[Caffe]:关于Eltwise layer

Eltwise : element-wise

eltwise layer是caffe提供的按元素操作层。它支持3种基本操作：
1. PROD：按元素乘积
2. SUM：按元素求和（默认）
3. MAX：保存元素大者

进行何种操作可以在layer里面通过定义EltwiseOp : x #x:=0,1,2 除此之外，该层还定义了
coeff 参数，该参数只对SUM操作起作用。
最后，caffe还设定了stable_prod_grad #[default = true ] 来选择是否渐进较慢的梯度计算方法，该方法只适用于PROD操作，对SUM操作无效。
更多细节参见下面的源码。

eltwise_layer 源码

#include <cfloat>
#include <vector>#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/util/math_functions.hpp"namespace caffe {template <typename Dtype>
void EltwiseLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top) {CHECK(this->layer_param().eltwise_param().coeff_size() == 0|| this->layer_param().eltwise_param().coeff_size() == bottom.size()) <<"Eltwise Layer takes one coefficient per bottom blob.";CHECK(!(this->layer_param().eltwise_param().operation()== EltwiseParameter_EltwiseOp_PROD&& this->layer_param().eltwise_param().coeff_size())) <<"Eltwise layer only takes coefficients for summation.";op_ = this->layer_param_.eltwise_param().operation();// Blob-wise coefficients for the elementwise operation.coeffs_ = vector<Dtype>(bottom.size(), 1);if (this->layer_param().eltwise_param().coeff_size()) {for (int i = 0; i < bottom.size(); ++i) {coeffs_[i] = this->layer_param().eltwise_param().coeff(i);}}stable_prod_grad_ = this->layer_param_.eltwise_param().stable_prod_grad();
}template <typename Dtype>
void EltwiseLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top) {for (int i = 1; i < bottom.size(); ++i) {CHECK(bottom[i]->shape() == bottom[0]->shape());}top[0]->ReshapeLike(*bottom[0]);// If max operation, we will initialize the vector index part.if (this->layer_param_.eltwise_param().operation() ==EltwiseParameter_EltwiseOp_MAX && top.size() == 1) {max_idx_.Reshape(bottom[0]->shape());}
}template <typename Dtype>
void EltwiseLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {int* mask = NULL;const Dtype* bottom_data_a = NULL;const Dtype* bottom_data_b = NULL;const int count = top[0]->count();Dtype* top_data = top[0]->mutable_cpu_data();switch (op_) {                         //choose different operations according to op_case EltwiseParameter_EltwiseOp_PROD:  //PROD,按位乘caffe_mul(count, bottom[0]->cpu_data(), bottom[1]->cpu_data(), top_data);for (int i = 2; i < bottom.size(); ++i) {caffe_mul(count, top_data, bottom[i]->cpu_data(), top_data);}break;case EltwiseParameter_EltwiseOp_SUM: //SUM 按位和，这里有coffs_为系数，可以通过指定某一输入的系数来完成按位加和按位减caffe_set(count, Dtype(0), top_data);// TODO(shelhamer) does BLAS optimize to sum for coeff = 1?for (int i = 0; i < bottom.size(); ++i) {caffe_axpy(count, coeffs_[i], bottom[i]->cpu_data(), top_data);}break;case EltwiseParameter_EltwiseOp_MAX: //按位取大数// Initializemask = max_idx_.mutable_cpu_data();caffe_set(count, -1, mask);caffe_set(count, Dtype(-FLT_MAX), top_data);// bottom 0 & 1bottom_data_a = bottom[0]->cpu_data();bottom_data_b = bottom[1]->cpu_data();for (int idx = 0; idx < count; ++idx) {if (bottom_data_a[idx] > bottom_data_b[idx]) {top_data[idx] = bottom_data_a[idx];  // maxvalmask[idx] = 0;  // maxid} else {top_data[idx] = bottom_data_b[idx];  // maxvalmask[idx] = 1;  // maxid}}// bottom 2++for (int blob_idx = 2; blob_idx < bottom.size(); ++blob_idx) {bottom_data_b = bottom[blob_idx]->cpu_data();for (int idx = 0; idx < count; ++idx) {if (bottom_data_b[idx] > top_data[idx]) {top_data[idx] = bottom_data_b[idx];  // maxvalmask[idx] = blob_idx;  // maxid}}}break;default:LOG(FATAL) << "Unknown elementwise operation.";}
}template <typename Dtype>
void EltwiseLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {const int* mask = NULL;const int count = top[0]->count();const Dtype* top_data = top[0]->cpu_data();const Dtype* top_diff = top[0]->cpu_diff();for (int i = 0; i < bottom.size(); ++i) {if (propagate_down[i]) {const Dtype* bottom_data = bottom[i]->cpu_data();Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();switch (op_) {case EltwiseParameter_EltwiseOp_PROD:if (stable_prod_grad_) {bool initialized = false;for (int j = 0; j < bottom.size(); ++j) {if (i == j) { continue; }if (!initialized) {caffe_copy(count, bottom[j]->cpu_data(), bottom_diff);initialized = true;} else {caffe_mul(count, bottom[j]->cpu_data(), bottom_diff,bottom_diff);}}} else {caffe_div(count, top_data, bottom_data, bottom_diff);}caffe_mul(count, bottom_diff, top_diff, bottom_diff);break;case EltwiseParameter_EltwiseOp_SUM:if (coeffs_[i] == Dtype(1)) {caffe_copy(count, top_diff, bottom_diff);} else {caffe_cpu_scale(count, coeffs_[i], top_diff, bottom_diff);}break;case EltwiseParameter_EltwiseOp_MAX:mask = max_idx_.cpu_data();for (int index = 0; index < count; ++index) {Dtype gradient = 0;if (mask[index] == i) {gradient += top_diff[index];}bottom_diff[index] = gradient;}break;default:LOG(FATAL) << "Unknown elementwise operation.";}}}
}#ifdef CPU_ONLY
STUB_GPU(EltwiseLayer);
#endifINSTANTIATE_CLASS(EltwiseLayer);
REGISTER_LAYER_CLASS(Eltwise);}  // namespace caffe

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[Caffe]:关于Eltwise layer

[Caffe]:关于Eltwise layer

Eltwise : element-wise

eltwise_layer 源码

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