mnet.prototxt

layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param {
    shape {
      dim: 1
      dim: 3
      dim: 1024     #显存不够 改小一点
      dim: 1980     #同上
    }
  }
}
layer {
  name: "mobilenet0_conv0"
  type: "Convolution"
  bottom: "data"
  top: "mobilenet0_conv0"
  convolution_param {
    num_output: 8
    bias_term: false
    group: 1
    stride: 2
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm0"
  type: "BatchNorm"
  bottom: "mobilenet0_conv0"
  top: "mobilenet0_batchnorm0"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm0_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm0"
  top: "mobilenet0_batchnorm0"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu0"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm0"
  top: "mobilenet0_batchnorm0"
}
layer {
  name: "mobilenet0_conv1"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm0"
  top: "mobilenet0_conv1"
  convolution_param {
    num_output: 8
    bias_term: false
    group: 8
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm1"
  type: "BatchNorm"
  bottom: "mobilenet0_conv1"
  top: "mobilenet0_batchnorm1"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm1_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm1"
  top: "mobilenet0_batchnorm1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu1"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm1"
  top: "mobilenet0_batchnorm1"
}
layer {
  name: "mobilenet0_conv2"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm1"
  top: "mobilenet0_conv2"
  convolution_param {
    num_output: 16
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm2"
  type: "BatchNorm"
  bottom: "mobilenet0_conv2"
  top: "mobilenet0_batchnorm2"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm2_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm2"
  top: "mobilenet0_batchnorm2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu2"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm2"
  top: "mobilenet0_batchnorm2"
}
layer {
  name: "mobilenet0_conv3"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm2"
  top: "mobilenet0_conv3"
  convolution_param {
    num_output: 16
    bias_term: false
    group: 16
    stride: 2
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm3"
  type: "BatchNorm"
  bottom: "mobilenet0_conv3"
  top: "mobilenet0_batchnorm3"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm3_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm3"
  top: "mobilenet0_batchnorm3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu3"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm3"
  top: "mobilenet0_batchnorm3"
}
layer {
  name: "mobilenet0_conv4"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm3"
  top: "mobilenet0_conv4"
  convolution_param {
    num_output: 32
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm4"
  type: "BatchNorm"
  bottom: "mobilenet0_conv4"
  top: "mobilenet0_batchnorm4"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm4_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm4"
  top: "mobilenet0_batchnorm4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu4"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm4"
  top: "mobilenet0_batchnorm4"
}
layer {
  name: "mobilenet0_conv5"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm4"
  top: "mobilenet0_conv5"
  convolution_param {
    num_output: 32
    bias_term: false
    group: 32
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm5"
  type: "BatchNorm"
  bottom: "mobilenet0_conv5"
  top: "mobilenet0_batchnorm5"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm5_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm5"
  top: "mobilenet0_batchnorm5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu5"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm5"
  top: "mobilenet0_batchnorm5"
}
layer {
  name: "mobilenet0_conv6"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm5"
  top: "mobilenet0_conv6"
  convolution_param {
    num_output: 32
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm6"
  type: "BatchNorm"
  bottom: "mobilenet0_conv6"
  top: "mobilenet0_batchnorm6"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm6_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm6"
  top: "mobilenet0_batchnorm6"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu6"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm6"
  top: "mobilenet0_batchnorm6"
}
layer {
  name: "mobilenet0_conv7"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm6"
  top: "mobilenet0_conv7"
  convolution_param {
    num_output: 32
    bias_term: false
    group: 32
    stride: 2
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm7"
  type: "BatchNorm"
  bottom: "mobilenet0_conv7"
  top: "mobilenet0_batchnorm7"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm7_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm7"
  top: "mobilenet0_batchnorm7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu7"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm7"
  top: "mobilenet0_batchnorm7"
}
layer {
  name: "mobilenet0_conv8"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm7"
  top: "mobilenet0_conv8"
  convolution_param {
    num_output: 64
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm8"
  type: "BatchNorm"
  bottom: "mobilenet0_conv8"
  top: "mobilenet0_batchnorm8"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm8_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm8"
  top: "mobilenet0_batchnorm8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu8"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm8"
  top: "mobilenet0_batchnorm8"
}
layer {
  name: "mobilenet0_conv9"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm8"
  top: "mobilenet0_conv9"
  convolution_param {
    num_output: 64
    bias_term: false
    group: 64
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm9"
  type: "BatchNorm"
  bottom: "mobilenet0_conv9"
  top: "mobilenet0_batchnorm9"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm9_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm9"
  top: "mobilenet0_batchnorm9"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu9"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm9"
  top: "mobilenet0_batchnorm9"
}
layer {
  name: "mobilenet0_conv10"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm9"
  top: "mobilenet0_conv10"
  convolution_param {
    num_output: 64
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm10"
  type: "BatchNorm"
  bottom: "mobilenet0_conv10"
  top: "mobilenet0_batchnorm10"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm10_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm10"
  top: "mobilenet0_batchnorm10"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu10"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm10"
  top: "mobilenet0_batchnorm10"
}
layer {
  name: "mobilenet0_conv11"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm10"
  top: "mobilenet0_conv11"
  convolution_param {
    num_output: 64
    bias_term: false
    group: 64
    stride: 2
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c1_red_conv"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm10"
  top: "rf_c1_red_conv"
  convolution_param {
    num_output: 64
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm11"
  type: "BatchNorm"
  bottom: "mobilenet0_conv11"
  top: "mobilenet0_batchnorm11"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm11_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm11"
  top: "mobilenet0_batchnorm11"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c1_red_conv_bn"
  type: "BatchNorm"
  bottom: "rf_c1_red_conv"
  top: "rf_c1_red_conv_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_red_conv_bn_scale"
  type: "Scale"
  bottom: "rf_c1_red_conv_bn"
  top: "rf_c1_red_conv_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu11"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm11"
  top: "mobilenet0_batchnorm11"
}
layer {
  name: "rf_c1_red_conv_relu"
  type: "ReLU"
  bottom: "rf_c1_red_conv_bn"
  top: "rf_c1_red_conv_bn"
}
layer {
  name: "mobilenet0_conv12"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm11"
  top: "mobilenet0_conv12"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm12"
  type: "BatchNorm"
  bottom: "mobilenet0_conv12"
  top: "mobilenet0_batchnorm12"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm12_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm12"
  top: "mobilenet0_batchnorm12"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu12"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm12"
  top: "mobilenet0_batchnorm12"
}
layer {
  name: "mobilenet0_conv13"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm12"
  top: "mobilenet0_conv13"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 128
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm13"
  type: "BatchNorm"
  bottom: "mobilenet0_conv13"
  top: "mobilenet0_batchnorm13"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm13_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm13"
  top: "mobilenet0_batchnorm13"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu13"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm13"
  top: "mobilenet0_batchnorm13"
}
layer {
  name: "mobilenet0_conv14"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm13"
  top: "mobilenet0_conv14"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm14"
  type: "BatchNorm"
  bottom: "mobilenet0_conv14"
  top: "mobilenet0_batchnorm14"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm14_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm14"
  top: "mobilenet0_batchnorm14"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu14"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm14"
  top: "mobilenet0_batchnorm14"
}
layer {
  name: "mobilenet0_conv15"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm14"
  top: "mobilenet0_conv15"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 128
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm15"
  type: "BatchNorm"
  bottom: "mobilenet0_conv15"
  top: "mobilenet0_batchnorm15"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm15_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm15"
  top: "mobilenet0_batchnorm15"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu15"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm15"
  top: "mobilenet0_batchnorm15"
}
layer {
  name: "mobilenet0_conv16"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm15"
  top: "mobilenet0_conv16"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm16"
  type: "BatchNorm"
  bottom: "mobilenet0_conv16"
  top: "mobilenet0_batchnorm16"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm16_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm16"
  top: "mobilenet0_batchnorm16"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu16"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm16"
  top: "mobilenet0_batchnorm16"
}
layer {
  name: "mobilenet0_conv17"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm16"
  top: "mobilenet0_conv17"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 128
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm17"
  type: "BatchNorm"
  bottom: "mobilenet0_conv17"
  top: "mobilenet0_batchnorm17"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm17_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm17"
  top: "mobilenet0_batchnorm17"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu17"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm17"
  top: "mobilenet0_batchnorm17"
}
layer {
  name: "mobilenet0_conv18"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm17"
  top: "mobilenet0_conv18"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm18"
  type: "BatchNorm"
  bottom: "mobilenet0_conv18"
  top: "mobilenet0_batchnorm18"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm18_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm18"
  top: "mobilenet0_batchnorm18"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu18"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm18"
  top: "mobilenet0_batchnorm18"
}
layer {
  name: "mobilenet0_conv19"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm18"
  top: "mobilenet0_conv19"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 128
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm19"
  type: "BatchNorm"
  bottom: "mobilenet0_conv19"
  top: "mobilenet0_batchnorm19"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm19_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm19"
  top: "mobilenet0_batchnorm19"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu19"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm19"
  top: "mobilenet0_batchnorm19"
}
layer {
  name: "mobilenet0_conv20"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm19"
  top: "mobilenet0_conv20"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm20"
  type: "BatchNorm"
  bottom: "mobilenet0_conv20"
  top: "mobilenet0_batchnorm20"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm20_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm20"
  top: "mobilenet0_batchnorm20"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu20"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm20"
  top: "mobilenet0_batchnorm20"
}
layer {
  name: "mobilenet0_conv21"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm20"
  top: "mobilenet0_conv21"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 128
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm21"
  type: "BatchNorm"
  bottom: "mobilenet0_conv21"
  top: "mobilenet0_batchnorm21"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm21_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm21"
  top: "mobilenet0_batchnorm21"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu21"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm21"
  top: "mobilenet0_batchnorm21"
}
layer {
  name: "mobilenet0_conv22"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm21"
  top: "mobilenet0_conv22"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm22"
  type: "BatchNorm"
  bottom: "mobilenet0_conv22"
  top: "mobilenet0_batchnorm22"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm22_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm22"
  top: "mobilenet0_batchnorm22"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu22"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm22"
  top: "mobilenet0_batchnorm22"
}
layer {
  name: "mobilenet0_conv23"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm22"
  top: "mobilenet0_conv23"
  convolution_param {
    num_output: 128
    bias_term: false
    group: 128
    stride: 2
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c2_lateral"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm22"
  top: "rf_c2_lateral"
  convolution_param {
    num_output: 64
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm23"
  type: "BatchNorm"
  bottom: "mobilenet0_conv23"
  top: "mobilenet0_batchnorm23"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm23_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm23"
  top: "mobilenet0_batchnorm23"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c2_lateral_bn"
  type: "BatchNorm"
  bottom: "rf_c2_lateral"
  top: "rf_c2_lateral_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_lateral_bn_scale"
  type: "Scale"
  bottom: "rf_c2_lateral_bn"
  top: "rf_c2_lateral_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu23"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm23"
  top: "mobilenet0_batchnorm23"
}
layer {
  name: "rf_c2_lateral_relu"
  type: "ReLU"
  bottom: "rf_c2_lateral_bn"
  top: "rf_c2_lateral_bn"
}
layer {
  name: "mobilenet0_conv24"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm23"
  top: "mobilenet0_conv24"
  convolution_param {
    num_output: 256
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm24"
  type: "BatchNorm"
  bottom: "mobilenet0_conv24"
  top: "mobilenet0_batchnorm24"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm24_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm24"
  top: "mobilenet0_batchnorm24"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu24"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm24"
  top: "mobilenet0_batchnorm24"
}
layer {
  name: "mobilenet0_conv25"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm24"
  top: "mobilenet0_conv25"
  convolution_param {
    num_output: 256
    bias_term: false
    group: 256
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "mobilenet0_batchnorm25"
  type: "BatchNorm"
  bottom: "mobilenet0_conv25"
  top: "mobilenet0_batchnorm25"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm25_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm25"
  top: "mobilenet0_batchnorm25"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu25"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm25"
  top: "mobilenet0_batchnorm25"
}
layer {
  name: "mobilenet0_conv26"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm25"
  top: "mobilenet0_conv26"
  convolution_param {
    num_output: 256
    bias_term: false
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "mobilenet0_batchnorm26"
  type: "BatchNorm"
  bottom: "mobilenet0_conv26"
  top: "mobilenet0_batchnorm26"
  batch_norm_param {
    use_global_stats: true
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "mobilenet0_batchnorm26_scale"
  type: "Scale"
  bottom: "mobilenet0_batchnorm26"
  top: "mobilenet0_batchnorm26"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "mobilenet0_relu26"
  type: "ReLU"
  bottom: "mobilenet0_batchnorm26"
  top: "mobilenet0_batchnorm26"
}
layer {
  name: "rf_c3_lateral"
  type: "Convolution"
  bottom: "mobilenet0_batchnorm26"
  top: "rf_c3_lateral"
  convolution_param {
    num_output: 64
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "rf_c3_lateral_bn"
  type: "BatchNorm"
  bottom: "rf_c3_lateral"
  top: "rf_c3_lateral_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c3_lateral_bn_scale"
  type: "Scale"
  bottom: "rf_c3_lateral_bn"
  top: "rf_c3_lateral_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c3_lateral_relu"
  type: "ReLU"
  bottom: "rf_c3_lateral_bn"
  top: "rf_c3_lateral_bn"
}
layer {
  name: "rf_c3_det_conv1"
  type: "Convolution"
  bottom: "rf_c3_lateral_bn"
  top: "rf_c3_det_conv1"
  convolution_param {
    num_output: 32
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c3_det_context_conv1"
  type: "Convolution"
  bottom: "rf_c3_lateral_bn"
  top: "rf_c3_det_context_conv1"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c3_upsampling_"
  type: "Deconvolution"
  bottom: "rf_c3_lateral_bn"
  top: "rf_c3_upsampling"
  param {
    lr_mult: 0.0
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 2
    group: 64
    stride: 2
    dilation: 1
    weight_filler: { 
        type: "constant"
        value: 1
    }
  }
}
layer {
  name: "rf_c3_det_conv1_bn"
  type: "BatchNorm"
  bottom: "rf_c3_det_conv1"
  top: "rf_c3_det_conv1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c3_det_conv1_bn_scale"
  type: "Scale"
  bottom: "rf_c3_det_conv1_bn"
  top: "rf_c3_det_conv1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c3_det_context_conv1_bn"
  type: "BatchNorm"
  bottom: "rf_c3_det_context_conv1"
  top: "rf_c3_det_context_conv1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c3_det_context_conv1_bn_scale"
  type: "Scale"
  bottom: "rf_c3_det_context_conv1_bn"
  top: "rf_c3_det_context_conv1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "crop0"
  type: "Crop"
  bottom: "rf_c3_upsampling"
  bottom: "rf_c2_lateral_bn"
  top: "crop0"
}
layer {
  name: "rf_c3_det_context_conv1_relu"
  type: "ReLU"
  bottom: "rf_c3_det_context_conv1_bn"
  top: "rf_c3_det_context_conv1_bn"
}
layer {
  name: "plus0"
  type: "Eltwise"
  bottom: "rf_c2_lateral_bn"
  bottom: "crop0"
  top: "plus0"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "rf_c3_det_context_conv2"
  type: "Convolution"
  bottom: "rf_c3_det_context_conv1_bn"
  top: "rf_c3_det_context_conv2"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c3_det_context_conv3_1"
  type: "Convolution"
  bottom: "rf_c3_det_context_conv1_bn"
  top: "rf_c3_det_context_conv3_1"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c2_aggr"
  type: "Convolution"
  bottom: "plus0"
  top: "rf_c2_aggr"
  convolution_param {
    num_output: 64
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c3_det_context_conv2_bn"
  type: "BatchNorm"
  bottom: "rf_c3_det_context_conv2"
  top: "rf_c3_det_context_conv2_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c3_det_context_conv2_bn_scale"
  type: "Scale"
  bottom: "rf_c3_det_context_conv2_bn"
  top: "rf_c3_det_context_conv2_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c3_det_context_conv3_1_bn"
  type: "BatchNorm"
  bottom: "rf_c3_det_context_conv3_1"
  top: "rf_c3_det_context_conv3_1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c3_det_context_conv3_1_bn_scale"
  type: "Scale"
  bottom: "rf_c3_det_context_conv3_1_bn"
  top: "rf_c3_det_context_conv3_1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c2_aggr_bn"
  type: "BatchNorm"
  bottom: "rf_c2_aggr"
  top: "rf_c2_aggr_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_aggr_bn_scale"
  type: "Scale"
  bottom: "rf_c2_aggr_bn"
  top: "rf_c2_aggr_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c3_det_context_conv3_1_relu"
  type: "ReLU"
  bottom: "rf_c3_det_context_conv3_1_bn"
  top: "rf_c3_det_context_conv3_1_bn"
}
layer {
  name: "rf_c2_aggr_relu"
  type: "ReLU"
  bottom: "rf_c2_aggr_bn"
  top: "rf_c2_aggr_bn"
}
layer {
  name: "rf_c3_det_context_conv3_2"
  type: "Convolution"
  bottom: "rf_c3_det_context_conv3_1_bn"
  top: "rf_c3_det_context_conv3_2"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c2_det_conv1"
  type: "Convolution"
  bottom: "rf_c2_aggr_bn"
  top: "rf_c2_det_conv1"
  convolution_param {
    num_output: 32
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c2_det_context_conv1"
  type: "Convolution"
  bottom: "rf_c2_aggr_bn"
  top: "rf_c2_det_context_conv1"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c2_upsampling_"
  type: "Deconvolution"
  bottom: "rf_c2_aggr_bn"
  top: "rf_c2_upsampling"
  param {
    lr_mult: 0.0
  }
  convolution_param {
     num_output: 64
     bias_term: false
     pad: 0
     kernel_size: 2
     group: 64
     stride: 2
     dilation: 1
     weight_filler: {
         type: "constant"
         value: 1
     }
  }
}
layer {
  name: "rf_c3_det_context_conv3_2_bn"
  type: "BatchNorm"
  bottom: "rf_c3_det_context_conv3_2"
  top: "rf_c3_det_context_conv3_2_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c3_det_context_conv3_2_bn_scale"
  type: "Scale"
  bottom: "rf_c3_det_context_conv3_2_bn"
  top: "rf_c3_det_context_conv3_2_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c2_det_conv1_bn"
  type: "BatchNorm"
  bottom: "rf_c2_det_conv1"
  top: "rf_c2_det_conv1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_det_conv1_bn_scale"
  type: "Scale"
  bottom: "rf_c2_det_conv1_bn"
  top: "rf_c2_det_conv1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c2_det_context_conv1_bn"
  type: "BatchNorm"
  bottom: "rf_c2_det_context_conv1"
  top: "rf_c2_det_context_conv1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_det_context_conv1_bn_scale"
  type: "Scale"
  bottom: "rf_c2_det_context_conv1_bn"
  top: "rf_c2_det_context_conv1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "crop1"
  type: "Crop"
  bottom: "rf_c2_upsampling"
  bottom: "rf_c1_red_conv_bn"
  top: "crop1"
}
layer {
  name: "rf_c3_det_concat"
  type: "Concat"
  bottom: "rf_c3_det_conv1_bn"
  bottom: "rf_c3_det_context_conv2_bn"
  bottom: "rf_c3_det_context_conv3_2_bn"
  top: "rf_c3_det_concat"
  concat_param {
    axis: 1
  }
}
layer {
  name: "rf_c2_det_context_conv1_relu"
  type: "ReLU"
  bottom: "rf_c2_det_context_conv1_bn"
  top: "rf_c2_det_context_conv1_bn"
}
layer {
  name: "plus1"
  type: "Eltwise"
  bottom: "rf_c1_red_conv_bn"
  bottom: "crop1"
  top: "plus1"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "rf_c3_det_concat_relu"
  type: "ReLU"
  bottom: "rf_c3_det_concat"
  top: "rf_c3_det_concat"
}
layer {
  name: "rf_c2_det_context_conv2"
  type: "Convolution"
  bottom: "rf_c2_det_context_conv1_bn"
  top: "rf_c2_det_context_conv2"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c2_det_context_conv3_1"
  type: "Convolution"
  bottom: "rf_c2_det_context_conv1_bn"
  top: "rf_c2_det_context_conv3_1"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c1_aggr"
  type: "Convolution"
  bottom: "plus1"
  top: "rf_c1_aggr"
  convolution_param {
    num_output: 64
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "face_rpn_cls_score_stride32"
  type: "Convolution"
  bottom: "rf_c3_det_concat"
  top: "face_rpn_cls_score_stride32"
  convolution_param {
    num_output: 4
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_bbox_pred_stride32"
  type: "Convolution"
  bottom: "rf_c3_det_concat"
  top: "face_rpn_bbox_pred_stride32"
  convolution_param {
    num_output: 8
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_landmark_pred_stride32"
  type: "Convolution"
  bottom: "rf_c3_det_concat"
  top: "face_rpn_landmark_pred_stride32"
  convolution_param {
    num_output: 20
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "rf_c2_det_context_conv2_bn"
  type: "BatchNorm"
  bottom: "rf_c2_det_context_conv2"
  top: "rf_c2_det_context_conv2_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_det_context_conv2_bn_scale"
  type: "Scale"
  bottom: "rf_c2_det_context_conv2_bn"
  top: "rf_c2_det_context_conv2_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c2_det_context_conv3_1_bn"
  type: "BatchNorm"
  bottom: "rf_c2_det_context_conv3_1"
  top: "rf_c2_det_context_conv3_1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_det_context_conv3_1_bn_scale"
  type: "Scale"
  bottom: "rf_c2_det_context_conv3_1_bn"
  top: "rf_c2_det_context_conv3_1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c1_aggr_bn"
  type: "BatchNorm"
  bottom: "rf_c1_aggr"
  top: "rf_c1_aggr_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_aggr_bn_scale"
  type: "Scale"
  bottom: "rf_c1_aggr_bn"
  top: "rf_c1_aggr_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "face_rpn_cls_score_reshape_stride32"
  type: "Reshape"
  bottom: "face_rpn_cls_score_stride32"
  top: "face_rpn_cls_score_reshape_stride32"
  reshape_param {
    shape {
      dim: 1
      dim: 2
      dim: -1
      dim: 0
    }
  }
}
layer {
  name: "rf_c2_det_context_conv3_1_relu"
  type: "ReLU"
  bottom: "rf_c2_det_context_conv3_1_bn"
  top: "rf_c2_det_context_conv3_1_bn"
}
layer {
  name: "rf_c1_aggr_relu"
  type: "ReLU"
  bottom: "rf_c1_aggr_bn"
  top: "rf_c1_aggr_bn"
}
layer {
  name: "face_rpn_cls_prob_stride32"
  type: "Softmax"
  bottom: "face_rpn_cls_score_reshape_stride32"
  top: "face_rpn_cls_prob_stride32"
  softmax_param {
      axis: 1
  }
}
layer {
  name: "rf_c2_det_context_conv3_2"
  type: "Convolution"
  bottom: "rf_c2_det_context_conv3_1_bn"
  top: "rf_c2_det_context_conv3_2"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c1_det_conv1"
  type: "Convolution"
  bottom: "rf_c1_aggr_bn"
  top: "rf_c1_det_conv1"
  convolution_param {
    num_output: 32
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c1_det_context_conv1"
  type: "Convolution"
  bottom: "rf_c1_aggr_bn"
  top: "rf_c1_det_context_conv1"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "face_rpn_cls_prob_reshape_stride32"
  type: "Reshape"
  bottom: "face_rpn_cls_prob_stride32"
  top: "face_rpn_cls_prob_reshape_stride32"
  reshape_param {
    shape {
      dim: 1
      dim: 4
      dim: -1
      dim: 0
    }
  }
}
layer {
  name: "rf_c2_det_context_conv3_2_bn"
  type: "BatchNorm"
  bottom: "rf_c2_det_context_conv3_2"
  top: "rf_c2_det_context_conv3_2_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c2_det_context_conv3_2_bn_scale"
  type: "Scale"
  bottom: "rf_c2_det_context_conv3_2_bn"
  top: "rf_c2_det_context_conv3_2_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c1_det_conv1_bn"
  type: "BatchNorm"
  bottom: "rf_c1_det_conv1"
  top: "rf_c1_det_conv1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_det_conv1_bn_scale"
  type: "Scale"
  bottom: "rf_c1_det_conv1_bn"
  top: "rf_c1_det_conv1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c1_det_context_conv1_bn"
  type: "BatchNorm"
  bottom: "rf_c1_det_context_conv1"
  top: "rf_c1_det_context_conv1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_det_context_conv1_bn_scale"
  type: "Scale"
  bottom: "rf_c1_det_context_conv1_bn"
  top: "rf_c1_det_context_conv1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c2_det_concat"
  type: "Concat"
  bottom: "rf_c2_det_conv1_bn"
  bottom: "rf_c2_det_context_conv2_bn"
  bottom: "rf_c2_det_context_conv3_2_bn"
  top: "rf_c2_det_concat"
  concat_param {
    axis: 1
  }
}
layer {
  name: "rf_c1_det_context_conv1_relu"
  type: "ReLU"
  bottom: "rf_c1_det_context_conv1_bn"
  top: "rf_c1_det_context_conv1_bn"
}
layer {
  name: "rf_c2_det_concat_relu"
  type: "ReLU"
  bottom: "rf_c2_det_concat"
  top: "rf_c2_det_concat"
}
layer {
  name: "rf_c1_det_context_conv2"
  type: "Convolution"
  bottom: "rf_c1_det_context_conv1_bn"
  top: "rf_c1_det_context_conv2"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "rf_c1_det_context_conv3_1"
  type: "Convolution"
  bottom: "rf_c1_det_context_conv1_bn"
  top: "rf_c1_det_context_conv3_1"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "face_rpn_cls_score_stride16"
  type: "Convolution"
  bottom: "rf_c2_det_concat"
  top: "face_rpn_cls_score_stride16"
  convolution_param {
    num_output: 4
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_bbox_pred_stride16"
  type: "Convolution"
  bottom: "rf_c2_det_concat"
  top: "face_rpn_bbox_pred_stride16"
  convolution_param {
    num_output: 8
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_landmark_pred_stride16"
  type: "Convolution"
  bottom: "rf_c2_det_concat"
  top: "face_rpn_landmark_pred_stride16"
  convolution_param {
    num_output: 20
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "rf_c1_det_context_conv2_bn"
  type: "BatchNorm"
  bottom: "rf_c1_det_context_conv2"
  top: "rf_c1_det_context_conv2_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_det_context_conv2_bn_scale"
  type: "Scale"
  bottom: "rf_c1_det_context_conv2_bn"
  top: "rf_c1_det_context_conv2_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c1_det_context_conv3_1_bn"
  type: "BatchNorm"
  bottom: "rf_c1_det_context_conv3_1"
  top: "rf_c1_det_context_conv3_1_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_det_context_conv3_1_bn_scale"
  type: "Scale"
  bottom: "rf_c1_det_context_conv3_1_bn"
  top: "rf_c1_det_context_conv3_1_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "face_rpn_cls_score_reshape_stride16"
  type: "Reshape"
  bottom: "face_rpn_cls_score_stride16"
  top: "face_rpn_cls_score_reshape_stride16"
  reshape_param {
    shape {
      dim: 1
      dim: 2
      dim: -1
      dim: 0
    }
  }
}
layer {
  name: "rf_c1_det_context_conv3_1_relu"
  type: "ReLU"
  bottom: "rf_c1_det_context_conv3_1_bn"
  top: "rf_c1_det_context_conv3_1_bn"
}
layer {
  name: "face_rpn_cls_prob_stride16"
  type: "Softmax"
  bottom: "face_rpn_cls_score_reshape_stride16"
  top: "face_rpn_cls_prob_stride16"
  softmax_param {
      axis: 1
  }
}
layer {
  name: "rf_c1_det_context_conv3_2"
  type: "Convolution"
  bottom: "rf_c1_det_context_conv3_1_bn"
  top: "rf_c1_det_context_conv3_2"
  convolution_param {
    num_output: 16
    bias_term: true
    group: 1
    stride: 1
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
  }
}
layer {
  name: "face_rpn_cls_prob_reshape_stride16"
  type: "Reshape"
  bottom: "face_rpn_cls_prob_stride16"
  top: "face_rpn_cls_prob_reshape_stride16"
  reshape_param {
    shape {
      dim: 1
      dim: 4
      dim: -1
      dim: 0
    }
  }
}
layer {
  name: "rf_c1_det_context_conv3_2_bn"
  type: "BatchNorm"
  bottom: "rf_c1_det_context_conv3_2"
  top: "rf_c1_det_context_conv3_2_bn"
  batch_norm_param {
    use_global_stats: true
    eps: 1.9999999494757503e-05
  }
}
layer {
  name: "rf_c1_det_context_conv3_2_bn_scale"
  type: "Scale"
  bottom: "rf_c1_det_context_conv3_2_bn"
  top: "rf_c1_det_context_conv3_2_bn"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "rf_c1_det_concat"
  type: "Concat"
  bottom: "rf_c1_det_conv1_bn"
  bottom: "rf_c1_det_context_conv2_bn"
  bottom: "rf_c1_det_context_conv3_2_bn"
  top: "rf_c1_det_concat"
  concat_param {
    axis: 1
  }
}
layer {
  name: "rf_c1_det_concat_relu"
  type: "ReLU"
  bottom: "rf_c1_det_concat"
  top: "rf_c1_det_concat"
}
layer {
  name: "face_rpn_cls_score_stride8"
  type: "Convolution"
  bottom: "rf_c1_det_concat"
  top: "face_rpn_cls_score_stride8"
  convolution_param {
    num_output: 4
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_bbox_pred_stride8"
  type: "Convolution"
  bottom: "rf_c1_det_concat"
  top: "face_rpn_bbox_pred_stride8"
  convolution_param {
    num_output: 8
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_landmark_pred_stride8"
  type: "Convolution"
  bottom: "rf_c1_det_concat"
  top: "face_rpn_landmark_pred_stride8"
  convolution_param {
    num_output: 20
    bias_term: true
    group: 1
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
  }
}
layer {
  name: "face_rpn_cls_score_reshape_stride8"
  type: "Reshape"
  bottom: "face_rpn_cls_score_stride8"
  top: "face_rpn_cls_score_reshape_stride8"
  reshape_param {
    shape {
      dim: 1
      dim: 2
      dim: -1
      dim: 0
    }
  }
}
layer {
  name: "face_rpn_cls_prob_stride8"
  type: "Softmax"
  bottom: "face_rpn_cls_score_reshape_stride8"
  top: "face_rpn_cls_prob_stride8"
  softmax_param {
      axis: 1
  }
}
layer {
  name: "face_rpn_cls_prob_reshape_stride8"
  type: "Reshape"
  bottom: "face_rpn_cls_prob_stride8"
  top: "face_rpn_cls_prob_reshape_stride8"
  reshape_param {
    shape {
      dim: 1
      dim: 4
      dim: -1
      dim: 0
    }
  }
}