name: "face_id_small_v1"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 224
input_dim: 224
layer {
  name: "backbone.input_block_1.0.0.conv"
  type: "Convolution"
  bottom: "data"
  top: "backbone.input_block_1.0.0"
  param {
    name: "backbone.input_block_1.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_1.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_1.0.0"
  top: "backbone.input_block_1.0.0"
  param {
    name: "backbone.input_block_1.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_1.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_1.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_1.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_1.0.0"
  top: "backbone.input_block_1.0.0"
  param {
    name: "backbone.input_block_1.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_1.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_1.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_1.0.0"
  top: "backbone.input_block_1.0.0"
}
layer {
  name: "backbone.input_block_1.0.0.pool1"
  type: "Pooling"
  bottom: "backbone.input_block_1.0.0"
  top: "backbone.input_block.0.1.pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
#==============================================
layer {
  name: "backbone.input_block_2.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block.0.1.pool1"
  top: "backbone.input_block_2.0.0"
  param {
    name: "backbone.input_block_2.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_2.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_2.0.0"
  top: "backbone.input_block_2.0.0"
  param {
    name: "backbone.input_block_2.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_2.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_2.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_2.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_2.0.0"
  top: "backbone.input_block_2.0.0"
  param {
    name: "backbone.input_block_2.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_2.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_2.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_2.0.0"
  top: "backbone.input_block_2.0.0"
}
layer {
  name: "backbone.input_block_2.0.0.pool1"
  type: "Pooling"
  bottom: "backbone.input_block_2.0.0"
  top: "backbone.input_block_2.0.0.pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
#==============================================
layer {
  name: "backbone.input_block_3.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_2.0.0.pool1"
  top: "backbone.input_block_3.0.0"
  param {
    name: "backbone.input_block_3.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_3.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_3.0.0"
  top: "backbone.input_block_3.0.0"
  param {
    name: "backbone.input_block_3.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_3.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_3.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_3.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_3.0.0"
  top: "backbone.input_block_3.0.0"
  param {
    name: "backbone.input_block_3.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_3.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_3.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_3.0.0"
  top: "backbone.input_block_3.0.0"
}
layer {
  name: "backbone.input_block_3.0.0.pool1"
  type: "Pooling"
  bottom: "backbone.input_block_3.0.0"
  top: "backbone.input_block_3.0.0.pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
#==============================================
layer {
  name: "backbone.input_block_4.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_3.0.0.pool1"
  top: "backbone.input_block_4.0.0"
  param {
    name: "backbone.input_block_4.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_4.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_4.0.0"
  top: "backbone.input_block_4.0.0"
  param {
    name: "backbone.input_block_4.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_4.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_4.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_4.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_4.0.0"
  top: "backbone.input_block_4.0.0"
  param {
    name: "backbone.input_block_4.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_4.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_4.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_4.0.0"
  top: "backbone.input_block_4.0.0"
}
layer {
  name: "backbone.input_block_4.0.0.pool1"
  type: "Pooling"
  bottom: "backbone.input_block_4.0.0"
  top: "backbone.input_block_4.0.0.pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
#==============================================
layer {
  name: "backbone.input_block_5.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_4.0.0.pool1"
  top: "backbone.input_block_5.0.0"
  param {
    name: "backbone.input_block_5.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_5.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_5.0.0"
  top: "backbone.input_block_5.0.0"
  param {
    name: "backbone.input_block_5.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_5.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_5.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_5.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_5.0.0"
  top: "backbone.input_block_5.0.0"
  param {
    name: "backbone.input_block_5.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_5.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_5.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_5.0.0"
  top: "backbone.input_block_5.0.0"
}
#==============================================
layer {
  name: "backbone.input_block_6.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_5.0.0"
  top: "backbone.input_block_6.0.0"
  param {
    name: "backbone.input_block_6.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_6.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_6.0.0"
  top: "backbone.input_block_6.0.0"
  param {
    name: "backbone.input_block_6.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_6.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_6.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_6.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_6.0.0"
  top: "backbone.input_block_6.0.0"
  param {
    name: "backbone.input_block_6.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_6.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_6.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_6.0.0"
  top: "backbone.input_block_6.0.0"
}
#==============================================
layer {
  name: "backbone.input_block_7.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_6.0.0"
  top: "backbone.input_block_7.0.0"
  param {
    name: "backbone.input_block_7.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_7.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_7.0.0"
  top: "backbone.input_block_7.0.0"
  param {
    name: "backbone.input_block_7.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_7.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_7.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_7.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_7.0.0"
  top: "backbone.input_block_7.0.0"
  param {
    name: "backbone.input_block_7.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_7.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_7.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_7.0.0"
  top: "backbone.input_block_7.0.0"
}
#==============================================
layer {
  name: "backbone.input_block_8.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_7.0.0"
  top: "backbone.input_block_8.0.0"
  param {
    name: "backbone.input_block_8.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_8.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_8.0.0"
  top: "backbone.input_block_8.0.0"
  param {
    name: "backbone.input_block_8.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_8.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_8.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_8.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_8.0.0"
  top: "backbone.input_block_8.0.0"
  param {
    name: "backbone.input_block_8.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_8.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_8.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_8.0.0"
  top: "backbone.input_block_8.0.0"
}
#==============================================
layer {
  name: "backbone.input_block_9.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_8.0.0"
  top: "backbone.input_block_9.0.0"
  param {
    name: "backbone.input_block_9.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "backbone.input_block_9.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.input_block_9.0.0"
  top: "backbone.input_block_9.0.0"
  param {
    name: "backbone.input_block_9.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_9.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.input_block_9.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.input_block_9.1.0.scale"
  type: "Scale"
  bottom: "backbone.input_block_9.0.0"
  top: "backbone.input_block_9.0.0"
  param {
    name: "backbone.input_block_9.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.input_block_9.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.input_block_9.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.input_block_9.0.0"
  top: "backbone.input_block_9.0.0"
}
#==============================================
layer {
  name: "backbone.final.0.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_9.0.0"
  top: "backbone.final.0.0"
  param {
    name: "backbone.final.0.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: true
  }
}
layer {
  name: "backbone.final.1.0.bn"
  type: "BatchNorm"
  bottom: "backbone.final.0.0"
  top: "backbone.final.0.0"
  param {
    name: "backbone.final.1.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "backbone.final.1.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "backbone.final.1.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "backbone.final.1.0.scale"
  type: "Scale"
  bottom: "backbone.final.0.0"
  top: "backbone.final.0.0"
  param {
    name: "backbone.final.1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "backbone.final.1.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "backbone.final.0.0.conv_relu"
  type: "ReLU"
  bottom: "backbone.final.0.0"
  top: "backbone.final.0.0"
}
layer {
  name: "backbone.final.0.0.pool1"
  type: "Pooling"
  bottom: "backbone.final.0.0"
  top: "backbone.final.0.0.pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
#==============================================
layer {
  name: "HWFPN.head1.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_3.0.0.pool1"
  top: "HWFPN.head1.0"
  param {
    name: "HWFPN.head1.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "HWFPN.head1.1.bn"
  type: "BatchNorm"
  bottom: "HWFPN.head1.0"
  top: "HWFPN.head1.0"
  param {
    name: "HWFPN.head1.1.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "HWFPN.head1.1.running_var"
    lr_mult: 0.0
  }
  param {
    name: "HWFPN.head1.1.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "HWFPN.head1.1.scale"
  type: "Scale"
  bottom: "HWFPN.head1.0"
  top: "HWFPN.head1.0"
  param {
    name: "HWFPN.head1.1.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "HWFPN.head1.1.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "HWFPN.head1.0.conv_relu"
  type: "ReLU"
  bottom: "HWFPN.head1.0"
  top: "HWFPN.head1.0"
}
#==============================================
layer {
  name: "HWFPN.head2.0.conv"
  type: "Convolution"
  bottom: "backbone.input_block_9.0.0"
  top: "HWFPN.head2.0"
  param {
    name: "HWFPN.head2.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "HWFPN.head2.1.bn"
  type: "BatchNorm"
  bottom: "HWFPN.head2.0"
  top: "HWFPN.head2.0"
  param {
    name: "HWFPN.head2.1.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "HWFPN.head2.1.running_var"
    lr_mult: 0.0
  }
  param {
    name: "HWFPN.head2.1.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "HWFPN.head2.1.scale"
  type: "Scale"
  bottom: "HWFPN.head2.0"
  top: "HWFPN.head2.0"
  param {
    name: "HWFPN.head2.1.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "HWFPN.head2.1.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "HWFPN.head2.0.conv_relu"
  type: "ReLU"
  bottom: "HWFPN.head2.0"
  top: "HWFPN.head2.0"
}

#==============================================
layer {
  name: "HWFPN.head3.0.conv"
  type: "Convolution"
  #bottom: "backbone.input_block_3.0.0"
  bottom: "backbone.final.0.0.pool1"
  top: "HWFPN.head3.0"
  param {
    name: "HWFPN.head3.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "HWFPN.head3.1.bn"
  type: "BatchNorm"
  bottom: "HWFPN.head3.0"
  top: "HWFPN.head3.0"
  param {
    name: "HWFPN.head3.1.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "HWFPN.head3.1.running_var"
    lr_mult: 0.0
  }
  param {
    name: "HWFPN.head3.1.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "HWFPN.head3.1.scale"
  type: "Scale"
  bottom: "HWFPN.head3.0"
  top: "HWFPN.head3.0"
  param {
    name: "HWFPN.head3.1.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "HWFPN.head3.1.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "HWFPN.head3.0.conv_relu"
  type: "ReLU"
  bottom: "HWFPN.head3.0"
  top: "HWFPN.head3.0"
}

#==============================================
layer {
    name: "up1"
    type: "Deconvolution"
    bottom: "HWFPN.head3.0"
    top: "up1"
    convolution_param {
    kernel_size: 2
    stride: 2
    pad: 0
    #group: 32
    num_output: 64
    bias_term: false
     weight_filler {
      type: "bilinear"
    }
  }
  param { lr_mult: 0 decay_mult: 0 } 
}
layer {
  name: "up1.add"
  type: "Eltwise"
  bottom: "up1"
  bottom: "HWFPN.head2.0"
  top: "up1_output"
  eltwise_param {
    operation: SUM
  }
}
layer {
    name: "up2"
    type: "Deconvolution"
    bottom: "up1_output"
    top: "up2"
    convolution_param {
    kernel_size: 2
    stride: 2
    pad: 0
    #group: 32
    num_output: 64
    bias_term: false
     weight_filler {
      type: "bilinear"
    }
  }
  param { lr_mult: 0 decay_mult: 0 }
}
 
layer {
  name: "up2.add"
  type: "Eltwise"
  bottom: "up2"
  bottom: "HWFPN.head1.0"
  top: "fpn_output_pre"
  eltwise_param {
    operation: SUM
  }
}

layer {
  name: "HWFPN.0.0.pool1"
  type: "Pooling"
  bottom: "fpn_output_pre"
  top: "fpn_output"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

#==============================================
#==============================================
layer {
  name: "heat_map_head.0.conv"
  type: "Convolution"
  bottom: "fpn_output"
  top: "heat_map_head"
  param {
    name: "heat_map_head.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
#==============================================
layer {
  name: "local_wh_head.0.conv"
  type: "Convolution"
  bottom: "fpn_output"
  top: "local_wh_head"
  param {
    name: "local_wh_head.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 2
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
#==============================================
layer {
  name: "local_xy_head.0.conv"
  type: "Convolution"
  bottom: "fpn_output"
  top: "local_xy_head"
  param {
    name: "local_xy_head.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 2
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
#==============================================


layer {
  name: "landmark_head.0.bn"
  type: "BatchNorm"
  bottom: "fpn_output"
  top: "landmark_head.0"
  param {
    name: "landmark_head.0.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "landmark_head.0.running_var"
    lr_mult: 0.0
  }
  param {
    name: "landmark_head.0.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "landmark_head.0.scale"
  type: "Scale"
  bottom: "landmark_head.0"
  top: "landmark_head.0"
  param {
    name: "landmark_head.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "landmark_head.0.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "landmark_head.0.conv_relu"
  type: "ReLU"
  bottom: "landmark_head.0"
  top: "landmark_head.0"
}
layer {
  name: "landmark_head.2.0.conv"
  type: "Convolution"
  bottom: "landmark_head.0"
  top: "landmark_head.0"
  param {
    name: "landmark_head.2.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}



#==============================================
layer {
  name: "landmark_head.3.bn"
  type: "BatchNorm"
  bottom: "landmark_head.0"
  top: "landmark_head.3"
  param {
    name: "landmark_head.3.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "landmark_head.3.running_var"
    lr_mult: 0.0
  }
  param {
    name: "landmark_head.3.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "landmark_head.3.scale"
  type: "Scale"
  bottom: "landmark_head.3"
  top: "landmark_head.3"
  param {
    name: "landmark_head.3.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "landmark_head.3.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "landmark_head.3.conv_relu"
  type: "ReLU"
  bottom: "landmark_head.3"
  top: "landmark_head.3"
}

layer {
  name: "landmark_head.5.0.conv"
  type: "Convolution"
  bottom: "landmark_head.3"
  top: "landmark_head.3"
  param {
    name: "landmark_head.5.0.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}

#==============================================
layer {
  name: "landmark_head.6.bn"
  type: "BatchNorm"
  bottom: "landmark_head.3"
  top: "landmark_head.6"
  param {
    name: "landmark_head.6.running_mean"
    lr_mult: 0.0
  }
  param {
    name: "landmark_head.6.running_var"
    lr_mult: 0.0
  }
  param {
    name: "landmark_head.6.for_train"
    lr_mult: 0.0
  }
}
layer {
  name: "landmark_head.6.scale"
  type: "Scale"
  bottom: "landmark_head.6"
  top: "landmark_head.6"
  param {
    name: "landmark_head.6.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    name: "landmark_head.6.bias"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "landmark_head.6.conv_relu"
  type: "ReLU"
  bottom: "landmark_head.6"
  top: "landmark_head.6"
}

layer {
  name: "landmark_head.8.conv"
  type: "Convolution"
  bottom: "landmark_head.6"
  top: "landmark_head"
  param {
    name: "landmark_head.8.weight"
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 136
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}