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<h1>Source code for torchreid.engine.engine</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">os.path</span> <span class="k">as</span> <span class="nn">osp</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">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">cv2</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">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
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<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
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<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="k">import</span> <span class="n">SummaryWriter</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">visualize_ranked_results</span><span class="p">,</span> <span class="n">save_checkpoint</span><span class="p">,</span> <span class="n">re_ranking</span><span class="p">,</span> <span class="n">mkdir_if_missing</span>
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<span class="kn">from</span> <span class="nn">torchreid.losses</span> <span class="k">import</span> <span class="n">DeepSupervision</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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<span class="n">GRID_SPACING</span> <span class="o">=</span> <span class="mi">10</span>
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<div class="viewcode-block" id="Engine"><a class="viewcode-back" href="../../../pkg/engine.html#torchreid.engine.engine.Engine">[docs]</a><span class="k">class</span> <span class="nc">Engine</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">"""A generic base Engine class for both image- and 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"> scheduler (LRScheduler, optional): if None, no learning rate decay will be performed.</span>
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<span class="sd"> use_gpu (bool, optional): use gpu. Default is True.</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="o">=</span><span class="kc">None</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_gpu</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">datamanager</span> <span class="o">=</span> <span class="n">datamanager</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">model</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">optimizer</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">scheduler</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="n">use_gpu</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">writer</span> <span class="o">=</span> <span class="kc">None</span>
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<span class="c1"># check attributes</span>
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<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</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">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
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<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'model must be an instance of nn.Module'</span><span class="p">)</span>
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<div class="viewcode-block" id="Engine.run"><a class="viewcode-back" href="../../../pkg/engine.html#torchreid.engine.engine.Engine.run">[docs]</a> <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="s1">'log'</span><span class="p">,</span> <span class="n">max_epoch</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">start_epoch</span><span class="o">=</span><span class="mi">0</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>
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<span class="n">start_eval</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">eval_freq</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span> <span class="n">test_only</span><span class="o">=</span><span class="kc">False</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">dist_metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">,</span> <span class="n">normalize_feature</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">visrank</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">visrank_topk</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
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<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ranks</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span> <span class="n">rerank</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">visactmap</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">"""A unified pipeline for training and evaluating a model.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> save_dir (str): directory to save model.</span>
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<span class="sd"> max_epoch (int): maximum epoch.</span>
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<span class="sd"> start_epoch (int, optional): starting epoch. Default is 0.</span>
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<span class="sd"> fixbase_epoch (int, optional): number of epochs to train ``open_layers`` (new layers)</span>
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<span class="sd"> while keeping base layers frozen. Default is 0. ``fixbase_epoch`` is counted</span>
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<span class="sd"> in ``max_epoch``.</span>
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<span class="sd"> open_layers (str or list, optional): layers (attribute names) open for training.</span>
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<span class="sd"> start_eval (int, optional): from which epoch to start evaluation. Default is 0.</span>
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<span class="sd"> eval_freq (int, optional): evaluation frequency. Default is -1 (meaning evaluation</span>
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<span class="sd"> is only performed at the end of training).</span>
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<span class="sd"> test_only (bool, optional): if True, only runs evaluation on test datasets.</span>
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<span class="sd"> Default is False.</span>
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<span class="sd"> print_freq (int, optional): print_frequency. Default is 10.</span>
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<span class="sd"> dist_metric (str, optional): distance metric used to compute distance matrix</span>
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<span class="sd"> between query and gallery. Default is "euclidean".</span>
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<span class="sd"> normalize_feature (bool, optional): performs L2 normalization on feature vectors before</span>
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<span class="sd"> computing feature distance. Default is False.</span>
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<span class="sd"> visrank (bool, optional): visualizes ranked results. Default is False. It is recommended to</span>
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<span class="sd"> enable ``visrank`` when ``test_only`` is True. The ranked images will be saved to</span>
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<span class="sd"> "save_dir/visrank_dataset", e.g. "save_dir/visrank_market1501".</span>
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<span class="sd"> visrank_topk (int, optional): top-k ranked images to be visualized. Default is 10.</span>
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<span class="sd"> use_metric_cuhk03 (bool, optional): use single-gallery-shot setting for cuhk03.</span>
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<span class="sd"> Default is False. This should be enabled when using cuhk03 classic split.</span>
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<span class="sd"> ranks (list, optional): cmc ranks to be computed. Default is [1, 5, 10, 20].</span>
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<span class="sd"> rerank (bool, optional): uses person re-ranking (by Zhong et al. CVPR'17).</span>
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<span class="sd"> Default is False. This is only enabled when test_only=True.</span>
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<span class="sd"> visactmap (bool, optional): visualizes activation maps. Default is False.</span>
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<span class="sd"> """</span>
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<span class="n">trainloader</span><span class="p">,</span> <span class="n">testloader</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">return_dataloaders</span><span class="p">()</span>
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<span class="k">if</span> <span class="n">visrank</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">test_only</span><span class="p">:</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'visrank=True is valid only if test_only=True'</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">test_only</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="p">(</span>
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<span class="mi">0</span><span class="p">,</span>
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<span class="n">testloader</span><span class="p">,</span>
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<span class="n">dist_metric</span><span class="o">=</span><span class="n">dist_metric</span><span class="p">,</span>
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<span class="n">normalize_feature</span><span class="o">=</span><span class="n">normalize_feature</span><span class="p">,</span>
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<span class="n">visrank</span><span class="o">=</span><span class="n">visrank</span><span class="p">,</span>
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<span class="n">visrank_topk</span><span class="o">=</span><span class="n">visrank_topk</span><span class="p">,</span>
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<span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span>
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<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="n">use_metric_cuhk03</span><span class="p">,</span>
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<span class="n">ranks</span><span class="o">=</span><span class="n">ranks</span><span class="p">,</span>
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<span class="n">rerank</span><span class="o">=</span><span class="n">rerank</span>
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<span class="p">)</span>
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<span class="k">return</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">writer</span> <span class="ow">is</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">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="n">log_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">visactmap</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">visactmap</span><span class="p">(</span><span class="n">testloader</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">datamanager</span><span class="o">.</span><span class="n">width</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">datamanager</span><span class="o">.</span><span class="n">height</span><span class="p">,</span> <span class="n">print_freq</span><span class="p">)</span>
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<span class="k">return</span>
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<span class="n">time_start</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="nb">print</span><span class="p">(</span><span class="s1">'=> Start training'</span><span class="p">)</span>
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<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">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="p">,</span> <span class="n">open_layers</span><span class="p">,</span> <span class="n">print_freq</span><span class="p">)</span>
|
|
|
|
<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">start_eval</span> <span class="ow">and</span> <span class="n">eval_freq</span><span class="o">></span><span class="mi">0</span> <span class="ow">and</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">eval_freq</span><span class="o">==</span><span class="mi">0</span> <span class="ow">and</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">max_epoch</span><span class="p">:</span>
|
|
<span class="n">rank1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="p">(</span>
|
|
<span class="n">epoch</span><span class="p">,</span>
|
|
<span class="n">testloader</span><span class="p">,</span>
|
|
<span class="n">dist_metric</span><span class="o">=</span><span class="n">dist_metric</span><span class="p">,</span>
|
|
<span class="n">normalize_feature</span><span class="o">=</span><span class="n">normalize_feature</span><span class="p">,</span>
|
|
<span class="n">visrank</span><span class="o">=</span><span class="n">visrank</span><span class="p">,</span>
|
|
<span class="n">visrank_topk</span><span class="o">=</span><span class="n">visrank_topk</span><span class="p">,</span>
|
|
<span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span>
|
|
<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="n">use_metric_cuhk03</span><span class="p">,</span>
|
|
<span class="n">ranks</span><span class="o">=</span><span class="n">ranks</span>
|
|
<span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">_save_checkpoint</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">rank1</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="n">max_epoch</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'=> Final test'</span><span class="p">)</span>
|
|
<span class="n">rank1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">test</span><span class="p">(</span>
|
|
<span class="n">epoch</span><span class="p">,</span>
|
|
<span class="n">testloader</span><span class="p">,</span>
|
|
<span class="n">dist_metric</span><span class="o">=</span><span class="n">dist_metric</span><span class="p">,</span>
|
|
<span class="n">normalize_feature</span><span class="o">=</span><span class="n">normalize_feature</span><span class="p">,</span>
|
|
<span class="n">visrank</span><span class="o">=</span><span class="n">visrank</span><span class="p">,</span>
|
|
<span class="n">visrank_topk</span><span class="o">=</span><span class="n">visrank_topk</span><span class="p">,</span>
|
|
<span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span>
|
|
<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="n">use_metric_cuhk03</span><span class="p">,</span>
|
|
<span class="n">ranks</span><span class="o">=</span><span class="n">ranks</span>
|
|
<span class="p">)</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">_save_checkpoint</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">rank1</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">)</span>
|
|
|
|
<span class="n">elapsed</span> <span class="o">=</span> <span class="nb">round</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">time_start</span><span class="p">)</span>
|
|
<span class="n">elapsed</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="n">elapsed</span><span class="p">))</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Elapsed </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">elapsed</span><span class="p">))</span>
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">writer</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
|
|
|
|
<div class="viewcode-block" id="Engine.train"><a class="viewcode-back" href="../../../pkg/engine.html#torchreid.engine.engine.Engine.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="sa">r</span><span class="sd">"""Performs training on source datasets for one epoch.</span>
|
|
|
|
<span class="sd"> This will be called every epoch in ``run()``, e.g.</span>
|
|
|
|
<span class="sd"> .. code-block:: python</span>
|
|
<span class="sd"> </span>
|
|
<span class="sd"> for epoch in range(start_epoch, max_epoch):</span>
|
|
<span class="sd"> self.train(some_arguments)</span>
|
|
|
|
<span class="sd"> .. note::</span>
|
|
<span class="sd"> </span>
|
|
<span class="sd"> This must be implemented in subclasses.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
|
|
|
|
<div class="viewcode-block" id="Engine.test"><a class="viewcode-back" href="../../../pkg/engine.html#torchreid.engine.engine.Engine.test">[docs]</a> <span class="k">def</span> <span class="nf">test</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">testloader</span><span class="p">,</span> <span class="n">dist_metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">,</span> <span class="n">normalize_feature</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
|
<span class="n">visrank</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">visrank_topk</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
|
<span class="n">ranks</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span> <span class="n">rerank</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Tests model on target datasets.</span>
|
|
|
|
<span class="sd"> .. note::</span>
|
|
|
|
<span class="sd"> This function has been called in ``run()``.</span>
|
|
|
|
<span class="sd"> .. note::</span>
|
|
|
|
<span class="sd"> The test pipeline implemented in this function suits both image- and</span>
|
|
<span class="sd"> video-reid. In general, a subclass of Engine only needs to re-implement</span>
|
|
<span class="sd"> ``_extract_features()`` and ``_parse_data_for_eval()`` (most of the time),</span>
|
|
<span class="sd"> but not a must. Please refer to the source code for more details.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">targets</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">testloader</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
|
|
|
|
<span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">targets</span><span class="p">:</span>
|
|
<span class="n">domain</span> <span class="o">=</span> <span class="s1">'source'</span> <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">datamanager</span><span class="o">.</span><span class="n">sources</span> <span class="k">else</span> <span class="s1">'target'</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'##### Evaluating </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">name</span><span class="p">,</span> <span class="n">domain</span><span class="p">))</span>
|
|
<span class="n">queryloader</span> <span class="o">=</span> <span class="n">testloader</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">'query'</span><span class="p">]</span>
|
|
<span class="n">galleryloader</span> <span class="o">=</span> <span class="n">testloader</span><span class="p">[</span><span class="n">name</span><span class="p">][</span><span class="s1">'gallery'</span><span class="p">]</span>
|
|
<span class="n">rank1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_evaluate</span><span class="p">(</span>
|
|
<span class="n">epoch</span><span class="p">,</span>
|
|
<span class="n">dataset_name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
|
|
<span class="n">queryloader</span><span class="o">=</span><span class="n">queryloader</span><span class="p">,</span>
|
|
<span class="n">galleryloader</span><span class="o">=</span><span class="n">galleryloader</span><span class="p">,</span>
|
|
<span class="n">dist_metric</span><span class="o">=</span><span class="n">dist_metric</span><span class="p">,</span>
|
|
<span class="n">normalize_feature</span><span class="o">=</span><span class="n">normalize_feature</span><span class="p">,</span>
|
|
<span class="n">visrank</span><span class="o">=</span><span class="n">visrank</span><span class="p">,</span>
|
|
<span class="n">visrank_topk</span><span class="o">=</span><span class="n">visrank_topk</span><span class="p">,</span>
|
|
<span class="n">save_dir</span><span class="o">=</span><span class="n">save_dir</span><span class="p">,</span>
|
|
<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="n">use_metric_cuhk03</span><span class="p">,</span>
|
|
<span class="n">ranks</span><span class="o">=</span><span class="n">ranks</span><span class="p">,</span>
|
|
<span class="n">rerank</span><span class="o">=</span><span class="n">rerank</span>
|
|
<span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">rank1</span></div>
|
|
|
|
<span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
|
|
<span class="k">def</span> <span class="nf">_evaluate</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">dataset_name</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">queryloader</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">galleryloader</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
|
|
<span class="n">dist_metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">,</span> <span class="n">normalize_feature</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">visrank</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
|
<span class="n">visrank_topk</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">save_dir</span><span class="o">=</span><span class="s1">''</span><span class="p">,</span> <span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ranks</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span>
|
|
<span class="n">rerank</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
|
<span class="n">batch_time</span> <span class="o">=</span> <span class="n">AverageMeter</span><span class="p">()</span>
|
|
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Extracting features from query set ...'</span><span class="p">)</span>
|
|
<span class="n">qf</span><span class="p">,</span> <span class="n">q_pids</span><span class="p">,</span> <span class="n">q_camids</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span> <span class="c1"># query features, query person IDs and query camera IDs</span>
|
|
<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">queryloader</span><span class="p">):</span>
|
|
<span class="n">imgs</span><span class="p">,</span> <span class="n">pids</span><span class="p">,</span> <span class="n">camids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_data_for_eval</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">:</span>
|
|
<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>
|
|
<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>
|
|
<span class="n">features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extract_features</span><span class="p">(</span><span class="n">imgs</span><span class="p">)</span>
|
|
<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>
|
|
<span class="n">features</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
|
|
<span class="n">qf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
|
|
<span class="n">q_pids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">pids</span><span class="p">)</span>
|
|
<span class="n">q_camids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">camids</span><span class="p">)</span>
|
|
<span class="n">qf</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">qf</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
|
<span class="n">q_pids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">q_pids</span><span class="p">)</span>
|
|
<span class="n">q_camids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">q_camids</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Done, obtained </span><span class="si">{}</span><span class="s1">-by-</span><span class="si">{}</span><span class="s1"> matrix'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">qf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">qf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
|
|
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Extracting features from gallery set ...'</span><span class="p">)</span>
|
|
<span class="n">gf</span><span class="p">,</span> <span class="n">g_pids</span><span class="p">,</span> <span class="n">g_camids</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span> <span class="c1"># gallery features, gallery person IDs and gallery camera IDs</span>
|
|
<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">galleryloader</span><span class="p">):</span>
|
|
<span class="n">imgs</span><span class="p">,</span> <span class="n">pids</span><span class="p">,</span> <span class="n">camids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_parse_data_for_eval</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">:</span>
|
|
<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>
|
|
<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>
|
|
<span class="n">features</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extract_features</span><span class="p">(</span><span class="n">imgs</span><span class="p">)</span>
|
|
<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>
|
|
<span class="n">features</span> <span class="o">=</span> <span class="n">features</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
|
|
<span class="n">gf</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">features</span><span class="p">)</span>
|
|
<span class="n">g_pids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">pids</span><span class="p">)</span>
|
|
<span class="n">g_camids</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">camids</span><span class="p">)</span>
|
|
<span class="n">gf</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">gf</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
|
<span class="n">g_pids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">g_pids</span><span class="p">)</span>
|
|
<span class="n">g_camids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">g_camids</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Done, obtained </span><span class="si">{}</span><span class="s1">-by-</span><span class="si">{}</span><span class="s1"> matrix'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">gf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">gf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span>
|
|
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Speed: </span><span class="si">{:.4f}</span><span class="s1"> sec/batch'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">batch_time</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
|
|
|
|
<span class="k">if</span> <span class="n">normalize_feature</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Normalzing features with L2 norm ...'</span><span class="p">)</span>
|
|
<span class="n">qf</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">qf</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
|
<span class="n">gf</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">gf</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
|
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Computing distance matrix with metric=</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">dist_metric</span><span class="p">))</span>
|
|
<span class="n">distmat</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">compute_distance_matrix</span><span class="p">(</span><span class="n">qf</span><span class="p">,</span> <span class="n">gf</span><span class="p">,</span> <span class="n">dist_metric</span><span class="p">)</span>
|
|
<span class="n">distmat</span> <span class="o">=</span> <span class="n">distmat</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
|
|
|
<span class="k">if</span> <span class="n">rerank</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Applying person re-ranking ...'</span><span class="p">)</span>
|
|
<span class="n">distmat_qq</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">compute_distance_matrix</span><span class="p">(</span><span class="n">qf</span><span class="p">,</span> <span class="n">qf</span><span class="p">,</span> <span class="n">dist_metric</span><span class="p">)</span>
|
|
<span class="n">distmat_gg</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">compute_distance_matrix</span><span class="p">(</span><span class="n">gf</span><span class="p">,</span> <span class="n">gf</span><span class="p">,</span> <span class="n">dist_metric</span><span class="p">)</span>
|
|
<span class="n">distmat</span> <span class="o">=</span> <span class="n">re_ranking</span><span class="p">(</span><span class="n">distmat</span><span class="p">,</span> <span class="n">distmat_qq</span><span class="p">,</span> <span class="n">distmat_gg</span><span class="p">)</span>
|
|
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Computing CMC and mAP ...'</span><span class="p">)</span>
|
|
<span class="n">cmc</span><span class="p">,</span> <span class="n">mAP</span> <span class="o">=</span> <span class="n">metrics</span><span class="o">.</span><span class="n">evaluate_rank</span><span class="p">(</span>
|
|
<span class="n">distmat</span><span class="p">,</span>
|
|
<span class="n">q_pids</span><span class="p">,</span>
|
|
<span class="n">g_pids</span><span class="p">,</span>
|
|
<span class="n">q_camids</span><span class="p">,</span>
|
|
<span class="n">g_camids</span><span class="p">,</span>
|
|
<span class="n">use_metric_cuhk03</span><span class="o">=</span><span class="n">use_metric_cuhk03</span>
|
|
<span class="p">)</span>
|
|
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'** Results **'</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'mAP: </span><span class="si">{:.1%}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mAP</span><span class="p">))</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'CMC curve'</span><span class="p">)</span>
|
|
<span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">ranks</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Rank-</span><span class="si">{:<3}</span><span class="s1">: </span><span class="si">{:.1%}</span><span class="s1">'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">cmc</span><span class="p">[</span><span class="n">r</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
|
|
|
|
<span class="k">if</span> <span class="n">visrank</span><span class="p">:</span>
|
|
<span class="n">visualize_ranked_results</span><span class="p">(</span>
|
|
<span class="n">distmat</span><span class="p">,</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">datamanager</span><span class="o">.</span><span class="n">return_testdataset_by_name</span><span class="p">(</span><span class="n">dataset_name</span><span class="p">),</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">datamanager</span><span class="o">.</span><span class="n">data_type</span><span class="p">,</span>
|
|
<span class="n">width</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">width</span><span class="p">,</span>
|
|
<span class="n">height</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">height</span><span class="p">,</span>
|
|
<span class="n">save_dir</span><span class="o">=</span><span class="n">osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'visrank_'</span><span class="o">+</span><span class="n">dataset_name</span><span class="p">),</span>
|
|
<span class="n">topk</span><span class="o">=</span><span class="n">visrank_topk</span>
|
|
<span class="p">)</span>
|
|
|
|
<span class="k">return</span> <span class="n">cmc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
|
|
|
<div class="viewcode-block" id="Engine.visactmap"><a class="viewcode-back" href="../../../pkg/engine.html#torchreid.engine.engine.Engine.visactmap">[docs]</a> <span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
|
|
<span class="k">def</span> <span class="nf">visactmap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">testloader</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">,</span> <span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">,</span> <span class="n">print_freq</span><span class="p">):</span>
|
|
<span class="sd">"""Visualizes CNN activation maps to see where the CNN focuses on to extract features.</span>
|
|
|
|
<span class="sd"> This function takes as input the query images of target datasets</span>
|
|
|
|
<span class="sd"> Reference:</span>
|
|
<span class="sd"> - Zagoruyko and Komodakis. Paying more attention to attention: Improving the</span>
|
|
<span class="sd"> performance of convolutional neural networks via attention transfer. ICLR, 2017</span>
|
|
<span class="sd"> - Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ICCV, 2019.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
|
|
|
<span class="n">imagenet_mean</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">]</span>
|
|
<span class="n">imagenet_std</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">]</span>
|
|
|
|
<span class="k">for</span> <span class="n">target</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">testloader</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
|
|
<span class="n">queryloader</span> <span class="o">=</span> <span class="n">testloader</span><span class="p">[</span><span class="n">target</span><span class="p">][</span><span class="s1">'query'</span><span class="p">]</span>
|
|
<span class="c1"># original images and activation maps are saved individually</span>
|
|
<span class="n">actmap_dir</span> <span class="o">=</span> <span class="n">osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">save_dir</span><span class="p">,</span> <span class="s1">'actmap_'</span><span class="o">+</span><span class="n">target</span><span class="p">)</span>
|
|
<span class="n">mkdir_if_missing</span><span class="p">(</span><span class="n">actmap_dir</span><span class="p">)</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Visualizing activation maps for </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">target</span><span class="p">))</span>
|
|
|
|
<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">queryloader</span><span class="p">):</span>
|
|
<span class="n">imgs</span><span class="p">,</span> <span class="n">paths</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">:</span>
|
|
<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>
|
|
|
|
<span class="c1"># forward to get convolutional feature maps</span>
|
|
<span class="k">try</span><span class="p">:</span>
|
|
<span class="n">outputs</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> <span class="n">return_featuremaps</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
|
<span class="k">except</span> <span class="ne">TypeError</span><span class="p">:</span>
|
|
<span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s1">'forward() got unexpected keyword argument "return_featuremaps". '</span> \
|
|
<span class="s1">'Please add return_featuremaps as an input argument to forward(). When '</span> \
|
|
<span class="s1">'return_featuremaps=True, return feature maps only.'</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="n">outputs</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">4</span><span class="p">:</span>
|
|
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'The model output is supposed to have '</span> \
|
|
<span class="s1">'shape of (b, c, h, w), i.e. 4 dimensions, but got </span><span class="si">{}</span><span class="s1"> dimensions. '</span>
|
|
<span class="s1">'Please make sure you set the model output at eval mode '</span>
|
|
<span class="s1">'to be the last convolutional feature maps'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
|
|
|
|
<span class="c1"># compute activation maps</span>
|
|
<span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">outputs</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
|
<span class="n">b</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
|
|
<span class="n">outputs</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">h</span><span class="o">*</span><span class="n">w</span><span class="p">)</span>
|
|
<span class="n">outputs</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">p</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
|
|
<span class="n">outputs</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_gpu</span><span class="p">:</span>
|
|
<span class="n">imgs</span><span class="p">,</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">cpu</span><span class="p">(),</span> <span class="n">outputs</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
|
|
|
|
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">outputs</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)):</span>
|
|
<span class="c1"># get image name</span>
|
|
<span class="n">path</span> <span class="o">=</span> <span class="n">paths</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
|
|
<span class="n">imname</span> <span class="o">=</span> <span class="n">osp</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">osp</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">path</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
|
|
|
|
<span class="c1"># RGB image</span>
|
|
<span class="n">img</span> <span class="o">=</span> <span class="n">imgs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
|
|
<span class="k">for</span> <span class="n">t</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">imagenet_mean</span><span class="p">,</span> <span class="n">imagenet_std</span><span class="p">):</span>
|
|
<span class="n">t</span><span class="o">.</span><span class="n">mul_</span><span class="p">(</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">add_</span><span class="p">(</span><span class="n">m</span><span class="p">)</span><span class="o">.</span><span class="n">clamp_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
|
|
<span class="n">img_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="o">*</span> <span class="mi">255</span><span class="p">))</span>
|
|
<span class="n">img_np</span> <span class="o">=</span> <span class="n">img_np</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span> <span class="c1"># (c, h, w) -> (h, w, c)</span>
|
|
|
|
<span class="c1"># activation map</span>
|
|
<span class="n">am</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="n">j</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
|
|
<span class="n">am</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">am</span><span class="p">,</span> <span class="p">(</span><span class="n">width</span><span class="p">,</span> <span class="n">height</span><span class="p">))</span>
|
|
<span class="n">am</span> <span class="o">=</span> <span class="mi">255</span> <span class="o">*</span> <span class="p">(</span><span class="n">am</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">am</span><span class="p">))</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">am</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">am</span><span class="p">)</span> <span class="o">+</span> <span class="mf">1e-12</span><span class="p">)</span>
|
|
<span class="n">am</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">am</span><span class="p">))</span>
|
|
<span class="n">am</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">applyColorMap</span><span class="p">(</span><span class="n">am</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLORMAP_JET</span><span class="p">)</span>
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<span class="c1"># overlapped</span>
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<span class="n">overlapped</span> <span class="o">=</span> <span class="n">img_np</span> <span class="o">*</span> <span class="mf">0.3</span> <span class="o">+</span> <span class="n">am</span> <span class="o">*</span> <span class="mf">0.7</span>
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<span class="n">overlapped</span><span class="p">[</span><span class="n">overlapped</span><span class="o">></span><span class="mi">255</span><span class="p">]</span> <span class="o">=</span> <span class="mi">255</span>
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<span class="n">overlapped</span> <span class="o">=</span> <span class="n">overlapped</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
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<span class="c1"># save images in a single figure (add white spacing between images)</span>
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<span class="c1"># from left to right: original image, activation map, overlapped image</span>
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<span class="n">grid_img</span> <span class="o">=</span> <span class="mi">255</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">height</span><span class="p">,</span> <span class="mi">3</span><span class="o">*</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">GRID_SPACING</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
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<span class="n">grid_img</span><span class="p">[:,</span> <span class="p">:</span><span class="n">width</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">img_np</span><span class="p">[:,</span> <span class="p">:,</span> <span class="p">::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
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<span class="n">grid_img</span><span class="p">[:,</span> <span class="n">width</span><span class="o">+</span><span class="n">GRID_SPACING</span><span class="p">:</span> <span class="mi">2</span><span class="o">*</span><span class="n">width</span><span class="o">+</span><span class="n">GRID_SPACING</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">am</span>
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<span class="n">grid_img</span><span class="p">[:,</span> <span class="mi">2</span><span class="o">*</span><span class="n">width</span><span class="o">+</span><span class="mi">2</span><span class="o">*</span><span class="n">GRID_SPACING</span><span class="p">:,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">overlapped</span>
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<span class="n">cv2</span><span class="o">.</span><span class="n">imwrite</span><span class="p">(</span><span class="n">osp</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">actmap_dir</span><span class="p">,</span> <span class="n">imname</span><span class="o">+</span><span class="s1">'.jpg'</span><span class="p">),</span> <span class="n">grid_img</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="nb">print</span><span class="p">(</span><span class="s1">'- done batch </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">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">queryloader</span><span class="p">)))</span></div>
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<span class="k">def</span> <span class="nf">_compute_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">):</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
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<span class="n">loss</span> <span class="o">=</span> <span class="n">DeepSupervision</span><span class="p">(</span><span class="n">criterion</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">targets</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">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">loss</span>
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<span class="k">def</span> <span class="nf">_extract_features</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</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">eval</span><span class="p">()</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">_parse_data_for_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
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<span class="n">imgs</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<span class="n">pids</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
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<span class="k">return</span> <span class="n">imgs</span><span class="p">,</span> <span class="n">pids</span>
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<span class="k">def</span> <span class="nf">_parse_data_for_eval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
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<span class="n">imgs</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
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<span class="n">pids</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
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<span class="n">camids</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
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<span class="k">return</span> <span class="n">imgs</span><span class="p">,</span> <span class="n">pids</span><span class="p">,</span> <span class="n">camids</span>
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<span class="k">def</span> <span class="nf">_save_checkpoint</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">rank1</span><span class="p">,</span> <span class="n">save_dir</span><span class="p">,</span> <span class="n">is_best</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
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<span class="n">save_checkpoint</span><span class="p">({</span>
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|
<span class="s1">'state_dict'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
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<span class="s1">'epoch'</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="s1">'rank1'</span><span class="p">:</span> <span class="n">rank1</span><span class="p">,</span>
|
|
<span class="s1">'optimizer'</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span>
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<span class="p">},</span> <span class="n">save_dir</span><span class="p">,</span> <span class="n">is_best</span><span class="o">=</span><span class="n">is_best</span><span class="p">)</span></div>
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</pre></div>
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