本文主要是介绍Caffe Prototxt 特征层系列：Scale Layer，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
Scale Layer是输入进行缩放和平移，常常出现在BatchNorm归一化后，Caffe中常用BatchNorm+Scale实现归一化操作（等同Pytorch中BatchNorm）
首先我们先看一下 ScaleParameter
message ScaleParameter {// The first axis of bottom[0] (the first input Blob) along which to apply// bottom[1] (the second input Blob). May be negative to index from the end// (e.g., -1 for the last axis).// 根据 bottom[0] 指定 bottom[1] 的形状// For example, if bottom[0] is 4D with shape 100x3x40x60, the output// top[0] will have the same shape, and bottom[1] may have any of the// following shapes (for the given value of axis):// (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60// (axis == 1 == -3) 3; 3x40; 3x40x60// (axis == 2 == -2) 40; 40x60// (axis == 3 == -1) 60// Furthermore, bottom[1] may have the empty shape (regardless of the value of// "axis") -- a scalar multiplier.// 例如，如果 bottom[0] 的 shape 为 100x3x40x60，则 top[0] 输出相同的 shape；// bottom[1] 可以包含上面 shapes 中的任一种(对于给定 axis 值). // 而且，bottom[1] 可以是 empty shape 的，没有任何的 axis 值，只是一个标量的乘子.optional int32 axis = 1 [default = 1];// (num_axes is ignored unless just one bottom is given and the scale is// a learned parameter of the layer. Otherwise, num_axes is determined by the// number of axes by the second bottom.)// (忽略 num_axes 参数，除非只给定一个 bottom 及 scale 是网络层的一个学习到的参数. // 否则，num_axes 是由第二个 bottom 的数量来决定的.)// The number of axes of the input (bottom[0]) covered by the scale// parameter, or -1 to cover all axes of bottom[0] starting from `axis`.// Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.// bottom[0] 的 num_axes 是由 scale 参数覆盖的；optional int32 num_axes = 2 [default = 1];// (filler is ignored unless just one bottom is given and the scale is// a learned parameter of the layer.)// (忽略 filler 参数，除非只给定一个 bottom 及 scale 是网络层的一个学习到的参数.// The initialization for the learned scale parameter.// scale 参数学习的初始化// Default is the unit (1) initialization, resulting in the ScaleLayer// initially performing the identity operation.// 默认是单位初始化，使 Scale 层初始进行单位操作.optional FillerParameter filler = 3;// Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but// may be more efficient). Initialized with bias_filler (defaults to 0).// 是否学习 bias，等价于 ScaleLayer+BiasLayer，只不过效率更高// 采用 bias_filler 进行初始化. 默认为 0.optional bool bias_term = 4 [default = false];optional FillerParameter bias_filler = 5;
}
Scale layer 在prototxt里面的书写：
layer {name: "scale_conv1"type: "Scale"bottom: "conv1"top: "conv1"scale_param {bias_term: true
}
例如在MobileNet中：
layer {name: "conv6_4/scale"type: "Scale"bottom: "conv6_4/bn"top: "conv6_4/bn"param {lr_mult: 1decay_mult: 0}param {lr_mult: 1decay_mult: 0}scale_param {bias_term: true}
}
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