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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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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.</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>datasets</strong> (<em>sequence</em>) – List of datasets to be concatenated</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>dataset</strong> (<a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><em>Dataset</em></a>) – The whole Dataset</li>
<li><strong>indices</strong> (<em>sequence</em>) – Indices in the whole set selected for subset</li>
</ul>
</td>
</tr>
</tbody>
</table>
</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=<function default_collate></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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><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.</li>
<li><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>).</li>
<li><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>).</li>
<li><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.</li>
<li><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>.</li>
<li><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>)</li>
<li><strong>collate_fn</strong> (<em>callable</em><em>, </em><em>optional</em>) – merges a list of samples to form a mini-batch.</li>
<li><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.</li>
<li><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>)</li>
<li><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>)</li>
<li><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>)</li>
</ul>
</td>
</tr>
</tbody>
</table>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">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="first admonition-title">Warning</p>
<p class="last">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>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><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</li>
<li><strong>lengths</strong> (<em>sequence</em>) – lengths of splits to be produced</li>
</ul>
</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><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</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><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</li>
<li><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)</li>
<li><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</li>
</ul>
</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>indices</strong> (<em>sequence</em>) – a sequence of indices</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>weights</strong> (<em>sequence</em>) – a sequence of weights, not necessary summing up to one</li>
<li><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</li>
<li><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.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>sampler</strong> (<a class="reference internal" href="#torch.utils.data.Sampler" title="torch.utils.data.Sampler"><em>Sampler</em></a>) – Base sampler.</li>
<li><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.</li>
<li><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></li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </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">>>> </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="first admonition-title">Note</p>
<p class="last">Dataset is assumed to be of constant size.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>dataset</strong> – Dataset used for sampling.</li>
<li><strong>num_replicas</strong> (<em>optional</em>) – Number of processes participating in
distributed training.</li>
<li><strong>rank</strong> (<em>optional</em>) – Rank of the current process within num_replicas.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
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