【caffe】Layer解讀之：Convolution

• Layer type: Convolution
• 頭文件位置：./include/caffe/layers/conv_layer.hpp
• CPU 執行源文件位置: ./src/caffe/layers/conv_layer.cpp
• CUDA GPU 執行源文件位置: ./src/caffe/layers/conv_layer.cu
• Convolution層的功能：使用一組可學習的濾波器對輸入圖像進行卷積，每個濾波器在輸出圖像中生成一個特徵映射。
• 輸入
  n * c_i * h_i * w_i
• 輸出
  n * c_o * h_o * w_o, where h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1 and w_o likewise.

參數解讀

  layer {
    name: "conv1"
    type: "Convolution"
    bottom: "data"
    top: "conv1"
    # 實際學習率 = 根據slover.prototxt中的base乘以下面係數
    # 權重學習率
    param { 
        lr_mult: 1 
        decay_mult: 1 
    }
    # 偏置學習率
    param { 
        lr_mult: 2 
        decay_mult: 0 
        }
    convolution_param {
      num_output: 96     
      kernel_size: 11     
      stride: 4          
      weight_filler {
        type: "gaussian" # 用高斯來初始化權重
        std: 0.01        # 
      }
      bias_filler {
        type: "constant" # 初始化偏置
        value: 0
      }
    }
  }

參數定義

Parameters (ConvolutionParameter convolution_param)

• 需要的參數
  num_output (c_o): 濾波器個數
  kernel_size (or kernel_h and kernel_w): 濾波器尺寸
• 強烈推薦
  weight_filler [default type: ‘constant’ value: 0]
• 可選參數
  bias_term [default true]: 指定是否學習並將一組加法偏差應用於濾波器輸出，就是有沒有偏置項。
  pad (or pad_h and pad_w) [default 0]: 指定（隱式）添加到輸入的每一側的像素數
  stride (or stride_h and stride_w) [default 1]:指定將過濾器在圖像上滑動的間隔
  group (g) [default 1]: 如果g> 1，我們將每個過濾器的連接限制爲輸入的子集。 具體地，輸入和輸出通道被分成g組，並且第i個輸出組通道將僅連接到第i個輸入組通道。這個操作可以參考shfflenet網絡。

message ConvolutionParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional bool bias_term = 2 [default = true]; // whether to have bias terms

  // Pad, kernel size, and stride are all given as a single value for equal
  // dimensions in all spatial dimensions, or once per spatial dimension.
  repeated uint32 pad = 3; // The padding size; defaults to 0
  repeated uint32 kernel_size = 4; // The kernel size
  repeated uint32 stride = 6; // The stride; defaults to 1
  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
  // holes. (Kernel dilation is sometimes referred to by its use in the
  // algorithme à trous from Holschneider et al. 1987.)
  repeated uint32 dilation = 18; // The dilation; defaults to 1

  // For 2D convolution only, the *_h and *_w versions may also be used to
  // specify both spatial dimensions.
  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
  optional uint32 kernel_h = 11; // The kernel height (2D only)
  optional uint32 kernel_w = 12; // The kernel width (2D only)
  optional uint32 stride_h = 13; // The stride height (2D only)
  optional uint32 stride_w = 14; // The stride width (2D only)

  optional uint32 group = 5 [default = 1]; // The group size for group conv

  optional FillerParameter weight_filler = 7; // The filler for the weight
  optional FillerParameter bias_filler = 8; // The filler for the bias
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 15 [default = DEFAULT];

  // The axis to interpret as "channels" when performing convolution.
  // Preceding dimensions are treated as independent inputs;
  // succeeding dimensions are treated as "spatial".
  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
  // groups g>1) filters across the spatial axes (H, W) of the input.
  // With (N, C, D, H, W) inputs, and axis == 1, we perform
  // N independent 3D convolutions, sliding (C/g)-channels
  // filters across the spatial axes (D, H, W) of the input.
  optional int32 axis = 16 [default = 1];

  // Whether to force use of the general ND convolution, even if a specific
  // implementation for blobs of the appropriate number of spatial dimensions
  // is available. (Currently, there is only a 2D-specific convolution
  // implementation; for input blobs with num_axes != 2, this option is
  // ignored and the ND implementation will be used.)
  optional bool force_nd_im2col = 17 [default = false];
}