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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. +--><div class="section" id="train-cnn-over-cifar-10"> +<h1>Train CNN over Cifar-10<a class="headerlink" href="#train-cnn-over-cifar-10" title="Permalink to this headline">¶</a></h1> +<p>Convolution neural network (CNN) is a type of feed-forward artificial neural +network widely used for image and video classification. In this example, we +will train three deep CNN models to do image classification for the CIFAR-10 dataset,</p> +<ol class="simple"> +<li><a class="reference external" href="https://code.google.com/p/cuda-convnet/source/browse/trunk/example-layers/layers-18pct.cfg";>AlexNet</a> +the best validation accuracy (without data augmentation) we achieved was about 82%.</li> +<li><a class="reference external" href="http://torch.ch/blog/2015/07/30/cifar.html";>VGGNet</a>, the best validation accuracy (without data augmentation) we achieved was about 89%.</li> +<li><a class="reference external" href="https://github.com/facebook/fb.resnet.torch";>ResNet</a>, the best validation accuracy (without data augmentation) we achieved was about 83%.</li> +<li><a class="reference external" href="https://github.com/BVLC/caffe/tree/master/examples/cifar10";>Alexnet from Caffe</a>, SINGA is able to convert model from Caffe seamlessly.</li> +</ol> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<div class="section" id="singa-installation"> +<h3>SINGA installation<a class="headerlink" href="#singa-installation" title="Permalink to this headline">¶</a></h3> +<p>Users can compile and install SINGA from source or install the Python version. +The code can ran on both CPU and GPU. For GPU training, CUDA and CUDNN (V4 or V5) +are required. Please refer to the installation page for detailed instructions.</p> +</div> +<div class="section" id="data-preparation"> +<h3>Data preparation<a class="headerlink" href="#data-preparation" title="Permalink to this headline">¶</a></h3> +<p>The binary Cifar-10 dataset could be downloaded by</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="nb">bin</span> +</pre></div> +</div> +<p>The Python version could be downloaded by</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">python</span> <span class="n">download_data</span><span class="o">.</span><span class="n">py</span> <span class="n">py</span> +</pre></div> +</div> +</div> +<div class="section" id="training"> +<h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3> +<p>There are four training programs</p> +<ol> +<li><p class="first">train.py. The following command would train the VGG model using the python +version of the Cifar-10 dataset in âcifar-10-batches-pyâ folder.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span> +</pre></div> +</div> +<p>To train other models, please replace âvggâ to âalexnetâ, âresnetâ or âcaffeâ, +where âcaffeâ refers to the alexnet model converted from Caffe. By default +the training would run on a CudaGPU device, to run it on CppCPU, add an additional +argument</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">train</span><span class="o">.</span><span class="n">py</span> <span class="n">vgg</span> <span class="n">cifar</span><span class="o">-</span><span class="mi">10</span><span class="o">-</span><span class="n">batches</span><span class="o">-</span><span class="n">py</span> <span class="o">--</span><span class="n">use_cpu</span> +</pre></div> +</div> +</li> +<li><p class="first">alexnet.cc. It trains the AlexNet model using the CPP APIs on a CudaGPU,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="o">./</span><span class="n">run</span><span class="o">.</span><span class="n">sh</span> +</pre></div> +</div> +</li> +<li><p class="first">alexnet-parallel.cc. It trains the AlexNet model using the CPP APIs on two CudaGPU devices. +The two devices run synchronously to compute the gradients of the mode parameters, which are +averaged on the host CPU device and then be applied to update the parameters.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="o">./</span><span class="n">run</span><span class="o">-</span><span class="n">parallel</span><span class="o">.</span><span class="n">sh</span> +</pre></div> +</div> +</li> +<li><p class="first">vgg-parallel.cc. It trains the VGG model using the CPP APIs on two CudaGPU devices similar to alexnet-parallel.cc.</p> +</li> +</ol> +</div> +<div class="section" id="prediction"> +<h3>Prediction<a class="headerlink" href="#prediction" title="Permalink to this headline">¶</a></h3> +<p>predict.py includes the prediction function</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">images</span><span class="p">,</span> <span class="n">dev</span><span class="p">,</span> <span class="n">topk</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> +</pre></div> +</div> +<p>The net is created by loading the previously trained model; Images consist of +a numpy array of images (one row per image); dev is the training device, e.g., +a CudaGPU device or the host CppCPU device; It returns the topk labels for each instance.</p> +<p>The predict.py fileâs main function provides an example of using the pre-trained alexnet model to do prediction for new images. +The âmodel.binâ file generated by the training program should be placed at the cifar10 folder to run</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">python</span> <span class="n">predict</span><span class="o">.</span><span class="n">py</span> +</pre></div> +</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. +--><div class="section" id="train-alexnet-over-imagenet"> +<h1>Train AlexNet over ImageNet<a class="headerlink" href="#train-alexnet-over-imagenet" title="Permalink to this headline">¶</a></h1> +<p>Convolution neural network (CNN) is a type of feed-forward neural +network widely used for image and video classification. In this example, we will +use a <a class="reference external" href="http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks";>deep CNN model</a> +to do image classification against the ImageNet dataset.</p> +<div class="section" id="instructions"> +<h2>Instructions<a class="headerlink" href="#instructions" title="Permalink to this headline">¶</a></h2> +<div class="section" id="compile-singa"> +<h3>Compile SINGA<a class="headerlink" href="#compile-singa" title="Permalink to this headline">¶</a></h3> +<p>Please compile SINGA with CUDA, CUDNN and OpenCV. You can manually turn on the +options in CMakeLists.txt or run <code class="docutils literal notranslate"><span class="pre">ccmake</span> <span class="pre">..</span></code> in build/ folder.</p> +<p>We have tested CUDNN V4 and V5 (V5 requires CUDA 7.5)</p> +</div> +<div class="section" id="data-download"> +<h3>Data download<a class="headerlink" href="#data-download" title="Permalink to this headline">¶</a></h3> +<ul class="simple"> +<li>Please refer to step1-3 on <a class="reference external" href="https://github.com/amd/OpenCL-caffe/wiki/Instructions-to-create-ImageNet-2012-data";>Instructions to create ImageNet 2012 data</a> +to download and decompress the data.</li> +<li>You can download the training and validation list by +<a class="reference external" href="https://github.com/BVLC/caffe/blob/master/data/ilsvrc12/get_ilsvrc_aux.sh";>get_ilsvrc_aux.sh</a> +or from <a class="reference external" href="http://www.image-net.org/download-images";>Imagenet</a>.</li> +</ul> +</div> +<div class="section" id="data-preprocessing"> +<h3>Data preprocessing<a class="headerlink" href="#data-preprocessing" title="Permalink to this headline">¶</a></h3> +<ul> +<li><p class="first">Assuming you have downloaded the data and the list. +Now we should transform the data into binary files. You can run:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">sh</span> <span class="n">create_data</span><span class="o">.</span><span class="n">sh</span> +</pre></div> +</div> +<p>The script will generate a test file(<code class="docutils literal notranslate"><span class="pre">test.bin</span></code>), a mean file(<code class="docutils literal notranslate"><span class="pre">mean.bin</span></code>) and +several training files(<code class="docutils literal notranslate"><span class="pre">trainX.bin</span></code>) in the specified output folder.</p> +</li> +<li><p class="first">You can also change the parameters in <code class="docutils literal notranslate"><span class="pre">create_data.sh</span></code>.</p> +<ul class="simple"> +<li><code class="docutils literal notranslate"><span class="pre">-trainlist</span> <span class="pre"><file></span></code>: the file of training list;</li> +<li><code class="docutils literal notranslate"><span class="pre">-trainfolder</span> <span class="pre"><folder></span></code>: the folder of training images;</li> +<li><code class="docutils literal notranslate"><span class="pre">-testlist</span> <span class="pre"><file></span></code>: the file of test list;</li> +<li><code class="docutils literal notranslate"><span class="pre">-testfolder</span> <span class="pre"><floder></span></code>: the folder of test images;</li> +<li><code class="docutils literal notranslate"><span class="pre">-outdata</span> <span class="pre"><folder></span></code>: the folder to save output files, including mean, training and test files. +The script will generate these files in the specified folder;</li> +<li><code class="docutils literal notranslate"><span class="pre">-filesize</span> <span class="pre"><int></span></code>: number of training images that stores in each binary file.</li> +</ul> +</li> +</ul> +</div> +<div class="section" id="training"> +<h3>Training<a class="headerlink" href="#training" title="Permalink to this headline">¶</a></h3> +<ul> +<li><p class="first">After preparing data, you can run the following command to train the Alexnet model.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> <span class="n">sh</span> <span class="n">run</span><span class="o">.</span><span class="n">sh</span> +</pre></div> +</div> +</li> +<li><p class="first">You may change the parameters in <code class="docutils literal notranslate"><span class="pre">run.sh</span></code>.</p> +<ul class="simple"> +<li><code class="docutils literal notranslate"><span class="pre">-epoch</span> <span class="pre"><int></span></code>: number of epoch to be trained, default is 90;</li> +<li><code class="docutils literal notranslate"><span class="pre">-lr</span> <span class="pre"><float></span></code>: base learning rate, the learning rate will decrease each 20 epochs, +more specifically, <code class="docutils literal notranslate"><span class="pre">lr</span> <span class="pre">=</span> <span class="pre">lr</span> <span class="pre">*</span> <span class="pre">exp(0.1</span> <span class="pre">*</span> <span class="pre">(epoch</span> <span class="pre">/</span> <span class="pre">20))</span></code>;</li> +<li><code class="docutils literal notranslate"><span class="pre">-batchsize</span> <span class="pre"><int></span></code>: batchsize, it should be changed regarding to your memory;</li> +<li><code class="docutils literal notranslate"><span class="pre">-filesize</span> <span class="pre"><int></span></code>: number of training images that stores in each binary file, it is the +same as the <code class="docutils literal notranslate"><span class="pre">filesize</span></code> in data preprocessing;</li> +<li><code class="docutils literal notranslate"><span class="pre">-ntrain</span> <span class="pre"><int></span></code>: number of training images;</li> +<li><code class="docutils literal notranslate"><span class="pre">-ntest</span> <span class="pre"><int></span></code>: number of test images;</li> +<li><code class="docutils literal notranslate"><span class="pre">-data</span> <span class="pre"><folder></span></code>: the folder which stores the binary files, it is exactly the output +folder in data preprocessing step;</li> +<li><code class="docutils literal notranslate"><span class="pre">-pfreq</span> <span class="pre"><int></span></code>: the frequency(in batch) of printing current model status(loss and accuracy);</li> +<li><code class="docutils literal notranslate"><span class="pre">-nthreads</span> <span class="pre"><int></span></code>: the number of threads to load data which feed to the model.</li> +</ul> +</li> +</ul> +</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-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 class="first">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 class="first">Usage</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> $ python serve.py -h +</pre></div> +</div> +</li> +<li><p class="first">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 class="first">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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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 class="first">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 class="first">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 class="first">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> 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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-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 class="first">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 class="first">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 class="first">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 class="first">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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