model.test.prototxt

force_backward: true
layer {
  name: "Python1"
  type: "Python"
  top: "Python1"
  top: "Python2"
  python_param {
    module: "read_data1"
    layer: "input_layer"
    param_str: "{\'split\': \'test\', \'data_dir\': \'/content/drive/My Drive/MEnet\', \'train_data_dir\': \'0408data1/Images_train\', \'train_sobel_dir\': \'0408data1/Images_train\', \'train_labels_dir\': \'0408data1/GT_train\', \'test_data_dir\': \'0408data1/Images_train\', \'test_sobel_dir\': \'0408data1/Images_train\', \'test_labels_dir\': \'0408data1/GT_train\', \'batch_size\': 1, \'test_batch_size\': 1, \'resize_size\': [224, 224]}"
  }
}
layer {
  name: "BatchNorm1"
  type: "BatchNorm"
  bottom: "Python1"
  top: "BatchNorm1"
  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: "Scale1"
  type: "Scale"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
}
layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "BatchNorm1"
  top: "Convolution1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "Python1"
  top: "Convolution2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm2"
  type: "BatchNorm"
  bottom: "Convolution2"
  top: "BatchNorm2"
  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: "Scale2"
  type: "Scale"
  bottom: "BatchNorm2"
  top: "Scale2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "Scale2"
  top: "ReLU2"
}
layer {
  name: "BatchNorm3"
  type: "BatchNorm"
  bottom: "ReLU2"
  top: "BatchNorm3"
  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: "Scale3"
  type: "Scale"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU3"
  type: "ReLU"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "BatchNorm3"
  top: "Convolution3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm4"
  type: "BatchNorm"
  bottom: "Convolution3"
  top: "Convolution3"
  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: "Scale4"
  type: "Scale"
  bottom: "Convolution3"
  top: "Convolution3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU4"
  type: "ReLU"
  bottom: "Convolution3"
  top: "Convolution3"
}
layer {
  name: "Convolution4"
  type: "Convolution"
  bottom: "Convolution3"
  top: "Convolution4"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise1"
  type: "Eltwise"
  bottom: "Convolution4"
  bottom: "ReLU2"
  top: "Eltwise1"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm5"
  type: "BatchNorm"
  bottom: "Eltwise1"
  top: "BatchNorm5"
  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: "Scale5"
  type: "Scale"
  bottom: "BatchNorm5"
  top: "BatchNorm5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU5"
  type: "ReLU"
  bottom: "BatchNorm5"
  top: "BatchNorm5"
}
layer {
  name: "Convolution5"
  type: "Convolution"
  bottom: "BatchNorm5"
  top: "Convolution5"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm6"
  type: "BatchNorm"
  bottom: "Convolution5"
  top: "Convolution5"
  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: "Scale6"
  type: "Scale"
  bottom: "Convolution5"
  top: "Convolution5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU6"
  type: "ReLU"
  bottom: "Convolution5"
  top: "Convolution5"
}
layer {
  name: "Convolution6"
  type: "Convolution"
  bottom: "Convolution5"
  top: "Convolution6"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise2"
  type: "Eltwise"
  bottom: "Convolution6"
  bottom: "Eltwise1"
  top: "Eltwise2"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm7"
  type: "BatchNorm"
  bottom: "Eltwise2"
  top: "BatchNorm7"
  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: "Scale7"
  type: "Scale"
  bottom: "BatchNorm7"
  top: "BatchNorm7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU7"
  type: "ReLU"
  bottom: "BatchNorm7"
  top: "BatchNorm7"
}
layer {
  name: "Convolution7"
  type: "Convolution"
  bottom: "BatchNorm7"
  top: "Convolution7"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm8"
  type: "BatchNorm"
  bottom: "Eltwise2"
  top: "BatchNorm8"
  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: "Scale8"
  type: "Scale"
  bottom: "BatchNorm8"
  top: "BatchNorm8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU8"
  type: "ReLU"
  bottom: "BatchNorm8"
  top: "BatchNorm8"
}
layer {
  name: "Convolution8"
  type: "Convolution"
  bottom: "BatchNorm8"
  top: "Convolution8"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm9"
  type: "BatchNorm"
  bottom: "Convolution8"
  top: "Convolution8"
  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: "Scale9"
  type: "Scale"
  bottom: "Convolution8"
  top: "Convolution8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU9"
  type: "ReLU"
  bottom: "Convolution8"
  top: "Convolution8"
}
layer {
  name: "Convolution9"
  type: "Convolution"
  bottom: "Convolution8"
  top: "Convolution9"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm10"
  type: "BatchNorm"
  bottom: "Eltwise2"
  top: "BatchNorm10"
  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: "Scale10"
  type: "Scale"
  bottom: "BatchNorm10"
  top: "BatchNorm10"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU10"
  type: "ReLU"
  bottom: "BatchNorm10"
  top: "BatchNorm10"
}
layer {
  name: "Convolution10"
  type: "Convolution"
  bottom: "BatchNorm10"
  top: "Convolution10"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise3"
  type: "Eltwise"
  bottom: "Convolution9"
  bottom: "Convolution10"
  top: "Eltwise3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm11"
  type: "BatchNorm"
  bottom: "Eltwise3"
  top: "BatchNorm11"
  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: "Scale11"
  type: "Scale"
  bottom: "BatchNorm11"
  top: "BatchNorm11"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU11"
  type: "ReLU"
  bottom: "BatchNorm11"
  top: "BatchNorm11"
}
layer {
  name: "Convolution11"
  type: "Convolution"
  bottom: "BatchNorm11"
  top: "Convolution11"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm12"
  type: "BatchNorm"
  bottom: "Convolution11"
  top: "Convolution11"
  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: "Scale12"
  type: "Scale"
  bottom: "Convolution11"
  top: "Convolution11"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU12"
  type: "ReLU"
  bottom: "Convolution11"
  top: "Convolution11"
}
layer {
  name: "Convolution12"
  type: "Convolution"
  bottom: "Convolution11"
  top: "Convolution12"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise4"
  type: "Eltwise"
  bottom: "Convolution12"
  bottom: "Eltwise3"
  top: "Eltwise4"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm13"
  type: "BatchNorm"
  bottom: "Eltwise4"
  top: "BatchNorm13"
  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: "Scale13"
  type: "Scale"
  bottom: "BatchNorm13"
  top: "BatchNorm13"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU13"
  type: "ReLU"
  bottom: "BatchNorm13"
  top: "BatchNorm13"
}
layer {
  name: "Convolution13"
  type: "Convolution"
  bottom: "BatchNorm13"
  top: "Convolution13"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm14"
  type: "BatchNorm"
  bottom: "Eltwise4"
  top: "BatchNorm14"
  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: "Scale14"
  type: "Scale"
  bottom: "BatchNorm14"
  top: "BatchNorm14"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU14"
  type: "ReLU"
  bottom: "BatchNorm14"
  top: "BatchNorm14"
}
layer {
  name: "Convolution14"
  type: "Convolution"
  bottom: "BatchNorm14"
  top: "Convolution14"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm15"
  type: "BatchNorm"
  bottom: "Convolution14"
  top: "Convolution14"
  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: "Scale15"
  type: "Scale"
  bottom: "Convolution14"
  top: "Convolution14"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU15"
  type: "ReLU"
  bottom: "Convolution14"
  top: "Convolution14"
}
layer {
  name: "Convolution15"
  type: "Convolution"
  bottom: "Convolution14"
  top: "Convolution15"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm16"
  type: "BatchNorm"
  bottom: "Eltwise4"
  top: "BatchNorm16"
  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: "Scale16"
  type: "Scale"
  bottom: "BatchNorm16"
  top: "BatchNorm16"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU16"
  type: "ReLU"
  bottom: "BatchNorm16"
  top: "BatchNorm16"
}
layer {
  name: "Convolution16"
  type: "Convolution"
  bottom: "BatchNorm16"
  top: "Convolution16"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise5"
  type: "Eltwise"
  bottom: "Convolution15"
  bottom: "Convolution16"
  top: "Eltwise5"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm17"
  type: "BatchNorm"
  bottom: "Eltwise5"
  top: "BatchNorm17"
  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: "Scale17"
  type: "Scale"
  bottom: "BatchNorm17"
  top: "BatchNorm17"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU17"
  type: "ReLU"
  bottom: "BatchNorm17"
  top: "BatchNorm17"
}
layer {
  name: "Convolution17"
  type: "Convolution"
  bottom: "BatchNorm17"
  top: "Convolution17"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm18"
  type: "BatchNorm"
  bottom: "Convolution17"
  top: "Convolution17"
  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: "Scale18"
  type: "Scale"
  bottom: "Convolution17"
  top: "Convolution17"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU18"
  type: "ReLU"
  bottom: "Convolution17"
  top: "Convolution17"
}
layer {
  name: "Convolution18"
  type: "Convolution"
  bottom: "Convolution17"
  top: "Convolution18"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise6"
  type: "Eltwise"
  bottom: "Convolution18"
  bottom: "Eltwise5"
  top: "Eltwise6"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm19"
  type: "BatchNorm"
  bottom: "Eltwise6"
  top: "BatchNorm19"
  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: "Scale19"
  type: "Scale"
  bottom: "BatchNorm19"
  top: "BatchNorm19"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU19"
  type: "ReLU"
  bottom: "BatchNorm19"
  top: "BatchNorm19"
}
layer {
  name: "Convolution19"
  type: "Convolution"
  bottom: "BatchNorm19"
  top: "Convolution19"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm20"
  type: "BatchNorm"
  bottom: "Eltwise6"
  top: "BatchNorm20"
  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: "Scale20"
  type: "Scale"
  bottom: "BatchNorm20"
  top: "BatchNorm20"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU20"
  type: "ReLU"
  bottom: "BatchNorm20"
  top: "BatchNorm20"
}
layer {
  name: "Convolution20"
  type: "Convolution"
  bottom: "BatchNorm20"
  top: "Convolution20"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm21"
  type: "BatchNorm"
  bottom: "Convolution20"
  top: "Convolution20"
  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: "Scale21"
  type: "Scale"
  bottom: "Convolution20"
  top: "Convolution20"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU21"
  type: "ReLU"
  bottom: "Convolution20"
  top: "Convolution20"
}
layer {
  name: "Convolution21"
  type: "Convolution"
  bottom: "Convolution20"
  top: "Convolution21"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm22"
  type: "BatchNorm"
  bottom: "Eltwise6"
  top: "BatchNorm22"
  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: "Scale22"
  type: "Scale"
  bottom: "BatchNorm22"
  top: "BatchNorm22"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU22"
  type: "ReLU"
  bottom: "BatchNorm22"
  top: "BatchNorm22"
}
layer {
  name: "Convolution22"
  type: "Convolution"
  bottom: "BatchNorm22"
  top: "Convolution22"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise7"
  type: "Eltwise"
  bottom: "Convolution21"
  bottom: "Convolution22"
  top: "Eltwise7"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm23"
  type: "BatchNorm"
  bottom: "Eltwise7"
  top: "BatchNorm23"
  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: "Scale23"
  type: "Scale"
  bottom: "BatchNorm23"
  top: "BatchNorm23"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU23"
  type: "ReLU"
  bottom: "BatchNorm23"
  top: "BatchNorm23"
}
layer {
  name: "Convolution23"
  type: "Convolution"
  bottom: "BatchNorm23"
  top: "Convolution23"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm24"
  type: "BatchNorm"
  bottom: "Convolution23"
  top: "Convolution23"
  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: "Scale24"
  type: "Scale"
  bottom: "Convolution23"
  top: "Convolution23"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU24"
  type: "ReLU"
  bottom: "Convolution23"
  top: "Convolution23"
}
layer {
  name: "Convolution24"
  type: "Convolution"
  bottom: "Convolution23"
  top: "Convolution24"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise8"
  type: "Eltwise"
  bottom: "Convolution24"
  bottom: "Eltwise7"
  top: "Eltwise8"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm25"
  type: "BatchNorm"
  bottom: "Eltwise8"
  top: "BatchNorm25"
  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: "Scale25"
  type: "Scale"
  bottom: "BatchNorm25"
  top: "BatchNorm25"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU25"
  type: "ReLU"
  bottom: "BatchNorm25"
  top: "BatchNorm25"
}
layer {
  name: "Convolution25"
  type: "Convolution"
  bottom: "BatchNorm25"
  top: "Convolution25"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm26"
  type: "BatchNorm"
  bottom: "Eltwise8"
  top: "BatchNorm26"
  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: "Scale26"
  type: "Scale"
  bottom: "BatchNorm26"
  top: "BatchNorm26"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU26"
  type: "ReLU"
  bottom: "BatchNorm26"
  top: "BatchNorm26"
}
layer {
  name: "Convolution26"
  type: "Convolution"
  bottom: "BatchNorm26"
  top: "Convolution26"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm27"
  type: "BatchNorm"
  bottom: "Convolution26"
  top: "Convolution26"
  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: "Scale27"
  type: "Scale"
  bottom: "Convolution26"
  top: "Convolution26"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU27"
  type: "ReLU"
  bottom: "Convolution26"
  top: "Convolution26"
}
layer {
  name: "Convolution27"
  type: "Convolution"
  bottom: "Convolution26"
  top: "Convolution27"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm28"
  type: "BatchNorm"
  bottom: "Eltwise8"
  top: "BatchNorm28"
  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: "Scale28"
  type: "Scale"
  bottom: "BatchNorm28"
  top: "BatchNorm28"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU28"
  type: "ReLU"
  bottom: "BatchNorm28"
  top: "BatchNorm28"
}
layer {
  name: "Convolution28"
  type: "Convolution"
  bottom: "BatchNorm28"
  top: "Convolution28"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise9"
  type: "Eltwise"
  bottom: "Convolution27"
  bottom: "Convolution28"
  top: "Eltwise9"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm29"
  type: "BatchNorm"
  bottom: "Eltwise9"
  top: "BatchNorm29"
  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: "Scale29"
  type: "Scale"
  bottom: "BatchNorm29"
  top: "BatchNorm29"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU29"
  type: "ReLU"
  bottom: "BatchNorm29"
  top: "BatchNorm29"
}
layer {
  name: "Convolution29"
  type: "Convolution"
  bottom: "BatchNorm29"
  top: "Convolution29"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm30"
  type: "BatchNorm"
  bottom: "Convolution29"
  top: "Convolution29"
  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: "Scale30"
  type: "Scale"
  bottom: "Convolution29"
  top: "Convolution29"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU30"
  type: "ReLU"
  bottom: "Convolution29"
  top: "Convolution29"
}
layer {
  name: "Convolution30"
  type: "Convolution"
  bottom: "Convolution29"
  top: "Convolution30"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise10"
  type: "Eltwise"
  bottom: "Convolution30"
  bottom: "Eltwise9"
  top: "Eltwise10"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm31"
  type: "BatchNorm"
  bottom: "Eltwise10"
  top: "BatchNorm31"
  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: "Scale31"
  type: "Scale"
  bottom: "BatchNorm31"
  top: "BatchNorm31"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU31"
  type: "ReLU"
  bottom: "BatchNorm31"
  top: "BatchNorm31"
}
layer {
  name: "Convolution31"
  type: "Convolution"
  bottom: "BatchNorm31"
  top: "Convolution31"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm32"
  type: "BatchNorm"
  bottom: "Eltwise10"
  top: "BatchNorm32"
  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: "Scale32"
  type: "Scale"
  bottom: "BatchNorm32"
  top: "BatchNorm32"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU32"
  type: "ReLU"
  bottom: "BatchNorm32"
  top: "BatchNorm32"
}
layer {
  name: "Convolution32"
  type: "Convolution"
  bottom: "BatchNorm32"
  top: "Convolution32"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm33"
  type: "BatchNorm"
  bottom: "Convolution32"
  top: "Convolution32"
  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: "Scale33"
  type: "Scale"
  bottom: "Convolution32"
  top: "Convolution32"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU33"
  type: "ReLU"
  bottom: "Convolution32"
  top: "Convolution32"
}
layer {
  name: "Convolution33"
  type: "Convolution"
  bottom: "Convolution32"
  top: "Convolution33"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm34"
  type: "BatchNorm"
  bottom: "Eltwise10"
  top: "BatchNorm34"
  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: "Scale34"
  type: "Scale"
  bottom: "BatchNorm34"
  top: "BatchNorm34"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU34"
  type: "ReLU"
  bottom: "BatchNorm34"
  top: "BatchNorm34"
}
layer {
  name: "Convolution34"
  type: "Convolution"
  bottom: "BatchNorm34"
  top: "Convolution34"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise11"
  type: "Eltwise"
  bottom: "Convolution33"
  bottom: "Convolution34"
  top: "Eltwise11"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm35"
  type: "BatchNorm"
  bottom: "Eltwise11"
  top: "BatchNorm35"
  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: "Scale35"
  type: "Scale"
  bottom: "BatchNorm35"
  top: "BatchNorm35"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU35"
  type: "ReLU"
  bottom: "BatchNorm35"
  top: "BatchNorm35"
}
layer {
  name: "Convolution35"
  type: "Convolution"
  bottom: "BatchNorm35"
  top: "Convolution35"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm36"
  type: "BatchNorm"
  bottom: "Convolution35"
  top: "Convolution35"
  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: "Scale36"
  type: "Scale"
  bottom: "Convolution35"
  top: "Convolution35"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU36"
  type: "ReLU"
  bottom: "Convolution35"
  top: "Convolution35"
}
layer {
  name: "Convolution36"
  type: "Convolution"
  bottom: "Convolution35"
  top: "Convolution36"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise12"
  type: "Eltwise"
  bottom: "Convolution36"
  bottom: "Eltwise11"
  top: "Eltwise12"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm37"
  type: "BatchNorm"
  bottom: "Eltwise12"
  top: "BatchNorm37"
  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: "Scale37"
  type: "Scale"
  bottom: "BatchNorm37"
  top: "BatchNorm37"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU37"
  type: "ReLU"
  bottom: "BatchNorm37"
  top: "BatchNorm37"
}
layer {
  name: "Convolution37"
  type: "Convolution"
  bottom: "BatchNorm37"
  top: "Convolution37"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm38"
  type: "BatchNorm"
  bottom: "Eltwise12"
  top: "BatchNorm38"
  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: "Scale38"
  type: "Scale"
  bottom: "BatchNorm38"
  top: "BatchNorm38"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU38"
  type: "ReLU"
  bottom: "BatchNorm38"
  top: "BatchNorm38"
}
layer {
  name: "Convolution38"
  type: "Convolution"
  bottom: "BatchNorm38"
  top: "Convolution38"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 0
    kernel_size: 7
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm39"
  type: "BatchNorm"
  bottom: "Convolution38"
  top: "BatchNorm39"
  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: "Scale39"
  type: "Scale"
  bottom: "BatchNorm39"
  top: "BatchNorm39"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU39"
  type: "ReLU"
  bottom: "BatchNorm39"
  top: "BatchNorm39"
}
layer {
  name: "Deconvolution1"
  type: "Deconvolution"
  bottom: "BatchNorm39"
  top: "Deconvolution1"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 7
    stride: 1
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "Concat1"
  type: "Concat"
  bottom: "Deconvolution1"
  bottom: "Eltwise12"
  top: "Concat1"
}
layer {
  name: "BatchNorm40"
  type: "BatchNorm"
  bottom: "Concat1"
  top: "BatchNorm40"
  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: "Scale40"
  type: "Scale"
  bottom: "BatchNorm40"
  top: "BatchNorm40"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU40"
  type: "ReLU"
  bottom: "BatchNorm40"
  top: "BatchNorm40"
}
layer {
  name: "Deconvolution2"
  type: "Deconvolution"
  bottom: "BatchNorm40"
  top: "Deconvolution2"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm41"
  type: "BatchNorm"
  bottom: "Deconvolution2"
  top: "Deconvolution2"
  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: "Scale41"
  type: "Scale"
  bottom: "Deconvolution2"
  top: "Deconvolution2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU41"
  type: "ReLU"
  bottom: "Deconvolution2"
  top: "Deconvolution2"
}
layer {
  name: "Convolution39"
  type: "Convolution"
  bottom: "Deconvolution2"
  top: "Convolution39"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm42"
  type: "BatchNorm"
  bottom: "Concat1"
  top: "BatchNorm42"
  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: "Scale42"
  type: "Scale"
  bottom: "BatchNorm42"
  top: "BatchNorm42"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU42"
  type: "ReLU"
  bottom: "BatchNorm42"
  top: "BatchNorm42"
}
layer {
  name: "Deconvolution3"
  type: "Deconvolution"
  bottom: "BatchNorm42"
  top: "Deconvolution3"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 2
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise13"
  type: "Eltwise"
  bottom: "Convolution39"
  bottom: "Deconvolution3"
  top: "Eltwise13"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm43"
  type: "BatchNorm"
  bottom: "Eltwise13"
  top: "BatchNorm43"
  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: "Scale43"
  type: "Scale"
  bottom: "BatchNorm43"
  top: "BatchNorm43"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU43"
  type: "ReLU"
  bottom: "BatchNorm43"
  top: "BatchNorm43"
}
layer {
  name: "Convolution40"
  type: "Convolution"
  bottom: "BatchNorm43"
  top: "Convolution40"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm44"
  type: "BatchNorm"
  bottom: "Convolution40"
  top: "Convolution40"
  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: "Scale44"
  type: "Scale"
  bottom: "Convolution40"
  top: "Convolution40"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU44"
  type: "ReLU"
  bottom: "Convolution40"
  top: "Convolution40"
}
layer {
  name: "Convolution41"
  type: "Convolution"
  bottom: "Convolution40"
  top: "Convolution41"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise14"
  type: "Eltwise"
  bottom: "Convolution41"
  bottom: "Eltwise13"
  top: "Eltwise14"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "Concat2"
  type: "Concat"
  bottom: "Eltwise14"
  bottom: "Eltwise10"
  top: "Concat2"
}
layer {
  name: "BatchNorm45"
  type: "BatchNorm"
  bottom: "Concat2"
  top: "BatchNorm45"
  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: "Scale45"
  type: "Scale"
  bottom: "BatchNorm45"
  top: "BatchNorm45"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU45"
  type: "ReLU"
  bottom: "BatchNorm45"
  top: "BatchNorm45"
}
layer {
  name: "Deconvolution4"
  type: "Deconvolution"
  bottom: "BatchNorm45"
  top: "Deconvolution4"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm46"
  type: "BatchNorm"
  bottom: "Deconvolution4"
  top: "Deconvolution4"
  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: "Scale46"
  type: "Scale"
  bottom: "Deconvolution4"
  top: "Deconvolution4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU46"
  type: "ReLU"
  bottom: "Deconvolution4"
  top: "Deconvolution4"
}
layer {
  name: "Convolution42"
  type: "Convolution"
  bottom: "Deconvolution4"
  top: "Convolution42"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm47"
  type: "BatchNorm"
  bottom: "Concat2"
  top: "BatchNorm47"
  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: "Scale47"
  type: "Scale"
  bottom: "BatchNorm47"
  top: "BatchNorm47"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU47"
  type: "ReLU"
  bottom: "BatchNorm47"
  top: "BatchNorm47"
}
layer {
  name: "Deconvolution5"
  type: "Deconvolution"
  bottom: "BatchNorm47"
  top: "Deconvolution5"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 2
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise15"
  type: "Eltwise"
  bottom: "Convolution42"
  bottom: "Deconvolution5"
  top: "Eltwise15"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm48"
  type: "BatchNorm"
  bottom: "Eltwise15"
  top: "BatchNorm48"
  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: "Scale48"
  type: "Scale"
  bottom: "BatchNorm48"
  top: "BatchNorm48"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU48"
  type: "ReLU"
  bottom: "BatchNorm48"
  top: "BatchNorm48"
}
layer {
  name: "Convolution43"
  type: "Convolution"
  bottom: "BatchNorm48"
  top: "Convolution43"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm49"
  type: "BatchNorm"
  bottom: "Convolution43"
  top: "Convolution43"
  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: "Scale49"
  type: "Scale"
  bottom: "Convolution43"
  top: "Convolution43"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU49"
  type: "ReLU"
  bottom: "Convolution43"
  top: "Convolution43"
}
layer {
  name: "Convolution44"
  type: "Convolution"
  bottom: "Convolution43"
  top: "Convolution44"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise16"
  type: "Eltwise"
  bottom: "Convolution44"
  bottom: "Eltwise15"
  top: "Eltwise16"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "Concat3"
  type: "Concat"
  bottom: "Eltwise16"
  bottom: "Eltwise8"
  top: "Concat3"
}
layer {
  name: "BatchNorm50"
  type: "BatchNorm"
  bottom: "Concat3"
  top: "BatchNorm50"
  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: "Scale50"
  type: "Scale"
  bottom: "BatchNorm50"
  top: "BatchNorm50"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU50"
  type: "ReLU"
  bottom: "BatchNorm50"
  top: "BatchNorm50"
}
layer {
  name: "Deconvolution6"
  type: "Deconvolution"
  bottom: "BatchNorm50"
  top: "Deconvolution6"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm51"
  type: "BatchNorm"
  bottom: "Deconvolution6"
  top: "Deconvolution6"
  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: "Scale51"
  type: "Scale"
  bottom: "Deconvolution6"
  top: "Deconvolution6"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU51"
  type: "ReLU"
  bottom: "Deconvolution6"
  top: "Deconvolution6"
}
layer {
  name: "Convolution45"
  type: "Convolution"
  bottom: "Deconvolution6"
  top: "Convolution45"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm52"
  type: "BatchNorm"
  bottom: "Concat3"
  top: "BatchNorm52"
  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: "Scale52"
  type: "Scale"
  bottom: "BatchNorm52"
  top: "BatchNorm52"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU52"
  type: "ReLU"
  bottom: "BatchNorm52"
  top: "BatchNorm52"
}
layer {
  name: "Deconvolution7"
  type: "Deconvolution"
  bottom: "BatchNorm52"
  top: "Deconvolution7"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 2
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise17"
  type: "Eltwise"
  bottom: "Convolution45"
  bottom: "Deconvolution7"
  top: "Eltwise17"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm53"
  type: "BatchNorm"
  bottom: "Eltwise17"
  top: "BatchNorm53"
  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: "Scale53"
  type: "Scale"
  bottom: "BatchNorm53"
  top: "BatchNorm53"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU53"
  type: "ReLU"
  bottom: "BatchNorm53"
  top: "BatchNorm53"
}
layer {
  name: "Convolution46"
  type: "Convolution"
  bottom: "BatchNorm53"
  top: "Convolution46"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm54"
  type: "BatchNorm"
  bottom: "Convolution46"
  top: "Convolution46"
  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: "Scale54"
  type: "Scale"
  bottom: "Convolution46"
  top: "Convolution46"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU54"
  type: "ReLU"
  bottom: "Convolution46"
  top: "Convolution46"
}
layer {
  name: "Convolution47"
  type: "Convolution"
  bottom: "Convolution46"
  top: "Convolution47"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise18"
  type: "Eltwise"
  bottom: "Convolution47"
  bottom: "Eltwise17"
  top: "Eltwise18"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "Concat4"
  type: "Concat"
  bottom: "Eltwise18"
  bottom: "Eltwise6"
  top: "Concat4"
}
layer {
  name: "BatchNorm55"
  type: "BatchNorm"
  bottom: "Concat4"
  top: "BatchNorm55"
  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: "Scale55"
  type: "Scale"
  bottom: "BatchNorm55"
  top: "BatchNorm55"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU55"
  type: "ReLU"
  bottom: "BatchNorm55"
  top: "BatchNorm55"
}
layer {
  name: "Deconvolution8"
  type: "Deconvolution"
  bottom: "BatchNorm55"
  top: "Deconvolution8"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm56"
  type: "BatchNorm"
  bottom: "Deconvolution8"
  top: "Deconvolution8"
  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: "Scale56"
  type: "Scale"
  bottom: "Deconvolution8"
  top: "Deconvolution8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU56"
  type: "ReLU"
  bottom: "Deconvolution8"
  top: "Deconvolution8"
}
layer {
  name: "Convolution48"
  type: "Convolution"
  bottom: "Deconvolution8"
  top: "Convolution48"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm57"
  type: "BatchNorm"
  bottom: "Concat4"
  top: "BatchNorm57"
  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: "Scale57"
  type: "Scale"
  bottom: "BatchNorm57"
  top: "BatchNorm57"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU57"
  type: "ReLU"
  bottom: "BatchNorm57"
  top: "BatchNorm57"
}
layer {
  name: "Deconvolution9"
  type: "Deconvolution"
  bottom: "BatchNorm57"
  top: "Deconvolution9"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 2
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise19"
  type: "Eltwise"
  bottom: "Convolution48"
  bottom: "Deconvolution9"
  top: "Eltwise19"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm58"
  type: "BatchNorm"
  bottom: "Eltwise19"
  top: "BatchNorm58"
  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: "Scale58"
  type: "Scale"
  bottom: "BatchNorm58"
  top: "BatchNorm58"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU58"
  type: "ReLU"
  bottom: "BatchNorm58"
  top: "BatchNorm58"
}
layer {
  name: "Convolution49"
  type: "Convolution"
  bottom: "BatchNorm58"
  top: "Convolution49"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm59"
  type: "BatchNorm"
  bottom: "Convolution49"
  top: "Convolution49"
  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: "Scale59"
  type: "Scale"
  bottom: "Convolution49"
  top: "Convolution49"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU59"
  type: "ReLU"
  bottom: "Convolution49"
  top: "Convolution49"
}
layer {
  name: "Convolution50"
  type: "Convolution"
  bottom: "Convolution49"
  top: "Convolution50"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise20"
  type: "Eltwise"
  bottom: "Convolution50"
  bottom: "Eltwise19"
  top: "Eltwise20"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "Concat5"
  type: "Concat"
  bottom: "Eltwise20"
  bottom: "Eltwise4"
  top: "Concat5"
}
layer {
  name: "BatchNorm60"
  type: "BatchNorm"
  bottom: "Concat5"
  top: "BatchNorm60"
  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: "Scale60"
  type: "Scale"
  bottom: "BatchNorm60"
  top: "BatchNorm60"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU60"
  type: "ReLU"
  bottom: "BatchNorm60"
  top: "BatchNorm60"
}
layer {
  name: "Deconvolution10"
  type: "Deconvolution"
  bottom: "BatchNorm60"
  top: "Deconvolution10"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 4
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm61"
  type: "BatchNorm"
  bottom: "Deconvolution10"
  top: "Deconvolution10"
  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: "Scale61"
  type: "Scale"
  bottom: "Deconvolution10"
  top: "Deconvolution10"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU61"
  type: "ReLU"
  bottom: "Deconvolution10"
  top: "Deconvolution10"
}
layer {
  name: "Convolution51"
  type: "Convolution"
  bottom: "Deconvolution10"
  top: "Convolution51"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm62"
  type: "BatchNorm"
  bottom: "Concat5"
  top: "BatchNorm62"
  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: "Scale62"
  type: "Scale"
  bottom: "BatchNorm62"
  top: "BatchNorm62"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU62"
  type: "ReLU"
  bottom: "BatchNorm62"
  top: "BatchNorm62"
}
layer {
  name: "Deconvolution11"
  type: "Deconvolution"
  bottom: "BatchNorm62"
  top: "Deconvolution11"
  param {
    lr_mult: 1.0
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 2
    stride: 2
    weight_filler {
      type: "xavier"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise21"
  type: "Eltwise"
  bottom: "Convolution51"
  bottom: "Deconvolution11"
  top: "Eltwise21"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm63"
  type: "BatchNorm"
  bottom: "Eltwise21"
  top: "BatchNorm63"
  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: "Scale63"
  type: "Scale"
  bottom: "BatchNorm63"
  top: "BatchNorm63"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU63"
  type: "ReLU"
  bottom: "BatchNorm63"
  top: "BatchNorm63"
}
layer {
  name: "Convolution52"
  type: "Convolution"
  bottom: "BatchNorm63"
  top: "Convolution52"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm64"
  type: "BatchNorm"
  bottom: "Convolution52"
  top: "Convolution52"
  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: "Scale64"
  type: "Scale"
  bottom: "Convolution52"
  top: "Convolution52"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU64"
  type: "ReLU"
  bottom: "Convolution52"
  top: "Convolution52"
}
layer {
  name: "Convolution53"
  type: "Convolution"
  bottom: "Convolution52"
  top: "Convolution53"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 32
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Eltwise22"
  type: "Eltwise"
  bottom: "Convolution53"
  bottom: "Eltwise21"
  top: "Eltwise22"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "BatchNorm65"
  type: "BatchNorm"
  bottom: "Eltwise22"
  top: "BatchNorm65"
  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: "Scale65"
  type: "Scale"
  bottom: "BatchNorm65"
  top: "BatchNorm65"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU65"
  type: "ReLU"
  bottom: "BatchNorm65"
  top: "BatchNorm65"
}
layer {
  name: "Convolution54"
  type: "Convolution"
  bottom: "BatchNorm65"
  top: "Convolution54"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm66"
  type: "BatchNorm"
  bottom: "Eltwise20"
  top: "BatchNorm66"
  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: "Scale66"
  type: "Scale"
  bottom: "BatchNorm66"
  top: "BatchNorm66"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU66"
  type: "ReLU"
  bottom: "BatchNorm66"
  top: "BatchNorm66"
}
layer {
  name: "Convolution55"
  type: "Convolution"
  bottom: "BatchNorm66"
  top: "Convolution55"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm67"
  type: "BatchNorm"
  bottom: "Eltwise18"
  top: "BatchNorm67"
  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: "Scale67"
  type: "Scale"
  bottom: "BatchNorm67"
  top: "BatchNorm67"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU67"
  type: "ReLU"
  bottom: "BatchNorm67"
  top: "BatchNorm67"
}
layer {
  name: "Convolution56"
  type: "Convolution"
  bottom: "BatchNorm67"
  top: "Convolution56"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm68"
  type: "BatchNorm"
  bottom: "Eltwise16"
  top: "BatchNorm68"
  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: "Scale68"
  type: "Scale"
  bottom: "BatchNorm68"
  top: "BatchNorm68"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU68"
  type: "ReLU"
  bottom: "BatchNorm68"
  top: "BatchNorm68"
}
layer {
  name: "Convolution57"
  type: "Convolution"
  bottom: "BatchNorm68"
  top: "Convolution57"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm69"
  type: "BatchNorm"
  bottom: "Eltwise14"
  top: "BatchNorm69"
  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: "Scale69"
  type: "Scale"
  bottom: "BatchNorm69"
  top: "BatchNorm69"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU69"
  type: "ReLU"
  bottom: "BatchNorm69"
  top: "BatchNorm69"
}
layer {
  name: "Convolution58"
  type: "Convolution"
  bottom: "BatchNorm69"
  top: "Convolution58"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm70"
  type: "BatchNorm"
  bottom: "Deconvolution1"
  top: "BatchNorm70"
  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: "Scale70"
  type: "Scale"
  bottom: "BatchNorm70"
  top: "BatchNorm70"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "ReLU70"
  type: "ReLU"
  bottom: "BatchNorm70"
  top: "BatchNorm70"
}
layer {
  name: "Convolution59"
  type: "Convolution"
  bottom: "BatchNorm70"
  top: "Convolution59"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 1
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Python3"
  type: "Python"
  bottom: "Convolution1"
  bottom: "Convolution7"
  bottom: "Convolution13"
  bottom: "Convolution19"
  bottom: "Convolution25"
  bottom: "Convolution31"
  bottom: "Convolution37"
  bottom: "Convolution54"
  bottom: "Convolution55"
  bottom: "Convolution56"
  bottom: "Convolution57"
  bottom: "Convolution58"
  bottom: "Convolution59"
  top: "Python3"
  python_param {
    module: "resize"
    layer: "resize_to_same_size"
    param_str: "{}"
  }
}
layer {
  name: "Convolution60"
  type: "Convolution"
  bottom: "Python3"
  top: "Convolution60"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 16
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm71"
  type: "BatchNorm"
  bottom: "Convolution60"
  top: "Convolution60"
  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: "Scale71"
  type: "Scale"
  bottom: "Convolution60"
  top: "Convolution60"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "TanH1"
  type: "TanH"
  bottom: "Convolution60"
  top: "Convolution60"
}
layer {
  name: "Convolution61"
  type: "Convolution"
  bottom: "Python3"
  top: "Convolution61"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 2
    bias_term: true
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "BatchNorm72"
  type: "BatchNorm"
  bottom: "Convolution61"
  top: "Convolution61"
  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: "Scale72"
  type: "Scale"
  bottom: "Convolution61"
  top: "Convolution61"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "Python4"
  type: "Python"
  bottom: "Convolution60"
  bottom: "Convolution61"
  bottom: "Python2"
  top: "Python4"
  loss_weight: 1.0
  python_param {
    module: "pyloss"
    layer: "EuclideanLossLayer"
    param_str: "{\'crop_size\': [64, 64], \'batches\': 20, \'sup_threshold\': 3, \'inf_threshold\': 0}"
  }
}