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<div class="section" id="torchvision-utils">
<h1>torchvision.utils<a class="headerlink" href="#torchvision-utils" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torchvision.utils.make_grid">
<code class="sig-prename descclassname">torchvision.utils.</code><code class="sig-name descname">make_grid</code><span class="sig-paren">(</span><em class="sig-param">tensor: Union[torch.Tensor, List[torch.Tensor]], nrow: int = 8, padding: int = 2, normalize: bool = False, range: Optional[Tuple[int, int]] = None, scale_each: bool = False, pad_value: int = 0</em><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="../_modules/torchvision/utils.html#make_grid"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.utils.make_grid" title="Permalink to this definition">¶</a></dt>
<dd><p>Make a grid of images.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.8)"><em>list</em></a>) – 4D mini-batch Tensor of shape (B x C x H x W)
or a list of images all of the same size.</p></li>
<li><p><strong>nrow</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – Number of images displayed in each row of the grid.
The final grid size is <code class="docutils literal notranslate"><span class="pre">(B</span> <span class="pre">/</span> <span class="pre">nrow,</span> <span class="pre">nrow)</span></code>. Default: <code class="docutils literal notranslate"><span class="pre">8</span></code>.</p></li>
<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – amount of padding. Default: <code class="docutils literal notranslate"><span class="pre">2</span></code>.</p></li>
<li><p><strong>normalize</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, shift the image to the range (0, 1),
by the min and max values specified by <code class="xref py py-attr docutils literal notranslate"><span class="pre">range</span></code>. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>range</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</em></a><em>, </em><em>optional</em>) – tuple (min, max) where min and max are numbers,
then these numbers are used to normalize the image. By default, min and max
are computed from the tensor.</p></li>
<li><p><strong>scale_each</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, scale each image in the batch of
images separately rather than the (min, max) over all images. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>pad_value</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a><em>, </em><em>optional</em>) – Value for the padded pixels. Default: <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<p>See this notebook <a class="reference external" href="https://gist.github.com/anonymous/bf16430f7750c023141c562f3e9f2a91">here</a></p>
</dd></dl>
<dl class="function">
<dt id="torchvision.utils.save_image">
<code class="sig-prename descclassname">torchvision.utils.</code><code class="sig-name descname">save_image</code><span class="sig-paren">(</span><em class="sig-param">tensor: Union[torch.Tensor, List[torch.Tensor]], fp: Union[str, pathlib.Path, BinaryIO], nrow: int = 8, padding: int = 2, normalize: bool = False, range: Optional[Tuple[int, int]] = None, scale_each: bool = False, pad_value: int = 0, format: Optional[str] = None</em><span class="sig-paren">)</span> → None<a class="reference internal" href="../_modules/torchvision/utils.html#save_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.utils.save_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Save a given Tensor into an image file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>tensor</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.8)"><em>list</em></a>) – Image to be saved. If given a mini-batch tensor,
saves the tensor as a grid of images by calling <code class="docutils literal notranslate"><span class="pre">make_grid</span></code>.</p></li>
<li><p><strong>fp</strong> (<em>string</em><em> or </em><em>file object</em>) – A filename or a file object</p></li>
<li><p><strong>format</strong> (<em>Optional</em>) – If omitted, the format to use is determined from the filename extension.
If a file object was used instead of a filename, this parameter should always be used.</p></li>
<li><p><strong>**kwargs</strong> – Other arguments are documented in <code class="docutils literal notranslate"><span class="pre">make_grid</span></code>.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
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