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<!DOCTYPE html>
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<title>torchvision.datasets — PyTorch master documentation</title>
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<p class="caption"><span class="caption-text">Notes</span></p>
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<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>
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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>
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<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>
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<li class="toctree-l2"><a class="reference internal" href="../notes/multiprocessing.html#sharing-cuda-tensors">Sharing CUDA tensors</a></li>
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<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>
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<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>
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<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>
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<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>
</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#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>
<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#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>
</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#id16"><span class="hidden-section">conv1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id17"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id18"><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-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-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>
</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#id19"><span class="hidden-section">threshold</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id20"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id21"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id22"><span class="hidden-section">relu6</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id23"><span class="hidden-section">elu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id24"><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#id25"><span class="hidden-section">prelu</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#id26"><span class="hidden-section">logsigmoid</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#id27"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id28"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id29"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id30"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id31"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id32"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id33"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id34"><span class="hidden-section">sigmoid</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#normalization-functions">Normalization functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#batch-norm"><span class="hidden-section">batch_norm</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#normalize"><span class="hidden-section">normalize</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#linear-functions">Linear functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id35"><span class="hidden-section">linear</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#dropout-functions">Dropout functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id36"><span class="hidden-section">dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#alpha-dropout"><span class="hidden-section">alpha_dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id37"><span class="hidden-section">dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id38"><span class="hidden-section">dropout3d</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#id39">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pairwise-distance"><span class="hidden-section">pairwise_distance</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cosine-similarity"><span class="hidden-section">cosine_similarity</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#id40">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#binary-cross-entropy"><span class="hidden-section">binary_cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#poisson-nll-loss"><span class="hidden-section">poisson_nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cosine-embedding-loss"><span class="hidden-section">cosine_embedding_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#cross-entropy"><span class="hidden-section">cross_entropy</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#hinge-embedding-loss"><span class="hidden-section">hinge_embedding_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#kl-div"><span class="hidden-section">kl_div</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#l1-loss"><span class="hidden-section">l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#mse-loss"><span class="hidden-section">mse_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#margin-ranking-loss"><span class="hidden-section">margin_ranking_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabel-margin-loss"><span class="hidden-section">multilabel_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multilabel-soft-margin-loss"><span class="hidden-section">multilabel_soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#multi-margin-loss"><span class="hidden-section">multi_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#nll-loss"><span class="hidden-section">nll_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#binary-cross-entropy-with-logits"><span class="hidden-section">binary_cross_entropy_with_logits</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#smooth-l1-loss"><span class="hidden-section">smooth_l1_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#soft-margin-loss"><span class="hidden-section">soft_margin_loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#triplet-margin-loss"><span class="hidden-section">triplet_margin_loss</span></a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../nn.html#vision-functions">Vision functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pixel-shuffle"><span class="hidden-section">pixel_shuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#pad"><span class="hidden-section">pad</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#id42"><span class="hidden-section">upsample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsample-nearest"><span class="hidden-section">upsample_nearest</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#upsample-bilinear"><span class="hidden-section">upsample_bilinear</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#grid-sample"><span class="hidden-section">grid_sample</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="../nn.html#affine-grid"><span class="hidden-section">affine_grid</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../nn.html#torch-nn-init">torch.nn.init</a></li>
<li class="toctree-l1"><a class="reference internal" href="../optim.html">torch.optim</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../optim.html#how-to-use-an-optimizer">How to use an optimizer</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../optim.html#constructing-it">Constructing it</a></li>
<li class="toctree-l3"><a class="reference internal" href="../optim.html#per-parameter-options">Per-parameter options</a></li>
<li class="toctree-l3"><a class="reference internal" href="../optim.html#taking-an-optimization-step">Taking an optimization step</a><ul>
<li class="toctree-l4"><a class="reference internal" href="../optim.html#optimizer-step"><code class="docutils literal"><span class="pre">optimizer.step()</span></code></a></li>
<li class="toctree-l4"><a class="reference internal" href="../optim.html#optimizer-step-closure"><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../optim.html#algorithms">Algorithms</a></li>
<li class="toctree-l2"><a class="reference internal" href="../optim.html#how-to-adjust-learning-rate">How to adjust Learning Rate</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../autograd.html">torch.autograd</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../autograd.html#variable">Variable</a><ul>
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</ul>
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<div class="section" id="torchvision-datasets">
<h1>torchvision.datasets<a class="headerlink" href="#torchvision-datasets" title="Permalink to this headline">¶</a></h1>
<p>All datasets are subclasses of <a class="reference internal" href="../data.html#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><code class="xref py py-class docutils literal"><span class="pre">torch.utils.data.Dataset</span></code></a>
i.e, they have <code class="docutils literal"><span class="pre">__getitem__</span></code> and <code class="docutils literal"><span class="pre">__len__</span></code> methods implemented.
Hence, they can all be passed to a <a class="reference internal" href="../data.html#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal"><span class="pre">torch.utils.data.DataLoader</span></code></a>
which can load multiple samples parallelly using <code class="docutils literal"><span class="pre">torch.multiprocessing</span></code> workers.
For example:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">imagenet_data</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageFolder</span><span class="p">(</span><span class="s1">'path/to/imagenet_root/'</span><span class="p">)</span>
<span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">imagenet_data</span><span class="p">,</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">num_workers</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">nThreads</span><span class="p">)</span>
</pre></div>
</div>
<p>The following datasets are available:</p>
<div class="contents local topic" id="datasets">
<p class="topic-title first">Datasets</p>
<ul class="simple">
<li><a class="reference internal" href="#mnist" id="id6">MNIST</a></li>
<li><a class="reference internal" href="#coco" id="id7">COCO</a><ul>
<li><a class="reference internal" href="#captions" id="id8">Captions</a></li>
<li><a class="reference internal" href="#detection" id="id9">Detection</a></li>
</ul>
</li>
<li><a class="reference internal" href="#lsun" id="id10">LSUN</a></li>
<li><a class="reference internal" href="#imagefolder" id="id11">ImageFolder</a></li>
<li><a class="reference internal" href="#imagenet-12" id="id12">Imagenet-12</a></li>
<li><a class="reference internal" href="#cifar" id="id13">CIFAR</a></li>
<li><a class="reference internal" href="#stl10" id="id14">STL10</a></li>
<li><a class="reference internal" href="#svhn" id="id15">SVHN</a></li>
<li><a class="reference internal" href="#phototour" id="id16">PhotoTour</a></li>
</ul>
</div>
<p>All the datasets have almost similar API. They all have two common arguments:
<code class="docutils literal"><span class="pre">transform</span></code> and <code class="docutils literal"><span class="pre">target_transform</span></code> to transform the input and target respectively.</p>
<div class="section" id="mnist">
<h2><a class="toc-backref" href="#id6">MNIST</a><a class="headerlink" href="#mnist" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.MNIST">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">MNIST</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/mnist.html#MNIST"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.MNIST" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://yann.lecun.com/exdb/mnist/">MNIST</a> 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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory of dataset where <code class="docutils literal"><span class="pre">processed/training.pt</span></code>
and <code class="docutils literal"><span class="pre">processed/test.pt</span></code> exist.</li>
<li><strong>train</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from <code class="docutils literal"><span class="pre">training.pt</span></code>,
otherwise from <code class="docutils literal"><span class="pre">test.pt</span></code>.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
</div>
<div class="section" id="coco">
<h2><a class="toc-backref" href="#id7">COCO</a><a class="headerlink" href="#coco" title="Permalink to this headline">¶</a></h2>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">These require the <a class="reference external" href="https://github.com/pdollar/coco/tree/master/PythonAPI">COCO API to be installed</a></p>
</div>
<div class="section" id="captions">
<h3><a class="toc-backref" href="#id8">Captions</a><a class="headerlink" href="#captions" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torchvision.datasets.CocoCaptions">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CocoCaptions</code><span class="sig-paren">(</span><em>root</em>, <em>annFile</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoCaptions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoCaptions" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://mscoco.org/dataset/#captions-challenge2015">MS Coco Captions</a> 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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory where images are downloaded to.</li>
<li><strong>annFile</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Path to json annotation file.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.ToTensor</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p class="rubric">Example</p>
<div class="code python highlight-default"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.datasets</span> <span class="k">as</span> <span class="nn">dset</span>
<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
<span class="n">cap</span> <span class="o">=</span> <span class="n">dset</span><span class="o">.</span><span class="n">CocoCaptions</span><span class="p">(</span><span class="n">root</span> <span class="o">=</span> <span class="s1">'dir where images are'</span><span class="p">,</span>
<span class="n">annFile</span> <span class="o">=</span> <span class="s1">'json annotation file'</span><span class="p">,</span>
<span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Number of samples: '</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">cap</span><span class="p">))</span>
<span class="n">img</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">cap</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># load 4th sample</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Image Size: "</span><span class="p">,</span> <span class="n">img</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
</pre></div>
</div>
<p>Output:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">Number</span> <span class="n">of</span> <span class="n">samples</span><span class="p">:</span> <span class="mi">82783</span>
<span class="n">Image</span> <span class="n">Size</span><span class="p">:</span> <span class="p">(</span><span class="mi">3</span><span class="n">L</span><span class="p">,</span> <span class="mi">427</span><span class="n">L</span><span class="p">,</span> <span class="mi">640</span><span class="n">L</span><span class="p">)</span>
<span class="p">[</span><span class="sa">u</span><span class="s1">'A plane emitting smoke stream flying over a mountain.'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A plane darts across a bright blue sky behind a mountain covered in snow'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A plane leaves a contrail above the snowy mountain top.'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A mountain that has a plane flying overheard in the distance.'</span><span class="p">,</span>
<span class="sa">u</span><span class="s1">'A mountain view with a plume of smoke in the background'</span><span class="p">]</span>
</pre></div>
</div>
<dl class="method">
<dt id="torchvision.datasets.CocoCaptions.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoCaptions.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoCaptions.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Tuple (image, target). target is a list of captions for the image.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="detection">
<h3><a class="toc-backref" href="#id9">Detection</a><a class="headerlink" href="#detection" title="Permalink to this headline">¶</a></h3>
<dl class="class">
<dt id="torchvision.datasets.CocoDetection">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CocoDetection</code><span class="sig-paren">(</span><em>root</em>, <em>annFile</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoDetection"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoDetection" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://mscoco.org/dataset/#detections-challenge2016">MS Coco Captions</a> 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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory where images are downloaded to.</li>
<li><strong>annFile</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Path to json annotation file.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.ToTensor</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.CocoDetection.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/coco.html#CocoDetection.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CocoDetection.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Tuple (image, target). target is the object returned by <code class="docutils literal"><span class="pre">coco.loadAnns</span></code>.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
</div>
<div class="section" id="lsun">
<h2><a class="toc-backref" href="#id10">LSUN</a><a class="headerlink" href="#lsun" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.LSUN">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">LSUN</code><span class="sig-paren">(</span><em>db_path</em>, <em>classes='train'</em>, <em>transform=None</em>, <em>target_transform=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/lsun.html#LSUN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.LSUN" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://lsun.cs.princeton.edu">LSUN</a> 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>db_path</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory for the database files.</li>
<li><strong>classes</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a><em> or </em><em>list</em>) – One of {‘train’, ‘val’, ‘test’} or a list of
categories to load. e,g. [‘bedroom_train’, ‘church_train’].</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.LSUN.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/lsun.html#LSUN.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.LSUN.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">Tuple (image, target) where target is the index of the target category.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="imagefolder">
<h2><a class="toc-backref" href="#id11">ImageFolder</a><a class="headerlink" href="#imagefolder" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.ImageFolder">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">ImageFolder</code><span class="sig-paren">(</span><em>root</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>loader=<function default_loader></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/folder.html#ImageFolder"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.ImageFolder" title="Permalink to this definition">¶</a></dt>
<dd><p>A generic data loader where the images are arranged in this way:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="n">root</span><span class="o">/</span><span class="n">dog</span><span class="o">/</span><span class="n">xxx</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">dog</span><span class="o">/</span><span class="n">xxy</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">dog</span><span class="o">/</span><span class="n">xxz</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">cat</span><span class="o">/</span><span class="mf">123.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">cat</span><span class="o">/</span><span class="n">nsdf3</span><span class="o">.</span><span class="n">png</span>
<span class="n">root</span><span class="o">/</span><span class="n">cat</span><span class="o">/</span><span class="n">asd932_</span><span class="o">.</span><span class="n">png</span>
</pre></div>
</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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory path.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>loader</strong> – A function to load an image given its path.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.ImageFolder.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/folder.html#ImageFolder.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.ImageFolder.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is class_index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="imagenet-12">
<h2><a class="toc-backref" href="#id12">Imagenet-12</a><a class="headerlink" href="#imagenet-12" title="Permalink to this headline">¶</a></h2>
<p>This should simply be implemented with an <code class="docutils literal"><span class="pre">ImageFolder</span></code> dataset.
The data is preprocessed <a class="reference external" href="https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md#download-the-imagenet-dataset">as described
here</a></p>
<p><a class="reference external" href="https://github.com/pytorch/examples/blob/27e2a46c1d1505324032b1d94fc6ce24d5b67e97/imagenet/main.py#L48-L62">Here is an
example</a>.</p>
</div>
<div class="section" id="cifar">
<h2><a class="toc-backref" href="#id13">CIFAR</a><a class="headerlink" href="#cifar" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.CIFAR10">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">CIFAR10</code><span class="sig-paren">(</span><em>root</em>, <em>train=True</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/cifar.html#CIFAR10"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CIFAR10" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://www.cs.toronto.edu/~kriz/cifar.html">CIFAR10</a> 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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory of dataset where directory
<code class="docutils literal"><span class="pre">cifar-10-batches-py</span></code> exists.</li>
<li><strong>train</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If True, creates dataset from training set, otherwise
creates from test set.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.CIFAR10.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/cifar.html#CIFAR10.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.CIFAR10.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="stl10">
<h2><a class="toc-backref" href="#id14">STL10</a><a class="headerlink" href="#stl10" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.STL10">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">STL10</code><span class="sig-paren">(</span><em>root</em>, <em>split='train'</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/stl10.html#STL10"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.STL10" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="https://cs.stanford.edu/~acoates/stl10/">STL10</a> 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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory of dataset where directory
<code class="docutils literal"><span class="pre">stl10_binary</span></code> exists.</li>
<li><strong>split</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – One of {‘train’, ‘test’, ‘unlabeled’, ‘train+unlabeled’}.
Accordingly dataset is selected.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.STL10.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/stl10.html#STL10.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.STL10.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>
</dd></dl>
</div>
<div class="section" id="svhn">
<h2><a class="toc-backref" href="#id15">SVHN</a><a class="headerlink" href="#svhn" title="Permalink to this headline">¶</a></h2>
<dl class="class">
<dt id="torchvision.datasets.SVHN">
<em class="property">class </em><code class="descclassname">torchvision.datasets.</code><code class="descname">SVHN</code><span class="sig-paren">(</span><em>root</em>, <em>split='train'</em>, <em>transform=None</em>, <em>target_transform=None</em>, <em>download=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/svhn.html#SVHN"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.SVHN" title="Permalink to this definition">¶</a></dt>
<dd><p><a class="reference external" href="http://ufldl.stanford.edu/housenumbers/">SVHN</a> 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>root</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – Root directory of dataset where directory
<code class="docutils literal"><span class="pre">SVHN</span></code> exists.</li>
<li><strong>split</strong> (<a class="reference external" href="https://docs.python.org/2/library/string.html#module-string" title="(in Python v2.7)"><em>string</em></a>) – One of {‘train’, ‘test’, ‘extra’}.
Accordingly dataset is selected. ‘extra’ is Extra training set.</li>
<li><strong>transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in an PIL image
and returns a transformed version. E.g, <code class="docutils literal"><span class="pre">transforms.RandomCrop</span></code></li>
<li><strong>target_transform</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#callable" title="(in Python v2.7)"><em>callable</em></a><em>, </em><em>optional</em>) – A function/transform that takes in the
target and transforms it.</li>
<li><strong>download</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If true, downloads the dataset from the internet and
puts it in root directory. If dataset is already downloaded, it is not
downloaded again.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<dl class="method">
<dt id="torchvision.datasets.SVHN.__getitem__">
<code class="descname">__getitem__</code><span class="sig-paren">(</span><em>index</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torchvision/datasets/svhn.html#SVHN.__getitem__"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torchvision.datasets.SVHN.__getitem__" 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>index</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – Index</td>
</tr>
<tr class="field-even field"><th class="field-name">Returns:</th><td class="field-body">(image, target) where target is index of the target class.</td>
</tr>
<tr class="field-odd field"><th class="field-name">Return type:</th><td class="field-body"><a class="reference external" href="https://docs.python.org/2/library/functions.html#tuple" title="(in Python v2.7)">tuple</a></td>
</tr>
</tbody>
</table>
</dd></dl>