Caffe Layer Library

Convolution layer

# convolution
layer {
  name: "loss1/conv"
  type: "Convolution"
  bottom: "loss1/ave_pool"
  top: "loss1/conv"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride:1 # default: stride=1
    pad: 1
    weight_filler {
      # xavier type
      type: "xavier"

      # gaussian type
      #type: "gaussian"
      #std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.2
    }
  }
}

Deconvolution

layer {
  name: "score2"
  type: "Deconvolution"
  bottom: "score"
  top: "score2"
  param {
    lr_mult: 1
  }
  convolution_param {
    num_output: 21
    kernel_size: 4
    stride: 2
    weight_filler: { type: "bilinear" }
  }
}

Dilation Convolution

layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 2
    dilation: 2 # Actually pad = dilation
  }
}

Pooling

max pool

layer {
  name: "pool1_3x3_s2"
  type: "Pooling"
  bottom: "conv1_3_3x3"
  top: "pool1_3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
  }
}

ave pool

layer {
  name: "conv5_3_pool1"
  type: "Pooling"
  bottom: "conv5_3"
  top: "conv5_3_pool1"
  pooling_param {
    pool: AVE
    kernel_size: 60
    stride: 60
  }
}

Upsample

layer {
  name: "upsample4"
  type: "Upsample"
  bottom: "conv5_1_D"
  top: "pool4_D"
  bottom: "pool4_mask"
  upsample_param {
    scale: 2
    upsample_w: 60
    upsample_h: 45
  }
}

Eltwise

layer {
    bottom: "conv4_3"
    bottom: "res_conv4"
    top: "fusion_res_cov4"
    name: "fusion_res_cov4"
    type: "Eltwise"
    eltwise_param { operation: SUM } # PROD SUM MAX
} 

Concat

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"
}

InnerProduct

layer {
  name: "imagenet_fc"
  type: "InnerProduct"
  bottom: "fc7"
  top: "imagenet_fc"
  param {
    lr_mult: 1
    decay_mult: 250
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: ${NUM_LABELS}
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.7
    }
  }
}

Dropout

layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}

Batch Normaliztion

# BatchNorm2  
layer {
  name: "BatchNorm2" 
  #type: "LRN"
  type: "BatchNorm" include { phase: TRAIN}
  bottom: "Concat1"
  top: "BatchNorm2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: false
  }
}

# BatchNorm
layer {
  name: "bn3"
  type: "BatchNorm"
  bottom: "conv3"
  top: "bn3"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
}

# BN
layer {
  name: "spp3_bn"
  type: "BN"
  bottom: "conv_spp_3_ave_pool"
  top: "spp3_bn"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  bn_param {
    slope_filler {
      type: "constant"
      value: 1
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    frozen: true
    momentum: 0.95
  }
}

LRN

layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}

Scale

layer {
  bottom: "conv1/7x7_s2/bn"
  top: "conv1/7x7_s2/bn/sc"
  name: "conv1/7x7_s2/bn/sc"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}

label interpolation

Threshold

layer {
  name: "threshold"
  type: "Threshold"
  bottom: "soft_prob_s1"
  top: "threshold"
  threshold_param {  
    threshold: 1e-36
  }
}

SigmoidGateLayer

layer {
  name: "gate"
  type: "SigmoidGate"
  bottom: "soft_prob_s1"
  top: "gate"
  gate_param {  
    threshold: 0.5
  }
}

#

ReLU

layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}

PReLU

layer {
  name: "relu6"
  bottom: "fc6"
  top: "relu6"
  type: "PReLU"
  prelu_param { 
    filler {
      type: "constant" 
      value: 0.3 
      } 
    channel_shared: false 
  }
}

label interpolation

layer {
  bottom: "label"
  top: "label_shrink"
  name: "label_shrink"
  type: "Interp"
  interp_param {
    shrink_factor: 8
    pad_beg: 0
    pad_end: 0
  }
}

data interpolation

layer {
  name: "fc8_interp"
  type: "Interp"
  bottom: "fc8_voc12"
  top: "fc8_interp"
  interp_param {
    zoom_factor: 8
  }
}