dataLayer作为整个网络的输入层，
数据从leveldb中取。leveldb的数据是通过图片转换过来的。
网络建立的时候，
datalayer主要是负责设置一些参数，比如batchsize，channels，height，width等。
这次会通过读leveldb一个数据块来获取这些信息。
然后启动一个线程来预先从leveldb拉取一批数据，这些数据是图像数据和图像标签。
正向传播的时候，
datalayer就把预先拉取好数据拷贝到指定的cpu或者gpu的内存。
然后启动新线程再预先拉取数据，这些数据留到下一次正向传播使用。
// Copyright 2013 Yangqing Jia
#include <stdint.h>
#include <leveldb/db.h>
#include <pthread.h>
#include <string>
#include <vector>
#include "caffe/layer.hpp"
#include "caffe/util/io.hpp"
#include "caffe/vision_layers.hpp"
using std::string;
namespace caffe {
template <typename Dtype>
void* DataLayerPrefetch(void* layer_pointer) {
CHECK(layer_pointer);
DataLayer<Dtype>* layer = reinterpret_cast<DataLayer<Dtype>*>(layer_pointer);
CHECK(layer);
Datum datum;
CHECK(layer->prefetch_data_);
Dtype* top_data = layer->prefetch_data_->mutable_cpu_data();//数据
Dtype* top_label = layer->prefetch_label_->mutable_cpu_data();//标签
const Dtype scale = layer->layer_param_.scale();
const int batchsize = layer->layer_param_.batchsize();
const int cropsize = layer->layer_param_.cropsize();
const bool mirror = layer->layer_param_.mirror();
if (mirror && cropsize == 0) {//当前实现需要同时设置mirror和cropsize
LOG(FATAL) << "Current implementation requires mirror and cropsize to be "
<< "set at the same time.";
}
// datum scales
const int channels = layer->datum_channels_;
const int height = layer->datum_height_;
const int width = layer->datum_width_;
const int size = layer->datum_size_;
const Dtype* mean = layer->data_mean_.cpu_data();
for (int itemid = 0; itemid < batchsize; ++itemid) {//每一批数据的数量是batchsize，一个循环拉取一张？
// get a blob
CHECK(layer->iter_);
CHECK(layer->iter_->Valid());
datum.ParseFromString(layer->iter_->value().ToString());//利用迭代器拉取下一批数据
const string& data = datum.data();
if (cropsize) {//如果需要裁剪
CHECK(data.size()) << "Image cropping only support uint8 data";
int h_off, w_off;
// We only do random crop when we do training.
//只是在训练阶段做随机裁剪
if (Caffe::phase() == Caffe::TRAIN) {
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
h_off = rand() % (height - cropsize);
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
w_off = rand() % (width - cropsize);
} else {//测试阶段固定裁剪
h_off = (height - cropsize) / 2;
w_off = (width - cropsize) / 2;
}
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
//怎么感觉下面两种情况的代码是一样的？
if (mirror && rand() % 2) {
// Copy mirrored version
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cropsize; ++h) {
for (int w = 0; w < cropsize; ++w) {
top_data[((itemid * channels + c) * cropsize + h) * cropsize
+ cropsize - 1 - w] =
(static_cast<Dtype>(
(uint8_t)data[(c * height + h + h_off) * width
+ w + w_off])
- mean[(c * height + h + h_off) * width + w + w_off])
* scale;
}
}
}
} else {
// Normal copy
for (int c = 0; c < channels; ++c) {
for (int h = 0; h < cropsize; ++h) {
for (int w = 0; w < cropsize; ++w) {
top_data[((itemid * channels + c) * cropsize + h) * cropsize + w]
= (static_cast<Dtype>(
(uint8_t)data[(c * height + h + h_off) * width
+ w + w_off])
- mean[(c * height + h + h_off) * width + w + w_off])
* scale;
}
}
}
}
} else {//如果不需要裁剪
// we will prefer to use data() first, and then try float_data()
//我们优先考虑data()，然后float_data()
if (data.size()) {
for (int j = 0; j < size; ++j) {
top_data[itemid * size + j] =
(static_cast<Dtype>((uint8_t)data[j]) - mean[j]) * scale;
}
} else {
for (int j = 0; j < size; ++j) {
top_data[itemid * size + j] =
(datum.float_data(j) - mean[j]) * scale;
}
}
}
top_label[itemid] = datum.label();
// go to the next iter
layer->iter_->Next();
if (!layer->iter_->Valid()) {
// We have reached the end. Restart from the first.
DLOG(INFO) << "Restarting data prefetching from start.";
layer->iter_->SeekToFirst();
}
}
return reinterpret_cast<void*>(NULL);
}
template <typename Dtype>
DataLayer<Dtype>::~DataLayer<Dtype>() {
// Finally, join the thread
CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
}
template <typename Dtype>
void DataLayer<Dtype>::SetUp(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
CHECK_EQ(bottom.size(), 0) << "Data Layer takes no input blobs.";
CHECK_EQ(top->size(), 2) << "Data Layer takes two blobs as output.";
// Initialize the leveldb
leveldb::DB* db_temp;
leveldb::Options options;
options.create_if_missing = false;
options.max_open_files = 100;
LOG(INFO) << "Opening leveldb " << this->layer_param_.source();
leveldb::Status status = leveldb::DB::Open(
options, this->layer_param_.source(), &db_temp);
CHECK(status.ok()) << "Failed to open leveldb "
<< this->layer_param_.source() << std::endl << status.ToString();
db_.reset(db_temp);
iter_.reset(db_->NewIterator(leveldb::ReadOptions()));//通过迭代器来操纵leveldb
iter_->SeekToFirst();
// Check if we would need to randomly skip a few data points
//是否要随机跳过一些数据
if (this->layer_param_.rand_skip()) {
// NOLINT_NEXT_LINE(runtime/threadsafe_fn)
unsigned int skip = rand() % this->layer_param_.rand_skip();
LOG(INFO) << "Skipping first " << skip << " data points.";
while (skip-- > 0) {//循环次数
iter_->Next();
if (!iter_->Valid()) {
iter_->SeekToFirst();
}
}
}
// Read a data point, and use it to initialize the top blob.
//读取一个数据点，用来初始化topblob。所谓初始化，只要是指reshape。
//可以观察到下面iter_调用调用next。所以这次读取只是用来读取出来channels等参数的，不作处理。
Datum datum;
datum.ParseFromString(iter_->value().ToString());//利用迭代器读取第一个数据点
// image图像数据
int cropsize = this->layer_param_.cropsize();//裁剪大小
if (cropsize > 0) {//需要裁剪
(*top)[0]->Reshape(
this->layer_param_.batchsize(), datum.channels(), cropsize, cropsize);
prefetch_data_.reset(new Blob<Dtype>(
this->layer_param_.batchsize(), datum.channels(), cropsize, cropsize));
} else {//不需要裁剪
(*top)[0]->Reshape(
this->layer_param_.batchsize(), datum.channels(), datum.height(),
datum.width());
prefetch_data_.reset(new Blob<Dtype>(
this->layer_param_.batchsize(), datum.channels(), datum.height(),
datum.width()));
}
LOG(INFO) << "output data size: " << (*top)[0]->num() << ","
<< (*top)[0]->channels() << "," << (*top)[0]->height() << ","
<< (*top)[0]->width();
// label标签数据
(*top)[1]->Reshape(this->layer_param_.batchsize(), 1, 1, 1);
prefetch_label_.reset(
new Blob<Dtype>(this->layer_param_.batchsize(), 1, 1, 1));
// datum size
datum_channels_ = datum.channels();
datum_height_ = datum.height();
datum_width_ = datum.width();
datum_size_ = datum.channels() * datum.height() * datum.width();
CHECK_GT(datum_height_, cropsize);
CHECK_GT(datum_width_, cropsize);
// check if we want to have mean是否要减去均值
if (this->layer_param_.has_meanfile()) {
BlobProto blob_proto;
LOG(INFO) << "Loading mean file from" << this->layer_param_.meanfile();
ReadProtoFromBinaryFile(this->layer_param_.meanfile().c_str(), &blob_proto);
data_mean_.FromProto(blob_proto);
CHECK_EQ(data_mean_.num(), 1);
CHECK_EQ(data_mean_.channels(), datum_channels_);
CHECK_EQ(data_mean_.height(), datum_height_);
CHECK_EQ(data_mean_.width(), datum_width_);
} else {
// Simply initialize an all-empty mean.
data_mean_.Reshape(1, datum_channels_, datum_height_, datum_width_);
}
// Now, start the prefetch thread. Before calling prefetch, we make two
// cpu_data calls so that the prefetch thread does not accidentally make
// simultaneous cudaMalloc calls when the main thread is running. In some
// GPUs this seems to cause failures if we do not so.
prefetch_data_->mutable_cpu_data();
prefetch_label_->mutable_cpu_data();
data_mean_.cpu_data();
DLOG(INFO) << "Initializing prefetch";
CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
reinterpret_cast<void*>(this))) << "Pthread execution failed.";
DLOG(INFO) << "Prefetch initialized.";
}
template <typename Dtype>
void DataLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
vector<Blob<Dtype>*>* top) {
// First, join the thread 等待线程结束
CHECK(!pthread_join(thread_, NULL)) << "Pthread joining failed.";
// Copy the data拷贝数据到top，即该层的输出
memcpy((*top)[0]->mutable_cpu_data(), prefetch_data_->cpu_data(),
sizeof(Dtype) * prefetch_data_->count());
memcpy((*top)[1]->mutable_cpu_data(), prefetch_label_->cpu_data(),
sizeof(Dtype) * prefetch_label_->count());
// Start a new prefetch thread启动新线程拉取下一批数据
CHECK(!pthread_create(&thread_, NULL, DataLayerPrefetch<Dtype>,
reinterpret_cast<void*>(this))) << "Pthread execution failed.";
}
// The backward operations are dummy - they do not carry any computation.
template <typename Dtype>
Dtype DataLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
const bool propagate_down, vector<Blob<Dtype>*>* bottom) {
return Dtype(0.);
}
INSTANTIATE_CLASS(DataLayer);
} // namespace caffe
神经网络caffe框架源码解析--data_layer.cpp类代码研究,布布扣,bubuko.com
神经网络caffe框架源码解析--data_layer.cpp类代码研究
原文地址：http://blog.csdn.net/linger2012liu/article/details/27348265
