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<title>快速入门 — netrans 0.1 文档</title>
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<section id="id1">
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<h1>快速入门<a class="headerlink" href="#id1" title="Link to this heading">¶</a></h1>
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<p>本文档以 onnx 格式的 yolov5s 为例，演示如何快速安装Nertans 并使用 Netrans 量化、编译模型并生成 nbg 文件。</p>
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<section id="id2">
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<h2>系统环境<a class="headerlink" href="#id2" title="Link to this heading">¶</a></h2>
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<ul class="simple">
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<li><p>Linux操作系统，推荐 Ubuntu 20.04 或 Debian12</p></li>
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<li><p>Python 3.8</p></li>
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<li><p>RAM 至少 8GB</p></li>
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</ul>
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</section>
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<section id="netrans">
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<h2>安装Netrans<a class="headerlink" href="#netrans" title="Link to this heading">¶</a></h2>
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<p>创建 python3.8 环境</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>wget<span class="w"> </span><span class="s2">"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-</span><span class="k">$(</span>uname<span class="k">)</span><span class="s2">-</span><span class="k">$(</span>uname<span class="w"> </span>-m<span class="k">)</span><span class="s2">.sh"</span>
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mkdir<span class="w"> </span>-p<span class="w"> </span>~/app
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<span class="nv">INSTALL_PATH</span><span class="o">=</span><span class="s2">"</span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span><span class="s2">/app/miniforge3"</span>
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bash<span class="w"> </span>Miniforge3-Linux-x86_64.sh<span class="w"> </span>-b<span class="w"> </span>-p<span class="w"> </span><span class="si">${</span><span class="nv">INSTALL_PATH</span><span class="si">}</span>
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<span class="nb">echo</span><span class="w"> </span><span class="s2">"source "</span><span class="si">${</span><span class="nv">INSTALL_PATH</span><span class="si">}</span>/etc/profile.d/conda.sh<span class="s2">""</span><span class="w"> </span>>><span class="w"> </span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/.bashrc
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<span class="nb">echo</span><span class="w"> </span><span class="s2">"source "</span><span class="si">${</span><span class="nv">INSTALL_PATH</span><span class="si">}</span>/etc/profile.d/mamba.sh<span class="s2">""</span><span class="w"> </span>>><span class="w"> </span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/.bashrc
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<span class="nb">source</span><span class="w"> </span><span class="si">${</span><span class="nv">HOME</span><span class="si">}</span>/.bashrc
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mamba<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>netrans<span class="w"> </span><span class="nv">python</span><span class="o">=</span><span class="m">3</span>.8<span class="w"> </span>-y
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mamba<span class="w"> </span>activate<span class="w"> </span>netrans
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</pre></div>
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</div>
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<p>下载 Netrans</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>~/app
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git<span class="w"> </span>clone<span class="w"> </span>https://gitlink.org.cn/nudt_dsp/netrans.git
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</pre></div>
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</div>
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<p>配置 Netrans</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>~/app/netrans
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./setup.sh
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</pre></div>
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</div>
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</section>
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<section id="netrans-yolov5s">
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<h2>使用 Netrans 编译 yolov5s 模型<a class="headerlink" href="#netrans-yolov5s" title="Link to this heading">¶</a></h2>
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<p>进入工作目录</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">cd</span><span class="w"> </span>～/app/netrans/examples/onnx
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</pre></div>
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</div>
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<p>此时目录如下：</p>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>onnx/
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├── README.md
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└── yolov5s
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├── 0.jpg
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├── dataset.txt
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└── yolov5s.onnx
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</pre></div>
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</div>
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<section id="netrans-cli-yolov5s">
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<h3>使用 netrans_cli 编译 yolov5s<a class="headerlink" href="#netrans-cli-yolov5s" title="Link to this heading">¶</a></h3>
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<section id="id3">
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<h4>导入模型<a class="headerlink" href="#id3" title="Link to this heading">¶</a></h4>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>load.sh<span class="w"> </span>yolov5s
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</pre></div>
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</div>
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<p>该命令会在工程目录下生成包含模型信息的 .json 和 .data 数据文件。</p>
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<p>此时 yolov5s 的目录结构如下</p>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>yolov5s/
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├── 0.jpg
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├── yolov5s.data
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├── yolov5s.json
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└── yolov5s.onnx
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</pre></div>
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</div>
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</section>
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<section id="id4">
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<h4>生成配置文件模板<a class="headerlink" href="#id4" title="Link to this heading">¶</a></h4>
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<p>配置文件定义输入数据前处理相关参数。Netrans预定义了配置文件模板生成脚本，用户需根据模型前处理参数对配置文件进行修改。</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>config.sh<span class="w"> </span>yolov5s
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</pre></div>
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</div>
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<p>此时 yolov5s 的目录结构如下：</p>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>yolov5s/
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├── 0.jpg
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├── dataset.txt
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├── yolov5s.data
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├── yolov5s_inputmeta.yml
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├── yolov5s.json
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└── yolov5s.onnx
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</pre></div>
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</div>
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<p>根据 yolov5s 的前处理参数 ，修改 yml 中的 scale 为 0.003921568627。
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打开 <code class="docutils literal notranslate"><span class="pre">yolov5s_inputmeta.yml</span></code> 文件，修改第30-33行：</p>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span> scale:
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- 0.003921568627
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- 0.003921568627
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- 0.003921568627
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</pre></div>
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</div>
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</section>
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<section id="id5">
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<h4>量化模型<a class="headerlink" href="#id5" title="Link to this heading">¶</a></h4>
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<p>生成 unit8 量化的量化参数文件</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>quantize.sh<span class="w"> </span>yolov5s<span class="w"> </span>uint8
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</pre></div>
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</div>
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<p>此时 yolov5s 的目录结构如下：</p>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>yolov5s/
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├── 0.jpg
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├── dataset.txt
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├── yolov5s_asymmetric_affine.quantize
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├── yolov5s.data
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├── yolov5s_inputmeta.yml
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├── yolov5s.json
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└── yolov5s.onnx
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</pre></div>
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</div>
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</section>
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<section id="id6">
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<h4>导出模型<a class="headerlink" href="#id6" title="Link to this heading">¶</a></h4>
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<p>导出 unit8 量化的模型项目工程</p>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>export.sh<span class="w"> </span>yolov5s<span class="w"> </span>uint8
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</pre></div>
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</div>
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<p>此时 yolov5s 的目录结构如下：</p>
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<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>yolov5s/
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├── 0.jpg
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├── dataset.txt
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├── wksp
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│ └── asymmetric_affine
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│ └── network_binary.nb
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├── yolov5s_asymmetric_affine.quantize
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├── yolov5s.data
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├── yolov5s_inputmeta.yml
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├── yolov5s.json
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└── yolov5s.onnx
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</pre></div>
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</div>
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</section>
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</section>
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<section id="netrans-py-yolov5s">
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<h3>使用 netrans_py 编译 yolov5s 模型<a class="headerlink" href="#netrans-py-yolov5s" title="Link to this heading">¶</a></h3>
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<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>example.py<span class="w"> </span>yolov5s<span class="w"> </span>-q<span class="w"> </span>uint8<span class="w"> </span>-m<span class="w"> </span><span class="m">0</span><span class="w"> </span>-s<span class="w"> </span><span class="m">0</span>.003921568627
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</pre></div>
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</div>
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</section>
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</section>
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</section>
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<h1 class="logo"><a href="index.html">netrans</a></h1>
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<script>document.getElementById('searchbox').style.display = "block"</script><h3>导航</h3>
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<p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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<ul class="current">
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<li class="toctree-l1 current"><a class="current reference internal" href="#">快速入门</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="#id2">系统环境</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#netrans">安装Netrans</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#netrans-yolov5s">使用 Netrans 编译 yolov5s 模型</a></li>
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</ul>
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</li>
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<li class="toctree-l1"><a class="reference internal" href="netrans_cli.html">netrans_cli 使用</a></li>
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<li class="toctree-l1"><a class="reference internal" href="netrans_py.html">netrans_py 使用</a></li>
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<li class="toctree-l1"><a class="reference internal" href="appendix.html">附录</a></li>
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</ul>
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<div class="relations">
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<h3>Related Topics</h3>
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<ul>
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<li><a href="index.html">Documentation overview</a><ul>
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<li>Previous: <a href="index.html" title="上一章">netrans documentation</a></li>
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<li>Next: <a href="netrans_cli.html" title="下一章">netrans_cli 使用</a></li>
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</ul></li>
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