name: "resnext50_deploy"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    name: "conv1_w"
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    kernel_size: 7
    stride: 2
    pad: 3
    bias_term: false
  }
}
layer {
  name: "conv1_bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1_scale"
  bottom: "conv1"
  top: "conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1_relu"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
    pad: 1
    #ceil_mode: false
  }
}
layer {
  name: "resx1_conv1"
  type: "Convolution"
  bottom: "pool1"
  top: "resx1_conv1"
  
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx1_conv1_bn"
  type: "BatchNorm"
  bottom: "resx1_conv1"
  top: "resx1_conv1" 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx1_conv1_scale"
  bottom: "resx1_conv1"
  top: "resx1_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx1_conv1_relu"
  type: "ReLU"
  bottom: "resx1_conv1"
  top: "resx1_conv1"
}
layer {
  name: "resx1_conv2"
  type: "Convolution"
  bottom: "resx1_conv1"
  top: "resx1_conv2"
  
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx1_conv2_bn"
  type: "BatchNorm"
  bottom: "resx1_conv2"
  top: "resx1_conv2"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx1_conv2_scale"
  bottom: "resx1_conv2"
  top: "resx1_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx1_conv2_relu"
  type: "ReLU"
  bottom: "resx1_conv2"
  top: "resx1_conv2"
}
layer {
  name: "resx1_conv3"
  type: "Convolution"
  bottom: "resx1_conv2"
  top: "resx1_conv3"

  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx1_conv3_bn"
  type: "BatchNorm"
  bottom: "resx1_conv3"
  top: "resx1_conv3"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx1_conv3_scale"
  bottom: "resx1_conv3"
  top: "resx1_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx1_match_conv"
  type: "Convolution"
  bottom: "pool1"
  top: "resx1_match_conv"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx1_match_conv_bn"
  type: "BatchNorm"
  bottom: "resx1_match_conv"
  top: "resx1_match_conv"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx1_match_conv_scale"
  bottom: "resx1_match_conv"
  top: "resx1_match_conv"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx1_elewise"
  type: "Eltwise"
  bottom: "resx1_match_conv"
  bottom: "resx1_conv3"
  top: "resx1_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx1_elewise_relu"
  type: "ReLU"
  bottom: "resx1_elewise"
  top: "resx1_elewise"
}
layer {
  name: "resx2_conv1"
  type: "Convolution"
  bottom: "resx1_elewise"
  top: "resx2_conv1"

  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx2_conv1_bn"
  type: "BatchNorm"
  bottom: "resx2_conv1"
  top: "resx2_conv1"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx2_conv1_scale"
  bottom: "resx2_conv1"
  top: "resx2_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx2_conv1_relu"
  type: "ReLU"
  bottom: "resx2_conv1"
  top: "resx2_conv1"
}
layer {
  name: "resx2_conv2"
  type: "Convolution"
  bottom: "resx2_conv1"
  top: "resx2_conv2"
  
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx2_conv2_bn"
  type: "BatchNorm"
  bottom: "resx2_conv2"
  top: "resx2_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx2_conv2_scale"
  bottom: "resx2_conv2"
  top: "resx2_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx2_conv2_relu"
  type: "ReLU"
  bottom: "resx2_conv2"
  top: "resx2_conv2"
}
layer {
  name: "resx2_conv3"
  type: "Convolution"
  bottom: "resx2_conv2"
  top: "resx2_conv3"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx2_conv3_bn"
  type: "BatchNorm"
  bottom: "resx2_conv3"
  top: "resx2_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx2_conv3_scale"
  bottom: "resx2_conv3"
  top: "resx2_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx2_elewise"
  type: "Eltwise"
  bottom: "resx1_elewise"
  bottom: "resx2_conv3"
  top: "resx2_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx2_elewise_relu"
  type: "ReLU"
  bottom: "resx2_elewise"
  top: "resx2_elewise"
}
layer {
  name: "resx3_conv1"
  type: "Convolution"
  bottom: "resx2_elewise"
  top: "resx3_conv1"

  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx3_conv1_bn"
  type: "BatchNorm"
  bottom: "resx3_conv1"
  top: "resx3_conv1"
    
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx3_conv1_scale"
  bottom: "resx3_conv1"
  top: "resx3_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx3_conv1_relu"
  type: "ReLU"
  bottom: "resx3_conv1"
  top: "resx3_conv1"
}
layer {
  name: "resx3_conv2"
  type: "Convolution"
  bottom: "resx3_conv1"
  top: "resx3_conv2"
  
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx3_conv2_bn"
  type: "BatchNorm"
  bottom: "resx3_conv2"
  top: "resx3_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx3_conv2_scale"
  bottom: "resx3_conv2"
  top: "resx3_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx3_conv2_relu"
  type: "ReLU"
  bottom: "resx3_conv2"
  top: "resx3_conv2"
}
layer {
  name: "resx3_conv3"
  type: "Convolution"
  bottom: "resx3_conv2"
  top: "resx3_conv3"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx3_conv3_bn"
  type: "BatchNorm"
  bottom: "resx3_conv3"
  top: "resx3_conv3"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx3_conv3_scale"
  bottom: "resx3_conv3"
  top: "resx3_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx3_elewise"
  type: "Eltwise"
  bottom: "resx2_elewise"
  bottom: "resx3_conv3"
  top: "resx3_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx3_elewise_relu"
  type: "ReLU"
  bottom: "resx3_elewise"
  top: "resx3_elewise"
}
layer {
  name: "resx4_conv1"
  type: "Convolution"
  bottom: "resx3_elewise"
  top: "resx4_conv1"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx4_conv1_bn"
  type: "BatchNorm"
  bottom: "resx4_conv1"
  top: "resx4_conv1"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx4_conv1_scale"
  bottom: "resx4_conv1"
  top: "resx4_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx4_conv1_relu"
  type: "ReLU"
  bottom: "resx4_conv1"
  top: "resx4_conv1"
}
layer {
  name: "resx4_conv2"
  type: "Convolution"
  bottom: "resx4_conv1"
  top: "resx4_conv2"
  
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 2
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx4_conv2_bn"
  type: "BatchNorm"
  bottom: "resx4_conv2"
  top: "resx4_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx4_conv2_scale"
  bottom: "resx4_conv2"
  top: "resx4_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx4_conv2_relu"
  type: "ReLU"
  bottom: "resx4_conv2"
  top: "resx4_conv2"
}
layer {
  name: "resx4_conv3"
  type: "Convolution"
  bottom: "resx4_conv2"
  top: "resx4_conv3"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx4_conv3_bn"
  type: "BatchNorm"
  bottom: "resx4_conv3"
  top: "resx4_conv3"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx4_conv3_scale"
  bottom: "resx4_conv3"
  top: "resx4_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx4_match_conv"
  type: "Convolution"
  bottom: "resx3_elewise"
  top: "resx4_match_conv"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 2
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx4_match_conv_bn"
  type: "BatchNorm"
  bottom: "resx4_match_conv"
  top: "resx4_match_conv"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx4_match_conv_scale"
  bottom: "resx4_match_conv"
  top: "resx4_match_conv"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx4_elewise"
  type: "Eltwise"
  bottom: "resx4_match_conv"
  bottom: "resx4_conv3"
  top: "resx4_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx4_elewise_relu"
  type: "ReLU"
  bottom: "resx4_elewise"
  top: "resx4_elewise"
}
layer {
  name: "resx5_conv1"
  type: "Convolution"
  bottom: "resx4_elewise"
  top: "resx5_conv1"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx5_conv1_bn"
  type: "BatchNorm"
  bottom: "resx5_conv1"
  top: "resx5_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx5_conv1_scale"
  bottom: "resx5_conv1"
  top: "resx5_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx5_conv1_relu"
  type: "ReLU"
  bottom: "resx5_conv1"
  top: "resx5_conv1"
}
layer {
  name: "resx5_conv2"
  type: "Convolution"
  bottom: "resx5_conv1"
  top: "resx5_conv2"
  
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx5_conv2_bn"
  type: "BatchNorm"
  bottom: "resx5_conv2"
  top: "resx5_conv2"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx5_conv2_scale"
  bottom: "resx5_conv2"
  top: "resx5_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx5_conv2_relu"
  type: "ReLU"
  bottom: "resx5_conv2"
  top: "resx5_conv2"
}
layer {
  name: "resx5_conv3"
  type: "Convolution"
  bottom: "resx5_conv2"
  top: "resx5_conv3"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx5_conv3_bn"
  type: "BatchNorm"
  bottom: "resx5_conv3"
  top: "resx5_conv3"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx5_conv3_scale"
  bottom: "resx5_conv3"
  top: "resx5_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx5_elewise"
  type: "Eltwise"
  bottom: "resx4_elewise"
  bottom: "resx5_conv3"
  top: "resx5_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx5_elewise_relu"
  type: "ReLU"
  bottom: "resx5_elewise"
  top: "resx5_elewise"
}
layer {
  name: "resx6_conv1"
  type: "Convolution"
  bottom: "resx5_elewise"
  top: "resx6_conv1"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx6_conv1_bn"
  type: "BatchNorm"
  bottom: "resx6_conv1"
  top: "resx6_conv1"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx6_conv1_scale"
  bottom: "resx6_conv1"
  top: "resx6_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx6_conv1_relu"
  type: "ReLU"
  bottom: "resx6_conv1"
  top: "resx6_conv1"
}
layer {
  name: "resx6_conv2"
  type: "Convolution"
  bottom: "resx6_conv1"
  top: "resx6_conv2"
  
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx6_conv2_bn"
  type: "BatchNorm"
  bottom: "resx6_conv2"
  top: "resx6_conv2"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx6_conv2_scale"
  bottom: "resx6_conv2"
  top: "resx6_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx6_conv2_relu"
  type: "ReLU"
  bottom: "resx6_conv2"
  top: "resx6_conv2"
}
layer {
  name: "resx6_conv3"
  type: "Convolution"
  bottom: "resx6_conv2"
  top: "resx6_conv3"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx6_conv3_bn"
  type: "BatchNorm"
  bottom: "resx6_conv3"
  top: "resx6_conv3"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx6_conv3_scale"
  bottom: "resx6_conv3"
  top: "resx6_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx6_elewise"
  type: "Eltwise"
  bottom: "resx5_elewise"
  bottom: "resx6_conv3"
  top: "resx6_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx6_elewise_relu"
  type: "ReLU"
  bottom: "resx6_elewise"
  top: "resx6_elewise"
}
layer {
  name: "resx7_conv1"
  type: "Convolution"
  bottom: "resx6_elewise"
  top: "resx7_conv1"
  
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx7_conv1_bn"
  type: "BatchNorm"
  bottom: "resx7_conv1"
  top: "resx7_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx7_conv1_scale"
  bottom: "resx7_conv1"
  top: "resx7_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx7_conv1_relu"
  type: "ReLU"
  bottom: "resx7_conv1"
  top: "resx7_conv1"
}
layer {
  name: "resx7_conv2"
  type: "Convolution"
  bottom: "resx7_conv1"
  top: "resx7_conv2"
  
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx7_conv2_bn"
  type: "BatchNorm"
  bottom: "resx7_conv2"
  top: "resx7_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx7_conv2_scale"
  bottom: "resx7_conv2"
  top: "resx7_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx7_conv2_relu"
  type: "ReLU"
  bottom: "resx7_conv2"
  top: "resx7_conv2"
}
layer {
  name: "resx7_conv3"
  type: "Convolution"
  bottom: "resx7_conv2"
  top: "resx7_conv3"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx7_conv3_bn"
  type: "BatchNorm"
  bottom: "resx7_conv3"
  top: "resx7_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx7_conv3_scale"
  bottom: "resx7_conv3"
  top: "resx7_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx7_elewise"
  type: "Eltwise"
  bottom: "resx6_elewise"
  bottom: "resx7_conv3"
  top: "resx7_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx7_elewise_relu"
  type: "ReLU"
  bottom: "resx7_elewise"
  top: "resx7_elewise"
}
layer {
  name: "resx8_conv1"
  type: "Convolution"
  bottom: "resx7_elewise"
  top: "resx8_conv1"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx8_conv1_bn"
  type: "BatchNorm"
  bottom: "resx8_conv1"
  top: "resx8_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx8_conv1_scale"
  bottom: "resx8_conv1"
  top: "resx8_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx8_conv1_relu"
  type: "ReLU"
  bottom: "resx8_conv1"
  top: "resx8_conv1"
}
layer {
  name: "resx8_conv2"
  type: "Convolution"
  bottom: "resx8_conv1"
  top: "resx8_conv2"
  
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 2
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx8_conv2_bn"
  type: "BatchNorm"
  bottom: "resx8_conv2"
  top: "resx8_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx8_conv2_scale"
  bottom: "resx8_conv2"
  top: "resx8_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx8_conv2_relu"
  type: "ReLU"
  bottom: "resx8_conv2"
  top: "resx8_conv2"
}
layer {
  name: "resx8_conv3"
  type: "Convolution"
  bottom: "resx8_conv2"
  top: "resx8_conv3"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx8_conv3_bn"
  type: "BatchNorm"
  bottom: "resx8_conv3"
  top: "resx8_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx8_conv3_scale"
  bottom: "resx8_conv3"
  top: "resx8_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx8_match_conv"
  type: "Convolution"
  bottom: "resx7_elewise"
  top: "resx8_match_conv"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 2
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx8_match_conv_bn"
  type: "BatchNorm"
  bottom: "resx8_match_conv"
  top: "resx8_match_conv"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx8_match_conv_scale"
  bottom: "resx8_match_conv"
  top: "resx8_match_conv"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx8_elewise"
  type: "Eltwise"
  bottom: "resx8_conv3"
  bottom: "resx8_match_conv"
  top: "resx8_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx8_elewise_relu"
  type: "ReLU"
  bottom: "resx8_elewise"
  top: "resx8_elewise"
}
layer {
  name: "resx9_conv1"
  type: "Convolution"
  bottom: "resx8_elewise"
  top: "resx9_conv1"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx9_conv1_bn"
  type: "BatchNorm"
  bottom: "resx9_conv1"
  top: "resx9_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx9_conv1_scale"
  bottom: "resx9_conv1"
  top: "resx9_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx9_conv1_relu"
  type: "ReLU"
  bottom: "resx9_conv1"
  top: "resx9_conv1"
}
layer {
  name: "resx9_conv2"
  type: "Convolution"
  bottom: "resx9_conv1"
  top: "resx9_conv2"
  
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx9_conv2_bn"
  type: "BatchNorm"
  bottom: "resx9_conv2"
  top: "resx9_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx9_conv2_scale"
  bottom: "resx9_conv2"
  top: "resx9_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx9_conv2_relu"
  type: "ReLU"
  bottom: "resx9_conv2"
  top: "resx9_conv2"
}
layer {
  name: "resx9_conv3"
  type: "Convolution"
  bottom: "resx9_conv2"
  top: "resx9_conv3"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx9_conv3_bn"
  type: "BatchNorm"
  bottom: "resx9_conv3"
  top: "resx9_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx9_conv3_scale"
  bottom: "resx9_conv3"
  top: "resx9_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx9_elewise"
  type: "Eltwise"
  bottom: "resx8_elewise"
  bottom: "resx9_conv3"
  top: "resx9_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx9_elewise_relu"
  type: "ReLU"
  bottom: "resx9_elewise"
  top: "resx9_elewise"
}
layer {
  name: "resx10_conv1"
  type: "Convolution"
  bottom: "resx9_elewise"
  top: "resx10_conv1"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx10_conv1_bn"
  type: "BatchNorm"
  bottom: "resx10_conv1"
  top: "resx10_conv1"
  
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx10_conv1_scale"
  bottom: "resx10_conv1"
  top: "resx10_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx10_conv1_relu"
  type: "ReLU"
  bottom: "resx10_conv1"
  top: "resx10_conv1"
}
layer {
  name: "resx10_conv2"
  type: "Convolution"
  bottom: "resx10_conv1"
  top: "resx10_conv2"
  
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx10_conv2_bn"
  type: "BatchNorm"
  bottom: "resx10_conv2"
  top: "resx10_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx10_conv2_scale"
  bottom: "resx10_conv2"
  top: "resx10_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx10_conv2_relu"
  type: "ReLU"
  bottom: "resx10_conv2"
  top: "resx10_conv2"
}
layer {
  name: "resx10_conv3"
  type: "Convolution"
  bottom: "resx10_conv2"
  top: "resx10_conv3"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx10_conv3_bn"
  type: "BatchNorm"
  bottom: "resx10_conv3"
  top: "resx10_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx10_conv3_scale"
  bottom: "resx10_conv3"
  top: "resx10_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx10_elewise"
  type: "Eltwise"
  bottom: "resx9_elewise"
  bottom: "resx10_conv3"
  top: "resx10_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx10_elewise_relu"
  type: "ReLU"
  bottom: "resx10_elewise"
  top: "resx10_elewise"
}
layer {
  name: "resx11_conv1"
  type: "Convolution"
  bottom: "resx10_elewise"
  top: "resx11_conv1"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx11_conv1_bn"
  type: "BatchNorm"
  bottom: "resx11_conv1"
  top: "resx11_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx11_conv1_scale"
  bottom: "resx11_conv1"
  top: "resx11_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx11_conv1_relu"
  type: "ReLU"
  bottom: "resx11_conv1"
  top: "resx11_conv1"
}
layer {
  name: "resx11_conv2"
  type: "Convolution"
  bottom: "resx11_conv1"
  top: "resx11_conv2"
  
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx11_conv2_bn"
  type: "BatchNorm"
  bottom: "resx11_conv2"
  top: "resx11_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx11_conv2_scale"
  bottom: "resx11_conv2"
  top: "resx11_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx11_conv2_relu"
  type: "ReLU"
  bottom: "resx11_conv2"
  top: "resx11_conv2"
}
layer {
  name: "resx11_conv3"
  type: "Convolution"
  bottom: "resx11_conv2"
  top: "resx11_conv3"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx11_conv3_bn"
  type: "BatchNorm"
  bottom: "resx11_conv3"
  top: "resx11_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx11_conv3_scale"
  bottom: "resx11_conv3"
  top: "resx11_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx11_elewise"
  type: "Eltwise"
  bottom: "resx10_elewise"
  bottom: "resx11_conv3"
  top: "resx11_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx11_elewise_relu"
  type: "ReLU"
  bottom: "resx11_elewise"
  top: "resx11_elewise"
}
layer {
  name: "resx12_conv1"
  type: "Convolution"
  bottom: "resx11_elewise"
  top: "resx12_conv1"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx12_conv1_bn"
  type: "BatchNorm"
  bottom: "resx12_conv1"
  top: "resx12_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx12_conv1_scale"
  bottom: "resx12_conv1"
  top: "resx12_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx12_conv1_relu"
  type: "ReLU"
  bottom: "resx12_conv1"
  top: "resx12_conv1"
}
layer {
  name: "resx12_conv2"
  type: "Convolution"
  bottom: "resx12_conv1"
  top: "resx12_conv2"
  
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx12_conv2_bn"
  type: "BatchNorm"
  bottom: "resx12_conv2"
  top: "resx12_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx12_conv2_scale"
  bottom: "resx12_conv2"
  top: "resx12_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx12_conv2_relu"
  type: "ReLU"
  bottom: "resx12_conv2"
  top: "resx12_conv2"
}
layer {
  name: "resx12_conv3"
  type: "Convolution"
  bottom: "resx12_conv2"
  top: "resx12_conv3"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx12_conv3_bn"
  type: "BatchNorm"
  bottom: "resx12_conv3"
  top: "resx12_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx12_conv3_scale"
  bottom: "resx12_conv3"
  top: "resx12_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx12_elewise"
  type: "Eltwise"
  bottom: "resx11_elewise"
  bottom: "resx12_conv3"
  top: "resx12_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx12_elewise_relu"
  type: "ReLU"
  bottom: "resx12_elewise"
  top: "resx12_elewise"
}
layer {
  name: "resx13_conv1"
  type: "Convolution"
  bottom: "resx12_elewise"
  top: "resx13_conv1"
  
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx13_conv1_bn"
  type: "BatchNorm"
  bottom: "resx13_conv1"
  top: "resx13_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx13_conv1_scale"
  bottom: "resx13_conv1"
  top: "resx13_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx13_conv1_relu"
  type: "ReLU"
  bottom: "resx13_conv1"
  top: "resx13_conv1"
}
layer {
  name: "resx13_conv2"
  type: "Convolution"
  bottom: "resx13_conv1"
  top: "resx13_conv2"
  
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx13_conv2_bn"
  type: "BatchNorm"
  bottom: "resx13_conv2"
  top: "resx13_conv2"
 
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx13_conv2_scale"
  bottom: "resx13_conv2"
  top: "resx13_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx13_conv2_relu"
  type: "ReLU"
  bottom: "resx13_conv2"
  top: "resx13_conv2"
}
layer {
  name: "resx13_conv3"
  type: "Convolution"
  bottom: "resx13_conv2"
  top: "resx13_conv3"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx13_conv3_bn"
  type: "BatchNorm"
  bottom: "resx13_conv3"
  top: "resx13_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx13_conv3_scale"
  bottom: "resx13_conv3"
  top: "resx13_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx13_elewise"
  type: "Eltwise"
  bottom: "resx12_elewise"
  bottom: "resx13_conv3"
  top: "resx13_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx13_elewise_relu"
  type: "ReLU"
  bottom: "resx13_elewise"
  top: "resx13_elewise"
}
layer {
  name: "resx14_conv1"
  type: "Convolution"
  bottom: "resx13_elewise"
  top: "resx14_conv1"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx14_conv1_bn"
  type: "BatchNorm"
  bottom: "resx14_conv1"
  top: "resx14_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx14_conv1_scale"
  bottom: "resx14_conv1"
  top: "resx14_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx14_conv1_relu"
  type: "ReLU"
  bottom: "resx14_conv1"
  top: "resx14_conv1"
}
layer {
  name: "resx14_conv2"
  type: "Convolution"
  bottom: "resx14_conv1"
  top: "resx14_conv2"
  
  convolution_param {
    num_output: 1024
    kernel_size: 3
    stride: 2
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx14_conv2_bn"
  type: "BatchNorm"
  bottom: "resx14_conv2"
  top: "resx14_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx14_conv2_scale"
  bottom: "resx14_conv2"
  top: "resx14_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx14_conv2_relu"
  type: "ReLU"
  bottom: "resx14_conv2"
  top: "resx14_conv2"
}
layer {
  name: "resx14_conv3"
  type: "Convolution"
  bottom: "resx14_conv2"
  top: "resx14_conv3"
  
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx14_conv3_bn"
  type: "BatchNorm"
  bottom: "resx14_conv3"
  top: "resx14_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx14_conv3_scale"
  bottom: "resx14_conv3"
  top: "resx14_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx14_match_conv"
  type: "Convolution"
  bottom: "resx13_elewise"
  top: "resx14_match_conv"
  
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 2
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx14_match_conv_bn"
  type: "BatchNorm"
  bottom: "resx14_match_conv"
  top: "resx14_match_conv"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx14_match_conv_scale"
  bottom: "resx14_match_conv"
  top: "resx14_match_conv"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx14_elewise"
  type: "Eltwise"
  bottom: "resx14_match_conv"
  bottom: "resx14_conv3"
  top: "resx14_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx14_elewise_relu"
  type: "ReLU"
  bottom: "resx14_elewise"
  top: "resx14_elewise"
}
layer {
  name: "resx15_conv1"
  type: "Convolution"
  bottom: "resx14_elewise"
  top: "resx15_conv1"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx15_conv1_bn"
  type: "BatchNorm"
  bottom: "resx15_conv1"
  top: "resx15_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx15_conv1_scale"
  bottom: "resx15_conv1"
  top: "resx15_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx15_conv1_relu"
  type: "ReLU"
  bottom: "resx15_conv1"
  top: "resx15_conv1"
}
layer {
  name: "resx15_conv2"
  type: "Convolution"
  bottom: "resx15_conv1"
  top: "resx15_conv2"
  
  convolution_param {
    num_output: 1024
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx15_conv2_bn"
  type: "BatchNorm"
  bottom: "resx15_conv2"
  top: "resx15_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx15_conv2_scale"
  bottom: "resx15_conv2"
  top: "resx15_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx15_conv2_relu"
  type: "ReLU"
  bottom: "resx15_conv2"
  top: "resx15_conv2"
}
layer {
  name: "resx15_conv3"
  type: "Convolution"
  bottom: "resx15_conv2"
  top: "resx15_conv3"
  
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx15_conv3_bn"
  type: "BatchNorm"
  bottom: "resx15_conv3"
  top: "resx15_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx15_conv3_scale"
  bottom: "resx15_conv3"
  top: "resx15_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx15_elewise"
  type: "Eltwise"
  bottom: "resx14_elewise"
  bottom: "resx15_conv3"
  top: "resx15_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx15_elewise_relu"
  type: "ReLU"
  bottom: "resx15_elewise"
  top: "resx15_elewise"
}
layer {
  name: "resx16_conv1"
  type: "Convolution"
  bottom: "resx15_elewise"
  top: "resx16_conv1"
  
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx16_conv1_bn"
  type: "BatchNorm"
  bottom: "resx16_conv1"
  top: "resx16_conv1"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx16_conv1_scale"
  bottom: "resx16_conv1"
  top: "resx16_conv1"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx16_conv1_relu"
  type: "ReLU"
  bottom: "resx16_conv1"
  top: "resx16_conv1"
}
layer {
  name: "resx16_conv2"
  type: "Convolution"
  bottom: "resx16_conv1"
  top: "resx16_conv2"
  
  convolution_param {
    num_output: 1024
    kernel_size: 3
    stride: 1
    group: 32
    pad: 1
    bias_term: false
  }
}
layer {
  name: "resx16_conv2_bn"
  type: "BatchNorm"
  bottom: "resx16_conv2"
  top: "resx16_conv2"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx16_conv2_scale"
  bottom: "resx16_conv2"
  top: "resx16_conv2"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx16_conv2_relu"
  type: "ReLU"
  bottom: "resx16_conv2"
  top: "resx16_conv2"
}
layer {
  name: "resx16_conv3"
  type: "Convolution"
  bottom: "resx16_conv2"
  top: "resx16_conv3"
  
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 1
    pad: 0
    bias_term: false
  }
}
layer {
  name: "resx16_conv3_bn"
  type: "BatchNorm"
  bottom: "resx16_conv3"
  top: "resx16_conv3"

  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "resx16_conv3_scale"
  bottom: "resx16_conv3"
  top: "resx16_conv3"
  type: "Scale"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "resx16_elewise"
  type: "Eltwise"
  bottom: "resx15_elewise"
  bottom: "resx16_conv3"
  top: "resx16_elewise"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "resx16_elewise_relu"
  type: "ReLU"
  bottom: "resx16_elewise"
  top: "resx16_elewise"
}
layer {
  name: "pool_ave"
  type: "Pooling"
  bottom: "resx16_elewise"
  top: "pool_ave"
  pooling_param {
    global_pooling : true
    pool: AVE
  }
}


layer {
  name: "feat1"
  type: "InnerProduct"
  bottom: "pool_ave"
  top: "feat1"
  
  inner_product_param {
    num_output: 1
  }
}