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 <p>name: GoogleNet on ImageNet
 SINGA version: 1.0.1
 SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744
 parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
 parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d
 license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</p>
 <hr class="docutils" />
 <div class="section" id="image-classification-using-googlenet">
 <span id="image-classification-using-googlenet"></span><h1>Image Classification using GoogleNet<a class="headerlink" href="#image-classification-using-googlenet" title="Permalink to this headline">¶</a></h1>
 <p>In this example, we convert GoogleNet trained on Caffe to SINGA for image classification.</p>
 <div class="section" id="instructions">
 <span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
 <ul>
 <li><p class="first">Download the parameter checkpoint file into this folder</p>
 <div class="highlight-python"><div class="highlight"><pre>  $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
   $ tar xvf bvlc_googlenet.tar.gz
 </pre></div>
 </div>
 </li>
 <li><p class="first">Run the program</p>
 <div class="highlight-python"><div class="highlight"><pre>  # use cpu
   $ python serve.py -C &amp;
   # use gpu
   $ python serve.py &amp;
 </pre></div>
 </div>
 </li>
 <li><p class="first">Submit images for classification</p>
 <div class="highlight-python"><div class="highlight"><pre>  $ 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 and image3.jpg should be downloaded before executing the above commands.</p>
 </div>
 <div class="section" id="details">
 <span id="details"></span><h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2>
 <p>We first extract the parameter values from <a class="reference external" href="http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel">Caffe&#8217;s checkpoint file</a> into a pickle version
 After downloading the checkpoint file into <code class="docutils literal"><span class="pre">caffe_root/python</span></code> folder, run the following script</p>
 <div class="highlight-python"><div class="highlight"><pre><span class="c1"># to be executed within caffe_root/python folder</span>
 <span class="kn">import</span> <span class="nn">caffe</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">cPickle</span> <span class="kn">as</span> <span class="nn">pickle</span>

 <span class="n">model_def</span> <span class="o">=</span> <span class="s1">&#39;../models/bvlc_googlenet/deploy.prototxt&#39;</span>
 <span class="n">weight</span> <span class="o">=</span> <span class="s1">&#39;bvlc_googlenet.caffemodel&#39;</span>  <span class="c1"># must be downloaded at first</span>
 <span class="n">net</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">Net</span><span class="p">(</span><span class="n">model_def</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">caffe</span><span class="o">.</span><span class="n">TEST</span><span class="p">)</span>

 <span class="n">params</span> <span class="o">=</span> <span class="p">{}</span>
 <span class="k">for</span> <span class="n">layer_name</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
     <span class="n">weights</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
     <span class="n">bias</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
     <span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">&#39;_weight&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">weights</span>
     <span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">&#39;_bias&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">bias</span>
     <span class="k">print</span> <span class="n">layer_name</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span>

 <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;bvlc_googlenet.pickle&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
     <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">fd</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Then we construct the GoogleNet using SINGA&#8217;s FeedForwardNet structure.
 Note that we added a EndPadding layer to resolve the issue from discrepancy
 of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor).
 Only the MaxPooling layers outside inception blocks have this problem.
 Refer to <a class="reference external" href="http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html">this</a> for more detials.</p>
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	<p>name: GoogleNet on ImageNet
	SINGA version: 1.0.1
	SINGA commit: 8c990f7da2de220e8a012c6a8ecc897dc7532744
	parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
	parameter_sha1: 0a88e8948b1abca3badfd8d090d6be03f8d7655d
	license: unrestricted https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet</p>
	<hr class="docutils" />
	<div class="section" id="image-classification-using-googlenet">
	<span id="image-classification-using-googlenet"></span><h1>Image Classification using GoogleNet<a class="headerlink" href="#image-classification-using-googlenet" title="Permalink to this headline">¶</a></h1>
	<p>In this example, we convert GoogleNet trained on Caffe to SINGA for image classification.</p>
	<div class="section" id="instructions">
	<span id="instructions"></span><h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2>
	<ul>
	<li><p class="first">Download the parameter checkpoint file into this folder</p>
	<div class="highlight-python"><div class="highlight"><pre> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/bvlc_googlenet.tar.gz
	$ tar xvf bvlc_googlenet.tar.gz
	</pre></div>
	</div>
	</li>
	<li><p class="first">Run the program</p>
	<div class="highlight-python"><div class="highlight"><pre> # use cpu
	$ python serve.py -C &
	# use gpu
	$ python serve.py &
	</pre></div>
	</div>
	</li>
	<li><p class="first">Submit images for classification</p>
	<div class="highlight-python"><div class="highlight"><pre> $ 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 and image3.jpg should be downloaded before executing the above commands.</p>
	</div>
	<div class="section" id="details">
	<span id="details"></span><h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2>
	<p>We first extract the parameter values from <a class="reference external" href="http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel">Caffe’s checkpoint file</a> into a pickle version
	After downloading the checkpoint file into <code class="docutils literal"><span class="pre">caffe_root/python</span></code> folder, run the following script</p>
	<div class="highlight-python"><div class="highlight"><pre><span class="c1"># to be executed within caffe_root/python folder</span>
	<span class="kn">import</span> <span class="nn">caffe</span>
	<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
	<span class="kn">import</span> <span class="nn">cPickle</span> <span class="kn">as</span> <span class="nn">pickle</span>

	<span class="n">model_def</span> <span class="o">=</span> <span class="s1">'../models/bvlc_googlenet/deploy.prototxt'</span>
	<span class="n">weight</span> <span class="o">=</span> <span class="s1">'bvlc_googlenet.caffemodel'</span> <span class="c1"># must be downloaded at first</span>
	<span class="n">net</span> <span class="o">=</span> <span class="n">caffe</span><span class="o">.</span><span class="n">Net</span><span class="p">(</span><span class="n">model_def</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">caffe</span><span class="o">.</span><span class="n">TEST</span><span class="p">)</span>

	<span class="n">params</span> <span class="o">=</span> <span class="p">{}</span>
	<span class="k">for</span> <span class="n">layer_name</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
	<span class="n">weights</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
	<span class="n">bias</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">)</span>
	<span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_weight'</span><span class="p">]</span><span class="o">=</span><span class="n">weights</span>
	<span class="n">params</span><span class="p">[</span><span class="n">layer_name</span><span class="o">+</span><span class="s1">'_bias'</span><span class="p">]</span><span class="o">=</span><span class="n">bias</span>
	<span class="k">print</span> <span class="n">layer_name</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">shape</span>

	<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'bvlc_googlenet.pickle'</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">fd</span><span class="p">:</span>
	<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">params</span><span class="p">,</span> <span class="n">fd</span><span class="p">)</span>
	</pre></div>
	</div>
	<p>Then we construct the GoogleNet using SINGA’s FeedForwardNet structure.
	Note that we added a EndPadding layer to resolve the issue from discrepancy
	of the rounding strategy of the pooling layer between Caffe (ceil) and cuDNN (floor).
	Only the MaxPooling layers outside inception blocks have this problem.
	Refer to <a class="reference external" href="http://joelouismarino.github.io/blog_posts/blog_googlenet_keras.html">this</a> for more detials.</p>
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