本机的mdc610版本为MDC 610 1.1.027-T000 产品文档 01.chm请问一下下载的Sample中没有分割的Sample，请问一下您这边是否有如MobileSam等语义分割的代码Sample

onnx模型成功转为om格式：atc --log=debug --model=./batch_map_encoder.onnx --framework=5 --output=./batch_map_encoder_dyn --soc_version=Ascend310M1 --input_shape="input_data_map_polygon_position:-1,3;input_data_map_polygon_orientation:-1;input_data_map_polygon_type:-1;input_data_map_polygon_is_intersection:-1;input_data_map_point_position:1669,3;input_data_map_point_orientation:1669;input_data_map_point_magnitude:1669;input_data_map_point_type:1669;input_data_map_point_side:1669;input_data_map_point_to_map_polygon_edge_index:2,1669;input_data_map_polygon_to_map_polygon_edge_index:2,208;input_data_map_polygon_to_map_polygon_type:208" --input_format=ND  --dynamic_dims="86,86,86,86;90,90,90,90;"在华为MDC510pro上加载报错：[mdc@AOS_A zhh]$ ./load_model batch_map_encoder_dyn.om [IAM][WARN][ClientSystemFops.cpp:136] Cannot find libc.so. [INFO] Acl Init Success [INFO] Acl Set Device Success,Current DeviceID:0 [INFO] Acl Create Context Success [INFO] Acl Create Stream Success file: load_model.cpp line: 85[ERROR] id: 500002 error_msg: EE1001: The argument is invalid.Reason: rtKernelLaunchFwk execute failed, reason=[feature not support] Solution: 1.Check the input parameter range of the function. 2.Check the function invocation relationship. TraceBack (most recent call last): not support aicpu task![FUNC:AiCpuTaskSupportCheck][FILE:api_impl.cc][LINE:5742] The argument is invalid.Reason: rtKernelLaunchFwk execute failed, reason=[feature not support] Call rtKernelLaunchEx(device_base, op_kernel_size, 0U, stream) fail, ret: 0x32898[FUNC:KernelLaunchEx][FILE:model_manager.cc][LINE:167] Failed to init task index 216, related node /pt2pl_layers.0/Mod_ascend_mbatch_batch_0[FUNC:InitTaskInfoV2][FILE:model_args_manager.cc][LINE:215] [Model][FromData]load model from data failed, ge result[1343225857][FUNC:ReportCallError][FILE:log_inner.cpp][LINE:161] [mdc@AOS_A zhh]$加载模型的 load_model 能够成功加载文档中给出的resnet示例模型，加载om模型的源码：#include <algorithm> #include <iostream> #include <vector> #include "acl/acl.h" #define INFO_LOG(fmt, args...) fprintf(stdout, "[INFO] " fmt "\n", ##args) #define WARN_LOG(fmt, args...) fprintf(stdout, "[WARN] " fmt "\n", ##args) #define ERROR_LOG(fmt, args...) fprintf(stdout, "[ERROR] " fmt "\n", ##args) #define CHECK_ERROR(ret) \ if (ret != ACL_ERROR_NONE) { \ std::cout << " file: " << __FILE__ << " line: " << __LINE__ \ << "[ERROR] id: " << ret \ << " error_msg: " << aclGetRecentErrMsg() << std::endl; \ return 1; \ } using namespace std; int main(int argc, char* argv[]) { uint32_t deviceId = 0; aclrtContext context = nullptr; aclrtStream stream = nullptr; const char* aclConfigPath = nullptr; aclError ret = aclInit(aclConfigPath); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Init Failed"); return 1; } INFO_LOG("Acl Init Success"); ret = aclrtSetDevice(deviceId); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Set Device Failed"); return 1; } INFO_LOG("Acl Set Device Success,Current DeviceID:%d", deviceId); ret = aclrtCreateContext(&context, deviceId); if (ret != ACL_ERROR_NONE) { fprintf(stderr, "%d", ret); ERROR_LOG("Acl Create Context Failed"); return 1; } INFO_LOG("Acl Create Context Success"); ret = aclrtCreateStream(&stream); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Create Stream Failed"); return 1; } INFO_LOG("Acl Create Stream Success"); /* * 业务执行 */ char omModelPath[100]; if (argc != 2) { std::cout << "input model's name in command!!!" << std::endl; } strcpy(omModelPath, argv[1]); size_t modelMemSize_; size_t modelWeightSize_; void* modelMemPtr_; void* modelWeightPtr_; uint32_t modelId_; ret = aclmdlLoadFromFile(omModelPath, &modelId_); CHECK_ERROR(ret); aclmdlDesc* modelDesc_ = aclmdlCreateDesc(); ret = aclmdlGetDesc(modelDesc_, modelId_); CHECK_ERROR(ret); size_t inputNum = aclmdlGetNumInputs(modelDesc_); aclmdlDataset* datasetInput_ = aclmdlCreateDataset(); std::vector<void*> inputBuffers(inputNum); std::vector<size_t> inputSizes(inputNum); for (int i = 0; i < inputNum; i++) { aclmdlIODims dims; aclmdlGetInputDims(modelDesc_, i, &dims); aclDataType dataType = aclmdlGetInputDataType(modelDesc_, i); inputSizes[i] = aclmdlGetInputSizeByIndex(modelDesc_, i); printf("Input %d: dims=[", i); for (size_t j = 0; j < dims.dimCount; ++j) { printf("%ld ", dims.dims[j]); } printf("], dtype=%d, size=%zu\n", dataType, inputSizes[i]); ret = aclrtMalloc(&inputBuffers[i], inputSizes[i], ACL_MEM_MALLOC_NORMAL_ONLY); aclDataBuffer* inputData = aclCreateDataBuffer(inputBuffers[i], inputSizes[i]); CHECK_ERROR(ret); ret = aclmdlAddDatasetBuffer(datasetInput_, inputData); CHECK_ERROR(ret); } size_t numOutputs = aclmdlGetNumOutputs(modelDesc_); aclmdlDataset* datasetOutput_ = aclmdlCreateDataset(); size_t modelOutputSize; void* modelOutputBuffer = nullptr; for (int i = 0; i < numOutputs; i++) { modelOutputSize = aclmdlGetOutputSizeByIndex(modelDesc_, i); ret = aclrtMalloc(&modelOutputBuffer, modelOutputSize, ACL_MEM_MALLOC_NORMAL_ONLY); CHECK_ERROR(ret); aclDataBuffer* outputData = aclCreateDataBuffer(modelOutputBuffer, modelOutputSize); ret = aclmdlAddDatasetBuffer(datasetOutput_, outputData); CHECK_ERROR(ret); } // ========================================================== ret = aclrtDestroyStream(stream); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Destroy Stream Failed"); return 1; } INFO_LOG("Acl Destroy Stream Success"); ret = aclrtDestroyContext(context); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Destroy Context Failed"); return 1; } INFO_LOG("Acl Destroy Context success"); ret = aclrtResetDevice(deviceId); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Reset Device Failed"); return 1; } INFO_LOG("Acl Reset Device Success"); ret = aclFinalize(); if (ret != ACL_ERROR_NONE) { ERROR_LOG("Acl Finalize Failed"); return 1; } INFO_LOG("Acl Finalize Success"); return 0; }

问题描述：使用aclInit () 调用，板端运行报错507017，可以确定的是要dump的算子scatternd不是cpu算子；看log aicpu运行有问题 使用pmupload 获取log如下：代码调用：acl.json:工具链版本：

工具链版本：现状描述：onnx模型进行aoe后，om模型对于小目标的低矮障碍物；有高度上的map值掉点，其他目标没有精度掉点；但是一些case对不齐，高分框车等目标完全没有检出提问：1. MDC610当前得到板端om模型dump文件的接口是支持的么？2. 关于异常case的对齐问题，要怎么去解决呢？可视化现状：

当前设备为MDC510pro，对应CANN为 7.10.t0.0.b370，soc version为 Ascend310M1。使用atc工具将onnx转om时会报各种关于Tensor维度的错误。转换格式时指定固定维度或者动态维度都会出现不同的维度错误。例如：[INFO] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.275 [stream.cc:195] 41 DeAllocStreamSqCq: [SqCqManage]end to release sq, sq is also reuse, sq_id=0, cq_id=0, stream_id=66, is_sq_need_release=0, drv_flag=0x1.[DEBUG] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.276 [stream.cc:449] 41 DelStreamIdToStream: streamId=66.[DEBUG] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.288 [callback.cc:38] 41 Notify: stub device is no need callback.[INFO] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.291 [stream.cc:618] 41 FreeStreamId: free stream_id=64,[DEBUG] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.293 [stream.cc:157] 41 DeAllocStreamSqCq: streamIdToSqIdMap remove:stream_id=64, sq_id=0, cq_id=0.[INFO] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.294 [stream.cc:192] 41 DeAllocStreamSqCq: [SqCqManage]success to release sq, sq_id=0, cq_id=0, stream_id=64, is_sq_need_release=1, drvFlag=0.[DEBUG] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.296 [stream.cc:449] 41 DelStreamIdToStream: streamId=64.[INFO] RUNTIME(41,atc.bin):2025-10-27-17:47:56.232.303 [device.cc:94] 41 ~Device: deconstruct device[ERROR] GE(41,atc.bin):2025-10-27-17:47:56.232.307 [graph_manager.cc:1227]41 StartForRunGraph: ErrorNo: 1343242270(Prepare Graph infershape failed) [COMP][PRE_OPT][Call][PreRun] Failed, graph_id:0, session_id:0.[ERROR] GE(41,atc.bin):2025-10-27-17:47:56.232.309 [graph_manager.cc:1668]41 BuildGraph: ErrorNo: 1343242270(Prepare Graph infershape failed) [COMP][PRE_OPT][Call][StartForRunGraph] failed! graph_id:0.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.316 [error_manager.cc:254]41 ReportInterErrMessage:report error_message, error_code:E19999, work_stream_id:4100041[ERROR] GE(41,atc.bin):2025-10-27-17:47:56.232.333 [ge_generator.cc:1506]41 BuildModel: ErrorNo: 1343266819(Graph manager build graph failed.) [COMP][DEFAULT][Build][Graph] fail, graph id: 0[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.342 [graph_var_manager.cc:757]41 Destory:VarManager::Destory, session id = 0.[ERROR] GE(41,atc.bin):2025-10-27-17:47:56.232.493 [ge_generator.cc:677]41 GenerateModel: ErrorNo: 1343242270(Prepare Graph infershape failed) [COMP][DEFAULT][Build][Model] failed, ret:1343242270.[WARNING] GE(41,atc.bin):2025-10-27-17:47:56.232.616 [analyzer.cc:143]41 DestroyGraphJsonObject:can not find the stored object by session_id[0].Do nothing[ERROR] GE(41,atc.bin):2025-10-27-17:47:56.232.773 [main_impl.cc:1238]41 GenerateModel: ErrorNo: 4294967295(failed) [COMP][DEFAULT]GE GenerateOfflineModel execute failed[ERROR] GE(41,atc.bin):2025-10-27-17:47:56.232.779 [main_impl.cc:1239]41 GenerateModel: ErrorNo: 4294967295(failed) [COMP][DEFAULT]ATC Generate execute failed[WARNING] GE(41,atc.bin):2025-10-27-17:47:56.232.782 [graph_manager.cc:439]41 Finalize:GraphManager has not been initialized.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.785 [gelib.cc:473]41 Finalize:finalization start[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.787 [gelib.cc:484]41 Finalize:engineManager finalization.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.789 [dnnengine_manager.cc:180]41 Finalize:DNNEngine name: AIcoreEngine.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.790 [dnnengine_manager.cc:180]41 Finalize:DNNEngine name: DNN_HCCL.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.792 [dnnengine_manager.cc:180]41 Finalize:DNNEngine name: DNN_VM_AICPU.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.232.793 [dnnengine_manager.cc:180]41 Finalize:DNNEngine name: DNN_VM_AICPU_ASCEND.和[INFO] GE(41,atc.bin):2025-10-27-17:47:56.936.942 [process_node_engine_manager.cc:87]41 Finalize:ProcessNodeEngine id:UDF.[DEBUG] GE(41,atc.bin):2025-10-27-17:47:56.937.254 [node_compile_cache_module.cc:443]41 Finalize:Finalize ccm.[INFO] GE(41,atc.bin):2025-10-27-17:47:56.937.267 [gelib.cc:548]41 Finalize:finalization success.[WARNING] GE(41,atc.bin):2025-10-27-17:47:56.937.433 [graph_manager.cc:439]41 Finalize:GraphManager has not been initialized....[WARNING] GE(41,atc.bin):2025-10-27-17:47:56.945.576 [main_impl.cc:1566]41 CheckRet:ATC generate offline model failed.ATC run failed, Please check the detail log, Try 'atc --help' for more information[INFO] GE(41,atc.bin):2025-10-27-17:47:56.945.586 [error_manager.cc:376]41 GetErrorMessage:current work_stream_id:4100041E89999: Inner Error!E89999  op[/Concat_19], the input shape dims should be equal except merge axis,shapes:[[0, 1, ], [1, 1, ], ]axis:-1[FUNC:ConcatInferShapeCommon][FILE:split_combination_ops.cc][LINE:892]        TraceBack (most recent call last):        Call InferShapeAndType for node:/Concat_19(ConcatD) failed[FUNC:Infer][FILE:infershape_pass.cc][LINE:119]        process pass InferShapePass on node:/Concat_19 failed, ret:4294967295[FUNC:RunPassesOnNode][FILE:base_pass.cc][LINE:571]        build graph failed, graph id:0, ret:1343242270[FUNC:BuildModel][FILE:ge_generator.cc][LINE:1505][INFO] GE(41,atc.bin):2025-10-27-17:47:56.946.172 [main_impl.cc:1637]41 GetMemInfo:Find mem [MemAvailable] info line [MemAvailable:   12196748 kB][INFO] GE(41,atc.bin):2025-10-27-17:47:56.946.186 [main_impl.cc:1645]41 GetMemInfo:Find mem [MemAvailable] info [12196748 kB]. 但原有的python代码运行正常且不存在维度错误。使用 onnx-runtime 加载 onnx 模型也可以正常运行。尝试通过升级CANN软件版本解决问题。从官网下载社区版 CANN8.1，其不支持当前 soc_version。当指定 soc_version 参数为 Ascend310P3或Ascend610 时，可以成功将当前onnx转为om格式。现有疑问：MDC510pro 的 atc 工具在格式转换时的维度错误该如何解决？能否通过升级软件版本解决问题？如何获取支持当前 soc_version（Ascend310M1） 的 CANN toolkit？

1、目前手上的MDC610板载版本是1.99.220，是否有部署过uniAD等端到端模型的案例？有相关链接参考吗？2、部署测试的range范围是多大的，整体耗时怎么样？

想请问下把mdc300F上的程序迁移到mdc510上，软件上需要做哪些适配，除了重新适配arxml文件，重新交叉编译程序用到的三方库以及自身程序，还有其他需要适配的嘛?激光雷达传感器和组合定位需要重新适配嘛？

环境：mdc610（风冷）系统版本：1.1.027-0000000T1问题： 我们现在想使用 sshfs 去挂载目录，不使用NFS，想问下是否只能通过交叉编译的方式部署，想问下有MDC610 专用的SSHFS 包可以直接安装嘛？

本机的mdc610版本为MDC 610 1.1.027-T000 产品文档 01.chm您看这个问题，您说的后续版本是什么版本？是软件版本还是mdc610自身产品不支持？2、如果是软件版本的话，产品支持软件版本升级吗？

MDC610可以部署transformer模型吗？版本：MDC 610 1.1.027-T000 产品文档 01.chm

MDC300出厂的帐号密码是什么？

 1.硬件平台：MDC 6102.软件版本：MDC_Development_Studio-3.0.002-0000000-Ubuntu203.问题描述：已按照产品文档进行交叉编译环境、SCFI环境等配置（SCFI例程已成功在计算平台上运行），当使用MDS软件或直接使用scfirepo showrpm时，均提示找不到RPM库4.一点疑问：产品文档中描述“确认当前工程所关联SDK中的交叉编译环境已配置lyum源”，但仅有计算平台的第三方库安装方式之一涉及到lyum，而交叉编译环境的第三方库完全不涉及lyum，该如何配置  

1.产品名称：MDC 6102.软件版本：MDC_Development_Studio-3.0.002-0000000-Ubuntu203.问题现象：已按照产品文档完成交叉编译环境、第三方库、SCFI等相关配置，使用MDS软件创建SCFI工程，右键该工程打开属性配置窗口后，在“Build & Execution > RPM Management”中输入当前开发机的root密码，提示无法找到rpm库。  4.已采取的措施：交叉编译、SCFI的环境变量已加载，交叉编译环境的第三方库已安装，计算平台的第三方库也已安装。5.主要疑问：文档中提到“确认当前工程关联SDK中的交叉编译环境已配置lyum源”，但在产品文档中，仅计算平台的第三方库安装有一种安装方式是采用lyum_install.sh脚本的，而交叉编译环境的第三方库安装并未涉及lyum，请问如何解决当前问题。   

VENC接口参数：在mini编码后转发给HOST A节点在节点A打印字节内容后发现字节内容错误码流缺失PPS和SPS帧，请问 如何进行修复，或者指出使用VENC接口的错误之处。

环境：MDC300F系统版本： 1.0.105.2t相机配置：问题现象：      经常出现MDC上电后，C1,C2,C3模组的相机没有数据，使用rtfevent工具看，79,80,81没有数据流。但是重启MDC之后，C1,C2,C3的相机数据流又会恢复，使用rtfevent工具看，79,80,81的相机都有数据流了。查看/disk4/hi3559_0/hi3559.log日志，当没有数据流的时候有如下报错：  想问下这是整个模组的线束问题，还是某个相机的问题，这个相机的报错日志要怎么看？