aclnnBlendImagesCustom
支持的产品型号
- Atlas 推理系列产品。
接口原型
每个算子分为两段式接口，必须先调用“aclnnBlendImagesCustomGetWorkspaceSize”接口获取计算所需workspace大小以及包含了算子计算流程的执行器，再调用“aclnnBlendImagesCustom”接口执行计算。
aclnnstatus aclnnBlendImagesCustomGetWorkspaceSize(const aclTensor *rgb, const aclTensor *alpha, const aclTensor *frame, aclTensor *out, uint64_t *workspaceSize, aclOpExecutor **executor)aclnnstatus aclnnBlendImagesCustom(void *workspace, uint64_t workspaceSize, aclOpExecutor *executor, aclrtStream stream)
功能描述
算子功能：完成张量rgb、frame和alpha的透明度乘法计算。
计算公式：
aclnnBlendImagesCustomGetWorkspaceSize
参数说明：
- rgb(const aclTensor*, 计算输入): Device侧的aclTensor，数据类型支持UINT8，shape支持HWC(C=3),与alpha满足broadcast关系。只支持连续Tensor,数据格式支持ND。
- alpha(const aclTensor*, 计算输入): Device侧的aclTensor，数据类型支持UINT8，shape支持HWC(C=1),与rgb满足broadcast关系。只支持连续Tensor,数据格式支持ND。
- frame(const aclTensor*, 计算输入): Device侧的aclTensor，数据类型支持UINT8，shape支持HWC(C=3),与alpha满足broadcast关系。只支持连续Tensor,数据格式支持ND。
- out(const aclTensor*, 计算输出): Device侧的aclTensor，数据类型支持UINT8，shape支持HWC(C=3),与frameshape一致。只支持连续Tensor,数据格式支持ND。
- workspaceSize(uint64_t*, 出参): 返回需要在Device侧申请的workspace大小。
- executor(aclOpExecutor**, 出参): 返回op执行器，包含了算子计算流程。
返回值：
aclnnStatus: 返回状态码，具体参见aclnn返回码。
第一段接口完成入参校验，出现以下场景时报错： 返回161001 (ACLNN_ERR_PARAM_NULLPTR)：1. 传入的rgb、alpha、frame或out是空指针。 返回161002 (ACLNN_ERR_PARAM_INVALID)： 1. rgb、alpha、frame的数据类型和数据格式不在支持的范围之内。 2. rgb、alpha、frame的shape无法做broadcast，rgb和frame支持HWC(C=3), alpha支持HWC(C=1)。
aclnnBlendImagesCustom
参数说明：
- workspace(void *, 入参): 在Device侧申请的workspace内存地址。
- workspaceSize(uint64_t, 入参): 在Device侧申请的workspace大小，由第一段接口aclnnBlendImagesGetWorkspaceSize获取。
- executor(aclOpExecutor *, 入参): op执行器，包含了算子计算流程。
- stream(aclrtStream, 入参): 指定执行任务的 AscendCL Stream流。
返回值：
aclnnStatus: 返回状态码，具体参见aclnn返回码。
约束与限制
无
调用示例
示例代码如下，仅供参考，具体编译和执行过程请参考编译与运行样例。
#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/aclnn_blend_images_custom.h"
#define CHECK_RET(cond, return_expr) \
do { \
if (!(cond)) { \
return_expr; \
} \
} while (0)
#define LOG_PRINT(message, ...) \
do { \
printf(message, ##__VA_ARGS__); \
} while (0)
int64_t GetShapeSize(const std::vector<int64_t>& shape) {
int64_t shapeSize = 1;
for (auto i : shape) {
shapeSize *= i;
}
return shapeSize;
}
void PrintOutResult(std::vector<int64_t> &shape, void** deviceAddr) {
auto size = GetShapeSize(shape);
std::vector<float> resultData(size, 0);
auto ret = aclrtMemcpy(resultData.data(), resultData.size() * sizeof(resultData[0]),
*deviceAddr, size * sizeof(resultData[0]), ACL_MEMCPY_DEVICE_TO_HOST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return);
for (int64_t i = 0; i < size; i++) {
LOG_PRINT("mean result[%ld] is: %f\n", i, resultData[i]);
}
}
int Init(int32_t deviceId, aclrtStream* stream) {
// 固定写法，AscendCL初始化
auto ret = aclInit(nullptr);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
ret = aclrtSetDevice(deviceId);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
ret = aclrtCreateStream(stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
return 0;
}
template <typename T>
int CreateAclTensor(const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr,
aclDataType dataType, aclTensor** tensor) {
auto size = GetShapeSize(shape) * sizeof(T);
// 调用aclrtMalloc申请device侧内存
auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
// 调用aclrtMemcpy将host侧数据拷贝到device侧内存上
ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);
// 计算连续tensor的strides
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];
}
// 调用aclCreateTensor接口创建aclTensor
*tensor = aclCreateTensor(shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND,
shape.data(), shape.size(), *deviceAddr);
return 0;
}
int main() {
// 1. （固定写法）device/stream初始化，参考AscendCL对外接口列表
// 根据自己的实际device填写deviceId
int32_t deviceId = 0;
aclrtStream stream;
auto ret = Init(deviceId, &stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
// 2. 构造输入与输出，需要根据API的接口自定义构造
std::vector<int64_t> rgbShape = {4, 3};
std::vector<int64_t> alphaShape = {4, 1};
std::vector<int64_t> frameShape = {4, 3};
std::vector<int64_t> outShape = {4, 3};
void* rgbDeviceAddr = nullptr;
void* alphaDeviceAddr = nullptr;
void* frameDeviceAddr = nullptr;
void* outDeviceAddr = nullptr;
aclTensor* rgb = nullptr;
aclTensor* alpha = nullptr;
aclTensor* frame = nullptr;
aclTensor* out = nullptr;
std::vector<float> rgbHostData = {10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120};
std::vector<float> alphaHostData = {255, 255, 255, 255};
std::vector<float> frameHostData = {10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120};
std::vector<float> outHostData = {10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120};
ret = CreateAclTensor(rgbHostData, rgbShape, &rgbDeviceAddr, aclDataType::ACL_UINT8, &rgb);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(alphaHostData, alphaShape, &alphaDeviceAddr, aclDataType::ACL_UINT8, &alpha);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(frameHostData, frameShape, &frameDeviceAddr, aclDataType::ACL_UINT8, &frame);
CHECK_RET(ret == ACL_SUCCESS, return ret);
ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_UINT8, &out);
CHECK_RET(ret == ACL_SUCCESS, return ret);
// 3. 调用CANN算子库API，需要修改为具体的Api名称
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
// 调用aclnnBlendImagesCustom第一段接口
ret = aclnnBlendImagesCustomGetWorkspaceSize(rgb, alpha, frame, out, &workspaceSize, &executor);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBlendImagesCustomGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
// 根据第一段接口计算出的workspaceSize申请device内存
void* workspaceAddr = nullptr;
if (workspaceSize > 0) {
ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
}
// 调用aclnnBlendImagesCustom第二段接口
ret = aclnnBlendImagesCustom(workspaceAddr, workspaceSize, executor, stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclnnBlendImagesCustom failed. ERROR: %d\n", ret); return ret);
// 4. （固定写法）同步等待任务执行结束
ret = aclrtSynchronizeStream(stream);
CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
// 5. 获取输出的值，将device侧内存上的结果拷贝至host侧，需要根据具体API的接口定义修改
PrintOutResult(outShape, &outDeviceAddr);
// 6. 释放aclTensor和aclScalar，需要根据具体API的接口定义修改
aclDestroyTensor(rgb);
aclDestroyTensor(alpha);
aclDestroyTensor(frame);
aclDestroyTensor(out);
// 7. 释放device资源
aclrtFree(rgbDeviceAddr);
aclrtFree(alphaDeviceAddr);
aclrtFree(frameDeviceAddr);
aclrtFree(outDeviceAddr);
if (workspaceSize > 0) {
aclrtFree(workspaceAddr);
}
aclrtDestroyStream(stream);
aclrtResetDevice(deviceId);
aclFinalize();
return 0;
}