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                <a href="https://pytorch.org/docs/versions.html"> 0.2.0_1 &#x25BC</a>
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              <p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../notes/autograd.html#requires-grad"><code class="docutils literal"><span class="pre">requires_grad</span></code></a></li>
<li class="toctree-l3"><a class="reference internal" href="../notes/autograd.html#volatile"><code class="docutils literal"><span class="pre">volatile</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../notes/autograd.html#how-autograd-encodes-the-history">How autograd encodes the history</a></li>
<li class="toctree-l2"><a class="reference internal" href="../notes/autograd.html#in-place-operations-on-variables">In-place operations on Variables</a></li>
<li class="toctree-l2"><a class="reference internal" href="../notes/autograd.html#in-place-correctness-checks">In-place correctness checks</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../notes/broadcasting.html">Broadcasting semantics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/broadcasting.html#general-semantics">General semantics</a></li>
<li class="toctree-l2"><a class="reference internal" href="../notes/broadcasting.html#in-place-semantics">In-place semantics</a></li>
<li class="toctree-l2"><a class="reference internal" href="../notes/broadcasting.html#backwards-compatibility">Backwards compatibility</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../notes/cuda.html">CUDA semantics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/cuda.html#best-practices">Best practices</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../notes/cuda.html#use-pinned-memory-buffers">Use pinned memory buffers</a></li>
<li class="toctree-l3"><a class="reference internal" href="../notes/cuda.html#use-nn-dataparallel-instead-of-multiprocessing">Use nn.DataParallel instead of multiprocessing</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../notes/extending.html">Extending PyTorch</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/extending.html#extending-torch-autograd">Extending <code class="docutils literal"><span class="pre">torch.autograd</span></code></a></li>
<li class="toctree-l2"><a class="reference internal" href="../notes/extending.html#extending-torch-nn">Extending <code class="docutils literal"><span class="pre">torch.nn</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="../notes/extending.html#adding-a-module">Adding a <code class="docutils literal"><span class="pre">Module</span></code></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../notes/extending.html#writing-custom-c-extensions">Writing custom C extensions</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../notes/multiprocessing.html">Multiprocessing best practices</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/multiprocessing.html#sharing-cuda-tensors">Sharing CUDA tensors</a></li>
<li class="toctree-l2"><a class="reference internal" href="../notes/multiprocessing.html#best-practices-and-tips">Best practices and tips</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../notes/multiprocessing.html#avoiding-and-fighting-deadlocks">Avoiding and fighting deadlocks</a></li>
<li class="toctree-l3"><a class="reference internal" href="../notes/multiprocessing.html#reuse-buffers-passed-through-a-queue">Reuse buffers passed through a Queue</a></li>
<li class="toctree-l3"><a class="reference internal" href="../notes/multiprocessing.html#asynchronous-multiprocess-training-e-g-hogwild">Asynchronous multiprocess training (e.g. Hogwild)</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../notes/multiprocessing.html#hogwild">Hogwild</a></li>
</ul>
</li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../notes/serialization.html">Serialization semantics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../notes/serialization.html#best-practices">Best practices</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../notes/serialization.html#recommended-approach-for-saving-a-model">Recommended approach for saving a model</a></li>
</ul>
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</ul>
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</ul>
<p class="caption"><span class="caption-text">Package Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../torch.html">torch</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../torch.html#tensors">Tensors</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#creation-ops">Creation Ops</a></li>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#indexing-slicing-joining-mutating-ops">Indexing, Slicing, Joining, Mutating Ops</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../torch.html#random-sampling">Random sampling</a></li>
<li class="toctree-l2"><a class="reference internal" href="../torch.html#serialization">Serialization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../torch.html#parallelism">Parallelism</a></li>
<li class="toctree-l2"><a class="reference internal" href="../torch.html#math-operations">Math operations</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#pointwise-ops">Pointwise Ops</a></li>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#reduction-ops">Reduction Ops</a></li>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#comparison-ops">Comparison Ops</a></li>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#other-operations">Other Operations</a></li>
<li class="toctree-l3"><a class="reference internal" href="../torch.html#blas-and-lapack-operations">BLAS and LAPACK Operations</a></li>
</ul>
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</ul>
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<li class="toctree-l1"><a class="reference internal" href="../tensors.html">torch.Tensor</a></li>
<li class="toctree-l1"><a class="reference internal" href="../sparse.html">torch.sparse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../storage.html">torch.Storage</a></li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html">torch.nn</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#parameters">Parameters</a></li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#containers">Containers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#module"><span class="hidden-section">Module</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#sequential"><span class="hidden-section">Sequential</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#modulelist"><span class="hidden-section">ModuleList</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#parameterlist"><span class="hidden-section">ParameterList</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../nn.html#convolution-layers">Convolution Layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv1d"><span class="hidden-section">Conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv2d"><span class="hidden-section">Conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#conv3d"><span class="hidden-section">Conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#convtranspose1d"><span class="hidden-section">ConvTranspose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#convtranspose2d"><span class="hidden-section">ConvTranspose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#convtranspose3d"><span class="hidden-section">ConvTranspose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#pooling-layers">Pooling Layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxpool1d"><span class="hidden-section">MaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxpool2d"><span class="hidden-section">MaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxpool3d"><span class="hidden-section">MaxPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxunpool1d"><span class="hidden-section">MaxUnpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxunpool2d"><span class="hidden-section">MaxUnpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#maxunpool3d"><span class="hidden-section">MaxUnpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avgpool1d"><span class="hidden-section">AvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avgpool2d"><span class="hidden-section">AvgPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#avgpool3d"><span class="hidden-section">AvgPool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#fractionalmaxpool2d"><span class="hidden-section">FractionalMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lppool2d"><span class="hidden-section">LPPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptivemaxpool1d"><span class="hidden-section">AdaptiveMaxPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptivemaxpool2d"><span class="hidden-section">AdaptiveMaxPool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptiveavgpool1d"><span class="hidden-section">AdaptiveAvgPool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#adaptiveavgpool2d"><span class="hidden-section">AdaptiveAvgPool2d</span></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../nn.html#padding-layers">Padding Layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#reflectionpad2d"><span class="hidden-section">ReflectionPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#replicationpad2d"><span class="hidden-section">ReplicationPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#replicationpad3d"><span class="hidden-section">ReplicationPad3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#zeropad2d"><span class="hidden-section">ZeroPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#constantpad2d"><span class="hidden-section">ConstantPad2d</span></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../nn.html#non-linear-activations">Non-linear Activations</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#relu"><span class="hidden-section">ReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#relu6"><span class="hidden-section">ReLU6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#elu"><span class="hidden-section">ELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#selu"><span class="hidden-section">SELU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#prelu"><span class="hidden-section">PReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#leakyrelu"><span class="hidden-section">LeakyReLU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#threshold"><span class="hidden-section">Threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#hardtanh"><span class="hidden-section">Hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#sigmoid"><span class="hidden-section">Sigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#tanh"><span class="hidden-section">Tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#logsigmoid"><span class="hidden-section">LogSigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softplus"><span class="hidden-section">Softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softshrink"><span class="hidden-section">Softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softsign"><span class="hidden-section">Softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#tanhshrink"><span class="hidden-section">Tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmin"><span class="hidden-section">Softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmax"><span class="hidden-section">Softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#logsoftmax"><span class="hidden-section">LogSoftmax</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#normalization-layers">Normalization layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batchnorm1d"><span class="hidden-section">BatchNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batchnorm2d"><span class="hidden-section">BatchNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#instancenorm1d"><span class="hidden-section">InstanceNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#instancenorm2d"><span class="hidden-section">InstanceNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#instancenorm3d"><span class="hidden-section">InstanceNorm3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#recurrent-layers">Recurrent layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#rnn"><span class="hidden-section">RNN</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lstm"><span class="hidden-section">LSTM</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#gru"><span class="hidden-section">GRU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#lstmcell"><span class="hidden-section">LSTMCell</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#grucell"><span class="hidden-section">GRUCell</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#linear-layers">Linear layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#linear"><span class="hidden-section">Linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#dropout-layers">Dropout layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#alphadropout"><span class="hidden-section">AlphaDropout</span></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="../nn.html#sparse-layers">Sparse layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#embedding"><span class="hidden-section">Embedding</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#embeddingbag"><span class="hidden-section">EmbeddingBag</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#distance-functions">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cosinesimilarity"><span class="hidden-section">CosineSimilarity</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pairwisedistance"><span class="hidden-section">PairwiseDistance</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#poissonnllloss"><span class="hidden-section">PoissonNLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#bceloss"><span class="hidden-section">BCELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#bcewithlogitsloss"><span class="hidden-section">BCEWithLogitsLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#tripletmarginloss"><span class="hidden-section">TripletMarginLoss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#vision-layers">Vision layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pixelshuffle"><span class="hidden-section">PixelShuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsample"><span class="hidden-section">Upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#dataparallel-layers-multi-gpu-distributed">DataParallel layers (multi-GPU, distributed)</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#dataparallel"><span class="hidden-section">DataParallel</span></a></li>
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<p class="caption"><span class="caption-text">torchvision Reference</span></p>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">torchvision.transforms</a><ul>
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  <div class="section" id="torchvision-transforms">
<h1>torchvision.transforms<a class="headerlink" href="#torchvision-transforms" title="Permalink to this headline">¶</a></h1>
<p>Transforms are common image transforms. They can be chained together using <a class="reference internal" href="#torchvision.transforms.Compose" title="torchvision.transforms.Compose"><code class="xref py py-class docutils literal"><span class="pre">Compose</span></code></a></p>
<dl class="class">
<dt id="torchvision.transforms.Compose">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">Compose</code><span class="sig-paren">(</span><em>transforms</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#Compose"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Compose" title="Permalink to this definition">¶</a></dt>
<dd><p>Composes several transforms together.</p>
<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>transforms</strong> (list of <code class="docutils literal"><span class="pre">Transform</span></code> objects) &#8211; list of transforms to compose.</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
<span class="gp">&gt;&gt;&gt; </span>    <span class="n">transforms</span><span class="o">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">10</span><span class="p">),</span>
<span class="gp">&gt;&gt;&gt; </span>    <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
<span class="gp">&gt;&gt;&gt; </span><span class="p">])</span>
</pre></div>
</div>
</dd></dl>

<div class="section" id="transforms-on-pil-image">
<h2>Transforms on PIL.Image<a class="headerlink" href="#transforms-on-pil-image" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.Scale">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">Scale</code><span class="sig-paren">(</span><em>size</em>, <em>interpolation=2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#Scale"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Scale" title="Permalink to this definition">¶</a></dt>
<dd><p>Rescale the input PIL.Image to the given size.</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>size</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) &#8211; Desired output size. If size is a sequence like
(w, h), output size will be matched to this. If size is an int,
smaller edge of the image will be matched to this number.
i.e, if height &gt; width, then image will be rescaled to
(size * height / width, size)</li>
<li><strong>interpolation</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em>, </em><em>optional</em>) &#8211; Desired interpolation. Default is
<code class="docutils literal"><span class="pre">PIL.Image.BILINEAR</span></code></li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="torchvision.transforms.CenterCrop">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">CenterCrop</code><span class="sig-paren">(</span><em>size</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#CenterCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.CenterCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crops the given PIL.Image at the center.</p>
<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>size</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) &#8211; Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="torchvision.transforms.RandomCrop">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">RandomCrop</code><span class="sig-paren">(</span><em>size</em>, <em>padding=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#RandomCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop the given PIL.Image at a random location.</p>
<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>size</strong> (<em>sequence</em><em> or </em><a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) &#8211; Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.</li>
<li><strong>padding</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><em>sequence</em><em>, </em><em>optional</em>) &#8211; Optional padding on each border
of the image. Default is 0, i.e no padding. If a sequence of length
4 is provided, it is used to pad left, top, right, bottom borders
respectively.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="torchvision.transforms.RandomHorizontalFlip">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">RandomHorizontalFlip</code><a class="reference internal" href="../_modules/torchvision/transforms.html#RandomHorizontalFlip"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomHorizontalFlip" title="Permalink to this definition">¶</a></dt>
<dd><p>Horizontally flip the given PIL.Image randomly with a probability of 0.5.</p>
</dd></dl>

<dl class="class">
<dt id="torchvision.transforms.RandomSizedCrop">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">RandomSizedCrop</code><span class="sig-paren">(</span><em>size</em>, <em>interpolation=2</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#RandomSizedCrop"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.RandomSizedCrop" title="Permalink to this definition">¶</a></dt>
<dd><p>Crop the given PIL.Image to random size and aspect ratio.</p>
<p>A crop of random size of (0.08 to 1.0) of the original size and a random
aspect ratio of 3/4 to 4/3 of the original aspect ratio is made. This crop
is finally resized to given size.
This is popularly used to train the Inception networks.</p>
<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>size</strong> &#8211; size of the smaller edge</li>
<li><strong>interpolation</strong> &#8211; Default: PIL.Image.BILINEAR</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

<dl class="class">
<dt id="torchvision.transforms.Pad">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">Pad</code><span class="sig-paren">(</span><em>padding</em>, <em>fill=0</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#Pad"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Pad" title="Permalink to this definition">¶</a></dt>
<dd><p>Pad the given PIL.Image on all sides with the given &#8220;pad&#8221; value.</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>padding</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a><em> or </em><em>sequence</em>) &#8211; Padding on each border. If a sequence of
length 4, it is used to pad left, top, right and bottom borders respectively.</li>
<li><strong>fill</strong> &#8211; Pixel fill value. Default is 0.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
<div class="section" id="transforms-on-torch-tensor">
<h2>Transforms on torch.*Tensor<a class="headerlink" href="#transforms-on-torch-tensor" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.Normalize">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">Normalize</code><span class="sig-paren">(</span><em>mean</em>, <em>std</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#Normalize"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Normalize" title="Permalink to this definition">¶</a></dt>
<dd><p>Normalize an tensor image with mean and standard deviation.</p>
<p>Given mean: (R, G, B) and std: (R, G, B),
will normalize each channel of the torch.*Tensor, i.e.
channel = (channel - mean) / std</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>mean</strong> (<em>sequence</em>) &#8211; Sequence of means for R, G, B channels respecitvely.</li>
<li><strong>std</strong> (<em>sequence</em>) &#8211; Sequence of standard deviations for R, G, B channels
respecitvely.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.transforms.Normalize.__call__">
<code class="descname">__call__</code><span class="sig-paren">(</span><em>tensor</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#Normalize.__call__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Normalize.__call__" title="Permalink to this definition">¶</a></dt>
<dd><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>tensor</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) &#8211; Tensor image of size (C, H, W) to be normalized.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Normalized image.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="conversion-transforms">
<h2>Conversion Transforms<a class="headerlink" href="#conversion-transforms" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.ToTensor">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">ToTensor</code><a class="reference internal" href="../_modules/torchvision/transforms.html#ToTensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToTensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a <code class="docutils literal"><span class="pre">PIL.Image</span></code> or <code class="docutils literal"><span class="pre">numpy.ndarray</span></code> to tensor.</p>
<p>Converts a PIL.Image or numpy.ndarray (H x W x C) in the range
[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].</p>
<dl class="method">
<dt id="torchvision.transforms.ToTensor.__call__">
<code class="descname">__call__</code><span class="sig-paren">(</span><em>pic</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#ToTensor.__call__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToTensor.__call__" title="Permalink to this definition">¶</a></dt>
<dd><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>pic</strong> (<em>PIL.Image</em><em> or </em><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.13)"><em>numpy.ndarray</em></a>) &#8211; Image to be converted to tensor.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Converted image.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a></td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

<dl class="class">
<dt id="torchvision.transforms.ToPILImage">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">ToPILImage</code><a class="reference internal" href="../_modules/torchvision/transforms.html#ToPILImage"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToPILImage" title="Permalink to this definition">¶</a></dt>
<dd><p>Convert a tensor to PIL Image.</p>
<p>Converts a torch.*Tensor of shape C x H x W or a numpy ndarray of shape
H x W x C to a PIL.Image while preserving the value range.</p>
<dl class="method">
<dt id="torchvision.transforms.ToPILImage.__call__">
<code class="descname">__call__</code><span class="sig-paren">(</span><em>pic</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#ToPILImage.__call__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.ToPILImage.__call__" title="Permalink to this definition">¶</a></dt>
<dd><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>pic</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.13)"><em>numpy.ndarray</em></a>) &#8211; Image to be converted to PIL.Image.</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Image converted to PIL.Image.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body">PIL.Image</td>
</tr>
</tbody>
</table>
</dd></dl>

</dd></dl>

</div>
<div class="section" id="generic-transforms">
<h2>Generic Transforms<a class="headerlink" href="#generic-transforms" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.transforms.Lambda">
<em class="property">class </em><code class="descclassname">torchvision.transforms.</code><code class="descname">Lambda</code><span class="sig-paren">(</span><em>lambd</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/transforms.html#Lambda"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.transforms.Lambda" title="Permalink to this definition">¶</a></dt>
<dd><p>Apply a user-defined lambda as a transform.</p>
<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>lambd</strong> (<em>function</em>) &#8211; Lambda/function to be used for transform.</td>
</tr>
</tbody>
</table>
</dd></dl>

</div>
</div>


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