之前一直使用610风冷版，现在有使用水冷版，这两个硬件can配置什么是否有区别呢

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

产品名称：MDC610软件版本：MDC_Development_Studio-2.2.001-0000000-Ubuntu20问题现象（问题描述）：根据“MDC 610 1.1.027-T000 产品文档”中的"安装第三方库>在编译环境中安装"章节步骤进行；我是用的操作系统：ubuntu20.04根据章节中的步骤需要安装“createrepo”命令，据了解该命令是centos系统中的，需要用yum安装，并且"MDC_AOS_rpmlist-{version}-llvm-dev.tar.gz"都是一些rpm包，这些应该都是centos系统的一些安装包，在ubuntu中的安装包因该是deb的，这是为啥；我尝试在ubuntu20.04系统中执行"apt install createrepo"安装，总是找不错，据说是python2中的东西已经移除，我现在不知道如何安装咱们的第三方库了。另外我需要"libpcap"、"Eigen3"、"PCL"、"Boost"这几个库，里面都有吗

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; }

产品名称：MDC610软件版本：MDC 610 1.1.027-T000问题现象：LidarDetection接收不到数据我在MMC工程搭建了如下框架，并进行如下配置连接MDC的雷达为速腾M1，配置信息如图。这里我参照了/opt/platform/mdc_platform/manual_service/lidar_a_cm/etc/LidarCmProcess中的network_binding.json接口我选的LidarDataFlowPPort，Instance Id设置为6会显示报错如下Failed to find the service。之后我又更换了个数据接口，尝试使用了LidarCmToAppPort接口，Instance Id配置从3-7之间都尝试了一次，均失败了。

MDC610里写了B3口支持1000M速率，但是在usr_network_port.json里将B3口的speed更改为1000,下电重启后，在B3口接了一个前兆雷达还是ping不通

MDC610使用ptp4l给雷达进行时间同步，在本地是可以的，但是在MDC上不行，而且其他雷达可以设置成功

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：

产品名称：MDC610软件版本：MDC 610 1.1.027-T000问题现象：FrontCameraTracker接收不到数据我在MMC工程搭建了如下框架，其会自动生成DDS通讯配置如下会显示找不到服务。之后我按照手册查阅了mbd_object_2d_array_service_interface.arxml，进行了如下配置。还是会显示Fail to find the service请问Service Instance ID、Domain Id、Qos Profile与Transport Plugins该如何取值？

使用MDC610host-image： 1.1.026-T0000000

硬件平台： 华为MDC510     CANN 7.0  Ascend310M1onnx 以及转换到om格式时均没有使用动态格式，只使用了单条数据用于格式转换。python 代码与 onnx-runtime 均能正常运行加法代码：    def message(self,                q_i: torch.Tensor,                k_j: torch.Tensor,                v_j: torch.Tensor,                r: Optional[torch.Tensor],                index: torch.Tensor,                ptr: Optional[torch.Tensor]) -> torch.Tensor:        if self.has_pos_emb and r is not None:            k_j = k_j + self.to_k_r(r).view(-1, self.num_heads, self.head_dim) # <====== 此处加法            v_j = v_j + self.to_v_r(r).view(-1, self.num_heads, self.head_dim) # <====== 此处加法        sim = (q_i * k_j).sum(dim=-1) * self.scale        attn = softmax(sim, dim=0)        attn = self.attn_drop(attn)        return v_j * attn.unsqueeze(-1)其中 k_j 与 k_r 的形状一致，v_j 与 v_r 的形状一致 乘法代码对应以下代码中的乘法：def angle_between_2d_vectors(        ctr_vector: torch.Tensor,        nbr_vector: torch.Tensor) -> torch.Tensor:    return custom_atan2(ctr_vector[..., 0] * nbr_vector[..., 1] - ctr_vector[..., 1] * nbr_vector[..., 0],                       (ctr_vector[..., :2] * nbr_vector[..., :2]).sum(dim=-1)) 通过debug确定了在python代码中不存在维度错误，且python代码与 onnx runtime 均能正常运行报错信息：root@acf7cc422d9d:/data# atc --log=debug --model=./batch_agent_encoder_modified.onnx --framework=5 --output=./batch_agent_encoder_modify --soc_version=Ascend310M1 --input_shape="input_x_pl:86,50,128;input_data_agent_valid_mask:39,110;input_data_agent_type:39;input_data_agent_position:39,110,3;input_data_agent_heading:39,110;input_data_agent_velocity:39,110,3;input_data_map_polygon_position:86,3;input_data_map_polygon_orientation:86;input_data_agent_batch:39;input_data_map_polygon_batch:86;input_data_num_graphs:1"ATC start working now, please wait for a moment....ATC run failed, Please check the detail log, Try 'atc --help' for more informationE40021: Failed to compile Op [/t_attn_layers.0/Add]. (oppath: [Pre-compile /usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/add.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/mdc/atc/python/site-packages/tbe/dsl/classifier/broadcast_classifier.py", line 188, in classify    return [classified_ins[-1]]IndexError: list index out of range], optype: [Add])        Solution: See the host log for details, and then check the Python stack where the error log is reported.        TraceBack (most recent call last):        Pre-compile op[/t_attn_layers.0/Add] failed, oppath[/usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/add.py], optype[Add], taskID[618]. Please check op's compilation error message.[FUNC:ReportBuildErrMessage][FILE:fusion_op.cc][LINE:882]        Failed to compile Op [/Mul_10]. (oppath: [Pre-compile /usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/mul.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/mdc/atc/python/site-packages/tbe/dsl/classifier/broadcast_classifier.py", line 188, in classify    return [classified_ins[-1]]IndexError: list index out of range], optype: [Mul])        Pre-compile op[/Mul_10] failed, oppath[/usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/mul.py], optype[Mul], taskID[625]. Please check op's compilation error message.[FUNC:ReportBuildErrMessage][FILE:fusion_op.cc][LINE:882]        [SubGraphOpt][Pre-Comp][Node /t_attn_layers.0/Add] Failed to pre-compile. Tid is [133010372425472], TaskId is [618] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133010372425472].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank19_new_sub_graph103][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank19_new_sub_graph103[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /Mul_11] Failed to pre-compile. Tid is [133011060266752], TaskId is [626] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp][Node /Mul_10] Failed to pre-compile. Tid is [133011060266752], TaskId is [625] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133011060266752].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank22_new_sub_graph106][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank22_new_sub_graph106[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /t_attn_layers.0/Add_1] Failed to pre-compile. Tid is [133011085444864], TaskId is [696][FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133011085444864].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank26_new_sub_graph110][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank26_new_sub_graph110[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        Failed to compile Op [/Mul_71]. (oppath: [Pre-compile /usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/mul.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/mdc/atc/python/site-packages/tbe/dsl/classifier/broadcast_classifier.py", line 188, in classify    return [classified_ins[-1]]IndexError: list index out of range], optype: [Mul])        Pre-compile op[/Mul_71] failed, oppath[/usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/mul.py], optype[Mul], taskID[777]. Please check op's compilation error message.[FUNC:ReportBuildErrMessage][FILE:fusion_op.cc][LINE:882]        [SubGraphOpt][Pre-Comp][Node /Mul_72] Failed to pre-compile. Tid is [133011068659456], TaskId is [778] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp][Node /Mul_71] Failed to pre-compile. Tid is [133011068659456], TaskId is [777] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133011068659456].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank63_new_sub_graph241][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank63_new_sub_graph241[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        Failed to compile Op [/Mul_67]. (oppath: [Pre-compile /usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/mul.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/mdc/atc/python/site-packages/tbe/dsl/classifier/broadcast_classifier.py", line 188, in classify    return [classified_ins[-1]]IndexError: list index out of range], optype: [Mul])        Pre-compile op[/Mul_67] failed, oppath[/usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/mul.py], optype[Mul], taskID[817]. Please check op's compilation error message.[FUNC:ReportBuildErrMessage][FILE:fusion_op.cc][LINE:882]        [SubGraphOpt][Pre-Comp][Node /Mul_68] Failed to pre-compile. Tid is [133010372425472], TaskId is [818] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp][Node /Mul_67] Failed to pre-compile. Tid is [133010372425472], TaskId is [817] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank76_new_sub_graph327][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank76_new_sub_graph327[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        Failed to compile Op [/pl2a_attn_layers.1/Add]. (oppath: [Pre-compile /usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/add.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/mdc/atc/python/site-packages/tbe/dsl/classifier/broadcast_classifier.py", line 188, in classify    return [classified_ins[-1]]IndexError: list index out of range], optype: [Add])        Pre-compile op[/pl2a_attn_layers.1/Add] failed, oppath[/usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/add.py], optype[Add], taskID[827]. Please check op's compilation error message.[FUNC:ReportBuildErrMessage][FILE:fusion_op.cc][LINE:882]        [SubGraphOpt][Pre-Comp][Node /pl2a_attn_layers.1/Add] Failed to pre-compile. Tid is [133010506643200], TaskId is [827] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133010506643200].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank79_new_sub_graph330][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        [SubGraphOpt][Pre-Comp][Node /pl2a_attn_layers.0/Add] Failed to pre-compile. Tid is [133010372425472], TaskId is [927] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank79_new_sub_graph330[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank83_new_sub_graph334][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank83_new_sub_graph334[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /pl2a_attn_layers.1/Add_1] Failed to pre-compile. Tid is [133011068659456], TaskId is [884] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank81_new_sub_graph332][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank81_new_sub_graph332[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /pl2a_attn_layers.0/Add_1] Failed to pre-compile. Tid is [133010456286976], TaskId is [936] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133010456286976].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank85_new_sub_graph336][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank85_new_sub_graph336[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /t_attn_layers.1/Add_1] Failed to pre-compile. Tid is [133010498250496], TaskId is [1096] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133010498250496].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank156_new_sub_graph452][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank156_new_sub_graph452[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /t_attn_layers.1/Add] Failed to pre-compile. Tid is [133010498250496], TaskId is [1099] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank158_new_sub_graph455][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank158_new_sub_graph455[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        Failed to compile Op [/a2a_attn_layers.0/Add_1]. (oppath: [Pre-compile /usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/add.py failed with errormsg/stack: File "/usr/local/Ascend/ascend-toolkit/mdc/atc/python/site-packages/tbe/dsl/classifier/broadcast_classifier.py", line 188, in classify    return [classified_ins[-1]]IndexError: list index out of range], optype: [Add])        Pre-compile op[/a2a_attn_layers.0/Add_1] failed, oppath[/usr/local/Ascend/ascend-toolkit/mdc/x86_64-linux/opp/built-in/op_impl/ai_core/tbe/impl/dynamic/add.py], optype[Add], taskID[963]. Please check op's compilation error message.[FUNC:ReportBuildErrMessage][FILE:fusion_op.cc][LINE:882]        [SubGraphOpt][Pre-Comp][Node /a2a_attn_layers.1/Add_1] Failed to pre-compile. Tid is [133011077052160], TaskId is [1238] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp][Node /a2a_attn_layers.0/Add_1] Failed to pre-compile. Tid is [133010456286976], TaskId is [963] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to process failed task. Thread_id is [133011077052160].[FUNC:ParallelPreCompileOp][FILE:tbe_op_store_adapter.cc][LINE:520]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank216_new_sub_graph586][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank216_new_sub_graph586[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank117_new_sub_graph409][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank117_new_sub_graph409[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /a2a_attn_layers.0/Add] Failed to pre-compile. Tid is [133011068659456], TaskId is [1019] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank121_new_sub_graph413][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank121_new_sub_graph413[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        [SubGraphOpt][Pre-Comp][Node /a2a_attn_layers.1/Add] Failed to pre-compile. Tid is [133011085444864], TaskId is [1241] [FUNC:ProcessFailPreCompTask][FILE:tbe_op_store_adapter.cc][LINE:179]        [SubGraphOpt][Pre-Comp]Failed to pre-compile graph [partition6_rank218_new_sub_graph589][FUNC:PreCompileOp][FILE:op_compiler.cc][LINE:724]        Call OptimizeFusedGraph failed, ret:-1, engine_name:AIcoreEngine, graph_name:partition6_rank218_new_sub_graph589[FUNC:OptimizeSubGraph][FILE:graph_optimize.cc][LINE:129]        subgraph 264 optimize failed[FUNC:OptimizeSubGraphWithMultiThreads][FILE:graph_manager.cc][LINE:962]        build graph failed, graph id:0, ret:-1[FUNC:BuildModel][FILE:ge_generator.cc][LINE:1505]root@acf7cc422d9d:/data#

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？

修改网络配置后，将B3口的速率改为100后，也通过校验。然后重启的时候就报错，错误如下：MDC reset: ERROR: MDC Reset failed! StateTransition Arbitration failed  这个怎么解决

1.产品名称：MDC6102.软件版本：MDC610 1.1.027-T000  MDC_Manifest_Configurator-2.2.001-0000000-Ubuntu20  MDC_Development_Studio-2.2.001-0000000-Ubuntu20相机为森云的可见光相机基于MMC搭建的节点通讯框架如下3.问题描述：在MDS中自动生成代码后，我们想测试MDC能否正常接收到相机的图像数据，并打印出一帧yuv图像或原始数据，请问应该如何操作？4.已采取的措施：在sample.cpp中编写了如下代码，希望获取到data（类型为CameraDecodedMbufStruct消息结构体）中的Rawdata，Rawdata在文档中的描述为图像数据#include "mdc/sample.h"#include <chrono>#include <functional>namespace mdc {Sample::Sample(): swcPtr_(std::make_unique<mdc::camera_det_base::CameraDetBaseSwC>()), workFlag_(true){}Sample::~Sample(){    if (workFlag_) {        Stop();    }}bool Sample::InitHandle(){    SPL_LOG_SPACE::GetLoggerIns("SPL")->LogInfo()<< "Some operations during initialization.";    return true;}void Sample::StopHandle(){    SPL_LOG_SPACE::GetLoggerIns("SPL")->LogInfo()<< "Some operations during stopping";    return;}bool Sample::Init(){    SPL_LOG_SPACE::InitAppLogging();    SPL_LOG_SPACE::InitLoggerCtx("SPL", "sample log contex");    if (!swcPtr_) {        SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "Failed to create the SWC object.";        return false;    }    swcPtr_->SetInitCallback(std::bind(&Sample::InitHandle));    swcPtr_->SetStopCallback(std::bind(&Sample::StopHandle));    if (!swcPtr_->Init()) {        SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "SWC initialization failed.";        return false;    }    return true;}void Sample::Run(){    Object2dArrayIntfServerThreadPtr_ =        std::make_unique<std::thread>(std::bind(&Sample::Object2dArrayIntfServerRun, this));    CameraDecodedMbufServiceInterfaceClientThreadPtr_ =        std::make_unique<std::thread>(std::bind(&Sample::CameraDecodedMbufServiceInterfaceClientRun, this));}void Sample::Object2dArrayIntfServerRun(){    if (!swcPtr_) {        SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "The swc object is empty.";        return;    }    /* 获取本应用所有的发送端口，获取到的端口名对应MMC上本应用配置的发送端口名 */    auto object2dArrayIntfServerPortVec = swcPtr_->GetObject2dArrayIntfServerVec();    while (workFlag_) {        /* 遍历port name，获取服务端对象并发送数据 */        for (const auto& portName : object2dArrayIntfServerPortVec) {            if (!workFlag_) {                return;            }            /* 获取发送端服务，其中portName对应MMC上配置的此应用对应的发送端portName <关键接口> */            auto serverPtr = swcPtr_->GetObject2dArrayIntfServer(portName);            if (!serverPtr) {                SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "Failed to initialize the instance: " << portName;                continue;            }            auto data = std::make_shared<adsfi::Object2dArrayDataType>();            /* Event事件，发送数据，非阻塞接口 <关键接口> */            serverPtr->SendObject2dArrayData(data);            std::this_thread::sleep_for(std::chrono::milliseconds(500U));        }    }}void Sample::CameraDecodedMbufServiceInterfaceClientRun(){    if (!swcPtr_) {        SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "The swc object is empty.";        return;    }    /* 获取本应用所有的接收端口，获取到的端口名对应MMC上本应用配置的接收端口名 */    auto cameraDecodedMbufServiceInterfaceClientPortVec = swcPtr_->GetCameraDecodedMbufServiceInterfaceClientVec();    while (workFlag_) {        /* 遍历port name，获取客户端对象并注册接收到数据后的回调函数 */        for (const auto& portName : cameraDecodedMbufServiceInterfaceClientPortVec) {            if (!workFlag_) {                return;            }            /* 获取接收端服务，其中portName对应MMC上配置的此应用对应的接收端portName <关键接口> */            auto clientPtr = swcPtr_->GetCameraDecodedMbufServiceInterfaceClient(portName);            if (!clientPtr) {                SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "Failed to initialize the instance: " << portName;                continue;            }            /* Event事件，注册接收到数据后的回调函数 */            clientPtr->RegisterCameraDecodedMbufEventNotifyHandler(                std::bind(&Sample::ReceiveMdcCamCameraCameraDecodedMbufEventDataHandle,                std::placeholders::_1));            /* 获取数据，非阻塞方式 <关键接口> */            auto oneData = clientPtr->GetCameraDecodedMbufEventOneData();            /* 获取数据，一次获取5个数据，非阻塞方式 <关键接口> */            auto nData = clientPtr->GetCameraDecodedMbufEventNdata(5U);            /* 获取数据，阻塞式接口，超时时间为1000ms <关键接口> */            auto oneDataBlocking = clientPtr->GetCameraDecodedMbufEventOneDataBlocking(1000U);        }    }}void Sample::Stop(){    workFlag_ = false;    if ((Object2dArrayIntfServerThreadPtr_ != nullptr) &&        (Object2dArrayIntfServerThreadPtr_->joinable())) {        Object2dArrayIntfServerThreadPtr_->join();    }    Object2dArrayIntfServerThreadPtr_ = nullptr;    if ((CameraDecodedMbufServiceInterfaceClientThreadPtr_ != nullptr) &&        (CameraDecodedMbufServiceInterfaceClientThreadPtr_->joinable())) {        CameraDecodedMbufServiceInterfaceClientThreadPtr_->join();    }    CameraDecodedMbufServiceInterfaceClientThreadPtr_ = nullptr;    SPL_LOG_SPACE::GetLoggerIns("SPL")->LogInfo()<< "Thread resources reclaimed successfully..";    if (!swcPtr_) {        SPL_LOG_SPACE::GetLoggerIns("SPL")->LogError()<< "The swc object is empty.";        return;    }    swcPtr_->Stop();}void Sample::ReceiveMdcCamCameraCameraDecodedMbufEventDataHandle(const mdc::cam::camera::cameraDecodedMbufEventDataType& data){    const ara::camera::CameraDecodedMbufStruct& mbufData =        static_cast<const ara::camera::CameraDecodedMbufStruct&>(data);    // 调用GetMbufPtr()方法获取缓冲区指针    ::rawBuffer* mbufPtr = mbufData.GetMbufPtr();    // 输出日志信息，包括缓冲区指针和关键属性    SPL_LOG_SPACE::GetLoggerIns("SPL")->LogInfo()        << "Received camera decoded mbuf data - "        << "Width: " << mbufData.Width << ", "        << "Height: " << mbufData.Height << ", "        << "DataSize: " << mbufData.DataSize << ", "        << "FrameType: " << mbufData.FrameType << ", "        << "MbufPtr: " << static_cast<void*>(mbufPtr);}}打包在mdc上运行，打印出的部分日志如下：2025/01/21 18:21:31.702187358 2025/01/21 18:21:31.702175665 018 A102 CLNT info [cameraDecodedMbufEvent: data received. instanceId: 21]2025/01/21 18:21:31.735434024 2025/01/21 18:21:31.735423373 006 A102 SPL info [Received camera decoded mbuf data - Width: 1920, Height: 1080, DataSize: 4147200, FrameType: 1004, MbufPtr: 3fa]2025/01/21 18:21:31.735484233 2025/01/21 18:21:31.735472800 022 A102 CLNT info [cameraDecodedMbufEvent: data received. instanceId: 21]2025/01/21 18:21:31.768783347 2025/01/21 18:21:31.768773087 007 A102 SPL info [Received camera decoded mbuf data - Width: 1920, Height: 1080, DataSize: 4147200, FrameType: 1004, MbufPtr: 3f9]可以看出Mbuf的值过低，不是有效地址，并不直接指向图像原始数据。也没有找到其他指向图像数据的接口。