deploy.prototxt

input: "data"
input_dim: 1
input_dim: 3
input_dim: 353
input_dim: 353
layer {
  name: "data_bn"
  type: "BatchNorm"
  bottom: "data"
  top: "data_bn"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "data_scale"
  type: "Scale"
  bottom: "data_bn"
  top: "data_bn"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data_bn"
  top: "conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 3
    kernel_size: 7
    stride: 2
    weight_filler {
      type: "msra"
      variance_norm: FAN_OUT
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "conv1_bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_scale"
  type: "Scale"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_relu"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv1_pool"
  type: "Pooling"
  bottom: "conv1"
  top: "conv1_pool"
  pooling_param {
    kernel_size: 3
    stride: 2
    pad: 1
  }
}
layer {
  name: "layer_64_1_conv1"
  type: "Convolution"
  bottom: "conv1_pool"
  top: "layer_64_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_bn2"
  type: "BatchNorm"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_1_scale2"
  type: "Scale"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_1_relu2"
  type: "ReLU"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv1"
}
layer {
  name: "layer_64_1_conv2"
  type: "Convolution"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_bn3"
  type: "BatchNorm"
  bottom: "layer_64_1_conv2"
  top: "layer_64_1_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_1_scale3"
  type: "Scale"
  bottom: "layer_64_1_conv2"
  top: "layer_64_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_1_relu3"
  type: "ReLU"
  bottom: "layer_64_1_conv2"
  top: "layer_64_1_conv2"
}
layer {
  name: "layer_64_1_conv3"
  type: "Convolution"
  bottom: "layer_64_1_conv2"
  top: "layer_64_1_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_conv_expand"
  type: "Convolution"
  bottom: "layer_64_1_conv1"
  top: "layer_64_1_conv_expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_1_sum"
  type: "Eltwise"
  bottom: "layer_64_1_conv3"
  bottom: "layer_64_1_conv_expand"
  top: "layer_64_1_sum"
}
layer {
  name: "layer_64_2_bn1"
  type: "BatchNorm"
  bottom: "layer_64_1_sum"
  top: "layer_64_2_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_2_scale1"
  type: "Scale"
  bottom: "layer_64_2_bn1"
  top: "layer_64_2_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_2_relu1"
  type: "ReLU"
  bottom: "layer_64_2_bn1"
  top: "layer_64_2_bn1"
}
layer {
  name: "layer_64_2_conv1"
  type: "Convolution"
  bottom: "layer_64_2_bn1"
  top: "layer_64_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_2_bn2"
  type: "BatchNorm"
  bottom: "layer_64_2_conv1"
  top: "layer_64_2_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_2_scale2"
  type: "Scale"
  bottom: "layer_64_2_conv1"
  top: "layer_64_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_2_relu2"
  type: "ReLU"
  bottom: "layer_64_2_conv1"
  top: "layer_64_2_conv1"
}
layer {
  name: "layer_64_2_conv2"
  type: "Convolution"
  bottom: "layer_64_2_conv1"
  top: "layer_64_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_2_bn3"
  type: "BatchNorm"
  bottom: "layer_64_2_conv2"
  top: "layer_64_2_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_2_scale3"
  type: "Scale"
  bottom: "layer_64_2_conv2"
  top: "layer_64_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_2_relu3"
  type: "ReLU"
  bottom: "layer_64_2_conv2"
  top: "layer_64_2_conv2"
}
layer {
  name: "layer_64_2_conv3"
  type: "Convolution"
  bottom: "layer_64_2_conv2"
  top: "layer_64_2_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_2_sum"
  type: "Eltwise"
  bottom: "layer_64_2_conv3"
  bottom: "layer_64_1_sum"
  top: "layer_64_2_sum"
}
layer {
  name: "layer_64_3_bn1"
  type: "BatchNorm"
  bottom: "layer_64_2_sum"
  top: "layer_64_3_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_3_scale1"
  type: "Scale"
  bottom: "layer_64_3_bn1"
  top: "layer_64_3_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_3_relu1"
  type: "ReLU"
  bottom: "layer_64_3_bn1"
  top: "layer_64_3_bn1"
}
layer {
  name: "layer_64_3_conv1"
  type: "Convolution"
  bottom: "layer_64_3_bn1"
  top: "layer_64_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_3_bn2"
  type: "BatchNorm"
  bottom: "layer_64_3_conv1"
  top: "layer_64_3_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_3_scale2"
  type: "Scale"
  bottom: "layer_64_3_conv1"
  top: "layer_64_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_3_relu2"
  type: "ReLU"
  bottom: "layer_64_3_conv1"
  top: "layer_64_3_conv1"
}
layer {
  name: "layer_64_3_conv2"
  type: "Convolution"
  bottom: "layer_64_3_conv1"
  top: "layer_64_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_3_bn3"
  type: "BatchNorm"
  bottom: "layer_64_3_conv2"
  top: "layer_64_3_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_64_3_scale3"
  type: "Scale"
  bottom: "layer_64_3_conv2"
  top: "layer_64_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_64_3_relu3"
  type: "ReLU"
  bottom: "layer_64_3_conv2"
  top: "layer_64_3_conv2"
}
layer {
  name: "layer_64_3_conv3"
  type: "Convolution"
  bottom: "layer_64_3_conv2"
  top: "layer_64_3_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_64_3_sum"
  type: "Eltwise"
  bottom: "layer_64_3_conv3"
  bottom: "layer_64_2_sum"
  top: "layer_64_3_sum"
}
layer {
  name: "layer_128_1_bn1"
  type: "BatchNorm"
  bottom: "layer_64_3_sum"
  top: "layer_128_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_1_scale1"
  type: "Scale"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu1"
  type: "ReLU"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_bn1"
}
layer {
  name: "layer_128_1_conv1"
  type: "Convolution"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_bn2"
  type: "BatchNorm"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_1_scale2"
  type: "Scale"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu2"
  type: "ReLU"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv1"
}
layer {
  name: "layer_128_1_conv2"
  type: "Convolution"
  bottom: "layer_128_1_conv1"
  top: "layer_128_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_bn3"
  type: "BatchNorm"
  bottom: "layer_128_1_conv2"
  top: "layer_128_1_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_1_scale3"
  type: "Scale"
  bottom: "layer_128_1_conv2"
  top: "layer_128_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_1_relu3"
  type: "ReLU"
  bottom: "layer_128_1_conv2"
  top: "layer_128_1_conv2"
}
layer {
  name: "layer_128_1_conv3"
  type: "Convolution"
  bottom: "layer_128_1_conv2"
  top: "layer_128_1_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_conv_expand"
  type: "Convolution"
  bottom: "layer_128_1_bn1"
  top: "layer_128_1_conv_expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_1_sum"
  type: "Eltwise"
  bottom: "layer_128_1_conv3"
  bottom: "layer_128_1_conv_expand"
  top: "layer_128_1_sum"
}
layer {
  name: "layer_128_2_bn1"
  type: "BatchNorm"
  bottom: "layer_128_1_sum"
  top: "layer_128_2_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_2_scale1"
  type: "Scale"
  bottom: "layer_128_2_bn1"
  top: "layer_128_2_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_2_relu1"
  type: "ReLU"
  bottom: "layer_128_2_bn1"
  top: "layer_128_2_bn1"
}
layer {
  name: "layer_128_2_conv1"
  type: "Convolution"
  bottom: "layer_128_2_bn1"
  top: "layer_128_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_2_bn2"
  type: "BatchNorm"
  bottom: "layer_128_2_conv1"
  top: "layer_128_2_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_2_scale2"
  type: "Scale"
  bottom: "layer_128_2_conv1"
  top: "layer_128_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_2_relu2"
  type: "ReLU"
  bottom: "layer_128_2_conv1"
  top: "layer_128_2_conv1"
}
layer {
  name: "layer_128_2_conv2"
  type: "Convolution"
  bottom: "layer_128_2_conv1"
  top: "layer_128_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_2_bn3"
  type: "BatchNorm"
  bottom: "layer_128_2_conv2"
  top: "layer_128_2_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_2_scale3"
  type: "Scale"
  bottom: "layer_128_2_conv2"
  top: "layer_128_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_2_relu3"
  type: "ReLU"
  bottom: "layer_128_2_conv2"
  top: "layer_128_2_conv2"
}
layer {
  name: "layer_128_2_conv3"
  type: "Convolution"
  bottom: "layer_128_2_conv2"
  top: "layer_128_2_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_2_sum"
  type: "Eltwise"
  bottom: "layer_128_2_conv3"
  bottom: "layer_128_1_sum"
  top: "layer_128_2_sum"
}
layer {
  name: "layer_128_3_bn1"
  type: "BatchNorm"
  bottom: "layer_128_2_sum"
  top: "layer_128_3_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_3_scale1"
  type: "Scale"
  bottom: "layer_128_3_bn1"
  top: "layer_128_3_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_3_relu1"
  type: "ReLU"
  bottom: "layer_128_3_bn1"
  top: "layer_128_3_bn1"
}
layer {
  name: "layer_128_3_conv1"
  type: "Convolution"
  bottom: "layer_128_3_bn1"
  top: "layer_128_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_3_bn2"
  type: "BatchNorm"
  bottom: "layer_128_3_conv1"
  top: "layer_128_3_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_3_scale2"
  type: "Scale"
  bottom: "layer_128_3_conv1"
  top: "layer_128_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_3_relu2"
  type: "ReLU"
  bottom: "layer_128_3_conv1"
  top: "layer_128_3_conv1"
}
layer {
  name: "layer_128_3_conv2"
  type: "Convolution"
  bottom: "layer_128_3_conv1"
  top: "layer_128_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_3_bn3"
  type: "BatchNorm"
  bottom: "layer_128_3_conv2"
  top: "layer_128_3_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_3_scale3"
  type: "Scale"
  bottom: "layer_128_3_conv2"
  top: "layer_128_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_3_relu3"
  type: "ReLU"
  bottom: "layer_128_3_conv2"
  top: "layer_128_3_conv2"
}
layer {
  name: "layer_128_3_conv3"
  type: "Convolution"
  bottom: "layer_128_3_conv2"
  top: "layer_128_3_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_3_sum"
  type: "Eltwise"
  bottom: "layer_128_3_conv3"
  bottom: "layer_128_2_sum"
  top: "layer_128_3_sum"
}
layer {
  name: "layer_128_4_bn1"
  type: "BatchNorm"
  bottom: "layer_128_3_sum"
  top: "layer_128_4_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_4_scale1"
  type: "Scale"
  bottom: "layer_128_4_bn1"
  top: "layer_128_4_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_4_relu1"
  type: "ReLU"
  bottom: "layer_128_4_bn1"
  top: "layer_128_4_bn1"
}
layer {
  name: "layer_128_4_conv1"
  type: "Convolution"
  bottom: "layer_128_4_bn1"
  top: "layer_128_4_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_4_bn2"
  type: "BatchNorm"
  bottom: "layer_128_4_conv1"
  top: "layer_128_4_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_4_scale2"
  type: "Scale"
  bottom: "layer_128_4_conv1"
  top: "layer_128_4_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_4_relu2"
  type: "ReLU"
  bottom: "layer_128_4_conv1"
  top: "layer_128_4_conv1"
}
layer {
  name: "layer_128_4_conv2"
  type: "Convolution"
  bottom: "layer_128_4_conv1"
  top: "layer_128_4_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_4_bn3"
  type: "BatchNorm"
  bottom: "layer_128_4_conv2"
  top: "layer_128_4_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_128_4_scale3"
  type: "Scale"
  bottom: "layer_128_4_conv2"
  top: "layer_128_4_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_128_4_relu3"
  type: "ReLU"
  bottom: "layer_128_4_conv2"
  top: "layer_128_4_conv2"
}
layer {
  name: "layer_128_4_conv3"
  type: "Convolution"
  bottom: "layer_128_4_conv2"
  top: "layer_128_4_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_128_4_sum"
  type: "Eltwise"
  bottom: "layer_128_4_conv3"
  bottom: "layer_128_3_sum"
  top: "layer_128_4_sum"
}
layer {
  name: "layer_256_1_bn1"
  type: "BatchNorm"
  bottom: "layer_128_4_sum"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_1_scale1"
  type: "Scale"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu1"
  type: "ReLU"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_bn1"
}
layer {
  name: "layer_256_1_conv1"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_bn2"
  type: "BatchNorm"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_1_scale2"
  type: "Scale"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu2"
  type: "ReLU"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv1"
}
layer {
  name: "layer_256_1_conv2"
  type: "Convolution"
  bottom: "layer_256_1_conv1"
  top: "layer_256_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_bn3"
  type: "BatchNorm"
  bottom: "layer_256_1_conv2"
  top: "layer_256_1_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_1_scale3"
  type: "Scale"
  bottom: "layer_256_1_conv2"
  top: "layer_256_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_1_relu3"
  type: "ReLU"
  bottom: "layer_256_1_conv2"
  top: "layer_256_1_conv2"
}
layer {
  name: "layer_256_1_conv3"
  type: "Convolution"
  bottom: "layer_256_1_conv2"
  top: "layer_256_1_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_conv_expand"
  type: "Convolution"
  bottom: "layer_256_1_bn1"
  top: "layer_256_1_conv_expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_1_sum"
  type: "Eltwise"
  bottom: "layer_256_1_conv3"
  bottom: "layer_256_1_conv_expand"
  top: "layer_256_1_sum"
}
layer {
  name: "layer_256_2_bn1"
  type: "BatchNorm"
  bottom: "layer_256_1_sum"
  top: "layer_256_2_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_2_scale1"
  type: "Scale"
  bottom: "layer_256_2_bn1"
  top: "layer_256_2_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_2_relu1"
  type: "ReLU"
  bottom: "layer_256_2_bn1"
  top: "layer_256_2_bn1"
}
layer {
  name: "layer_256_2_conv1"
  type: "Convolution"
  bottom: "layer_256_2_bn1"
  top: "layer_256_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_2_bn2"
  type: "BatchNorm"
  bottom: "layer_256_2_conv1"
  top: "layer_256_2_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_2_scale2"
  type: "Scale"
  bottom: "layer_256_2_conv1"
  top: "layer_256_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_2_relu2"
  type: "ReLU"
  bottom: "layer_256_2_conv1"
  top: "layer_256_2_conv1"
}
layer {
  name: "layer_256_2_conv2"
  type: "Convolution"
  bottom: "layer_256_2_conv1"
  top: "layer_256_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_2_bn3"
  type: "BatchNorm"
  bottom: "layer_256_2_conv2"
  top: "layer_256_2_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_2_scale3"
  type: "Scale"
  bottom: "layer_256_2_conv2"
  top: "layer_256_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_2_relu3"
  type: "ReLU"
  bottom: "layer_256_2_conv2"
  top: "layer_256_2_conv2"
}
layer {
  name: "layer_256_2_conv3"
  type: "Convolution"
  bottom: "layer_256_2_conv2"
  top: "layer_256_2_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_2_sum"
  type: "Eltwise"
  bottom: "layer_256_2_conv3"
  bottom: "layer_256_1_sum"
  top: "layer_256_2_sum"
}
layer {
  name: "layer_256_3_bn1"
  type: "BatchNorm"
  bottom: "layer_256_2_sum"
  top: "layer_256_3_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_3_scale1"
  type: "Scale"
  bottom: "layer_256_3_bn1"
  top: "layer_256_3_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_3_relu1"
  type: "ReLU"
  bottom: "layer_256_3_bn1"
  top: "layer_256_3_bn1"
}
layer {
  name: "layer_256_3_conv1"
  type: "Convolution"
  bottom: "layer_256_3_bn1"
  top: "layer_256_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_3_bn2"
  type: "BatchNorm"
  bottom: "layer_256_3_conv1"
  top: "layer_256_3_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_3_scale2"
  type: "Scale"
  bottom: "layer_256_3_conv1"
  top: "layer_256_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_3_relu2"
  type: "ReLU"
  bottom: "layer_256_3_conv1"
  top: "layer_256_3_conv1"
}
layer {
  name: "layer_256_3_conv2"
  type: "Convolution"
  bottom: "layer_256_3_conv1"
  top: "layer_256_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_3_bn3"
  type: "BatchNorm"
  bottom: "layer_256_3_conv2"
  top: "layer_256_3_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_3_scale3"
  type: "Scale"
  bottom: "layer_256_3_conv2"
  top: "layer_256_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_3_relu3"
  type: "ReLU"
  bottom: "layer_256_3_conv2"
  top: "layer_256_3_conv2"
}
layer {
  name: "layer_256_3_conv3"
  type: "Convolution"
  bottom: "layer_256_3_conv2"
  top: "layer_256_3_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_3_sum"
  type: "Eltwise"
  bottom: "layer_256_3_conv3"
  bottom: "layer_256_2_sum"
  top: "layer_256_3_sum"
}
layer {
  name: "layer_256_4_bn1"
  type: "BatchNorm"
  bottom: "layer_256_3_sum"
  top: "layer_256_4_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_4_scale1"
  type: "Scale"
  bottom: "layer_256_4_bn1"
  top: "layer_256_4_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_4_relu1"
  type: "ReLU"
  bottom: "layer_256_4_bn1"
  top: "layer_256_4_bn1"
}
layer {
  name: "layer_256_4_conv1"
  type: "Convolution"
  bottom: "layer_256_4_bn1"
  top: "layer_256_4_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_4_bn2"
  type: "BatchNorm"
  bottom: "layer_256_4_conv1"
  top: "layer_256_4_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_4_scale2"
  type: "Scale"
  bottom: "layer_256_4_conv1"
  top: "layer_256_4_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_4_relu2"
  type: "ReLU"
  bottom: "layer_256_4_conv1"
  top: "layer_256_4_conv1"
}
layer {
  name: "layer_256_4_conv2"
  type: "Convolution"
  bottom: "layer_256_4_conv1"
  top: "layer_256_4_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_4_bn3"
  type: "BatchNorm"
  bottom: "layer_256_4_conv2"
  top: "layer_256_4_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_4_scale3"
  type: "Scale"
  bottom: "layer_256_4_conv2"
  top: "layer_256_4_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_4_relu3"
  type: "ReLU"
  bottom: "layer_256_4_conv2"
  top: "layer_256_4_conv2"
}
layer {
  name: "layer_256_4_conv3"
  type: "Convolution"
  bottom: "layer_256_4_conv2"
  top: "layer_256_4_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_4_sum"
  type: "Eltwise"
  bottom: "layer_256_4_conv3"
  bottom: "layer_256_3_sum"
  top: "layer_256_4_sum"
}
layer {
  name: "layer_256_5_bn1"
  type: "BatchNorm"
  bottom: "layer_256_4_sum"
  top: "layer_256_5_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_5_scale1"
  type: "Scale"
  bottom: "layer_256_5_bn1"
  top: "layer_256_5_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_5_relu1"
  type: "ReLU"
  bottom: "layer_256_5_bn1"
  top: "layer_256_5_bn1"
}
layer {
  name: "layer_256_5_conv1"
  type: "Convolution"
  bottom: "layer_256_5_bn1"
  top: "layer_256_5_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_5_bn2"
  type: "BatchNorm"
  bottom: "layer_256_5_conv1"
  top: "layer_256_5_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_5_scale2"
  type: "Scale"
  bottom: "layer_256_5_conv1"
  top: "layer_256_5_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_5_relu2"
  type: "ReLU"
  bottom: "layer_256_5_conv1"
  top: "layer_256_5_conv1"
}
layer {
  name: "layer_256_5_conv2"
  type: "Convolution"
  bottom: "layer_256_5_conv1"
  top: "layer_256_5_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_5_bn3"
  type: "BatchNorm"
  bottom: "layer_256_5_conv2"
  top: "layer_256_5_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_5_scale3"
  type: "Scale"
  bottom: "layer_256_5_conv2"
  top: "layer_256_5_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_5_relu3"
  type: "ReLU"
  bottom: "layer_256_5_conv2"
  top: "layer_256_5_conv2"
}
layer {
  name: "layer_256_5_conv3"
  type: "Convolution"
  bottom: "layer_256_5_conv2"
  top: "layer_256_5_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_5_sum"
  type: "Eltwise"
  bottom: "layer_256_5_conv3"
  bottom: "layer_256_4_sum"
  top: "layer_256_5_sum"
}
layer {
  name: "layer_256_6_bn1"
  type: "BatchNorm"
  bottom: "layer_256_5_sum"
  top: "layer_256_6_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_6_scale1"
  type: "Scale"
  bottom: "layer_256_6_bn1"
  top: "layer_256_6_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_6_relu1"
  type: "ReLU"
  bottom: "layer_256_6_bn1"
  top: "layer_256_6_bn1"
}
layer {
  name: "layer_256_6_conv1"
  type: "Convolution"
  bottom: "layer_256_6_bn1"
  top: "layer_256_6_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_6_bn2"
  type: "BatchNorm"
  bottom: "layer_256_6_conv1"
  top: "layer_256_6_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_6_scale2"
  type: "Scale"
  bottom: "layer_256_6_conv1"
  top: "layer_256_6_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_6_relu2"
  type: "ReLU"
  bottom: "layer_256_6_conv1"
  top: "layer_256_6_conv1"
}
layer {
  name: "layer_256_6_conv2"
  type: "Convolution"
  bottom: "layer_256_6_conv1"
  top: "layer_256_6_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_6_bn3"
  type: "BatchNorm"
  bottom: "layer_256_6_conv2"
  top: "layer_256_6_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_256_6_scale3"
  type: "Scale"
  bottom: "layer_256_6_conv2"
  top: "layer_256_6_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_256_6_relu3"
  type: "ReLU"
  bottom: "layer_256_6_conv2"
  top: "layer_256_6_conv2"
}
layer {
  name: "layer_256_6_conv3"
  type: "Convolution"
  bottom: "layer_256_6_conv2"
  top: "layer_256_6_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_256_6_sum"
  type: "Eltwise"
  bottom: "layer_256_6_conv3"
  bottom: "layer_256_5_sum"
  top: "layer_256_6_sum"
}
layer {
  name: "layer_512_1_bn1"
  type: "BatchNorm"
  bottom: "layer_256_6_sum"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_1_scale1"
  type: "Scale"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu1"
  type: "ReLU"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_bn1"
}
layer {
  name: "layer_512_1_conv1"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_bn2"
  type: "BatchNorm"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_1_scale2"
  type: "Scale"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu2"
  type: "ReLU"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv1"
}
layer {
  name: "layer_512_1_conv2"
  type: "Convolution"
  bottom: "layer_512_1_conv1"
  top: "layer_512_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_bn3"
  type: "BatchNorm"
  bottom: "layer_512_1_conv2"
  top: "layer_512_1_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_1_scale3"
  type: "Scale"
  bottom: "layer_512_1_conv2"
  top: "layer_512_1_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_1_relu3"
  type: "ReLU"
  bottom: "layer_512_1_conv2"
  top: "layer_512_1_conv2"
}
layer {
  name: "layer_512_1_conv3"
  type: "Convolution"
  bottom: "layer_512_1_conv2"
  top: "layer_512_1_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 2048
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_conv_expand"
  type: "Convolution"
  bottom: "layer_512_1_bn1"
  top: "layer_512_1_conv_expand"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 2048
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_1_sum"
  type: "Eltwise"
  bottom: "layer_512_1_conv3"
  bottom: "layer_512_1_conv_expand"
  top: "layer_512_1_sum"
}
layer {
  name: "layer_512_2_bn1"
  type: "BatchNorm"
  bottom: "layer_512_1_sum"
  top: "layer_512_2_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_2_scale1"
  type: "Scale"
  bottom: "layer_512_2_bn1"
  top: "layer_512_2_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_2_relu1"
  type: "ReLU"
  bottom: "layer_512_2_bn1"
  top: "layer_512_2_bn1"
}
layer {
  name: "layer_512_2_conv1"
  type: "Convolution"
  bottom: "layer_512_2_bn1"
  top: "layer_512_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_2_bn2"
  type: "BatchNorm"
  bottom: "layer_512_2_conv1"
  top: "layer_512_2_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_2_scale2"
  type: "Scale"
  bottom: "layer_512_2_conv1"
  top: "layer_512_2_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_2_relu2"
  type: "ReLU"
  bottom: "layer_512_2_conv1"
  top: "layer_512_2_conv1"
}
layer {
  name: "layer_512_2_conv2"
  type: "Convolution"
  bottom: "layer_512_2_conv1"
  top: "layer_512_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_2_bn3"
  type: "BatchNorm"
  bottom: "layer_512_2_conv2"
  top: "layer_512_2_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_2_scale3"
  type: "Scale"
  bottom: "layer_512_2_conv2"
  top: "layer_512_2_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_2_relu3"
  type: "ReLU"
  bottom: "layer_512_2_conv2"
  top: "layer_512_2_conv2"
}
layer {
  name: "layer_512_2_conv3"
  type: "Convolution"
  bottom: "layer_512_2_conv2"
  top: "layer_512_2_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 2048
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_2_sum"
  type: "Eltwise"
  bottom: "layer_512_2_conv3"
  bottom: "layer_512_1_sum"
  top: "layer_512_2_sum"
}
layer {
  name: "layer_512_3_bn1"
  type: "BatchNorm"
  bottom: "layer_512_2_sum"
  top: "layer_512_3_bn1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_3_scale1"
  type: "Scale"
  bottom: "layer_512_3_bn1"
  top: "layer_512_3_bn1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_3_relu1"
  type: "ReLU"
  bottom: "layer_512_3_bn1"
  top: "layer_512_3_bn1"
}
layer {
  name: "layer_512_3_conv1"
  type: "Convolution"
  bottom: "layer_512_3_bn1"
  top: "layer_512_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_3_bn2"
  type: "BatchNorm"
  bottom: "layer_512_3_conv1"
  top: "layer_512_3_conv1"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_3_scale2"
  type: "Scale"
  bottom: "layer_512_3_conv1"
  top: "layer_512_3_conv1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_3_relu2"
  type: "ReLU"
  bottom: "layer_512_3_conv1"
  top: "layer_512_3_conv1"
}
layer {
  name: "layer_512_3_conv2"
  type: "Convolution"
  bottom: "layer_512_3_conv1"
  top: "layer_512_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_3_bn3"
  type: "BatchNorm"
  bottom: "layer_512_3_conv2"
  top: "layer_512_3_conv2"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "layer_512_3_scale3"
  type: "Scale"
  bottom: "layer_512_3_conv2"
  top: "layer_512_3_conv2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "layer_512_3_relu3"
  type: "ReLU"
  bottom: "layer_512_3_conv2"
  top: "layer_512_3_conv2"
}
layer {
  name: "layer_512_3_conv3"
  type: "Convolution"
  bottom: "layer_512_3_conv2"
  top: "layer_512_3_conv3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 2048
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "layer_512_3_sum"
  type: "Eltwise"
  bottom: "layer_512_3_conv3"
  bottom: "layer_512_2_sum"
  top: "layer_512_3_sum"
}
layer {
  name: "last_bn"
  type: "BatchNorm"
  bottom: "layer_512_3_sum"
  top: "layer_512_3_sum"
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  param {
    lr_mult: 0.0
  }
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "last_scale"
  type: "Scale"
  bottom: "layer_512_3_sum"
  top: "layer_512_3_sum"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 1.0
  }
  scale_param {
    bias_term: true
  }
}
layer {
  name: "last_relu"
  type: "ReLU"
  bottom: "layer_512_3_sum"
  top: "layer_512_3_sum"
}

#
# pyramid pooling
#
layer {
  name: "conv5_4"
  type: "Convolution"
  bottom: "layer_512_3_sum"
  top: "conv5_4"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "msra"
    }
    bias_term: false
  }
}
layer {
  name: "conv5_4/dropout"
  type: "Dropout"
  bottom: "conv5_4"
  top: "conv5_4"
  dropout_param {
    dropout_ratio: 0.1
  }
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5_4"
  top: "conv6"
  param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 1
  }
  convolution_param {
    num_output: 2
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "conv6_interp"
  type: "Interp"
  bottom: "conv6"
  top: "conv6_interp"
  interp_param {
    zoom_factor: 32
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "conv6_interp"
  top: "prob"
}