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<div class="section" id="torch-utils-tensorboard">
<h1>torch.utils.tensorboard<a class="headerlink" href="#torch-utils-tensorboard" title="Permalink to this headline">¶</a></h1>
<p>Before going further, more details on TensorBoard can be found at
<a class="reference external" href="https://www.tensorflow.org/tensorboard/">https://www.tensorflow.org/tensorboard/</a></p>
<p>Once you’ve installed TensorBoard, these utilities let you log PyTorch models
and metrics into a directory for visualization within the TensorBoard UI.
Scalars, images, histograms, graphs, and embedding visualizations are all
supported for PyTorch models and tensors as well as Caffe2 nets and blobs.</p>
<p>The SummaryWriter class is your main entry to log data for consumption
and visualization by TensorBoard. For example:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torchvision</span>
<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
<span class="c1"># Writer will output to ./runs/ directory by default</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.5</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.5</span><span class="p">,))])</span>
<span class="n">trainset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s1">'mnist_train'</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
<span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet50</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># Have ResNet model take in grayscale rather than RGB</span>
<span class="n">model</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">images</span><span class="p">,</span> <span class="n">labels</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">trainloader</span><span class="p">))</span>
<span class="n">grid</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'images'</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_graph</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">images</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>This can then be visualized with TensorBoard, which should be installable
and runnable with:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">tensorboard</span>
<span class="n">tensorboard</span> <span class="o">--</span><span class="n">logdir</span><span class="o">=</span><span class="n">runs</span>
</pre></div>
</div>
<p>Lots of information can be logged for one experiment. To avoid cluttering
the UI and have better result clustering, we can group plots by naming them
hierarchically. For example, “Loss/train” and “Loss/test” will be grouped
together, while “Accuracy/train” and “Accuracy/test” will be grouped separately
in the TensorBoard interface.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">n_iter</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Loss/train'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Loss/test'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Accuracy/train'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'Accuracy/test'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(),</span> <span class="n">n_iter</span><span class="p">)</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/hier_tags.png"><img alt="_images/hier_tags.png" src="_images/hier_tags.png" style="width: 545.25px; height: 525.75px;" /></a>
<div class="line-block">
<div class="line"><br /></div>
<div class="line"><br /></div>
</div>
<dl class="class">
<dt id="torch.utils.tensorboard.writer.SummaryWriter">
<em class="property">class </em><code class="sig-prename descclassname">torch.utils.tensorboard.writer.</code><code class="sig-name descname">SummaryWriter</code><span class="sig-paren">(</span><em class="sig-param">log_dir=None</em>, <em class="sig-param">comment=''</em>, <em class="sig-param">purge_step=None</em>, <em class="sig-param">max_queue=10</em>, <em class="sig-param">flush_secs=120</em>, <em class="sig-param">filename_suffix=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter" title="Permalink to this definition">¶</a></dt>
<dd><p>Writes entries directly to event files in the log_dir to be
consumed by TensorBoard.</p>
<p>The <cite>SummaryWriter</cite> class provides a high-level API to create an event file
in a given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.</p>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.__init__">
<code class="sig-name descname">__init__</code><span class="sig-paren">(</span><em class="sig-param">log_dir=None</em>, <em class="sig-param">comment=''</em>, <em class="sig-param">purge_step=None</em>, <em class="sig-param">max_queue=10</em>, <em class="sig-param">flush_secs=120</em>, <em class="sig-param">filename_suffix=''</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.__init__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.__init__" title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a <cite>SummaryWriter</cite> that will write out events and summaries
to the event file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>log_dir</strong> (<em>string</em>) – Save directory location. Default is
runs/<strong>CURRENT_DATETIME_HOSTNAME</strong>, which changes after each run.
Use hierarchical folder structure to compare
between runs easily. e.g. pass in ‘runs/exp1’, ‘runs/exp2’, etc.
for each new experiment to compare across them.</p></li>
<li><p><strong>comment</strong> (<em>string</em>) – Comment log_dir suffix appended to the default
<code class="docutils literal notranslate"><span class="pre">log_dir</span></code>. If <code class="docutils literal notranslate"><span class="pre">log_dir</span></code> is assigned, this argument has no effect.</p></li>
<li><p><strong>purge_step</strong> (<em>python:int</em>) – When logging crashes at step <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi><mo>+</mo><mi>X</mi></mrow><annotation encoding="application/x-tex">T+X</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.76666em;vertical-align:-0.08333em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathdefault" style="margin-right:0.07847em;">X</span></span></span></span>
</span> and restarts at step <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi></mrow><annotation encoding="application/x-tex">T</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span></span></span></span>
</span>,
any events whose global_step larger or equal to <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>T</mi></mrow><annotation encoding="application/x-tex">T</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span></span></span></span>
</span> will be
purged and hidden from TensorBoard.
Note that crashed and resumed experiments should have the same <code class="docutils literal notranslate"><span class="pre">log_dir</span></code>.</p></li>
<li><p><strong>max_queue</strong> (<em>python:int</em>) – Size of the queue for pending events and
summaries before one of the ‘add’ calls forces a flush to disk.
Default is ten items.</p></li>
<li><p><strong>flush_secs</strong> (<em>python:int</em>) – How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.</p></li>
<li><p><strong>filename_suffix</strong> (<em>string</em>) – Suffix added to all event filenames in
the log_dir directory. More details on filename construction in
tensorboard.summary.writer.event_file_writer.EventFileWriter.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="c1"># create a summary writer with automatically generated folder name.</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="c1"># folder location: runs/May04_22-14-54_s-MacBook-Pro.local/</span>
<span class="c1"># create a summary writer using the specified folder name.</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="s2">"my_experiment"</span><span class="p">)</span>
<span class="c1"># folder location: my_experiment</span>
<span class="c1"># create a summary writer with comment appended.</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">(</span><span class="n">comment</span><span class="o">=</span><span class="s2">"LR_0.1_BATCH_16"</span><span class="p">)</span>
<span class="c1"># folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_scalar">
<code class="sig-name descname">add_scalar</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">scalar_value</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalar"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalar" title="Permalink to this definition">¶</a></dt>
<dd><p>Add scalar data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>scalar_value</strong> (<em>python:float</em><em> or </em><em>string/blobname</em>) – Value to save</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
with seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">x</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalar</span><span class="p">(</span><span class="s1">'y=2x'</span><span class="p">,</span> <span class="n">i</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_scalar.png"><img alt="_images/add_scalar.png" src="_images/add_scalar.png" style="width: 312.0px; height: 238.0px;" /></a>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_scalars">
<code class="sig-name descname">add_scalars</code><span class="sig-paren">(</span><em class="sig-param">main_tag</em>, <em class="sig-param">tag_scalar_dict</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalars"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalars" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds many scalar data to summary.</p>
<p>Note that this function also keeps logged scalars in memory. In extreme case it explodes your RAM.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>main_tag</strong> (<em>string</em>) – The parent name for the tags</p></li>
<li><p><strong>tag_scalar_dict</strong> (<em>dict</em>) – Key-value pair storing the tag and corresponding values</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">r</span> <span class="o">=</span> <span class="mi">5</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_scalars</span><span class="p">(</span><span class="s1">'run_14h'</span><span class="p">,</span> <span class="p">{</span><span class="s1">'xsinx'</span><span class="p">:</span><span class="n">i</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">i</span><span class="o">/</span><span class="n">r</span><span class="p">),</span>
<span class="s1">'xcosx'</span><span class="p">:</span><span class="n">i</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">i</span><span class="o">/</span><span class="n">r</span><span class="p">),</span>
<span class="s1">'tanx'</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">tan</span><span class="p">(</span><span class="n">i</span><span class="o">/</span><span class="n">r</span><span class="p">)},</span> <span class="n">i</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
<span class="c1"># This call adds three values to the same scalar plot with the tag</span>
<span class="c1"># 'run_14h' in TensorBoard's scalar section.</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_scalars.png"><img alt="_images/add_scalars.png" src="_images/add_scalars.png" style="width: 348.0px; height: 264.0px;" /></a>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_histogram">
<code class="sig-name descname">add_histogram</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">values</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">bins='tensorflow'</em>, <em class="sig-param">walltime=None</em>, <em class="sig-param">max_bins=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_histogram" title="Permalink to this definition">¶</a></dt>
<dd><p>Add histogram to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>values</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><em>numpy.array</em><em>, or </em><em>string/blobname</em>) – Values to build histogram</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>bins</strong> (<em>string</em>) – One of {‘tensorflow’,’auto’, ‘fd’, …}. This determines how the bins are made. You can find
other options in: <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html">https://docs.scipy.org/doc/numpy/reference/generated/numpy.histogram.html</a></p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">):</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="mi">1000</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_histogram</span><span class="p">(</span><span class="s1">'distribution centers'</span><span class="p">,</span> <span class="n">x</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="n">i</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_histogram.png"><img alt="_images/add_histogram.png" src="_images/add_histogram.png" style="width: 275.0px; height: 217.0px;" /></a>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_image">
<code class="sig-name descname">add_image</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">img_tensor</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">walltime=None</em>, <em class="sig-param">dataformats='CHW'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Add image data to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>img_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><em>numpy.array</em><em>, or </em><em>string/blobname</em>) – Image data</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>img_tensor: Default is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mn>3</mn><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(3, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">3</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span>. You can use <code class="docutils literal notranslate"><span class="pre">torchvision.utils.make_grid()</span></code> to
convert a batch of tensor into 3xHxW format or call <code class="docutils literal notranslate"><span class="pre">add_images</span></code> and let us do the job.
Tensor with <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mn>1</mn><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(1, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">1</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(H, W, 3)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span>
</span> is also suitible as long as
corresponding <code class="docutils literal notranslate"><span class="pre">dataformats</span></code> argument is passed. e.g. CHW, HWC, HW.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">img</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">img_HWC</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="n">img_HWC</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">img_HWC</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'my_image'</span><span class="p">,</span> <span class="n">img</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="c1"># If you have non-default dimension setting, set the dataformats argument.</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">'my_image_HWC'</span><span class="p">,</span> <span class="n">img_HWC</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">dataformats</span><span class="o">=</span><span class="s1">'HWC'</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_image.png"><img alt="_images/add_image.png" src="_images/add_image.png" style="width: 365.0px; height: 411.0px;" /></a>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_images">
<code class="sig-name descname">add_images</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">img_tensor</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">walltime=None</em>, <em class="sig-param">dataformats='NCHW'</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_images"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_images" title="Permalink to this definition">¶</a></dt>
<dd><p>Add batched image data to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>img_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><em>numpy.array</em><em>, or </em><em>string/blobname</em>) – Image data</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
<li><p><strong>dataformats</strong> (<em>string</em>) – Image data format specification of the form
NCHW, NHWC, CHW, HWC, HW, WH, etc.</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>img_tensor: Default is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, 3, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">3</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span>. If <code class="docutils literal notranslate"><span class="pre">dataformats</span></code> is specified, other shape will be
accepted. e.g. NCHW or NHWC.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">img_batch</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">16</span><span class="p">):</span>
<span class="n">img_batch</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span> <span class="o">/</span> <span class="mi">16</span> <span class="o">*</span> <span class="n">i</span>
<span class="n">img_batch</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span> <span class="o">/</span> <span class="mi">10000</span><span class="p">)</span> <span class="o">/</span> <span class="mi">16</span> <span class="o">*</span> <span class="n">i</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_images</span><span class="p">(</span><span class="s1">'my_image_batch'</span><span class="p">,</span> <span class="n">img_batch</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_images.png"><img alt="_images/add_images.png" src="_images/add_images.png" style="width: 488.4px; height: 147.6px;" /></a>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_figure">
<code class="sig-name descname">add_figure</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">figure</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">close=True</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_figure"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_figure" title="Permalink to this definition">¶</a></dt>
<dd><p>Render matplotlib figure into an image and add it to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>figure</strong> (<em>matplotlib.pyplot.figure</em>) – Figure or a list of figures</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>close</strong> (<em>bool</em>) – Flag to automatically close the figure</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_video">
<code class="sig-name descname">add_video</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">vid_tensor</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">fps=4</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_video"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_video" title="Permalink to this definition">¶</a></dt>
<dd><p>Add video data to summary.</p>
<p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">moviepy</span></code> package.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>vid_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Video data</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>fps</strong> (<em>python:float</em><em> or </em><em>python:int</em>) – Frames per second</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>vid_tensor: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>T</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, T, C, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">T</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span>. The values should lie in [0, 255] for type <cite>uint8</cite> or [0, 1] for type <cite>float</cite>.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_audio">
<code class="sig-name descname">add_audio</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">snd_tensor</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">sample_rate=44100</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_audio"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_audio" title="Permalink to this definition">¶</a></dt>
<dd><p>Add audio data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>snd_tensor</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Sound data</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>sample_rate</strong> (<em>python:int</em>) – sample rate in Hz</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl class="simple">
<dt>Shape:</dt><dd><p>snd_tensor: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mn>1</mn><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(1, L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">1</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault">L</span><span class="mclose">)</span></span></span></span>
</span>. The values should lie between [-1, 1].</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_text">
<code class="sig-name descname">add_text</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">text_string</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_text" title="Permalink to this definition">¶</a></dt>
<dd><p>Add text data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>text_string</strong> (<em>string</em>) – String to save</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">writer</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="s1">'lstm'</span><span class="p">,</span> <span class="s1">'This is an lstm'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="s1">'rnn'</span><span class="p">,</span> <span class="s1">'This is an rnn'</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_graph">
<code class="sig-name descname">add_graph</code><span class="sig-paren">(</span><em class="sig-param">model</em>, <em class="sig-param">input_to_model=None</em>, <em class="sig-param">verbose=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_graph"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_graph" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_embedding">
<code class="sig-name descname">add_embedding</code><span class="sig-paren">(</span><em class="sig-param">mat</em>, <em class="sig-param">metadata=None</em>, <em class="sig-param">label_img=None</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">tag='default'</em>, <em class="sig-param">metadata_header=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_embedding"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_embedding" title="Permalink to this definition">¶</a></dt>
<dd><p>Add embedding projector data to summary.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mat</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em> or </em><em>numpy.array</em>) – A matrix which each row is the feature vector of the data point</p></li>
<li><p><strong>metadata</strong> (<em>list</em>) – A list of labels, each element will be convert to string</p></li>
<li><p><strong>label_img</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Images correspond to each data point</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>tag</strong> (<em>string</em>) – Name for the embedding</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Shape:</dt><dd><p>mat: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>D</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, D)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.02778em;">D</span><span class="mclose">)</span></span></span></span>
</span>, where N is number of data and D is feature dimension</p>
<p>label_img: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span>
</span></p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">keyword</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">meta</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span><span class="o"><</span><span class="mi">100</span><span class="p">:</span>
<span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span><span class="o">+</span><span class="n">keyword</span><span class="o">.</span><span class="n">kwlist</span> <span class="c1"># get some strings</span>
<span class="n">meta</span> <span class="o">=</span> <span class="n">meta</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">meta</span><span class="p">):</span>
<span class="n">meta</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">label_img</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
<span class="n">label_img</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">*=</span><span class="n">i</span><span class="o">/</span><span class="mf">100.0</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">metadata</span><span class="o">=</span><span class="n">meta</span><span class="p">,</span> <span class="n">label_img</span><span class="o">=</span><span class="n">label_img</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">label_img</span><span class="o">=</span><span class="n">label_img</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_embedding</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">metadata</span><span class="o">=</span><span class="n">meta</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve">
<code class="sig-name descname">add_pr_curve</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">labels</em>, <em class="sig-param">predictions</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">num_thresholds=127</em>, <em class="sig-param">weights=None</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_pr_curve"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve" title="Permalink to this definition">¶</a></dt>
<dd><p>Adds precision recall curve.
Plotting a precision-recall curve lets you understand your model’s
performance under different threshold settings. With this function,
you provide the ground truth labeling (T/F) and prediction confidence
(usually the output of your model) for each target. The TensorBoard UI
will let you choose the threshold interactively.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>labels</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><em>numpy.array</em><em>, or </em><em>string/blobname</em>) – Ground truth data. Binary label for each element.</p></li>
<li><p><strong>predictions</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a><em>, </em><em>numpy.array</em><em>, or </em><em>string/blobname</em>) – The probability that an element be classified as true.
Value should in [0, 1]</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>num_thresholds</strong> (<em>python:int</em>) – Number of thresholds used to draw the curve.</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="n">labels</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="c1"># binary label</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">100</span><span class="p">)</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_pr_curve</span><span class="p">(</span><span class="s1">'pr_curve'</span><span class="p">,</span> <span class="n">labels</span><span class="p">,</span> <span class="n">predictions</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars">
<code class="sig-name descname">add_custom_scalars</code><span class="sig-paren">(</span><em class="sig-param">layout</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_custom_scalars"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars" title="Permalink to this definition">¶</a></dt>
<dd><p>Create special chart by collecting charts tags in ‘scalars’. Note that this function can only be called once
for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called
before or after the training loop.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>layout</strong> (<em>dict</em>) – {categoryName: <em>charts</em>}, where <em>charts</em> is also a dictionary
{chartName: <em>ListOfProperties</em>}. The first element in <em>ListOfProperties</em> is the chart’s type
(one of <strong>Multiline</strong> or <strong>Margin</strong>) and the second element should be a list containing the tags
you have used in add_scalar function, which will be collected into the new chart.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">layout</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'Taiwan'</span><span class="p">:{</span><span class="s1">'twse'</span><span class="p">:[</span><span class="s1">'Multiline'</span><span class="p">,[</span><span class="s1">'twse/0050'</span><span class="p">,</span> <span class="s1">'twse/2330'</span><span class="p">]]},</span>
<span class="s1">'USA'</span><span class="p">:{</span> <span class="s1">'dow'</span><span class="p">:[</span><span class="s1">'Margin'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'dow/aaa'</span><span class="p">,</span> <span class="s1">'dow/bbb'</span><span class="p">,</span> <span class="s1">'dow/ccc'</span><span class="p">]],</span>
<span class="s1">'nasdaq'</span><span class="p">:[</span><span class="s1">'Margin'</span><span class="p">,</span> <span class="p">[</span><span class="s1">'nasdaq/aaa'</span><span class="p">,</span> <span class="s1">'nasdaq/bbb'</span><span class="p">,</span> <span class="s1">'nasdaq/ccc'</span><span class="p">]]}}</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_custom_scalars</span><span class="p">(</span><span class="n">layout</span><span class="p">)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_mesh">
<code class="sig-name descname">add_mesh</code><span class="sig-paren">(</span><em class="sig-param">tag</em>, <em class="sig-param">vertices</em>, <em class="sig-param">colors=None</em>, <em class="sig-param">faces=None</em>, <em class="sig-param">config_dict=None</em>, <em class="sig-param">global_step=None</em>, <em class="sig-param">walltime=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_mesh"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_mesh" title="Permalink to this definition">¶</a></dt>
<dd><p>Add meshes or 3D point clouds to TensorBoard. The visualization is based on Three.js,
so it allows users to interact with the rendered object. Besides the basic definitions
such as vertices, faces, users can further provide camera parameter, lighting condition, etc.
Please check <a class="reference external" href="https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene">https://threejs.org/docs/index.html#manual/en/introduction/Creating-a-scene</a> for
advanced usage.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tag</strong> (<em>string</em>) – Data identifier</p></li>
<li><p><strong>vertices</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – List of the 3D coordinates of vertices.</p></li>
<li><p><strong>colors</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Colors for each vertex</p></li>
<li><p><strong>faces</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>torch.Tensor</em></a>) – Indices of vertices within each triangle. (Optional)</p></li>
<li><p><strong>config_dict</strong> – Dictionary with ThreeJS classes names and configuration.</p></li>
<li><p><strong>global_step</strong> (<em>python:int</em>) – Global step value to record</p></li>
<li><p><strong>walltime</strong> (<em>python:float</em>) – Optional override default walltime (time.time())
seconds after epoch of event</p></li>
</ul>
</dd>
</dl>
<dl>
<dt>Shape:</dt><dd><p>vertices: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>B</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(B, N, 3)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.05017em;">B</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span>
</span>. (batch, number_of_vertices, channels)</p>
<p>colors: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>B</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(B, N, 3)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.05017em;">B</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span>
</span>. The values should lie in [0, 255] for type <cite>uint8</cite> or [0, 1] for type <cite>float</cite>.</p>
<p>faces: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>B</mi><mo separator="true">,</mo><mi>N</mi><mo separator="true">,</mo><mn>3</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(B, N, 3)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathdefault" style="margin-right:0.05017em;">B</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathdefault" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">3</span><span class="mclose">)</span></span></span></span>
</span>. The values should lie in [0, number_of_vertices] for type <cite>uint8</cite>.</p>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="n">vertices_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">colors_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span>
<span class="p">[</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">],</span>
<span class="p">[</span><span class="mi">255</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">],</span>
<span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">faces_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">([</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</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="mi">2</span><span class="p">],</span>
<span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span>
<span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
<span class="n">writer</span><span class="o">.</span><span class="n">add_mesh</span><span class="p">(</span><span class="s1">'my_mesh'</span><span class="p">,</span> <span class="n">vertices</span><span class="o">=</span><span class="n">vertices_tensor</span><span class="p">,</span> <span class="n">colors</span><span class="o">=</span><span class="n">colors_tensor</span><span class="p">,</span> <span class="n">faces</span><span class="o">=</span><span class="n">faces_tensor</span><span class="p">)</span>
<span class="n">writer</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.add_hparams">
<code class="sig-name descname">add_hparams</code><span class="sig-paren">(</span><em class="sig-param">hparam_dict=None</em>, <em class="sig-param">metric_dict=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_hparams"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_hparams" title="Permalink to this definition">¶</a></dt>
<dd><p>Add a set of hyperparameters to be compared in TensorBoard.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>hparam_dict</strong> (<em>dict</em>) – Each key-value pair in the dictionary is the
name of the hyper parameter and it’s corresponding value.</p></li>
<li><p><strong>metric_dict</strong> (<em>dict</em>) – Each key-value pair in the dictionary is the
name of the metric and it’s corresponding value. Note that the key used
here should be unique in the tensorboard record. Otherwise the value
you added by <code class="docutils literal notranslate"><span class="pre">add_scalar</span></code> will be displayed in hparam plugin. In most
cases, this is unwanted.</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
<span class="k">with</span> <span class="n">SummaryWriter</span><span class="p">()</span> <span class="k">as</span> <span class="n">w</span><span class="p">:</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">5</span><span class="p">):</span>
<span class="n">w</span><span class="o">.</span><span class="n">add_hparams</span><span class="p">({</span><span class="s1">'lr'</span><span class="p">:</span> <span class="mf">0.1</span><span class="o">*</span><span class="n">i</span><span class="p">,</span> <span class="s1">'bsize'</span><span class="p">:</span> <span class="n">i</span><span class="p">},</span>
<span class="p">{</span><span class="s1">'hparam/accuracy'</span><span class="p">:</span> <span class="mi">10</span><span class="o">*</span><span class="n">i</span><span class="p">,</span> <span class="s1">'hparam/loss'</span><span class="p">:</span> <span class="mi">10</span><span class="o">*</span><span class="n">i</span><span class="p">})</span>
</pre></div>
</div>
<p>Expected result:</p>
<a class="reference internal image-reference" href="_images/add_hparam.png"><img alt="_images/add_hparam.png" src="_images/add_hparam.png" style="width: 571.0px; height: 230.0px;" /></a>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.flush">
<code class="sig-name descname">flush</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.flush"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.flush" title="Permalink to this definition">¶</a></dt>
<dd><p>Flushes the event file to disk.
Call this method to make sure that all pending events have been written to
disk.</p>
</dd></dl>
<dl class="method">
<dt id="torch.utils.tensorboard.writer.SummaryWriter.close">
<code class="sig-name descname">close</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.close"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.close" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
</article>
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