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#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#endif // USE_OPENCV
#include <vector>
#include "caffe/data_layers.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/io.hpp"
namespace caffe {
template <typename Dtype>
void MemoryDataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
batch_size_ = this->layer_param_.memory_data_param().batch_size();
channels_ = this->layer_param_.memory_data_param().channels();
height_ = this->layer_param_.memory_data_param().height();
width_ = this->layer_param_.memory_data_param().width();
size_ = channels_ * height_ * width_;
CHECK_GT(batch_size_ * size_, 0) <<
"batch_size, channels, height, and width must be specified and"
" positive in memory_data_param";
vector<int> label_shape(1, batch_size_);
top[0]->Reshape(batch_size_, channels_, height_, width_);
top[1]->Reshape(label_shape);
added_data_.Reshape(batch_size_, channels_, height_, width_);
added_label_.Reshape(label_shape);
data_ = NULL;
labels_ = NULL;
added_data_.cpu_data();
added_label_.cpu_data();
}
template <typename Dtype>
void MemoryDataLayer<Dtype>::AddDatumVector(const vector<Datum>& datum_vector) {
CHECK(!has_new_data_) <<
"Can't add data until current data has been consumed.";
size_t num = datum_vector.size();
CHECK_GT(num, 0) << "There is no datum to add.";
CHECK_EQ(num % batch_size_, 0) <<
"The added data must be a multiple of the batch size.";
added_data_.Reshape(num, channels_, height_, width_);
added_label_.Reshape(num, 1, 1, 1);
// Apply data transformations (mirror, scale, crop...)
this->data_transformer_->Transform(datum_vector, &added_data_);
// Copy Labels
Dtype* top_label = added_label_.mutable_cpu_data();
for (int item_id = 0; item_id < num; ++item_id) {
top_label[item_id] = datum_vector[item_id].label();
}
// num_images == batch_size_
Dtype* top_data = added_data_.mutable_cpu_data();
Reset(top_data, top_label, num);
has_new_data_ = true;
}
#ifdef USE_OPENCV
template <typename Dtype>
void MemoryDataLayer<Dtype>::AddMatVector(const vector<cv::Mat>& mat_vector,
const vector<int>& labels) {
size_t num = mat_vector.size();
CHECK(!has_new_data_) <<
"Can't add mat until current data has been consumed.";
CHECK_GT(num, 0) << "There is no mat to add";
CHECK_EQ(num % batch_size_, 0) <<
"The added data must be a multiple of the batch size.";
added_data_.Reshape(num, channels_, height_, width_);
added_label_.Reshape(num, 1, 1, 1);
// Apply data transformations (mirror, scale, crop...)
this->data_transformer_->Transform(mat_vector, &added_data_);
// Copy Labels
Dtype* top_label = added_label_.mutable_cpu_data();
for (int item_id = 0; item_id < num; ++item_id) {
top_label[item_id] = labels[item_id];
}
// num_images == batch_size_
Dtype* top_data = added_data_.mutable_cpu_data();
Reset(top_data, top_label, num);
has_new_data_ = true;
}
#endif // USE_OPENCV
template <typename Dtype>
void MemoryDataLayer<Dtype>::Reset(Dtype* data, Dtype* labels, int n) {
CHECK(data);
CHECK(labels);
CHECK_EQ(n % batch_size_, 0) << "n must be a multiple of batch size";
// Warn with transformation parameters since a memory array is meant to
// be generic and no transformations are done with Reset().
if (this->layer_param_.has_transform_param()) {
LOG(WARNING) << this->type() << " does not transform array data on Reset()";
}
data_ = data;
labels_ = labels;
n_ = n;
pos_ = 0;
}
template <typename Dtype>
void MemoryDataLayer<Dtype>::set_batch_size(int new_size) {
CHECK(!has_new_data_) <<
"Can't change batch_size until current data has been consumed.";
batch_size_ = new_size;
added_data_.Reshape(batch_size_, channels_, height_, width_);
added_label_.Reshape(batch_size_, 1, 1, 1);
}
template <typename Dtype>
void MemoryDataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
CHECK(data_) << "MemoryDataLayer needs to be initalized by calling Reset";
top[0]->Reshape(batch_size_, channels_, height_, width_);
top[1]->Reshape(batch_size_, 1, 1, 1);
top[0]->set_cpu_data(data_ + pos_ * size_);
top[1]->set_cpu_data(labels_ + pos_);
pos_ = (pos_ + batch_size_) % n_;
if (pos_ == 0)
has_new_data_ = false;
}
INSTANTIATE_CLASS(MemoryDataLayer);
REGISTER_LAYER_CLASS(MemoryData);
} // namespace caffe
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