name: "2012_ATLANTA_10class_20190614_v4.1"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 352
input_dim: 352
layer {
  name: "image_process"
  type: "Scale"
  bottom: "data"
  top: "image_process"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv1_conv"
  type: "Convolution"
  bottom: "image_process"
  top: "conv1_conv"
  convolution_param {
    num_output: 16
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "conv1_bn"
  type: "BatchNorm"
  bottom: "conv1_conv"
  top: "conv1_conv"
   
}
layer {
  name: "conv1_sc"
  type: "Scale"
  bottom: "conv1_conv"
  top: "conv1_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "conv1_relu"
  type: "ReLU"
  bottom: "conv1_conv"
  top: "conv1_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv2_conv"
  type: "Convolution"
  bottom: "conv1_conv"
  top: "conv2_conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "conv2_bn"
  type: "BatchNorm"
  bottom: "conv2_conv"
  top: "conv2_conv"
   
}
layer {
  name: "conv2_sc"
  type: "Scale"
  bottom: "conv2_conv"
  top: "conv2_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "conv2_relu"
  type: "ReLU"
  bottom: "conv2_conv"
  top: "conv2_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv3_conv"
  type: "Convolution"
  bottom: "conv2_conv"
  top: "conv3_conv"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "conv3_bn"
  type: "BatchNorm"
  bottom: "conv3_conv"
  top: "conv3_conv"
   
}
layer {
  name: "conv3_sc"
  type: "Scale"
  bottom: "conv3_conv"
  top: "conv3_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "conv3_relu"
  type: "ReLU"
  bottom: "conv3_conv"
  top: "conv3_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res2a_branch2a_conv"
  type: "Convolution"
  bottom: "conv3_conv"
  top: "res2a_branch2a_conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res2a_branch2a_bn"
  type: "BatchNorm"
  bottom: "res2a_branch2a_conv"
  top: "res2a_branch2a_conv"
   
}
layer {
  name: "res2a_branch2a_sc"
  type: "Scale"
  bottom: "res2a_branch2a_conv"
  top: "res2a_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2a_branch2a_relu"
  type: "ReLU"
  bottom: "res2a_branch2a_conv"
  top: "res2a_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res2a_branch2b_conv"
  type: "Convolution"
  bottom: "res2a_branch2a_conv"
  top: "res2a_branch2b_conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res2a_branch2b_bn"
  type: "BatchNorm"
  bottom: "res2a_branch2b_conv"
  top: "res2a_branch2b_conv"
   
}
layer {
  name: "res2a_branch2b_sc"
  type: "Scale"
  bottom: "res2a_branch2b_conv"
  top: "res2a_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2a_branch2b_relu"
  type: "ReLU"
  bottom: "res2a_branch2b_conv"
  top: "res2a_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res2a_branch2c_conv"
  type: "Convolution"
  bottom: "res2a_branch2b_conv"
  top: "res2a_branch2c_conv"
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res2a_branch2c_bn"
  type: "BatchNorm"
  bottom: "res2a_branch2c_conv"
  top: "res2a_branch2c_conv"
   
}
layer {
  name: "res2a_branch2c_sc"
  type: "Scale"
  bottom: "res2a_branch2c_conv"
  top: "res2a_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2a_branch1_conv"
  type: "Convolution"
  bottom: "conv3_conv"
  top: "res2a_branch1_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res2a_branch1_bn"
  type: "BatchNorm"
  bottom: "res2a_branch1_conv"
  top: "res2a_branch1_conv"
   
}
layer {
  name: "res2a_branch1_sc"
  type: "Scale"
  bottom: "res2a_branch1_conv"
  top: "res2a_branch1_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2a"
  type: "Eltwise"
  bottom: "res2a_branch1_conv"
  bottom: "res2a_branch2c_conv"
  top: "res2a"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res2a_relu"
  type: "ReLU"
  bottom: "res2a"
  top: "res2a"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res2b_branch2a_conv"
  type: "Convolution"
  bottom: "res2a"
  top: "res2b_branch2a_conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res2b_branch2a_bn"
  type: "BatchNorm"
  bottom: "res2b_branch2a_conv"
  top: "res2b_branch2a_conv"
   
}
layer {
  name: "res2b_branch2a_sc"
  type: "Scale"
  bottom: "res2b_branch2a_conv"
  top: "res2b_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2b_branch2a_relu"
  type: "ReLU"
  bottom: "res2b_branch2a_conv"
  top: "res2b_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res2b_branch2b_conv"
  type: "Convolution"
  bottom: "res2b_branch2a_conv"
  top: "res2b_branch2b_conv"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res2b_branch2b_bn"
  type: "BatchNorm"
  bottom: "res2b_branch2b_conv"
  top: "res2b_branch2b_conv"
   
}
layer {
  name: "res2b_branch2b_sc"
  type: "Scale"
  bottom: "res2b_branch2b_conv"
  top: "res2b_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2b_branch2b_relu"
  type: "ReLU"
  bottom: "res2b_branch2b_conv"
  top: "res2b_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res2b_branch2c_conv"
  type: "Convolution"
  bottom: "res2b_branch2b_conv"
  top: "res2b_branch2c_conv"
  convolution_param {
    num_output: 128
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res2b_branch2c_bn"
  type: "BatchNorm"
  bottom: "res2b_branch2c_conv"
  top: "res2b_branch2c_conv"
   
}
layer {
  name: "res2b_branch2c_sc"
  type: "Scale"
  bottom: "res2b_branch2c_conv"
  top: "res2b_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res2b"
  type: "Eltwise"
  bottom: "res2a"
  bottom: "res2b_branch2c_conv"
  top: "res2b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res2b_relu"
  type: "ReLU"
  bottom: "res2b"
  top: "res2b"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "res2b"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "res3a_branch2a_conv"
  type: "Convolution"
  bottom: "pool2"
  top: "res3a_branch2a_conv"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res3a_branch2a_bn"
  type: "BatchNorm"
  bottom: "res3a_branch2a_conv"
  top: "res3a_branch2a_conv"
   
}
layer {
  name: "res3a_branch2a_sc"
  type: "Scale"
  bottom: "res3a_branch2a_conv"
  top: "res3a_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3a_branch2a_relu"
  type: "ReLU"
  bottom: "res3a_branch2a_conv"
  top: "res3a_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res3a_branch2b_conv"
  type: "Convolution"
  bottom: "res3a_branch2a_conv"
  top: "res3a_branch2b_conv"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res3a_branch2b_bn"
  type: "BatchNorm"
  bottom: "res3a_branch2b_conv"
  top: "res3a_branch2b_conv"
   
}
layer {
  name: "res3a_branch2b_sc"
  type: "Scale"
  bottom: "res3a_branch2b_conv"
  top: "res3a_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3a_branch2b_relu"
  type: "ReLU"
  bottom: "res3a_branch2b_conv"
  top: "res3a_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res3a_branch2c_conv"
  type: "Convolution"
  bottom: "res3a_branch2b_conv"
  top: "res3a_branch2c_conv"
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res3a_branch2c_bn"
  type: "BatchNorm"
  bottom: "res3a_branch2c_conv"
  top: "res3a_branch2c_conv"
   
}
layer {
  name: "res3a_branch2c_sc"
  type: "Scale"
  bottom: "res3a_branch2c_conv"
  top: "res3a_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3a_branch1_conv"
  type: "Convolution"
  bottom: "pool2"
  top: "res3a_branch1_conv"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res3a_branch1_bn"
  type: "BatchNorm"
  bottom: "res3a_branch1_conv"
  top: "res3a_branch1_conv"
   
}
layer {
  name: "res3a_branch1_sc"
  type: "Scale"
  bottom: "res3a_branch1_conv"
  top: "res3a_branch1_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3a"
  type: "Eltwise"
  bottom: "res3a_branch1_conv"
  bottom: "res3a_branch2c_conv"
  top: "res3a"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res3a_relu"
  type: "ReLU"
  bottom: "res3a"
  top: "res3a"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res3b_branch2a_conv"
  type: "Convolution"
  bottom: "res3a"
  top: "res3b_branch2a_conv"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res3b_branch2a_bn"
  type: "BatchNorm"
  bottom: "res3b_branch2a_conv"
  top: "res3b_branch2a_conv"
   
}
layer {
  name: "res3b_branch2a_sc"
  type: "Scale"
  bottom: "res3b_branch2a_conv"
  top: "res3b_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3b_branch2a_relu"
  type: "ReLU"
  bottom: "res3b_branch2a_conv"
  top: "res3b_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res3b_branch2b_conv"
  type: "Convolution"
  bottom: "res3b_branch2a_conv"
  top: "res3b_branch2b_conv"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res3b_branch2b_bn"
  type: "BatchNorm"
  bottom: "res3b_branch2b_conv"
  top: "res3b_branch2b_conv"
   
}
layer {
  name: "res3b_branch2b_sc"
  type: "Scale"
  bottom: "res3b_branch2b_conv"
  top: "res3b_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3b_branch2b_relu"
  type: "ReLU"
  bottom: "res3b_branch2b_conv"
  top: "res3b_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res3b_branch2c_conv"
  type: "Convolution"
  bottom: "res3b_branch2b_conv"
  top: "res3b_branch2c_conv"
  convolution_param {
    num_output: 256
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res3b_branch2c_bn"
  type: "BatchNorm"
  bottom: "res3b_branch2c_conv"
  top: "res3b_branch2c_conv"
   
}
layer {
  name: "res3b_branch2c_sc"
  type: "Scale"
  bottom: "res3b_branch2c_conv"
  top: "res3b_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res3b"
  type: "Eltwise"
  bottom: "res3a"
  bottom: "res3b_branch2c_conv"
  top: "res3b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res3b_relu"
  type: "ReLU"
  bottom: "res3b"
  top: "res3b"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "res3b"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "res4a_branch2a_conv"
  type: "Convolution"
  bottom: "pool3"
  top: "res4a_branch2a_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res4a_branch2a_bn"
  type: "BatchNorm"
  bottom: "res4a_branch2a_conv"
  top: "res4a_branch2a_conv"
   
}
layer {
  name: "res4a_branch2a_sc"
  type: "Scale"
  bottom: "res4a_branch2a_conv"
  top: "res4a_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4a_branch2a_relu"
  type: "ReLU"
  bottom: "res4a_branch2a_conv"
  top: "res4a_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4a_branch2b_conv"
  type: "Convolution"
  bottom: "res4a_branch2a_conv"
  top: "res4a_branch2b_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res4a_branch2b_bn"
  type: "BatchNorm"
  bottom: "res4a_branch2b_conv"
  top: "res4a_branch2b_conv"
   
}
layer {
  name: "res4a_branch2b_sc"
  type: "Scale"
  bottom: "res4a_branch2b_conv"
  top: "res4a_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4a_branch2b_relu"
  type: "ReLU"
  bottom: "res4a_branch2b_conv"
  top: "res4a_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4a_branch2c_conv"
  type: "Convolution"
  bottom: "res4a_branch2b_conv"
  top: "res4a_branch2c_conv"
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res4a_branch2c_bn"
  type: "BatchNorm"
  bottom: "res4a_branch2c_conv"
  top: "res4a_branch2c_conv"
   
}
layer {
  name: "res4a_branch2c_sc"
  type: "Scale"
  bottom: "res4a_branch2c_conv"
  top: "res4a_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4a_branch1_conv"
  type: "Convolution"
  bottom: "pool3"
  top: "res4a_branch1_conv"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res4a_branch1_bn"
  type: "BatchNorm"
  bottom: "res4a_branch1_conv"
  top: "res4a_branch1_conv"
   
}
layer {
  name: "res4a_branch1_sc"
  type: "Scale"
  bottom: "res4a_branch1_conv"
  top: "res4a_branch1_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4a"
  type: "Eltwise"
  bottom: "res4a_branch1_conv"
  bottom: "res4a_branch2c_conv"
  top: "res4a"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res4a_relu"
  type: "ReLU"
  bottom: "res4a"
  top: "res4a"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4b_branch2a_conv"
  type: "Convolution"
  bottom: "res4a"
  top: "res4b_branch2a_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res4b_branch2a_bn"
  type: "BatchNorm"
  bottom: "res4b_branch2a_conv"
  top: "res4b_branch2a_conv"
   
}
layer {
  name: "res4b_branch2a_sc"
  type: "Scale"
  bottom: "res4b_branch2a_conv"
  top: "res4b_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4b_branch2a_relu"
  type: "ReLU"
  bottom: "res4b_branch2a_conv"
  top: "res4b_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4b_branch2b_conv"
  type: "Convolution"
  bottom: "res4b_branch2a_conv"
  top: "res4b_branch2b_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res4b_branch2b_bn"
  type: "BatchNorm"
  bottom: "res4b_branch2b_conv"
  top: "res4b_branch2b_conv"
   
}
layer {
  name: "res4b_branch2b_sc"
  type: "Scale"
  bottom: "res4b_branch2b_conv"
  top: "res4b_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4b_branch2b_relu"
  type: "ReLU"
  bottom: "res4b_branch2b_conv"
  top: "res4b_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4b_branch2c_conv"
  type: "Convolution"
  bottom: "res4b_branch2b_conv"
  top: "res4b_branch2c_conv"
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res4b_branch2c_bn"
  type: "BatchNorm"
  bottom: "res4b_branch2c_conv"
  top: "res4b_branch2c_conv"
   
}
layer {
  name: "res4b_branch2c_sc"
  type: "Scale"
  bottom: "res4b_branch2c_conv"
  top: "res4b_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4b"
  type: "Eltwise"
  bottom: "res4a"
  bottom: "res4b_branch2c_conv"
  top: "res4b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res4b_relu"
  type: "ReLU"
  bottom: "res4b"
  top: "res4b"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4c_branch2a_conv"
  type: "Convolution"
  bottom: "res4b"
  top: "res4c_branch2a_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res4c_branch2a_bn"
  type: "BatchNorm"
  bottom: "res4c_branch2a_conv"
  top: "res4c_branch2a_conv"
   
}
layer {
  name: "res4c_branch2a_sc"
  type: "Scale"
  bottom: "res4c_branch2a_conv"
  top: "res4c_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4c_branch2a_relu"
  type: "ReLU"
  bottom: "res4c_branch2a_conv"
  top: "res4c_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4c_branch2b_conv"
  type: "Convolution"
  bottom: "res4c_branch2a_conv"
  top: "res4c_branch2b_conv"
   
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 2
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 2
  }
}
layer {
  name: "res4c_branch2b_bn"
  type: "BatchNorm"
  bottom: "res4c_branch2b_conv"
  top: "res4c_branch2b_conv"
   
}
layer {
  name: "res4c_branch2b_sc"
  type: "Scale"
  bottom: "res4c_branch2b_conv"
  top: "res4c_branch2b_conv"
   
   
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4c_branch2b_relu"
  type: "ReLU"
  bottom: "res4c_branch2b_conv"
  top: "res4c_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res4c_branch2c_conv"
  type: "Convolution"
  bottom: "res4c_branch2b_conv"
  top: "res4c_branch2c_conv"
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res4c_branch2c_bn"
  type: "BatchNorm"
  bottom: "res4c_branch2c_conv"
  top: "res4c_branch2c_conv"
   
}
layer {
  name: "res4c_branch2c_sc"
  type: "Scale"
  bottom: "res4c_branch2c_conv"
  top: "res4c_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res4c"
  type: "Eltwise"
  bottom: "res4b"
  bottom: "res4c_branch2c_conv"
  top: "res4c"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res4c_relu"
  type: "ReLU"
  bottom: "res4c"
  top: "res4c"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "res4c"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "res5a_branch2a_conv"
  type: "Convolution"
  bottom: "pool4"
  top: "res5a_branch2a_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res5a_branch2a_bn"
  type: "BatchNorm"
  bottom: "res5a_branch2a_conv"
  top: "res5a_branch2a_conv"
   
}
layer {
  name: "res5a_branch2a_sc"
  type: "Scale"
  bottom: "res5a_branch2a_conv"
  top: "res5a_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5a_branch2a_relu"
  type: "ReLU"
  bottom: "res5a_branch2a_conv"
  top: "res5a_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5a_branch2b_conv"
  type: "Convolution"
  bottom: "res5a_branch2a_conv"
  top: "res5a_branch2b_conv"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res5a_branch2b_bn"
  type: "BatchNorm"
  bottom: "res5a_branch2b_conv"
  top: "res5a_branch2b_conv"
   
}
layer {
  name: "res5a_branch2b_sc"
  type: "Scale"
  bottom: "res5a_branch2b_conv"
  top: "res5a_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5a_branch2b_relu"
  type: "ReLU"
  bottom: "res5a_branch2b_conv"
  top: "res5a_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5a_branch2c_conv"
  type: "Convolution"
  bottom: "res5a_branch2b_conv"
  top: "res5a_branch2c_conv"
  convolution_param {
    num_output: 512
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res5a_branch2c_bn"
  type: "BatchNorm"
  bottom: "res5a_branch2c_conv"
  top: "res5a_branch2c_conv"
   
}
layer {
  name: "res5a_branch2c_sc"
  type: "Scale"
  bottom: "res5a_branch2c_conv"
  top: "res5a_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5a"
  type: "Eltwise"
  bottom: "pool4"
  bottom: "res5a_branch2c_conv"
  top: "res5a"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res5a_relu"
  type: "ReLU"
  bottom: "res5a"
  top: "res5a"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5b_branch2a_conv"
  type: "Convolution"
  bottom: "res5a"
  top: "res5b_branch2a_conv"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res5b_branch2a_bn"
  type: "BatchNorm"
  bottom: "res5b_branch2a_conv"
  top: "res5b_branch2a_conv"
   
}
layer {
  name: "res5b_branch2a_sc"
  type: "Scale"
  bottom: "res5b_branch2a_conv"
  top: "res5b_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5b_branch2a_relu"
  type: "ReLU"
  bottom: "res5b_branch2a_conv"
  top: "res5b_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5b_branch2b_conv"
  type: "Convolution"
  bottom: "res5b_branch2a_conv"
  top: "res5b_branch2b_conv"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res5b_branch2b_bn"
  type: "BatchNorm"
  bottom: "res5b_branch2b_conv"
  top: "res5b_branch2b_conv"
   
}
layer {
  name: "res5b_branch2b_sc"
  type: "Scale"
  bottom: "res5b_branch2b_conv"
  top: "res5b_branch2b_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5b_branch2b_relu"
  type: "ReLU"
  bottom: "res5b_branch2b_conv"
  top: "res5b_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5b_branch2c_conv"
  type: "Convolution"
  bottom: "res5b_branch2b_conv"
  top: "res5b_branch2c_conv"
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res5b_branch2c_bn"
  type: "BatchNorm"
  bottom: "res5b_branch2c_conv"
  top: "res5b_branch2c_conv"
   
}
layer {
  name: "res5b_branch2c_sc"
  type: "Scale"
  bottom: "res5b_branch2c_conv"
  top: "res5b_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5b_branch1_conv"
  type: "Convolution"
  bottom: "res5a"
  top: "res5b_branch1_conv"
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res5b_branch1_bn"
  type: "BatchNorm"
  bottom: "res5b_branch1_conv"
  top: "res5b_branch1_conv"
   
}
layer {
  name: "res5b_branch1_sc"
  type: "Scale"
  bottom: "res5b_branch1_conv"
  top: "res5b_branch1_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5b"
  type: "Eltwise"
  bottom: "res5b_branch1_conv"
  bottom: "res5b_branch2c_conv"
  top: "res5b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res5b_relu"
  type: "ReLU"
  bottom: "res5b"
  top: "res5b"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5c_branch2a_conv"
  type: "Convolution"
  bottom: "res5b"
  top: "res5c_branch2a_conv"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
     
  }
}
layer {
  name: "res5c_branch2a_bn"
  type: "BatchNorm"
  bottom: "res5c_branch2a_conv"
  top: "res5c_branch2a_conv"
   
}
layer {
  name: "res5c_branch2a_sc"
  type: "Scale"
  bottom: "res5c_branch2a_conv"
  top: "res5c_branch2a_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5c_branch2a_relu"
  type: "ReLU"
  bottom: "res5c_branch2a_conv"
  top: "res5c_branch2a_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5c_branch2b_conv"
  type: "Convolution"
  bottom: "res5c_branch2a_conv"
  top: "res5c_branch2b_conv"
   
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 2
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 2
  }
}
layer {
  name: "res5c_branch2b_bn"
  type: "BatchNorm"
  bottom: "res5c_branch2b_conv"
  top: "res5c_branch2b_conv"
   
}
layer {
  name: "res5c_branch2b_sc"
  type: "Scale"
  bottom: "res5c_branch2b_conv"
  top: "res5c_branch2b_conv"
   
   
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5c_branch2b_relu"
  type: "ReLU"
  bottom: "res5c_branch2b_conv"
  top: "res5c_branch2b_conv"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "res5c_branch2c_conv"
  type: "Convolution"
  bottom: "res5c_branch2b_conv"
  top: "res5c_branch2c_conv"
  convolution_param {
    num_output: 1024
    bias_term: true
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "res5c_branch2c_bn"
  type: "BatchNorm"
  bottom: "res5c_branch2c_conv"
  top: "res5c_branch2c_conv"
   
}
layer {
  name: "res5c_branch2c_sc"
  type: "Scale"
  bottom: "res5c_branch2c_conv"
  top: "res5c_branch2c_conv"
  scale_param {
    filler {
      value: 1.0
    }
    bias_term: true
    bias_filler {
      value: 0.0
    }
  }
}
layer {
  name: "res5c"
  type: "Eltwise"
  bottom: "res5b"
  bottom: "res5c_branch2c_conv"
  top: "res5c"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res5c_relu"
  type: "ReLU"
  bottom: "res5c"
  top: "res5c"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv_4x_conv"
  type: "Convolution"
  bottom: "res2b"
  top: "Convolution1"
   
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv_4x_bn"
  type: "BatchNorm"
  bottom: "Convolution1"
  top: "Convolution1"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv_4x_scale"
  type: "Scale"
  bottom: "Convolution1"
  top: "Convolution1"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv_8x_conv"
  type: "Convolution"
  bottom: "res3b"
  top: "Convolution2"
  
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv_8x_bn"
  type: "BatchNorm"
  bottom: "Convolution2"
  top: "Convolution2"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv_8x_scale"
  type: "Scale"
  bottom: "Convolution2"
  top: "Convolution2"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv_16x_conv"
  type: "Convolution"
  bottom: "res4c"
  top: "Convolution3"
  
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv_16x_bn"
  type: "BatchNorm"
  bottom: "Convolution3"
  top: "Convolution3"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv_16x_scale"
  type: "Scale"
  bottom: "Convolution3"
  top: "Convolution3"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_1x1a_conv"
  type: "Convolution"
  bottom: "res5c"
  top: "Convolution4"
  
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "conv6_1x1a_bn"
  type: "BatchNorm"
  bottom: "Convolution4"
  top: "Convolution4"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv6_1x1a_scale"
  type: "Scale"
  bottom: "Convolution4"
  top: "Convolution4"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_1x1a_relu"
  type: "ReLU"
  bottom: "Convolution4"
  top: "Convolution4"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv6_1x1b_b0_kx1_conv"
  type: "Convolution"
  bottom: "Convolution4"
  top: "Convolution5"
  
  convolution_param {
    num_output: 128
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    pad_h: 0
    pad_w: 4
    kernel_h: 1
    kernel_w: 9
  }
}
layer {
  name: "conv6_1x1b_b0_kx1_bn"
  type: "BatchNorm"
  bottom: "Convolution5"
  top: "Convolution5"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv6_1x1b_b0_kx1_scale"
  type: "Scale"
  bottom: "Convolution5"
  top: "Convolution5"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_1x1b_b1_1xk_conv"
  type: "Convolution"
  bottom: "Convolution4"
  top: "Convolution6"
  
  convolution_param {
    num_output: 128
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    pad_h: 4
    pad_w: 0
    kernel_h: 9
    kernel_w: 1
  }
}
layer {
  name: "conv6_1x1b_b1_1xk_bn"
  type: "BatchNorm"
  bottom: "Convolution6"
  top: "Convolution6"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv6_1x1b_b1_1xk_scale"
  type: "Scale"
  bottom: "Convolution6"
  top: "Convolution6"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv6_1x1b_gcn_eltwise"
  type: "Eltwise"
  bottom: "Convolution5"
  bottom: "Convolution6"
  top: "conv6_1x1b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "interp1"
  type: "Interp"
  bottom: "conv6_1x1b"
  top: "conv6_deconv"
  interp_param {
    height: 22
    width: 22
  }
}
layer {
  name: "conv6_eltwise"
  type: "Eltwise"
  bottom: "conv6_deconv"
  bottom: "Convolution3"
  top: "conv6_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv6_relu"
  type: "ReLU"
  bottom: "conv6_eltwise"
  top: "conv6_eltwise"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv7_1x1b_b0_kx1_conv"
  type: "Convolution"
  bottom: "conv6_eltwise"
  top: "Convolution7"
  
  convolution_param {
    num_output: 128
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    pad_h: 0
    pad_w: 4
    kernel_h: 1
    kernel_w: 9
  }
}
layer {
  name: "conv7_1x1b_b0_kx1_bn"
  type: "BatchNorm"
  bottom: "Convolution7"
  top: "Convolution7"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv7_1x1b_b0_kx1_scale"
  type: "Scale"
  bottom: "Convolution7"
  top: "Convolution7"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv7_1x1b_b1_1xk_conv"
  type: "Convolution"
  bottom: "conv6_eltwise"
  top: "Convolution8"
  
  convolution_param {
    num_output: 128
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    pad_h: 4
    pad_w: 0
    kernel_h: 9
    kernel_w: 1
  }
}
layer {
  name: "conv7_1x1b_b1_1xk_bn"
  type: "BatchNorm"
  bottom: "Convolution8"
  top: "Convolution8"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv7_1x1b_b1_1xk_scale"
  type: "Scale"
  bottom: "Convolution8"
  top: "Convolution8"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv7_1x1b_gcn_eltwise"
  type: "Eltwise"
  bottom: "Convolution7"
  bottom: "Convolution8"
  top: "conv7_1x1b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "interp2"
  type: "Interp"
  bottom: "conv7_1x1b"
  top: "conv7_deconv"
  interp_param {
    height: 44
    width: 44
  }
}
layer {
  name: "conv7_eltwise"
  type: "Eltwise"
  bottom: "conv7_deconv"
  bottom: "Convolution2"
  top: "conv7_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv7_relu"
  type: "ReLU"
  bottom: "conv7_eltwise"
  top: "conv7_eltwise"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv8_1x1b_b0_kx1_conv"
  type: "Convolution"
  bottom: "conv7_eltwise"
  top: "Convolution9"
  
  convolution_param {
    num_output: 128
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    pad_h: 0
    pad_w: 4
    kernel_h: 1
    kernel_w: 9
  }
}
layer {
  name: "conv8_1x1b_b0_kx1_bn"
  type: "BatchNorm"
  bottom: "Convolution9"
  top: "Convolution9"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv8_1x1b_b0_kx1_scale"
  type: "Scale"
  bottom: "Convolution9"
  top: "Convolution9"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv8_1x1b_b1_1xk_conv"
  type: "Convolution"
  bottom: "conv7_eltwise"
  top: "Convolution10"
  
  convolution_param {
    num_output: 128
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
    pad_h: 4
    pad_w: 0
    kernel_h: 9
    kernel_w: 1
  }
}
layer {
  name: "conv8_1x1b_b1_1xk_bn"
  type: "BatchNorm"
  bottom: "Convolution10"
  top: "Convolution10"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv8_1x1b_b1_1xk_scale"
  type: "Scale"
  bottom: "Convolution10"
  top: "Convolution10"
  
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv8_1x1b_gcn_eltwise"
  type: "Eltwise"
  bottom: "Convolution9"
  bottom: "Convolution10"
  top: "conv8_1x1b"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "interp3"
  type: "Interp"
  bottom: "conv8_1x1b"
  top: "conv8_deconv"
  interp_param {
    height: 88
    width: 88
  }
}
layer {
  name: "conv8_eltwise"
  type: "Eltwise"
  bottom: "conv8_deconv"
  bottom: "Convolution1"
  top: "conv8_eltwise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv8_relu"
  type: "ReLU"
  bottom: "conv8_eltwise"
  top: "conv8_eltwise"
  relu_param{negative_slope: 0.10000000149}
}
layer {
  name: "conv10_b0_kx1_m_LeakyRelu"
  type: "Convolution"
  bottom: "conv8_eltwise"
  top: "Convolution11"
   
  convolution_param {
    num_output: 11
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
    pad_h: 0
    pad_w: 4
    kernel_h: 1
    kernel_w: 9
  }
}
layer {
  name: "conv10_b0_1xk_m_LeakyRelu"
  type: "Convolution"
  bottom: "conv8_eltwise"
  top: "Convolution12"
  
  convolution_param {
    num_output: 11
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
    pad_h: 4
    pad_w: 0
    kernel_h: 9
    kernel_w: 1
  }
}
layer {
  name: "conv10_lk_eltwise"
  type: "Eltwise"
  bottom: "Convolution11"
  bottom: "Convolution12"
  top: "conv10"
  eltwise_param {
    operation: SUM
  }
}