媒体数据处理算子【免费下载链接】docs该仓库用于维护cann公共文档项目地址: https://gitcode.com/cann/docs基本概念调用媒体数据处理算子通常采用“两段式接口”形式具体如下其中_“acldvpp”表示算子接口前缀而“Xxx”_表示对应的算子类型如EncodeJpeg算子。aclnnStatus acldvppXxxGetWorkspaceSize(const aclTensor *src, ..., aclTensor *out, ..., uint64_t *workspaceSize, aclOpExecutor **executor); aclnnStatus acldvppXxx(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream);两段式接口的作用分别为第一段接口acldvpp_Xxx_GetWorkspaceSize该接口内部执行入参校验、在动态Shape场景下推导输出Shape、数据切块Tiling以及计算执行算子所需的workspace内存大小等任务。第二段接口acldvpp_Xxx_执行算子计算接口内部涉及DFX例如Dump、溢出检测等、调用Runtime提供的LaunchKernel接口等_。_接口调用流程如下所示示例代码以JPEGEJPEG Encoder算子调用为例介绍算子两段式接口调用的基本逻辑其他算子的调用过程类似请根据实际情况自行修改。已知JPEGE算子用于将单通道GRAY或三通道RGB图像编码为JPEG图像。您可以获取如下示例代码并将文件命名为“jpege_demo.cpp”代码如下#include vector #include string #include cstdint #include functional #include acl/acl.h #include acldvpp_op_api.h #include memory #define ALIGN_UP(x, a) ((((x) ((a) - 1U)) / (a)) * (a)) typedef int32_t (*InitFunc)(const char *configPath); typedef int32_t (*FinalizeFunc)(); InitFunc initFunc; FinalizeFunc finalizeFunc; class ScopeGuard { public: // Noncopyable ScopeGuard(ScopeGuard const ) delete; ScopeGuard operator(ScopeGuard const ) delete; explicit ScopeGuard(const std::functionvoid() on_exit_scope) : on_exit_scope_(on_exit_scope), dismissed_(false) {} ~ScopeGuard() { if (!dismissed_) { if (on_exit_scope_ ! nullptr) { try { on_exit_scope_(); } catch (std::bad_function_call ) { } catch (...) { } } } } void Dismiss() { dismissed_ true; } private: std::functionvoid() on_exit_scope_; bool dismissed_; }; int64_t GetShapeSize(const std::vectorint64_t shape) { int64_t shape_size 1; for (auto i : shape) { shape_size * i; } return shape_size; } // 自定义函数用于创建Tensor template typename T int32_t CreateAclTensor(const std::vectorT hostData, const std::vectorint64_t shape, void** deviceAddr, aclDataType dataType, aclTensor** tensor, aclFormat tensorFormat, bool needCopy true) { auto size GetShapeSize(shape) * sizeof(T); // 申请Device内存 aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST); aclrtMemset(*deviceAddr, size, 0, size); // 将Host侧数据拷贝到Device侧 aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE); // 计算连续Tensor的访问步长 std::vectorint64_t strides(shape.size(), 1); for (int64_t i shape.size() - 2; i 0; i--) { strides[i] shape[i 1] * strides[i 1]; } // 创建aclTensor *tensor aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, tensorFormat, shape.data(), shape.size(), *deviceAddr); return 0; } int32_t encode_jpeg(aclrtStream stream) { constexpr uint32_t jpegeHeaderSize 640U; constexpr uint32_t startAlignBytes 128U; constexpr uint32_t memoryAlignSize 2097152U; // 2M: 2*1024*1024 int64_t inChannel 0; uint32_t inWidth 1920; uint32_t inHeight 1080; // 1. 初始化参数 std::vectorint64_t selfShape {1, inChannel, inHeight, inWidth}; // 默认 NCHW uint32_t encode_size ALIGN_UP(inWidth, 16U) * ALIGN_UP(inHeight, 16U) * 3 / 2 jpegeHeaderSize startAlignBytes; encode_size ALIGN_UP(encode_size, memoryAlignSize); std::vectorint64_t outShape {encode_size}; std::vectorfloat inputPic(inWidth * inHeight * inChannel, 0.0); std::vectorfloat outputPic(encode_size, 0.0); size_t inputPicSize inWidth * inHeight * inChannel; std::shared_ptrFILE srcFp(fopen(./1920x1080_nv12.yuv, rb), fclose); fread(inputPic.data(), 1, inputPicSize, srcFp.get()); // 2. 创建输入输出将vector转成aclTensor void* selfDeviceAddr nullptr; void* outDeviceAddr nullptr; aclTensor* self nullptr; aclTensor* out nullptr; CreateAclTensor(inputPic, selfShape, selfDeviceAddr, aclDataType::ACL_UINT8, self, aclFormat::ACL_FORMAT_NCHW); ScopeGuard autoCloseInTensor([self, selfDeviceAddr] { aclrtFree(self);aclDestroyTensor((const aclTensor *)selfDeviceAddr);}); // 每次执行完输出Tensor Shape会修改因此性能测试将输出Tensor构造放到循环内部不然会第二次执行会被内部拦截 // 放到内部时最后执行完文件无法保留因为内存提前释放了 CreateAclTensor(outputPic, outShape, outDeviceAddr, aclDataType::ACL_UINT8, out, ACL_FORMAT_ND, false); ScopeGuard autoCloseOutTensor([out, outDeviceAddr] { aclrtFree(out);aclDestroyTensor((const aclTensor *)outDeviceAddr);}); // 3. 调用CANN算子库API uint64_t workspaceSize 0; aclOpExecutor* executor; const uint32_t quality 75; // 调用第一段接口 acldvppEncodeJpegGetWorkspaceSize(self, quality, out, workspaceSize, executor); // 根据第一段接口计算出的workspaceSize并申请Device内存 void* workspaceAddr nullptr; if (workspaceSize 0) { aclrtMalloc(workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST); } ScopeGuard autoWorkspace([workspaceAddr] { aclrtFree(workspaceAddr); }); // 调用第二段接口 acldvppEncodeJpeg(workspaceAddr, workspaceSize, executor, stream); // 获取编码后JPEG图片长度 int64_t* viewDims nullptr; uint64_t viewDimsNum 0; aclGetViewShape(out, viewDims, viewDimsNum); std::vectorint64_t outSize(viewDims, viewDims viewDimsNum); size_t outputPicSize outSize[0]; // 获取输出的值将Device内存中的结果数据拷贝至Host侧 aclrtMemcpy(outputPic.data(), outputPicSize, outDeviceAddr, outputPicSize, ACL_MEMCPY_DEVICE_TO_HOST); std::shared_ptrFILE dstFp(fopen(./1920x1080_nv12.jpg, wb), fclose); fwrite(outputPic.data(), 1, outputPicSize, dstFp.get()); return 0; } int32_t Init(int32_t deviceId, aclrtContext* context, aclrtStream* stream) { // 涉及Profiling功能需要调用aclint接口初始化如果不需要Profiling功能则直接调用acldvppInit接口即可 initFunc acldvppInit; finalizeFunc acldvppFinalize; auto initFunc(nullptr); ScopeGuard autoDeinit([] { finalizeFunc(); }); aclrtSetDevice(deviceId); ScopeGuard autoResetDevice([deviceId] { aclrtResetDevice(deviceId); }); aclrtCreateContext(context, deviceId); ScopeGuard autoDestroyContext([context] { aclrtDestroyContext(context); }); aclrtSetCurrentContext(*context); aclrtCreateStream(stream); ScopeGuard autoDestroyStream([stream] { aclrtDestroyStream(stream); }); autoResetDevice.Dismiss(); autoDestroyContext.Dismiss(); autoDestroyStream.Dismiss(); autoDeinit.Dismiss(); return 0; } // 销毁Stream、Context资源复位Device void UnInit(int32_t deviceId, aclrtContext context, aclrtStream stream) { aclrtDestroyStream(stream); aclrtDestroyContext(context); aclrtResetDevice(deviceId); finalizeFunc(); } int32_t main() { // 初始化系统指定计算设备依次创建Context、Stream int32_t deviceId 0; aclrtContext context; aclrtStream stream; Init(deviceId, context, stream); // jpeg图片编码 encode_jpeg(stream); // 资源释放 UnInit(deviceId, context, stream); return 0; }编译与运行准备编译脚本CMakeLists文件。cmake_minimum_required(VERSION 3.14) set(ASCEND_PATH $ENV{ASCEND_HOME_PATH}) # 设置可执行文件名如opapi_test并指定待运行cpp文件所在目录 add_executable(opapi_test jpege_demo.cpp) # 设置库文件路径 find_library(NNOPBASE_LIBRARY_DIR libnnopbase.so ${ASCEND_PATH}/lib64) # aclTensor 相关接口 find_library(ACLDVPPOP_LIBRARY_DIR libacl_dvpp_op.so ${ASCEND_PATH}/lib64) find_library(DVPPOPBASE_LIBRARY_DIR libdvpp_op_base.so ${ASCEND_PATH}/lib64) find_library(ASCENDCL_LIBRARY_DIR libascendcl.so ${ASCEND_PATH}/lib64) find_library(ASCENDCL_C_SEC_DIR libc_sec.so ${ASCEND_PATH}/lib64) target_link_libraries(opapi_test PRIVATE -Wl,--no-as-needed ${NNOPBASE_LIBRARY_DIR} ${ACLDVPPOP_LIBRARY_DIR} ${ASCENDCL_LIBRARY_DIR} ${ASCENDCL_C_SEC_DIR} ${DVPPOPBASE_LIBRARY_DIR} -Wl,--as-needed ) # 设置头文件路径 target_include_directories(opapi_test PRIVATE ${ASCEND_PATH}/include/acldvppop/ ${ASCEND_PATH}/include/ )编译并运行。进入CMakeLists.txt所在目录执行如下命令新建build目录存放生成的编译文件。mkdir -p build进入build所在目录执行cmake命令编译再执行make命令生成可执行文件。cd build cmake .. make编译成功后会在build目录下生成opapi_test可执行文件。运行可执行文件opapi_test。./opapi_test【免费下载链接】docs该仓库用于维护cann公共文档项目地址: https://gitcode.com/cann/docs创作声明：本文部分内容由AI辅助生成（AIGC），仅供参考