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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="quickstart-cifar10-example"> +<h1>Quickstart - Cifar10 example<a class="headerlink" href="#quickstart-cifar10-example" 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 classification. In this example, we will use a deep CNN model to do image classification for the <a class="reference external" href="http://www.cs.toronto.edu/%7Ekriz/cifar.html";>CIFAR10 dataset</a>.</p> +<div class="section" id="running-instructions-for-cpp-version"> +<h2>Running instructions for CPP version<a class="headerlink" href="#running-instructions-for-cpp-version" title="Permalink to this headline">¶</a></h2> +<p>Please refer to <a class="reference external" href="installation.html">Installation</a> page for how to install SINGA. Currently, we CNN requires CUDNN, hence both CUDA and CUDNN should be installed and SINGA should be compiled with CUDA and CUDNN.</p> +<p>The Cifar10 dataset could be downloaded by running</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># switch to cifar10 directory +$ cd ../examples/cifar10 +# download data for CPP version +$ python download_data.py bin +</pre></div> +</div> +<p>âbinâ is for downloading binary version of Cifar10 data.</p> +<p>During downloading, you should see the detailed output like</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz + The tar file does exist. Extracting it now.. + Finished! +</pre></div> +</div> +<p>Now you have prepared the data for this Cifar10 example, the final step is to execute the <code class="docutils literal notranslate"><span class="pre">run.sh</span></code> script,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># in SINGA_ROOT/examples/cifar10/ +$ ./run.sh +</pre></div> +</div> +<p>You should see the detailed output as follows: first read the data files in order, show the statistics of training and testing data, then show the details of neural net structure with some parameter information, finally illustrate the performance details during training and validation process. The number of epochs can be specified in <code class="docutils literal notranslate"><span class="pre">run.sh</span></code> file.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Start</span> <span class="n">training</span> +<span class="n">Reading</span> <span class="n">file</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="nb">bin</span><span class="o">/</span><span class="n">data_batch_1</span><span class="o">.</span><span class="n">bin</span> +<span class="n">Reading</span> <span class="n">file</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="nb">bin</span><span class="o">/</span><span class="n">data_batch_2</span><span class="o">.</span><span class="n">bin</span> +<span class="n">Reading</span> <span class="n">file</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="nb">bin</span><span class="o">/</span><span class="n">data_batch_3</span><span class="o">.</span><span class="n">bin</span> +<span class="n">Reading</span> <span class="n">file</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="nb">bin</span><span class="o">/</span><span class="n">data_batch_4</span><span class="o">.</span><span class="n">bin</span> +<span class="n">Reading</span> <span class="n">file</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="nb">bin</span><span class="o">/</span><span class="n">data_batch_5</span><span class="o">.</span><span class="n">bin</span> +<span class="n">Reading</span> <span class="n">file</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="nb">bin</span><span class="o">/</span><span class="n">test_batch</span><span class="o">.</span><span class="n">bin</span> +<span class="n">Training</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">50000</span><span class="p">,</span> <span class="n">Test</span> <span class="n">samples</span> <span class="o">=</span> <span class="mi">10000</span> +<span class="n">conv1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="p">)</span> +<span class="n">pool1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> +<span class="n">relu1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> +<span class="n">lrn1</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> +<span class="n">conv2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> +<span class="n">relu2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="p">)</span> +<span class="n">pool2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> +<span class="n">lrn2</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> +<span class="n">conv3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> +<span class="n">relu3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">)</span> +<span class="n">pool3</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="p">)</span> +<span class="n">flat</span><span class="p">(</span><span class="mi">1024</span><span class="p">,</span> <span class="p">)</span> +<span class="n">ip</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="p">)</span> +<span class="n">conv1_weight</span> <span class="p">:</span> <span class="mf">8.09309e-05</span> +<span class="n">conv1_bias</span> <span class="p">:</span> <span class="mi">0</span> +<span class="n">conv2_weight</span> <span class="p">:</span> <span class="mf">0.00797731</span> +<span class="n">conv2_bias</span> <span class="p">:</span> <span class="mi">0</span> +<span class="n">conv3_weight</span> <span class="p">:</span> <span class="mf">0.00795888</span> +<span class="n">conv3_bias</span> <span class="p">:</span> <span class="mi">0</span> +<span class="n">ip_weight</span> <span class="p">:</span> <span class="mf">0.00798683</span> +<span class="n">ip_bias</span> <span class="p">:</span> <span class="mi">0</span> +<span class="n">Messages</span> <span class="n">will</span> <span class="n">be</span> <span class="n">appended</span> <span class="n">to</span> <span class="n">an</span> <span class="n">existed</span> <span class="n">file</span><span class="p">:</span> <span class="n">train_perf</span> +<span class="n">Messages</span> <span class="n">will</span> <span class="n">be</span> <span class="n">appended</span> <span class="n">to</span> <span class="n">an</span> <span class="n">existed</span> <span class="n">file</span><span class="p">:</span> <span class="n">val_perf</span> +<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.828369</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.329420</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> +<span class="n">Epoch</span> <span class="mi">0</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.561823</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.420600</span> +<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.465898</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.469940</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> +<span class="n">Epoch</span> <span class="mi">1</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.361778</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.513300</span> +<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.320708</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.529000</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> +<span class="n">Epoch</span> <span class="mi">2</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.242080</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.549100</span> +<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span> <span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.213776</span><span class="p">,</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.571620</span><span class="p">,</span> <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.001000</span> +<span class="n">Epoch</span> <span class="mi">3</span><span class="p">,</span> <span class="n">val</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.175346</span><span class="p">,</span> <span class="n">metric</span> <span class="o">=</span> <span class="mf">0.582000</span> +</pre></div> +</div> +<p>The training details are stored in <code class="docutils literal notranslate"><span class="pre">train_perf</span></code> file in the same directory and the validation details in <code class="docutils literal notranslate"><span class="pre">val_perf</span></code> file.</p> +</div> +<div class="section" id="running-instructions-for-python-version"> +<h2>Running instructions for Python version<a class="headerlink" href="#running-instructions-for-python-version" title="Permalink to this headline">¶</a></h2> +<p>To run CNN example in Python version, we need to compile SINGA with Python binding,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ mkdir build && cd build +$ cmake -DUSE_PYTHON=ON .. +$ make +</pre></div> +</div> +<p>Now download the Cifar10 dataset,</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span># switch to cifar10 directory +$ cd ../examples/cifar10 +# download data for Python version +$ python download_data.py py +</pre></div> +</div> +<p>During downloading, you should see the detailed output like</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span> Downloading CIFAR10 from http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz + The tar file does exist. Extracting it now.. + Finished! +</pre></div> +</div> +<p>Then execute the <code class="docutils literal notranslate"><span class="pre">train.py</span></code> script to build the model</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python train.py +</pre></div> +</div> +<p>You should see the output as follows including the details of neural net structure with some parameter information, reading data files, and the performance details during training and testing process.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">32</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">,</span> <span class="mi">16</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">32</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">,</span> <span class="mi">8</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">64</span><span class="n">L</span><span class="p">,</span> <span class="mi">4</span><span class="n">L</span><span class="p">,</span> <span class="mi">4</span><span class="n">L</span><span class="p">)</span> +<span class="p">(</span><span class="mi">1024</span><span class="n">L</span><span class="p">,)</span> +<span class="n">Start</span> <span class="n">intialization</span><span class="o">............</span> +<span class="n">conv1_weight</span> <span class="n">gaussian</span> <span class="mf">7.938460476e-05</span> +<span class="n">conv1_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> +<span class="n">conv2_weight</span> <span class="n">gaussian</span> <span class="mf">0.00793507322669</span> +<span class="n">conv2_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> +<span class="n">conv3_weight</span> <span class="n">gaussian</span> <span class="mf">0.00799657031894</span> +<span class="n">conv3_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> +<span class="n">dense_weight</span> <span class="n">gaussian</span> <span class="mf">0.00804364029318</span> +<span class="n">dense_bias</span> <span class="n">constant</span> <span class="mf">0.0</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="o">..................</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</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">data_batch_1</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</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">data_batch_2</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</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">data_batch_3</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</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">data_batch_4</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</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">data_batch_5</span> +<span class="n">Loading</span> <span class="n">data</span> <span class="n">file</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">test_batch</span> +<span class="n">Epoch</span> <span class="mi">0</span> +<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.881866</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.306360</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.420000</span> +<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.602577</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.412200</span> +<span class="n">Epoch</span> <span class="mi">1</span> +<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.536011</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.441940</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.500000</span> +<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.378170</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.507600</span> +<span class="n">Epoch</span> <span class="mi">2</span> +<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.333137</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.519960</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.520000</span> +<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.272205</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.540600</span> +<span class="n">Epoch</span> <span class="mi">3</span> +<span class="n">training</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.185212</span><span class="p">,</span> <span class="n">training</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.574120</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.540000</span> +<span class="n">test</span> <span class="n">loss</span> <span class="o">=</span> <span class="mf">1.211573</span><span class="p">,</span> <span class="n">test</span> <span class="n">accuracy</span> <span class="o">=</span> <span class="mf">0.567600</span> +</pre></div> +</div> +<p>This script will call <code class="docutils literal notranslate"><span class="pre">alexnet.py</span></code> file to build the alexnet model. After the training is finished, SINGA will save the model parameters into a checkpoint file <code class="docutils literal notranslate"><span class="pre">model.bin</span></code> in the same directory. Then we can use this <code class="docutils literal notranslate"><span class="pre">model.bin</span></code> file for prediction.</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ python predict.py +</pre></div> +</div> +</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. 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<li><a href="index.html">Documentation</a> »</li> + + <li>Data</li> + + + <li class="wy-breadcrumbs-aside"> + + + + </li> + + </ul> + + + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article";> + <div itemprop="articleBody"> + + <div class="section" id="module-singa.data"> +<span id="data"></span><h1>Data<a class="headerlink" href="#module-singa.data" title="Permalink to this headline">¶</a></h1> +<p>This module includes classes for loading and prefetching data batches.</p> +<p>Example usage:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">image_tool</span> +<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span> + +<span class="n">tool</span> <span class="o">=</span> <span class="n">image_tool</span><span class="o">.</span><span class="n">ImageTool</span><span class="p">()</span> + +<span class="k">def</span> <span class="nf">image_transform</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="k">global</span> <span class="n">tool</span> + <span class="k">return</span> <span class="n">tool</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span><span class="o">.</span><span class="n">resize_by_range</span><span class="p">(</span> + <span class="p">(</span><span class="mi">112</span><span class="p">,</span> <span class="mi">128</span><span class="p">))</span><span class="o">.</span><span class="n">random_crop</span><span class="p">(</span> + <span class="p">(</span><span class="mi">96</span><span class="p">,</span> <span class="mi">96</span><span class="p">))</span><span class="o">.</span><span class="n">flip</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">()</span> + +<span class="n">data</span> <span class="o">=</span> <span class="n">ImageBatchIter</span><span class="p">(</span><span class="s1">'train.txt'</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> + <span class="n">image_transform</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">','</span><span class="p">,</span> + <span class="n">image_folder</span><span class="o">=</span><span class="s1">'images/'</span><span class="p">,</span> + <span class="n">capacity</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span> +<span class="n">data</span><span class="o">.</span><span class="n">start</span><span class="p">()</span> +<span class="c1"># imgs is a numpy array for a batch of images,</span> +<span class="c1"># shape: batch_size, 3 (RGB), height, width</span> +<span class="n">imgs</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">next</span><span class="p">()</span> + +<span class="c1"># convert numpy array back into images</span> +<span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> + <span class="s1">'RGB'</span><span class="p">)</span> + <span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'img</span><span class="si">%d</span><span class="s1">.png'</span> <span class="o">%</span> <span class="n">idx</span><span class="p">)</span> +<span class="n">data</span><span class="o">.</span><span class="n">end</span><span class="p">()</span> +</pre></div> +</div> +<dl class="class"> +<dt id="singa.data.ImageBatchIter"> +<em class="property">class </em><code class="descclassname">singa.data.</code><code class="descname">ImageBatchIter</code><span class="sig-paren">(</span><em>img_list_file</em>, <em>batch_size</em>, <em>image_transform</em>, <em>shuffle=True</em>, <em>delimiter=' '</em>, <em>image_folder=None</em>, <em>capacity=10</em><span class="sig-paren">)</span><a class="headerlink" href="#singa.data.ImageBatchIter" title="Permalink to this definition">¶</a></dt> +<dd><p>Utility for iterating over an image dataset to get mini-batches.</p> +<dl class="field-list simple"> +<dt class="field-odd">Parameters</dt> +<dd class="field-odd"><ul class="simple"> +<li><p><strong>img_list_file</strong> (<em>str</em>) â name of the file containing image meta data; each +line consists of image_path_suffix delimiter meta_info, +where meta info could be label index or label strings, etc. +meta_info should not contain the delimiter. If the meta_info +of each image is just the label index, then we will parse the +label index into a numpy array with length=batchsize +(for compatibility); otherwise, we return a list of meta_info; +if meta info is available, we return a list of None.</p></li> +<li><p><strong>batch_size</strong> (<em>int</em>) â num of samples in one mini-batch</p></li> +<li><p><strong>image_transform</strong> â a function for image augmentation; it accepts the full +image path and outputs a list of augmented images.</p></li> +<li><p><strong>shuffle</strong> (<em>boolean</em>) â True for shuffling images in the list</p></li> +<li><p><strong>delimiter</strong> (<em>char</em>) â delimiter between image_path_suffix and label, e.g., +space or comma</p></li> +<li><p><strong>image_folder</strong> (<em>boolean</em>) â prefix of the image path</p></li> +<li><p><strong>capacity</strong> (<em>int</em>) â the max num of mini-batches in the internal queue.</p></li> +</ul> +</dd> +</dl> +</dd></dl> + +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="image_tool.html" class="btn btn-neutral float-right" title="Image Tool" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="autograd.html" class="btn btn-neutral float-left" title="Autograd in Singa" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <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. +--><div class="section" id="dependent-library-installation"> +<h1>Dependent library installation<a class="headerlink" href="#dependent-library-installation" title="Permalink to this headline">¶</a></h1> +<div class="section" id="windows"> +<h2>Windows<a class="headerlink" href="#windows" title="Permalink to this headline">¶</a></h2> +<p>This section is used to compile and install the dependent libraries under +windows system from source codes. The following instructions ONLY work for Visual Studio 2015 as +previous VS does not support <a class="reference external" href="https://msdn.microsoft.com/en-us/library/hh567368.aspx";>C++11 features</a> well (including generic lambdas, auto, non-static +data member initializers). If you intend to generate a 32-bit/64-bit singa solution, please configure all the +VS projects for the dependent libraries as 32-bit/64-bit. This can be done by +âConfiguration Managerâ in VS 2015 or use corresponding generator in cmake. When compiling the following libraries, you +may get system-specific warnings/errors. Please fix them according to the +prompts by VS.</p> +<div class="section" id="google-logging"> +<h3>Google Logging<a class="headerlink" href="#google-logging" title="Permalink to this headline">¶</a></h3> +<p>The glog library is an optional library for singa project. But it is currently necessary for Window compilation. +Since the latest release version of glog will encounter error C2084 on sprintf function +under VS2015, we test the compilation and installation using the master branch from <a class="reference external" href="https://github.com/google/glog";>github</a>.</p> +<p>Step 1: Download and decompress the source code. Or use <code class="docutils literal notranslate"><span class="pre">git</span> <span class="pre">clone</span> <span class="pre">https://github.com/google/glog</span></code> to get the code.</p> +<p>Step 2: Open âglog.slnâ file under project folder. You will get a conversion +dialog and please finish it by the prompts. Compile all the projects in the solution after +proper configuration, especially âlibglogâ and âlibglog_staticâ projects.</p> +<p>Step 3: Copy all the header files and the entire directory named âglogâ under +âsrc\windowsâ folder into the installation include folder (or system folder). +Copy all the generated library files into the installation library folder (or +system folder).</p> +<p>Step 4: Done.</p> +</div> +<div class="section" id="google-protobuf"> +<h3>Google protobuf<a class="headerlink" href="#google-protobuf" title="Permalink to this headline">¶</a></h3> +<p>Tested on version 2.6.1:</p> +<p>Step 1: Download and decompress the source code.</p> +<p>Step 2: Open âprotobuf.slnâ file under âvsprojectsâ folder. You will get a conversion +dialog and please finish it by the prompts. Compile all the projects in the solution after proper +configuration. Especially âlibprotobufâ, âlibprotobuf-liteâ, âlibprotocâ and +âprotocâ projects.</p> +<p>Step 3: Run âextract_includes.batâ script under âvsprojectsâ folder, you will +get a new âincludeâ folder with all the headers.</p> +<p>Step 4: Copy the library files, such as âlibprotobuf.libâ, +âlibprotobuf-lite.libâ, âlibprotoc.libâ, etc., into your installation library folder (or +system folder). Copy the binary file âprotocâ into your installation binary +folder (or system folder). Copy all the headers and folders in âincludeâ folder into your +installation include folder (or system folder).</p> +<p>Step 5: Done.</p> +</div> +<div class="section" id="cblas"> +<h3>CBLAS<a class="headerlink" href="#cblas" title="Permalink to this headline">¶</a></h3> +<p>There are ready-to-use binary packages online +(<a class="reference external" href="https://sourceforge.net/projects/openblas/files/";>link</a>). However, we still install +OpenBLAS with version 0.2.18 as test:</p> +<p>Step 1: Download and decompress the source code.</p> +<p>Step 2: Start a cmd window under the OpenBLAS folder then run the following +commands to generate the solution:</p> +<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ md build $$ cd build +$ cmake -G "Visual Studio 14" .. +</pre></div> +</div> +<p>Or run <code class="docutils literal notranslate"><span class="pre">cmake</span> <span class="pre">-G</span> <span class="pre">"Visual</span> <span class="pre">Studio</span> <span class="pre">14</span> <span class="pre">Win64"</span></code> as you wish.</p> +<p>Step 3: Install Perl into your system and put perl.exe on your path. Open âOpenBlas.slnâ and build the solution, especially âlibopenblasâ +project.</p> +<p>Step 4: Copy the library files under âbuild\libâ folder and all header files +under OpenBLAS folder into installation library and include folders (or system +folders).</p> +<p>Step 5: Done.</p> +</div> +</div> +<div class="section" id="faq"> +<h2>FAQ<a class="headerlink" href="#faq" title="Permalink to this headline">¶</a></h2> +<ol> +<li><p>Error C2375 âsnprintfâ: redefinition; different linkage</p> +<p>Add âHAVE_SNPRINTFâ to âC/C++ - Preprocessor - Preprocessor definitionsâ</p> +</li> +<li><p>Error due to hash map</p> +<p>Add â_SILENCE_STDEXT_HASH_DEPRECATION_WARNINGSâ to Preprocessor Definitions.</p> +</li> +</ol> +</div> +</div> + + + </div> + + </div> + <footer> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2019 The Apache Software Foundation. All rights reserved. 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