caffe网络结构可视化

可视化网址

http://dgschwend.github.io/netscope/#/editor

deploy_vgg11_regression.prototxt

# Enter your network definition here.
# Use Shift+Enter to update the visualization.
###----------------
name: "vgg11_regression_posture"

layer { 
  name: "data" 
  type: "Input" 
  top: "data" 
  input_param { 
	shape: { 
    dim: 1 
    dim: 3 
    dim: 224 
    dim: 224 
    } 
  } 
}

layer { 
  name: "conv1_1"  
  type: "Convolution"  
  bottom: "data" 
  top: "conv1_1"  
  convolution_param { 
    num_output: 6 
    pad: 1 
    kernel_size: 3 
    stride: 1 
   } 
}

layer { 
  name: "con1_1/bn" 
  type: "BatchNorm"  
  bottom: "conv1_1" 
  top: "conv1_1"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }  
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}


layer { 
  name: "conv1_1/scale" 
  type: "Scale"  
  bottom: "conv1_1"  
  top: "conv1_1"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
   } 
  scale_param {
    filler { 
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
     }
  }
}


layer { 
  name: "relu1_1"  
  type: "ReLU"  
  bottom: "conv1_1"  
  top: "conv1_1"
}


layer { 
  bottom: "conv1_1" 
  top: "pool1" 
  name: "pool1" 
  type: "Pooling" 
  pooling_param {
    kernel_size: 3 
    stride: 2 
    pool: AVE
  }
}

###----------------

layer { 
  name: "conv2_1"  
  type: "Convolution" 
  bottom: "pool1" 
  top: "conv2_1"  
  convolution_param { 
    num_output: 12 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}

layer { 
  name: "con2_1/bn" 
  type: "BatchNorm"  
  bottom: "conv2_1" 
  top: "conv2_1"  
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }  
  param {
  lr_mult: 0.0 
  decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }  
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}

layer { 
  name: "conv2_1/scale"
  type: "Scale"  
  bottom: "conv2_1"  
  top: "conv2_1"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  scale_param {
    filler { 
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
    }
  }
}

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


layer { 
  bottom: "conv2_1" 
  top: "pool2" 
  name: "pool2" 
  type: "Pooling" 
  pooling_param {
    kernel_size: 3 
    stride: 2 
    pool: AVE
  } 
}

###----------------

layer { 
  name: "conv3_1"  
  type: "Convolution"  
  bottom: "pool2"  
  top: "conv3_1"  
  convolution_param { 
    num_output: 24 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}

layer { 
  name: "conv3_1/bn" 
  type: "BatchNorm" 
  bottom: "conv3_1" 
  top: "conv3_1"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  }  
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }   
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}


layer { 
  name: "conv3_1/scale" 
  type: "Scale"  
  bottom: "conv3_1"  
  top: "conv3_1" 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  scale_param {
    filler { 帮我
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
    }
  }
}

layer { 
  name: "relu3_1"  
  type: "ReLU"  
  bottom: "conv3_1"  
  top: "conv3_1"
}

layer { 
  name: "conv3_2" 
  type: "Convolution"  
  bottom: "conv3_1"  
  top: "conv3_2" 
  convolution_param { 
    num_output: 24 
    pad: 1
    kernel_size: 3 
    stride: 1 
  } 
}
  

layer { 
  name: "conv3_2/bn" 
  type: "BatchNorm" 
  bottom: "conv3_2" 
  top: "conv3_2"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  }  
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }   
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}

layer { 
  name: "conv3_2/scale" 
  type: "Scale"  
  bottom: "conv3_2"  
  top: "conv3_2"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000} 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000}
  scale_param {
    filler { 
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
    }
  }
}


layer { 
  name: "relu3_2" 
  type: "ReLU"  
  bottom: "conv3_2"  
  top: "conv3_2"}


layer { 
  bottom: "conv3_2" 
  top: "pool3"
  name: "pool3" 
  type: "Pooling" 
  pooling_param {
    kernel_size: 3 
    stride: 2 
    pool: AVE
  } 
}
###----------------

layer { 
  name: "conv4_1"  
  type: "Convolution"  
  bottom: "pool3"  
  top: "conv4_1"  
  convolution_param { 
    num_output: 32 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}


layer { 
  name: "conv4_1/bn" 
  type: "BatchNorm"  
  bottom: "conv4_1"
  top: "conv4_1"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  }  
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }   
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}

layer { 
  name: "conv4_1/scale" 
  type: "Scale"  
  bottom: "conv4_1"  
  top: "conv4_1"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  scale_param {
    filler { type: "constant" value: 1.0} 
    bias_term: true 
    bias_filler { type: "constant" value: 0.0}}}

layer {
  name: "relu4_1"  
  type: "ReLU"  
  bottom: "conv4_1"  
  top: "conv4_1"
}


layer { 
  name: "conv4_2"  
  type: "Convolution" 
  bottom: "conv4_1"  
  top: "conv4_2"  
  convolution_param { 
    num_output: 32 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}
  

layer { 
  name: "conv4_2/bn" 
  type: "BatchNorm"  
  bottom: "conv4_2" 
  top: "conv4_2"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0} 
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}

layer { 
  name: "conv4_2/scale"
  type: "Scale"  
  bottom: "conv4_2"  
  top: "conv4_2"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000} 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  scale_param {
    filler { 
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
    }
  }
}


layer { 
  name: "relu4_2"  
  type: "ReLU"  
  bottom: "conv4_2"  
  top: "conv4_2"
}


layer { 
  bottom: "conv4_2" 
  top: "pool4" 
  name: "pool4" 
  type: "Pooling" 
  pooling_param {
    kernel_size: 3 
    stride: 2 
  pool: AVE
  } 
}

###----------------

layer { 
  name: "conv5_1"  
  type: "Convolution"  
  bottom: "pool4"  
  top: "conv5_1"  
  convolution_param { 
    num_output: 32 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}

layer { 
  name: "conv5_1/bn" 
  type: "BatchNorm"  
  bottom: "conv5_1" 
  top: "conv5_1"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  }  
  param {
  lr_mult: 0.0 
  decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }   
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}


layer { 
  name: "conv5_1/scale" 
  type: "Scale"  
  bottom: "conv5_1" 
  top: "conv5_1"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  } 
  scale_param {
    filler { 
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
     type: "constant" 
     value: 0.0
    }
  }
}

layer { 
  name: "relu5_1"  
  type: "ReLU"  
  bottom: "conv5_1"  
  top: "conv5_1"
}

layer { 
  name: "conv5_2"  
  type: "Convolution" 
  bottom: "conv5_1"  
  top: "conv5_2"  
  convolution_param { 
    num_output: 32 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}

layer { 
  name: "conv5_2/bn" 
  type: "BatchNorm"  
  bottom: "conv5_2" 
  top: "conv5_2"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0} 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0} 
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}

layer { 
  name: "conv5_2/scale" 
  type: "Scale"  
  bottom: "conv5_2"  
  top: "conv5_2"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000} 
  param {
    lr_mult: 1.0 
    decay_mult: 0.000} 
  scale_param {
    filler {
      type: "constant" 
      value: 1.0
    } 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
    }
  }
}

layer { 
  name: "relu5_2" 
  type: "ReLU" 
  bottom: "conv5_2"  
  top: "conv5_2"
}

layer { 
  bottom: "conv5_2" 
  top: "pool5" 
  name: "pool5" 
  type: "Pooling" 
  pooling_param {
    kernel_size: 3 
    stride: 2 
  pool: AVE
  } 
}

###----------------

layer { 
  name: "conv6_1"  
  type: "Convolution"  
  bottom: "pool5"  
  top: "conv6_1"  
  convolution_param { 
    num_output: 48 
    pad: 1 
    kernel_size: 3 
    stride: 1 
  } 
}


layer { 
  name: "conv6_1/bn" 
  type: "BatchNorm" 
  bottom: "conv6_1" 
  top: "conv6_1"  
  param {
    lr_mult: 0.0  
    decay_mult: 0.0
  }  
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  } 
  param {
    lr_mult: 0.0 
    decay_mult: 0.0
  }   
  batch_norm_param {
    moving_average_fraction: 0.999000012875 
    eps: 9.99999993923e-09 
    synchronize: true
  }
}


layer { 
  name: "conv6_1/scale"
  type: "Scale"  
  bottom: "conv6_1"  
  top: "conv6_1"  
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  }
  param {
    lr_mult: 1.0 
    decay_mult: 0.000
  }
  scale_param {
    filler { 
      type: "constant" 
      value: 1.0} 
    bias_term: true 
    bias_filler { 
      type: "constant" 
      value: 0.0
    }
  }
}


layer { 
  name: "relu6_1"  
  type: "ReLU"  
  bottom: "conv6_1" 
  top: "conv6_1"}

layer { 
  bottom: "conv6_1"
  top: "pool6"
  name: "pool6" 
  type: "Pooling" 
  pooling_param {
    kernel_size: 3 
    stride: 2 
    pool: AVE
  } 
}


###----------------
layer { 
  name: "conv_loc"  
  type: "Convolution" 
  bottom: "pool6" 
  top: "conv_loc" 
  convolution_param {
    num_output: 8  
    pad: 0  
    kernel_size: 3
  }
}

layer { 
  name: "sigmoid"  
  type: "Sigmoid" 
  bottom: "conv_loc" 
  top: "sigmoid"
}

layer { 
  name: "final_out" 
  type: "Reshape" 
  bottom: "sigmoid" 
  top: "final_out" 
  reshape_param { 
    shape { 
    dim: 0   
    dim: -1
    } 
  }
}

可视化结果