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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>
<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.</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>
</dd></dl>
<div class="section" id="transforms-on-pil-image">
<h2>Transforms on PIL Image<a class="headerlink" href="#transforms-on-pil-image" 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 PIL Image at the center.</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 v3.8)"><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.</p>
</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 v3.8)"><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 v3.8)"><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 v3.8)"><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 v3.8)"><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>
</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 PIL Image into four corners and the central crop</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 v3.8)"><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.</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>
</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.</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 v3.8)"><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>
</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 PIL Image on all sides with the given “pad” value.</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 v3.8)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</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.</p></li>
<li><p><strong>fill</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><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 v3.8)"><em>str</em></a>) – <p>Type of padding. Should be: constant, edge, reflect or symmetric.
Default is constant.</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>
</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=False</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</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 v3.8)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><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 v3.8)"><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 v3.8)"><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 v3.8)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><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 apllied. 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> (<em>{PIL.Image.NEAREST</em><em>, </em><em>PIL.Image.BILINEAR</em><em>, </em><em>PIL.Image.BICUBIC}</em><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 PIL.Image.NEAREST.</p></li>
<li><p><strong>fillcolor</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><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)</p></li>
</ul>
</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>
<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 v3.8)"><em>list</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.8)"><em>tuple</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 v3.8)"><em>float</em></a>) – probability</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomChoice">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomChoice</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#RandomChoice"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomChoice" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply single transformation randomly picked from a list</p>
</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 PIL Image at a random location.</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 v3.8)"><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.</p></li>
<li><p><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em> or </em><em>sequence</em><em>, </em><em>optional</em>) – Optional padding on each border
of the image. Default is None, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively. If a sequence of length 2 is provided, it is used to
pad left/right, top/bottom borders, respectively.</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> – 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> – <p>Type of padding. Should be: constant, edge, reflect or symmetric. Default is constant.</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 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>
</p></li>
</ul>
</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).</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 v3.8)"><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</p>
</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 v3.8)"><em>float</em></a>) – probability of the image being flipped. Default value is 0.5</p>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomOrder">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomOrder</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#RandomOrder"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomOrder" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply a list of transformations in a random order</p>
</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=3</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 Perspective transformation of the given PIL Image randomly with a given probability.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>interpolation</strong> – Default- Image.BICUBIC</p></li>
<li><p><strong>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a>) – probability of the image being perspectively transformed. Default value is 0.5</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 v3.8)"><em>float</em></a>) – it controls the degree of distortion and ranges from 0 to 1. Default value is 0.5.</p></li>
<li><p><strong>fill</strong> (<em>3-tuple</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – RGB pixel fill value for area outside the rotated image.
If int, it is used for all channels respectively. Default value is 0.</p></li>
</ul>
</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 PIL Image to random size and aspect ratio.</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> – expected output size of each edge</p></li>
<li><p><strong>scale</strong> – range of size of the origin size cropped</p></li>
<li><p><strong>ratio</strong> – range of aspect ratio of the origin aspect ratio cropped</p></li>
<li><p><strong>interpolation</strong> – Default: PIL.Image.BILINEAR</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomRotation">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomRotation</code><span class="sig-paren">(</span><em class="sig-param">degrees</em>, <em class="sig-param">resample=False</em>, <em class="sig-param">expand=False</em>, <em class="sig-param">center=None</em>, <em class="sig-param">fill=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomRotation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomRotation" title="Permalink to this definition">¶</a></dt>
<dd><p>Rotate the image by angle.</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 v3.8)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><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).</p></li>
<li><p><strong>resample</strong> (<em>{PIL.Image.NEAREST</em><em>, </em><em>PIL.Image.BILINEAR</em><em>, </em><em>PIL.Image.BICUBIC}</em><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 PIL.Image.NEAREST.</p></li>
<li><p><strong>expand</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – Optional expansion flag.
If true, expands the output to make it large enough to hold the entire rotated image.
If false or omitted, make the output image the same size as the input image.
Note that the expand flag assumes rotation around the center and no translation.</p></li>
<li><p><strong>center</strong> (<em>2-tuple</em><em>, </em><em>optional</em>) – Optional center of rotation.
Origin is the upper left corner.
Default is the center of the image.</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 v3.8)"><em>int</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><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.
Defaults to 0 for all bands. This option is only available for <code class="docutils literal notranslate"><span class="pre">pillow>=5.2.0</span></code>.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomSizedCrop">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomSizedCrop</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomSizedCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomSizedCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Note: This transform is deprecated in favor of RandomResizedCrop.</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomVerticalFlip">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomVerticalFlip</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#RandomVerticalFlip"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomVerticalFlip" title="Permalink to this definition">¶</a></dt>
<dd><p>Vertically flip the given PIL 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 v3.8)"><em>float</em></a>) – probability of the image being flipped. Default value is 0.5</p>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.Resize">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Resize</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">interpolation=2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Resize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Resize" title="Permalink to this definition">¶</a></dt>
<dd><p>Resize the input PIL Image to the given size.</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 v3.8)"><em>int</em></a>) – Desired output size. If size is a sequence like
(h, w), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height > width, then image will be rescaled to
(size * height / width, size)</p></li>
<li><p><strong>interpolation</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a><em>, </em><em>optional</em>) – Desired interpolation. Default is
<code class="docutils literal notranslate"><span class="pre">PIL.Image.BILINEAR</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.Scale">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Scale</code><span class="sig-paren">(</span><em class="sig-param">*args</em>, <em class="sig-param">**kwargs</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Scale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Scale" title="Permalink to this definition">¶</a></dt>
<dd><p>Note: This transform is deprecated in favor of Resize.</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.TenCrop">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">TenCrop</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">vertical_flip=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#TenCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.TenCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop the given PIL Image into four corners and the central crop plus the flipped version of
these (horizontal flipping is used by default)</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"><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 v3.8)"><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.</p></li>
<li><p><strong>vertical_flip</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a>) – Use vertical flipping instead of horizontal</p></li>
</ul>
</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">TenCrop</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>
</dd></dl>
</div>
<div class="section" id="transforms-on-torch-tensor">
<h2>Transforms on torch.*Tensor<a class="headerlink" href="#transforms-on-torch-tensor" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.LinearTransformation">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">LinearTransformation</code><span class="sig-paren">(</span><em class="sig-param">transformation_matrix</em>, <em class="sig-param">mean_vector</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#LinearTransformation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.LinearTransformation" title="Permalink to this definition">¶</a></dt>
<dd><p>Transform a tensor image with a square transformation matrix and a mean_vector computed
offline.
Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and
subtract mean_vector from it which is then followed by computing the dot
product with the transformation matrix and then reshaping the tensor to its
original shape.</p>
<dl class="simple">
<dt>Applications:</dt><dd><p>whitening transformation: Suppose X is a column vector zero-centered data.
Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X),
perform SVD on this matrix and pass it as transformation_matrix.</p>
</dd>
</dl>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>transformation_matrix</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – tensor [D x D], D = C x H x W</p></li>
<li><p><strong>mean_vector</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – tensor [D], D = C x H x W</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.Normalize">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Normalize</code><span class="sig-paren">(</span><em class="sig-param">mean</em>, <em class="sig-param">std</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Normalize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Normalize" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize a tensor image with mean and standard deviation.
Given mean: <code class="docutils literal notranslate"><span class="pre">(mean[1],...,mean[n])</span></code> and std: <code class="docutils literal notranslate"><span class="pre">(std[1],..,std[n])</span></code> for <code class="docutils literal notranslate"><span class="pre">n</span></code>
channels, this transform will normalize each channel of the input
<code class="docutils literal notranslate"><span class="pre">torch.*Tensor</span></code> i.e.,
<code class="docutils literal notranslate"><span class="pre">output[channel]</span> <span class="pre">=</span> <span class="pre">(input[channel]</span> <span class="pre">-</span> <span class="pre">mean[channel])</span> <span class="pre">/</span> <span class="pre">std[channel]</span></code></p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This transform acts out of place, i.e., it does not mutate the input tensor.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>mean</strong> (<em>sequence</em>) – Sequence of means for each channel.</p></li>
<li><p><strong>std</strong> (<em>sequence</em>) – Sequence of standard deviations for each channel.</p></li>
<li><p><strong>inplace</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>,</em><em>optional</em>) – Bool to make this operation in-place.</p></li>
</ul>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.Normalize.__call__">
<code class="sig-name descname">__call__</code><span class="sig-paren">(</span><em class="sig-param">tensor</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Normalize.__call__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Normalize.__call__" 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>tensor</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Tensor image of size (C, H, W) to be normalized.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Normalized Tensor image.</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></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.RandomErasing">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">RandomErasing</code><span class="sig-paren">(</span><em class="sig-param">p=0.5</em>, <em class="sig-param">scale=(0.02</em>, <em class="sig-param">0.33)</em>, <em class="sig-param">ratio=(0.3</em>, <em class="sig-param">3.3)</em>, <em class="sig-param">value=0</em>, <em class="sig-param">inplace=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#RandomErasing"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomErasing" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly selects a rectangle region in an image and erases its pixels.
‘Random Erasing Data Augmentation’ by Zhong et al. See <a class="reference external" href="https://arxiv.org/pdf/1708.04896.pdf">https://arxiv.org/pdf/1708.04896.pdf</a></p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>p</strong> – probability that the random erasing operation will be performed.</p></li>
<li><p><strong>scale</strong> – range of proportion of erased area against input image.</p></li>
<li><p><strong>ratio</strong> – range of aspect ratio of erased area.</p></li>
<li><p><strong>value</strong> – erasing value. Default is 0. If a single int, it is used to
erase all pixels. If a tuple of length 3, it is used to erase
R, G, B channels respectively.
If a str of ‘random’, erasing each pixel with random values.</p></li>
<li><p><strong>inplace</strong> – boolean to make this transform inplace. Default set to False.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Erased Image.</p>
</dd>
</dl>
<dl>
<dt># Examples:</dt><dd><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">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">RandomHorizontalFlip</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="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="n">transforms</span><span class="o">.</span><span class="n">RandomErasing</span><span class="p">(),</span>
<span class="gp">>>> </span><span class="p">])</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="conversion-transforms">
<h2>Conversion Transforms<a class="headerlink" href="#conversion-transforms" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.ToPILImage">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">ToPILImage</code><span class="sig-paren">(</span><em class="sig-param">mode=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ToPILImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToPILImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a tensor or an ndarray to PIL Image.</p>
<p>Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape
H x W x C to a PIL Image while preserving the value range.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>mode</strong> (<a class="reference external" href="https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes">PIL.Image mode</a>) – <p>color space and pixel depth of input data (optional).
If <code class="docutils literal notranslate"><span class="pre">mode</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> (default) there are some assumptions made about the input data:</p>
<blockquote>
<div><ul class="simple">
<li><p>If the input has 4 channels, the <code class="docutils literal notranslate"><span class="pre">mode</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">RGBA</span></code>.</p></li>
<li><p>If the input has 3 channels, the <code class="docutils literal notranslate"><span class="pre">mode</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">RGB</span></code>.</p></li>
<li><p>If the input has 2 channels, the <code class="docutils literal notranslate"><span class="pre">mode</span></code> is assumed to be <code class="docutils literal notranslate"><span class="pre">LA</span></code>.</p></li>
<li><p>If the input has 1 channel, the <code class="docutils literal notranslate"><span class="pre">mode</span></code> is determined by the data type (i.e <code class="docutils literal notranslate"><span class="pre">int</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code>,
<code class="docutils literal notranslate"><span class="pre">short</span></code>).</p></li>
</ul>
</div></blockquote>
</p>
</dd>
</dl>
<dl class="method">
<dt id="torchvision.transforms.ToPILImage.__call__">
<code class="sig-name descname">__call__</code><span class="sig-paren">(</span><em class="sig-param">pic</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ToPILImage.__call__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToPILImage.__call__" 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>pic</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.18)"><em>numpy.ndarray</em></a>) – Image to be converted to PIL Image.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Image converted to PIL Image.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>PIL Image</p>
</dd>
</dl>
</dd></dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.transforms.ToTensor">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">ToTensor</code><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ToTensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToTensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a <code class="docutils literal notranslate"><span class="pre">PIL</span> <span class="pre">Image</span></code> or <code class="docutils literal notranslate"><span class="pre">numpy.ndarray</span></code> to tensor.</p>
<p>Converts a PIL Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0]
if the PIL Image belongs to one of the modes (L, LA, P, I, F, RGB, YCbCr, RGBA, CMYK, 1)
or if the numpy.ndarray has dtype = np.uint8</p>
<p>In the other cases, tensors are returned without scaling.</p>
<dl class="method">
<dt id="torchvision.transforms.ToTensor.__call__">
<code class="sig-name descname">__call__</code><span class="sig-paren">(</span><em class="sig-param">pic</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#ToTensor.__call__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToTensor.__call__" 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>pic</strong> (<em>PIL Image</em><em> or </em><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.18)"><em>numpy.ndarray</em></a>) – Image to be converted to tensor.</p>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Converted image.</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></p>
</dd>
</dl>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="generic-transforms">
<h2>Generic Transforms<a class="headerlink" href="#generic-transforms" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.Lambda">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.transforms.</code><code class="sig-name descname">Lambda</code><span class="sig-paren">(</span><em class="sig-param">lambd</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms/transforms.html#Lambda"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Lambda" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply a user-defined lambda as a transform.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>lambd</strong> (<em>function</em>) – Lambda/function to be used for transform.</p>
</dd>
</dl>
</dd></dl>
</div>
<div class="section" id="functional-transforms">
<h2>Functional Transforms<a class="headerlink" href="#functional-transforms" title="Permalink to this headline">¶</a></h2>
<p>Functional transforms give you fine-grained control of the transformation pipeline.
As opposed to the transformations above, functional transforms don’t contain a random number
generator for their parameters.
That means you have to specify/generate all parameters, but you can reuse the functional transform.</p>
<p>Example:
you can apply a functional transform with the same parameters to multiple images like this:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.transforms.functional</span> <span class="k">as</span> <span class="nn">TF</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="k">def</span> <span class="nf">my_segmentation_transforms</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">segmentation</span><span class="p">):</span>
<span class="k">if</span> <span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">()</span> <span class="o">></span> <span class="mf">0.5</span><span class="p">:</span>
<span class="n">angle</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">TF</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
<span class="n">segmentation</span> <span class="o">=</span> <span class="n">TF</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">segmentation</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
<span class="c1"># more transforms ...</span>
<span class="k">return</span> <span class="n">image</span><span class="p">,</span> <span class="n">segmentation</span>
</pre></div>
</div>
<p>Example:
you can use a functional transform to build transform classes with custom behavior:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.transforms.functional</span> <span class="k">as</span> <span class="nn">TF</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="k">class</span> <span class="nc">MyRotationTransform</span><span class="p">:</span>
<span class="sd">"""Rotate by one of the given angles."""</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">angles</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">angles</span> <span class="o">=</span> <span class="n">angles</span>
<span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
<span class="n">angle</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angles</span><span class="p">)</span>
<span class="k">return</span> <span class="n">TF</span><span class="o">.</span><span class="n">rotate</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">angle</span><span class="p">)</span>
<span class="n">rotation_transform</span> <span class="o">=</span> <span class="n">MyRotationTransform</span><span class="p">(</span><span class="n">angles</span><span class="o">=</span><span class="p">[</span><span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="o">-</span><span class="mi">15</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="mi">30</span><span class="p">])</span>
</pre></div>
</div>
<span class="target" id="module-torchvision.transforms.functional"></span><dl class="function">
<dt id="torchvision.transforms.functional.adjust_brightness">