SSD 源码（1）
训练数据加载，与读取：主要由 AnnotatedDataLayer 承担
其构造函数如下：
template type>
AnnotatedDataLayertype>::AnnotatedDataLayer(const LayerParameter& param)
: BasePrefetchingDataLayertype>(param),
reader_(param) {
} 构造函数很简单，但是，创建了2个主要的数据读取线程：
1#基础数据（LMDB）读取线程
2#目标数据读取与分析线程。
第一个目标由成员变量reader_实现，第二个目标结合BasePrefetchingDataLayer层实现。为了了解实现原理，先梳理一下他们的类之间的继承关系。
如上图所示，class IntrnalThread 中：
- StartInternalThread（）函数线程开启
- InternalThreadEntry（）具体实现线程功能，此函数为虚函数，根据需要进行重写。
class DataReader 中的成员变量body，重写了InternalThreadEntry（）函数，实现基本数据读取功能。
class BasePrefetchingDataLayer 重写了InternalThreadEntry（）函数，实现标注数据信息的读取功能。AnnotatedDataLayer类中的load_batch()函数，重写了数据加载功能。
template <typename Dtype>
void AnnotatedDataLayer::DataLayerSetUp(
const vector::LayerSetUp() requires to call
// cpu_data and gpu_data for consistent prefetch thread. Thus we make
// sure there is at least one bbox.
label_shape[2] = std::max(num_bboxes, 1);
label_shape[3] = 8;
} else {
LOG(FATAL) << "Unknown annotation type.";
}
} else {
label_shape[0] = batch_size;
}
top[1]->Reshape(label_shape);
for (int i = 0; i < this->PREFETCH_COUNT; ++i) {
this->prefetch_[i].label_.Reshape(label_shape);
}
}
}
// This function is called on prefetch thread
template<typename Dtype>
void AnnotatedDataLayer::load_batch(Batch* batch) {
CPUTimer batch_timer;
batch_timer.Start();
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
CHECK(batch->data_.count());
CHECK(this->transformed_data_.count());
// Reshape according to the first anno_datum of each batch
// on single input batches allows for inputs of varying dimension.
const int batch_size = this->layer_param_.data_param().batch_size();
AnnotatedDatum& anno_datum = *(reader_.full().peek()); //加载一副图像
// Use data_transformer to infer the expected blob shape from anno_datum.
vector<int> top_shape =
this->data_transformer_->InferBlobShape(anno_datum.datum());
this->transformed_data_.Reshape(top_shape);
// Reshape batch according to the batch_size.
top_shape[0] = batch_size;
batch->data_.Reshape(top_shape);
Dtype* top_data = batch->data_.mutable_cpu_data();
Dtype* top_label = NULL; // suppress warnings about uninitialized variables
if (this->output_labels_ && !has_anno_type_) {
top_label = batch->label_.mutable_cpu_data();
}
// Store transformed annotation.
map<int, vector(&anno_datum));
}
///保存最终的目标结果
// Store "rich" annotation if needed.
if (this->output_labels_ && has_anno_type_) {
vector<int> label_shape(4);
if (anno_type_ == AnnotatedDatum_AnnotationType_BBOX) {
label_shape[0] = 1;
label_shape[1] = 1;
label_shape[3] = 8;
if (num_bboxes == 0) {
// Store all -1 in the label.
label_shape[2] = 1;
batch->label_.Reshape(label_shape);
caffe_set(8, -1, batch->label_.mutable_cpu_data());
} else {
// Reshape the label and store the annotation.
label_shape[2] = num_bboxes;
batch->label_.Reshape(label_shape);
top_label = batch->label_.mutable_cpu_data();
int idx = 0;
for (int item_id = 0; item_id < batch_size; ++item_id) {
const vector