IterableTorchMnistDataset.functions#
- IterableTorchMnistDataset.functions: Dict[str, Callable] = {'_dataframes_as_tuples': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.datapipes.DataFramesAsTuplesPipe'>, False), '_dataframes_concat': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.datapipes.ConcatDataFramesPipe'>, True), '_dataframes_filter': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.datapipes.FilterDataFramesPipe'>, True), '_dataframes_per_row': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.datapipes.PerRowDataFramesPipe'>, True), '_dataframes_shuffle': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.datapipes.ShuffleDataFramesPipe'>, True), '_to_dataframes_pipe': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.datapipes.ExampleAggregateAsDataFrames'>, True), 'batch': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.grouping.BatcherIterDataPipe'>, False), 'collate': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.callable.CollatorIterDataPipe'>, False), 'concat': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.combining.ConcaterIterDataPipe'>, False), 'decode': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.routeddecoder.RoutedDecoderIterDataPipe'>, False), 'demux': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.combining.DemultiplexerIterDataPipe'>, False), 'filter': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.selecting.FilterIterDataPipe'>, False), 'fork': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.combining.ForkerIterDataPipe'>, False), 'groupby': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.grouping.GrouperIterDataPipe'>, False), 'map': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.callable.MapperIterDataPipe'>, False), 'mux': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.combining.MultiplexerIterDataPipe'>, False), 'sharding_filter': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.grouping.ShardingFilterIterDataPipe'>, False), 'shuffle': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.combinatorics.ShufflerIterDataPipe'>, False), 'trace_as_dataframe': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.dataframe.dataframes.DataFrameTracer'>, False), 'unbatch': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.grouping.UnBatcherIterDataPipe'>, False), 'zip': functools.partial(<function Dataset.register_datapipe_as_function.<locals>.class_function>, <class 'torch.utils.data.datapipes.iter.combining.ZipperIterDataPipe'>, False)}#