deploy.prototxt

name: "nyu4-sf1"
layer {
  name: "rgb"
  type: "Input"
  top: "rgb"
  input_param { shape {dim: 1 dim: 3 dim: 240 dim: 320} }
}
layer {
  name: "depth"
  type: "Input"
  top: "depth"
  input_param { shape {dim: 1 dim: 1 dim: 240 dim: 320} }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "rgb"
  top: "conv1_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_1_bn"
  type: "BatchNorm"
  bottom: "conv1_1"
  top: "conv1_1"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_1_scale"
  type: "Scale"
  bottom: "conv1_1"
  top: "conv1_1"
  scale_param {
    bias_term: true
  }
}
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: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_2_bn"
  type: "BatchNorm"
  bottom: "conv1_2"
  top: "conv1_2"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_2_scale"
  type: "Scale"
  bottom: "conv1_2"
  top: "conv1_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_2_relu"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "conv1_1_pair1ch"
  type: "Convolution"
  bottom: "depth"
  top: "conv1_1_pair"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_1_bn_pair"
  type: "BatchNorm"
  bottom: "conv1_1_pair"
  top: "conv1_1_pair"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_1_scale_pair"
  type: "Scale"
  bottom: "conv1_1_pair"
  top: "conv1_1_pair"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu1_1_pair"
  type: "ReLU"
  bottom: "conv1_1_pair"
  top: "conv1_1_pair"
}
layer {
  name: "conv1_2_pair"
  type: "Convolution"
  bottom: "conv1_1_pair"
  top: "conv1_2_pair"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_2_bn_pair"
  type: "BatchNorm"
  bottom: "conv1_2_pair"
  top: "conv1_2_pair"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_2_scale_pair"
  type: "Scale"
  bottom: "conv1_2_pair"
  top: "conv1_2_pair"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_2_pair_relu"
  type: "ReLU"
  bottom: "conv1_2_pair"
  top: "conv1_2_pair"
}
layer {
  name: "conv1_fuse"
  type: "Eltwise"
  bottom: "conv1_2"
  bottom: "conv1_2_pair"
  top: "conv1_fuse"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_fuse"
  top: "pool1"
  top: "pool1_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv2_1_bn"
  type: "BatchNorm"
  bottom: "conv2_1"
  top: "conv2_1_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_1_scale"
  type: "Scale"
  bottom: "conv2_1_bn"
  top: "conv2_1_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1_scale"
  top: "conv2_1_scale"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1_scale"
  top: "conv2_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv2_2_bn"
  type: "BatchNorm"
  bottom: "conv2_2"
  top: "conv2_2_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_2_scale"
  type: "Scale"
  bottom: "conv2_2_bn"
  top: "conv2_2_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2_scale"
  top: "conv2_2_scale"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2_scale"
  top: "pool2"
  top: "pool2_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_1_bn"
  type: "BatchNorm"
  bottom: "conv3_1"
  top: "conv3_1_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_1_scale"
  type: "Scale"
  bottom: "conv3_1_bn"
  top: "conv3_1_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1_scale"
  top: "conv3_1_scale"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1_scale"
  top: "conv3_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_2_bn"
  type: "BatchNorm"
  bottom: "conv3_2"
  top: "conv3_2_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_2_scale"
  type: "Scale"
  bottom: "conv3_2_bn"
  top: "conv3_2_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2_scale"
  top: "conv3_2_scale"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2_scale"
  top: "conv3_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_3_bn"
  type: "BatchNorm"
  bottom: "conv3_3"
  top: "conv3_3_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_3_scale"
  type: "Scale"
  bottom: "conv3_3_bn"
  top: "conv3_3_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3_scale"
  top: "conv3_3_scale"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3_scale"
  top: "pool3"
  top: "pool3_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop3"
  type: "Dropout"
  bottom: "pool3"
  top: "pool3"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_1_bn"
  type: "BatchNorm"
  bottom: "conv4_1"
  top: "conv4_1_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_1_scale"
  type: "Scale"
  bottom: "conv4_1_bn"
  top: "conv4_1_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1_scale"
  top: "conv4_1_scale"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1_scale"
  top: "conv4_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_2_bn"
  type: "BatchNorm"
  bottom: "conv4_2"
  top: "conv4_2_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_2_scale"
  type: "Scale"
  bottom: "conv4_2_bn"
  top: "conv4_2_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2_scale"
  top: "conv4_2_scale"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2_scale"
  top: "conv4_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_3_bn"
  type: "BatchNorm"
  bottom: "conv4_3"
  top: "conv4_3_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_3_scale"
  type: "Scale"
  bottom: "conv4_3_bn"
  top: "conv4_3_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3_scale"
  top: "conv4_3_scale"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3_scale"
  top: "pool4"
  top: "pool4_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop4"
  type: "Dropout"
  bottom: "pool4"
  top: "pool4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_1_bn"
  type: "BatchNorm"
  bottom: "conv5_1"
  top: "conv5_1_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_1_scale"
  type: "Scale"
  bottom: "conv5_1_bn"
  top: "conv5_1_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1_scale"
  top: "conv5_1_scale"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1_scale"
  top: "conv5_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_2_bn"
  type: "BatchNorm"
  bottom: "conv5_2"
  top: "conv5_2_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_2_scale"
  type: "Scale"
  bottom: "conv5_2_bn"
  top: "conv5_2_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2_scale"
  top: "conv5_2_scale"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2_scale"
  top: "conv5_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_3_bn"
  type: "BatchNorm"
  bottom: "conv5_3"
  top: "conv5_3_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_3_scale"
  type: "Scale"
  bottom: "conv5_3_bn"
  top: "conv5_3_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3_scale"
  top: "conv5_3_scale"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3_scale"
  top: "pool5"
  top: "pool5_mask"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "encdrop5"
  type: "Dropout"
  bottom: "pool5"
  top: "pool5"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "unpool5"
  type: "Unpooling"
  bottom: "pool5"
  bottom: "pool5_mask"
  top: "pool5_D"
  unpooling_param {
    output_h: 15
    output_w: 20
  }
}
layer {
  name: "conv5_3_D"
  type: "Deconvolution"
  bottom: "pool5_D"
  top: "conv5_3_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_3_D_bn"
  type: "BatchNorm"
  bottom: "conv5_3_D"
  top: "conv5_3_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_3_D_scale"
  type: "Scale"
  bottom: "conv5_3_D_bn"
  top: "conv5_3_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5_3_D"
  type: "ReLU"
  bottom: "conv5_3_D_scale"
  top: "conv5_3_D_scale"
}
layer {
  name: "conv5_2_D"
  type: "Deconvolution"
  bottom: "conv5_3_D_scale"
  top: "conv5_2_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_2_D_bn"
  type: "BatchNorm"
  bottom: "conv5_2_D"
  top: "conv5_2_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_2_D_scale"
  type: "Scale"
  bottom: "conv5_2_D_bn"
  top: "conv5_2_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5_2_D"
  type: "ReLU"
  bottom: "conv5_2_D_scale"
  top: "conv5_2_D_scale"
}
layer {
  name: "conv5_1_D"
  type: "Deconvolution"
  bottom: "conv5_2_D_scale"
  top: "conv5_1_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv5_1_D_bn"
  type: "BatchNorm"
  bottom: "conv5_1_D"
  top: "conv5_1_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_1_D_scale"
  type: "Scale"
  bottom: "conv5_1_D_bn"
  top: "conv5_1_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5_1_D"
  type: "ReLU"
  bottom: "conv5_1_D_scale"
  top: "conv5_1_D_scale"
}
layer {
  name: "decdrop5"
  type: "Dropout"
  bottom: "conv5_1_D_scale"
  top: "conv5_1_D_scale"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "unpool4"
  type: "Unpooling"
  bottom: "conv5_1_D_scale"
  bottom: "pool4_mask"
  top: "pool4_D"
  unpooling_param {
    scale: 2
  }
}
layer {
  name: "conv4_3_D"
  type: "Deconvolution"
  bottom: "pool4_D"
  top: "conv4_3_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_3_D_bn"
  type: "BatchNorm"
  bottom: "conv4_3_D"
  top: "conv4_3_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_3_D_scale"
  type: "Scale"
  bottom: "conv4_3_D_bn"
  top: "conv4_3_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4_3_D"
  type: "ReLU"
  bottom: "conv4_3_D_scale"
  top: "conv4_3_D_scale"
}
layer {
  name: "conv4_2_D"
  type: "Deconvolution"
  bottom: "conv4_3_D_scale"
  top: "conv4_2_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_2_D_bn"
  type: "BatchNorm"
  bottom: "conv4_2_D"
  top: "conv4_2_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_2_D_scale"
  type: "Scale"
  bottom: "conv4_2_D_bn"
  top: "conv4_2_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4_2_D"
  type: "ReLU"
  bottom: "conv4_2_D_scale"
  top: "conv4_2_D_scale"
}
layer {
  name: "conv4_1_D"
  type: "Deconvolution"
  bottom: "conv4_2_D_scale"
  top: "conv4_1_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv4_1_D_bn"
  type: "BatchNorm"
  bottom: "conv4_1_D"
  top: "conv4_1_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_1_D_scale"
  type: "Scale"
  bottom: "conv4_1_D_bn"
  top: "conv4_1_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4_1_D"
  type: "ReLU"
  bottom: "conv4_1_D_scale"
  top: "conv4_1_D_scale"
}
layer {
  name: "decdrop4"
  type: "Dropout"
  bottom: "conv4_1_D_scale"
  top: "conv4_1_D_scale"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "unpool3"
  type: "Unpooling"
  bottom: "conv4_1_D_scale"
  bottom: "pool3_mask"
  top: "pool3_D"
  unpooling_param {
    scale: 2
  }
}
layer {
  name: "conv3_3_D"
  type: "Deconvolution"
  bottom: "pool3_D"
  top: "conv3_3_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_3_D_bn"
  type: "BatchNorm"
  bottom: "conv3_3_D"
  top: "conv3_3_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_3_D_scale"
  type: "Scale"
  bottom: "conv3_3_D_bn"
  top: "conv3_3_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3_3_D"
  type: "ReLU"
  bottom: "conv3_3_D_scale"
  top: "conv3_3_D_scale"
}
layer {
  name: "conv3_2_D"
  type: "Deconvolution"
  bottom: "conv3_3_D_scale"
  top: "conv3_2_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_2_D_bn"
  type: "BatchNorm"
  bottom: "conv3_2_D"
  top: "conv3_2_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_2_D_scale"
  type: "Scale"
  bottom: "conv3_2_D_bn"
  top: "conv3_2_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3_2_D"
  type: "ReLU"
  bottom: "conv3_2_D_scale"
  top: "conv3_2_D_scale"
}
layer {
  name: "conv3_1_D"
  type: "Deconvolution"
  bottom: "conv3_2_D_scale"
  top: "conv3_1_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv3_1_D_bn"
  type: "BatchNorm"
  bottom: "conv3_1_D"
  top: "conv3_1_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_1_D_scale"
  type: "Scale"
  bottom: "conv3_1_D_bn"
  top: "conv3_1_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3_1_D"
  type: "ReLU"
  bottom: "conv3_1_D_scale"
  top: "conv3_1_D_scale"
}
layer {
  name: "decdrop3"
  type: "Dropout"
  bottom: "conv3_1_D_scale"
  top: "conv3_1_D_scale"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "unpool2"
  type: "Unpooling"
  bottom: "conv3_1_D_scale"
  bottom: "pool2_mask"
  top: "pool2_D"
  unpooling_param {
    scale: 2
  }
}
layer {
  name: "conv2_2_D"
  type: "Deconvolution"
  bottom: "pool2_D"
  top: "conv2_2_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv2_2_D_bn"
  type: "BatchNorm"
  bottom: "conv2_2_D"
  top: "conv2_2_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_2_D_scale"
  type: "Scale"
  bottom: "conv2_2_D_bn"
  top: "conv2_2_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2_2_D"
  type: "ReLU"
  bottom: "conv2_2_D_scale"
  top: "conv2_2_D_scale"
}
layer {
  name: "conv2_1_D"
  type: "Deconvolution"
  bottom: "conv2_2_D_scale"
  top: "conv2_1_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv2_1_D_bn"
  type: "BatchNorm"
  bottom: "conv2_1_D"
  top: "conv2_1_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_1_D_scale"
  type: "Scale"
  bottom: "conv2_1_D_bn"
  top: "conv2_1_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2_1_D"
  type: "ReLU"
  bottom: "conv2_1_D_scale"
  top: "conv2_1_D_scale"
}
layer {
  name: "unpool1"
  type: "Unpooling"
  bottom: "conv2_1_D_scale"
  bottom: "pool1_mask"
  top: "pool1_D"
  unpooling_param {
    scale: 2
  }
}
layer {
  name: "conv1_2_D"
  type: "Deconvolution"
  bottom: "pool1_D"
  top: "conv1_2_D"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv1_2_D_bn"
  type: "BatchNorm"
  bottom: "conv1_2_D"
  top: "conv1_2_D_bn"
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_2_D_scale"
  type: "Scale"
  bottom: "conv1_2_D_bn"
  top: "conv1_2_D_scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu1_2_D"
  type: "ReLU"
  bottom: "conv1_2_D_scale"
  top: "conv1_2_D_scale"
}
layer {
  name: "score"
  type: "Convolution"
  bottom: "conv1_2_D_scale"
  top: "score"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 41
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "score"
  top: "prob"
}