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<h1>Source code for torchreid.utils.torchtools</h1><div class="highlight"><pre>
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<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">absolute_import</span>
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<span class="kn">import</span> <span class="nn">pickle</span>
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<span class="kn">import</span> <span class="nn">shutil</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">warnings</span>
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<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span>
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<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</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">.tools</span> <span class="kn">import</span> <span class="n">mkdir_if_missing</span>
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<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
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<span class="s1">'save_checkpoint'</span><span class="p">,</span> <span class="s1">'load_checkpoint'</span><span class="p">,</span> <span class="s1">'resume_from_checkpoint'</span><span class="p">,</span>
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<span class="s1">'open_all_layers'</span><span class="p">,</span> <span class="s1">'open_specified_layers'</span><span class="p">,</span> <span class="s1">'count_num_param'</span><span class="p">,</span>
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<span class="s1">'load_pretrained_weights'</span>
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<span class="p">]</span>
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<div class="viewcode-block" id="save_checkpoint"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.save_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">save_checkpoint</span><span class="p">(</span>
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<span class="n">state</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> <span class="n">remove_module_from_keys</span><span class="o">=</span><span class="kc">False</span>
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<span class="p">):</span>
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<span class="sa">r</span><span class="sd">"""Saves checkpoint.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> state (dict): dictionary.</span>
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<span class="sd"> save_dir (str): directory to save checkpoint.</span>
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<span class="sd"> is_best (bool, optional): if True, this checkpoint will be copied and named</span>
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<span class="sd"> ``model-best.pth.tar``. Default is False.</span>
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<span class="sd"> remove_module_from_keys (bool, optional): whether to remove "module."</span>
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<span class="sd"> from layer names. Default is False.</span>
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<span class="sd"> Examples::</span>
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<span class="sd"> >>> state = {</span>
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<span class="sd"> >>> 'state_dict': model.state_dict(),</span>
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<span class="sd"> >>> 'epoch': 10,</span>
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<span class="sd"> >>> 'rank1': 0.5,</span>
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<span class="sd"> >>> 'optimizer': optimizer.state_dict()</span>
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<span class="sd"> >>> }</span>
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<span class="sd"> >>> save_checkpoint(state, 'log/my_model')</span>
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<span class="sd"> """</span>
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<span class="n">mkdir_if_missing</span><span class="p">(</span><span class="n">save_dir</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">remove_module_from_keys</span><span class="p">:</span>
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<span class="c1"># remove 'module.' in state_dict's keys</span>
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<span class="n">state_dict</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="s1">'state_dict'</span><span class="p">]</span>
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<span class="n">new_state_dict</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
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<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
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<span class="k">if</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'module.'</span><span class="p">):</span>
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<span class="n">k</span> <span class="o">=</span> <span class="n">k</span><span class="p">[</span><span class="mi">7</span><span class="p">:]</span>
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<span class="n">new_state_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
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<span class="n">state</span><span class="p">[</span><span class="s1">'state_dict'</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_state_dict</span>
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<span class="c1"># save</span>
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<span class="n">epoch</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="s1">'epoch'</span><span class="p">]</span>
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<span class="n">fpath</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">'model.pth.tar-'</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">epoch</span><span class="p">))</span>
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<span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">state</span><span class="p">,</span> <span class="n">fpath</span><span class="p">)</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'Checkpoint saved to "</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">fpath</span><span class="p">))</span>
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<span class="k">if</span> <span class="n">is_best</span><span class="p">:</span>
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<span class="n">shutil</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">fpath</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">osp</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">fpath</span><span class="p">),</span> <span class="s1">'model-best.pth.tar'</span><span class="p">))</span></div>
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<div class="viewcode-block" id="load_checkpoint"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.load_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">load_checkpoint</span><span class="p">(</span><span class="n">fpath</span><span class="p">):</span>
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<span class="sa">r</span><span class="sd">"""Loads checkpoint.</span>
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<span class="sd"> ``UnicodeDecodeError`` can be well handled, which means</span>
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<span class="sd"> python2-saved files can be read from python3.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> fpath (str): path to checkpoint.</span>
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<span class="sd"> Returns:</span>
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<span class="sd"> dict</span>
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<span class="sd"> Examples::</span>
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|
<span class="sd"> >>> from torchreid.utils import load_checkpoint</span>
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<span class="sd"> >>> fpath = 'log/my_model/model.pth.tar-10'</span>
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<span class="sd"> >>> checkpoint = load_checkpoint(fpath)</span>
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<span class="sd"> """</span>
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<span class="k">if</span> <span class="n">fpath</span> <span class="ow">is</span> <span class="kc">None</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">'File path is None'</span><span class="p">)</span>
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<span class="k">if</span> <span class="ow">not</span> <span class="n">osp</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">fpath</span><span class="p">):</span>
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<span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="s1">'File is not found at "</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">fpath</span><span class="p">))</span>
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<span class="n">map_location</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</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="k">else</span> <span class="s1">'cpu'</span>
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<span class="k">try</span><span class="p">:</span>
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<span class="n">checkpoint</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">fpath</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">map_location</span><span class="p">)</span>
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<span class="k">except</span> <span class="ne">UnicodeDecodeError</span><span class="p">:</span>
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<span class="n">pickle</span><span class="o">.</span><span class="n">load</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"latin1"</span><span class="p">)</span>
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<span class="n">pickle</span><span class="o">.</span><span class="n">Unpickler</span> <span class="o">=</span> <span class="n">partial</span><span class="p">(</span><span class="n">pickle</span><span class="o">.</span><span class="n">Unpickler</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">"latin1"</span><span class="p">)</span>
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<span class="n">checkpoint</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span>
|
|
<span class="n">fpath</span><span class="p">,</span> <span class="n">pickle_module</span><span class="o">=</span><span class="n">pickle</span><span class="p">,</span> <span class="n">map_location</span><span class="o">=</span><span class="n">map_location</span>
|
|
<span class="p">)</span>
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|
<span class="k">except</span> <span class="ne">Exception</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Unable to load checkpoint from "</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">fpath</span><span class="p">))</span>
|
|
<span class="k">raise</span>
|
|
<span class="k">return</span> <span class="n">checkpoint</span></div>
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|
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<div class="viewcode-block" id="resume_from_checkpoint"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.resume_from_checkpoint">[docs]</a><span class="k">def</span> <span class="nf">resume_from_checkpoint</span><span class="p">(</span><span class="n">fpath</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="sa">r</span><span class="sd">"""Resumes training from a checkpoint.</span>
|
|
|
|
<span class="sd"> This will load (1) model weights and (2) ``state_dict``</span>
|
|
<span class="sd"> of optimizer if ``optimizer`` is not None.</span>
|
|
|
|
<span class="sd"> Args:</span>
|
|
<span class="sd"> fpath (str): path to checkpoint.</span>
|
|
<span class="sd"> model (nn.Module): model.</span>
|
|
<span class="sd"> optimizer (Optimizer, optional): an Optimizer.</span>
|
|
<span class="sd"> scheduler (LRScheduler, optional): an LRScheduler.</span>
|
|
|
|
<span class="sd"> Returns:</span>
|
|
<span class="sd"> int: start_epoch.</span>
|
|
|
|
<span class="sd"> Examples::</span>
|
|
<span class="sd"> >>> from torchreid.utils import resume_from_checkpoint</span>
|
|
<span class="sd"> >>> fpath = 'log/my_model/model.pth.tar-10'</span>
|
|
<span class="sd"> >>> start_epoch = resume_from_checkpoint(</span>
|
|
<span class="sd"> >>> fpath, model, optimizer, scheduler</span>
|
|
<span class="sd"> >>> )</span>
|
|
<span class="sd"> """</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Loading checkpoint from "</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">fpath</span><span class="p">))</span>
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|
<span class="n">checkpoint</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">fpath</span><span class="p">)</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">'state_dict'</span><span class="p">])</span>
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<span class="nb">print</span><span class="p">(</span><span class="s1">'Loaded model weights'</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">optimizer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">'optimizer'</span> <span class="ow">in</span> <span class="n">checkpoint</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
|
<span class="n">optimizer</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">'optimizer'</span><span class="p">])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Loaded optimizer'</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="n">scheduler</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="s1">'scheduler'</span> <span class="ow">in</span> <span class="n">checkpoint</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
|
<span class="n">scheduler</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">checkpoint</span><span class="p">[</span><span class="s1">'scheduler'</span><span class="p">])</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Loaded scheduler'</span><span class="p">)</span>
|
|
<span class="n">start_epoch</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">'epoch'</span><span class="p">]</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Last epoch = </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">start_epoch</span><span class="p">))</span>
|
|
<span class="k">if</span> <span class="s1">'rank1'</span> <span class="ow">in</span> <span class="n">checkpoint</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
|
<span class="nb">print</span><span class="p">(</span><span class="s1">'Last rank1 = </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">checkpoint</span><span class="p">[</span><span class="s1">'rank1'</span><span class="p">]))</span>
|
|
<span class="k">return</span> <span class="n">start_epoch</span></div>
|
|
|
|
|
|
<span class="k">def</span> <span class="nf">adjust_learning_rate</span><span class="p">(</span>
|
|
<span class="n">optimizer</span><span class="p">,</span>
|
|
<span class="n">base_lr</span><span class="p">,</span>
|
|
<span class="n">epoch</span><span class="p">,</span>
|
|
<span class="n">stepsize</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span>
|
|
<span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
|
|
<span class="n">linear_decay</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
|
<span class="n">final_lr</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
|
|
<span class="n">max_epoch</span><span class="o">=</span><span class="mi">100</span>
|
|
<span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Adjusts learning rate.</span>
|
|
|
|
<span class="sd"> Deprecated.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="n">linear_decay</span><span class="p">:</span>
|
|
<span class="c1"># linearly decay learning rate from base_lr to final_lr</span>
|
|
<span class="n">frac_done</span> <span class="o">=</span> <span class="n">epoch</span> <span class="o">/</span> <span class="n">max_epoch</span>
|
|
<span class="n">lr</span> <span class="o">=</span> <span class="n">frac_done</span><span class="o">*</span><span class="n">final_lr</span> <span class="o">+</span> <span class="p">(</span><span class="mf">1.</span><span class="o">-</span><span class="n">frac_done</span><span class="p">)</span> <span class="o">*</span> <span class="n">base_lr</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="c1"># decay learning rate by gamma for every stepsize</span>
|
|
<span class="n">lr</span> <span class="o">=</span> <span class="n">base_lr</span> <span class="o">*</span> <span class="p">(</span><span class="n">gamma</span><span class="o">**</span><span class="p">(</span><span class="n">epoch</span> <span class="o">//</span> <span class="n">stepsize</span><span class="p">))</span>
|
|
|
|
<span class="k">for</span> <span class="n">param_group</span> <span class="ow">in</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">param_groups</span><span class="p">:</span>
|
|
<span class="n">param_group</span><span class="p">[</span><span class="s1">'lr'</span><span class="p">]</span> <span class="o">=</span> <span class="n">lr</span>
|
|
|
|
|
|
<span class="k">def</span> <span class="nf">set_bn_to_eval</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Sets BatchNorm layers to eval mode."""</span>
|
|
<span class="c1"># 1. no update for running mean and var</span>
|
|
<span class="c1"># 2. scale and shift parameters are still trainable</span>
|
|
<span class="n">classname</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
|
|
<span class="k">if</span> <span class="n">classname</span><span class="o">.</span><span class="n">find</span><span class="p">(</span><span class="s1">'BatchNorm'</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
|
|
<span class="n">m</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
|
|
|
|
|
<div class="viewcode-block" id="open_all_layers"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.open_all_layers">[docs]</a><span class="k">def</span> <span class="nf">open_all_layers</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Opens all layers in model for training.</span>
|
|
|
|
<span class="sd"> Examples::</span>
|
|
<span class="sd"> >>> from torchreid.utils import open_all_layers</span>
|
|
<span class="sd"> >>> open_all_layers(model)</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
|
|
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
|
|
<span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span></div>
|
|
|
|
|
|
<div class="viewcode-block" id="open_specified_layers"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.open_specified_layers">[docs]</a><span class="k">def</span> <span class="nf">open_specified_layers</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">open_layers</span><span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Opens specified layers in model for training while keeping</span>
|
|
<span class="sd"> other layers frozen.</span>
|
|
|
|
<span class="sd"> Args:</span>
|
|
<span class="sd"> model (nn.Module): neural net model.</span>
|
|
<span class="sd"> open_layers (str or list): layers open for training.</span>
|
|
|
|
<span class="sd"> Examples::</span>
|
|
<span class="sd"> >>> from torchreid.utils import open_specified_layers</span>
|
|
<span class="sd"> >>> # Only model.classifier will be updated.</span>
|
|
<span class="sd"> >>> open_layers = 'classifier'</span>
|
|
<span class="sd"> >>> open_specified_layers(model, open_layers)</span>
|
|
<span class="sd"> >>> # Only model.fc and model.classifier will be updated.</span>
|
|
<span class="sd"> >>> open_layers = ['fc', 'classifier']</span>
|
|
<span class="sd"> >>> open_specified_layers(model, open_layers)</span>
|
|
<span class="sd"> """</span>
|
|
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">):</span>
|
|
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">module</span>
|
|
|
|
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">open_layers</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
|
|
<span class="n">open_layers</span> <span class="o">=</span> <span class="p">[</span><span class="n">open_layers</span><span class="p">]</span>
|
|
|
|
<span class="k">for</span> <span class="n">layer</span> <span class="ow">in</span> <span class="n">open_layers</span><span class="p">:</span>
|
|
<span class="k">assert</span> <span class="nb">hasattr</span><span class="p">(</span>
|
|
<span class="n">model</span><span class="p">,</span> <span class="n">layer</span>
|
|
<span class="p">),</span> <span class="s1">'"</span><span class="si">{}</span><span class="s1">" is not an attribute of the model, please provide the correct name'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
|
|
<span class="n">layer</span>
|
|
<span class="p">)</span>
|
|
|
|
<span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
|
|
<span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">open_layers</span><span class="p">:</span>
|
|
<span class="n">module</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
|
|
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
|
|
<span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
|
|
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
|
|
<span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">False</span></div>
|
|
|
|
|
|
<div class="viewcode-block" id="count_num_param"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.count_num_param">[docs]</a><span class="k">def</span> <span class="nf">count_num_param</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Counts number of parameters in a model while ignoring ``self.classifier``.</span>
|
|
|
|
<span class="sd"> Args:</span>
|
|
<span class="sd"> model (nn.Module): network model.</span>
|
|
|
|
<span class="sd"> Examples::</span>
|
|
<span class="sd"> >>> from torchreid.utils import count_num_param</span>
|
|
<span class="sd"> >>> model_size = count_num_param(model)</span>
|
|
|
|
<span class="sd"> .. warning::</span>
|
|
<span class="sd"> </span>
|
|
<span class="sd"> This method is deprecated in favor of</span>
|
|
<span class="sd"> ``torchreid.utils.compute_model_complexity``.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
|
|
<span class="s1">'This method is deprecated and will be removed in the future.'</span>
|
|
<span class="p">)</span>
|
|
|
|
<span class="n">num_param</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
|
|
|
|
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">):</span>
|
|
<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">module</span>
|
|
|
|
<span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">model</span><span class="p">,</span>
|
|
<span class="s1">'classifier'</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
|
|
<span class="c1"># we ignore the classifier because it is unused at test time</span>
|
|
<span class="n">num_param</span> <span class="o">-=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">classifier</span><span class="o">.</span><span class="n">parameters</span><span class="p">())</span>
|
|
|
|
<span class="k">return</span> <span class="n">num_param</span></div>
|
|
|
|
|
|
<div class="viewcode-block" id="load_pretrained_weights"><a class="viewcode-back" href="../../../pkg/utils.html#torchreid.utils.torchtools.load_pretrained_weights">[docs]</a><span class="k">def</span> <span class="nf">load_pretrained_weights</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">weight_path</span><span class="p">):</span>
|
|
<span class="sa">r</span><span class="sd">"""Loads pretrianed weights to model.</span>
|
|
|
|
<span class="sd"> Features::</span>
|
|
<span class="sd"> - Incompatible layers (unmatched in name or size) will be ignored.</span>
|
|
<span class="sd"> - Can automatically deal with keys containing "module.".</span>
|
|
|
|
<span class="sd"> Args:</span>
|
|
<span class="sd"> model (nn.Module): network model.</span>
|
|
<span class="sd"> weight_path (str): path to pretrained weights.</span>
|
|
|
|
<span class="sd"> Examples::</span>
|
|
<span class="sd"> >>> from torchreid.utils import load_pretrained_weights</span>
|
|
<span class="sd"> >>> weight_path = 'log/my_model/model-best.pth.tar'</span>
|
|
<span class="sd"> >>> load_pretrained_weights(model, weight_path)</span>
|
|
<span class="sd"> """</span>
|
|
<span class="n">checkpoint</span> <span class="o">=</span> <span class="n">load_checkpoint</span><span class="p">(</span><span class="n">weight_path</span><span class="p">)</span>
|
|
<span class="k">if</span> <span class="s1">'state_dict'</span> <span class="ow">in</span> <span class="n">checkpoint</span><span class="p">:</span>
|
|
<span class="n">state_dict</span> <span class="o">=</span> <span class="n">checkpoint</span><span class="p">[</span><span class="s1">'state_dict'</span><span class="p">]</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="n">state_dict</span> <span class="o">=</span> <span class="n">checkpoint</span>
|
|
|
|
<span class="n">model_dict</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
|
|
<span class="n">new_state_dict</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
|
|
<span class="n">matched_layers</span><span class="p">,</span> <span class="n">discarded_layers</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
|
|
|
|
<span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">state_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
|
|
<span class="k">if</span> <span class="n">k</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s1">'module.'</span><span class="p">):</span>
|
|
<span class="n">k</span> <span class="o">=</span> <span class="n">k</span><span class="p">[</span><span class="mi">7</span><span class="p">:]</span> <span class="c1"># discard module.</span>
|
|
|
|
<span class="k">if</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model_dict</span> <span class="ow">and</span> <span class="n">model_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()</span> <span class="o">==</span> <span class="n">v</span><span class="o">.</span><span class="n">size</span><span class="p">():</span>
|
|
<span class="n">new_state_dict</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
|
|
<span class="n">matched_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="n">discarded_layers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
|
|
|
|
<span class="n">model_dict</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">new_state_dict</span><span class="p">)</span>
|
|
<span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">model_dict</span><span class="p">)</span>
|
|
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">matched_layers</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
|
<span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
|
|
<span class="s1">'The pretrained weights "</span><span class="si">{}</span><span class="s1">" cannot be loaded, '</span>
|
|
<span class="s1">'please check the key names manually '</span>
|
|
<span class="s1">'(** ignored and continue **)'</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">weight_path</span><span class="p">)</span>
|
|
<span class="p">)</span>
|
|
<span class="k">else</span><span class="p">:</span>
|
|
<span class="nb">print</span><span class="p">(</span>
|
|
<span class="s1">'Successfully loaded pretrained weights from "</span><span class="si">{}</span><span class="s1">"'</span><span class="o">.</span>
|
|
<span class="nb">format</span><span class="p">(</span><span class="n">weight_path</span><span class="p">)</span>
|
|
<span class="p">)</span>
|
|
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">discarded_layers</span><span class="p">)</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">'** The following layers are discarded '</span>
|
|
<span class="s1">'due to unmatched keys or layer size: </span><span class="si">{}</span><span class="s1">'</span><span class="o">.</span>
|
|
<span class="nb">format</span><span class="p">(</span><span class="n">discarded_layers</span><span class="p">)</span>
|
|
<span class="p">)</span></div>
|
|
</pre></div>
|
|
|
|
</div>
|
|
|
|
</div>
|
|
<footer>
|
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<hr/>
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<div role="contentinfo">
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<p>
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