![]() In this case, the default collate_fn simply converts NumPy When automatic batching is disabled, collate_fn is called withĮach individual data sample, and the output is yielded from the data loader The use of collate_fn is slightly different when automatic batching is In this case, loading from a map-style dataset is roughly equivalent with: When automatic batching is disabled, the default collate_fn simplyĬonverts NumPy arrays into PyTorch Tensors, and keeps everything else untouched. Each sample obtained from the dataset is processed with theįunction passed as the collate_fn argument. Value for batch_sampler is already None), automatic batching isĭisabled. When both batch_size and batch_sampler are None (default ![]() Under these scenarios, it’s likelyīetter to not use automatic batching (where collate_fn is used toĬollate the samples), but let the data loader directly return each member of Load batched data (e.g., bulk reads from a database or reading continuousĬhunks of memory), or the batch size is data dependent, or the program isĭesigned to work on individual samples. For example, it could be cheaper to directly In certain cases, users may want to handle batching manually in dataset code, Indices at a time can be passed as the batch_sampler argument.Īutomatic batching can also be enabled via batch_size and Sampler could randomly permute a list of indicesĪnd yield each one at a time, or yield a small number of them for mini-batchĪ sequential or shuffled sampler will be automatically constructed based on the shuffle argument to a DataLoader.Īlternatively, users may use the sampler argument to specify aĬustom Sampler object that at each time yieldsĪ custom Sampler that yields a list of batch E.g., in theĬommon case with stochastic gradient decent (SGD), a They represent iterable objects over the indices to datasets. Ĭlasses are used to specify the sequence of indices/keys used in data loading. The rest of this section concerns the case with Implementations of chunk-reading and dynamic batch size (e.g., by yielding a Is entirely controlled by the user-defined iterable. CPU threading and TorchScript inferenceįor iterable-style datasets, data loading order. ![]() CUDA Automatic Mixed Precision examples. ![]()
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