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<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>
</ul>
</li>
<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#lppool1d"><span class="hidden-section">LPPool1d</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#adaptivemaxpool3d"><span class="hidden-section">AdaptiveMaxPool3d</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptiveavgpool3d"><span class="hidden-section">AdaptiveAvgPool3d</span></a></li>
</ul>
</li>
<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#reflectionpad1d"><span class="hidden-section">ReflectionPad1d</span></a></li>
<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#replicationpad1d"><span class="hidden-section">ReplicationPad1d</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#constantpad1d"><span class="hidden-section">ConstantPad1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#constantpad2d"><span class="hidden-section">ConstantPad2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#constantpad3d"><span class="hidden-section">ConstantPad3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activations-weighted-sum-nonlinearity">Non-linear activations (weighted sum, nonlinearity)</a><ul>
<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#hardshrink"><span class="hidden-section">Hardshrink</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#leakyrelu"><span class="hidden-section">LeakyReLU</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#prelu"><span class="hidden-section">PReLU</span></a></li>
<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#rrelu"><span class="hidden-section">RReLU</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#sigmoid"><span class="hidden-section">Sigmoid</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#tanh"><span class="hidden-section">Tanh</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#threshold"><span class="hidden-section">Threshold</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activations-other">Non-linear activations (other)</a><ul>
<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#softmax2d"><span class="hidden-section">Softmax2d</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#layernorm"><span class="hidden-section">LayerNorm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#localresponsenorm"><span class="hidden-section">LocalResponseNorm</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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bilinear"><span class="hidden-section">Bilinear</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>
</li>
<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#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>
<li class="toctree-l3"><a class="reference internal" href="nn.html#distributeddataparallel"><span class="hidden-section">DistributedDataParallel</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#utilities">Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#clip-grad-norm"><span class="hidden-section">clip_grad_norm_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#clip-grad-value"><span class="hidden-section">clip_grad_value_</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#weight-norm"><span class="hidden-section">weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#remove-weight-norm"><span class="hidden-section">remove_weight_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#packedsequence"><span class="hidden-section">PackedSequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-sequence"><span class="hidden-section">pad_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pack-sequence"><span class="hidden-section">pack_sequence</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn.html#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn.html#convolution-functions">Convolution functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id20"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id21"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose3d"><span class="hidden-section">conv_transpose3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#pooling-functions">Pooling functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool1d"><span class="hidden-section">max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool2d"><span class="hidden-section">max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool3d"><span class="hidden-section">max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool1d"><span class="hidden-section">max_unpool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool3d"><span class="hidden-section">max_unpool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lp-pool1d"><span class="hidden-section">lp_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool3d"><span class="hidden-section">adaptive_max_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool3d"><span class="hidden-section">adaptive_avg_pool3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id23"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id25"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id26"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id27"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">selu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#leaky-relu"><span class="hidden-section">leaky_relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">prelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><span class="hidden-section">rrelu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#glu"><span class="hidden-section">glu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id31"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id32"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id33"><span class="hidden-section">tanhshrink</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="distributions.html#module-torch.distributions.kl"><cite>KL Divergence</cite></a></li>
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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"><span class="pre">__len__</span></code>, that provides the size of the dataset, and <code class="docutils literal"><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>iterable</em>) – List of datasets to be concatenated</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 v3.6)"><em>int</em></a><em>, </em><em>optional</em>) – how many samples per batch to load
(default: 1).</li>
<li><strong>shuffle</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.6)"><em>bool</em></a><em>, </em><em>optional</em>) – set to <code class="docutils literal"><span class="pre">True</span></code> to have the data reshuffled
at every epoch (default: False).</li>
<li><strong>sampler</strong> (<a class="reference internal" href="#torch.utils.data.sampler.Sampler" title="torch.utils.data.sampler.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"><span class="pre">shuffle</span></code> must be False.</li>
<li><strong>batch_sampler</strong> (<a class="reference internal" href="#torch.utils.data.sampler.Sampler" title="torch.utils.data.sampler.Sampler"><em>Sampler</em></a><em>, </em><em>optional</em>) – like sampler, but returns a batch of
indices at a time. Mutually exclusive with batch_size, shuffle,
sampler, and drop_last.</li>
<li><strong>num_workers</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.6)"><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: 0)</li>
<li><strong>collate_fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#callable" title="(in Python v3.6)"><em>callable</em></a><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 v3.6)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal"><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 v3.6)"><em>bool</em></a><em>, </em><em>optional</em>) – set to <code class="docutils literal"><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"><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: False)</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: 0)</li>
<li><strong>worker_init_fn</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#callable" title="(in Python v3.6)"><em>callable</em></a><em>, </em><em>optional</em>) – If not None, this will be called on each
worker subprocess with the worker id (an int in <code class="docutils literal"><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: None)</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"><span class="pre">base_seed</span> <span class="pre">+</span> <span class="pre">worker_id</span></code>, where <code class="docutils literal"><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 <code class="docutils literal"><span class="pre">torch.initial_seed()</span></code> to access the PyTorch seed for each
worker in <code class="xref py py-attr docutils literal"><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"><span class="pre">spawn</span></code> start method is used, <code class="xref py py-attr docutils literal"><span class="pre">worker_init_fn</span></code> cannot be an
unpicklable object, e.g., a lambda function.</p>
</div>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.Sampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</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.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.sampler.SequentialSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</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.sampler.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.sampler.RandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</code><code class="descname">RandomSampler</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#RandomSampler"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.utils.data.sampler.RandomSampler" title="Permalink to this definition">¶</a></dt>
<dd><p>Samples elements randomly, 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>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.sampler.SubsetRandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</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.sampler.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> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.6)"><em>list</em></a>) – a list of indices</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="class">
<dt id="torch.utils.data.sampler.WeightedRandomSampler">
<em class="property">class </em><code class="descclassname">torch.utils.data.sampler.</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.sampler.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> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.6)"><em>list</em></a>) – a list 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 v3.6)"><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 v3.6)"><em>bool</em></a>) – if <code class="docutils literal"><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.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"><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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