caffe.prototxt

name: "AlexNet for Office"

# -----------------------------------------------------------------------------
# ----------------------------------------------------------------- Data layer
# -----------------------------------------------------------------------------

# ---------------------------------------------------------------------- Source
# Train phase
layer {
  name: "source_data"
  type: "Data"
  top: "source_data"
  top: "lp_labels"
  data_param {
    source: "train_0_lmdb"
    backend: LMDB
    batch_size: 64
    cursor: SHUFFLING
  }
  transform_param {
    crop_size: 227
    mean_file: "imagenet_mean_path"
    mirror: true
  }
  include: { phase: TRAIN }
}
layer {
  name: "source_domain_labels"
  type: "DummyData"
  top: "source_domain_labels"
  dummy_data_param {
    data_filler {
      type: "constant"
      value: 0
    }
    num: 64
    channels: 1
    height: 1
    width: 1
  }
  include: { phase: TRAIN }
}

# ---------------------------------------------------------------------- Target
# Train phase
layer {
  name: "target_data"
  type: "Data"
  top: "target_data"
  data_param {
    source: "_train_0_lmdb"
    backend: LMDB
    batch_size: 64
    cursor: SHUFFLING
  }
  transform_param {
    crop_size: 227
    mean_file: "imagenet_mean_path"
    mirror: true
  }
  include: { phase: TRAIN }
}
layer {
  name: "target_domain_labels"
  type: "DummyData"
  top: "target_domain_labels"
  dummy_data_param {
    data_filler {
      type: "constant"
      value: 1
    }
    num: 64
    channels: 1
    height: 1
    width: 1
  }
  include: { phase: TRAIN }
}
# Test phase
layer {
  name: "target_data"
  type: "Data"
  top: "data"
  top: "lp_labels"
  data_param {
    source: "train_0_lmdb"
    backend: LMDB
    batch_size: 1
  }
  transform_param {
    crop_size: 227
    mean_file: "imagenet_mean_path"
  }
  include: { phase: TEST }
}
layer {
  name: "target_domain_labels"
  type: "DummyData"
  top: "dc_labels"
  dummy_data_param {
    data_filler {
      type: "constant"
      value: 1
    }
    num: 1
    channels: 1
    height: 1
    width: 1
  }
  include: { phase: TEST }
}

# ---------------------------------------------------------- Data concatenation
layer {
  name: "concat_data"
  type: "Concat"
  bottom: "source_data"
  bottom: "target_data"
  top: "data"
  concat_param {
    concat_dim: 0
  }
  include: { phase: TRAIN }
}
layer {
  name: "concat_domain_labels"
  type: "Concat"
  bottom: "source_domain_labels"
  bottom: "target_domain_labels"
  top: "dc_labels"
  concat_param {
    concat_dim: 0
  }
  include: { phase: TRAIN }
}

# ----------------------------------------------------------------------------
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "conv1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
# ----------------------------------------------------------------------------
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
# ----------------------------------------------------------------------------
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "bottleneck"
  type: "InnerProduct"
  bottom: "fc7"
  top: "bottleneck"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

# -----------------------------------------------------------------------------
# ------------------------------------------------------------- Label predictor
# -----------------------------------------------------------------------------

# ------------------------------------------------------ Exclude target samples

# -----------------------------------------------------------------------------
# ----------------------------------------------------------- Gradient reversal
# -----------------------------------------------------------------------------
layer {
  name: "grl"
  type: "GradientScaler"
  bottom: "bottleneck"
  top: "grl"
  gradient_scaler_param {
    lower_bound: 0.0
    upper_bound: 1.0
    alpha: 0.5
    max_iter: 123
  }
}

# -----------------------------------------------------------------------------
# ----------------------------------------------------------- Domain classifier
# -----------------------------------------------------------------------------
layer {
  name: "dc_ip1"
  type: "InnerProduct"
  bottom: "grl"
  top: "dc_ip1"
  param {
    lr_mult: 10
  }
  param {
    lr_mult: 20
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "dc_relu1"
  type: "ReLU"
  bottom: "dc_ip1"
  top: "dc_ip1"
}
layer {
  name: "dc_drop1"
  type: "Dropout"
  bottom: "dc_ip1"
  top: "dc_ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "dc_ip2"
  type: "InnerProduct"
  bottom: "dc_ip1"
  top: "dc_ip2"
  param {
    lr_mult: 10
  }
  param {
    lr_mult: 20
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "dc_relu2"
  type: "ReLU"
  bottom: "dc_ip2"
  top: "dc_ip2"
}
layer {
  name: "dc_drop2"
  type: "Dropout"
  bottom: "dc_ip2"
  top: "dc_ip2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
# ----------------------------------------------------------------------------
layer {
  name: "dc_ip3"
  type: "InnerProduct"
  bottom: "dc_ip2"
  top: "dc_ip3"
  param {
    lr_mult: 10
  }
  param {
    lr_mult: 20
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "gaussian"
      std: 0.3
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "dc_loss"
  type: "SigmoidCrossEntropyLoss"
  bottom: "dc_ip3"
  bottom: "dc_labels"
  top: "dc_loss"
  loss_weight: 0.1
}