hand_trainval.prototxt

layer {
  name: "data"
  type: "CPMData"
  top: "data"
  top: "label"
  data_param {
    source: "/data/xiaobing.wang/pingjun.li/yfji/hand_labels_synth/lmdb"
    batch_size: 10
    backend: LMDB
  }
  cpm_transform_param {
    stride: 8
    max_rotate_degree: 40
    visualize: false
    crop_size_x: 256
    crop_size_y: 256
    scale_prob: 1
    scale_min: 0.5
    scale_max: 1.1
    target_dist: 0.6
    center_perterb_max: 40
    do_clahe: false
    num_parts: 21
    np_in_lmdb: 21
    dataset: "HAND"
  }
}
layer {
  name: "image"
  type: "Slice"
  bottom: "data"
  top: "image"
  top: "center_map"
  slice_param {
    slice_point: 3
    axis: 1
  }
}
layer {
  name: "silence2"
  type: "Silence"
  bottom: "center_map"
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "image"
  top: "conv1_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1_stage1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1_stage1"
  top: "conv2_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2_stage1"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2_stage1"
  top: "conv3_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_4"
  type: "Convolution"
  bottom: "conv3_3"
  top: "conv3_4"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu3_4"
  type: "ReLU"
  bottom: "conv3_4"
  top: "conv3_4"
}
layer {
  name: "pool3_stage1"
  type: "Pooling"
  bottom: "conv3_4"
  top: "pool3_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3_stage1"
  top: "conv4_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "conv4_4"
  type: "Convolution"
  bottom: "conv4_3"
  top: "conv4_4"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu4_4"
  type: "ReLU"
  bottom: "conv4_4"
  top: "conv4_4"
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "conv4_4"
  top: "conv5_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3_CPM"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3_CPM"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu5_4_stage1_3"
  type: "ReLU"
  bottom: "conv5_3_CPM"
  top: "conv5_3_CPM"
}
layer {
  name: "conv6_1_CPM"
  type: "Convolution"
  bottom: "conv5_3_CPM"
  top: "conv6_1_CPM"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "relu6_4_stage1_1"
  type: "ReLU"
  bottom: "conv6_1_CPM"
  top: "conv6_1_CPM"
}
layer {
  name: "conv6_2_CPM"
  type: "Convolution"
  bottom: "conv6_1_CPM"
  top: "conv6_2_CPM"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 22
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "loss_stage1"
  type: "EuclideanLoss"
  bottom: "conv6_2_CPM"
  bottom: "label"
  top: "loss_stage1"
  loss_weight: 1
}

layer {
  name: "concat_stage2"
  type: "Concat"
  bottom: "conv6_2_CPM"
  bottom: "conv5_3_CPM"
  top: "concat_stage2"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage2"
  type: "Convolution"
  bottom: "concat_stage2"
  top: "Mconv1_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_2_stage2_1"
  type: "ReLU"
  bottom: "Mconv1_stage2"
  top: "Mconv1_stage2"
}
layer {
  name: "Mconv2_stage2"
  type: "Convolution"
  bottom: "Mconv1_stage2"
  top: "Mconv2_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_3_stage2_2"
  type: "ReLU"
  bottom: "Mconv2_stage2"
  top: "Mconv2_stage2"
}
layer {
  name: "Mconv3_stage2"
  type: "Convolution"
  bottom: "Mconv2_stage2"
  top: "Mconv3_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_4_stage2_3"
  type: "ReLU"
  bottom: "Mconv3_stage2"
  top: "Mconv3_stage2"
}
layer {
  name: "Mconv4_stage2"
  type: "Convolution"
  bottom: "Mconv3_stage2"
  top: "Mconv4_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_5_stage2_4"
  type: "ReLU"
  bottom: "Mconv4_stage2"
  top: "Mconv4_stage2"
}
layer {
  name: "Mconv5_stage2"
  type: "Convolution"
  bottom: "Mconv4_stage2"
  top: "Mconv5_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_6_stage2_5"
  type: "ReLU"
  bottom: "Mconv5_stage2"
  top: "Mconv5_stage2"
}
layer {
  name: "Mconv6_stage2"
  type: "Convolution"
  bottom: "Mconv5_stage2"
  top: "Mconv6_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_7_stage2_6"
  type: "ReLU"
  bottom: "Mconv6_stage2"
  top: "Mconv6_stage2"
}
layer {
  name: "Mconv7_stage2"
  type: "Convolution"
  bottom: "Mconv6_stage2"
  top: "Mconv7_stage2"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 22
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}

layer {
  name: "loss_stage2"
  type: "EuclideanLoss"
  bottom: "Mconv7_stage2"
  bottom: "label"
  top: "loss_stage2"
  loss_weight: 1
}

layer {
  name: "concat_stage3"
  type: "Concat"
  bottom: "Mconv7_stage2"
  bottom: "conv5_3_CPM"
  top: "concat_stage3"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage3"
  type: "Convolution"
  bottom: "concat_stage3"
  top: "Mconv1_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_2_stage3_1"
  type: "ReLU"
  bottom: "Mconv1_stage3"
  top: "Mconv1_stage3"
}
layer {
  name: "Mconv2_stage3"
  type: "Convolution"
  bottom: "Mconv1_stage3"
  top: "Mconv2_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_3_stage3_2"
  type: "ReLU"
  bottom: "Mconv2_stage3"
  top: "Mconv2_stage3"
}
layer {
  name: "Mconv3_stage3"
  type: "Convolution"
  bottom: "Mconv2_stage3"
  top: "Mconv3_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_4_stage3_3"
  type: "ReLU"
  bottom: "Mconv3_stage3"
  top: "Mconv3_stage3"
}
layer {
  name: "Mconv4_stage3"
  type: "Convolution"
  bottom: "Mconv3_stage3"
  top: "Mconv4_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_5_stage3_4"
  type: "ReLU"
  bottom: "Mconv4_stage3"
  top: "Mconv4_stage3"
}
layer {
  name: "Mconv5_stage3"
  type: "Convolution"
  bottom: "Mconv4_stage3"
  top: "Mconv5_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_6_stage3_5"
  type: "ReLU"
  bottom: "Mconv5_stage3"
  top: "Mconv5_stage3"
}
layer {
  name: "Mconv6_stage3"
  type: "Convolution"
  bottom: "Mconv5_stage3"
  top: "Mconv6_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_7_stage3_6"
  type: "ReLU"
  bottom: "Mconv6_stage3"
  top: "Mconv6_stage3"
}
layer {
  name: "Mconv7_stage3"
  type: "Convolution"
  bottom: "Mconv6_stage3"
  top: "Mconv7_stage3"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 22
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}

layer {
  name: "loss_stage3"
  type: "EuclideanLoss"
  bottom: "Mconv7_stage3"
  bottom: "label"
  top: "loss_stage3"
  loss_weight: 1
}

layer {
  name: "concat_stage4"
  type: "Concat"
  bottom: "Mconv7_stage3"
  bottom: "conv5_3_CPM"
  top: "concat_stage4"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage4"
  type: "Convolution"
  bottom: "concat_stage4"
  top: "Mconv1_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_2_stage4_1"
  type: "ReLU"
  bottom: "Mconv1_stage4"
  top: "Mconv1_stage4"
}
layer {
  name: "Mconv2_stage4"
  type: "Convolution"
  bottom: "Mconv1_stage4"
  top: "Mconv2_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_3_stage4_2"
  type: "ReLU"
  bottom: "Mconv2_stage4"
  top: "Mconv2_stage4"
}
layer {
  name: "Mconv3_stage4"
  type: "Convolution"
  bottom: "Mconv2_stage4"
  top: "Mconv3_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_4_stage4_3"
  type: "ReLU"
  bottom: "Mconv3_stage4"
  top: "Mconv3_stage4"
}
layer {
  name: "Mconv4_stage4"
  type: "Convolution"
  bottom: "Mconv3_stage4"
  top: "Mconv4_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_5_stage4_4"
  type: "ReLU"
  bottom: "Mconv4_stage4"
  top: "Mconv4_stage4"
}
layer {
  name: "Mconv5_stage4"
  type: "Convolution"
  bottom: "Mconv4_stage4"
  top: "Mconv5_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_6_stage4_5"
  type: "ReLU"
  bottom: "Mconv5_stage4"
  top: "Mconv5_stage4"
}
layer {
  name: "Mconv6_stage4"
  type: "Convolution"
  bottom: "Mconv5_stage4"
  top: "Mconv6_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_7_stage4_6"
  type: "ReLU"
  bottom: "Mconv6_stage4"
  top: "Mconv6_stage4"
}
layer {
  name: "Mconv7_stage4"
  type: "Convolution"
  bottom: "Mconv6_stage4"
  top: "Mconv7_stage4"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 22
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}

layer {
  name: "loss_stage4"
  type: "EuclideanLoss"
  bottom: "Mconv7_stage4"
  bottom: "label"
  top: "loss_stage4"
  loss_weight: 1
}

layer {
  name: "concat_stage5"
  type: "Concat"
  bottom: "Mconv7_stage4"
  bottom: "conv5_3_CPM"
  top: "concat_stage5"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage5"
  type: "Convolution"
  bottom: "concat_stage5"
  top: "Mconv1_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_2_stage5_1"
  type: "ReLU"
  bottom: "Mconv1_stage5"
  top: "Mconv1_stage5"
}
layer {
  name: "Mconv2_stage5"
  type: "Convolution"
  bottom: "Mconv1_stage5"
  top: "Mconv2_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_3_stage5_2"
  type: "ReLU"
  bottom: "Mconv2_stage5"
  top: "Mconv2_stage5"
}
layer {
  name: "Mconv3_stage5"
  type: "Convolution"
  bottom: "Mconv2_stage5"
  top: "Mconv3_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_4_stage5_3"
  type: "ReLU"
  bottom: "Mconv3_stage5"
  top: "Mconv3_stage5"
}
layer {
  name: "Mconv4_stage5"
  type: "Convolution"
  bottom: "Mconv3_stage5"
  top: "Mconv4_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_5_stage5_4"
  type: "ReLU"
  bottom: "Mconv4_stage5"
  top: "Mconv4_stage5"
}
layer {
  name: "Mconv5_stage5"
  type: "Convolution"
  bottom: "Mconv4_stage5"
  top: "Mconv5_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_6_stage5_5"
  type: "ReLU"
  bottom: "Mconv5_stage5"
  top: "Mconv5_stage5"
}
layer {
  name: "Mconv6_stage5"
  type: "Convolution"
  bottom: "Mconv5_stage5"
  top: "Mconv6_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_7_stage5_6"
  type: "ReLU"
  bottom: "Mconv6_stage5"
  top: "Mconv6_stage5"
}
layer {
  name: "Mconv7_stage5"
  type: "Convolution"
  bottom: "Mconv6_stage5"
  top: "Mconv7_stage5"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 22
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}

layer {
  name: "loss_stage5"
  type: "EuclideanLoss"
  bottom: "Mconv7_stage5"
  bottom: "label"
  top: "loss_stage5"
  loss_weight: 1
}

layer {
  name: "concat_stage6"
  type: "Concat"
  bottom: "Mconv7_stage5"
  bottom: "conv5_3_CPM"
  top: "concat_stage6"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage6"
  type: "Convolution"
  bottom: "concat_stage6"
  top: "Mconv1_stage6"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_2_stage6_1"
  type: "ReLU"
  bottom: "Mconv1_stage6"
  top: "Mconv1_stage6"
}
layer {
  name: "Mconv2_stage6"
  type: "Convolution"
  bottom: "Mconv1_stage6"
  top: "Mconv2_stage6"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_3_stage6_2"
  type: "ReLU"
  bottom: "Mconv2_stage6"
  top: "Mconv2_stage6"
}
layer {
  name: "Mconv3_stage6"
  type: "Convolution"
  bottom: "Mconv2_stage6"
  top: "Mconv3_stage6"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_4_stage6_3"
  type: "ReLU"
  bottom: "Mconv3_stage6"
  top: "Mconv3_stage6"
}
layer {
  name: "Mconv4_stage6"
  type: "Convolution"
  bottom: "Mconv3_stage6"
  top: "Mconv4_stage6"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_5_stage6_4"
  type: "ReLU"
  bottom: "Mconv4_stage6"
  top: "Mconv4_stage6"
}
layer {
  name: "Mconv5_stage6"
  type: "Convolution"
  bottom: "Mconv4_stage6"
  top: "Mconv5_stage6"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_6_stage6_5"
  type: "ReLU"
  bottom: "Mconv5_stage6"
  top: "Mconv5_stage6"
}
layer {
  name: "Mconv6_stage6"
  type: "Convolution"
  bottom: "Mconv5_stage6"
  top: "Mconv6_stage6"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}
layer {
  name: "Mrelu1_7_stage6_6"
  type: "ReLU"
  bottom: "Mconv6_stage6"
  top: "Mconv6_stage6"
}
layer {
  name: "Mconv7_stage6"
  type: "Convolution"
  bottom: "Mconv6_stage6"
#   top: "Mconv7_stage6"
  top: "net_output"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 22
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
    dilation: 1
  }
}

layer {
  name: "loss_stage6"
  type: "EuclideanLoss"
  bottom: "net_output"
  bottom: "label"
  top: "loss_stage6"
  loss_weight: 1
}