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<div class="section" id="torchvision-transforms">
<h1>torchvision.transforms<a class="headerlink" href="#torchvision-transforms" title="Permalink to this headline">¶</a></h1>
<p>Transforms are common image transformations. They can be chained together using <a class="reference internal" href="#torchvision.transforms.Compose" title="torchvision.transforms.Compose"><code class="xref py py-class docutils literal notranslate"><span class="pre">Compose</span></code></a>.
Additionally, there is the <a class="reference internal" href="#module-torchvision.transforms.functional" title="torchvision.transforms.functional"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torchvision.transforms.functional</span></code></a> module.
Functional transforms give fine-grained control over the transformations.
This is useful if you have to build a more complex transformation pipeline
(e.g. in the case of segmentation tasks).</p>
<p>All transformations accept PIL Image, Tensor Image or batch of Tensor Images as input. Tensor Image is a tensor with
<code class="docutils literal notranslate"><span class="pre">(C,</span> <span class="pre">H,</span> <span class="pre">W)</span></code> shape, where <code class="docutils literal notranslate"><span class="pre">C</span></code> is a number of channels, <code class="docutils literal notranslate"><span class="pre">H</span></code> and <code class="docutils literal notranslate"><span class="pre">W</span></code> are image height and width. Batch of
Tensor Images is a tensor of <code class="docutils literal notranslate"><span class="pre">(B,</span> <span class="pre">C,</span> <span class="pre">H,</span> <span class="pre">W)</span></code> shape, where <code class="docutils literal notranslate"><span class="pre">B</span></code> is a number of images in the batch. Deterministic or
random transformations applied on the batch of Tensor Images identically transform all the images of the batch.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Since v0.8.0 all random transformations are using torch default random generator to sample random parameters.
It is a backward compatibility breaking change and user should set the random state as following:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Previous versions</span>
<span class="c1"># import random</span>
<span class="c1"># random.seed(12)</span>
<span class="c1"># Now</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">17</span><span class="p">)</span>
</pre></div>
</div>
<p>Please, keep in mind that the same seed for torch random generator and Python random generator will not
produce the same results.</p>
</div>
<div class="section" id="scriptable-transforms">
<h2>Scriptable transforms<a class="headerlink" href="#scriptable-transforms" title="Permalink to this headline">¶</a></h2>
<p>In order to script the transformations, please use <code class="docutils literal notranslate"><span class="pre">torch.nn.Sequential</span></code> instead of <a class="reference internal" href="#torchvision.transforms.Compose" title="torchvision.transforms.Compose"><code class="xref py py-class docutils literal notranslate"><span class="pre">Compose</span></code></a>.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">transforms</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">Sequential</span><span class="p">(</span>
<span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">10</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">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">)),</span>
<span class="p">)</span>
<span class="n">scripted_transforms</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">transforms</span><span class="p">)</span>
</pre></div>
</div>
<p>Make sure to use only scriptable transformations, i.e. that work with <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> and does not require
<cite>lambda</cite> functions or <code class="docutils literal notranslate"><span class="pre">PIL.Image</span></code>.</p>
<p>For any custom transformations to be used with <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code>, they should be derived from <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>.</p>
</div>
<div class="section" id="compositions-of-transforms">
<h2>Compositions of transforms<a class="headerlink" href="#compositions-of-transforms" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.Compose">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Compose</code><span class="sig-paren">(</span><em class="sig-param">transforms</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Compose"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Compose" title="Permalink to this definition">¶</a></dt>
<dd><p>Composes several transforms together. This transform does not support torchscript.
Please, see the note below.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>transforms</strong> (list of <code class="docutils literal notranslate"><span class="pre">Transform</span></code> objects) – list of transforms to compose.</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="gp">>>> </span> <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="gp">>>> </span> <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="gp">>>> </span><span class="p">])</span>
</pre></div>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In order to script the transformations, please use <code class="docutils literal notranslate"><span class="pre">torch.nn.Sequential</span></code> as below.</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">transforms</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">Sequential</span><span class="p">(</span>
<span class="gp">>>> </span> <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="gp">>>> </span> <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">)),</span>
<span class="gp">>>> </span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">scripted_transforms</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">transforms</span><span class="p">)</span>
</pre></div>
</div>
<p>Make sure to use only scriptable transformations, i.e. that work with <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, does not require
<cite>lambda</cite> functions or <code class="docutils literal notranslate"><span class="pre">PIL.Image</span></code>.</p>
</div>
</dd></dl>
</div>
<div class="section" id="transforms-on-pil-image-and-torch-tensor">
<h2>Transforms on PIL Image and torch.*Tensor<a class="headerlink" href="#transforms-on-pil-image-and-torch-tensor" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.CenterCrop">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">CenterCrop</code><span class="sig-paren">(</span><em class="sig-param">size</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#CenterCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.CenterCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crops the given image at the center.
The image can be a PIL Image or a torch Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>size</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).</p>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.CenterCrop.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#CenterCrop.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.CenterCrop.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be cropped.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Cropped image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.ColorJitter">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">ColorJitter</code><span class="sig-paren">(</span><em class="sig-param">brightness=0</em>, <em class="sig-param">contrast=0</em>, <em class="sig-param">saturation=0</em>, <em class="sig-param">hue=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ColorJitter"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ColorJitter" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly change the brightness, contrast and saturation of an image.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>brightness</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>tuple of python:float</em><em> (</em><em>min</em><em>, </em><em>max</em><em>)</em>) – How much to jitter brightness.
brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
or the given [min, max]. Should be non negative numbers.</p></li>
<li><p><strong>contrast</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>tuple of python:float</em><em> (</em><em>min</em><em>, </em><em>max</em><em>)</em>) – How much to jitter contrast.
contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
or the given [min, max]. Should be non negative numbers.</p></li>
<li><p><strong>saturation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>tuple of python:float</em><em> (</em><em>min</em><em>, </em><em>max</em><em>)</em>) – How much to jitter saturation.
saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
or the given [min, max]. Should be non negative numbers.</p></li>
<li><p><strong>hue</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><em>tuple of python:float</em><em> (</em><em>min</em><em>, </em><em>max</em><em>)</em>) – How much to jitter hue.
hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.ColorJitter.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ColorJitter.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ColorJitter.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Input image.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Color jittered image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.transforms.ColorJitter.get_params">
<em class="property">static </em><code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">brightness</em>, <em class="sig-param">contrast</em>, <em class="sig-param">saturation</em>, <em class="sig-param">hue</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ColorJitter.get_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ColorJitter.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get a randomized transform to be applied on image.</p>
<p>Arguments are same as that of __init__.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Transform which randomly adjusts brightness, contrast and
saturation in a random order.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.FiveCrop">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">FiveCrop</code><span class="sig-paren">(</span><em class="sig-param">size</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#FiveCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.FiveCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop the given image into four corners and the central crop.
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading
dimensions</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This transform returns a tuple of images and there may be a mismatch in the number of
inputs and targets your Dataset returns. See below for an example of how to deal with
this.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>size</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Desired output size of the crop. If size is an <code class="docutils literal notranslate"><span class="pre">int</span></code>
instead of sequence like (h, w), a square crop of size (size, size) is made.
If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).</p>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">transform</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
<span class="gp">>>> </span> <span class="n">FiveCrop</span><span class="p">(</span><span class="n">size</span><span class="p">),</span> <span class="c1"># this is a list of PIL Images</span>
<span class="gp">>>> </span> <span class="n">Lambda</span><span class="p">(</span><span class="k">lambda</span> <span class="n">crops</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">ToTensor</span><span class="p">()(</span><span class="n">crop</span><span class="p">)</span> <span class="k">for</span> <span class="n">crop</span> <span class="ow">in</span> <span class="n">crops</span><span class="p">]))</span> <span class="c1"># returns a 4D tensor</span>
<span class="gp">>>> </span><span class="p">])</span>
<span class="gp">>>> </span><span class="c1">#In your test loop you can do the following:</span>
<span class="gp">>>> </span><span class="nb">input</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">batch</span> <span class="c1"># input is a 5d tensor, target is 2d</span>
<span class="gp">>>> </span><span class="n">bs</span><span class="p">,</span> <span class="n">ncrops</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">result</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span> <span class="c1"># fuse batch size and ncrops</span>
<span class="gp">>>> </span><span class="n">result_avg</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">bs</span><span class="p">,</span> <span class="n">ncrops</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="c1"># avg over crops</span>
</pre></div>
</div>
<dl class="method">
<dt id="torchvision.transforms.FiveCrop.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#FiveCrop.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.FiveCrop.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be cropped.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>tuple of 5 images. Image can be PIL Image or Tensor</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.Grayscale">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Grayscale</code><span class="sig-paren">(</span><em class="sig-param">num_output_channels=1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Grayscale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Grayscale" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert image to grayscale.
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, 3, H, W] shape, where … means an arbitrary number of leading
dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>num_output_channels</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – (1 or 3) number of channels desired for output image</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><dl class="simple">
<dt>Grayscale version of the input.</dt><dd><ul class="simple">
<li><p>If <code class="docutils literal notranslate"><span class="pre">num_output_channels</span> <span class="pre">==</span> <span class="pre">1</span></code> : returned image is single channel</p></li>
<li><p>If <code class="docutils literal notranslate"><span class="pre">num_output_channels</span> <span class="pre">==</span> <span class="pre">3</span></code> : returned image is 3 channel with r == g == b</p></li>
</ul>
</dd>
</dl>
</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image</p>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.Grayscale.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Grayscale.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Grayscale.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be converted to grayscale.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Grayscaled image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.Pad">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Pad</code><span class="sig-paren">(</span><em class="sig-param">padding</em>, <em class="sig-param">fill=0</em>, <em class="sig-param">padding_mode='constant'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Pad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Pad" title="Permalink to this definition">¶</a></dt>
<dd><p>Pad the given image on all sides with the given “pad” value.
The image can be a PIL Image or a torch Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><em>list</em></a>) – Padding on each border. If a single int is provided this
is used to pad all borders. If tuple of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a tuple of length 4 is provided
this is the padding for the left, top, right and bottom borders respectively.
In torchscript mode padding as single int is not supported, use a tuple or
list of length 1: <code class="docutils literal notranslate"><span class="pre">[padding,</span> <span class="pre">]</span></code>.</p></li>
<li><p><strong>fill</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a>) – Pixel fill value for constant fill. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant</p></li>
<li><p><strong>padding_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – <p>Type of padding. Should be: constant, edge, reflect or symmetric.
Default is constant. Mode symmetric is not yet supported for Tensor inputs.</p>
<ul>
<li><p>constant: pads with a constant value, this value is specified with fill</p></li>
<li><p>edge: pads with the last value at the edge of the image</p></li>
<li><p>reflect: pads with reflection of image without repeating the last value on the edge</p>
<blockquote>
<div><p>For example, padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]</p>
</div></blockquote>
</li>
<li><p>symmetric: pads with reflection of image repeating the last value on the edge</p>
<blockquote>
<div><p>For example, padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]</p>
</div></blockquote>
</li>
</ul>
</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.Pad.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Pad.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Pad.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be padded.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Padded image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomAffine">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomAffine</code><span class="sig-paren">(</span><em class="sig-param">degrees</em>, <em class="sig-param">translate=None</em>, <em class="sig-param">scale=None</em>, <em class="sig-param">shear=None</em>, <em class="sig-param">resample=0</em>, <em class="sig-param">fillcolor=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomAffine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomAffine" title="Permalink to this definition">¶</a></dt>
<dd><p>Random affine transformation of the image keeping center invariant.
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading dimensions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>degrees</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Range of degrees to select from.
If degrees is a number instead of sequence like (min, max), the range of degrees
will be (-degrees, +degrees). Set to 0 to deactivate rotations.</p></li>
<li><p><strong>translate</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a><em>, </em><em>optional</em>) – tuple of maximum absolute fraction for horizontal
and vertical translations. For example translate=(a, b), then horizontal shift
is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is
randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.</p></li>
<li><p><strong>scale</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a><em>, </em><em>optional</em>) – scaling factor interval, e.g (a, b), then scale is
randomly sampled from the range a <= scale <= b. Will keep original scale by default.</p></li>
<li><p><strong>shear</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – Range of degrees to select from.
If shear is a number, a shear parallel to the x axis in the range (-shear, +shear)
will be applied. Else if shear is a tuple or list of 2 values a shear parallel to the x axis in the
range (shear[0], shear[1]) will be applied. Else if shear is a tuple or list of 4 values,
a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
Will not apply shear by default.</p></li>
<li><p><strong>resample</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – An optional resampling filter. See <a class="reference external" href="https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters">filters</a> for more information.
If omitted, or if the image has mode “1” or “P”, it is set to <code class="docutils literal notranslate"><span class="pre">PIL.Image.NEAREST</span></code>.
If input is Tensor, only <code class="docutils literal notranslate"><span class="pre">PIL.Image.NEAREST</span></code> and <code class="docutils literal notranslate"><span class="pre">PIL.Image.BILINEAR</span></code> are supported.</p></li>
<li><p><strong>fillcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Optional fill color (Tuple for RGB Image and int for grayscale) for the area
outside the transform in the output image (Pillow>=5.0.0). This option is not supported for Tensor
input. Fill value for the area outside the transform in the output image is always 0.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.RandomAffine.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomAffine.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomAffine.forward" title="Permalink to this definition">¶</a></dt>
<dd><blockquote>
<div><p>img (PIL Image or Tensor): Image to be transformed.</p>
</div></blockquote>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>Affine transformed image.</p>
</dd>
<dt class="field-even">Return type</dt>
<dd class="field-even"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.transforms.RandomAffine.get_params">
<em class="property">static </em><code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">degrees: List[float], translate: Optional[List[float]], scale_ranges: Optional[List[float]], shears: Optional[List[float]], img_size: List[int]</em><span class="sig-paren">)</span> → Tuple[float, Tuple[int, int], float, Tuple[float, float]]<a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomAffine.get_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomAffine.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for affine transformation</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
<dd class="field-odd"><p>params to be passed to the affine transformation</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomApply">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomApply</code><span class="sig-paren">(</span><em class="sig-param">transforms</em>, <em class="sig-param">p=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomApply"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomApply" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply randomly a list of transformations with a given probability.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In order to script the transformation, please use <code class="docutils literal notranslate"><span class="pre">torch.nn.ModuleList</span></code> as input instead of list/tuple of
transforms as shown below:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">RandomApply</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">([</span>
<span class="gp">>>> </span> <span class="n">transforms</span><span class="o">.</span><span class="n">ColorJitter</span><span class="p">(),</span>
<span class="gp">>>> </span><span class="p">]),</span> <span class="n">p</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">scripted_transforms</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">transforms</span><span class="p">)</span>
</pre></div>
</div>
<p>Make sure to use only scriptable transformations, i.e. that work with <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, does not require
<cite>lambda</cite> functions or <code class="docutils literal notranslate"><span class="pre">PIL.Image</span></code>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>transforms</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a><em> or </em><a class="reference internal" href="../generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><em>torch.nn.Module</em></a>) – list of transformations</p></li>
<li><p><strong>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – probability</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomCrop">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomCrop</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">padding=None</em>, <em class="sig-param">pad_if_needed=False</em>, <em class="sig-param">fill=0</em>, <em class="sig-param">padding_mode='constant'</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop the given image at a random location.
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading
dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).</p></li>
<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>sequence</em><em>, </em><em>optional</em>) – Optional padding on each border
of the image. Default is None. If a single int is provided this
is used to pad all borders. If tuple of length 2 is provided this is the padding
on left/right and top/bottom respectively. If a tuple of length 4 is provided
this is the padding for the left, top, right and bottom borders respectively.
In torchscript mode padding as single int is not supported, use a tuple or
list of length 1: <code class="docutils literal notranslate"><span class="pre">[padding,</span> <span class="pre">]</span></code>.</p></li>
<li><p><strong>pad_if_needed</strong> (<em>boolean</em>) – It will pad the image if smaller than the
desired size to avoid raising an exception. Since cropping is done
after padding, the padding seems to be done at a random offset.</p></li>
<li><p><strong>fill</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a>) – Pixel fill value for constant fill. Default is 0. If a tuple of
length 3, it is used to fill R, G, B channels respectively.
This value is only used when the padding_mode is constant</p></li>
<li><p><strong>padding_mode</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.9)"><em>str</em></a>) – <p>Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.
Mode symmetric is not yet supported for Tensor inputs.</p>
<blockquote>
<div><ul>
<li><p>constant: pads with a constant value, this value is specified with fill</p></li>
<li><p>edge: pads with the last value on the edge of the image</p></li>
<li><p>reflect: pads with reflection of image (without repeating the last value on the edge)</p>
<blockquote>
<div><p>padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode
will result in [3, 2, 1, 2, 3, 4, 3, 2]</p>
</div></blockquote>
</li>
<li><p>symmetric: pads with reflection of image (repeating the last value on the edge)</p>
<blockquote>
<div><p>padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode
will result in [2, 1, 1, 2, 3, 4, 4, 3]</p>
</div></blockquote>
</li>
</ul>
</div></blockquote>
</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.RandomCrop.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomCrop.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomCrop.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be cropped.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Cropped image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.transforms.RandomCrop.get_params">
<em class="property">static </em><code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">img: torch.Tensor, output_size: Tuple[int, int]</em><span class="sig-paren">)</span> → Tuple[int, int, int, int]<a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomCrop.get_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomCrop.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for <code class="docutils literal notranslate"><span class="pre">crop</span></code> for a random crop.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be cropped.</p></li>
<li><p><strong>output_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a>) – Expected output size of the crop.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>params (i, j, h, w) to be passed to <code class="docutils literal notranslate"><span class="pre">crop</span></code> for random crop.</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/stdtypes.html#tuple" title="(in Python v3.9)">tuple</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomGrayscale">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomGrayscale</code><span class="sig-paren">(</span><em class="sig-param">p=0.1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomGrayscale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomGrayscale" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly convert image to grayscale with a probability of p (default 0.1).
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, 3, H, W] shape, where … means an arbitrary number of leading
dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – probability that image should be converted to grayscale.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Grayscale version of the input image with probability p and unchanged
with probability (1-p).
- If input image is 1 channel: grayscale version is 1 channel
- If input image is 3 channel: grayscale version is 3 channel with r == g == b</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.RandomGrayscale.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomGrayscale.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomGrayscale.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be converted to grayscale.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Randomly grayscaled image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomHorizontalFlip">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomHorizontalFlip</code><span class="sig-paren">(</span><em class="sig-param">p=0.5</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomHorizontalFlip"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomHorizontalFlip" title="Permalink to this definition">¶</a></dt>
<dd><p>Horizontally flip the given image randomly with a given probability.
The image can be a PIL Image or a torch Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading
dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – probability of the image being flipped. Default value is 0.5</p>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.RandomHorizontalFlip.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomHorizontalFlip.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomHorizontalFlip.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be flipped.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Randomly flipped image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomPerspective">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomPerspective</code><span class="sig-paren">(</span><em class="sig-param">distortion_scale=0.5</em>, <em class="sig-param">p=0.5</em>, <em class="sig-param">interpolation=2</em>, <em class="sig-param">fill=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomPerspective"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomPerspective" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a random perspective transformation of the given image with a given probability.
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading dimensions.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>distortion_scale</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – argument to control the degree of distortion and ranges from 0 to 1.
Default is 0.5.</p></li>
<li><p><strong>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – probability of the image being transformed. Default is 0.5.</p></li>
<li><p><strong>interpolation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Interpolation type. If input is Tensor, only <code class="docutils literal notranslate"><span class="pre">PIL.Image.NEAREST</span></code> and
<code class="docutils literal notranslate"><span class="pre">PIL.Image.BILINEAR</span></code> are supported. Default, <code class="docutils literal notranslate"><span class="pre">PIL.Image.BILINEAR</span></code> for PIL images and Tensors.</p></li>
<li><p><strong>fill</strong> (<em>n-tuple</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – Pixel fill value for area outside the rotated
image. If int or float, the value is used for all bands respectively. Default is 0.
This option is only available for <code class="docutils literal notranslate"><span class="pre">pillow>=5.0.0</span></code>. This option is not supported for Tensor
input. Fill value for the area outside the transform in the output image is always 0.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.RandomPerspective.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomPerspective.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomPerspective.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be Perspectively transformed.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Randomly transformed image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.transforms.RandomPerspective.get_params">
<em class="property">static </em><code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">width: int</em>, <em class="sig-param">height: int</em>, <em class="sig-param">distortion_scale: float</em><span class="sig-paren">)</span> → Tuple[List[List[int]], List[List[int]]]<a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomPerspective.get_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomPerspective.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for <code class="docutils literal notranslate"><span class="pre">perspective</span></code> for a random perspective transform.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>width</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – width of the image.</p></li>
<li><p><strong>height</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – height of the image.</p></li>
<li><p><strong>distortion_scale</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – argument to control the degree of distortion and ranges from 0 to 1.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>List containing [top-left, top-right, bottom-right, bottom-left] of the original image,
List containing [top-left, top-right, bottom-right, bottom-left] of the transformed image.</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomResizedCrop">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomResizedCrop</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">scale=(0.08</em>, <em class="sig-param">1.0)</em>, <em class="sig-param">ratio=(0.75</em>, <em class="sig-param">1.3333333333333333)</em>, <em class="sig-param">interpolation=2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomResizedCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomResizedCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop the given image to random size and aspect ratio.
The image can be a PIL Image or a Tensor, in which case it is expected
to have […, H, W] shape, where … means an arbitrary number of leading dimensions</p>
<p>A crop of random size (default: of 0.08 to 1.0) of the original size and a random
aspect ratio (default: of 3/4 to 4/3) of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em> or </em><em>sequence</em>) – expected output size of each edge. If size is an
int instead of sequence like (h, w), a square output size <code class="docutils literal notranslate"><span class="pre">(size,</span> <span class="pre">size)</span></code> is
made. If provided a tuple or list of length 1, it will be interpreted as (size[0], size[0]).</p></li>
<li><p><strong>scale</strong> (<em>tuple of python:float</em>) – range of size of the origin size cropped</p></li>
<li><p><strong>ratio</strong> (<em>tuple of python:float</em>) – range of aspect ratio of the origin aspect ratio cropped.</p></li>
<li><p><strong>interpolation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – Desired interpolation enum defined by <a class="reference external" href="https://pillow.readthedocs.io/en/latest/handbook/concepts.html#filters">filters</a>.
Default is <code class="docutils literal notranslate"><span class="pre">PIL.Image.BILINEAR</span></code>. If input is Tensor, only <code class="docutils literal notranslate"><span class="pre">PIL.Image.NEAREST</span></code>, <code class="docutils literal notranslate"><span class="pre">PIL.Image.BILINEAR</span></code>
and <code class="docutils literal notranslate"><span class="pre">PIL.Image.BICUBIC</span></code> are supported.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.RandomResizedCrop.forward">
<code class="sig-name descname">forward</code><span class="sig-paren">(</span><em class="sig-param">img</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomResizedCrop.forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomResizedCrop.forward" title="Permalink to this definition">¶</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>img</strong> (<em>PIL Image</em><em> or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Image to be cropped and resized.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Randomly cropped and resized image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image or <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torchvision.transforms.RandomResizedCrop.get_params">
<em class="property">static </em><code class="sig-name descname">get_params</code><span class="sig-paren">(</span><em class="sig-param">img: torch.Tensor, scale: List[float], ratio: List[float]</em><span class="sig-paren">)</span> → Tuple[int, int, int, int]<a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomResizedCrop.get_params"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomResizedCrop.get_params" title="Permalink to this definition">¶</a></dt>
<dd><p>Get parameters for <code class="docutils literal notranslate"><span class="pre">crop</span></code> for a random sized crop.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">