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// Copyright 2014 BVLC and contributors.
#include <vector>
#include "caffe/layer.hpp"
#include "caffe/vision_layers.hpp"
#include "caffe/util/math_functions.hpp"
namespace caffe {
template <typename Dtype>
void ConcatLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
CHECK_GT(bottom.size(), 1) <<
"Concat Layer takes at least two blobs as input.";
CHECK_EQ(top->size(), 1) <<
"Concat Layer takes a single blob as output.";
concat_dim_ = this->layer_param_.concat_dim();
CHECK_GE(concat_dim_, 0) << "concat_dim should be >= 0";
CHECK_LE(concat_dim_, 1) <<
"For now concat_dim <=1, it can only concat num and channels";
// Intialize with the first blob
COUNT_ = bottom[0]->count();
NUM_ = bottom[0]->num();
CHANNELS_ = bottom[0]->channels();
HEIGHT_ = bottom[0]->height();
WIDTH_ = bottom[0]->width();
for (int i = 1; i < bottom.size(); ++i) {
COUNT_ += bottom[i]->count();
if (concat_dim_== 0) {
NUM_ += bottom[i]->num();
} else if (concat_dim_ == 1) {
CHANNELS_ += bottom[i]->channels();
} else if (concat_dim_ == 2) {
HEIGHT_ += bottom[i]->height();
} else if (concat_dim_ == 3) {
WIDTH_ += bottom[i]->width();
}
}
(*top)[0]->Reshape(NUM_, CHANNELS_, HEIGHT_, WIDTH_);
CHECK_EQ(COUNT_, (*top)[0]->count());
}
template <typename Dtype>
Dtype ConcatLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
Dtype* top_data = (*top)[0]->mutable_cpu_data();
if (concat_dim_== 0) {
int offset_num = 0;
for (int i = 0; i < bottom.size(); ++i) {
const Dtype* bottom_data = bottom[i]->cpu_data();
int num_elem = bottom[i]->count();
caffe_copy(num_elem, bottom_data, top_data+(*top)[0]->offset(offset_num));
offset_num += bottom[i]->num();
}
} else if (concat_dim_ == 1) {
int offset_channel = 0;
for (int i = 0; i < bottom.size(); ++i) {
const Dtype* bottom_data = bottom[i]->cpu_data();
int num_elem =
bottom[i]->channels()*bottom[i]->height()*bottom[i]->width();
for (int n = 0; n < NUM_; ++n) {
caffe_copy(num_elem, bottom_data+bottom[i]->offset(n),
top_data+(*top)[0]->offset(n, offset_channel));
}
offset_channel += bottom[i]->channels();
}
} else {
LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
" not implemented yet";
}
return Dtype(0.);
}
template <typename Dtype>
void ConcatLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
const Dtype* top_diff = top[0]->cpu_diff();
if (concat_dim_ == 0) {
int offset_num = 0;
for (int i = 0; i < bottom->size(); ++i) {
Blob<Dtype>* blob = (*bottom)[i];
Dtype* bottom_diff = blob->mutable_cpu_diff();
caffe_copy(blob->count(),
top_diff+top[0]->offset(offset_num), bottom_diff);
offset_num += blob->num();
}
} else if (concat_dim_ == 1) {
int offset_channel = 0;
for (int i = 0; i < bottom->size(); ++i) {
Blob<Dtype>* blob = (*bottom)[i];
Dtype* bottom_diff = blob->mutable_cpu_diff();
int num_elem = blob->channels()*blob->height()*blob->width();
for (int n = 0; n < NUM_; ++n) {
caffe_copy(num_elem, top_diff+top[0]->offset(n, offset_channel),
bottom_diff+blob->offset(n));
}
offset_channel += blob->channels();
}
} else {
LOG(FATAL) << "concat_dim along dim" << concat_dim_ <<
" not implemented yet";
}
}
INSTANTIATE_CLASS(ConcatLayer);
} // namespace caffe
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