caffe源代码分析--data_layer.cpp

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參数，比方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复制到指定的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) {//当前实现须要同一时候设置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) {//假设须要裁剪
      CHECK(data.size()) << "Image cropping only support uint8 data";
      int h_off, w_off;
      // We only do random crop when we do training.
      //仅仅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 {//測试阶段固定裁剪
        h_off = (height - cropsize) / 2;
        w_off = (width - cropsize) / 2;
      }
      // NOLINT_NEXT_LINE(runtime/threadsafe_fn)
      //怎么感觉以下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 {//假设不须要裁剪
      // 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，仅仅要是指reshape。
  //能够观察到以下iter_调用调用next。所以这次读取仅仅是用来读取出来channels等參数的，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) {//须要裁剪
    (*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 {//不须要裁剪
    (*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

本文作者：linger

本文链接：http://blog.csdn.net/lingerlanlan/article/details/27348265