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// Copyright 2013 Yangqing Jia
#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/vision_layers.hpp"
namespace caffe {
template <typename Dtype>
void DropoutLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
NeuronLayer<Dtype>::SetUp(bottom, top);
// Set up the cache for random number generation
rand_vec_.reset(new SyncedMemory(bottom[0]->count() * sizeof(int)));
threshold_ = this->layer_param_.dropout_ratio();
DCHECK(threshold_ > 0.);
DCHECK(threshold_ < 1.);
scale_ = 1. / (1. - threshold_);
uint_thres_ = (unsigned int)(UINT_MAX * threshold_);
}
template <typename Dtype>
void DropoutLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
const Dtype* bottom_data = bottom[0]->cpu_data();
Dtype* top_data = (*top)[0]->mutable_cpu_data();
int* mask = reinterpret_cast<int*>(rand_vec_->mutable_cpu_data());
const int count = bottom[0]->count();
if (Caffe::phase() == Caffe::TRAIN) {
// Create random numbers
viRngBernoulli(VSL_RNG_METHOD_BERNOULLI_ICDF, Caffe::vsl_stream(),
count, mask, 1. - threshold_);
for (int i = 0; i < count; ++i) {
top_data[i] = bottom_data[i] * mask[i] * scale_;
}
} else {
memcpy(top_data, bottom_data, bottom[0]->count() * sizeof(Dtype));
}
}
template <typename Dtype>
Dtype DropoutLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down,
vector<Blob<Dtype>*>* bottom) {
CHECK(Caffe::phase() == Caffe::TRAIN);
if (propagate_down) {
const Dtype* top_diff = top[0]->cpu_diff();
Dtype* bottom_diff = (*bottom)[0]->mutable_cpu_diff();
const int* mask = reinterpret_cast<const int*>(rand_vec_->cpu_data());
const int count = (*bottom)[0]->count();
for (int i = 0; i < count; ++i) {
bottom_diff[i] = top_diff[i] * mask[i] * scale_;
}
}
return Dtype(0);
}
INSTANTIATE_CLASS(DropoutLayer);
} // namespace caffe
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