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<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
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<h1>Source code for torchreid.engine.image.triplet</h1><div class="highlight"><pre>
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<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
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<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
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<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
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<span class="kn">import</span> <span class="nn">time</span>
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<span class="kn">import</span> <span class="nn">datetime</span>
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<span class="kn">import</span> <span class="nn">torch</span>
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<span class="kn">import</span> <span class="nn">torchreid</span>
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<span class="kn">from</span> <span class="nn">torchreid.engine</span> <span class="k">import</span> <span class="n">engine</span>
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<span class="kn">from</span> <span class="nn">torchreid.losses</span> <span class="k">import</span> <span class="n">CrossEntropyLoss</span><span class="p">,</span> <span class="n">TripletLoss</span>
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<span class="kn">from</span> <span class="nn">torchreid.utils</span> <span class="k">import</span> <span class="n">AverageMeter</span><span class="p">,</span> <span class="n">open_specified_layers</span><span class="p">,</span> <span class="n">open_all_layers</span>
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<span class="kn">from</span> <span class="nn">torchreid</span> <span class="k">import</span> <span class="n">metrics</span>
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<div class="viewcode-block" id="ImageTripletEngine"><a class="viewcode-back" href="../../../../pkg/engine.html#torchreid.engine.image.triplet.ImageTripletEngine">[docs]</a><span class="k">class</span> <span class="nc">ImageTripletEngine</span><span class="p">(</span><span class="n">engine</span><span class="o">.</span><span class="n">Engine</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">"""Triplet-loss engine for image-reid.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> datamanager (DataManager): an instance of ``torchreid.data.ImageDataManager``</span>
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<span class="sd"> or ``torchreid.data.VideoDataManager``.</span>
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<span class="sd"> model (nn.Module): model instance.</span>
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<span class="sd"> optimizer (Optimizer): an Optimizer.</span>
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<span class="sd"> margin (float, optional): margin for triplet loss. Default is 0.3.</span>
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<span class="sd"> weight_t (float, optional): weight for triplet loss. Default is 1.</span>
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<span class="sd"> weight_x (float, optional): weight for softmax loss. Default is 1.</span>
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<span class="sd"> scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.</span>
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<span class="sd"> use_cpu (bool, optional): use cpu. Default is False.</span>
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<span class="sd"> label_smooth (bool, optional): use label smoothing regularizer. Default is True.</span>
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<span class="sd"> Examples::</span>
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<span class="sd"> </span>
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<span class="sd"> import torch</span>
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<span class="sd"> import torchreid</span>
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<span class="sd"> datamanager = torchreid.data.ImageDataManager(</span>
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<span class="sd"> root='path/to/reid-data',</span>
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<span class="sd"> sources='market1501',</span>
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<span class="sd"> height=256,</span>
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<span class="sd"> width=128,</span>
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<span class="sd"> combineall=False,</span>
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<span class="sd"> batch_size=32,</span>
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<span class="sd"> num_instances=4,</span>
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<span class="sd"> train_sampler='RandomIdentitySampler' # this is important</span>
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<span class="sd"> )</span>
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<span class="sd"> model = torchreid.models.build_model(</span>
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<span class="sd"> name='resnet50',</span>
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<span class="sd"> num_classes=datamanager.num_train_pids,</span>
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<span class="sd"> loss='triplet'</span>
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<span class="sd"> )</span>
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<span class="sd"> model = model.cuda()</span>
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<span class="sd"> optimizer = torchreid.optim.build_optimizer(</span>
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<span class="sd"> model, optim='adam', lr=0.0003</span>
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<span class="sd"> )</span>
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<span class="sd"> scheduler = torchreid.optim.build_lr_scheduler(</span>
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<span class="sd"> optimizer,</span>
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<span class="sd"> lr_scheduler='single_step',</span>
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<span class="sd"> stepsize=20</span>
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<span class="sd"> )</span>
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<span class="sd"> engine = torchreid.engine.ImageTripletEngine(</span>
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<span class="sd"> datamanager, model, optimizer, margin=0.3,</span>
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<span class="sd"> weight_t=0.7, weight_x=1, scheduler=scheduler</span>
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<span class="sd"> )</span>
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<span class="sd"> engine.run(</span>
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<span class="sd"> max_epoch=60,</span>
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<span class="sd"> save_dir='log/resnet50-triplet-market1501',</span>
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<span class="sd"> print_freq=10</span>
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<span class="sd"> )</span>
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</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>
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<span class="n">weight_t</span><span class="o">=</span><span class="mi">1</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="kc">None</span><span class="p">,</span> <span class="n">use_cpu</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
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<span class="n">label_smooth</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">ImageTripletEngine</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</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="p">,</span> <span class="n">use_cpu</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">weight_t</span> <span class="o">=</span> <span class="n">weight_t</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">weight_x</span> <span class="o">=</span> <span class="n">weight_x</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">criterion_t</span> <span class="o">=</span> <span class="n">TripletLoss</span><span class="p">(</span><span class="n">margin</span><span class="o">=</span><span class="n">margin</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">criterion_x</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">(</span>
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<span class="n">num_classes</span><span class="o">=</span><span class="bp">self</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>
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<span class="n">use_gpu</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">,</span>
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<span class="n">label_smooth</span><span class="o">=</span><span class="n">label_smooth</span>
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<span class="p">)</span>
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<div class="viewcode-block" id="ImageTripletEngine.train"><a class="viewcode-back" href="../../../../pkg/engine.html#torchreid.engine.image.triplet.ImageTripletEngine.train">[docs]</a> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">epoch</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">,</span> <span class="n">trainloader</span><span class="p">,</span> <span class="n">fixbase_epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">open_layers</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">print_freq</span><span class="o">=</span><span class="mi">10</span><span class="p">):</span>
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<span class="n">losses_t</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
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<span class="n">losses_x</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
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<span class="n">accs</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
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<span class="n">batch_time</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
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<span class="n">data_time</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
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<span class="k">if</span> <span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o"><=</span><span class="n">fixbase_epoch</span> <span class="ow">and</span> <span class="n">open_layers</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'* Only train </span><span class="si">{}</span><span class="s1"> (epoch: </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">)'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">open_layers</span><span class="p">,</span> <span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">fixbase_epoch</span><span class="p">))</span>
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<span class="n">open_specified_layers</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">,</span> <span class="n">open_layers</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">open_all_layers</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
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<span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
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<span class="k">for</span> <span class="n">batch_idx</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">trainloader</span><span class="p">):</span>
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<span class="n">data_time</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">end</span><span class="p">)</span>
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<span class="n">imgs</span><span class="p">,</span> <span class="n">pids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_data_for_train</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">:</span>
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<span class="n">imgs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<span class="n">pids</span> <span class="o">=</span> <span class="n">pids</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
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<span class="n">outputs</span><span class="p">,</span> <span class="n">features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">imgs</span><span class="p">)</span>
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<span class="n">loss_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_loss</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">criterion_t</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">pids</span><span class="p">)</span>
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<span class="n">loss_x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compute_loss</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">criterion_x</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">pids</span><span class="p">)</span>
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<span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight_t</span> <span class="o">*</span> <span class="n">loss_t</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight_x</span> <span class="o">*</span> <span class="n">loss_x</span>
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<span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
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<span class="n">batch_time</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">end</span><span class="p">)</span>
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<span class="n">losses_t</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loss_t</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">pids</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
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<span class="n">losses_x</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">loss_x</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">pids</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
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<span class="n">accs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">pids</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">())</span>
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<span class="k">if</span> <span class="p">(</span><span class="n">batch_idx</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">print_freq</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
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<span class="c1"># estimate remaining time</span>
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<span class="n">num_batches</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainloader</span><span class="p">)</span>
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<span class="n">eta_seconds</span> <span class="o">=</span> <span class="n">batch_time</span><span class="o">.</span><span class="n">avg</span> <span class="o">*</span> <span class="p">(</span><span class="n">num_batches</span><span class="o">-</span><span class="p">(</span><span class="n">batch_idx</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="n">max_epoch</span><span class="o">-</span><span class="p">(</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span><span class="o">*</span><span class="n">num_batches</span><span class="p">)</span>
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<span class="n">eta_str</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">timedelta</span><span class="p">(</span><span class="n">seconds</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="n">eta_seconds</span><span class="p">)))</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'Epoch: [</span><span class="si">{0}</span><span class="s1">/</span><span class="si">{1}</span><span class="s1">][</span><span class="si">{2}</span><span class="s1">/</span><span class="si">{3}</span><span class="s1">]</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Time </span><span class="si">{batch_time.val:.3f}</span><span class="s1"> (</span><span class="si">{batch_time.avg:.3f}</span><span class="s1">)</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Data </span><span class="si">{data_time.val:.3f}</span><span class="s1"> (</span><span class="si">{data_time.avg:.3f}</span><span class="s1">)</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Loss_t </span><span class="si">{loss_t.val:.4f}</span><span class="s1"> (</span><span class="si">{loss_t.avg:.4f}</span><span class="s1">)</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Loss_x </span><span class="si">{loss_x.val:.4f}</span><span class="s1"> (</span><span class="si">{loss_x.avg:.4f}</span><span class="s1">)</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Acc </span><span class="si">{acc.val:.2f}</span><span class="s1"> (</span><span class="si">{acc.avg:.2f}</span><span class="s1">)</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Lr </span><span class="si">{lr:.6f}</span><span class="se">\t</span><span class="s1">'</span>
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<span class="s1">'Eta </span><span class="si">{eta}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
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<span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">max_epoch</span><span class="p">,</span> <span class="n">batch_idx</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainloader</span><span class="p">),</span>
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<span class="n">batch_time</span><span class="o">=</span><span class="n">batch_time</span><span class="p">,</span>
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<span class="n">data_time</span><span class="o">=</span><span class="n">data_time</span><span class="p">,</span>
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<span class="n">loss_t</span><span class="o">=</span><span class="n">losses_t</span><span class="p">,</span>
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<span class="n">loss_x</span><span class="o">=</span><span class="n">losses_x</span><span class="p">,</span>
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<span class="n">acc</span><span class="o">=</span><span class="n">accs</span><span class="p">,</span>
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<span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s1">'lr'</span><span class="p">],</span>
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<span class="n">eta</span><span class="o">=</span><span class="n">eta_str</span>
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<span class="p">)</span>
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<span class="p">)</span>
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<span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">()</span></div></div>
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</pre></div>
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