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colornet.prototxt

name: "color"
input: "data"
input_dim: 1
input_dim: 4
input_dim: 256
input_dim: 256
layer {
  name: "data_l_ab_mask"
  type: "Input"
  top: "data_l_ab_mask"
  input_param { shape { dim: 1 dim: 4 dim: 256 dim: 256 } }
}

layer {
  name: "Slice"
  type: "Slice"
  bottom: "data_l_ab_mask"
  top: "data_l"
  top: "data_ab_mask"
  slice_param { axis: 1 slice_point: 1 }
}
# *****************
# ***** conv1 *****
# *****************
layer {
  name: "ab_conv1_1"
  type: "Convolution"
  bottom: "data_ab_mask"
  top: "ab_conv1_1"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "bw_conv1_1"
  type: "Convolution"
  bottom: "data_l"
  top: "bw_conv1_1"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "conv1_1"
  type: "Eltwise"
  bottom: "bw_conv1_1"
  bottom: "ab_conv1_1"
  top: "conv1_1"
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
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}
}
layer {
  name: "conv1_2norm_ss"
  type: "Convolution"
  bottom: "conv1_2norm"
  top: "conv1_2norm_ss"
  param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    bias_term: false
    num_output: 64
    kernel_size: 1
    stride: 2
    weight_filler { type: 'constant' value: 1 }
    group: 64
  }
}
# *****************
# ***** conv2 *****
# *****************
layer {
  name: "conv2_1"
  type: "Convolution"
  # bottom: "conv1_2"
  # bottom: "conv1_2norm"
  bottom: "conv1_2norm_ss"
  # bottom: "pool1"
  top: "conv2_1"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
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}
}
layer {
  name: "conv2_2norm_ss"
  type: "Convolution"
  bottom: "conv2_2norm"
  top: "conv2_2norm_ss"
  param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    bias_term: false
    num_output: 128
    kernel_size: 1
    stride: 2
    weight_filler { type: 'constant' value: 1 }
    group: 128
  }
}
# *****************
# ***** conv3 *****
# *****************
layer {
  name: "conv3_1"
  type: "Convolution"
  # bottom: "conv2_2"
  bottom: "conv2_2norm_ss"
  # bottom: "pool2"
  top: "conv3_1"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
  }
}
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}
}
layer {
  name: "conv3_3norm_ss"
  type: "Convolution"
  bottom: "conv3_3norm"
  top: "conv3_3norm_ss"
  param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  convolution_param {
    bias_term: false
    num_output: 256
    kernel_size: 1
    stride: 2
    weight_filler { type: 'constant' value: 1 }
    group: 256
  }
}
# *****************
# ***** conv4 *****
# *****************
layer {
  name: "conv4_1"
  type: "Convolution"
  # bottom: "conv3_3"
  # bottom: "conv3_3norm"
  bottom: "conv3_3norm_ss"
  # bottom: "pool3"
  top: "conv4_1"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE  
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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"
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  # param {lr_mult: 0 decay_mult: 0} # UNARY_BRANCH_PROPAGATE
  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
  }
}
# ***** Shortcut *****
layer {
  name: "conv3_3_short"
  type: "Convolution"
  bottom: "conv3_3norm"
  top: "conv3_3_short"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}
layer {
  name: "conv8_1_comb"
  type: "Eltwise"
  bottom: "conv8_1"
  bottom: "conv3_3_short"
  top: "conv8_1_comb"
}
# ***** End Shortcut *****
layer {
  name: "conv8_1_comb"
  type: "ReLU"
  bottom: "conv8_1_comb"
  top: "conv8_1_comb"
}
layer {
  name: "conv8_2"
  type: "Convolution"
  bottom: "conv8_1_comb"
  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"
}
layer {
  name: "conv8_3norm"
  type: "BatchNorm"
  bottom: "conv8_3"
  top: "conv8_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}
}

# ****************************
# ***** Unary prediction *****
# ****************************
layer {
  name: "conv3_pred"
  type: "Convolution"
  bottom: "conv3_3norm"
  top: "conv3_pred"
  propagate_down: false
  convolution_param {
    num_output: 384
    kernel_size: 3
    stride: 1
    pad: 1
    dilation: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}
layer {
  name: "conv4_pred"
  type: "Deconvolution"
  bottom: "conv4_3norm"
  top: "conv4_pred"
  propagate_down: false
  convolution_param {
    num_output: 384
    kernel_size: 4
    stride: 2
    pad: 1
    dilation: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}
layer {
  name: "conv5_pred"
  type: "Deconvolution"
  bottom: "conv5_3norm"
  top: "conv5_pred"
  propagate_down: false
  convolution_param {
    num_output: 384
    kernel_size: 4
    stride: 2
    pad: 1
    dilation: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}
layer {
  name: "conv6_pred"
  type: "Deconvolution"
  bottom: "conv6_3norm"
  top: "conv6_pred"
  propagate_down: false
  convolution_param {
    num_output: 384
    kernel_size: 4
    stride: 2
    pad: 1
    dilation: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}
layer {
  name: "conv7_pred"
  type: "Deconvolution"
  bottom: "conv7_3norm"
  top: "conv7_pred"
  propagate_down: false
  convolution_param {
    num_output: 384
    kernel_size: 4
    stride: 2
    pad: 1
    dilation: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}
layer {
  name: "conv8_pred"
  type: "Convolution"
  bottom: "conv8_3norm"
  top: "conv8_pred"
  propagate_down: false
  convolution_param {
    num_output: 384
    kernel_size: 3
    stride: 1
    pad: 1
    dilation: 1
    weight_filler { type: "gaussian" std: .01 }
    bias_filler { type: "constant" value: 1 }
  }
}

layer {
  name: "conv345678_pred"
  type: "Eltwise"
  bottom: "conv3_pred"
  bottom: "conv4_pred"
  bottom: "conv5_pred"
  bottom: "conv6_pred"
  bottom: "conv7_pred"
  bottom: "conv8_pred"
  top: "conv345678_pred"
}
layer {
  name: "relu345678_pred"
  type: "ReLU"
  bottom: "conv345678_pred"
  top: "conv345678_pred"
}
layer {
  name: "pred_313"
  type: "Convolution"
  bottom: "conv345678_pred"
  top: "pred_313"
  convolution_param {
    num_output: 313
    kernel_size: 1
    stride: 1
    dilation: 1
  }
}
layer {
  name: "pred_313_us"
  type: "Deconvolution"
  bottom: "pred_313"
  top: "pred_313_us"
  param { name: "kern_us" }
  convolution_param {
    bias_term: false
    num_output: 313
    kernel_size: 4
    stride: 2
    pad: 1
    group: 313
  }
}
layer {
  name: "pred_313_rs"
  type: "Deconvolution"
  bottom: "pred_313_us"
  top: "pred_313_rs"
  param { name: "kern_us" }
  convolution_param {
    bias_term: false
    num_output: 313
    kernel_size: 4
    stride: 2
    pad: 1
    group: 313
  }
}

layer { # make less peaky
  name: "scale_S"
  bottom: "pred_313_rs"
  type: "Scale"
  top: "scale_S"
  scale_param {
    filler { type: "constant" value: .2 }
  }
}
layer {
  name: "dist_ab_S"
  type: "Softmax"
  bottom: "scale_S"
  top: "dist_ab_S"
}

# **************************
# ***** Point Estimate *****
# **************************
layer { # make more peaky
  name: "scale_T"
  bottom: "pred_313_rs"
  type: "Scale"
  top: "scale_T"
  scale_param {
    filler { type: "constant" value: 2.6 }
  }
}
layer {
  name: "dist_ab"
  type: "Softmax"
  bottom: "scale_T"
  top: "dist_ab_T"
}
layer {
  name: "pred_ab"
  type: "Convolution"
  bottom: "dist_ab_T"
  top: "pred_ab"
  convolution_param {
    kernel_size: 1
    num_output: 2
  }
}