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<div class="section" id="torchreid-engine">
<span id="id1"></span><h1>torchreid.engine<a class="headerlink" href="#torchreid-engine" title="Permalink to this headline"></a></h1>
<div class="section" id="base-engine">
<h2>Base Engine<a class="headerlink" href="#base-engine" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="torchreid.engine.engine.Engine">
<em class="property">class </em><code class="descclassname">torchreid.engine.engine.</code><code class="descname">Engine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer=None</em>, <em>scheduler=None</em>, <em>use_cpu=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine" title="Permalink to this definition"></a></dt>
<dd><p>A generic base Engine class for both image- and video-reid.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_cpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cpu. Default is False.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchreid.engine.engine.Engine.run">
<code class="descname">run</code><span class="sig-paren">(</span><em>save_dir='log', max_epoch=0, start_epoch=0, fixbase_epoch=0, open_layers=None, start_eval=0, eval_freq=-1, test_only=False, print_freq=10, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False, visactmap=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.run" title="Permalink to this definition"></a></dt>
<dd><p>A unified pipeline for training and evaluating a model.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>save_dir</strong> (<em>str</em>) – directory to save model.</li>
<li><strong>max_epoch</strong> (<em>int</em>) – maximum epoch.</li>
<li><strong>start_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – starting epoch. Default is 0.</li>
<li><strong>fixbase_epoch</strong> (<em>int</em><em>, </em><em>optional</em>) – number of epochs to train <code class="docutils literal notranslate"><span class="pre">open_layers</span></code> (new layers)
while keeping base layers frozen. Default is 0. <code class="docutils literal notranslate"><span class="pre">fixbase_epoch</span></code> is counted
in <code class="docutils literal notranslate"><span class="pre">max_epoch</span></code>.</li>
<li><strong>open_layers</strong> (<em>str</em><em> or </em><em>list</em><em>, </em><em>optional</em>) – layers (attribute names) open for training.</li>
<li><strong>start_eval</strong> (<em>int</em><em>, </em><em>optional</em>) – from which epoch to start evaluation. Default is 0.</li>
<li><strong>eval_freq</strong> (<em>int</em><em>, </em><em>optional</em>) – evaluation frequency. Default is -1 (meaning evaluation
is only performed at the end of training).</li>
<li><strong>test_only</strong> (<em>bool</em><em>, </em><em>optional</em>) – if True, only runs evaluation on test datasets.
Default is False.</li>
<li><strong>print_freq</strong> (<em>int</em><em>, </em><em>optional</em>) – print_frequency. Default is 10.</li>
<li><strong>dist_metric</strong> (<em>str</em><em>, </em><em>optional</em>) – distance metric used to compute distance matrix
between query and gallery. Default is “euclidean”.</li>
<li><strong>normalize_feature</strong> (<em>bool</em><em>, </em><em>optional</em>) – performs L2 normalization on feature vectors before
computing feature distance. Default is False.</li>
<li><strong>visrank</strong> (<em>bool</em><em>, </em><em>optional</em>) – visualizes ranked results. Default is False. It is recommended to
enable <code class="docutils literal notranslate"><span class="pre">visrank</span></code> when <code class="docutils literal notranslate"><span class="pre">test_only</span></code> is True. The ranked images will be saved to
“save_dir/visrank_dataset”, e.g. “save_dir/visrank_market1501”.</li>
<li><strong>visrank_topk</strong> (<em>int</em><em>, </em><em>optional</em>) – top-k ranked images to be visualized. Default is 10.</li>
<li><strong>use_metric_cuhk03</strong> (<em>bool</em><em>, </em><em>optional</em>) – use single-gallery-shot setting for cuhk03.
Default is False. This should be enabled when using cuhk03 classic split.</li>
<li><strong>ranks</strong> (<em>list</em><em>, </em><em>optional</em>) – cmc ranks to be computed. Default is [1, 5, 10, 20].</li>
<li><strong>rerank</strong> (<em>bool</em><em>, </em><em>optional</em>) – uses person re-ranking (by Zhong et al. CVPR‘17).
Default is False. This is only enabled when test_only=True.</li>
<li><strong>visactmap</strong> (<em>bool</em><em>, </em><em>optional</em>) – visualizes activation maps. Default is False.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torchreid.engine.engine.Engine.test">
<code class="descname">test</code><span class="sig-paren">(</span><em>epoch, testloader, dist_metric='euclidean', normalize_feature=False, visrank=False, visrank_topk=10, save_dir='', use_metric_cuhk03=False, ranks=[1, 5, 10, 20], rerank=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.test"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.test" title="Permalink to this definition"></a></dt>
<dd><p>Tests model on target datasets.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This function has been called in <code class="docutils literal notranslate"><span class="pre">run()</span></code>.</p>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">The test pipeline implemented in this function suits both image- and
video-reid. In general, a subclass of Engine only needs to re-implement
<code class="docutils literal notranslate"><span class="pre">_extract_features()</span></code> and <code class="docutils literal notranslate"><span class="pre">_parse_data_for_eval()</span></code> (most of the time),
but not a must. Please refer to the source code for more details.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torchreid.engine.engine.Engine.train">
<code class="descname">train</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.train" title="Permalink to this definition"></a></dt>
<dd><p>Performs training on source datasets for one epoch.</p>
<p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">some_arguments</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This must be implemented in subclasses.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torchreid.engine.engine.Engine.visactmap">
<code class="descname">visactmap</code><span class="sig-paren">(</span><em>testloader</em>, <em>save_dir</em>, <em>width</em>, <em>height</em>, <em>print_freq</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/engine.html#Engine.visactmap"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.engine.Engine.visactmap" title="Permalink to this definition"></a></dt>
<dd><p>Visualizes CNN activation maps to see where the CNN focuses on to extract features.</p>
<p>This function takes as input the query images of target datasets</p>
<dl class="docutils">
<dt>Reference:</dt>
<dd><ul class="first last simple">
<li>Zagoruyko and Komodakis. Paying more attention to attention: Improving the
performance of convolutional neural networks via attention transfer. ICLR, 2017</li>
<li>Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</li>
</ul>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="image-engines">
<h2>Image Engines<a class="headerlink" href="#image-engines" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="torchreid.engine.image.softmax.ImageSoftmaxEngine">
<em class="property">class </em><code class="descclassname">torchreid.engine.image.softmax.</code><code class="descname">ImageSoftmaxEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>scheduler=None</em>, <em>use_cpu=False</em>, <em>label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/softmax.html#ImageSoftmaxEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.softmax.ImageSoftmaxEngine" title="Permalink to this definition"></a></dt>
<dd><p>Softmax-loss engine for image-reid.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_cpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cpu. Default is False.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchreid</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>
<span class="n">root</span><span class="o">=</span><span class="s1">&#39;path/to/reid-data&#39;</span><span class="p">,</span>
<span class="n">sources</span><span class="o">=</span><span class="s1">&#39;market1501&#39;</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="n">combineall</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span>
<span class="n">num_classes</span><span class="o">=</span><span class="n">datamanager</span><span class="o">.</span><span class="n">num_train_pids</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0003</span>
<span class="p">)</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span>
<span class="n">optimizer</span><span class="p">,</span>
<span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">&#39;single_step&#39;</span><span class="p">,</span>
<span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span>
<span class="p">)</span>
<span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">ImageSoftmaxEngine</span><span class="p">(</span>
<span class="n">datamanager</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">scheduler</span><span class="o">=</span><span class="n">scheduler</span>
<span class="p">)</span>
<span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="n">max_epoch</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span>
<span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;log/resnet50-softmax-market1501&#39;</span><span class="p">,</span>
<span class="n">print_freq</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
</pre></div>
</div>
<dl class="method">
<dt id="torchreid.engine.image.softmax.ImageSoftmaxEngine.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>epoch</em>, <em>max_epoch</em>, <em>trainloader</em>, <em>fixbase_epoch=0</em>, <em>open_layers=None</em>, <em>print_freq=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/softmax.html#ImageSoftmaxEngine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.softmax.ImageSoftmaxEngine.train" title="Permalink to this definition"></a></dt>
<dd><p>Performs training on source datasets for one epoch.</p>
<p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">some_arguments</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This must be implemented in subclasses.</p>
</div>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchreid.engine.image.triplet.ImageTripletEngine">
<em class="property">class </em><code class="descclassname">torchreid.engine.image.triplet.</code><code class="descname">ImageTripletEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>margin=0.3</em>, <em>weight_t=1</em>, <em>weight_x=1</em>, <em>scheduler=None</em>, <em>use_cpu=False</em>, <em>label_smooth=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/triplet.html#ImageTripletEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.triplet.ImageTripletEngine" title="Permalink to this definition"></a></dt>
<dd><p>Triplet-loss engine for image-reid.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet loss. Default is 0.3.</li>
<li><strong>weight_t</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for triplet loss. Default is 1.</li>
<li><strong>weight_x</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for softmax loss. Default is 1.</li>
<li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_cpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cpu. Default is False.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchreid</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">ImageDataManager</span><span class="p">(</span>
<span class="n">root</span><span class="o">=</span><span class="s1">&#39;path/to/reid-data&#39;</span><span class="p">,</span>
<span class="n">sources</span><span class="o">=</span><span class="s1">&#39;market1501&#39;</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="n">combineall</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
<span class="n">num_instances</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">train_sampler</span><span class="o">=</span><span class="s1">&#39;RandomIdentitySampler&#39;</span> <span class="c1"># this is important</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span>
<span class="n">num_classes</span><span class="o">=</span><span class="n">datamanager</span><span class="o">.</span><span class="n">num_train_pids</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="s1">&#39;triplet&#39;</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0003</span>
<span class="p">)</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span>
<span class="n">optimizer</span><span class="p">,</span>
<span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">&#39;single_step&#39;</span><span class="p">,</span>
<span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span>
<span class="p">)</span>
<span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">ImageTripletEngine</span><span class="p">(</span>
<span class="n">datamanager</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">margin</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
<span class="n">weight_t</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">weight_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">scheduler</span><span class="o">=</span><span class="n">scheduler</span>
<span class="p">)</span>
<span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="n">max_epoch</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span>
<span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;log/resnet50-triplet-market1501&#39;</span><span class="p">,</span>
<span class="n">print_freq</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
</pre></div>
</div>
<dl class="method">
<dt id="torchreid.engine.image.triplet.ImageTripletEngine.train">
<code class="descname">train</code><span class="sig-paren">(</span><em>epoch</em>, <em>max_epoch</em>, <em>trainloader</em>, <em>fixbase_epoch=0</em>, <em>open_layers=None</em>, <em>print_freq=10</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/image/triplet.html#ImageTripletEngine.train"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.image.triplet.ImageTripletEngine.train" title="Permalink to this definition"></a></dt>
<dd><p>Performs training on source datasets for one epoch.</p>
<p>This will be called every epoch in <code class="docutils literal notranslate"><span class="pre">run()</span></code>, e.g.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">start_epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">some_arguments</span><span class="p">)</span>
</pre></div>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This must be implemented in subclasses.</p>
</div>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="video-engines">
<h2>Video Engines<a class="headerlink" href="#video-engines" title="Permalink to this headline"></a></h2>
<dl class="class">
<dt id="torchreid.engine.video.softmax.VideoSoftmaxEngine">
<em class="property">class </em><code class="descclassname">torchreid.engine.video.softmax.</code><code class="descname">VideoSoftmaxEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>scheduler=None</em>, <em>use_cpu=False</em>, <em>label_smooth=True</em>, <em>pooling_method='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/video/softmax.html#VideoSoftmaxEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.video.softmax.VideoSoftmaxEngine" title="Permalink to this definition"></a></dt>
<dd><p>Softmax-loss engine for video-reid.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_cpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cpu. Default is False.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
<li><strong>pooling_method</strong> (<em>str</em><em>, </em><em>optional</em>) – how to pool features for a tracklet.
Default is “avg” (average). Choices are [“avg”, “max”].</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchreid</span>
<span class="c1"># Each batch contains batch_size*seq_len images</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">VideoDataManager</span><span class="p">(</span>
<span class="n">root</span><span class="o">=</span><span class="s1">&#39;path/to/reid-data&#39;</span><span class="p">,</span>
<span class="n">sources</span><span class="o">=</span><span class="s1">&#39;mars&#39;</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="n">combineall</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="c1"># number of tracklets</span>
<span class="n">seq_len</span><span class="o">=</span><span class="mi">15</span> <span class="c1"># number of images in each tracklet</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span>
<span class="n">num_classes</span><span class="o">=</span><span class="n">datamanager</span><span class="o">.</span><span class="n">num_train_pids</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0003</span>
<span class="p">)</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span>
<span class="n">optimizer</span><span class="p">,</span>
<span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">&#39;single_step&#39;</span><span class="p">,</span>
<span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span>
<span class="p">)</span>
<span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">VideoSoftmaxEngine</span><span class="p">(</span>
<span class="n">datamanager</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">scheduler</span><span class="o">=</span><span class="n">scheduler</span><span class="p">,</span>
<span class="n">pooling_method</span><span class="o">=</span><span class="s1">&#39;avg&#39;</span>
<span class="p">)</span>
<span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="n">max_epoch</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span>
<span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;log/resnet50-softmax-mars&#39;</span><span class="p">,</span>
<span class="n">print_freq</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="class">
<dt id="torchreid.engine.video.triplet.VideoTripletEngine">
<em class="property">class </em><code class="descclassname">torchreid.engine.video.triplet.</code><code class="descname">VideoTripletEngine</code><span class="sig-paren">(</span><em>datamanager</em>, <em>model</em>, <em>optimizer</em>, <em>margin=0.3</em>, <em>weight_t=1</em>, <em>weight_x=1</em>, <em>scheduler=None</em>, <em>use_cpu=False</em>, <em>label_smooth=True</em>, <em>pooling_method='avg'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchreid/engine/video/triplet.html#VideoTripletEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchreid.engine.video.triplet.VideoTripletEngine" title="Permalink to this definition"></a></dt>
<dd><p>Triplet-loss engine for video-reid.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>datamanager</strong> (<a class="reference internal" href="data.html#torchreid.data.datamanager.DataManager" title="torchreid.data.datamanager.DataManager"><em>DataManager</em></a>) – an instance of <code class="docutils literal notranslate"><span class="pre">torchreid.data.ImageDataManager</span></code>
or <code class="docutils literal notranslate"><span class="pre">torchreid.data.VideoDataManager</span></code>.</li>
<li><strong>model</strong> (<em>nn.Module</em>) – model instance.</li>
<li><strong>optimizer</strong> (<em>Optimizer</em>) – an Optimizer.</li>
<li><strong>margin</strong> (<em>float</em><em>, </em><em>optional</em>) – margin for triplet loss. Default is 0.3.</li>
<li><strong>weight_t</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for triplet loss. Default is 1.</li>
<li><strong>weight_x</strong> (<em>float</em><em>, </em><em>optional</em>) – weight for softmax loss. Default is 1.</li>
<li><strong>scheduler</strong> (<em>LRScheduler</em><em>, </em><em>optional</em>) – if None, no learning rate decay will be performed.</li>
<li><strong>use_cpu</strong> (<em>bool</em><em>, </em><em>optional</em>) – use cpu. Default is False.</li>
<li><strong>label_smooth</strong> (<em>bool</em><em>, </em><em>optional</em>) – use label smoothing regularizer. Default is True.</li>
<li><strong>pooling_method</strong> (<em>str</em><em>, </em><em>optional</em>) – how to pool features for a tracklet.
Default is “avg” (average). Choices are [“avg”, “max”].</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchreid</span>
<span class="c1"># Each batch contains batch_size*seq_len images</span>
<span class="c1"># Each identity is sampled with num_instances tracklets</span>
<span class="n">datamanager</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">VideoDataManager</span><span class="p">(</span>
<span class="n">root</span><span class="o">=</span><span class="s1">&#39;path/to/reid-data&#39;</span><span class="p">,</span>
<span class="n">sources</span><span class="o">=</span><span class="s1">&#39;mars&#39;</span><span class="p">,</span>
<span class="n">height</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
<span class="n">combineall</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">num_instances</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">train_sampler</span><span class="o">=</span><span class="s1">&#39;RandomIdentitySampler&#39;</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">8</span><span class="p">,</span> <span class="c1"># number of tracklets</span>
<span class="n">seq_len</span><span class="o">=</span><span class="mi">15</span> <span class="c1"># number of images in each tracklet</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">build_model</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s1">&#39;resnet50&#39;</span><span class="p">,</span>
<span class="n">num_classes</span><span class="o">=</span><span class="n">datamanager</span><span class="o">.</span><span class="n">num_train_pids</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="s1">&#39;triplet&#39;</span>
<span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_optimizer</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span> <span class="n">optim</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.0003</span>
<span class="p">)</span>
<span class="n">scheduler</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">build_lr_scheduler</span><span class="p">(</span>
<span class="n">optimizer</span><span class="p">,</span>
<span class="n">lr_scheduler</span><span class="o">=</span><span class="s1">&#39;single_step&#39;</span><span class="p">,</span>
<span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span>
<span class="p">)</span>
<span class="n">engine</span> <span class="o">=</span> <span class="n">torchreid</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">VideoTripletEngine</span><span class="p">(</span>
<span class="n">datamanager</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">margin</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span>
<span class="n">weight_t</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">weight_x</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">scheduler</span><span class="o">=</span><span class="n">scheduler</span><span class="p">,</span>
<span class="n">pooling_method</span><span class="o">=</span><span class="s1">&#39;avg&#39;</span>
<span class="p">)</span>
<span class="n">engine</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="n">max_epoch</span><span class="o">=</span><span class="mi">60</span><span class="p">,</span>
<span class="n">save_dir</span><span class="o">=</span><span class="s1">&#39;log/resnet50-triplet-mars&#39;</span><span class="p">,</span>
<span class="n">print_freq</span><span class="o">=</span><span class="mi">10</span>
<span class="p">)</span>
</pre></div>
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