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  <div class="section" id="module-torch.utils.data">
<span id="torch-utils-data"></span><h1>torch.utils.data<a class="headerlink" href="#module-torch.utils.data" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torch.utils.data.Dataset">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">Dataset</code><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Dataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.Dataset" title="Permalink to this definition">¶</a></dt>
<dd><p>An abstract class representing a Dataset.</p>
<p>All other datasets should subclass it. All subclasses should override
<code class="docutils literal notranslate"><span class="pre">__len__</span></code>, that provides the size of the dataset, and <code class="docutils literal notranslate"><span class="pre">__getitem__</span></code>,
supporting integer indexing in range from 0 to len(self) exclusive.</p>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.TensorDataset">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">TensorDataset</code><span class="sig-paren">(</span><em>*tensors</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#TensorDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.TensorDataset" title="Permalink to this definition">¶</a></dt>
<dd><p>Dataset wrapping tensors.</p>
<p>Each sample will be retrieved by indexing tensors along the first dimension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>*tensors</strong> (<a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – tensors that have the same size of the first dimension.</p>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.ConcatDataset">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">ConcatDataset</code><span class="sig-paren">(</span><em>datasets</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ConcatDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.ConcatDataset" title="Permalink to this definition">¶</a></dt>
<dd><p>Dataset to concatenate multiple datasets.
Purpose: useful to assemble different existing datasets, possibly
large-scale datasets as the concatenation operation is done in an
on-the-fly manner.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>datasets</strong> (<em>sequence</em>) – List of datasets to be concatenated</p>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.Subset">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">Subset</code><span class="sig-paren">(</span><em>dataset</em>, <em>indices</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Subset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.Subset" title="Permalink to this definition">¶</a></dt>
<dd><p>Subset of a dataset at specified indices.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – The whole Dataset</p></li>
<li><p><strong>indices</strong> (<em>sequence</em>) – Indices in the whole set selected for subset</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.DataLoader">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">DataLoader</code><span class="sig-paren">(</span><em>dataset</em>, <em>batch_size=1</em>, <em>shuffle=False</em>, <em>sampler=None</em>, <em>batch_sampler=None</em>, <em>num_workers=0</em>, <em>collate_fn=&lt;function default_collate&gt;</em>, <em>pin_memory=False</em>, <em>drop_last=False</em>, <em>timeout=0</em>, <em>worker_init_fn=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataloader.html#DataLoader"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.DataLoader" title="Permalink to this definition">¶</a></dt>
<dd><p>Data loader. Combines a dataset and a sampler, and provides
single- or multi-process iterators over the dataset.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – dataset from which to load the data.</p></li>
<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – how many samples per batch to load
(default: <code class="docutils literal notranslate"><span class="pre">1</span></code>).</p></li>
<li><p><strong>shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – set to <code class="docutils literal notranslate"><span class="pre">True</span></code> to have the data reshuffled
at every epoch (default: <code class="docutils literal notranslate"><span class="pre">False</span></code>).</p></li>
<li><p><strong>sampler</strong> (<a class="reference internal" href="#torch.utils.data.Sampler" title="torch.utils.data.Sampler"><em>Sampler</em></a><em>, </em><em>optional</em>) – defines the strategy to draw samples from
the dataset. If specified, <code class="docutils literal notranslate"><span class="pre">shuffle</span></code> must be False.</p></li>
<li><p><strong>batch_sampler</strong> (<a class="reference internal" href="#torch.utils.data.Sampler" title="torch.utils.data.Sampler"><em>Sampler</em></a><em>, </em><em>optional</em>) – like sampler, but returns a batch of
indices at a time. Mutually exclusive with <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_size</span></code>,
<code class="xref py py-attr docutils literal notranslate"><span class="pre">shuffle</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">sampler</span></code>, and <code class="xref py py-attr docutils literal notranslate"><span class="pre">drop_last</span></code>.</p></li>
<li><p><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a><em>, </em><em>optional</em>) – how many subprocesses to use for data
loading. 0 means that the data will be loaded in the main process.
(default: <code class="docutils literal notranslate"><span class="pre">0</span></code>)</p></li>
<li><p><strong>collate_fn</strong> (<em>callable</em><em>, </em><em>optional</em>) – merges a list of samples to form a mini-batch.</p></li>
<li><p><strong>pin_memory</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the data loader will copy tensors
into CUDA pinned memory before returning them.  If your data elements
are a custom type, or your <code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> returns a batch that is a custom type
see the example below.</p></li>
<li><p><strong>drop_last</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – set to <code class="docutils literal notranslate"><span class="pre">True</span></code> to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If <code class="docutils literal notranslate"><span class="pre">False</span></code> and
the size of dataset is not divisible by the batch size, then the last batch
will be smaller. (default: <code class="docutils literal notranslate"><span class="pre">False</span></code>)</p></li>
<li><p><strong>timeout</strong> (<em>numeric</em><em>, </em><em>optional</em>) – if positive, the timeout value for collecting a batch
from workers. Should always be non-negative. (default: <code class="docutils literal notranslate"><span class="pre">0</span></code>)</p></li>
<li><p><strong>worker_init_fn</strong> (<em>callable</em><em>, </em><em>optional</em>) – If not <code class="docutils literal notranslate"><span class="pre">None</span></code>, this will be called on each
worker subprocess with the worker id (an int in <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">num_workers</span> <span class="pre">-</span> <span class="pre">1]</span></code>) as
input, after seeding and before data loading. (default: <code class="docutils literal notranslate"><span class="pre">None</span></code>)</p></li>
</ul>
</dd>
</dl>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>When <code class="docutils literal notranslate"><span class="pre">num_workers</span> <span class="pre">!=</span> <span class="pre">0</span></code>, the corresponding worker processes are created each time
iterator for the DataLoader is obtained (as in when you call
<code class="docutils literal notranslate"><span class="pre">enumerate(dataloader,0)</span></code>).
At this point, the dataset, <code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> and <code class="docutils literal notranslate"><span class="pre">worker_init_fn</span></code> are passed to each
worker, where they are used to access and initialize data based on the indices
queued up from the main process. This means that dataset access together with
its internal IO, transforms and collation runs in the worker, while any
shuffle randomization is done in the main process which guides loading by assigning
indices to load. Workers are shut down once the end of the iteration is reached.</p>
<p>Since workers rely on Python multiprocessing, worker launch behavior is different
on Windows compared to Unix. On Unix fork() is used as the default
muliprocessing start method, so child workers typically can access the dataset and
Python argument functions directly through the cloned address space. On Windows, another
interpreter is launched which runs your main script, followed by the internal
worker function that receives the dataset, collate_fn and other arguments
through Pickle serialization.</p>
<p>This separate serialization means that you should take two steps to ensure you
are compatible with Windows while using workers
(this also works equally well on Unix):</p>
<ul class="simple">
<li><p>Wrap most of you main script’s code within <code class="docutils literal notranslate"><span class="pre">if</span> <span class="pre">__name__</span> <span class="pre">==</span> <span class="pre">'__main__':</span></code> block,
to make sure it doesn’t run again (most likely generating error) when each worker
process is launched. You can place your dataset and DataLoader instance creation
logic here, as it doesn’t need to be re-executed in workers.</p></li>
<li><p>Make sure that <code class="docutils literal notranslate"><span class="pre">collate_fn</span></code>, <code class="docutils literal notranslate"><span class="pre">worker_init_fn</span></code> or any custom dataset code
is declared as a top level def, outside of that <code class="docutils literal notranslate"><span class="pre">__main__</span></code> check. This ensures
they are available in workers as well
(this is needed since functions are pickled as references only, not bytecode).</p></li>
</ul>
<p>By default, each worker will have its PyTorch seed set to
<code class="docutils literal notranslate"><span class="pre">base_seed</span> <span class="pre">+</span> <span class="pre">worker_id</span></code>, where <code class="docutils literal notranslate"><span class="pre">base_seed</span></code> is a long generated
by main process using its RNG. However, seeds for other libraies
may be duplicated upon initializing workers (w.g., NumPy), causing
each worker to return identical random numbers. (See
<a class="reference internal" href="notes/faq.html#dataloader-workers-random-seed"><span class="std std-ref">My data loader workers return identical random numbers</span></a> section in FAQ.) You may
use <a class="reference internal" href="torch.html#torch.initial_seed" title="torch.initial_seed"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.initial_seed()</span></code></a> to access the PyTorch seed for
each worker in <code class="xref py py-attr docutils literal notranslate"><span class="pre">worker_init_fn</span></code>, and use it to set other
seeds before data loading.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>If <code class="docutils literal notranslate"><span class="pre">spawn</span></code> start method is used, <code class="xref py py-attr docutils literal notranslate"><span class="pre">worker_init_fn</span></code> cannot be an
unpicklable object, e.g., a lambda function.</p>
</div>
<p>The default memory pinning logic only recognizes Tensors and maps and iterables
containg Tensors.  By default, if the pinning logic sees a batch that is a custom type
(which will occur if you have a <code class="docutils literal notranslate"><span class="pre">collate_fn</span></code> that returns a custom batch type),
or if each element of your batch is a custom type, the pinning logic will not
recognize them, and it will return that batch (or those elements)
without pinning the memory.  To enable memory pinning for custom batch or data types,
define a <code class="docutils literal notranslate"><span class="pre">pin_memory</span></code> method on your custom type(s).</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">SimpleCustomBatch</span><span class="p">:</span>
    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
        <span class="n">transposed_data</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="o">*</span><span class="n">data</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">inp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">transposed_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">tgt</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">transposed_data</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">pin_memory</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">inp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inp</span><span class="o">.</span><span class="n">pin_memory</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">tgt</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tgt</span><span class="o">.</span><span class="n">pin_memory</span><span class="p">()</span>
        <span class="k">return</span> <span class="bp">self</span>

<span class="k">def</span> <span class="nf">collate_wrapper</span><span class="p">(</span><span class="n">batch</span><span class="p">):</span>
    <span class="k">return</span> <span class="n">SimpleCustomBatch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>

<span class="n">inps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">tgts</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
<span class="n">dataset</span> <span class="o">=</span> <span class="n">TensorDataset</span><span class="p">(</span><span class="n">inps</span><span class="p">,</span> <span class="n">tgts</span><span class="p">)</span>

<span class="n">loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">collate_fn</span><span class="o">=</span><span class="n">collate_wrapper</span><span class="p">,</span>
                    <span class="n">pin_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="k">for</span> <span class="n">batch_ndx</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">loader</span><span class="p">):</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">inp</span><span class="o">.</span><span class="n">is_pinned</span><span class="p">())</span>
    <span class="nb">print</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">tgt</span><span class="o">.</span><span class="n">is_pinned</span><span class="p">())</span>
</pre></div>
</div>
</dd></dl>

<dl class="function">
<dt id="torch.utils.data.random_split">
<code class="descclassname">torch.utils.data.</code><code class="descname">random_split</code><span class="sig-paren">(</span><em>dataset</em>, <em>lengths</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#random_split"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.random_split" title="Permalink to this definition">¶</a></dt>
<dd><p>Randomly split a dataset into non-overlapping new datasets of given lengths.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – Dataset to be split</p></li>
<li><p><strong>lengths</strong> (<em>sequence</em>) – lengths of splits to be produced</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.Sampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">Sampler</code><span class="sig-paren">(</span><em>data_source</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#Sampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.Sampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Base class for all Samplers.</p>
<p>Every Sampler subclass has to provide an __iter__ method, providing a way
to iterate over indices of dataset elements, and a __len__ method that
returns the length of the returned iterators.</p>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.SequentialSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">SequentialSampler</code><span class="sig-paren">(</span><em>data_source</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SequentialSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.SequentialSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements sequentially, always in the same order.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data_source</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – dataset to sample from</p>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.RandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">RandomSampler</code><span class="sig-paren">(</span><em>data_source</em>, <em>replacement=False</em>, <em>num_samples=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#RandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.RandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements randomly. If without replacement, then sample from a shuffled dataset.
If with replacement, then user can specify <code class="docutils literal notranslate"><span class="pre">num_samples</span></code> to draw.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data_source</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – dataset to sample from</p></li>
<li><p><strong>replacement</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – samples are drawn with replacement if <code class="docutils literal notranslate"><span class="pre">True</span></code>, default=``False``</p></li>
<li><p><strong>num_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – number of samples to draw, default=`len(dataset)`. This argument
is supposed to be specified only when <cite>replacement</cite> is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.SubsetRandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">SubsetRandomSampler</code><span class="sig-paren">(</span><em>indices</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SubsetRandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.SubsetRandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements randomly from a given list of indices, without replacement.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>indices</strong> (<em>sequence</em>) – a sequence of indices</p>
</dd>
</dl>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.WeightedRandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">WeightedRandomSampler</code><span class="sig-paren">(</span><em>weights</em>, <em>num_samples</em>, <em>replacement=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#WeightedRandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.WeightedRandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements from [0,..,len(weights)-1] with given probabilities (weights).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>weights</strong> (<em>sequence</em>) – a sequence of weights, not necessary summing up to one</p></li>
<li><p><strong>num_samples</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – number of samples to draw</p></li>
<li><p><strong>replacement</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – if <code class="docutils literal notranslate"><span class="pre">True</span></code>, samples are drawn with replacement.
If not, they are drawn without replacement, which means that when a
sample index is drawn for a row, it cannot be drawn again for that row.</p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">WeightedRandomSampler</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">replacement</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="go">[0, 0, 0, 1, 0]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">WeightedRandomSampler</span><span class="p">([</span><span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.4</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.3</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">],</span> <span class="mi">5</span><span class="p">,</span> <span class="n">replacement</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="go">[0, 1, 4, 3, 2]</span>
</pre></div>
</div>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.BatchSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.</code><code class="descname">BatchSampler</code><span class="sig-paren">(</span><em>sampler</em>, <em>batch_size</em>, <em>drop_last</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#BatchSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.BatchSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Wraps another sampler to yield a mini-batch of indices.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sampler</strong> (<a class="reference internal" href="#torch.utils.data.Sampler" title="torch.utils.data.Sampler"><em>Sampler</em></a>) – Base sampler.</p></li>
<li><p><strong>batch_size</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.7)"><em>int</em></a>) – Size of mini-batch.</p></li>
<li><p><strong>drop_last</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the sampler will drop the last batch if
its size would be less than <code class="docutils literal notranslate"><span class="pre">batch_size</span></code></p></li>
</ul>
</dd>
</dl>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">BatchSampler</span><span class="p">(</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
<span class="go">[[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]]</span>
<span class="gp">&gt;&gt;&gt; </span><span class="nb">list</span><span class="p">(</span><span class="n">BatchSampler</span><span class="p">(</span><span class="n">SequentialSampler</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">10</span><span class="p">)),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">drop_last</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
<span class="go">[[0, 1, 2], [3, 4, 5], [6, 7, 8]]</span>
</pre></div>
</div>
</dd></dl>

<dl class="class">
<dt id="torch.utils.data.distributed.DistributedSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.distributed.</code><code class="descname">DistributedSampler</code><span class="sig-paren">(</span><em>dataset</em>, <em>num_replicas=None</em>, <em>rank=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/distributed.html#DistributedSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.distributed.DistributedSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Sampler that restricts data loading to a subset of the dataset.</p>
<p>It is especially useful in conjunction with
<a class="reference internal" href="nn.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code></a>. In such case, each
process can pass a DistributedSampler instance as a DataLoader sampler,
and load a subset of the original dataset that is exclusive to it.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Dataset is assumed to be of constant size.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dataset</strong> – Dataset used for sampling.</p></li>
<li><p><strong>num_replicas</strong> (<em>optional</em>) – Number of processes participating in
distributed training.</p></li>
<li><p><strong>rank</strong> (<em>optional</em>) – Rank of the current process within num_replicas.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

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