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<div class="section" id="torchvision-ops">
<h1>torchvision.ops<a class="headerlink" href="#torchvision-ops" title="Permalink to this headline">¶</a></h1>
<p><code class="xref py py-mod docutils literal notranslate"><span class="pre">torchvision.ops</span></code> implements operators that are specific for Computer Vision.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>All operators have native support for TorchScript.</p>
</div>
<dl class="function">
<dt id="torchvision.ops.nms">
<code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">nms</code><span class="sig-paren">(</span><em class="sig-param">boxes</em>, <em class="sig-param">scores</em>, <em class="sig-param">iou_threshold</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/boxes.html#nms"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.nms" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs non-maximum suppression (NMS) on the boxes according
to their intersection-over-union (IoU).</p>
<p>NMS iteratively removes lower scoring boxes which have an
IoU greater than iou_threshold with another (higher scoring)
box.</p>
<p>If multiple boxes have the exact same score and satisfy the IoU
criterion with respect to a reference box, the selected box is
not guaranteed to be the same between CPU and GPU. This is similar
to the behavior of argsort in PyTorch when repeated values are present.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>boxes</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>N</em><em>, </em><em>4</em><em>]</em><em>)</em>) – boxes to perform NMS on. They
are expected to be in (x1, y1, x2, y2) format</p></li>
<li><p><strong>scores</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>N</em><em>]</em>) – scores for each one of the boxes</p></li>
<li><p><strong>iou_threshold</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a>) – discards all overlapping
boxes with IoU > iou_threshold</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p><strong>keep</strong> – int64 tensor with the indices
of the elements that have been kept
by NMS, sorted in decreasing order of scores</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>
<dl class="function">
<dt id="torchvision.ops.roi_align">
<code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">roi_align</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">boxes</em>, <em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale=1.0</em>, <em class="sig-param">sampling_ratio=-1</em>, <em class="sig-param">aligned=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/roi_align.html#roi_align"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.roi_align" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Region of Interest (RoI) Align operator described in Mask R-CNN</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>N</em><em>, </em><em>C</em><em>, </em><em>H</em><em>, </em><em>W</em><em>]</em>) – input tensor</p></li>
<li><p><strong>boxes</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>K</em><em>, </em><em>5</em><em>] or </em><em>List</em><em>[</em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>L</em><em>, </em><em>4</em><em>]</em><em>]</em>) – the box coordinates in (x1, y1, x2, y2)
format where the regions will be taken from. If a single Tensor is passed,
then the first column should contain the batch index. If a list of Tensors
is passed, then each Tensor will correspond to the boxes for an element i
in a batch</p></li>
<li><p><strong>output_size</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>Tuple</em><em>[</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><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>) – the size of the output after the cropping
is performed, as (height, width)</p></li>
<li><p><strong>spatial_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>) – a scaling factor that maps the input coordinates to
the box coordinates. Default: 1.0</p></li>
<li><p><strong>sampling_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – number of sampling points in the interpolation grid
used to compute the output value of each pooled output bin. If > 0,
then exactly sampling_ratio x sampling_ratio grid points are used. If
<= 0, then an adaptive number of grid points are used (computed as
ceil(roi_width / pooled_w), and likewise for height). Default: -1</p></li>
<li><p><strong>aligned</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a>) – If False, use the legacy implementation.
If True, pixel shift it by -0.5 for align more perfectly about two neighboring pixel indices.
This version in Detectron2</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>output (Tensor[K, C, output_size[0], output_size[1]])</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.ops.ps_roi_align">
<code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">ps_roi_align</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">boxes</em>, <em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale=1.0</em>, <em class="sig-param">sampling_ratio=-1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/ps_roi_align.html#ps_roi_align"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.ps_roi_align" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Position-Sensitive Region of Interest (RoI) Align operator
mentioned in Light-Head R-CNN.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>N</em><em>, </em><em>C</em><em>, </em><em>H</em><em>, </em><em>W</em><em>]</em>) – input tensor</p></li>
<li><p><strong>boxes</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>K</em><em>, </em><em>5</em><em>] or </em><em>List</em><em>[</em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>L</em><em>, </em><em>4</em><em>]</em><em>]</em>) – the box coordinates in (x1, y1, x2, y2)
format where the regions will be taken from. If a single Tensor is passed,
then the first column should contain the batch index. If a list of Tensors
is passed, then each Tensor will correspond to the boxes for an element i
in a batch</p></li>
<li><p><strong>output_size</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>Tuple</em><em>[</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><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>) – the size of the output after the cropping
is performed, as (height, width)</p></li>
<li><p><strong>spatial_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>) – a scaling factor that maps the input coordinates to
the box coordinates. Default: 1.0</p></li>
<li><p><strong>sampling_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – number of sampling points in the interpolation grid
used to compute the output value of each pooled output bin. If > 0
then exactly sampling_ratio x sampling_ratio grid points are used.
If <= 0, then an adaptive number of grid points are used (computed as
ceil(roi_width / pooled_w), and likewise for height). Default: -1</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>output (Tensor[K, C, output_size[0], output_size[1]])</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.ops.roi_pool">
<code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">roi_pool</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">boxes</em>, <em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/roi_pool.html#roi_pool"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.roi_pool" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Region of Interest (RoI) Pool operator described in Fast R-CNN</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>N</em><em>, </em><em>C</em><em>, </em><em>H</em><em>, </em><em>W</em><em>]</em>) – input tensor</p></li>
<li><p><strong>boxes</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>K</em><em>, </em><em>5</em><em>] or </em><em>List</em><em>[</em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>L</em><em>, </em><em>4</em><em>]</em><em>]</em>) – the box coordinates in (x1, y1, x2, y2)
format where the regions will be taken from. If a single Tensor is passed,
then the first column should contain the batch index. If a list of Tensors
is passed, then each Tensor will correspond to the boxes for an element i
in a batch</p></li>
<li><p><strong>output_size</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>Tuple</em><em>[</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><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>) – the size of the output after the cropping
is performed, as (height, width)</p></li>
<li><p><strong>spatial_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>) – a scaling factor that maps the input coordinates to
the box coordinates. Default: 1.0</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>output (Tensor[K, C, output_size[0], output_size[1]])</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.ops.ps_roi_pool">
<code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">ps_roi_pool</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">boxes</em>, <em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale=1.0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/ps_roi_pool.html#ps_roi_pool"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.ps_roi_pool" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Position-Sensitive Region of Interest (RoI) Pool operator
described in R-FCN</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>N</em><em>, </em><em>C</em><em>, </em><em>H</em><em>, </em><em>W</em><em>]</em>) – input tensor</p></li>
<li><p><strong>boxes</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>K</em><em>, </em><em>5</em><em>] or </em><em>List</em><em>[</em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>L</em><em>, </em><em>4</em><em>]</em><em>]</em>) – the box coordinates in (x1, y1, x2, y2)
format where the regions will be taken from. If a single Tensor is passed,
then the first column should contain the batch index. If a list of Tensors
is passed, then each Tensor will correspond to the boxes for an element i
in a batch</p></li>
<li><p><strong>output_size</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>Tuple</em><em>[</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><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>) – the size of the output after the cropping
is performed, as (height, width)</p></li>
<li><p><strong>spatial_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>) – a scaling factor that maps the input coordinates to
the box coordinates. Default: 1.0</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>output (Tensor[K, C, output_size[0], output_size[1]])</p>
</dd>
</dl>
</dd></dl>
<dl class="function">
<dt id="torchvision.ops.deform_conv2d">
<code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">deform_conv2d</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">offset</em>, <em class="sig-param">weight</em>, <em class="sig-param">bias=None</em>, <em class="sig-param">stride=(1</em>, <em class="sig-param">1)</em>, <em class="sig-param">padding=(0</em>, <em class="sig-param">0)</em>, <em class="sig-param">dilation=(1</em>, <em class="sig-param">1)</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/deform_conv.html#deform_conv2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.deform_conv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>Performs Deformable Convolution, described in Deformable Convolutional Networks</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>batch_size</em><em>, </em><em>in_channels</em><em>, </em><em>in_height</em><em>, </em><em>in_width</em><em>]</em>) – input tensor</p></li>
<li><p><strong>(Tensor[batch_size, 2 * offset_groups * kernel_height * kernel_width,</strong> (<em>offset</em>) – out_height, out_width]): offsets to be applied for each position in the
convolution kernel.</p></li>
<li><p><strong>weight</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>out_channels</em><em>, </em><em>in_channels // groups</em><em>, </em><em>kernel_height</em><em>, </em><em>kernel_width</em><em>]</em>) – convolution weights, split into groups of size (in_channels // groups)</p></li>
<li><p><strong>bias</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>[</em><em>out_channels</em><em>]</em>) – optional bias of shape (out_channels,). Default: None</p></li>
<li><p><strong>stride</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>Tuple</em><em>[</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><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>) – distance between convolution centers. Default: 1</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>Tuple</em><em>[</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><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>) – height/width of padding of zeroes around
each image. Default: 0</p></li>
<li><p><strong>dilation</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>Tuple</em><em>[</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><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>) – the spacing between kernel elements. Default: 1</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>result of convolution</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>output (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>[batch_sz, out_channels, out_h, out_w])</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="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">kh</span><span class="p">,</span> <span class="n">kw</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span>
<span class="gp">>>> </span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">kh</span><span class="p">,</span> <span class="n">kw</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># offset should have the same spatial size as the output</span>
<span class="gp">>>> </span><span class="c1"># of the convolution. In this case, for an input of 10, stride of 1</span>
<span class="gp">>>> </span><span class="c1"># and kernel size of 3, without padding, the output size is 8</span>
<span class="gp">>>> </span><span class="n">offset</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">kh</span> <span class="o">*</span> <span class="n">kw</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">out</span> <span class="o">=</span> <span class="n">deform_conv2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">offset</span><span class="p">,</span> <span class="n">weight</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># returns</span>
<span class="gp">>>> </span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">])</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.RoIAlign">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">RoIAlign</code><span class="sig-paren">(</span><em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale</em>, <em class="sig-param">sampling_ratio</em>, <em class="sig-param">aligned=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/roi_align.html#RoIAlign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.RoIAlign" title="Permalink to this definition">¶</a></dt>
<dd><p>See roi_align</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.PSRoIAlign">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">PSRoIAlign</code><span class="sig-paren">(</span><em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale</em>, <em class="sig-param">sampling_ratio</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/ps_roi_align.html#PSRoIAlign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.PSRoIAlign" title="Permalink to this definition">¶</a></dt>
<dd><p>See ps_roi_align</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.RoIPool">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">RoIPool</code><span class="sig-paren">(</span><em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/roi_pool.html#RoIPool"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.RoIPool" title="Permalink to this definition">¶</a></dt>
<dd><p>See roi_pool</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.PSRoIPool">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">PSRoIPool</code><span class="sig-paren">(</span><em class="sig-param">output_size</em>, <em class="sig-param">spatial_scale</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/ps_roi_pool.html#PSRoIPool"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.PSRoIPool" title="Permalink to this definition">¶</a></dt>
<dd><p>See ps_roi_pool</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.DeformConv2d">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">DeformConv2d</code><span class="sig-paren">(</span><em class="sig-param">in_channels</em>, <em class="sig-param">out_channels</em>, <em class="sig-param">kernel_size</em>, <em class="sig-param">stride=1</em>, <em class="sig-param">padding=0</em>, <em class="sig-param">dilation=1</em>, <em class="sig-param">groups=1</em>, <em class="sig-param">bias=True</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/deform_conv.html#DeformConv2d"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.DeformConv2d" title="Permalink to this definition">¶</a></dt>
<dd><p>See deform_conv2d</p>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.MultiScaleRoIAlign">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">MultiScaleRoIAlign</code><span class="sig-paren">(</span><em class="sig-param">featmap_names</em>, <em class="sig-param">output_size</em>, <em class="sig-param">sampling_ratio</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/poolers.html#MultiScaleRoIAlign"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.MultiScaleRoIAlign" title="Permalink to this definition">¶</a></dt>
<dd><p>Multi-scale RoIAlign pooling, which is useful for detection with or without FPN.</p>
<p>It infers the scale of the pooling via the heuristics present in the FPN paper.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>featmap_names</strong> (<em>List</em><em>[</em><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.8)"><em>str</em></a><em>]</em>) – the names of the feature maps that will be used
for the pooling.</p></li>
<li><p><strong>output_size</strong> (<em>List</em><em>[</em><em>Tuple</em><em>[</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><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>] or </em><em>List</em><em>[</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>) – output size for the pooled region</p></li>
<li><p><strong>sampling_ratio</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – sampling ratio for ROIAlign</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">MultiScaleRoIAlign</span><span class="p">([</span><span class="s1">'feat1'</span><span class="p">,</span> <span class="s1">'feat3'</span><span class="p">],</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">i</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">i</span><span class="p">[</span><span class="s1">'feat1'</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">i</span><span class="p">[</span><span class="s1">'feat2'</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span> <span class="c1"># this feature won't be used in the pooling</span>
<span class="gp">>>> </span><span class="n">i</span><span class="p">[</span><span class="s1">'feat3'</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># create some random bounding boxes</span>
<span class="gp">>>> </span><span class="n">boxes</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="mi">256</span><span class="p">;</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">:]</span> <span class="o">+=</span> <span class="n">boxes</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="gp">>>> </span><span class="c1"># original image size, before computing the feature maps</span>
<span class="gp">>>> </span><span class="n">image_sizes</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">)]</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">[</span><span class="n">boxes</span><span class="p">],</span> <span class="n">image_sizes</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
</pre></div>
</div>
</dd></dl>
<dl class="class">
<dt id="torchvision.ops.FeaturePyramidNetwork">
<em class="property">class </em><code class="sig-prename descclassname">torchvision.ops.</code><code class="sig-name descname">FeaturePyramidNetwork</code><span class="sig-paren">(</span><em class="sig-param">in_channels_list</em>, <em class="sig-param">out_channels</em>, <em class="sig-param">extra_blocks=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/ops/feature_pyramid_network.html#FeaturePyramidNetwork"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.ops.FeaturePyramidNetwork" title="Permalink to this definition">¶</a></dt>
<dd><p>Module that adds a FPN from on top of a set of feature maps. This is based on
<a class="reference external" href="https://arxiv.org/abs/1612.03144">“Feature Pyramid Network for Object Detection”</a>.</p>
<p>The feature maps are currently supposed to be in increasing depth
order.</p>
<p>The input to the model is expected to be an OrderedDict[Tensor], containing
the feature maps on top of which the FPN will be added.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>in_channels_list</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>[</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>) – number of channels for each feature map that
is passed to the module</p></li>
<li><p><strong>out_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>) – number of channels of the FPN representation</p></li>
<li><p><strong>extra_blocks</strong> (<em>ExtraFPNBlock</em><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.8)"><em>None</em></a>) – if provided, extra operations will
be performed. It is expected to take the fpn features, the original
features and the names of the original features as input, and returns
a new list of feature maps and their corresponding names</p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">m</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">FeaturePyramidNetwork</span><span class="p">([</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">],</span> <span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># get some dummy data</span>
<span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="s1">'feat0'</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="s1">'feat2'</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="s1">'feat3'</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="c1"># compute the FPN on top of x</span>
<span class="gp">>>> </span><span class="n">output</span> <span class="o">=</span> <span class="n">m</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">([(</span><span class="n">k</span><span class="p">,</span> <span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">items</span><span class="p">()])</span>
<span class="gp">>>> </span><span class="c1"># returns</span>
<span class="gp">>>> </span> <span class="p">[(</span><span class="s1">'feat0'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">])),</span>
<span class="gp">>>> </span> <span class="p">(</span><span class="s1">'feat2'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">])),</span>
<span class="gp">>>> </span> <span class="p">(</span><span class="s1">'feat3'</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">]))]</span>
</pre></div>
</div>
</dd></dl>
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