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 --><p>name: Resnets on ImageNet SINGA version: 1.1 SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</p>
 <hr class="docutils" />
 <div class="section" id="resnet">
 <h1>用ResNet做图像分类<a class="headerlink" href="#resnet" title="Permalink to this headline">¶</a></h1>
 <p>这个例子中，我们将Torch训练好的ResNet转换为SINGA模型以用作图像分类。</p>
 <div class="section" id="id1">
 <h2>操作说明<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">下载参数的checkpoint文件到如下目录</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
   $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
   $ tar xvf resnet-18.tar.gz
 </pre></div>
 </div>
 </li>
 <li><p class="first">运行程序</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ python serve.py -h
 </pre></div>
 </div>
 </li>
 <li><p class="first">运行程序</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  # use cpu
   $ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &amp;
   # use gpu
   $ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &amp;
 </pre></div>
 </div>
 <p>我们提供了以下模型和深度配置的参数文件:</p>
 <ul class="simple">
 <li>resnet (原始 resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz">18</a>|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz">34</a>|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz">101</a>|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz">152</a></li>
 <li>包括批量正则, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz">50</a></li>
 <li>wrn (宽 resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz">50</a></li>
 <li>preact (包括 pre-activation 的 resnet) <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz">200</a></li>
 </ul>
 </li>
 <li><p class="first">提交图片进行分类</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ curl -i -F image=@image1.jpg http://localhost:9999/api
   $ curl -i -F image=@image2.jpg http://localhost:9999/api
   $ curl -i -F image=@image3.jpg http://localhost:9999/api
 </pre></div>
 </div>
 </li>
 </ul>
 <p>image1.jpg, image2.jpg和image3.jpg应该在执行指令前就已被下载。</p>
 </div>
 <div class="section" id="id2">
 <h2>详细信息<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
 <p>用<code class="docutils literal"><span class="pre">convert.py</span></code>从torch参数文件中提取参数值</p>
 <ul>
 <li><p class="first">运行程序</p>
 <div class="highlight-default"><div class="highlight"><pre><span></span>  $ python convert.py -h
 </pre></div>
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 </li>
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	--><p>name: Resnets on ImageNet SINGA version: 1.1 SINGA commit: 45ec92d8ffc1fa1385a9307fdf07e21da939ee2f parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz license: Apache V2, https://github.com/facebook/fb.resnet.torch/blob/master/LICENSE</p>
	<hr class="docutils" />
	<div class="section" id="resnet">
	<h1>用ResNet做图像分类<a class="headerlink" href="#resnet" title="Permalink to this headline">¶</a></h1>
	<p>这个例子中，我们将Torch训练好的ResNet转换为SINGA模型以用作图像分类。</p>
	<div class="section" id="id1">
	<h2>操作说明<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
	<ul>
	<li><p class="first">下载参数的checkpoint文件到如下目录</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz
	$ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt
	$ tar xvf resnet-18.tar.gz
	</pre></div>
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	</li>
	<li><p class="first">运行程序</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ python serve.py -h
	</pre></div>
	</div>
	</li>
	<li><p class="first">运行程序</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> # use cpu
	$ python serve.py --use_cpu --parameter_file resnet-18.pickle --model resnet --depth 18 &
	# use gpu
	$ python serve.py --parameter_file resnet-18.pickle --model resnet --depth 18 &
	</pre></div>
	</div>
	<p>我们提供了以下模型和深度配置的参数文件:</p>
	<ul class="simple">
	<li>resnet (原始 resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-18.tar.gz">18</a>\|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-34.tar.gz">34</a>\|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-101.tar.gz">101</a>\|<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-152.tar.gz">152</a></li>
	<li>包括批量正则, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz">50</a></li>
	<li>wrn (宽 resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz">50</a></li>
	<li>preact (包括 pre-activation 的 resnet) <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz">200</a></li>
	</ul>
	</li>
	<li><p class="first">提交图片进行分类</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ curl -i -F image=@image1.jpg http://localhost:9999/api
	$ curl -i -F image=@image2.jpg http://localhost:9999/api
	$ curl -i -F image=@image3.jpg http://localhost:9999/api
	</pre></div>
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	</li>
	</ul>
	<p>image1.jpg, image2.jpg和image3.jpg应该在执行指令前就已被下载。</p>
	</div>
	<div class="section" id="id2">
	<h2>详细信息<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
	<p>用<code class="docutils literal"><span class="pre">convert.py</span></code>从torch参数文件中提取参数值</p>
	<ul>
	<li><p class="first">运行程序</p>
	<div class="highlight-default"><div class="highlight"><pre><span></span> $ python convert.py -h
	</pre></div>
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