_l"
  top: "conv1_1"
  convolution_param {
    num_output: 64
    pad: 1
    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"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "conv1_2norm"
  type: "BatchNorm"
  bottom: "conv1_2"
  top: "conv1_2norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv2 *****
# *****************
layer {
  name: "conv2_1"
  type: "Convolution"
  # bottom: "conv1_2"
  bottom: "conv1_2norm"
  # bottom: "pool1"
  top: "conv2_1"
  convolution_param {
    num_output: 128
    pad: 1
    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"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "conv2_2norm"
  type: "BatchNorm"
  bottom: "conv2_2"
  top: "conv2_2norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv3 *****
# *****************
layer {
  name: "conv3_1"
  type: "Convolution"
  # bottom: "conv2_2"
  bottom: "conv2_2norm"
  # bottom: "pool2"
  top: "conv3_1"
  convolution_param {
    num_output: 256
    pad: 1
    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"
  convolution_param {
    num_output: 256
    pad: 1
    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"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_3norm"
  type: "BatchNorm"
  bottom: "conv3_3"
  top: "conv3_3norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv4 *****
# *****************
layer {
  name: "conv4_1"
  type: "Convolution"
  # bottom: "conv3_3"
  bottom: "conv3_3norm"
  # bottom: "pool3"
  top: "conv4_1"
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "conv4_3norm"
  type: "BatchNorm"
  bottom: "conv4_3"
  top: "conv4_3norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv5 *****
# *****************
layer {
  name: "conv5_1"
  type: "Convolution"
  # bottom: "conv4_3"
  bottom: "conv4_3norm"
  # bottom: "pool4"
  top: "conv5_1"
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 2
    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"
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 2
    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"
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 2
    dilation: 2
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "conv5_3norm"
  type: "BatchNorm"
  bottom: "conv5_3"
  top: "conv5_3norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv6 *****
# *****************
layer {
  name: "conv6_1"
  type: "Convolution"
  bottom: "conv5_3norm"
  top: "conv6_1"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 2
    dilation: 2
  }
}
layer {
  name: "relu6_1"
  type: "ReLU"
  bottom: "conv6_1"
  top: "conv6_1"
}
layer {
  name: "conv6_2"
  type: "Convolution"
  bottom: "conv6_1"
  top: "conv6_2"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 2
    dilation: 2
  }
}
layer {
  name: "relu6_2"
  type: "ReLU"
  bottom: "conv6_2"
  top: "conv6_2"
}
layer {
  name: "conv6_3"
  type: "Convolution"
  bottom: "conv6_2"
  top: "conv6_3"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 2
    dilation: 2
  }
}
layer {
  name: "relu6_3"
  type: "ReLU"
  bottom: "conv6_3"
  top: "conv6_3"
}
layer {
  name: "conv6_3norm"
  type: "BatchNorm"
  bottom: "conv6_3"
  top: "conv6_3norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv7 *****
# *****************
layer {
  name: "conv7_1"
  type: "Convolution"
  bottom: "conv6_3norm"
  top: "conv7_1"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu7_1"
  type: "ReLU"
  bottom: "conv7_1"
  top: "conv7_1"
}
layer {
  name: "conv7_2"
  type: "Convolution"
  bottom: "conv7_1"
  top: "conv7_2"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu7_2"
  type: "ReLU"
  bottom: "conv7_2"
  top: "conv7_2"
}
layer {
  name: "conv7_3"
  type: "Convolution"
  bottom: "conv7_2"
  top: "conv7_3"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu7_3"
  type: "ReLU"
  bottom: "conv7_3"
  top: "conv7_3"
}
layer {
  name: "conv7_3norm"
  type: "BatchNorm"
  bottom: "conv7_3"
  top: "conv7_3norm"
  batch_norm_param{ }
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
  param {lr_mult: 0 decay_mult: 0}
}
# *****************
# ***** conv8 *****
# *****************
layer {
  name: "conv8_1"
  type: "Deconvolution"
  bottom: "conv7_3norm"
  top: "conv8_1"
  convolution_param {
    num_output: 256
    kernel_size: 4
    pad: 1
    dilation: 1
    stride: 2
  }
}
layer {
  name: "relu8_1"
  type: "ReLU"
  bottom: "conv8_1"
  top: "conv8_1"
}
layer {
  name: "conv8_2"
  # name: "conv8_2_"
  type: "Convolution"
  bottom: "conv8_1"
  top: "conv8_2"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu8_2"
  type: "ReLU"
  bottom: "conv8_2"
  top: "conv8_2"
}
layer {
  name: "conv8_3"
  type: "Convolution"
  bottom: "conv8_2"
  top: "conv8_3"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    dilation: 1
  }
}
layer {
  name: "relu8_3"
  type: "ReLU"
  bottom: "conv8_3"
  top: "conv8_3"
}
# ****************************
# ***** Unary prediction *****
# ****************************
layer {
  name: "conv8_313"
  type: "Convolution"
  bottom: "conv8_3"
  top: "conv8_313"
  convolution_param {
    num_output: 313
    kernel_size: 1
    stride: 1
    dilation: 1
  }
}
# ***************************
# ***** Boosting priors *****
# ***************************
layer {
  name: "conv8_313_boost"
  type: "Python"
  bottom: "conv8_313"
  bottom: "prior_boost_nongray"
  top: "conv8_313_boost"
  python_param {
    module: 'caffe_traininglayers'
    layer: 'ClassRebalanceMultLayer'
  }
}
# ************************
# ***** Softmax loss *****
# ************************
layer {
  name: "loss8_313"
  type: "SoftmaxCrossEntropyLoss"
  bottom: "conv8_313_boost"
  bottom: "gt_ab_313"
  top: 'loss8_313'
  loss_weight: 1.0
}