安装驱动和固件检查服务器是否安装驱动与固件终端输入npu-smi info如果正常返回显卡信息说明驱动已安装好。使用python测试acl.rt.ser_device(0)命令正常返回说明固件已安装好卡死无返回说明固件没装。如果没有可从官网下载安装文件拉到服务器上进行安装安装命令如下bash Ascend-hdk-310p-npu-driver_25.5.2_linux-aarch64.run # 安装驱动 bash Ascend-hdk-310p-npu-firmware_7.8.0.7.220.run #安装固件安装dockeryum install -y dockeryum install -y docker拉取镜像参考方式一拉取容器镜像-安装CANN容器场景-软件安装-CANN商用版8.0.RC2开发文档-昇腾社区docker pull swr.cn-south-1.myhuaweicloud.com/ascendhub/mindie:3.0.0b2-300I-Duo-py311-openeuler24.03-lts启动容器启动脚本如下#!/bin/bash docker stop ascend-310p_3 docker rm ascend-310p_3 docker run -itd --privileged \ --nethost \ --ipchost \ --name ascend-310p_3 \ --device/dev/davinci0 \ --device/dev/davinci2 \ --device/dev/davinci4 \ --device/dev/davinci6 \ --device/dev/davinci_manager \ --device/dev/devmm_svm \ --device/dev/hisi_hdc \ --device/dev/isomgr \ -v /cv:/cv \ -v /usr/local/Ascend/driver:/usr/local/Ascend/driver:ro \ -v /usr/local/Ascend/add-ons:/usr/local/Ascend/add-ons:ro \ -v /var/log/npu/:/var/log/npu/ \ -v /usr/local/sbin/npu-smi:/usr/local/sbin/npu-smi:ro \ -v /var/log/npu/conf/devmm/common_devmm.cfg:/var/log/npu/conf/devmm/common_devmm.cfg \ 1f6e1a6cee5f \ /bin/bash容器内安装相关软件#安装开发工具包组 (包含 make, gcc, g 等) dnf groupinstall Development Tools -y # 安装opencv cd opencv-4.11.0/ mkdir build cd build cmake -D CMAKE_BUILD_TYPERELEASE -D CMAKE_INSTALL_PREFIX/usr/local -D WITH_TBBON -D WITH_V4LON -D WITH_QTOFF -D WITH_GTKOFF -D WITH_OPENGLOFF -D BUILD_EXAMPLESOFF .. make -j$(nproc) sudo make install # 安装ros2 bash -c cat EOF /etc/yum.repos.d/ROS.repo [openEulerROS-humble] nameopenEulerROS-humble baseurlhttps://eulermaker.compass-ci.openeuler.openatom.cn/api/ems1/repositories/ROS-SIG-Multi-Version_ros-humble_openEuler-24.03-LTS-TEST4/openEuler%3A24.03-LTS/aarch64/ enabled1 gpgcheck0 EOF sudo dnf update sudo dnf install ros-humble-ros-base -y参考gitee.com安装acliteonnx转om# yolov8 atc --modelyolov8n.onnx \ --framework5 \ --outputyolov8n \ --input_shapeimages:1,3,640,640 \ --soc_versionAscend310P3 \ --logerror推理import acl import sys import numpy as np import cv2 # 假设你的 acllite 路径如下 sys.path.append(/home/samples/inference/acllite/python) from acllite_model import AclLiteModel from acllite_resource import AclLiteResource def post_process(infer_output, conf_threshold0.25, iou_threshold0.45): YOLOv8 后处理 infer_output: model.execute 的返回值 (list of numpy arrays) # 1. 提取输出YOLOv8 默认输出 shape 为 [1, 84, 8400] # 84 代表: 4个坐标 80个类别 # 8400 代表: 3个尺度的预测框总和 data infer_output[0][0] data data.transpose() # 转置为 [8400, 84] boxes [] scores [] class_ids [] for row in data: classes_scores row[4:] max_score np.amax(classes_scores) if max_score conf_threshold: # YOLOv8 输出的是 [center_x, center_y, w, h] cx, cy, w, h row[:4] # 转换为 [x1, y1, w, h] 用于 cv2.dnn.NMSBoxes left int(cx - w / 2) top int(cy - h / 2) boxes.append([left, top, int(w), int(h)]) scores.append(float(max_score)) class_ids.append(np.argmax(classes_scores)) # 2. 非极大值抑制 (NMS) 过滤重叠框 indices cv2.dnn.NMSBoxes(boxes, scores, conf_threshold, iou_threshold) results [] if len(indices) 0: for i in indices.flatten(): results.append({ box: boxes[i], score: scores[i], class_id: class_ids[i] }) return results # --- 主程序 --- resource AclLiteResource(2) resource.init() model AclLiteModel(yolov8n.om) # 预处理注意 YOLOv8 通常需要归一化 (1/255.0) start_event, ret acl.rt.create_event() end_event, ret acl.rt.create_event() stream resource.stream image cv2.imread(/cv/1713418474155.jpg) img_h, img_w image.shape[:2] while 1: #acl.rt.record_event(start_event, stream) image_resized cv2.resize(image, (640, 640)) image_data image_resized.astype(np.float32) / 255.0 # 归一化 image_data image_data.transpose(2, 0, 1) # HWC 转 CHW # 执行推理 acl.rt.record_event(start_event, stream) result_list model.execute([image_data,]) acl.rt.record_event(end_event, stream) acl.rt.synchronize_stream(stream) time_ms, ret acl.rt.event_elapsed_time(start_event, end_event) # 后处理 detections post_process(result_list) #acl.rt.record_event(end_event, stream) #acl.rt.synchronize_stream(stream) #time_ms, ret acl.rt.event_elapsed_time(start_event, end_event) print(f硬件纯推理耗时 (NPU Pure Inference): {time_ms:.2f} ms) # 3. 可视化结果 for det in detections: x, y, w, h det[box] score det[score] class_id det[class_id] # 坐标还原到原图大小 x int(x * img_w / 640) y int(y * img_h / 640) w int(w * img_w / 640) h int(h * img_h / 640) cv2.rectangle(image, (x, y), (x w, y h), (0, 255, 0), 2) cv2.putText(image, fID:{class_id} {score:.2f}, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2) print(f检测到 {len(detections)} 个目标) #cv2.imwrite(result.jpg, image) acl.rt.destroy_event(start_event) acl.rt.destroy_event(end_event)