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<article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
<div class="section" id="torchvision-io">
<h1>torchvision.io<a class="headerlink" href="#torchvision-io" title="Permalink to this headline">¶</a></h1>
<p>The <code class="xref py py-mod docutils literal notranslate"><span class="pre">torchvision.io</span></code> package provides functions for performing IO
operations. They are currently specific to reading and writing video and
images.</p>
<div class="section" id="video">
<h2>Video<a class="headerlink" href="#video" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.io.read_video">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">read_video</code><span class="sig-paren">(</span><em class="sig-param">filename: str</em>, <em class="sig-param">start_pts: int = 0</em>, <em class="sig-param">end_pts: Optional[float] = None</em>, <em class="sig-param">pts_unit: str = 'pts'</em><span class="sig-paren">)</span> → Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]<a class="reference internal" href="../_modules/torchvision/io/video.html#read_video"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.read_video" title="Permalink to this definition">¶</a></dt>
<dd><p>Reads a video from a file, returning both the video frames as well as
the audio frames</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – path to the video file</p></li>
<li><p><strong>start_pts</strong> (<em>int if pts_unit = 'pts'</em><em>, </em><em>optional</em>) – float / Fraction if pts_unit = ‘sec’, optional
the start presentation time of the video</p></li>
<li><p><strong>end_pts</strong> (<em>int if pts_unit = 'pts'</em><em>, </em><em>optional</em>) – float / Fraction if pts_unit = ‘sec’, optional
the end presentation time</p></li>
<li><p><strong>pts_unit</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em>, </em><em>optional</em>) – unit in which start_pts and end_pts values will be interpreted, either ‘pts’ or ‘sec’. Defaults to ‘pts’.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><ul class="simple">
<li><p><strong>vframes</strong> (<em>Tensor[T, H, W, C]</em>) – the <cite>T</cite> video frames</p></li>
<li><p><strong>aframes</strong> (<em>Tensor[K, L]</em>) – the audio frames, where <cite>K</cite> is the number of channels and <cite>L</cite> is the
number of points</p></li>
<li><p><strong>info</strong> (<em>Dict</em>) – metadata for the video and audio. Can contain the fields video_fps (float)
and audio_fps (int)</p></li>
</ul>
</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.read_video_timestamps">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">read_video_timestamps</code><span class="sig-paren">(</span><em class="sig-param">filename: str</em>, <em class="sig-param">pts_unit: str = 'pts'</em><span class="sig-paren">)</span> → Tuple[List[int], Optional[float]]<a class="reference internal" href="../_modules/torchvision/io/video.html#read_video_timestamps"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.read_video_timestamps" title="Permalink to this definition">¶</a></dt>
<dd><p>List the video frames timestamps.</p>
<p>Note that the function decodes the whole video frame-by-frame.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – path to the video file</p></li>
<li><p><strong>pts_unit</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a><em>, </em><em>optional</em>) – unit in which timestamp values will be returned either ‘pts’ or ‘sec’. Defaults to ‘pts’.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><ul class="simple">
<li><p><strong>pts</strong> (<em>List[int] if pts_unit = ‘pts’</em>) – List[Fraction] if pts_unit = ‘sec’
presentation timestamps for each one of the frames in the video.</p></li>
<li><p><strong>video_fps</strong> (<em>float, optional</em>) – the frame rate for the video</p></li>
</ul>
</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.write_video">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">write_video</code><span class="sig-paren">(</span><em class="sig-param">filename: str</em>, <em class="sig-param">video_array: torch.Tensor</em>, <em class="sig-param">fps: float</em>, <em class="sig-param">video_codec: str = 'libx264'</em>, <em class="sig-param">options: Optional[Dict[str</em>, <em class="sig-param">Any]] = None</em><span class="sig-paren">)</span> → None<a class="reference internal" href="../_modules/torchvision/io/video.html#write_video"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.write_video" title="Permalink to this definition">¶</a></dt>
<dd><p>Writes a 4d tensor in [T, H, W, C] format in a video file</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – path where the video will be saved</p></li>
<li><p><strong>video_array</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>T</em><em>, </em><em>H</em><em>, </em><em>W</em><em>, </em><em>C</em><em>]</em>) – tensor containing the individual frames, as a uint8 tensor in [T, H, W, C] format</p></li>
<li><p><strong>fps</strong> (<em>Number</em>) – frames per second</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="fine-grained-video-api">
<h2>Fine-grained video API<a class="headerlink" href="#fine-grained-video-api" title="Permalink to this headline">¶</a></h2>
<p>In addition to the <code class="xref py py-mod docutils literal notranslate"><span class="pre">read_video</span></code> function, we provide a high-performance
lower-level API for more fine-grained control compared to the <code class="xref py py-mod docutils literal notranslate"><span class="pre">read_video</span></code> function.
It does all this whilst fully supporting torchscript.</p>
<dl class="class">
<dt id="torchvision.io.VideoReader">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">VideoReader</code><span class="sig-paren">(</span><em class="sig-param">path</em>, <em class="sig-param">stream='video'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io.html#VideoReader"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.VideoReader" title="Permalink to this definition">¶</a></dt>
<dd><p>Fine-grained video-reading API.
Supports frame-by-frame reading of various streams from a single video
container.</p>
<p class="rubric">Example</p>
<p>The following examples creates a <code class="xref py py-mod docutils literal notranslate"><span class="pre">VideoReader</span></code> object, seeks into 2s
point, and returns a single frame:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
<span class="n">video_path</span> <span class="o">=</span> <span class="s2">"path_to_a_test_video"</span>
<span class="n">reader</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">VideoReader</span><span class="p">(</span><span class="n">video_path</span><span class="p">,</span> <span class="s2">"video"</span><span class="p">)</span>
<span class="n">reader</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mf">2.0</span><span class="p">)</span>
<span class="n">frame</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">reader</span><span class="p">)</span>
</pre></div>
</div>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">VideoReader</span></code> implements the iterable API, which makes it suitable to
using it in conjunction with <a class="reference external" href="https://docs.python.org/3/library/itertools.html#module-itertools" title="(in Python v3.9)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">itertools</span></code></a> for more advanced reading.
As such, we can use a <code class="xref py py-mod docutils literal notranslate"><span class="pre">VideoReader</span></code> instance inside for loops:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">reader</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="k">for</span> <span class="n">frame</span> <span class="ow">in</span> <span class="n">reader</span><span class="p">:</span>
<span class="n">frames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">frame</span><span class="p">[</span><span class="s1">'data'</span><span class="p">])</span>
<span class="c1"># additionally, `seek` implements a fluent API, so we can do</span>
<span class="k">for</span> <span class="n">frame</span> <span class="ow">in</span> <span class="n">reader</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
<span class="n">frames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">frame</span><span class="p">[</span><span class="s1">'data'</span><span class="p">])</span>
</pre></div>
</div>
<p>With <a class="reference external" href="https://docs.python.org/3/library/itertools.html#module-itertools" title="(in Python v3.9)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">itertools</span></code></a>, we can read all frames between 2 and 5 seconds with the
following code:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">frame</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">takewhile</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">[</span><span class="s1">'pts'</span><span class="p">]</span> <span class="o"><=</span> <span class="mi">5</span><span class="p">,</span> <span class="n">reader</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">2</span><span class="p">)):</span>
<span class="n">frames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">frame</span><span class="p">[</span><span class="s1">'data'</span><span class="p">])</span>
</pre></div>
</div>
<p>and similarly, reading 10 frames after the 2s timestamp can be achieved
as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">frame</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">islice</span><span class="p">(</span><span class="n">reader</span><span class="o">.</span><span class="n">seek</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="mi">10</span><span class="p">):</span>
<span class="n">frames</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">frame</span><span class="p">[</span><span class="s1">'data'</span><span class="p">])</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Each stream descriptor consists of two parts: stream type (e.g. ‘video’) and
a unique stream id (which are determined by the video encoding).
In this way, if the video contaner contains multiple
streams of the same type, users can acces the one they want.
If only stream type is passed, the decoder auto-detects first stream of that type.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>path</strong> (<em>string</em>) – Path to the video file in supported format</p></li>
<li><p><strong>stream</strong> (<em>string</em><em>, </em><em>optional</em>) – descriptor of the required stream, followed by the stream id,
in the format <code class="docutils literal notranslate"><span class="pre">{stream_type}:{stream_id}</span></code>. Defaults to <code class="docutils literal notranslate"><span class="pre">"video:0"</span></code>.
Currently available options include <code class="docutils literal notranslate"><span class="pre">['video',</span> <span class="pre">'audio']</span></code></p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.io.VideoReader.__next__">
<code class="sig-name descname">__next__</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io.html#VideoReader.__next__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.VideoReader.__next__" title="Permalink to this definition">¶</a></dt>
<dd><p>Decodes and returns the next frame of the current stream</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>a dictionary with fields <code class="docutils literal notranslate"><span class="pre">data</span></code> and <code class="docutils literal notranslate"><span class="pre">pts</span></code>
containing decoded frame and corresponding timestamp</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>(<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)">dict</a>)</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.io.VideoReader.get_metadata">
<code class="sig-name descname">get_metadata</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io.html#VideoReader.get_metadata"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.VideoReader.get_metadata" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns video metadata</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>dictionary containing duration and frame rate for every stream</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>(<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.9)">dict</a>)</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.io.VideoReader.seek">
<code class="sig-name descname">seek</code><span class="sig-paren">(</span><em class="sig-param">time_s: float</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io.html#VideoReader.seek"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.VideoReader.seek" title="Permalink to this definition">¶</a></dt>
<dd><p>Seek within current stream.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>time_s</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – seek time in seconds</p>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Current implementation is the so-called precise seek. This
means following seek, call to <code class="xref py py-mod docutils literal notranslate"><span class="pre">next()</span></code> will return the
frame with the exact timestamp if it exists or
the first frame with timestamp larger than <code class="docutils literal notranslate"><span class="pre">time_s</span></code>.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torchvision.io.VideoReader.set_current_stream">
<code class="sig-name descname">set_current_stream</code><span class="sig-paren">(</span><em class="sig-param">stream: str</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io.html#VideoReader.set_current_stream"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.VideoReader.set_current_stream" title="Permalink to this definition">¶</a></dt>
<dd><p>Set current stream.
Explicitly define the stream we are operating on.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>stream</strong> (<em>string</em>) – descriptor of the required stream. Defaults to <code class="docutils literal notranslate"><span class="pre">"video:0"</span></code>
Currently available stream types include <code class="docutils literal notranslate"><span class="pre">['video',</span> <span class="pre">'audio']</span></code>.
Each descriptor consists of two parts: stream type (e.g. ‘video’) and
a unique stream id (which are determined by video encoding).
In this way, if the video contaner contains multiple
streams of the same type, users can acces the one they want.
If only stream type is passed, the decoder auto-detects first stream
of that type and returns it.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>True on succes, False otherwise</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>(<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)">bool</a>)</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<p>Example of inspecting a video:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
<span class="n">video_path</span> <span class="o">=</span> <span class="s2">"path to a test video"</span>
<span class="c1"># Constructor allocates memory and a threaded decoder</span>
<span class="c1"># instance per video. At the momet it takes two arguments:</span>
<span class="c1"># path to the video file, and a wanted stream.</span>
<span class="n">reader</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">VideoReader</span><span class="p">(</span><span class="n">video_path</span><span class="p">,</span> <span class="s2">"video"</span><span class="p">)</span>
<span class="c1"># The information about the video can be retrieved using the</span>
<span class="c1"># `get_metadata()` method. It returns a dictionary for every stream, with</span>
<span class="c1"># duration and other relevant metadata (often frame rate)</span>
<span class="n">reader_md</span> <span class="o">=</span> <span class="n">reader</span><span class="o">.</span><span class="n">get_metadata</span><span class="p">()</span>
<span class="c1"># metadata is structured as a dict of dicts with following structure</span>
<span class="c1"># {"stream_type": {"attribute": [attribute per stream]}}</span>
<span class="c1">#</span>
<span class="c1"># following would print out the list of frame rates for every present video stream</span>
<span class="nb">print</span><span class="p">(</span><span class="n">reader_md</span><span class="p">[</span><span class="s2">"video"</span><span class="p">][</span><span class="s2">"fps"</span><span class="p">])</span>
<span class="c1"># we explicitly select the stream we would like to operate on. In</span>
<span class="c1"># the constructor we select a default video stream, but</span>
<span class="c1"># in practice, we can set whichever stream we would like</span>
<span class="n">video</span><span class="o">.</span><span class="n">set_current_stream</span><span class="p">(</span><span class="s2">"video:0"</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="image">
<h2>Image<a class="headerlink" href="#image" title="Permalink to this headline">¶</a></h2>
<dl class="function">
<dt id="torchvision.io.read_image">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">read_image</code><span class="sig-paren">(</span><em class="sig-param">path: str</em><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="../_modules/torchvision/io/image.html#read_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.read_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Reads a JPEG or PNG image into a 3 dimensional RGB Tensor.
The values of the output tensor are uint8 between 0 and 255.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>path</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – path of the JPEG or PNG image.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>output</strong></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>[3, image_height, image_width]</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.decode_image">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">decode_image</code><span class="sig-paren">(</span><em class="sig-param">input: torch.Tensor</em><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="../_modules/torchvision/io/image.html#decode_image"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.decode_image" title="Permalink to this definition">¶</a></dt>
<dd><p>Detects whether an image is a JPEG or PNG and performs the appropriate
operation to decode the image into a 3 dimensional RGB Tensor.</p>
<p>The values of the output tensor are uint8 between 0 and 255.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – a one dimensional uint8 tensor containing the raw bytes of the
PNG or JPEG image.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>output</strong></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>[3, image_height, image_width]</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.encode_jpeg">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">encode_jpeg</code><span class="sig-paren">(</span><em class="sig-param">input: torch.Tensor</em>, <em class="sig-param">quality: int = 75</em><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="../_modules/torchvision/io/image.html#encode_jpeg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.encode_jpeg" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes an input tensor in CHW layout and returns a buffer with the contents
of its corresponding JPEG file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>channels</em><em>, </em><em>image_height</em><em>, </em><em>image_width</em><em>]</em><em>)</em>) – int8 image tensor of <cite>c</cite> channels, where <cite>c</cite> must be 1 or 3.</p></li>
<li><p><strong>quality</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Quality of the resulting JPEG file, it must be a number between
1 and 100. Default: 75</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>output</strong> – A one dimensional int8 tensor that contains the raw bytes of the
JPEG file.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>[1]</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.write_jpeg">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">write_jpeg</code><span class="sig-paren">(</span><em class="sig-param">input: torch.Tensor</em>, <em class="sig-param">filename: str</em>, <em class="sig-param">quality: int = 75</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io/image.html#write_jpeg"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.write_jpeg" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes an input tensor in CHW layout and saves it in a JPEG file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>channels</em><em>, </em><em>image_height</em><em>, </em><em>image_width</em><em>]</em>) – int8 image tensor of <cite>c</cite> channels, where <cite>c</cite> must be 1 or 3.</p></li>
<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – Path to save the image.</p></li>
<li><p><strong>quality</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Quality of the resulting JPEG file, it must be a number
between 1 and 100. Default: 75</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.encode_png">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">encode_png</code><span class="sig-paren">(</span><em class="sig-param">input: torch.Tensor</em>, <em class="sig-param">compression_level: int = 6</em><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="../_modules/torchvision/io/image.html#encode_png"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.encode_png" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes an input tensor in CHW layout and returns a buffer with the contents
of its corresponding PNG file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>channels</em><em>, </em><em>image_height</em><em>, </em><em>image_width</em><em>]</em>) – int8 image tensor of <cite>c</cite> channels, where <cite>c</cite> must 3 or 1.</p></li>
<li><p><strong>compression_level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Compression factor for the resulting file, it must be a number
between 0 and 9. Default: 6</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>output</strong> – A one dimensional int8 tensor that contains the raw bytes of the
PNG file.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>[1]</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.io.write_png">
<code class="sig-prename descclassname">torchvision.io.</code><code class="sig-name descname">write_png</code><span class="sig-paren">(</span><em class="sig-param">input: torch.Tensor</em>, <em class="sig-param">filename: str</em>, <em class="sig-param">compression_level: int = 6</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/io/image.html#write_png"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.io.write_png" title="Permalink to this definition">¶</a></dt>
<dd><p>Takes an input tensor in CHW layout (or HW in the case of grayscale images)
and saves it in a PNG file.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>channels</em><em>, </em><em>image_height</em><em>, </em><em>image_width</em><em>]</em>) – int8 image tensor of <cite>c</cite> channels, where <cite>c</cite> must be 1 or 3.</p></li>
<li><p><strong>filename</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – Path to save the image.</p></li>
<li><p><strong>compression_level</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Compression factor for the resulting file, it must be a number
between 0 and 9. Default: 6</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</div>
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