【Caffe代码分析】DataLayer

函数功能：

DataLayer 用于将数据库上的内容，一个batch一个batch的读入到相对应的Blob中，

首先查看其继承关系



注意其不是直接继承于BaseDatalayer，因为，它需要并行的读取数据库上的数据，需要新开线程来预先读入数据。

DataLayer 有两个指针成员用来存放数据库和游标，

shared_ptr<db::DB> db_;
shared_ptr<db::Cursor> cursor_;

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其继承自基类的成员变量有

protected:
Blob<Dtype> prefetch_data_;
Blob<Dtype> prefetch_label_;
Blob<Dtype> transformed_data_;

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用于保存预读取的数据，标签，以及转换过的数据

继承自BaseDataLayer的成员变量有：

bool output_labels_;

其成员函数

InternalThreadEntry()

用于真正的数据读入操作，

其中

Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
Dtype* top_label = NULL;  // suppress warnings about uninitialized variables

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指针用于保留输入的批数据。

数据库里面的数据依然是先转化为Datum，

Datum datum;

datum.ParseFromString(cursor_->value());

int offset = this->prefetch_data_.offset(item_id);

this->transformed_data_.set_cpu_data(top_data + offset);

top_label[item_id] = datum.label();

其读取数据库输入也是通过游标来操作，

cursor_->Next();

,注意这里都是按照顺序读入的，所以，需要自己保证在输入存入数据库的时候确保其是无序的。

=============

源代码：

#include <opencv2/core/core.hpp>

#include <stdint.h>

#include <string>
#include <vector>

#include "caffe/common.hpp"
#include "caffe/data_layers.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/benchmark.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/util/rng.hpp"

namespace caffe {

template <typename Dtype>
DataLayer<Dtype>::~DataLayer<Dtype>() {
this->JoinPrefetchThread();
}

template <typename Dtype>
void DataLayer<Dtype>::DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
// Initialize DB
db_.reset(db::GetDB(this->layer_param_.data_param().backend()));
db_->Open(this->layer_param_.data_param().source(), db::READ);
cursor_.reset(db_->NewCursor());

// Check if we should randomly skip a few data points
if (this->layer_param_.data_param().rand_skip()) {
unsigned int skip = caffe_rng_rand() %
this->layer_param_.data_param().rand_skip();
LOG(INFO) << "Skipping first " << skip << " data points.";
while (skip-- > 0) {
cursor_->Next();
}
}
// Read a data point, and use it to initialize the top blob.
Datum datum;
datum.ParseFromString(cursor_->value());

bool force_color = this->layer_param_.data_param().force_encoded_color();
if ((force_color && DecodeDatum(&datum, true)) ||
DecodeDatumNative(&datum)) {
LOG(INFO) << "Decoding Datum";
}
// image
int crop_size = this->layer_param_.transform_param().crop_size();
if (crop_size > 0) {
top[0]->Reshape(this->layer_param_.data_param().batch_size(),
datum.channels(), crop_size, crop_size);
this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
datum.channels(), crop_size, crop_size);
this->transformed_data_.Reshape(1, datum.channels(), crop_size, crop_size);
} else {
top[0]->Reshape(
this->layer_param_.data_param().batch_size(), datum.channels(),
datum.height(), datum.width());
this->prefetch_data_.Reshape(this->layer_param_.data_param().batch_size(),
datum.channels(), datum.height(), datum.width());
this->transformed_data_.Reshape(1, datum.channels(),
datum.height(), datum.width());
}
LOG(INFO) << "output data size: " << top[0]->num() << ","
<< top[0]->channels() << "," << top[0]->height() << ","
<< top[0]->width();
// label
if (this->output_labels_) {
vector<int> label_shape(1, this->layer_param_.data_param().batch_size());
top[1]->Reshape(label_shape);
this->prefetch_label_.Reshape(label_shape);
}
}

// This function is used to create a thread that prefetches the data.
template <typename Dtype>
void DataLayer<Dtype>::InternalThreadEntry() {
CPUTimer batch_timer;
batch_timer.Start();
double read_time = 0;
double trans_time = 0;
CPUTimer timer;
CHECK(this->prefetch_data_.count());
CHECK(this->transformed_data_.count());

// Reshape on single input batches for inputs of varying dimension.
const int batch_size = this->layer_param_.data_param().batch_size();
const int crop_size = this->layer_param_.transform_param().crop_size();
bool force_color = this->layer_param_.data_param().force_encoded_color();
if (batch_size == 1 && crop_size == 0) {
Datum datum;
datum.ParseFromString(cursor_->value());
if (datum.encoded()) {
if (force_color) {
DecodeDatum(&datum, true);
} else {
DecodeDatumNative(&datum);
}
}
this->prefetch_data_.Reshape(1, datum.channels(),
datum.height(), datum.width());
this->transformed_data_.Reshape(1, datum.channels(),
datum.height(), datum.width());
}

Dtype* top_data = this->prefetch_data_.mutable_cpu_data();
Dtype* top_label = NULL;  // suppress warnings about uninitialized variables

if (this->output_labels_) {
top_label = this->prefetch_label_.mutable_cpu_data();
}
for (int item_id = 0; item_id < batch_size; ++item_id) {
timer.Start();
// get a blob
Datum datum;
datum.ParseFromString(cursor_->value());

cv::Mat cv_img;
if (datum.encoded()) {
if (force_color) {
cv_img = DecodeDatumToCVMat(datum, true);
} else {
cv_img = DecodeDatumToCVMatNative(datum);
}
if (cv_img.channels() != this->transformed_data_.channels()) {
LOG(WARNING) << "Your dataset contains encoded images with mixed "
<< "channel sizes. Consider adding a 'force_color' flag to the "
<< "model definition, or rebuild your dataset using "
<< "convert_imageset.";
}
}
read_time += timer.MicroSeconds();
timer.Start();

// Apply data transformations (mirror, scale, crop...)
int offset = this->prefetch_data_.offset(item_id);
this->transformed_data_.set_cpu_data(top_data + offset);
if (datum.encoded()) {
this->data_transformer_->Transform(cv_img, &(this->transformed_data_));
} else {
this->data_transformer_->Transform(datum, &(this->transformed_data_));
}
if (this->output_labels_) {
top_label[item_id] = datum.label();
}
trans_time += timer.MicroSeconds();
// go to the next iter
cursor_->Next();
if (!cursor_->valid()) {
DLOG(INFO) << "Restarting data prefetching from start.";
cursor_->SeekToFirst();
}
}
batch_timer.Stop();
DLOG(INFO) << "Prefetch batch: " << batch_timer.MilliSeconds() << " ms.";
DLOG(INFO) << "     Read time: " << read_time / 1000 << " ms.";
DLOG(INFO) << "Transform time: " << trans_time / 1000 << " ms.";
}

INSTANTIATE_CLASS(DataLayer);
REGISTER_LAYER_CLASS(Data);

}  // namespace caffe

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其相对应的头文件信息：

template <typename Dtype>
class BaseDataLayer : public Layer<Dtype> {
public:
explicit BaseDataLayer(const LayerParameter& param);
virtual ~BaseDataLayer() {}
// LayerSetUp: implements common data layer setup functionality, and calls
// DataLayerSetUp to do special data layer setup for individual layer types.
// This method may not be overridden except by the BasePrefetchingDataLayer.
virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void DataLayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}
// Data layers have no bottoms, so reshaping is trivial.
virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {}

virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}
virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {}

protected:
TransformationParameter transform_param_;
shared_ptr<DataTransformer<Dtype> > data_transformer_;
bool output_labels_;
};

template <typename Dtype>
class BasePrefetchingDataLayer :
public BaseDataLayer<Dtype>, public InternalThread {
public:
explicit BasePrefetchingDataLayer(const LayerParameter& param)
: BaseDataLayer<Dtype>(param) {}
virtual ~BasePrefetchingDataLayer() {}
// LayerSetUp: implements common data layer setup functionality, and calls
// DataLayerSetUp to do special data layer setup for individual layer types.
// This method may not be overridden.
void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);

virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);
virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top);

virtual void CreatePrefetchThread();
virtual void JoinPrefetchThread();
// The thread's function
virtual void InternalThreadEntry() {}

protected:
Blob<Dtype> prefetch_data_;
Blob<Dtype> prefetch_label_;
Blob<Dtype> transformed_data_;
};

template <typename Dtype>

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