绘制神经网络结构图

最新推荐文章于 2023-11-16 18:00:52 发布

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最新推荐文章于 2023-11-16 18:00:52 发布

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分类专栏：深度学习

本文链接：https://blog.youkuaiyun.com/cc__cc__/article/details/103737655

→→绘制神经网络结构图工具链接

以Faster RCNN中VGG16/faster_rcnn_alt_opt/stage1_rpn_train.pt为例

网络结构图如下：
在这里插入图片描述
原pt文件：

name: "VGG_ILSVRC_16_layers"
layer {
   
  name: 'input-data'
  type: 'Python'
  top: 'data'
  top: 'im_info'
  top: 'gt_boxes'
  python_param {
   
    module: 'roi_data_layer.layer'
    layer: 'RoIDataLayer'
    param_str: "'num_classes': 21"  
  }
}
layer {
   
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
   
    lr_mult: 0
    decay_mult: 0
  }
  param {
   
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
   
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
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: 0
    decay_mult: 0
  }
  param {
   
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
   
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
   
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
   
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
   
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
   
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
   
    lr_mult: 0
    decay_mult: 0
  }
  param {
   
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
   
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
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: 0
    decay_mult: 0
  }
  param {
   
    lr_mult: 0
    decay_mult

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