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#include <vector>
#include "caffe/layer.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
void EltwiseLayer<Dtype>::Forward_gpu(
const vector<Blob<Dtype>*>& bottom, vector<Blob<Dtype>*>* top) {
const int count = (*top)[0]->count();
Dtype* top_data = (*top)[0]->mutable_gpu_data();
switch (op_) {
case EltwiseParameter_EltwiseOp_PROD:
caffe_gpu_mul(count, bottom[0]->gpu_data(),
bottom[1]->gpu_data(), top_data);
for (int i = 2; i < bottom.size(); ++i) {
caffe_gpu_mul(count, top_data, bottom[i]->gpu_data(), top_data);
}
break;
case EltwiseParameter_EltwiseOp_SUM:
caffe_gpu_set(count, Dtype(0.), top_data);
// TODO(shelhamer) does cuBLAS optimize to sum for coeff = 1?
for (int i = 0; i < bottom.size(); ++i) {
caffe_gpu_axpy(count, coeffs_[i], bottom[i]->gpu_data(), top_data);
}
break;
default:
LOG(FATAL) << "Unknown elementwise operation.";
}
}
template <typename Dtype>
void EltwiseLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, vector<Blob<Dtype>*>* bottom) {
const int count = top[0]->count();
const Dtype* top_data = top[0]->gpu_data();
const Dtype* top_diff = top[0]->gpu_diff();
for (int i = 0; i < bottom->size(); ++i) {
if (propagate_down[i]) {
const Dtype* bottom_data = (*bottom)[i]->gpu_data();
Dtype* bottom_diff = (*bottom)[i]->mutable_gpu_diff();
switch (op_) {
case EltwiseParameter_EltwiseOp_PROD:
caffe_gpu_div(count, top_data, bottom_data, bottom_diff);
caffe_gpu_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_gpu_scale(count, coeffs_[i], top_diff, bottom_diff);
}
break;
default:
LOG(FATAL) << "Unknown elementwise operation.";
}
}
}
}
INSTANTIATE_CLASS(EltwiseLayer);
} // namespace caffe
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