Data Producer

class neural_pipeline.data_producer.data_producer.DataProducer(datasets: [<class 'neural_pipeline.data_producer.data_producer.AbstractDataset'>], batch_size: int = 1, num_workers: int = 0)[source]

Data Producer. Accumulate one or more datasets and pass it’s data by batches for processing. This use PyTorch builtin DataLoader for increase performance of data delivery.

  • datasets – list of datasets. Every dataset might be iterable (contans methods __getitem__ and __len__)
  • batch_size – size of output batch
  • num_workers – number of processes, that load data from datasets and pass it for output
get_data(dataset_idx: int, data_idx: int) → object[source]

Get single data by dataset idx and data_idx

  • dataset_idx – index of dataset
  • data_idx – index of data in this dataset

dataset output

get_loader(indices: [<class 'str'>] = None) →[source]

Get PyTorch DataLoader object, that aggregate DataProducer. If indices is specified - DataLoader wil output data only by this indices. In this case indices will not passed.

Parameters:indices – list of indices. Each item of list is a string in format ‘{}_{}’.format(dataset_idx, data_idx)
Returns:DataLoader object
global_shuffle(is_need: bool) → neural_pipeline.data_producer.data_producer.DataProducer[source]

Is need global shuffling. If global shuffling enable - batches will compile from random indices of all datasets. In this case datasets order shuffling was ignoring

Parameters:is_need – is need global shuffling
Returns:self object
pass_indices(need_pass: bool) → neural_pipeline.data_producer.data_producer.DataProducer[source]

Pass indices of data in every batch. By default disabled

Parameters:need_pass – is need to pass indices
pin_memory(is_need: bool) → neural_pipeline.data_producer.data_producer.DataProducer[source]

Is need to pin memory on loading. Pinning memory was increase data loading performance (especially when data loads to GPU) but incompatible with swap

Parameters:is_need – is need
Returns:self object
shuffle_datasets_order(is_need: bool) → neural_pipeline.data_producer.data_producer.DataProducer[source]

Is need to shuffle datasets order. Shuffling performs after every 0 index access

Parameters:is_need – is need
Returns:self object