--- title: RpSort16 description: "| 产品 | 是否支持 |" url: https://www.hiascend.com/document/detail/zh/canncommercial/latest/API/ascendcopapi/atlasascendc_api_07_0229.html sourcePath: /source/zh/canncommercial/900/API/ascendcopapi/atlasascendc_api_07_0229.html indexId: 55c71266235dc6284098e2999e33d48ad2f0ae76a5b566d1864af58aa9d3c51176 --- # RpSort16 #### 产品支持情况 | 产品 | 是否支持 | | --- | --- | | Atlas 350 加速卡 | x | | Atlas A3 训练系列产品 / Atlas A3 推理系列产品 | x | | Atlas A2 训练系列产品 / Atlas A2 推理系列产品 | x | | Atlas 200I/500 A2 推理产品 | x | | Atlas 推理系列产品 AI Core | √ | | Atlas 推理系列产品 Vector Core | x | | Atlas 训练系列产品 | √ | #### 功能说明 根据Region Proposals中的score域对其进行排序（score大的排前面），每次排16个Region Proposals。 #### 函数原型 ``` template __aicore__ inline void RpSort16(const LocalTensor& dst, const LocalTensor& src, const int32_t repeatTime) ``` #### 参数说明 **表1 模板参数说明** | 参数名 | 描述 | | --- | --- | | T | 操作数数据类型。 Atlas 训练系列产品 ，支持的数据类型为：half Atlas 推理系列产品 AI Core，支持的数据类型为：half/float | **表2 参数说明** | 参数名称 | 输入/输出 | 含义 | | --- | --- | --- | | dst | 输出 | 目的操作数，存储经过排序后的Region Proposals。 类型为LocalTensor，支持的TPosition为VECIN/VECCALC/VECOUT。 LocalTensor的起始地址需要32字节对齐。 | | src | 输入 | 源操作数，存储未经过排序的Region Proposals。 类型为LocalTensor，支持的TPosition为VECIN/VECCALC/VECOUT。 LocalTensor的起始地址需要32字节对齐。 | | repeatTime | 输入 | 重复迭代次数，int32\_t类型，每次排16个Region Proposals。取值范围：repeatTime∈[0,255]。 | #### 约束说明 - 用户需保证src和dst中存储的Region Proposal数目大于实际所需数据，否则会存在tensor越界错误。 - 当存在proposal[i]与proposal[j]的score值相同时，如果i>j，则proposal[j]将首先被选出来，排在前面。 - 操作数地址对齐要求请参见 通用地址对齐约束 。 #### 调用示例 - 接口使用样例 1 2 3 4 // ProposalConcat将连续元素合入Region Proposal内对应位置 // repeatTime = 2, 对2个Region Proposal进行排序，model=4起始位置为4 AscendC::ProposalConcat(dstLocal, srcLocal, 2, 4); AscendC::RpSort16(dstLocal, dstLocal, 2); ``` 示例结果 输入数据srcLocal: [ -1.624 -42.3 -54.12 91.25 -99.4 36.72 67.44 -66.3 -52.53 3.377 -62.47 -15.85 -31.47 3.143 58.47 -83.75 21.58 63.47 7.234 35.16 -39.72 37.8 73.06 -98.7 44.1 -77.2 67.2 19.62 -87.9 -14.875 15.86 -77.75] 经过ProposalConcat后的dstLocal数据，repeat=2计算32个元素，model=4起始位置为4 [ 0. 0. 0. 0. -1.624 0. 0. 0. 0. 0. 0. 0. -42.3 0. 0. 0. 0. 0. 0. 0. -54.12 0. 0. 0. 0. 0. 0. 0. 91.25 0. 0. 0. 0. 0. 0. 0. -99.4 0. 0. 0. 0. 0. 0. 0. 36.72 0. 0. 0. 0. 0. 0. 0. 67.44 0. 0. 0. 0. 0. 0. 0. -66.3 0. 0. 0. 0. 0. 0. 0. -52.53 0. 0. 0. 0. 0. 0. 0. 3.377 0. 0. 0. 0. 0. 0. 0. -62.47 0. 0. 0. 0. 0. 0. 0. -15.85 0. 0. 0. 0. 0. 0. 0. -31.47 0. 0. 0. 0. 0. 0. 0. 3.143 0. 0. 0. 0. 0. 0. 0. 58.47 0. 0. 0. 0. 0. 0. 0. -83.75 0. 0. 0. 0. 0. 0. 0. 21.58 0. 0. 0. 0. 0. 0. 0. 63.47 0. 0. 0. 0. 0. 0. 0. 7.234 0. 0. 0. 0. 0. 0. 0. 35.16 0. 0. 0. 0. 0. 0. 0. -39.72 0. 0. 0. 0. 0. 0. 0. 37.8 0. 0. 0. 0. 0. 0. 0. 73.06 0. 0. 0. 0. 0. 0. 0. -98.7 0. 0. 0. 0. 0. 0. 0. 44.1 0. 0. 0. 0. 0. 0. 0. -77.2 0. 0. 0. 0. 0. 0. 0. 67.2 0. 0. 0. 0. 0. 0. 0. 19.62 0. 0. 0. 0. 0. 0. 0. -87.9 0. 0. 0. 0. 0. 0. 0. -14.875 0. 0. 0. 0. 0. 0. 0. 15.86 0. 0. 0. 0. 0. 0. 0. -77.75 0. 0. 0. ] 输出数据(dst_gm): [ 0. 0. 0. 0. 91.25 0. 0. 0. 0. 0. 0. 0. 67.44 0. 0. 0. 0. 0. 0. 0. 58.47 0. 0. 0. 0. 0. 0. 0. 36.72 0. 0. 0. 0. 0. 0. 0. 3.377 0. 0. 0. 0. 0. 0. 0. 3.143 0. 0. 0. 0. 0. 0. 0. -1.624 0. 0. 0. 0. 0. 0. 0. -15.85 0. 0. 0. 0. 0. 0. 0. -31.47 0. 0. 0. 0. 0. 0. 0. -42.3 0. 0. 0. 0. 0. 0. 0. -52.53 0. 0. 0. 0. 0. 0. 0. -54.12 0. 0. 0. 0. 0. 0. 0. -62.47 0. 0. 0. 0. 0. 0. 0. -66.3 0. 0. 0. 0. 0. 0. 0 -83.75 0. 0. 0. 0. 0. 0. 0. -99.4 0. 0. 0. 0. 0. 0. 0. 73.06 0. 0. 0. 0. 0. 0. 0. 67.2 0. 0. 0. 0. 0. 0. 0. 63.47 0. 0. 0. 0. 0. 0. 0. 44.1 0. 0. 0. 0. 0. 0. 0. 37.8 0. 0. 0. 0. 0. 0. 0. 35.16 0. 0. 0. 0. 0. 0. 0. 21.58 0. 0. 0. 0. 0. 0. 0. 19.62 0. 0. 0. 0. 0. 0. 0. 15.86 0. 0. 0. 0. 0. 0. 0. 7.234 0. 0. 0. 0. 0. 0. 0. -14.875 0. 0. 0. 0. 0. 0. 0. -39.72 0. 0. 0. 0. 0. 0. 0. -77.2 0. 0. 0. 0. 0. 0. 0. -77.75 0. 0. 0. 0. 0. 0. 0. -87.9 0. 0. 0. 0. 0. 0. 0. -98.7 0. 0. 0. ] ```