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<h1>Source code for torchreid.engine.video.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.image</span> <span class="k">import</span> <span class="n">ImageTripletEngine</span>
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<span class="kn">from</span> <span class="nn">torchreid.engine.video</span> <span class="k">import</span> <span class="n">VideoSoftmaxEngine</span>
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<div class="viewcode-block" id="VideoTripletEngine"><a class="viewcode-back" href="../../../../pkg/engine.html#torchreid.engine.video.triplet.VideoTripletEngine">[docs]</a><span class="k">class</span> <span class="nc">VideoTripletEngine</span><span class="p">(</span><span class="n">ImageTripletEngine</span><span class="p">,</span> <span class="n">VideoSoftmaxEngine</span><span class="p">):</span>
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<span class="sd">"""Triplet-loss engine for video-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"> pooling_method (str, optional): how to pool features for a tracklet.</span>
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<span class="sd"> Default is "avg" (average). Choices are ["avg", "max"].</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"> # Each batch contains batch_size*seq_len images</span>
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<span class="sd"> # Each identity is sampled with num_instances tracklets </span>
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<span class="sd"> datamanager = torchreid.data.VideoDataManager(</span>
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<span class="sd"> root='path/to/reid-data',</span>
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<span class="sd"> sources='mars',</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"> num_instances=4,</span>
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<span class="sd"> train_sampler='RandomIdentitySampler'</span>
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<span class="sd"> batch_size=8, # number of tracklets</span>
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<span class="sd"> seq_len=15 # number of images in each tracklet</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.VideoTripletEngine(</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"> pooling_method='avg'</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-mars',</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> <span class="n">pooling_method</span><span class="o">=</span><span class="s1">'avg'</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">(</span><span class="n">VideoTripletEngine</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">margin</span><span class="o">=</span><span class="n">margin</span><span class="p">,</span>
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<span class="n">weight_t</span><span class="o">=</span><span class="n">weight_t</span><span class="p">,</span> <span class="n">weight_x</span><span class="o">=</span><span class="n">weight_x</span><span class="p">,</span>
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<span class="n">scheduler</span><span class="o">=</span><span class="n">scheduler</span><span class="p">,</span> <span class="n">use_cpu</span><span class="o">=</span><span class="n">use_cpu</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><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">pooling_method</span> <span class="o">=</span> <span class="n">pooling_method</span></div>
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