libfacedetection库 yufacedetectnet-open-v1.prototxt详解

libfacedetection库是深圳大学的于仕琪老师发布的开源人脸检测库,该库使用的是SSD网络框架,SSD框架详情参见博客:https://blog.csdn.net/qq_30815237/article/details/90292639

下面对其网库框架的prototxt文件进行详解:

name: "YuFaceDetectNet"
  input: "data"#输入 
  input_shape {   #输入数据的尺寸:1张图片,3个通道(BGR),width,height=320,240
  dim: 1
  dim: 3
  dim: 240
  dim: 320
  }
  #CONV1###########################################################
  layer {
  name: "conv1_1"   #,name任取,表示这一层的名字
  type: "Convolution"#卷积层
  bottom: "data"#bottom为此层输入
 

top: "conv1_1" #top为此层输出

在数据层中,至少有一个命名为data的top。如果有第二个top,一般命名为label。这种(data, label)的配对是分类模型所必需的.

  param {
  lr_mult: 1.0  #学习率的系数,最终的学习率是这个数乘以solver.prototxt配置文件中的base_lr。如有两个lr_mult,则第一个表示权值w的学习率,第二个表示偏置项的学习率。一般偏置项的学习率是权值学习率的两倍。
  decay_mult: 1.0 #权值衰减,为了避免模型的over-fitting,需要对cost function加入规范项
  }
  convolution_param {     #卷积核的参数定义
  num_output: 16   #卷积核的个数
  pad: 1  #扩充边缘,默认为0,不扩充。 扩充的时候是左右、上下对称的
  stride: 2 #卷积核的步长,默认为1, 也可以用stride_h, stride_w来设置。
  kernel_size: 3 #卷积核的大小,如果kernel_size长宽不一样,则需要通过kernel_h,kernel_w分别设定
 

weight_filler {

#权值初始化。默认为“constant”,值权威0,很多时候我们用“xavier”算法来进行初始化,也可以设置为“gaussian

  type: "xavier"
  }
  bias_term: false #是否开启偏置项,默认为true
  }
  }
  layer {#激活函数
  name: "relu1_1"
  type: "ReLU"激活函数,对输入数据进行激活操作(一种函数变换),从bottom得到一个blob数据输入,运算后,从top输出一个blob数据, 在运算过程中,只对输入数据逐个元素进行函数变化,不改变数据维度大小
  bottom: "conv1_1"  输入:n*c*h*w  输出:n*c*h*w
 

top: "conv1_1"      Relu层支持in-place计算,因此Relu层的输入输出可以相同,这意味着该层的输入输出可以共享同一块内存,减少内存消耗,其它层不支持

  }
 

##################第一组卷积层的第2个小层,1.2层

layer {

  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"#第1小层卷积层输出作为输入
  top: "conv1_2"
  param {
  lr_mult: 1.0  #learning rate
  decay_mult: 1.0
  }
  convolution_param {
  num_output: 16
  pad: 0
  kernel_size: 1
  weight_filler {   type: "xavier"    }
  bias_term: false
  }
  }
 

#卷积层1.2的激活层

layer {

  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
 

}

####################CONV2

 
 

#池化层,对第一组卷积层池化

layer {

  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"   #1.2层经过激活函数的输出作为输入
  top: "pool1"
  pooling_param {
  pool: MAX
  kernel_size: 2
  stride: 2}
  }
 

#2.1卷积层

layer {

  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
  lr_mult: 1.0
  decay_mult: 1.0
  }
  convolution_param {
  num_output: 16
  pad: 1
  kernel_size: 3
  weight_filler {
  type: "xavier"}
  bias_term: false
  }
  }
 

#2.1激活层

layer {

  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
  }
 

#2.2卷积层

layer {

  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
  lr_mult: 1.0
  decay_mult: 1.0
  }
  convolution_param {
  num_output: 16
  pad: 0
  kernel_size: 1
  weight_filler {
  type: "xavier"}
  bias_term: false
  }
  }
 

#2.2激活层

layer {

  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
  }

#CONV3###########先池化,卷积激活3.1,3.2.3.3,该大层一共有三个3个卷积层,最终num_output: 32
 

layer {
  name: "pool2"
  type: "Pooling"
}
layer {
  name: "conv3_1"
  type: "Convolution"
}

layer {
  name: "relu3_1"
  type: "ReLU"
}
layer {
  name: "conv3_2"
  type: "Convolution"
}
layer {
  name: "relu3_2"
  type: "ReLU"
}
layer {
  name: "conv3_3"
  type: "Convolution"
 }

layer {
  name: "relu3_3"
  type: "ReLU"
}

#CONV4同上,num_output: 64

#CONV5同上, num_output: 128

#CONV6同上,num_output: 128

layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "conv6_1"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu6_1"
  type: "ReLU"
  bottom: "conv6_1"
  top: "conv6_1"
}
layer {
  name: "conv6_2"
  type: "Convolution"
  bottom: "conv6_1"
  top: "conv6_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu6_2"
  type: "ReLU"
  bottom: "conv6_2"
  top: "conv6_2"
}
layer {
  name: "conv6_3"
  type: "Convolution"
  bottom: "conv6_2"
  top: "conv6_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu6_3"
  type: "ReLU"
  bottom: "conv6_3"
  top: "conv6_3"
}
from:Caffe各层参数解析https://www.jianshu.com/p/d7bcd3338c6c

from:https://blog.csdn.net/github_37973614/article/details/81810327 

layer {
  name: "conv3_3_norm"
  type: "Normalize"
  bottom: "conv3_3"
  top: "conv3_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 10.0
    }
    channel_shared: false
  }
}

     PriorBox层用于部署特征图中每个位置(像素点)处的默认框(即计算每个默认框相对于网络输入层输入图像的归一化左上角和右下角坐标以及设置的坐标variance值)

#PRIORBOX3#
 

#对第3大卷积层输出进行批量归一化

layer {

  name: "conv3_3_norm"
  type: "Normalize"#归一化层主要作用是将空间或者通道内的元素归一化到0到1之间
  bottom: "conv3_3"
  top: "conv3_3_norm"
  norm_param {
  across_spatial: false#是否对整个图片进行归一化,归一化的维度为:1 x c x h x w,否则对每个像素点进行归一化:1 x c x 1 x 1。
  scale_filler {#包含可学习的参数。和卷积层参数一样,可以通过设置学习率来决定是否对该参数进行更新,例如设置学习率为0,实现常数的缩放。
  type: "constant"
  value: 10.0}    #scale_filler 中的value值会随着迭代的更新而更新,
  channel_shared: false      #表示scale是否相同,如果为true,则scale都是一样的,否则对于同像素点位置一样,对不同像素点是不一样的!
  }
  }

#conv3_3_norm"在这里会有两个分支,一个分支用于估计边框回归,另一个用于边框类别判断

 

layer {

  name: "conv3_3_norm_mbox_loc_new"
  type: "Convolution"
  bottom: "conv3_3_norm"
  top: "conv3_3_norm_mbox_loc_new"
  param {
  lr_mult: 1.0
  decay_mult: 1.0
  }
  convolution_param {
  num_output: 12#12个卷积核,即3个边框,每个边框4个参数x,y,w,h
  pad: 1
  kernel_size: 3
  stride: 1
  weight_filler {
  type: "xavier"}
  bias_term: false
  }
  }

#该permute层相当于交换caffe blob中的数据维度。在正常情况下caffe blob的顺序为:

bottom blob = [batch_num, channel, height, width],经过变换后,top blob = [batch_num, height, width, channel]

比如你卷积后的维度是32×24×19×19,那么经过交换层后就变成32×19×19×2

 

layer {

  name: "conv3_3_norm_mbox_loc_perm"
  type: "Permute"
  bottom: "conv3_3_norm_mbox_loc_new"
  top: "conv3_3_norm_mbox_loc_perm"
  permute_param { #调整顺序
  order: 0
  order: 2
  order: 3
  order: 1}
  }

#flatten层的作用就是将32×19×19×24变成32*8664,32是batchsize的大小。

layer {

  name: "conv3_3_norm_mbox_loc_flat"
  type: "Flatten"
  bottom: "conv3_3_norm_mbox_loc_perm"
  top: "conv3_3_norm_mbox_loc_flat"
  flatten_param {
  axis: 1}
  }

#conv3_3_norm"有两个分支,一个分支用于估计边框回归,另一个用于边框类别判断,下面的是类别置信度判断

layer {

  name: "conv3_3_norm_mbox_conf_new"
  type: "Convolution"
  bottom: "conv3_3_norm"
  top: "conv3_3_norm_mbox_conf_new"
  param {
  lr_mult: 1.0
  decay_mult: 1.0}
  convolution_param {
  num_output: 6     #6个卷积核,2类(人脸与非人脸),每个锚点默认3个边框
  pad: 1
  kernel_size: 3
  stride: 1
  weight_filler {
  type: "xavier"}
  bias_term: false
  }
  }

#互换维度顺序

layer {

  name: "conv3_3_norm_mbox_conf_perm"
  type: "Permute"
  bottom: "conv3_3_norm_mbox_conf_new"
  top: "conv3_3_norm_mbox_conf_perm"
  permute_param {
  order: 0
  order: 2
  order: 3
  order: 1}
  }

layer {

  name: "conv3_3_norm_mbox_conf_flat"
  type: "Flatten"
  bottom: "conv3_3_norm_mbox_conf_perm"
  top: "conv3_3_norm_mbox_conf_flat"
  flatten_param {
  axis: 1}
  }
 

#用于部署特征图中每个位置(像素点)处的默认框(即计算每个默认框相对于输入图像的归一化左上角和右下角坐标以及设置的坐标variance值),这里边框个数设置为3.

layer {

  name: "conv3_3_norm_mbox_priorbox"
  type: "PriorBox"   
  bottom: "conv3_3_norm"
  bottom: "data"    #输入有两个,一个是源图像,另一个是归一化的特征图
  top: "conv3_3_norm_mbox_priorbox"
  prior_box_param {
  min_size: 10.0  
  min_size: 16.0
  min_size: 24.0# s_{k}=s_{min}+\frac{s_{max}-s_{min}}{m-1}(k-1), k\in [1,m]
  clip: false
  variance: 0.10000000149
  variance: 0.10000000149
  variance: 0.20000000298
  variance: 0.20000000298
  step: 8.0   #step参数本质上是该层的特征图相对于网络输入层输入图像的下采样率,用于计算当前特征图上某一位置处所有默认框中心坐标在网络输入层输入图像坐标系下的坐标
  offset: 0.5}
  }

#PRIORBOX4与PRIORBOX3基本一样,区别在于特征图上每个点对应的默认矿不再是3个,而是2个。
layer {
  name: "conv4_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_loc"
  convolution_param {
    num_output: 8    #  2个边框*4个参数x,y,w,h
    pad: 1
    kernel_size: 3
    stride: 1
}
 "Permute"
 "Flatten"
layer {
  name: "conv4_3_norm_mbox_conf"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_conf"
  convolution_param {
    num_output: 4   #  2类*2个边框
    pad: 1
    kernel_size: 3
    stride: 1
}
 "Permute"
 "Flatten"
layer {

  name: "conv4_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv4_3_norm"
  bottom: "data"
  top: "conv4_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 32.0
    min_size: 48.0
}
#PRIORBOX5与PRIORBOX4相同,边框数为2.
layer {

  name: "conv5_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv5_3_norm"
  bottom: "data"
  top: "conv5_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 64.0
    min_size: 96.0
  }
}

#PRIORBOX6,边框数为3.
layer {

  name: "conv6_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv6_3_norm"
  bottom: "data"
  top: "conv6_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 128.0
    min_size: 192.0
    min_size: 256.0
  }
}

########################################################
libfacedetection库 yufacedetectnet-open-v1.prototxt详解_第1张图片libfacedetection库 yufacedetectnet-open-v1.prototxt详解_第2张图片


layer {
  name: "mbox_loc"
  type: "Concat"
  bottom: "conv3_3_norm_mbox_loc_flat"
  bottom: "conv4_3_norm_mbox_loc_flat"
  bottom: "conv5_3_norm_mbox_loc_flat"
  bottom: "conv6_3_norm_mbox_loc_flat"
  top: "mbox_loc"
  concat_param {
    axis: 1 }
}


layer {
  name: "mbox_conf"
  type: "Concat"
  bottom: "conv3_3_norm_mbox_conf_flat"
  bottom: "conv4_3_norm_mbox_conf_flat"
  bottom: "conv5_3_norm_mbox_conf_flat"
  bottom: "conv6_3_norm_mbox_conf_flat"
  top: "mbox_conf"
  concat_param {
    axis: 1}
}

libfacedetection库 yufacedetectnet-open-v1.prototxt详解_第3张图片
layer {
  name: "mbox_priorbox"
  type: "Concat"
  bottom: "conv3_3_norm_mbox_priorbox"
  bottom: "conv4_3_norm_mbox_priorbox"
  bottom: "conv5_3_norm_mbox_priorbox"
  bottom: "conv6_3_norm_mbox_priorbox"
  top: "mbox_priorbox"
  concat_param {
    axis: 2 }
}

#####################################################


layer {
  name: "mbox_conf_reshape"
  type: "Reshape"
  bottom: "mbox_conf"
  top: "mbox_conf_reshape"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 2  }
  }
}
layer {
  name: "mbox_conf_softmax"
  type: "Softmax"
  bottom: "mbox_conf_reshape"
  top: "mbox_conf_softmax"
  softmax_param {
    axis: 2}
}
layer {#
  name: "mbox_conf_flatten"
  type: "Flatten"
  bottom: "mbox_conf_softmax"
  top: "mbox_conf_flatten"
  flatten_param {
    axis: 1  }
}

###################################################

libfacedetection库 yufacedetectnet-open-v1.prototxt详解_第4张图片

layer {
  name: "detection_out"
  type: "DetectionOutput"
  bottom: "mbox_loc"
  bottom: "mbox_conf_flatten"
  bottom: "mbox_priorbox"
  top: "detection_out"
  include {
    phase: TEST
  }
  transform_param {
    mean_value: 103.94
    mean_value: 116.78
    mean_value: 123.68
  }
  detection_output_param {
    num_classes: 2    #两个类别,人脸非人脸
    share_location: true
    background_label_id: 0
    nms_param {    #极大值抑制
      nms_threshold: 0.15
      top_k: 100
    }
    code_type: CENTER_SIZE
    keep_top_k: 50
    confidence_threshold: 0.01
    visualize: false
    visualize_threshold: 0.3
  }
}

完整prototxt文件:https://github.com/ShiqiYu/libfacedetection/blob/master/models/caffe/yufacedetectnet-open-v1.prototxt

 

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