Caffe-代码解析-SyncedMemory

#ifndef CAFFE_SYNCEDMEM_HPP_
#define CAFFE_SYNCEDMEM_HPP_

#include <cstdlib>

#include "caffe/common.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {

inline void CaffeMallocHost(void** ptr, size_t size) {
  *ptr = malloc(size);
  CHECK(*ptr) << "host allocation of size " << size << " failed";
}

inline void CaffeFreeHost(void* ptr) {
  free(ptr);
}

/**
 * @brief Manages memory allocation and synchronization between the host (CPU)
 *        and device (GPU).
 *
 * TODO(dox): more thorough description.
 */
class SyncedMemory {
 public:
  SyncedMemory()
      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(0), head_(UNINITIALIZED),
        own_cpu_data_(false) {}
  explicit SyncedMemory(size_t size)
      : cpu_ptr_(NULL), gpu_ptr_(NULL), size_(size), head_(UNINITIALIZED),
        own_cpu_data_(false) {}
  ~SyncedMemory();
  const void* cpu_data();//获取cpu数据，返回void * 指针
  void set_cpu_data(void* data);//用一个void * 指针修改指针
  const void* gpu_data();//获取gpu数据，返回void * 指针
  void* mutable_cpu_data();//获取可以更改cpu数据，返回void * 指针
  void* mutable_gpu_data();//获取可以更改gpu数据，返回void * 指针
  enum SyncedHead { UNINITIALIZED, HEAD_AT_CPU, HEAD_AT_GPU, SYNCED };//enum枚举值
  SyncedHead head() { return head_; }//获得枚举值
  size_t size() { return size_; }//获得数据大小

 private:
  void to_cpu();//转为cpu模式
  void to_gpu(); //转为gpu模式
  void* cpu_ptr_;//指向cpu的指针
  void* gpu_ptr_;//指向gpu的指指针
  size_t size_; //大小
  SyncedHead head_; //数据存放的位置，枚举值之一
  bool own_cpu_data_;//是否有cpu数据

  DISABLE_COPY_AND_ASSIGN(SyncedMemory);
};  // class SyncedMemory

}  // namespace caffe

#endif  // CAFFE_SYNCEDMEM_HPP_

#include <cstring>

#include "caffe/common.hpp"
#include "caffe/syncedmem.hpp"
#include "caffe/util/math_functions.hpp"

namespace caffe {
//析构函数，调用caffe函数来释放空间
SyncedMemory::~SyncedMemory() {
  if (cpu_ptr_ && own_cpu_data_) {
    CaffeFreeHost(cpu_ptr_);
  }
//如果有gpu数据，也进行释放。
#ifndef CPU_ONLY
  if (gpu_ptr_) {
    CUDA_CHECK(cudaFree(gpu_ptr_));
  }
#endif  // CPU_ONLY
}
// 根据head信息，选择：1， 分类cpu空间，2.拷贝gpu数据值cpu
inline void SyncedMemory::to_cpu() {
  switch (head_) {
  case UNINITIALIZED:
    CaffeMallocHost(&cpu_ptr_, size_);
    caffe_memset(size_, 0, cpu_ptr_);
    head_ = HEAD_AT_CPU;
    own_cpu_data_ = true;
    break;
  case HEAD_AT_GPU:
#ifndef CPU_ONLY
    if (cpu_ptr_ == NULL) {
      CaffeMallocHost(&cpu_ptr_, size_);
      own_cpu_data_ = true;
    }
    caffe_gpu_memcpy(size_, gpu_ptr_, cpu_ptr_);
    head_ = SYNCED;
#else
    NO_GPU;
#endif
    break;
  case HEAD_AT_CPU:
  case SYNCED:
    break;
  }
}
// 根据head信息，选择：1， 分类gpu空间，2.拷贝cpu数据值gpu
inline void SyncedMemory::to_gpu() {
#ifndef CPU_ONLY
  switch (head_) {
  case UNINITIALIZED:
    CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
    caffe_gpu_memset(size_, 0, gpu_ptr_);
    head_ = HEAD_AT_GPU;
    break;
  case HEAD_AT_CPU:
    if (gpu_ptr_ == NULL) {
      CUDA_CHECK(cudaMalloc(&gpu_ptr_, size_));
    }
    caffe_gpu_memcpy(size_, cpu_ptr_, gpu_ptr_);
    head_ = SYNCED;
    break;
  case HEAD_AT_GPU:
  case SYNCED:
    break;
  }
#else
  NO_GPU;
#endif
}
//返回cpu指针 void * 类型
const void* SyncedMemory::cpu_data() {
  to_cpu();
  return (const void*)cpu_ptr_;
}
// 设置cpu数据，利用另外一个指针的数据来初始化
void SyncedMemory::set_cpu_data(void* data) {
  CHECK(data);
  if (own_cpu_data_) {
    CaffeFreeHost(cpu_ptr_);
  }
  cpu_ptr_ = data;//直接重置指针，
  head_ = HEAD_AT_CPU;
  own_cpu_data_ = false;//设false
}
//获得gpu指针
const void* SyncedMemory::gpu_data() {
#ifndef CPU_ONLY
  to_gpu();
  return (const void*)gpu_ptr_;
#else
  NO_GPU;
#endif
}
//获得可更改的cpu指针
void* SyncedMemory::mutable_cpu_data() {
  to_cpu();
  head_ = HEAD_AT_CPU;
  return cpu_ptr_;
}
//获得可更改的gpu指针
void* SyncedMemory::mutable_gpu_data() {
#ifndef CPU_ONLY
  to_gpu();
  head_ = HEAD_AT_GPU;
  return gpu_ptr_;
#else
  NO_GPU;
#endif
}

}  // namespace caffe