Labview结合Yolov5与TensorRT（wangxingyu版）实现快速并行推理，dll封装与调用，模型转换至Engine并支持视频图片识别，6ms极速响应,Labview结合Yolov5与

YHqPNQdioFGDZIP版推理封装调用支持同时加油多个模型并行推理识别视频  2MB

资源文件列表:

ZIP 版推理封装调用支持同时加油多个模型并行推理识别视频 大约有12个文件
  1. 1.jpg 427.75KB
  2. 2.jpg 63.28KB
  3. 与深度学习模型的高效推理与封装.docx 44.55KB
  4. 与版技术详解多模型推理与封装的实.docx 44.42KB
  5. 在技术快速发展的当.html 684.45KB
  6. 标题调用封装的版本支持多模型并行推理摘要本.docx 20.7KB
  7. 版技术解析与应用在数字化时代随着深度学习技.docx 44.56KB
  8. 版推理封装调用支持同时加油多个模型并行推理识别视.html 683.81KB
  9. 王兴宇版技术分析博客一引言.html 683.41KB
  10. 王兴宇版技术解析与应用一引言随着深度学习技.docx 44.55KB
  11. 题目中使用和进行模型推理的封装.docx 16.44KB
  12. 题目中推理的封装与调用摘要本文介绍了在中使用进行推.docx 44.86KB

资源介绍:

Labview结合Yolov5与TensorRT（wangxingyu版）实现快速并行推理，dll封装与调用，模型转换至Engine并支持视频图片识别，6ms极速响应,Labview结合Yolov5与TensorRT（Wangxingyu版）实现快速并行推理，dll封装与调用，模型转换及视频图片识别,labview yolov5 tensorrt(wangxingyu版)推理，封装dll, labview调用dll，支持同时加油多个模型并行推理，识别视频和图片，速度6ms内，模型需要pt->wts->engine, 由于不同电脑和平台需要重新wts->engine，所以包含一个wts模型转engine软件，只需要替模型的engin和nameclass即可 ,关键词提取结果：labview; yolov5; tensorrt; wangxingyu版; 推理; 封装dll; 调用dll; 同时加油多个模型; 并行推理; 识别视频和图片; 速度6ms内; 模型转换; pt->wts->engine; 不同电脑和平台; wts模型转engine软件; 替换模型; enginenamecla
<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90432396/2/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/90432396/bg1.jpg"/><div class="t m0 x1 h2 y1 ff1 fs0 fc0 sc0 ls0 ws0">【题目】<span class="ff2">LabVIEW<span class="_ _0"> </span></span>中<span class="_ _0"> </span><span class="ff2">Yolov5 TensorRT<span class="_ _0"> </span></span>推理的<span class="_ _0"> </span><span class="ff2">DLL<span class="_ _0"> </span></span>封装与调用</div><div class="t m0 x1 h2 y2 ff1 fs0 fc0 sc0 ls0 ws0">【摘要】<span class="_ _1"></span>本文介绍了在<span class="_ _0"> </span><span class="ff2">LabVIEW<span class="_ _0"> </span></span>中使用<span class="_ _0"> </span><span class="ff2">Yolov5 <span class="_ _2"></span>TensorRT<span class="_ _0"> </span><span class="ff1">进行推理的方法。<span class="_ _1"></span>通过封装<span class="_ _0"> </span><span class="ff2">DLL<span class="_ _0"> </span></span>并</span></span></div><div class="t m0 x1 h2 y3 ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">LabVIEW<span class="_"> </span></span>中调用，实现<span class="_ _3"></span>了同时处<span class="_ _3"></span>理多个模<span class="_ _3"></span>型的并行推<span class="_ _3"></span>理，能够<span class="_ _3"></span>对视频和<span class="_ _3"></span>图片进行识<span class="_ _3"></span>别。</div><div class="t m0 x1 h2 y4 ff1 fs0 fc0 sc0 ls0 ws0">该方法的速度快，推理时间在<span class="_ _0"> </span><span class="ff2">6<span class="_"> </span></span>毫秒内。由于不同电脑和平台需要重新生成<span class="_ _0"> </span><span class="ff2">TensorRT<span class="_"> </span></span>引擎</div><div class="t m0 x1 h2 y5 ff1 fs0 fc0 sc0 ls0 ws0">文件，<span class="_ _4"></span>因此提供了一个用于转换模型的<span class="_ _0"> </span><span class="ff2">WTS<span class="_ _0"> </span></span>模型转<span class="_ _0"> </span><span class="ff2">Engine<span class="_ _0"> </span></span>软件。<span class="_ _4"></span>用户只需替换模型的引擎</div><div class="t m0 x1 h2 y6 ff1 fs0 fc0 sc0 ls0 ws0">文件和类别名称即可使用。</div><div class="t m0 x1 h2 y7 ff1 fs0 fc0 sc0 ls0 ws0">【关键词<span class="_ _3"></span>】<span class="ff2">LabVIEW</span>，<span class="ff2">Yolov5<span class="_ _3"></span></span>，<span class="ff2">TensorRT</span>，<span class="ff2">DLL<span class="_ _3"></span></span>，推理，<span class="_ _3"></span>并行处理<span class="_ _3"></span>，视频识<span class="_ _3"></span>别，图片<span class="_ _3"></span>识别，</div><div class="t m0 x1 h2 y8 ff1 fs0 fc0 sc0 ls0 ws0">性能优化</div><div class="t m0 x1 h2 y9 ff1 fs0 fc0 sc0 ls0 ws0">【正文】</div><div class="t m0 x1 h2 ya ff1 fs0 fc0 sc0 ls0 ws0">一、引言</div><div class="t m0 x1 h2 yb ff1 fs0 fc0 sc0 ls0 ws0">近年来<span class="_ _3"></span>，深<span class="_ _3"></span>度学习<span class="_ _3"></span>技术在<span class="_ _3"></span>计算<span class="_ _3"></span>机视觉<span class="_ _3"></span>领域的<span class="_ _3"></span>应用<span class="_ _3"></span>越来越<span class="_ _3"></span>广泛。<span class="_ _3"></span><span class="ff2">Yolov5<span class="_"> </span></span>是目标检<span class="_ _3"></span>测领<span class="_ _3"></span>域的一</div><div class="t m0 x1 h2 yc ff1 fs0 fc0 sc0 ls0 ws0">种经典<span class="_ _3"></span>神经网络<span class="_ _3"></span>模型，它<span class="_ _3"></span>结合了<span class="_ _3"></span>速度和准<span class="_ _3"></span>确率的优<span class="_ _3"></span>势，受<span class="_ _3"></span>到了广大<span class="_ _3"></span>开发者的<span class="_ _3"></span>热情追<span class="_ _3"></span>捧。而</div><div class="t m0 x1 h2 yd ff2 fs0 fc0 sc0 ls0 ws0">TensorRT<span class="_ _0"> </span><span class="ff1">是由<span class="_ _0"> </span></span>NVIDIA<span class="_ _0"> </span><span class="ff1">开发的用于深度学习推理的高性能推理引擎，<span class="_ _5"></span>可以大幅度提升神经网</span></div><div class="t m0 x1 h2 ye ff1 fs0 fc0 sc0 ls0 ws0">络的推理速度。<span class="_ _6"></span>本文将介绍在<span class="_ _0"> </span><span class="ff2">LabVIEW<span class="_ _0"> </span></span>中使用<span class="_ _0"> </span><span class="ff2">Yolov5 TensorRT<span class="_ _0"> </span></span>进行推理的方法，<span class="_ _1"></span>并将其封</div><div class="t m0 x1 h2 yf ff1 fs0 fc0 sc0 ls0 ws0">装成<span class="_ _0"> </span><span class="ff2">DLL<span class="_ _0"> </span></span>供<span class="_ _0"> </span><span class="ff2">LabVIEW<span class="_ _0"> </span></span>调用，实现在<span class="_ _0"> </span><span class="ff2">LabVIEW<span class="_ _0"> </span></span>平台上快速高效地进行目标检测。</div><div class="t m0 x1 h2 y10 ff1 fs0 fc0 sc0 ls0 ws0">二、<span class="ff2">Yolov5 TensorRT<span class="_ _0"> </span></span>推理原理</div><div class="t m0 x1 h2 y11 ff2 fs0 fc0 sc0 ls0 ws0">Yolov5 TensorRT<span class="_"> </span><span class="ff1">推理的基<span class="_ _3"></span>本原理是<span class="_ _3"></span>将<span class="_ _0"> </span></span>Yolov5<span class="_"> </span><span class="ff1">模型转换<span class="_ _3"></span>为<span class="_ _0"> </span></span>TensorRT<span class="_"> </span><span class="ff1">引擎，通过<span class="_ _3"></span>对输入数<span class="_ _3"></span>据</span></div><div class="t m0 x1 h2 y12 ff1 fs0 fc0 sc0 ls0 ws0">进行前向推理，得到目标检测结果。具体步骤如下：</div><div class="t m0 x1 h2 y13 ff2 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _7"> </span><span class="ff1">将<span class="_ _0"> </span></span>PyTorch<span class="_ _0"> </span><span class="ff1">训练好的<span class="_ _0"> </span></span>Yolov5<span class="_ _0"> </span><span class="ff1">模型转换为<span class="_ _0"> </span></span>TensorRT<span class="_ _7"> </span><span class="ff1">引擎所需的权重文件（</span>WTS<span class="_"> </span><span class="ff1">文件）<span class="_ _8"></span>。</span></div><div class="t m0 x1 h2 y14 ff2 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _7"> </span><span class="ff1">使用<span class="_ _0"> </span></span>TensorRT API<span class="_ _7"> </span><span class="ff1">加载<span class="_ _0"> </span></span>WTS<span class="_"> </span><span class="ff1">文件并生成<span class="_ _7"> </span></span>TensorRT<span class="_"> </span><span class="ff1">引擎文件（</span>Engine<span class="_ _7"> </span><span class="ff1">文件）<span class="_ _9"></span>。</span></div><div class="t m0 x1 h2 y15 ff2 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _7"> </span><span class="ff1">在<span class="_ _a"> </span></span>LabVIEW<span class="_"> </span><span class="ff1">中使用<span class="_ _a"> </span></span>DLL<span class="_"> </span><span class="ff1">调用<span class="_ _0"> </span></span>TensorRT<span class="_"> </span><span class="ff1">引擎<span class="_ _3"></span>，对输<span class="_ _3"></span>入数<span class="_ _3"></span>据进<span class="_ _3"></span>行推理<span class="_ _3"></span>，得<span class="_ _3"></span>到目标<span class="_ _3"></span>检测<span class="_ _3"></span>结果<span class="_ _3"></span>。</span></div><div class="t m0 x1 h2 y16 ff1 fs0 fc0 sc0 ls0 ws0">三、封装<span class="_ _0"> </span><span class="ff2">Yolov5 TensorRT<span class="_ _7"> </span></span>推理<span class="_ _0"> </span><span class="ff2">DLL</span></div><div class="t m0 x1 h2 y17 ff1 fs0 fc0 sc0 ls0 ws0">为<span class="_ _3"></span>了<span class="_ _b"></span>在<span class="_ _a"> </span><span class="ff2">LabVIEW<span class="_ _c"> </span></span>中<span class="_ _3"></span>调<span class="_ _b"></span>用<span class="_ _a"> </span><span class="ff2">Yolov5 <span class="_ _b"></span>TensorRT<span class="_"> </span></span>引<span class="_ _b"></span>擎<span class="_ _3"></span>进<span class="_ _b"></span>行<span class="_ _3"></span>推<span class="_ _b"></span>理<span class="_ _3"></span>，<span class="_ _b"></span>我<span class="_ _3"></span>们<span class="_ _b"></span>使<span class="_ _3"></span>用<span class="_ _c"> </span><span class="ff2">C++<span class="_ _3"></span></span>语<span class="_ _b"></span>言<span class="_ _3"></span>封<span class="_ _b"></span>装<span class="_ _3"></span>了<span class="_ _b"></span>一<span class="_ _3"></span>个</div><div class="t m0 x1 h2 y18 ff2 fs0 fc0 sc0 ls0 ws0">DLL<span class="ff1">。该<span class="_ _0"> </span></span>DLL<span class="_ _0"> </span><span class="ff1">提供了以下功能：</span></div><div class="t m0 x1 h2 y19 ff2 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _7"> </span><span class="ff1">加载<span class="_ _0"> </span></span>TensorRT<span class="_ _7"> </span><span class="ff1">引擎文件并创建<span class="_ _0"> </span></span>TensorRT<span class="_"> </span><span class="ff1">推理实例。</span></div><div class="t m0 x1 h2 y1a ff2 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _7"> </span><span class="ff1">输入图像数据，进行目标检测推理，并返回检测结果。</span></div><div class="t m0 x1 h2 y1b ff2 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _7"> </span><span class="ff1">支持同时加油多个模型的并行推理，提高处理速度和效率。</span></div><div class="t m0 x1 h2 y1c ff2 fs0 fc0 sc0 ls0 ws0">4. <span class="_ _7"> </span><span class="ff1">支持视频和图片的识别，满足不同场景的需求。</span></div><div class="t m0 x1 h2 y1d ff1 fs0 fc0 sc0 ls0 ws0">四、<span class="ff2">LabVIEW<span class="_ _0"> </span></span>中的<span class="_ _0"> </span><span class="ff2">DLL<span class="_ _0"> </span></span>调用</div><div class="t m0 x1 h2 y1e ff1 fs0 fc0 sc0 ls0 ws0">在<span class="_ _0"> </span><span class="ff2">LabVIEW<span class="_ _0"> </span></span>中调用<span class="_ _0"> </span><span class="ff2">Yolov5 TensorRT<span class="_ _7"> </span></span>推理<span class="_ _0"> </span><span class="ff2">DLL</span>，可以通过以下步骤实现：</div><div class="t m0 x1 h2 y1f ff2 fs0 fc0 sc0 ls0 ws0">1. <span class="_ _7"> </span><span class="ff1">将<span class="_ _0"> </span></span>DLL<span class="_ _0"> </span><span class="ff1">文件导入到<span class="_ _0"> </span></span>LabVIEW<span class="_ _0"> </span><span class="ff1">中，创建函数节点。</span></div><div class="t m0 x1 h2 y20 ff2 fs0 fc0 sc0 ls0 ws0">2. <span class="_ _7"> </span><span class="ff1">配置输入参数，包括图像数据、模型引擎文件和类别名称。</span></div><div class="t m0 x1 h2 y21 ff2 fs0 fc0 sc0 ls0 ws0">3. <span class="_ _7"> </span><span class="ff1">调用函数节点进行推理，获取检测结果。</span></div><div class="t m0 x1 h2 y22 ff2 fs0 fc0 sc0 ls0 ws0">4. <span class="_ _7"> </span><span class="ff1">根据需要，对检测结果进行后续处理，如可视化、保存等。</span></div><div class="t m0 x1 h2 y23 ff1 fs0 fc0 sc0 ls0 ws0">通过在<span class="_ _a"> </span><span class="ff2">LabVIEW<span class="_"> </span></span>中调用<span class="_ _a"> </span><span class="ff2">Yolov5 TensorRT<span class="_"> </span></span>推理<span class="_ _0"> </span><span class="ff2">DLL<span class="_ _3"></span></span>，开<span class="_ _3"></span>发者可<span class="_ _3"></span>以方便<span class="_ _3"></span>地使<span class="_ _3"></span>用<span class="_ _0"> </span><span class="ff2">Yolov5<span class="_"> </span></span>模型进</div><div class="t m0 x1 h2 y24 ff1 fs0 fc0 sc0 ls0 ws0">行目标检测，<span class="_ _2"></span>并对多个模型进行并行推理，<span class="_ _d"></span>提高处理速度。<span class="_ _d"></span>同时，<span class="_ _2"></span>可以根据实际需求，<span class="_ _d"></span>对输</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>
100+评论
captcha
    相关资源