媒体数据处理算子
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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JPEGE（JPEG 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::function<void()> &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::function<void()> on_exit_scope_; bool dismissed_; }; int64_t GetShapeSize(const std::vector<int64_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::vector<T>& hostData, const std::vector<int64_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::vector<int64_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::vector<int64_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::vector<int64_t> outShape = {encode_size}; std::vector<float> inputPic(inWidth * inHeight * inChannel, 0.0); std::vector<float> outputPic(encode_size, 0.0); size_t inputPicSize = inWidth * inHeight * inChannel; std::shared_ptr<FILE> 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::vector<int64_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_ptr<FILE> 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
