#include <algorithm>

#include <vector>

#include "caffe/layer.hpp"

#include "caffe/neuron_layers.hpp"

#include "caffe/util/math_functions.hpp"

namespace caffe {

template <typename Dtype>

void LogLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top) {

NeuronLayer<Dtype>::LayerSetUp(bottom, top);

const Dtype base = this->layer_param_.log_param().base();

if (base != Dtype(-1)) {

CHECK_GT(base, 0) << "base must be strictly positive.";

}

// If base == -1, interpret the base as e and set log_base = 1 exactly.

// Otherwise, calculate its log explicitly.

const Dtype log_base = (base == Dtype(-1)) ? Dtype(1) : log(base);

CHECK(!isnan(log_base))

<< "NaN result: log(base) = log(" << base << ") = " << log_base;

CHECK(!isinf(log_base))

<< "Inf result: log(base) = log(" << base << ") = " << log_base;

base_scale_ = Dtype(1) / log_base;

CHECK(!isnan(base_scale_))

<< "NaN result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;

CHECK(!isinf(base_scale_))

<< "Inf result: 1/log(base) = 1/log(" << base << ") = " << base_scale_;

input_scale_ = this->layer_param_.log_param().scale();

input_shift_ = this->layer_param_.log_param().shift();

backward_num_scale_ = input_scale_ / log_base;

}

template <typename Dtype>

void LogLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,

const vector<Blob<Dtype>*>& top) {

const int count = bottom[0]->count();

const Dtype* bottom_data = bottom[0]->cpu_data();

Dtype* top_data = top[0]->mutable_cpu_data();

if (input_scale_ == Dtype(1) && input_shift_ == Dtype(0)) {

caffe_log(count, bottom_data, top_data);

} else {

caffe_copy(count, bottom_data, top_data);

if (input_scale_ != Dtype(1)) {

caffe_scal(count, input_scale_, top_data);

}

if (input_shift_ != Dtype(0)) {

caffe_add_scalar(count, input_shift_, top_data);

}

caffe_log(count, top_data, top_data);

}

if (base_scale_ != Dtype(1)) {

caffe_scal(count, base_scale_, top_data);

}

}

template <typename Dtype>

void LogLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,

const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {

if (!propagate_down[0]) { return; }

const int count = bottom[0]->count();

const Dtype* bottom_data = bottom[0]->cpu_data();

const Dtype* top_diff = top[0]->cpu_diff();

Dtype* bottom_diff = bottom[0]->mutable_cpu_diff();

caffe_copy(count, bottom_data, bottom_diff);

if (input_scale_ != Dtype(1)) {

caffe_scal(count, input_scale_, bottom_diff);

}

if (input_shift_ != Dtype(0)) {

caffe_add_scalar(count, input_shift_, bottom_diff);

}

caffe_powx(count, bottom_diff, Dtype(-1), bottom_diff);

if (backward_num_scale_ != Dtype(1)) {

caffe_scal(count, backward_num_scale_, bottom_diff);

}

caffe_mul(count, top_diff, bottom_diff, bottom_diff);

}

#ifdef CPU_ONLY

STUB_GPU(LogLayer);

#endif

INSTANTIATE_CLASS(LogLayer);

REGISTER_LAYER_CLASS(Log);

} // namespace caffe