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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><hr class="docutils" /> +<div class="section" id="name-densenet-models-on-imagenet-singa-version-1-1-1-singa-commit-license-https-github-com-pytorch-vision-blob-master-torchvision-models-densenet-py"> +<h1>name: DenseNet models on ImageNet +SINGA version: 1.1.1 +SINGA commit: +license: https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py<a class="headerlink" href="#name-densenet-models-on-imagenet-singa-version-1-1-1-singa-commit-license-https-github-com-pytorch-vision-blob-master-torchvision-models-densenet-py" title="Permalink to this headline">¶</a></h1> +</div> +<div class="section" id="image-classification-using-densenet"> +<h1>Image Classification using DenseNet<a class="headerlink" href="#image-classification-using-densenet" title="Permalink to this headline">¶</a></h1> +<p>In this example, we convert DenseNet on <a class="reference external" href="https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py";>PyTorch</a> +to SINGA for image classification.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz + $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt + $ tar xvf densenet-121.tar.gz +</pre></div> +</div> +</li> +<li><p>Usage</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ python serve.py -h +</pre></div> +</div> +</li> +<li><p>Example</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # use cpu + $ python serve.py --use_cpu --parameter_file densenet-121.pickle --depth 121 & + # use gpu + $ python serve.py --parameter_file densenet-121.pickle --depth 121 & +</pre></div> +</div> +<p>The parameter files for the following model and depth configuration pairs are provided: +<a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-121.tar.gz";>121</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-169.tar.gz";>169</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-201.tar.gz";>201</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/densenet/densenet-161.tar.gz";>161</a></p> +</li> +<li><p>Submit images for classification</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] 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"> +<h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2> +<p>The parameter files were converted from the pytorch via the convert.py program.</p> +<p>Usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python convert.py -h +</pre></div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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Added: incubator/singa/site/trunk/en/docs/model_zoo/examples/imagenet/googlenet/README.html URL: http://svn.apache.org/viewvc/incubator/singa/site/trunk/en/docs/model_zoo/examples/imagenet/googlenet/README.html?rev=1858059&view=auto ============================================================================== --- incubator/singa/site/trunk/en/docs/model_zoo/examples/imagenet/googlenet/README.html (added) +++ incubator/singa/site/trunk/en/docs/model_zoo/examples/imagenet/googlenet/README.html Wed Apr 24 14:57:35 2019 @@ -0,0 +1,356 @@ + + + +<!DOCTYPE html> +<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]--> +<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]--> +<head> + <meta charset="utf-8"> + + <meta name="viewport" content="width=device-width, initial-scale=1.0"> + + <title>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 — incubator-singa 1.1.0 documentation</title> + + + + + + + + + <script type="text/javascript" src="../../../../../_static/js/modernizr.min.js"></script> + + + <script type="text/javascript" id="documentation_options" data-url_root="../../../../../" src="../../../../../_static/documentation_options.js"></script> + <script type="text/javascript" src="../../../../../_static/jquery.js"></script> + <script type="text/javascript" src="../../../../../_static/underscore.js"></script> + <script type="text/javascript" src="../../../../../_static/doctools.js"></script> + <script type="text/javascript" src="../../../../../_static/language_data.js"></script> + + <script type="text/javascript" src="../../../../../_static/js/theme.js"></script> + + + + + <link rel="stylesheet" href="../../../../../_static/css/theme.css" type="text/css" /> + <link rel="stylesheet" href="../../../../../_static/pygments.css" type="text/css" /> + <link rel="index" title="Index" href="../../../../../genindex.html" /> + <link rel="search" title="Search" href="../../../../../search.html" /> + <link href="../../../../../_static/style.css" rel="stylesheet" type="text/css"> + <!--link href="../../../../../_static/fontawesome-all.min.css" rel="stylesheet" type="text/css"--> + <link rel="stylesheet" href="https://use.fontawesome.com/releases/v5.0.13/css/all.css"; 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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><hr class="docutils" /> +<div class="section" id="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"> +<h1>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<a class="headerlink" href="#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" title="Permalink to this headline">¶</a></h1> +</div> +<div class="section" id="image-classification-using-googlenet"> +<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"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Download the parameter checkpoint file into this folder</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ 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>Run the program</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # use cpu + $ python serve.py -C & + # use gpu + $ python serve.py & +</pre></div> +</div> +</li> +<li><p>Submit images for classification</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] 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"> +<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";>Caffeâs checkpoint file</a> into a pickle version +After downloading the checkpoint file into <code class="docutils literal notranslate"><span class="pre">caffe_root/python</span></code> folder, run the following script</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><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="k">as</span> <span class="nn">np</span> +<span class="kn">import</span> <span class="nn">cPickle</span> <span class="k">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="nb">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 SINGAâs 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> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. All rights reserved. Apache SINGA, Apache, the Apache feather logo, and the Apache SINGA project logos are trademarks of The Apache Software Foundation. All other marks mentioned may be trademarks or registered trademarks of their respective owners. + + </p> + </div> + Built with <a href="http://sphinx-doc.org/";>Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme";>theme</a> provided by <a href="https://readthedocs.org";>Read the Docs</a>. + +</footer> + + </div> + </div> + + </section> + + </div> + + + + <script type="text/javascript"> + jQuery(function () { + SphinxRtdTheme.Navigation.enable(true); + }); + </script> + + + + + + +<div class="rst-versions" data-toggle="rst-versions" role="note" aria-label="versions"> + <span class="rst-current-version" data-toggle="rst-current-version"> + <span class="fa fa-book"> incubator-singa </span> + v: latest + <span class="fa fa-caret-down"></span> + </span> + <div class="rst-other-versions"> + <dl> + <dt>Languages</dt> + <dd><a href="../../../../../../en/index.html">English</a></dd> + <dd><a href="../../../../../../zh/index.html">ä¸æ</a></dd> + </dl> + <dl> + <dt>Versions</dt> + <dd><a href="http://singa.apache.org/v0.3.0/";>0.3</a></dd> + <dd><a href="http://singa.apache.org/v1.1.0/";>1.1</a></dd> + </dl> + + </div> + <a href="http://incubator.apache.org/";> <img src= "../../../../../_static/apache.png" style="background-color:white;"> </a> + + <a href="https://github.com/apache/incubator-singa"; 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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><hr class="docutils" /> +<div class="section" id="name-inception-v4-on-imagenet-singa-version-1-1-1-singa-commit-parameter-url-https-s3-ap-southeast-1-amazonaws-com-dlfile-inception-v4-tar-gz-parameter-sha1-5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56-license-https-github-com-tensorflow-models-tree-master-slim"> +<h1>name: Inception V4 on ImageNet +SINGA version: 1.1.1 +SINGA commit: +parameter_url: https://s3-ap-southeast-1.amazonaws.com/dlfile/inception_v4.tar.gz +parameter_sha1: 5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56 +license: https://github.com/tensorflow/models/tree/master/slim<a class="headerlink" href="#name-inception-v4-on-imagenet-singa-version-1-1-1-singa-commit-parameter-url-https-s3-ap-southeast-1-amazonaws-com-dlfile-inception-v4-tar-gz-parameter-sha1-5fdd6f5d8af8fd10e7321d9b38bb87ef14e80d56-license-https-github-com-tensorflow-models-tree-master-slim" title="Permalink to this headline">¶</a></h1> +</div> +<div class="section" id="image-classification-using-inception-v4"> +<h1>Image Classification using Inception V4<a class="headerlink" href="#image-classification-using-inception-v4" title="Permalink to this headline">¶</a></h1> +<p>In this example, we convert Inception V4 trained on Tensorflow to SINGA for image classification.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Download the parameter checkpoint file</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ wget + $ tar xvf inception_v4.tar.gz +</pre></div> +</div> +</li> +<li><p>Download <a class="reference external" href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh";>synset_word.txt</a> file.</p></li> +<li><p>Run the program</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # use cpu + $ python serve.py -C & + # use gpu + $ python serve.py & +</pre></div> +</div> +</li> +<li><p>Submit images for classification</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] 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"> +<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://download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz";>Tensorflowâs checkpoint file</a> into a pickle version. +After downloading and decompressing the checkpoint file, run the following script</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python convert.py --file_name=inception_v4.ckpt +</pre></div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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See the License for the + specific language governing permissions and limitations + under the License. +--><hr class="docutils" /> +<div class="section" id="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"> +<h1>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<a class="headerlink" href="#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" title="Permalink to this headline">¶</a></h1> +</div> +<div class="section" id="image-classification-using-residual-networks"> +<h1>Image Classification using Residual Networks<a class="headerlink" href="#image-classification-using-residual-networks" title="Permalink to this headline">¶</a></h1> +<p>In this example, we convert Residual Networks trained on <a class="reference external" href="https://github.com/facebook/fb.resnet.torch";>Torch</a> to SINGA for image classification.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,</p> +<div class="highlight-default notranslate"><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>Usage</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ python serve.py -h +</pre></div> +</div> +</li> +<li><p>Example</p> +<div class="highlight-default notranslate"><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>The parameter files for the following model and depth configuration pairs are provided:</p> +<ul class="simple"> +<li><p>resnet (original 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></p></li> +<li><p>addbn (resnet with a batch normalization layer after the addition), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-50.tar.gz";>50</a></p></li> +<li><p>wrn (wide resnet), <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/wrn-50-2.tar.gz";>50</a></p></li> +<li><p>preact (resnet with pre-activation) <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/resnet-200.tar.gz";>200</a></p></li> +</ul> +</li> +<li><p>Submit images for classification</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] 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"> +<h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2> +<p>The parameter files were extracted from the original <a class="reference external" href="https://github.com/facebook/fb.resnet.torch/tree/master/pretrained";>torch files</a> via +the convert.py program.</p> +<p>Usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python convert.py -h +</pre></div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--><hr class="docutils" /> +<div class="section" id="name-vgg-models-on-imagenet-singa-version-1-1-1-singa-commit-license-https-github-com-pytorch-vision-blob-master-torchvision-models-vgg-py"> +<h1>name: VGG models on ImageNet +SINGA version: 1.1.1 +SINGA commit: +license: https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py<a class="headerlink" href="#name-vgg-models-on-imagenet-singa-version-1-1-1-singa-commit-license-https-github-com-pytorch-vision-blob-master-torchvision-models-vgg-py" title="Permalink to this headline">¶</a></h1> +</div> +<div class="section" id="image-classification-using-vgg"> +<h1>Image Classification using VGG<a class="headerlink" href="#image-classification-using-vgg" title="Permalink to this headline">¶</a></h1> +<p>In this example, we convert VGG on <a class="reference external" href="https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py";>PyTorch</a> +to SINGA for image classification.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<ul> +<li><p>Download one parameter checkpoint file (see below) and the synset word file of ImageNet into this folder, e.g.,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz + $ wget https://s3-ap-southeast-1.amazonaws.com/dlfile/resnet/synset_words.txt + $ tar xvf vgg11.tar.gz +</pre></div> +</div> +</li> +<li><p>Usage</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ python serve.py -h +</pre></div> +</div> +</li> +<li><p>Example</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> # use cpu + $ python serve.py --use_cpu --parameter_file vgg11.pickle --depth 11 & + # use gpu + $ python serve.py --parameter_file vgg11.pickle --depth 11 & +</pre></div> +</div> +<p>The parameter files for the following model and depth configuration pairs are provided:</p> +<ul class="simple"> +<li><p>Without batch-normalization, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11.tar.gz";>11</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13.tar.gz";>13</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16.tar.gz";>16</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19.tar.gz";>19</a></p></li> +<li><p>With batch-normalization, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg11_bn.tar.gz";>11</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg13_bn.tar.gz";>13</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg16_bn.tar.gz";>16</a>, <a class="reference external" href="https://s3-ap-southeast-1.amazonaws.com/dlfile/vgg/vgg19_bn.tar.gz";>19</a></p></li> +</ul> +</li> +<li><p>Submit images for classification</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] http://localhost:9999/api + $ curl -i -F [email protected] 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"> +<h2>Details<a class="headerlink" href="#details" title="Permalink to this headline">¶</a></h2> +<p>The parameter files were converted from the pytorch via the convert.py program.</p> +<p>Usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python convert.py -h +</pre></div> +</div> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. 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