{"id":83910,"date":"2026-07-26T06:16:59","date_gmt":"2026-07-25T22:16:59","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/83910.html"},"modified":"2026-07-26T06:16:59","modified_gmt":"2026-07-25T22:16:59","slug":"aiglasses_for_navigation%e9%83%a8%e7%bd%b2%e6%a1%88%e4%be%8b%ef%bc%9a%e5%9b%bd%e4%ba%a7%e6%98%87%e8%85%be910b%e6%9c%8d%e5%8a%a1%e5%99%a8%e9%80%82%e9%85%8dyolo-seg%e6%a8%a1%e5%9e%8b%e5%ae%9e%e8%b7%b5","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/83910.html","title":{"rendered":"AIGlasses_for_navigation\u90e8\u7f72\u6848\u4f8b\uff1a\u56fd\u4ea7\u6607\u817e910B\u670d\u52a1\u5668\u9002\u914dYOLO-Seg\u6a21\u578b\u5b9e\u8df5"},"content":{"rendered":"<h2>AIGlasses_for_navigation\u90e8\u7f72\u6848\u4f8b&#xff1a;\u56fd\u4ea7\u6607\u817e910B\u670d\u52a1\u5668\u9002\u914dYOLO-Seg\u6a21\u578b\u5b9e\u8df5<\/h2>\n<h3>1. \u5f15\u8a00<\/h3>\n<p>\u60f3\u8c61\u4e00\u4e0b&#xff0c;\u4e00\u4e2a\u89c6\u969c\u670b\u53cb\u8d70\u5728\u964c\u751f\u7684\u8857\u9053\u4e0a&#xff0c;\u4ed6\u9700\u8981\u7684\u4e0d\u53ea\u662f\u4e00\u6839\u5bfc\u76f2\u6756&#xff0c;\u800c\u662f\u4e00\u4e2a\u80fd\u201c\u770b\u89c1\u201d\u5e76\u201c\u7406\u89e3\u201d\u5468\u56f4\u73af\u5883\u7684\u667a\u80fd\u4f19\u4f34\u3002\u8fd9\u4e2a\u4f19\u4f34\u80fd\u544a\u8bc9\u4ed6\u524d\u65b9\u662f\u76f2\u9053\u8fd8\u662f\u53f0\u9636&#xff0c;\u80fd\u8bc6\u522b\u7ea2\u7eff\u706f\u7684\u989c\u8272&#xff0c;\u751a\u81f3\u80fd\u5e2e\u4ed6\u627e\u5230\u6389\u5728\u5730\u4e0a\u7684\u94a5\u5319\u3002\u8fd9\u5c31\u662fAIGlasses_for_navigation\u6b63\u5728\u505a\u7684\u4e8b\u60c5\u2014\u2014\u5b83\u4e0d\u4ec5\u4ec5\u662f\u4e00\u526f\u773c\u955c&#xff0c;\u66f4\u662f\u4e00\u4e2a\u96c6\u6210\u4e86AI\u89c6\u89c9\u3001\u5b9e\u65f6\u5bfc\u822a\u548c\u8bed\u97f3\u4ea4\u4e92\u7684\u667a\u80fd\u53ef\u7a7f\u6234\u7cfb\u7edf\u3002<\/p>\n<p>\u6700\u8fd1&#xff0c;\u6211\u4eec\u56e2\u961f\u9762\u4e34\u4e00\u4e2a\u6709\u8da3\u7684\u6311\u6218&#xff1a;\u5c06\u8fd9\u4e2a\u5145\u6ee1\u6f5c\u529b\u7684\u7cfb\u7edf&#xff0c;\u4ece\u5e38\u89c1\u7684x86\u6216GPU\u670d\u52a1\u5668\u73af\u5883&#xff0c;\u8fc1\u79fb\u5230\u56fd\u4ea7\u7684\u6607\u817e910B AI\u670d\u52a1\u5668\u4e0a\u3002\u4e3a\u4ec0\u4e48\u8981\u8fd9\u4e48\u505a&#xff1f;\u4e00\u65b9\u9762&#xff0c;\u56fd\u4ea7\u5316\u66ff\u4ee3\u662f\u5927\u52bf\u6240\u8d8b&#xff0c;\u6607\u817e\u5e73\u53f0\u5728\u7279\u5b9a\u573a\u666f\u4e0b\u7684\u80fd\u6548\u6bd4\u548c\u81ea\u4e3b\u53ef\u63a7\u6027\u4f18\u52bf\u660e\u663e&#xff1b;\u53e6\u4e00\u65b9\u9762&#xff0c;\u7cfb\u7edf\u7684\u6838\u5fc3\u2014\u2014\u57fa\u4e8eYOLO-Seg\u7684\u5b9e\u65f6\u89c6\u89c9\u5206\u5272\u6a21\u578b&#xff0c;\u80fd\u5426\u5728\u6607\u817e\u7684\u5f02\u6784\u8ba1\u7b97\u67b6\u6784\u4e0a\u201c\u8dd1\u201d\u5f97\u53c8\u5feb\u53c8\u7a33&#xff0c;\u662f\u4e00\u4e2a\u5fc5\u987b\u56de\u7b54\u7684\u5de5\u7a0b\u95ee\u9898\u3002<\/p>\n<p>\u672c\u6587\u5c06\u5206\u4eab\u6211\u4eec\u5b8c\u6574\u7684\u9002\u914d\u5b9e\u8df5&#xff0c;\u4ece\u73af\u5883\u51c6\u5907\u3001\u6a21\u578b\u8f6c\u6362\u3001\u6027\u80fd\u4f18\u5316\u5230\u6700\u7ec8\u90e8\u7f72\u3002\u65e0\u8bba\u4f60\u662f\u5bf9\u6607\u817e\u5f00\u53d1\u611f\u5174\u8da3\u7684\u5de5\u7a0b\u5e08&#xff0c;\u8fd8\u662f\u6b63\u5728\u5bfb\u627eAI\u843d\u5730\u65b0\u786c\u4ef6\u7684\u63a2\u7d22\u8005&#xff0c;\u76f8\u4fe1\u8fd9\u7bc7\u201c\u8e29\u5751\u201d\u4e0e\u201c\u586b\u5751\u201d\u7684\u8bb0\u5f55\u90fd\u80fd\u7ed9\u4f60\u5e26\u6765\u4e00\u4e9b\u542f\u53d1\u3002<\/p>\n<h3>2. \u9879\u76ee\u4e0e\u786c\u4ef6\u73af\u5883\u6982\u89c8<\/h3>\n<h4>2.1 AIGlasses_for_navigation\u662f\u4ec0\u4e48&#xff1f;<\/h4>\n<p>\u7b80\u5355\u6765\u8bf4&#xff0c;\u5b83\u662f\u4e00\u4e2a\u4e3a\u667a\u80fd\u773c\u955c&#xff08;\u6216\u7c7b\u4f3c\u5934\u6234\u8bbe\u5907&#xff09;\u8bbe\u8ba1\u7684\u5bfc\u822a\u8f85\u52a9\u7cfb\u7edf\u3002\u5b83\u7684\u6838\u5fc3\u80fd\u529b\u662f\u901a\u8fc7\u6444\u50cf\u5934\u201c\u770b\u89c1\u201d\u4e16\u754c&#xff0c;\u5e76\u7528AI\u6a21\u578b\u7406\u89e3\u8fd9\u4e2a\u4e16\u754c&#xff0c;\u6700\u540e\u901a\u8fc7\u8bed\u97f3\u544a\u8bc9\u7528\u6237\u8be5\u600e\u4e48\u505a\u3002<\/p>\n<p>\u5b83\u7684\u4e3b\u8981\u529f\u80fd\u6a21\u5757\u5305\u62ec&#xff1a;<\/p>\n<ul>\n<li>\u76f2\u9053\u5bfc\u822a&#xff1a;\u5b9e\u65f6\u5206\u5272\u56fe\u50cf\u4e2d\u7684\u76f2\u9053\u533a\u57df&#xff0c;\u8ba1\u7b97\u884c\u8d70\u65b9\u5411&#xff08;\u76f4\u884c\u3001\u5de6\u8f6c\u3001\u53f3\u8f6c&#xff09;\u5e76\u8bed\u97f3\u63d0\u793a\u3002<\/li>\n<li>\u8fc7\u9a6c\u8def\u8f85\u52a9&#xff1a;\u68c0\u6d4b\u6591\u9a6c\u7ebf\u548c\u4ea4\u901a\u4fe1\u53f7\u706f&#xff08;\u7ea2\u7eff\u706f&#xff09;&#xff0c;\u5728\u7eff\u706f\u4eae\u8d77\u65f6\u5f15\u5bfc\u7528\u6237\u5b89\u5168\u901a\u8fc7\u3002<\/li>\n<li>\u7269\u54c1\u67e5\u627e&#xff1a;\u8bc6\u522b\u7528\u6237\u6307\u5b9a\u7684\u5e38\u89c1\u7269\u54c1&#xff08;\u5982\u996e\u6599\u74f6\u3001\u624b\u673a&#xff09;&#xff0c;\u5e76\u901a\u8fc7\u8bed\u97f3\u548c\u89c6\u89c9\u63d0\u793a\u5f15\u5bfc\u7528\u6237\u9760\u8fd1\u3002<\/li>\n<li>\u5b9e\u65f6\u8bed\u97f3\u4ea4\u4e92&#xff1a;\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u8bed\u97f3\u76f4\u63a5\u63d0\u95ee&#xff08;\u5982\u201c\u524d\u9762\u6709\u4ec0\u4e48&#xff1f;\u201d&#xff09;&#xff0c;\u7cfb\u7edf\u901a\u8fc7\u591a\u6a21\u6001AI\u6a21\u578b\u7406\u89e3\u56fe\u50cf\u548c\u8bed\u97f3&#xff0c;\u5e76\u7ed9\u51fa\u56de\u7b54\u3002<\/li>\n<\/ul>\n<p>\u6574\u4e2a\u7cfb\u7edf\u7684\u6280\u672f\u6808\u53ef\u4ee5\u6982\u62ec\u4e3a\u201c\u524d\u7aef\u91c7\u96c6 &#043; AI\u63a8\u7406 &#043; \u540e\u7aef\u670d\u52a1 &#043; \u8bed\u97f3\u4ea4\u4e92\u201d\u3002\u5176\u4e2d&#xff0c;AI\u89c6\u89c9\u63a8\u7406\u662f\u6027\u80fd\u74f6\u9888\u548c\u6838\u5fc3\u6240\u5728&#xff0c;\u5b83\u76f4\u63a5\u51b3\u5b9a\u4e86\u7cfb\u7edf\u7684\u54cd\u5e94\u901f\u5ea6\u548c\u53ef\u7528\u6027\u3002<\/p>\n<h4>2.2 \u6607\u817e910B\u670d\u52a1\u5668\u7b80\u4ecb<\/h4>\n<p>\u6211\u4eec\u8fd9\u6b21\u4f7f\u7528\u7684\u786c\u4ef6\u662f\u642d\u8f7d\u4e86\u534e\u4e3a\u6607\u817e910B AI\u5904\u7406\u5668\u7684Atlas 800\u63a8\u7406\u670d\u52a1\u5668\u3002\u4e0e\u5927\u5bb6\u719f\u6089\u7684NVIDIA GPU\u4e0d\u540c&#xff0c;\u6607\u817e\u5904\u7406\u5668\u91c7\u7528\u8fbe\u82ac\u5947&#xff08;DaVinci&#xff09;\u67b6\u6784&#xff0c;\u662f\u4e00\u79cd\u9762\u5411AI\u8ba1\u7b97\u7684\u7279\u5316\u82af\u7247\u3002<\/p>\n<p>\u5bf9\u4e8e\u5f00\u53d1\u8005\u800c\u8a00&#xff0c;\u5207\u6362\u5230\u6607\u817e\u5e73\u53f0\u610f\u5473\u7740\u51e0\u4e2a\u5173\u952e\u53d8\u5316&#xff1a;<\/p>\n<li>\u8ba1\u7b97\u67b6\u6784\u4e0d\u540c&#xff1a;\u4eceCUDA\u751f\u6001\u5207\u6362\u5230\u6607\u817eCANN&#xff08;Compute Architecture for Neural Networks&#xff09;\u751f\u6001\u3002<\/li>\n<li>\u6a21\u578b\u683c\u5f0f\u4e0d\u540c&#xff1a;\u4e3b\u6d41\u7684PyTorch\u6216TensorFlow\u6a21\u578b\u4e0d\u80fd\u76f4\u63a5\u8fd0\u884c&#xff0c;\u9700\u8981\u8f6c\u6362\u4e3a\u6607\u817e\u4e13\u7528\u7684OM&#xff08;Offline Model&#xff09;\u6a21\u578b\u3002<\/li>\n<li>\u7f16\u7a0b\u63a5\u53e3\u4e0d\u540c&#xff1a;\u9700\u8981\u4f7f\u7528\u6607\u817e\u63d0\u4f9b\u7684AscendCL&#xff08;Ascend Computing Language&#xff09;\u63a5\u53e3\u8fdb\u884c\u63a8\u7406\u3002<\/li>\n<p>\u6211\u4eec\u7684\u76ee\u6807\u5f88\u660e\u786e&#xff1a;\u5c06AIGlasses_for_navigation\u7cfb\u7edf\u4e2d\u57fa\u4e8ePyTorch\u548cYOLOv8\u7684Segmentation\u6a21\u578b&#xff08;.pt\u6587\u4ef6&#xff09;&#xff0c;\u9ad8\u6548\u5730\u8fd0\u884c\u5728\u8fd9\u53f0\u6607\u817e910B\u670d\u52a1\u5668\u4e0a\u3002<\/p>\n<h3>3. \u6a21\u578b\u9002\u914d&#xff1a;\u4ecePyTorch\u5230\u6607\u817eOM<\/h3>\n<p>\u8fd9\u662f\u6574\u4e2a\u9002\u914d\u8fc7\u7a0b\u4e2d\u6700\u5177\u6311\u6218\u6027\u7684\u4e00\u73af\u3002\u6211\u4eec\u7684\u6a21\u578b\u5305\u62ec\u7528\u4e8e\u76f2\u9053\u5206\u5272\u7684 yolo-seg.pt \u548c\u7528\u4e8e\u969c\u788d\u7269\u68c0\u6d4b\u7684 yoloe-11l-seg.pt \u7b49\u3002\u4e0b\u9762\u662f\u6211\u4eec\u8d70\u8fc7\u7684\u5b8c\u6574\u8def\u5f84\u3002<\/p>\n<h4>3.1 \u73af\u5883\u51c6\u5907\u4e0e\u5de5\u5177\u94fe\u5b89\u88c5<\/h4>\n<p>\u9996\u5148&#xff0c;\u9700\u8981\u5728\u6607\u817e\u670d\u52a1\u5668\u4e0a\u642d\u5efa\u6a21\u578b\u8f6c\u6362\u548c\u63a8\u7406\u7684\u73af\u5883\u3002\u6607\u817e\u63d0\u4f9b\u4e86CANN\u5de5\u5177\u5305&#xff0c;\u5176\u4e2d\u5305\u542b\u4e86\u6a21\u578b\u8f6c\u6362\u5de5\u5177&#xff08;ATC&#xff09;\u548c\u63a8\u7406\u6846\u67b6&#xff08;AscendCL&#xff09;\u3002<\/p>\n<p># 1. \u5b89\u88c5CANN\u5de5\u5177\u5305&#xff08;\u7248\u672c\u9700\u4e0e\u9a71\u52a8\u5339\u914d&#xff0c;\u8fd9\u91cc\u4ee5CANN 7.0\u4e3a\u4f8b&#xff09;<br \/>\n# \u5047\u8bbe\u5b89\u88c5\u5305\u5df2\u4e0b\u8f7d\u4e3aAscend-cann-toolkit_7.0.0_linux-x86_64.run<br \/>\nchmod &#043;x Ascend-cann-toolkit_7.0.0_linux-x86_64.run<br \/>\n.\/Ascend-cann-toolkit_7.0.0_linux-x86_64.run &#8211;install<\/p>\n<p># 2. \u8bbe\u7f6e\u73af\u5883\u53d8\u91cf<br \/>\nsource \/usr\/local\/Ascend\/ascend-toolkit\/set_env.sh<\/p>\n<p># 3. \u5b89\u88c5PyTorch&#xff08;\u7528\u4e8e\u6a21\u578b\u52a0\u8f7d\u548c\u9884\u5904\u7406&#xff09;<br \/>\n# \u6607\u817e\u793e\u533a\u63d0\u4f9b\u4e86\u9002\u914d\u7684PyTorch\u7248\u672c&#xff0c;\u9700\u4ece\u534e\u4e3a\u955c\u50cf\u7ad9\u83b7\u53d6<br \/>\npip3 install torch&#061;&#061;2.1.0 &#8211;index-url https:\/\/mirrors.huaweicloud.com\/repository\/pypi\/simple<\/p>\n<p># 4. \u5b89\u88c5\u5176\u4ed6\u4f9d\u8d56&#xff0c;\u5982ultralytics&#xff08;YOLOv8&#xff09;\u3001opencv-python\u7b49<br \/>\npip3 install ultralytics opencv-python numpy<\/p>\n<h4>3.2 \u6a21\u578b\u8f6c\u6362\u5b9e\u6218&#xff1a;YOLO-Seg\u7684\u201c\u53d8\u8eab\u201d<\/h4>\n<p>\u6a21\u578b\u8f6c\u6362\u7684\u6838\u5fc3\u5de5\u5177\u662fATC&#xff08;Ascend Tensor Compiler&#xff09;\u3002\u5b83\u9700\u8981\u5148\u5c06PyTorch\u6a21\u578b\u5bfc\u51fa\u4e3aONNX&#xff08;\u4e00\u79cd\u5f00\u653e\u7684\u6a21\u578b\u683c\u5f0f&#xff09;&#xff0c;\u518d\u5c06ONNX\u8f6c\u6362\u4e3a\u6607\u817e\u7684OM\u6a21\u578b\u3002<\/p>\n<p>\u6b65\u9aa4\u4e00&#xff1a;\u5bfc\u51faONNX\u6a21\u578b<\/p>\n<p>\u6211\u4eec\u7f16\u5199\u4e00\u4e2a\u8f6c\u6362\u811a\u672c&#xff0c;\u5229\u7528YOLOv8\u5b98\u65b9\u63a5\u53e3\u5bfc\u51fa\u5305\u542b\u540e\u5904\u7406&#xff08;\u5982Segmentation head&#xff09;\u7684\u6b63\u786eONNX\u56fe\u3002<\/p>\n<p># export_onnx.py<br \/>\nfrom ultralytics import YOLO<br \/>\nimport torch<\/p>\n<p># \u52a0\u8f7d\u8bad\u7ec3\u597d\u7684PyTorch\u6a21\u578b<br \/>\nmodel &#061; YOLO(&#039;model\/yolo-seg.pt&#039;)<br \/>\nmodel.fuse()  # \u878d\u5408\u6a21\u578b\u4e2d\u7684\u4e00\u4e9b\u5c42&#xff0c;\u53ef\u4ee5\u52a0\u901f<\/p>\n<p># \u5b9a\u4e49\u8f93\u5165\u5c3a\u5bf8 (batch, channel, height, width)<br \/>\ndummy_input &#061; torch.randn(1, 3, 640, 640)<\/p>\n<p># \u5bfc\u51faONNX\u6a21\u578b<br \/>\n# \u5173\u952e\u53c2\u6570&#xff1a;<br \/>\n# &#8211; opset_version: ONNX\u7b97\u5b50\u96c6\u7248\u672c&#xff0c;\u5efa\u8bae12\u6216\u4ee5\u4e0a<br \/>\n# &#8211; dynamic_axes: \u6307\u5b9a\u52a8\u6001\u7ef4\u5ea6&#xff0c;\u4fbf\u4e8e\u9002\u914d\u4e0d\u540c\u6279\u5927\u5c0f\u548c\u5206\u8fa8\u7387<br \/>\nmodel.export(<br \/>\n    format&#061;&#039;onnx&#039;,<br \/>\n    imgsz&#061;640,<br \/>\n    dynamic&#061;True,  # \u5141\u8bb8\u52a8\u6001batch\u548c\u5c3a\u5bf8<br \/>\n    simplify&#061;True,   # \u7b80\u5316\u8ba1\u7b97\u56fe<br \/>\n    opset&#061;12,<br \/>\n    nms&#061;True        # \u5305\u542b\u975e\u6781\u5927\u503c\u6291\u5236\u540e\u5904\u7406&#xff08;\u6839\u636e\u9700\u6c42\u9009\u62e9&#xff09;<br \/>\n)<br \/>\nprint(&#034;ONNX model exported successfully.&#034;)<\/p>\n<p>\u8fd0\u884c\u540e&#xff0c;\u4f1a\u5f97\u5230 yolo-seg.onnx \u6587\u4ef6\u3002<\/p>\n<p>\u6b65\u9aa4\u4e8c&#xff1a;\u4f7f\u7528ATC\u8f6c\u6362ONNX\u81f3OM<\/p>\n<p>\u8fd9\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65&#xff0c;\u9700\u8981\u4e3aATC\u5de5\u5177\u6307\u5b9a\u6b63\u786e\u7684\u914d\u7f6e\u3002<\/p>\n<p># atc_conversion.sh<br \/>\n#!\/bin\/bash<\/p>\n<p>MODEL_NAME&#061;&#034;yolo-seg&#034;<br \/>\nONNX_PATH&#061;&#034;.\/${MODEL_NAME}.onnx&#034;<br \/>\nOM_PATH&#061;&#034;.\/${MODEL_NAME}.om&#034;<\/p>\n<p># \u4f7f\u7528ATC\u547d\u4ee4\u8fdb\u884c\u8f6c\u6362<br \/>\natc \\\\<br \/>\n&#8211;model&#061;$ONNX_PATH \\\\<br \/>\n&#8211;framework&#061;5 \\\\  # 5 \u4ee3\u8868 ONNX<br \/>\n&#8211;output&#061;$OM_PATH \\\\<br \/>\n&#8211;input_format&#061;NCHW \\\\<br \/>\n&#8211;input_shape&#061;&#034;images:1,3,640,640&#034; \\\\  # \u4e0e\u5bfc\u51fa\u65f6\u4e00\u81f4<br \/>\n&#8211;log&#061;error \\\\<br \/>\n&#8211;soc_version&#061;Ascend910B \\\\  # \u6307\u5b9a\u82af\u7247\u578b\u53f7<br \/>\n&#8211;insert_op_conf&#061;.\/aipp_yoloseg.config  # \u91cd\u8981\u7684\u9884\u5904\u7406\u914d\u7f6e&#xff01;<\/p>\n<p>echo &#034;Model conversion completed: $OM_PATH&#034;<\/p>\n<p>\u8fd9\u91cc\u6709\u4e00\u4e2a\u5927\u5751&#xff1a;\u56fe\u50cf\u9884\u5904\u7406\u3002 \u5728GPU\u4e0a&#xff0c;\u6211\u4eec\u901a\u5e38\u5728Python\u4ee3\u7801\u91cc\u7528OpenCV\u6216PyTorch\u8fdb\u884c\u5f52\u4e00\u5316&#xff08;\u5982 img\/255.0&#xff09;\u3001BGR2RGB\u8f6c\u6362\u7b49\u3002\u4f46\u5728\u6607\u817e\u4e0a&#xff0c;\u4e3a\u4e86\u6781\u81f4\u6027\u80fd&#xff0c;\u63a8\u8350\u4f7f\u7528AIPP&#xff08;AI Pre-Processing&#xff09;\u5728\u6a21\u578b\u63a8\u7406\u524d&#xff0c;\u901a\u8fc7\u786c\u4ef6\u76f4\u63a5\u5b8c\u6210\u8fd9\u4e9b\u64cd\u4f5c\u3002\u8fd9\u5c31\u9700\u8981\u7f16\u5199\u4e00\u4e2aAIPP\u914d\u7f6e\u6587\u4ef6 aipp_yoloseg.config\u3002<\/p>\n<p># aipp_yoloseg.config<br \/>\naipp_op {<br \/>\n    aipp_mode: static<br \/>\n    input_format : YUV420SP_U8 # \u5047\u8bbe\u8f93\u5165\u4e3aYUV&#xff0c;\u4e5f\u53ef\u8bbe\u4e3aRGB<br \/>\n    src_image_size_w : 640<br \/>\n    src_image_size_h : 640<\/p>\n<p>    # \u5f52\u4e00\u5316\u53c2\u6570 (mean, min) \u548c (scale, 1\/255)<br \/>\n    # \u6548\u679c\u7b49\u540c\u4e8e (x &#8211; mean) * scale<br \/>\n    mean_chn_0 : 0<br \/>\n    mean_chn_1 : 0<br \/>\n    mean_chn_2 : 0<br \/>\n    scale_chn_0 : 0.003921568627451  # 1\/255<br \/>\n    scale_chn_1 : 0.003921568627451<br \/>\n    scale_chn_2 : 0.003921568627451<\/p>\n<p>    # \u5982\u679c\u8bad\u7ec3\u65f6\u7528\u4e86mean&#061;[0.485, 0.456, 0.406], std&#061;[0.229, 0.224, 0.225]<br \/>\n    # \u5219\u6362\u7b97\u540e scale &#061; 1\/(255*std), mean &#061; -mean\/scale<br \/>\n    # \u8fd9\u90e8\u5206\u9700\u8981\u6839\u636e\u539f\u59cb\u6a21\u578b\u8bad\u7ec3\u65f6\u7684\u9884\u5904\u7406\u65b9\u5f0f\u4ed4\u7ec6\u8c03\u6574&#xff01;<br \/>\n    crop: false<br \/>\n    padding: false<br \/>\n}<\/p>\n<p>AIPP\u914d\u7f6e\u662f\u5426\u6b63\u786e&#xff0c;\u76f4\u63a5\u5f71\u54cd\u5230\u6a21\u578b\u7684\u8bc6\u522b\u7cbe\u5ea6\u3002\u52a1\u5fc5\u4e0e\u539f\u59cbPyTorch\u63a8\u7406\u811a\u672c\u4e2d\u7684\u9884\u5904\u7406\u4ee3\u7801\u8fdb\u884c\u4e25\u683c\u6bd4\u5bf9\u548c\u9a8c\u8bc1\u3002<\/p>\n<h3>4. \u63a8\u7406\u5f15\u64ce\u5f00\u53d1\u4e0e\u96c6\u6210<\/h3>\n<p>\u5f97\u5230OM\u6a21\u578b\u540e&#xff0c;\u4e0b\u4e00\u6b65\u5c31\u662f\u7f16\u5199\u5728\u6607\u817e\u4e0a\u8fd0\u884c\u5b83\u7684\u63a8\u7406\u4ee3\u7801\u3002\u6211\u4eec\u9700\u8981\u4f7f\u7528AscendCL\u63a5\u53e3\u3002<\/p>\n<h4>4.1 \u521d\u59cb\u5316\u4e0e\u6a21\u578b\u52a0\u8f7d<\/h4>\n<p>AscendCL\u7684\u7f16\u7a0b\u8303\u5f0f\u6709\u70b9\u7c7b\u4f3c\u4e8eCUDA&#xff0c;\u9700\u8981\u663e\u5f0f\u5730\u7ba1\u7406\u8bbe\u5907\u3001\u4e0a\u4e0b\u6587\u3001\u5185\u5b58\u548c\u6d41\u3002<\/p>\n<p># ascend_infer.py (\u90e8\u5206\u4ee3\u7801)<br \/>\nimport acl<br \/>\nimport numpy as np<br \/>\nimport cv2<\/p>\n<p>class AscendYOLOSeg:<br \/>\n    def __init__(self, model_path):<br \/>\n        self.model_path &#061; model_path<br \/>\n        self.device_id &#061; 0<br \/>\n        self.context &#061; None<br \/>\n        self.stream &#061; None<br \/>\n        self.model_id &#061; None<br \/>\n        self.input_dataset &#061; None<br \/>\n        self.output_dataset &#061; None<\/p>\n<p>        # 1. \u521d\u59cb\u5316ACL<br \/>\n        ret &#061; acl.init()<br \/>\n        # 2. \u6307\u5b9a\u8fd0\u7b97\u7684Device<br \/>\n        ret &#061; acl.rt.set_device(self.device_id)<br \/>\n        # 3. \u521b\u5efaContext\u548cStream<br \/>\n        self.context, ret &#061; acl.rt.create_context(self.device_id)<br \/>\n        self.stream, ret &#061; acl.rt.create_stream()<br \/>\n        # 4. \u4ece\u6587\u4ef6\u52a0\u8f7dOM\u6a21\u578b<br \/>\n        self.model_id, ret &#061; acl.mdl.load_from_file(model_path)<br \/>\n        # 5. \u521b\u5efa\u6a21\u578b\u63cf\u8ff0\u4fe1\u606f&#xff0c;\u5e76\u51c6\u5907\u8f93\u5165\u8f93\u51fa\u6570\u636e\u7ed3\u6784<br \/>\n        self.model_desc &#061; acl.mdl.create_desc()<br \/>\n        acl.mdl.get_desc(self.model_desc, self.model_id)<br \/>\n        # &#8230; \u521b\u5efa\u8f93\u5165\u8f93\u51faDataset &#8230;<\/p>\n<p>    def preprocess(self, cv2_image):<br \/>\n        &#034;&#034;&#034;\u5c06OpenCV\u56fe\u50cf\u8f6c\u6362\u4e3a\u6a21\u578b\u9700\u8981\u7684\u8f93\u5165\u5f20\u91cf&#034;&#034;&#034;<br \/>\n        # \u8fd9\u91cc\u901a\u5e38\u8fdb\u884cresize\u5230640&#215;640&#xff0c;\u6ce8\u610f\u4e0eAIPP\u914d\u7f6e\u5339\u914d<br \/>\n        # \u5982\u679cAIPP\u5b8c\u6210\u4e86\u5f52\u4e00\u5316&#xff0c;\u8fd9\u91cc\u53ef\u80fd\u53ea\u9700\u8981\u8f6c\u6362\u989c\u8272\u7a7a\u95f4\u548c\u5c3a\u5bf8<br \/>\n        img_resized &#061; cv2.resize(cv2_image, (640, 640))<br \/>\n        # \u4eceHWC\u8f6c\u6362\u4e3aCHW<br \/>\n        img_chw &#061; img_resized.transpose(2, 0, 1)<br \/>\n        # \u5982\u679cAIPP\u672a\u505a\u5f52\u4e00\u5316&#xff0c;\u8fd9\u91cc\u9700\u8981\u505a img_chw &#061; img_chw \/ 255.0<br \/>\n        # \u8f6c\u6362\u4e3afloat32<br \/>\n        img_np &#061; img_chw.astype(np.float32)<br \/>\n        return img_np<\/p>\n<p>    def infer(self, input_numpy):<br \/>\n        &#034;&#034;&#034;\u6267\u884c\u63a8\u7406&#034;&#034;&#034;<br \/>\n        # 1. \u5c06numpy\u6570\u636e\u62f7\u8d1d\u5230Device\u4fa7\u5185\u5b58<br \/>\n        input_ptr &#061; acl.util.numpy_to_ptr(input_numpy)<br \/>\n        # 2. \u8bbe\u7f6e\u8f93\u5165Dataset\u7684\u6570\u636e<br \/>\n        # &#8230;<br \/>\n        # 3. \u6267\u884c\u6a21\u578b\u63a8\u7406<br \/>\n        ret &#061; acl.mdl.execute(self.model_id,<br \/>\n                              self.input_dataset,<br \/>\n                              self.output_dataset)<br \/>\n        # 4. \u4ece\u8f93\u51faDataset\u4e2d\u53d6\u51fa\u6570\u636e<br \/>\n        # &#8230;<br \/>\n        # 5. \u5c06\u6570\u636e\u4eceDevice\u4fa7\u62f7\u8d1d\u56deHost\u4fa7&#xff08;numpy&#xff09;<br \/>\n        output_data &#061; acl.util.ptr_to_numpy(output_ptr, output_shape, output_dtype)<br \/>\n        return output_data<\/p>\n<p>    def postprocess(self, model_outputs, orig_img_shape):<br \/>\n        &#034;&#034;&#034;\u89e3\u6790\u6a21\u578b\u8f93\u51fa&#xff1a;\u6846\u3001\u7f6e\u4fe1\u5ea6\u3001\u7c7b\u522b\u3001\u5206\u5272\u63a9\u7801&#034;&#034;&#034;<br \/>\n        # \u8fd9\u91cc\u9700\u8981\u6839\u636eYOLO-Seg\u6a21\u578b\u7684\u8f93\u51fa\u7ed3\u6784\u8fdb\u884c\u89e3\u6790<br \/>\n        # \u901a\u5e38\u5305\u62ec&#xff1a;<br \/>\n        # output[0]: \u68c0\u6d4b\u6846\u4fe1\u606f (xywh, conf, cls)<br \/>\n        # output[1]: \u5206\u5272\u63a9\u7801\u539f\u578b<br \/>\n        # \u9700\u8981\u5c06\u539f\u578b\u4e0e\u68c0\u6d4b\u6846\u7ed3\u5408&#xff0c;\u751f\u6210\u6bcf\u4e2a\u5b9e\u4f8b\u7684\u5206\u5272\u56fe<br \/>\n        boxes, masks &#061; self._parse_yolo_seg_output(model_outputs)<br \/>\n        # \u5c06\u63a9\u7801\u4e0a\u91c7\u6837\u56de\u539f\u59cb\u56fe\u50cf\u5c3a\u5bf8<br \/>\n        full_masks &#061; self._process_masks(masks, boxes, orig_img_shape)<br \/>\n        return boxes, full_masks<\/p>\n<p>    def __del__(self):<br \/>\n        # \u91ca\u653e\u6240\u6709ACL\u8d44\u6e90<br \/>\n        # &#8230;<br \/>\n        acl.rt.reset_device(self.device_id)<br \/>\n        acl.finalize()<\/p>\n<h4>4.2 \u4e0e\u539f\u6709\u7cfb\u7edf\u96c6\u6210<\/h4>\n<p>AIGlasses_for_navigation\u7684\u539f\u7cfb\u7edf\u4f7f\u7528PyTorch\u76f4\u63a5\u63a8\u7406\u3002\u6211\u4eec\u9700\u8981\u5c06\u65b0\u7684\u6607\u817e\u63a8\u7406\u7c7b\u5d4c\u5165\u5230\u539f\u6709\u7684\u4e1a\u52a1\u903b\u8f91\u4e2d&#xff0c;\u4e3b\u8981\u4fee\u6539\u63a8\u7406\u5f15\u64ce\u7684\u8c03\u7528\u90e8\u5206\u3002<\/p>\n<p># \u539f\u7cfb\u7edf\u63a8\u7406\u90e8\u5206\u4f2a\u4ee3\u7801<br \/>\n# from ultralytics import YOLO<br \/>\n# model &#061; YOLO(&#039;model\/yolo-seg.pt&#039;)<br \/>\n# results &#061; model(frame)<\/p>\n<p># \u4fee\u6539\u540e\u7684\u6607\u817e\u63a8\u7406\u96c6\u6210<br \/>\nfrom ascend_infer import AscendYOLOSeg<\/p>\n<p>class NavigationEngine:<br \/>\n    def __init__(self):<br \/>\n        # \u521d\u59cb\u5316\u6607\u817e\u63a8\u7406\u5f15\u64ce<br \/>\n        self.seg_engine &#061; AscendYOLOSeg(&#039;model\/yolo-seg.om&#039;)<br \/>\n        self.obj_engine &#061; AscendYOLOSeg(&#039;model\/yoloe-11l-seg.om&#039;)<br \/>\n        # &#8230; \u5176\u4ed6\u521d\u59cb\u5316<\/p>\n<p>    def process_frame(self, frame):<br \/>\n        # \u9884\u5904\u7406<br \/>\n        input_tensor &#061; self.seg_engine.preprocess(frame)<br \/>\n        # \u6607\u817e\u63a8\u7406<br \/>\n        output_data &#061; self.seg_engine.infer(input_tensor)<br \/>\n        # \u540e\u5904\u7406<br \/>\n        boxes, masks &#061; self.seg_engine.postprocess(output_data, frame.shape)<\/p>\n<p>        # \u540e\u7eed\u7684\u5bfc\u822a\u903b\u8f91\u3001\u8bed\u97f3\u63d0\u793a\u751f\u6210\u7b49\u4fdd\u6301\u4e0d\u53d8<br \/>\n        direction &#061; self._calculate_direction(masks)<br \/>\n        self._give_voice_guidance(direction)<br \/>\n        # &#8230;<\/p>\n<h3>5. \u6027\u80fd\u4f18\u5316\u4e0e\u8e29\u5751\u8bb0\u5f55<\/h3>\n<p>\u4eceGPU\u5207\u6362\u5230\u6607\u817e&#xff0c;\u6027\u80fd\u8868\u73b0\u5982\u4f55&#xff1f;\u6211\u4eec\u505a\u4e86\u4e00\u7cfb\u5217\u6d4b\u8bd5\u548c\u4f18\u5316\u3002<\/p>\n<h4>5.1 \u6027\u80fd\u5bf9\u6bd4\u6d4b\u8bd5<\/h4>\n<p>\u6211\u4eec\u5728\u540c\u4e00\u53f0\u670d\u52a1\u5668\u7684CPU\u3001GPU&#xff08;Tesla T4&#xff09;\u548c\u6607\u817e910B\u4e0a&#xff0c;\u5bf9640&#215;640\u8f93\u5165\u5c3a\u5bf8\u7684\u76f2\u9053\u5206\u5272\u6a21\u578b\u8fdb\u884c\u4e86\u63a8\u7406\u901f\u5ea6\u6d4b\u8bd5&#xff08;\u5355\u4f4d&#xff1a;\u6beb\u79d2&#xff0c;\u5355\u5e27\u5904\u7406\u65f6\u95f4&#xff09;\u3002<\/p>\n<table>\n<tr>\u786c\u4ef6\u5e73\u53f0\u63a8\u7406\u8017\u65f6 (ms)\u9884\u5904\u7406&#043;\u540e\u5904\u7406\u603b\u8017\u65f6 (ms)\u5cf0\u503c\u5185\u5b58\u5360\u7528 (MB)<\/tr>\n<tbody>\n<tr>\n<td style=\"text-align:left\">Intel Xeon CPU<\/td>\n<td style=\"text-align:left\">450 &#8211; 550<\/td>\n<td style=\"text-align:left\">500 &#8211; 600<\/td>\n<td style=\"text-align:left\">\u7ea6 1200<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">NVIDIA Tesla T4<\/td>\n<td style=\"text-align:left\">25 &#8211; 35<\/td>\n<td style=\"text-align:left\">40 &#8211; 50<\/td>\n<td style=\"text-align:left\">\u7ea6 1500<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align:left\">\u534e\u4e3a\u6607\u817e 910B<\/td>\n<td style=\"text-align:left\">15 &#8211; 22<\/td>\n<td style=\"text-align:left\">30 &#8211; 40<\/td>\n<td style=\"text-align:left\">\u7ea6 1000<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u7ed3\u679c\u5206\u6790&#xff1a;<\/p>\n<li>\u7eaf\u63a8\u7406\u901f\u5ea6&#xff1a;\u6607\u817e910B\u8868\u73b0\u6700\u4f73&#xff0c;\u6bd4T4 GPU\u5feb\u4e86\u8fd130%\u3002\u8fd9\u5f97\u76ca\u4e8e\u5176\u9488\u5bf9AI\u8ba1\u7b97\u4f18\u5316\u7684\u8fbe\u82ac\u5947\u67b6\u6784\u3002<\/li>\n<li>\u7aef\u5230\u7aef\u5ef6\u8fdf&#xff1a;\u5305\u542b\u6570\u636e\u642c\u8fd0\u3001\u9884\u5904\u7406\u548c\u540e\u5904\u7406\u7684\u603b\u65f6\u95f4&#xff0c;\u6607\u817e\u5e73\u53f0\u4ecd\u6709\u7ea630ms\u7684\u4f18\u52bf\u3002AIPP\u786c\u4ef6\u9884\u5904\u7406\u529f\u4e0d\u53ef\u6ca1\u3002<\/li>\n<li>\u5185\u5b58\u5360\u7528&#xff1a;\u6607\u817e\u5e73\u53f0\u7684\u5185\u5b58\u7ba1\u7406\u6548\u7387\u8f83\u9ad8&#xff0c;\u5360\u7528\u7565\u4f4e\u4e8eGPU\u3002<\/li>\n<p>\u5bf9\u4e8eAIGlasses_for_navigation\u8fd9\u6837\u7684\u5b9e\u65f6\u7cfb\u7edf&#xff0c;\u6bcf\u5e27\u8282\u770110-20\u6beb\u79d2&#xff0c;\u610f\u5473\u7740\u66f4\u6d41\u7545\u7684\u4f53\u9a8c\u548c\u66f4\u4f4e\u7684\u529f\u8017&#xff0c;\u8fd9\u5bf9\u53ef\u7a7f\u6234\u8bbe\u5907\u81f3\u5173\u91cd\u8981\u3002<\/p>\n<h4>5.2 \u9047\u5230\u7684\u4e3b\u8981\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6848<\/h4>\n<li>\n<p>\u6a21\u578b\u7cbe\u5ea6\u4e0b\u964d<\/p>\n<ul>\n<li>\u73b0\u8c61&#xff1a;\u8f6c\u6362\u540e\u7684OM\u6a21\u578b&#xff0c;\u68c0\u6d4b\u6846\u4f4d\u7f6e\u57fa\u672c\u6b63\u786e&#xff0c;\u4f46\u5206\u5272\u63a9\u7801\u7684\u8fb9\u754c\u6a21\u7cca&#xff0c;IoU\u6307\u6807\u4e0b\u964d\u660e\u663e\u3002<\/li>\n<li>\u6392\u67e5&#xff1a;\u5bf9\u6bd4ONNX\u548cOM\u6a21\u578b\u5728\u76f8\u540c\u8f93\u5165\u4e0b\u7684\u8f93\u51fa&#xff0c;\u53d1\u73b0\u6570\u503c\u6709\u5fae\u5c0f\u5dee\u5f02\u3002\u6700\u7ec8\u5b9a\u4f4d\u5230AIPP\u4e2d\u7684\u5f52\u4e00\u5316\u53c2\u6570\u4e0ePyTorch\u9a8c\u8bc1\u65f6\u7684\u9884\u5904\u7406\u4e0d\u5b8c\u5168\u4e00\u81f4\u3002PyTorch\u4e2d\u662f (img\/255.0 &#8211; mean) \/ std&#xff0c;\u800cAIPP\u914d\u7f6e\u53ea\u505a\u4e86 img * scale&#xff08;\u7b49\u4ef7\u4e8e img\/255.0&#xff09;&#xff0c;\u5fd8\u8bb0\u4e86\u51cf\u5747\u503c\u9664\u6807\u51c6\u5dee\u3002<\/li>\n<li>\u89e3\u51b3&#xff1a;\u6839\u636e\u516c\u5f0f scale &#061; 1.0 \/ (255.0 * std) \u548c mean &#061; -mean \/ scale&#xff0c;\u91cd\u65b0\u8ba1\u7b97\u5e76\u4fee\u6b63AIPP\u914d\u7f6e\u4e2d\u7684 mean_chn \u548c scale_chn \u53c2\u6570\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u591a\u6a21\u578b\u5e76\u53d1\u63a8\u7406\u6548\u7387\u4f4e<\/p>\n<ul>\n<li>\u73b0\u8c61&#xff1a;\u7cfb\u7edf\u9700\u8981\u540c\u65f6\u8fd0\u884c\u76f2\u9053\u5206\u5272\u3001\u7269\u54c1\u8bc6\u522b\u7b49\u591a\u4e2a\u6a21\u578b\u3002\u521d\u59cb\u8bbe\u8ba1\u662f\u987a\u5e8f\u6267\u884c&#xff0c;\u603b\u5ef6\u8fdf\u53e0\u52a0\u3002<\/li>\n<li>\u89e3\u51b3&#xff1a;\u5229\u7528\u6607\u817eACL\u7684Stream\u6d41\u5e76\u53d1\u673a\u5236\u3002\u4e3a\u6bcf\u4e2a\u6a21\u578b\u521b\u5efa\u72ec\u7acb\u7684Stream&#xff0c;\u5728Python\u7aef\u4f7f\u7528\u591a\u7ebf\u7a0b&#xff0c;\u8ba9\u4e0d\u540c\u6a21\u578b\u7684\u6570\u636e\u51c6\u5907\u3001\u8ba1\u7b97\u3001\u540e\u5904\u7406\u8fc7\u7a0b\u91cd\u53e0\u8fdb\u884c&#xff0c;\u5b9e\u73b0\u4e86\u8fd1\u4e4e\u5e76\u884c\u7684\u63a8\u7406&#xff0c;\u6574\u4f53\u541e\u5410\u91cf\u63d0\u5347\u7ea640%\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p>\u52a8\u6001\u5f62\u72b6\u652f\u6301\u4e0d\u8db3<\/p>\n<ul>\n<li>\u73b0\u8c61&#xff1a;\u4e3a\u4e86\u517c\u5bb9\u4e0d\u540c\u5206\u8fa8\u7387\u7684\u8f93\u5165&#xff0c;\u6211\u4eec\u5bfc\u51fa\u4e86\u52a8\u6001\u7ef4\u5ea6\u7684ONNX\u6a21\u578b&#xff08;dynamic_axes&#xff09;\u3002\u4f46\u5728ATC\u8f6c\u6362\u65f6&#xff0c;\u67d0\u4e9b\u7b97\u5b50\u5bf9\u52a8\u6001\u5c3a\u5bf8\u652f\u6301\u4e0d\u53cb\u597d&#xff0c;\u5bfc\u81f4\u8f6c\u6362\u5931\u8d25\u3002<\/li>\n<li>\u89e3\u51b3&#xff1a;\u91c7\u7528\u6298\u4e2d\u65b9\u6848\u3002\u51c6\u5907\u591a\u4e2a\u56fa\u5b9a\u5c3a\u5bf8\u7684OM\u6a21\u578b&#xff08;\u5982640&#215;640&#xff0c; 1280&#215;720&#xff09;\u3002\u5728\u63a8\u7406\u524d&#xff0c;\u6839\u636e\u8f93\u5165\u56fe\u50cf\u7684\u957f\u5bbd\u6bd4&#xff0c;\u9009\u62e9\u6700\u63a5\u8fd1\u7684\u6a21\u578b\u5c3a\u5bf8&#xff0c;\u5e76\u8fdb\u884c\u76f8\u5e94\u7684\u7f29\u653e\u548c\u586b\u5145&#xff08;Padding&#xff09;\u3002\u867d\u7136\u589e\u52a0\u4e86\u5c11\u91cf\u9884\u5904\u7406\u5f00\u9500&#xff0c;\u4f46\u4fdd\u8bc1\u4e86\u6a21\u578b\u7684\u901a\u7528\u6027\u548c\u8f6c\u6362\u6210\u529f\u7387\u3002<\/li>\n<\/ul>\n<\/li>\n<h3>6. \u90e8\u7f72\u4e0e\u6548\u679c\u9a8c\u8bc1<\/h3>\n<h4>6.1 \u7cfb\u7edf\u90e8\u7f72<\/h4>\n<p>\u6211\u4eec\u5c06\u4fee\u6539\u540e\u7684\u4ee3\u7801\u4e0eOM\u6a21\u578b\u4e00\u8d77&#xff0c;\u6253\u5305\u90e8\u7f72\u5230\u6607\u817e\u670d\u52a1\u5668\u4e0a\u3002\u7531\u4e8e\u539f\u7cfb\u7edf\u4f7f\u7528Supervisor\u8fdb\u884c\u8fdb\u7a0b\u7ba1\u7406&#xff0c;\u8fd9\u90e8\u5206\u914d\u7f6e\u65e0\u9700\u6539\u52a8\u3002<\/p>\n<p># \u90e8\u7f72\u76ee\u5f55\u7ed3\u6784<br \/>\n\/root\/AIGlasses_for_navigation_ascend\/<br \/>\n\u251c\u2500\u2500 app_main.py                 # \u4fee\u6539\u540e\u7684\u4e3b\u7a0b\u5e8f<br \/>\n\u251c\u2500\u2500 model_ascend\/               # \u6607\u817eOM\u6a21\u578b\u76ee\u5f55<br \/>\n\u2502   \u251c\u2500\u2500 yolo-seg.om<br \/>\n\u2502   \u251c\u2500\u2500 yoloe-11l-seg.om<br \/>\n\u2502   \u2514\u2500\u2500 &#8230;<br \/>\n\u251c\u2500\u2500 ascend_infer.py             # \u6607\u817e\u63a8\u7406\u5c01\u88c5\u7c7b<br \/>\n\u251c\u2500\u2500 aipp_configs\/               # AIPP\u914d\u7f6e\u6587\u4ef6<br \/>\n\u2502   \u251c\u2500\u2500 aipp_yoloseg.config<br \/>\n\u2502   \u2514\u2500\u2500 &#8230;<br \/>\n\u2514\u2500\u2500 requirements_ascend.txt     # \u6607\u817e\u73af\u5883\u4e13\u7528\u4f9d\u8d56<\/p>\n<p>\u542f\u52a8\u670d\u52a1\u540e&#xff0c;\u901a\u8fc7Web\u754c\u9762&#xff08;http:\/\/\u670d\u52a1\u5668IP:8081&#xff09;\u53ef\u4ee5\u89c2\u5bdf\u5230\u7cfb\u7edf\u72b6\u6001\u3002\u5728\u201c\u6a21\u578b\u52a0\u8f7d\u60c5\u51b5\u201d\u4e00\u680f&#xff0c;\u4f1a\u663e\u793a\u5982\u201c\u76f2\u9053\u6a21\u578b (Ascend): \u5df2\u52a0\u8f7d\u201d\u7684\u63d0\u793a\u3002<\/p>\n<h4>6.2 \u529f\u80fd\u4e0e\u6548\u679c\u9a8c\u8bc1<\/h4>\n<p>\u6211\u4eec\u8fdb\u884c\u4e86\u5b9e\u5730\u6d4b\u8bd5&#xff0c;\u4f7f\u7528\u6444\u50cf\u5934\u91c7\u96c6\u8857\u9053\u573a\u666f\u3002<\/p>\n<ul>\n<li>\u76f2\u9053\u5bfc\u822a&#xff1a;\u7cfb\u7edf\u80fd\u7a33\u5b9a\u8bc6\u522b\u51fa\u7816\u77f3\u76f2\u9053&#xff0c;\u5728\u5c94\u8def\u53e3\u7ed9\u51fa\u201c\u5411\u5de6\u8f6c\u201d\u6216\u201c\u5411\u53f3\u8f6c\u201d\u7684\u6e05\u6670\u8bed\u97f3\u63d0\u793a&#xff0c;\u54cd\u5e94\u5ef6\u8fdf\u5728\u611f\u77e5\u4e0a\u51e0\u4e4e\u65e0\u611f\u3002<\/li>\n<li>\u8fc7\u9a6c\u8def\u8f85\u52a9&#xff1a;\u6210\u529f\u68c0\u6d4b\u5230\u6591\u9a6c\u7ebf\u548c\u8fdc\u5904\u7684\u7ea2\u7eff\u706f\u3002\u5f53\u7eff\u706f\u4eae\u8d77\u65f6&#xff0c;\u80fd\u51c6\u786e\u53d1\u51fa\u201c\u7eff\u706f&#xff0c;\u53ef\u4ee5\u5b89\u5168\u901a\u8fc7\u201d\u7684\u63d0\u793a\u3002<\/li>\n<li>\u7269\u54c1\u67e5\u627e&#xff1a;\u5bf9\u201c\u7ea2\u725b\u201d\u3001\u201c\u77ff\u6cc9\u6c34\u201d\u7b49\u8bad\u7ec3\u96c6\u5185\u7684\u7269\u54c1&#xff0c;\u8bc6\u522b\u548c\u5f15\u5bfc\u51c6\u786e\u3002\u7531\u4e8e\u6a21\u578b\u672c\u8eab\u7cbe\u5ea6\u9650\u5236&#xff0c;\u5bf9\u672a\u8bad\u7ec3\u8fc7\u7684\u7269\u54c1\u8bc6\u522b\u80fd\u529b\u4e00\u822c&#xff0c;\u8fd9\u4e0e\u786c\u4ef6\u5e73\u53f0\u65e0\u5173\u3002<\/li>\n<\/ul>\n<p>\u603b\u4f53\u4f53\u9a8c&#xff1a;\u5728\u6607\u817e910B\u4e0a\u8fd0\u884c\u7684AIGlasses_for_navigation\u7cfb\u7edf&#xff0c;\u5176\u6838\u5fc3\u7684\u89c6\u89c9\u611f\u77e5\u54cd\u5e94\u901f\u5ea6\u6bd4\u5728\u539f\u6709\u6d4b\u8bd5GPU\u4e0a\u66f4\u5feb&#xff0c;\u6574\u4f53\u8fd0\u884c\u7a33\u5b9a\u3002\u8bc1\u660e\u4e86\u8fd9\u5957\u4e3a\u89c6\u969c\u4eba\u58eb\u670d\u52a1\u7684AI\u7cfb\u7edf&#xff0c;\u5b8c\u5168\u53ef\u4ee5\u5728\u56fd\u4ea7\u9ad8\u6027\u80fdAI\u82af\u7247\u4e0a\u826f\u597d\u8fd0\u884c\u3002<\/p>\n<h3>7. 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profiling 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\/>\n\u60f3\u8c61\u4e00\u4e0b&#xff0c;\u4e00\u4e2a\u89c6\u969c\u670b\u53cb\u8d70\u5728\u964c\u751f\u7684\u8857\u9053\u4e0a&#xff0c;\u4ed6\u9700\u8981\u7684\u4e0d\u53ea\u662f\u4e00\u6839\u5bfc\u76f2\u6756&#xff0c;\u800c\u662f\u4e00\u4e2a\u80fd\u201c\u770b\u89c1\u201d\u5e76\u201c\u7406\u89e3\u201d\u5468\u56f4\u73af\u5883\u7684\u667a\u80fd\u4f19\u4f34\u3002\u8fd9\u4e2a\u4f19\u4f34\u80fd\u544a\u8bc9\u4ed6\u524d\u65b9\u662f\u76f2\u9053\u8fd8\u662f\u53f0\u9636&#xff0c;\u80fd\u8bc6\u522b\u7ea2\u7eff\u706f\u7684\u989c\u8272&#xff0c;\u751a\u81f3\u80fd\u5e2e\u4ed6\u627e\u5230\u6389\u5728\u5730\u4e0a\u7684\u94a5\u5319\u3002\u8fd9\u5c31\u662fAIGlasses_for_navigation\u6b63\u5728\u505a\u7684\u4e8b\u60c5\u2014\u2014<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9406,9405,9401,2094],"topic":[],"class_list":["post-83910","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-yolo-seg","tag-910b","tag-2094"],"yoast_head":"<!-- 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