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| <div class="section" id="predict-with-pre-trained-models"> |
| <span id="predict-with-pre-trained-models"></span><h1>Predict with pre-trained models<a class="headerlink" href="#predict-with-pre-trained-models" title="Permalink to this headline">¶</a></h1> |
| <p>This tutorial explains how to recognize objects in an image with a |
| pre-trained model, and how to perform feature extraction.</p> |
| <div class="section" id="prerequisites"> |
| <span id="prerequisites"></span><h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h2> |
| <p>To complete this tutorial, we need:</p> |
| <ul class="simple"> |
| <li>MXNet. See the instructions for your operating system in <a class="reference external" href="http://mxnet.io/get_started/install.html">Setup and Installation</a></li> |
| <li><a class="reference external" href="http://docs.python-requests.org/en/master/">Python Requests</a>, <a class="reference external" href="https://matplotlib.org/">Matplotlib</a> and <a class="reference external" href="http://jupyter.org/index.html">Jupyter Notebook</a>.</li> |
| </ul> |
| <div class="highlight-python"><div class="highlight"><pre><span></span>$ pip install requests matplotlib jupyter |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="loading"> |
| <span id="loading"></span><h2>Loading<a class="headerlink" href="#loading" title="Permalink to this headline">¶</a></h2> |
| <p>We first download a pre-trained ResNet 152 layer that is trained on the full |
| ImageNet dataset with over 10 million images and 10 thousand classes. A |
| pre-trained model contains two parts, a json file containing the model |
| definition and a binary file containing the parameters. In addition, there may be |
| a text file for the labels.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mxnet</span> <span class="kn">as</span> <span class="nn">mx</span> |
| <span class="n">path</span><span class="o">=</span><span class="s1">'http://data.mxnet.io/models/imagenet-11k/'</span> |
| <span class="p">[</span><span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">'resnet-152/resnet-152-symbol.json'</span><span class="p">),</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">'resnet-152/resnet-152-0000.params'</span><span class="p">),</span> |
| <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">path</span><span class="o">+</span><span class="s1">'synset.txt'</span><span class="p">)]</span> |
| </pre></div> |
| </div> |
| <p>Next, we load the downloaded model. <em>Note:</em> If GPU is available, we can replace all |
| occurrences of <code class="docutils literal"><span class="pre">mx.cpu()</span></code> with <code class="docutils literal"><span class="pre">mx.gpu()</span></code> to accelerate the computation.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">sym</span><span class="p">,</span> <span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">load_checkpoint</span><span class="p">(</span><span class="s1">'resnet-152'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> |
| <span class="n">mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">sym</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">label_names</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span> |
| <span class="n">mod</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">for_training</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">data_shapes</span><span class="o">=</span><span class="p">[(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">))],</span> |
| <span class="n">label_shapes</span><span class="o">=</span><span class="n">mod</span><span class="o">.</span><span class="n">_label_shapes</span><span class="p">)</span> |
| <span class="n">mod</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">,</span> <span class="n">allow_missing</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> |
| <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'synset.txt'</span><span class="p">,</span> <span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span> |
| <span class="n">labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">l</span><span class="o">.</span><span class="n">rstrip</span><span class="p">()</span> <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="n">f</span><span class="p">]</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="predicting"> |
| <span id="predicting"></span><h2>Predicting<a class="headerlink" href="#predicting" title="Permalink to this headline">¶</a></h2> |
| <p>We first define helper functions for downloading an image and performing the |
| prediction:</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span> |
| <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span> |
| <span class="kn">import</span> <span class="nn">cv2</span> |
| <span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span> |
| <span class="c1"># define a simple data batch</span> |
| <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span> |
| <span class="n">Batch</span> <span class="o">=</span> <span class="n">namedtuple</span><span class="p">(</span><span class="s1">'Batch'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'data'</span><span class="p">])</span> |
| |
| <span class="k">def</span> <span class="nf">get_image</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span> |
| <span class="c1"># download and show the image</span> |
| <span class="n">fname</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">test_utils</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="n">url</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">fname</span><span class="p">),</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span> |
| <span class="k">if</span> <span class="n">img</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span> |
| <span class="k">return</span> <span class="bp">None</span> |
| <span class="k">if</span> <span class="n">show</span><span class="p">:</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> |
| <span class="n">plt</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">'off'</span><span class="p">)</span> |
| <span class="c1"># convert into format (batch, RGB, width, height)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">resize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="n">img</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="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="p">:]</span> |
| <span class="k">return</span> <span class="n">img</span> |
| |
| <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="n">url</span><span class="p">):</span> |
| <span class="n">img</span> <span class="o">=</span> <span class="n">get_image</span><span class="p">(</span><span class="n">url</span><span class="p">,</span> <span class="n">show</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> |
| <span class="c1"># compute the predict probabilities</span> |
| <span class="n">mod</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Batch</span><span class="p">([</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="p">)]))</span> |
| <span class="n">prob</span> <span class="o">=</span> <span class="n">mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="c1"># print the top-5</span> |
| <span class="n">prob</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="n">prob</span><span class="p">)</span> |
| <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">prob</span><span class="p">)[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> |
| <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">5</span><span class="p">]:</span> |
| <span class="k">print</span><span class="p">(</span><span class="s1">'probability=</span><span class="si">%f</span><span class="s1">, class=</span><span class="si">%s</span><span class="s1">'</span> <span class="o">%</span><span class="p">(</span><span class="n">prob</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">labels</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> |
| </pre></div> |
| </div> |
| <p>Now, we can perform prediction with any downloadable URL:</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">predict</span><span class="p">(</span><span class="s1">'http://writm.com/wp-content/uploads/2016/08/Cat-hd-wallpapers.jpg'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">predict</span><span class="p">(</span><span class="s1">'http://thenotoriouspug.com/wp-content/uploads/2015/01/Pug-Cookie-1920x1080-1024x576.jpg'</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| </div> |
| <div class="section" id="feature-extraction"> |
| <span id="feature-extraction"></span><h2>Feature extraction<a class="headerlink" href="#feature-extraction" title="Permalink to this headline">¶</a></h2> |
| <p>By feature extraction, we mean presenting the input images by the output of an |
| internal layer rather than the last softmax layer. These outputs, which can be |
| viewed as the feature of the raw input image, can then be used by other |
| applications such as object detection.</p> |
| <p>We can use the <code class="docutils literal"><span class="pre">get_internals</span></code> method to get all internal layers from a |
| Symbol.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="c1"># list the last 10 layers</span> |
| <span class="n">all_layers</span> <span class="o">=</span> <span class="n">sym</span><span class="o">.</span><span class="n">get_internals</span><span class="p">()</span> |
| <span class="n">all_layers</span><span class="o">.</span><span class="n">list_outputs</span><span class="p">()[</span><span class="o">-</span><span class="mi">10</span><span class="p">:]</span> |
| </pre></div> |
| </div> |
| <p>An often used layer for feature extraction is the one before the last fully |
| connected layer. For ResNet, and also Inception, it is the flattened layer with |
| name <code class="docutils literal"><span class="pre">flatten0</span></code> which reshapes the 4-D convolutional layer output into 2-D for |
| the fully connected layer. The following source code extracts a new Symbol which |
| outputs the flattened layer and creates a model.</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">fe_sym</span> <span class="o">=</span> <span class="n">all_layers</span><span class="p">[</span><span class="s1">'flatten0_output'</span><span class="p">]</span> |
| <span class="n">fe_mod</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">mod</span><span class="o">.</span><span class="n">Module</span><span class="p">(</span><span class="n">symbol</span><span class="o">=</span><span class="n">fe_sym</span><span class="p">,</span> <span class="n">context</span><span class="o">=</span><span class="n">mx</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">label_names</span><span class="o">=</span><span class="bp">None</span><span class="p">)</span> |
| <span class="n">fe_mod</span><span class="o">.</span><span class="n">bind</span><span class="p">(</span><span class="n">for_training</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">data_shapes</span><span class="o">=</span><span class="p">[(</span><span class="s1">'data'</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">224</span><span class="p">,</span><span class="mi">224</span><span class="p">))])</span> |
| <span class="n">fe_mod</span><span class="o">.</span><span class="n">set_params</span><span class="p">(</span><span class="n">arg_params</span><span class="p">,</span> <span class="n">aux_params</span><span class="p">)</span> |
| </pre></div> |
| </div> |
| <p>We can now invoke <code class="docutils literal"><span class="pre">forward</span></code> to obtain the features:</p> |
| <div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">get_image</span><span class="p">(</span><span class="s1">'http://writm.com/wp-content/uploads/2016/08/Cat-hd-wallpapers.jpg'</span><span class="p">)</span> |
| <span class="n">fe_mod</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="n">Batch</span><span class="p">([</span><span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">img</span><span class="p">)]))</span> |
| <span class="n">features</span> <span class="o">=</span> <span class="n">fe_mod</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span> |
| <span class="k">print</span><span class="p">(</span><span class="n">features</span><span class="p">)</span> |
| <span class="k">assert</span> <span class="n">features</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2048</span><span class="p">)</span> |
| </pre></div> |
| </div> |
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| <div class="download_btn"><a download="predict_image_python.ipynb" href="predict_image_python.ipynb"><span class="glyphicon glyphicon-download-alt"></span> predict_image_python.ipynb</a></div></div></div> |
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| <h3><a href="../../index.html">Table Of Contents</a></h3> |
| <ul> |
| <li><a class="reference internal" href="#">Predict with pre-trained models</a><ul> |
| <li><a class="reference internal" href="#prerequisites">Prerequisites</a></li> |
| <li><a class="reference internal" href="#loading">Loading</a></li> |
| <li><a class="reference internal" href="#predicting">Predicting</a></li> |
| <li><a class="reference internal" href="#feature-extraction">Feature extraction</a></li> |
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