查看DeepFace网络的proto文件发现使用Slice做blob数据分割，Eltwise层做blob数据融合，这里详细说明这个layer的用法。

1、Slice Layer

Slice Layer接收top blob的数据，并再指定维度做分割处理。

caffe.proto中定义如下：

message SliceParameter {
  // The axis along which to slice -- may be negative to index from the end
  // (e.g., -1 for the last axis).
  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
  #在制定维度上做分割
  optional int32 axis = 3 [default = 1];
  #指定分割点，不过不指定则均匀分割
  repeated uint32 slice_point = 2;

  //等同于axis
  // DEPRECATED: alias for "axis" -- does not support negative indexing.
  optional uint32 slice_dim = 1 [default = 1];
}

prototxt文件定义示例1：

layer{
  name: "slice1"
  type:"Slice"
  slice_param {
	slice_dim: 1    #在维度一上做均匀分割
  }
  bottom: "conv1"
  top: "slice1_1"    #分割后的第一部分赋值个blob slice1_1
  top: "slice1_2"    #分割后的第二部分赋值个blob slice2_2
}

prototxt文件定义示例2：

layer {
  name:"data_each"
  type:"Slice"
  bottom:"data_all"
  top:"data_classfier"    #输出blob1
  top:"data_boundingbox"  #输出blob2
  top:"data_facialpoints"    #输出blob3
  slice_param {
    axis:0
    slice_point:100        #指定blob1和blob2的分割点
    slice_point: 150       #指定blob2和blob3的分割点
  }
}

其中slice_point的个数等于top的个数减1。

加入的data_all为 250×3×24×24的blob

拆分后的3个输出的维度依次为 100×3×24×24，50×3×24×24，100×3×24×24

2、Eltwise Layer

是对两个blob按位置做数据融合，数据融合方式包括product（点乘）、sum（相加减）和 max（取大值），其中sum是默认操作。

cafe.proto文件中定义如下：

message EltwiseParameter {
  enum EltwiseOp {
    PROD = 0;
    SUM = 1;
    MAX = 2;
  }
  #支持按位操作类型
  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
  repeated float coeff = 2; // blob-wise coefficient for SUM operation

  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
  // of computing the gradient for the PROD operation. (No effect for SUM op.)
  optional bool stable_prod_grad = 3 [default = true];
}

proto文件语法如下：

layer{
  name: "etlwise1"
  type: "Eltwise"
  bottom: "slice1_1"
  bottom: "slice1_2"
  top: "eltwise1"
  eltwise_param {
	operation: MAX    #按位取slice1_1和slice1_2中较大值
  }
}

在来一段FaceNet中定义，加深理解：

name: "DeepFace_set003_net"
input: "data"
input_dim: 1
input_dim: 1
input_dim: 128
input_dim: 128

layer{
  name: "conv1"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
	num_output: 96
	kernel_size: 5
	stride: 1
	pad: 2
	weight_filler {
	  type: "xavier"
	}
	bias_filler {
	  type: "constant"
	  value: 0.1
	}
  }
  bottom: "data"
  top: "conv1"
}

layer{
  name: "slice1"
  type:"Slice"
  slice_param {
	slice_dim: 1
  }
  bottom: "conv1"
  top: "slice1_1"
  top: "slice1_2"
}
layer{
  name: "etlwise1"
  type: "Eltwise"
  bottom: "slice1_1"
  bottom: "slice1_2"
  top: "eltwise1"
  eltwise_param {
	operation: MAX
  }
}
layer{
  name: "pool1"
  type: "Pooling"
  pooling_param {
	pool: MAX
	kernel_size: 2
	stride: 2
  }
  bottom: "eltwise1"
  top: "pool1"
}

layer{
  name: "conv2a"
  type: "Convolution"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
	num_output: 96
	kernel_size: 1
	stride: 1
	weight_filler {
	  type: "xavier"
	}
	bias_filler {
	  type: "constant"
	  value: 0.1
	}
  }
  bottom: "pool1"
  top: "conv2a"
}

data 层接收图片数据
conv1 提取特征，生成96通道的blob
slice1 将96通道blob分成 48和48的两个blob
eltwise 按位取Max，合并成一个48通道的blob
conv2a是一个NIN，实现48通道间的数据融合

这个网络还是很高效的

Caffe Layer 系列(二)：Slice层、Eltwise层

1、Slice Layer

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