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// Copyright 2013 Yangqing Jia
// Fillers are random number generators that fills a blob using the specified
// algorithm. The expectation is that they are only going to be used during
// initialization time and will not involve any GPUs.
#ifndef CAFFE_FILLER_HPP
#define CAFFE_FILLER_HPP
#include <string>
#include "caffe/common.hpp"
#include "caffe/blob.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/proto/caffe.pb.h"
namespace caffe {
template <typename Dtype>
class Filler {
public:
explicit Filler(const FillerParameter& param) : filler_param_(param) {}
virtual ~Filler() {}
virtual void Fill(Blob<Dtype>* blob) = 0;
protected:
FillerParameter filler_param_;
}; // class Filler
template <typename Dtype>
class ConstantFiller : public Filler<Dtype> {
public:
explicit ConstantFiller(const FillerParameter& param)
: Filler<Dtype>(param) {}
virtual void Fill(Blob<Dtype>* blob) {
Dtype* data = blob->mutable_cpu_data();
const int count = blob->count();
const Dtype value = this->filler_param_.value();
CHECK(count);
for (int i = 0; i < count; ++i) {
data[i] = value;
}
}
};
template <typename Dtype>
class UniformFiller : public Filler<Dtype> {
public:
explicit UniformFiller(const FillerParameter& param)
: Filler<Dtype>(param) {}
virtual void Fill(Blob<Dtype>* blob) {
CHECK(blob->count());
caffe_vRngUniform<Dtype>(blob->count(), blob->mutable_cpu_data(),
Dtype(this->filler_param_.min()),
Dtype(this->filler_param_.max()));
}
};
template <typename Dtype>
class GaussianFiller : public Filler<Dtype> {
public:
explicit GaussianFiller(const FillerParameter& param)
: Filler<Dtype>(param) {}
virtual void Fill(Blob<Dtype>* blob) {
Dtype* data = blob->mutable_cpu_data();
CHECK(blob->count());
caffe_vRngGaussian<Dtype>(blob->count(), blob->mutable_cpu_data(),
Dtype(this->filler_param_.mean()),
Dtype(this->filler_param_.std()));
}
};
template <typename Dtype>
class PositiveUnitballFiller : public Filler<Dtype> {
public:
explicit PositiveUnitballFiller(const FillerParameter& param)
: Filler<Dtype>(param) {}
virtual void Fill(Blob<Dtype>* blob) {
Dtype* data = blob->mutable_cpu_data();
DCHECK(blob->count());
caffe_vRngUniform<Dtype>(blob->count(), blob->mutable_cpu_data(), 0, 1);
// We expect the filler to not be called very frequently, so we will
// just use a simple implementation
int dim = blob->count() / blob->num();
CHECK(dim);
for (int i = 0; i < blob->num(); ++i) {
Dtype sum = 0;
for (int j = 0; j < dim; ++j) {
sum += data[i * dim + j];
}
for (int j = 0; j < dim; ++j) {
data[i * dim + j] /= sum;
}
}
}
};
// A filler based on the paper [Bengio and Glorot 2010]: Understanding
// the difficulty of training deep feedforward neuralnetworks, but does not
// use the fan_out value.
//
// It fills the incoming matrix by randomly sampling uniform data from
// [-scale, scale] where scale = sqrt(3 / fan_in) where fan_in is the number
// of input nodes. You should make sure the input blob has shape (num, a, b, c)
// where a * b * c = fan_in.
template <typename Dtype>
class XavierFiller : public Filler<Dtype> {
public:
explicit XavierFiller(const FillerParameter& param)
: Filler<Dtype>(param) {}
virtual void Fill(Blob<Dtype>* blob) {
CHECK(blob->count());
int fan_in = blob->count() / blob->num();
Dtype scale = sqrt(Dtype(3) / fan_in);
caffe_vRngUniform<Dtype>(blob->count(), blob->mutable_cpu_data(),
-scale, scale);
}
};
// A function to get a specific filler from the specification given in
// FillerParameter. Ideally this would be replaced by a factory pattern,
// but we will leave it this way for now.
template <typename Dtype>
Filler<Dtype>* GetFiller(const FillerParameter& param) {
const std::string& type = param.type();
if (type == "constant") {
return new ConstantFiller<Dtype>(param);
} else if (type == "gaussian") {
return new GaussianFiller<Dtype>(param);
} else if (type == "positive_unitball") {
return new PositiveUnitballFiller<Dtype>(param);
} else if (type == "uniform") {
return new UniformFiller<Dtype>(param);
} else if (type == "xavier") {
return new XavierFiller<Dtype>(param);
} else {
CHECK(false) << "Unknown filler name: " << param.type();
}
return (Filler<Dtype>*)(NULL);
}
} // namespace caffe
#endif // CAFFE_FILLER_HPP_
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