功能：
Caffe的底层数据的切换（cpu模式和gpu模式），需要用到内存同步模块。
源码：头文件
#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_- 1
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实现文件：
#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