RGBDNet_test.prototxt

name: "saliencyRGBD"
layer {
  name: "data"
  type: "MemoryData"
  top: "data"
  top: "fakelabel"
  memory_data_param {
    batch_size: 1
    channels: 3
    height: 324
    width: 324
  }
}
layer {
  name: "depthdata"
  type: "MemoryData"
  top: "depthdata"
  top: "depth_fakelabel"
  memory_data_param {
    batch_size: 1
    channels: 3
    height: 324
    width: 324
  }
}
layer {
  name: "filldata"
  type: "MemoryData"
  top: "filldata"
  top: "fill_fakelabel"
  memory_data_param {
    batch_size: 1
    channels: 3
    height: 324
    width: 324
  }
}
layer {
  name: "gapdata"
  type: "MemoryData"
  top: "gapdata"
  top: "gap_fakelabel"
  memory_data_param {
    batch_size: 1
    channels: 3
    height: 324
    width: 324
  }
}
layer {
  name: "silence"
  type: "Silence"
  bottom: "fakelabel"
  #bottom: "fakelabel2"
}

#------------------------------------------------------------------

layer {
  name: "genslic"
  type: "GenSlicinfo"
  bottom: "data"
  top: "slic"
  top: "deadmap"
  gen_slicinfo_param {
    image_size: 324
    slic_size: 18
    coh_weight: 1.4
    cielab: true 
    min_pixel_num: 100
  }
}

layer {
  name: "initial_low"
  type: "LowlevelDistance"
  bottom: "data"
  bottom: "slic"
  bottom: "deadmap"
  top: "region_features"
  top: "region_labels"
  top: "query_index_cue" # order of picked region   
  include {
    phase: TRAIN
  }
  lowlevel_distance_param {
    feature_option: 3
    grid_size: 20
    train_region_per_image: 50
    spixel_size: 18
  }
}

layer {
  name: "initial_low"
  type: "LowlevelDistance"
  bottom: "data"
  bottom: "slic"
  bottom: "deadmap"
  top: "region_features"
  top: "region_labels"
  top: "query_index_cue" # order of picked region   
  include {
    phase: TEST
  }
  lowlevel_distance_param {
    feature_option: 3
    grid_size: 20
    train_region_per_image: 0 # this means train all regions
    spixel_size: 18
  }
}

layer {
  name: "low_conv1"
  type: "Convolution"
  bottom: "region_features"
  top: "region_conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 100
    pad:0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "low_bn1"
  type: "BatchNorm"
  bottom: "region_conv1"
  top: "region_conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "low_relu1"
  type: "ReLU"
  bottom: "region_conv1"
  top: "region_conv1"
}

layer {
  name: "low_conv2"
  type: "Convolution"
  bottom: "region_conv1"
  top: "region_conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 100
    pad:0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "low_bn2"
  type: "BatchNorm"
  bottom: "region_conv2"
  top: "region_conv2"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "low_relu2"
  type: "ReLU"
  bottom: "region_conv2"
  top: "region_conv2"
}

layer {
  name: "low_conv3"
  type: "Convolution"
  bottom: "region_conv2"
  top: "region_conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 3
    pad:0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "low_bn3"
  type: "BatchNorm"
  bottom: "region_conv3"
  top: "region_conv3"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "low_relu3"
  type: "ReLU"
  bottom: "region_conv3"
  top: "region_conv3"
}

#----------------------------------------
# depict depth features
#----------------------------------------

layer {
  name: "depth_low"
  type: "LowlevelDepth"
  bottom: "depthdata"
  bottom: "filldata"
  bottom: "gapdata"
  bottom: "slic" #slic contains from 0 to 323
  bottom: "deadmap"
  bottom: "query_index_cue"
  top: "depth_features"
  include {
    phase: TRAIN
  }
  lowlevel_depth_param {
    grid_size: 20
    train_region_per_image: 50
    spixel_size: 18
  }
}
layer {
  name: "depth_low"
  type: "LowlevelDepth"
  bottom: "depthdata"
  bottom: "filldata"
  bottom: "gapdata"
  bottom: "slic"
  bottom: "deadmap"
  bottom: "query_index_cue"
  top: "depth_features"
  include {
    phase: TEST
  }
  lowlevel_depth_param {
    grid_size: 20
    train_region_per_image: 0
    spixel_size: 18
  }
}

layer {
  name: "depth_conv1"
  type: "Convolution"
  bottom: "depth_features"
  top: "depth_conv1"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad:0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "depth_bn1"
  type: "BatchNorm"
  bottom: "depth_conv1"
  top: "depth_conv1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "depth_relu1"
  type: "ReLU"
  bottom: "depth_conv1"
  top: "depth_conv1"
}

layer {
  name: "depth_conv2"
  type: "Convolution"
  bottom: "depth_conv1"
  top: "depth_conv2"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad:0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "depth_bn2"
  type: "BatchNorm"
  bottom: "depth_conv2"
  top: "depth_conv2"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "depth_relu2"
  type: "ReLU"
  bottom: "depth_conv2"
  top: "depth_conv2"
}

layer {
  name: "depth_conv3"
  type: "Convolution"
  bottom: "depth_conv2"
  top: "depth_conv3"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  convolution_param {
    num_output: 1 # change this input from 3 to 1 as depth has 1 channel
    pad:0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "depth_bn3"
  type: "BatchNorm"
  bottom: "depth_conv3"
  top: "depth_conv3"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "depth_relu3"
  type: "ReLU"
  bottom: "depth_conv3"
  top: "depth_conv3"
}

#-----------------------------------------
# CaffeNet/VGG16 features
#-----------------------------------------
layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "data"
  top: "conv1/7x7_s2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv1/relu_7x7"
  type: "ReLU"
  bottom: "conv1/7x7_s2"
  top: "conv1/7x7_s2"
}
layer {
  name: "pool1/3x3_s2"
  type: "Pooling"
  bottom: "conv1/7x7_s2"
  top: "pool1/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "pool1/norm1"
  type: "LRN"
  bottom: "pool1/3x3_s2"
  top: "pool1/norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2/3x3_reduce"
  type: "Convolution"
  bottom: "pool1/norm1"
  top: "conv2/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv2/relu_3x3_reduce"
  type: "ReLU"
  bottom: "conv2/3x3_reduce"
  top: "conv2/3x3_reduce"
}
layer {
  name: "conv2/3x3"
  type: "Convolution"
  bottom: "conv2/3x3_reduce"
  top: "conv2/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "conv2/relu_3x3"
  type: "ReLU"
  bottom: "conv2/3x3"
  top: "conv2/3x3"
}
layer {
  name: "conv2/norm2"
  type: "LRN"
  bottom: "conv2/3x3"
  top: "conv2/norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2/3x3_s2"
  type: "Pooling"
  bottom: "conv2/norm2"
  top: "pool2/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "inception_3a/1x1"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_1x1"
  type: "ReLU"
  bottom: "inception_3a/1x1"
  top: "inception_3a/1x1"
}
layer {
  name: "inception_3a/3x3_reduce"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_3a/3x3_reduce"
  top: "inception_3a/3x3_reduce"
}
layer {
  name: "inception_3a/3x3"
  type: "Convolution"
  bottom: "inception_3a/3x3_reduce"
  top: "inception_3a/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_3x3"
  type: "ReLU"
  bottom: "inception_3a/3x3"
  top: "inception_3a/3x3"
}
layer {
  name: "inception_3a/5x5_reduce"
  type: "Convolution"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_3a/5x5_reduce"
  top: "inception_3a/5x5_reduce"
}
layer {
  name: "inception_3a/5x5"
  type: "Convolution"
  bottom: "inception_3a/5x5_reduce"
  top: "inception_3a/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_5x5"
  type: "ReLU"
  bottom: "inception_3a/5x5"
  top: "inception_3a/5x5"
}
layer {
  name: "inception_3a/pool"
  type: "Pooling"
  bottom: "pool2/3x3_s2"
  top: "inception_3a/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_3a/pool_proj"
  type: "Convolution"
  bottom: "inception_3a/pool"
  top: "inception_3a/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3a/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_3a/pool_proj"
  top: "inception_3a/pool_proj"
}
layer {
  name: "inception_3a/output"
  type: "Concat"
  bottom: "inception_3a/1x1"
  bottom: "inception_3a/3x3"
  bottom: "inception_3a/5x5"
  bottom: "inception_3a/pool_proj"
  top: "inception_3a/output"
}
layer {
  name: "inception_3b/1x1"
  type: "Convolution"
  bottom: "inception_3a/output"
  top: "inception_3b/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_1x1"
  type: "ReLU"
  bottom: "inception_3b/1x1"
  top: "inception_3b/1x1"
}
layer {
  name: "inception_3b/3x3_reduce"
  type: "Convolution"
  bottom: "inception_3a/output"
  top: "inception_3b/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_3b/3x3_reduce"
  top: "inception_3b/3x3_reduce"
}
layer {
  name: "inception_3b/3x3"
  type: "Convolution"
  bottom: "inception_3b/3x3_reduce"
  top: "inception_3b/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_3x3"
  type: "ReLU"
  bottom: "inception_3b/3x3"
  top: "inception_3b/3x3"
}
layer {
  name: "inception_3b/5x5_reduce"
  type: "Convolution"
  bottom: "inception_3a/output"
  top: "inception_3b/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_3b/5x5_reduce"
  top: "inception_3b/5x5_reduce"
}
layer {
  name: "inception_3b/5x5"
  type: "Convolution"
  bottom: "inception_3b/5x5_reduce"
  top: "inception_3b/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_5x5"
  type: "ReLU"
  bottom: "inception_3b/5x5"
  top: "inception_3b/5x5"
}
layer {
  name: "inception_3b/pool"
  type: "Pooling"
  bottom: "inception_3a/output"
  top: "inception_3b/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_3b/pool_proj"
  type: "Convolution"
  bottom: "inception_3b/pool"
  top: "inception_3b/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_3b/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_3b/pool_proj"
  top: "inception_3b/pool_proj"
}
layer {
  name: "inception_3b/output"
  type: "Concat"
  bottom: "inception_3b/1x1"
  bottom: "inception_3b/3x3"
  bottom: "inception_3b/5x5"
  bottom: "inception_3b/pool_proj"
  top: "inception_3b/output"
}
layer {
  name: "pool3/3x3_s2"
  type: "Pooling"
  bottom: "inception_3b/output"
  top: "pool3/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "inception_4a/1x1"
  type: "Convolution"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 192
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_1x1"
  type: "ReLU"
  bottom: "inception_4a/1x1"
  top: "inception_4a/1x1"
}
layer {
  name: "inception_4a/3x3_reduce"
  type: "Convolution"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4a/3x3_reduce"
  top: "inception_4a/3x3_reduce"
}
layer {
  name: "inception_4a/3x3"
  type: "Convolution"
  bottom: "inception_4a/3x3_reduce"
  top: "inception_4a/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 208
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_3x3"
  type: "ReLU"
  bottom: "inception_4a/3x3"
  top: "inception_4a/3x3"
}
layer {
  name: "inception_4a/5x5_reduce"
  type: "Convolution"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4a/5x5_reduce"
  top: "inception_4a/5x5_reduce"
}
layer {
  name: "inception_4a/5x5"
  type: "Convolution"
  bottom: "inception_4a/5x5_reduce"
  top: "inception_4a/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 48
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_5x5"
  type: "ReLU"
  bottom: "inception_4a/5x5"
  top: "inception_4a/5x5"
}
layer {
  name: "inception_4a/pool"
  type: "Pooling"
  bottom: "pool3/3x3_s2"
  top: "inception_4a/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4a/pool_proj"
  type: "Convolution"
  bottom: "inception_4a/pool"
  top: "inception_4a/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4a/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4a/pool_proj"
  top: "inception_4a/pool_proj"
}
layer {
  name: "inception_4a/output"
  type: "Concat"
  bottom: "inception_4a/1x1"
  bottom: "inception_4a/3x3"
  bottom: "inception_4a/5x5"
  bottom: "inception_4a/pool_proj"
  top: "inception_4a/output"
}
layer {
  name: "inception_4b/1x1"
  type: "Convolution"
  bottom: "inception_4a/output"
  top: "inception_4b/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_1x1"
  type: "ReLU"
  bottom: "inception_4b/1x1"
  top: "inception_4b/1x1"
}
layer {
  name: "inception_4b/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4a/output"
  top: "inception_4b/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 112
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4b/3x3_reduce"
  top: "inception_4b/3x3_reduce"
}
layer {
  name: "inception_4b/3x3"
  type: "Convolution"
  bottom: "inception_4b/3x3_reduce"
  top: "inception_4b/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 224
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_3x3"
  type: "ReLU"
  bottom: "inception_4b/3x3"
  top: "inception_4b/3x3"
}
layer {
  name: "inception_4b/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4a/output"
  top: "inception_4b/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4b/5x5_reduce"
  top: "inception_4b/5x5_reduce"
}
layer {
  name: "inception_4b/5x5"
  type: "Convolution"
  bottom: "inception_4b/5x5_reduce"
  top: "inception_4b/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_5x5"
  type: "ReLU"
  bottom: "inception_4b/5x5"
  top: "inception_4b/5x5"
}
layer {
  name: "inception_4b/pool"
  type: "Pooling"
  bottom: "inception_4a/output"
  top: "inception_4b/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4b/pool_proj"
  type: "Convolution"
  bottom: "inception_4b/pool"
  top: "inception_4b/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4b/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4b/pool_proj"
  top: "inception_4b/pool_proj"
}
layer {
  name: "inception_4b/output"
  type: "Concat"
  bottom: "inception_4b/1x1"
  bottom: "inception_4b/3x3"
  bottom: "inception_4b/5x5"
  bottom: "inception_4b/pool_proj"
  top: "inception_4b/output"
}
layer {
  name: "inception_4c/1x1"
  type: "Convolution"
  bottom: "inception_4b/output"
  top: "inception_4c/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_1x1"
  type: "ReLU"
  bottom: "inception_4c/1x1"
  top: "inception_4c/1x1"
}
layer {
  name: "inception_4c/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4b/output"
  top: "inception_4c/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4c/3x3_reduce"
  top: "inception_4c/3x3_reduce"
}
layer {
  name: "inception_4c/3x3"
  type: "Convolution"
  bottom: "inception_4c/3x3_reduce"
  top: "inception_4c/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_3x3"
  type: "ReLU"
  bottom: "inception_4c/3x3"
  top: "inception_4c/3x3"
}
layer {
  name: "inception_4c/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4b/output"
  top: "inception_4c/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 24
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4c/5x5_reduce"
  top: "inception_4c/5x5_reduce"
}
layer {
  name: "inception_4c/5x5"
  type: "Convolution"
  bottom: "inception_4c/5x5_reduce"
  top: "inception_4c/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_5x5"
  type: "ReLU"
  bottom: "inception_4c/5x5"
  top: "inception_4c/5x5"
}
layer {
  name: "inception_4c/pool"
  type: "Pooling"
  bottom: "inception_4b/output"
  top: "inception_4c/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4c/pool_proj"
  type: "Convolution"
  bottom: "inception_4c/pool"
  top: "inception_4c/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4c/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4c/pool_proj"
  top: "inception_4c/pool_proj"
}
layer {
  name: "inception_4c/output"
  type: "Concat"
  bottom: "inception_4c/1x1"
  bottom: "inception_4c/3x3"
  bottom: "inception_4c/5x5"
  bottom: "inception_4c/pool_proj"
  top: "inception_4c/output"
}
layer {
  name: "inception_4d/1x1"
  type: "Convolution"
  bottom: "inception_4c/output"
  top: "inception_4d/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 112
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_1x1"
  type: "ReLU"
  bottom: "inception_4d/1x1"
  top: "inception_4d/1x1"
}
layer {
  name: "inception_4d/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4c/output"
  top: "inception_4d/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 144
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4d/3x3_reduce"
  top: "inception_4d/3x3_reduce"
}
layer {
  name: "inception_4d/3x3"
  type: "Convolution"
  bottom: "inception_4d/3x3_reduce"
  top: "inception_4d/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 288
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_3x3"
  type: "ReLU"
  bottom: "inception_4d/3x3"
  top: "inception_4d/3x3"
}
layer {
  name: "inception_4d/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4c/output"
  top: "inception_4d/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4d/5x5_reduce"
  top: "inception_4d/5x5_reduce"
}
layer {
  name: "inception_4d/5x5"
  type: "Convolution"
  bottom: "inception_4d/5x5_reduce"
  top: "inception_4d/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_5x5"
  type: "ReLU"
  bottom: "inception_4d/5x5"
  top: "inception_4d/5x5"
}
layer {
  name: "inception_4d/pool"
  type: "Pooling"
  bottom: "inception_4c/output"
  top: "inception_4d/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4d/pool_proj"
  type: "Convolution"
  bottom: "inception_4d/pool"
  top: "inception_4d/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4d/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4d/pool_proj"
  top: "inception_4d/pool_proj"
}
layer {
  name: "inception_4d/output"
  type: "Concat"
  bottom: "inception_4d/1x1"
  bottom: "inception_4d/3x3"
  bottom: "inception_4d/5x5"
  bottom: "inception_4d/pool_proj"
  top: "inception_4d/output"
}
layer {
  name: "inception_4e/1x1"
  type: "Convolution"
  bottom: "inception_4d/output"
  top: "inception_4e/1x1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_1x1"
  type: "ReLU"
  bottom: "inception_4e/1x1"
  top: "inception_4e/1x1"
}
layer {
  name: "inception_4e/3x3_reduce"
  type: "Convolution"
  bottom: "inception_4d/output"
  top: "inception_4e/3x3_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 160
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_3x3_reduce"
  type: "ReLU"
  bottom: "inception_4e/3x3_reduce"
  top: "inception_4e/3x3_reduce"
}
layer {
  name: "inception_4e/3x3"
  type: "Convolution"
  bottom: "inception_4e/3x3_reduce"
  top: "inception_4e/3x3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 320
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_3x3"
  type: "ReLU"
  bottom: "inception_4e/3x3"
  top: "inception_4e/3x3"
}
layer {
  name: "inception_4e/5x5_reduce"
  type: "Convolution"
  bottom: "inception_4d/output"
  top: "inception_4e/5x5_reduce"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_5x5_reduce"
  type: "ReLU"
  bottom: "inception_4e/5x5_reduce"
  top: "inception_4e/5x5_reduce"
}
layer {
  name: "inception_4e/5x5"
  type: "Convolution"
  bottom: "inception_4e/5x5_reduce"
  top: "inception_4e/5x5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 2
    kernel_size: 5
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_5x5"
  type: "ReLU"
  bottom: "inception_4e/5x5"
  top: "inception_4e/5x5"
}
layer {
  name: "inception_4e/pool"
  type: "Pooling"
  bottom: "inception_4d/output"
  top: "inception_4e/pool"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
    pad: 1
  }
}
layer {
  name: "inception_4e/pool_proj"
  type: "Convolution"
  bottom: "inception_4e/pool"
  top: "inception_4e/pool_proj"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}
layer {
  name: "inception_4e/relu_pool_proj"
  type: "ReLU"
  bottom: "inception_4e/pool_proj"
  top: "inception_4e/pool_proj"
}
layer {
  name: "inception_4e/output"
  type: "Concat"
  bottom: "inception_4e/1x1"
  bottom: "inception_4e/3x3"
  bottom: "inception_4e/5x5"
  bottom: "inception_4e/pool_proj"
  top: "inception_4e/output"
}

layer {
  bottom: "inception_4e/output"
  top: "conv6_1"
  name: "conv6_1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu_conv6"
  type: "ReLU"
  bottom: "conv6_1"
  top: "conv6_1"
}
#-----------------------------------------
# Concat low and high
#-----------------------------------------

layer {
  name: "tile"
  type: "Tile"
  bottom: "conv6_1"
  top: "tiled_highfeatures"
  include {
    phase: TRAIN
  }
  tile_param {
    axis: 1
    tiles: 50 # same with train_region_per_image param in lowlevel layer
  }
}
layer {
  name: "reshape"
  type: "Reshape"
  bottom: "tiled_highfeatures"
  top: "reshaped_highfeatures"
  include {
    phase: TRAIN
  }
  reshape_param {
    shape {
      dim: 1500 # N*R (batch size * train_region_per_image)
      dim: -1
      dim: 20
      dim: 20
    }
  }
}
layer {
  name: "tile"
  type: "Tile"
  bottom: "conv6_1"
  top: "tiled_highfeatures"
  include {
    phase: TEST
  }
  tile_param {
    axis: 1
    tiles: 324 # same with train_region_per_image param in lowlevel layer
  }
}
layer {
  name: "reshape"
  type: "Reshape"
  bottom: "tiled_highfeatures"
  top: "reshaped_highfeatures"
  include {
    phase: TEST
  }
  reshape_param {
    shape {
      dim: 324 # N*R (batch size * train_region_per_image)
      dim: -1
      dim: 20
      dim: 20
    }
  }
}

layer {
  name: "concat"
  type: "Concat"
  bottom: "region_conv3"
  bottom: "reshaped_highfeatures"
  top: "concat_features"
  concat_param {
    axis: 1
  }
}

layer {
  bottom: "concat_features"
  top: "comp1"
  name: "compression1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 100
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "comp1_bn"
  type: "BatchNorm"
  bottom: "comp1"
  top: "comp1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu_comp1"
  type: "ReLU"
  bottom: "comp1"
  top: "comp1"
}

layer {
  bottom: "comp1"
  top: "comp2"
  name: "compression2"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 100
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "comp2_bn"
  type: "BatchNorm"
  bottom: "comp2"
  top: "comp2"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu_comp2"
  type: "ReLU"
  bottom: "comp2"
  top: "comp2"
}
layer {
  bottom: "comp2"
  top: "comp3"
  name: "compression3"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "comp3_bn"
  type: "BatchNorm"
  bottom: "comp3"
  top: "comp3"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}
layer {
  name: "relu_comp3"
  type: "ReLU"
  bottom: "comp3"
  top: "comp3"
}


#-----------------------------------------
# concat mcs and depth features
#-----------------------------------------


layer {
  name: "depth_concat"
  type: "Concat"
  bottom: "comp3"
  bottom: "depth_conv3"
  top: "depth_concat_features"
  concat_param {
    axis: 1
  }
}

layer {
  name: "depth_concat_ip1" # if want to initialize the caffemodel just concat_ip3
  type: "InnerProduct"
  bottom: "depth_concat_features"
  top: "depth_comp_ip1"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    } 
  }
}

layer {
  name: "depth_concat_ip3" # if want to initialize the caffemodel just concat_ip3
  type: "InnerProduct"
  bottom: "depth_comp_ip1"
  top: "depth_concat_ip3"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    } 
  }
}

#-----------------------------------------

layer {
  name: "loss"
  type: "Sigmoid"
  bottom: "depth_concat_ip3"
  top: "score"
}