Data Loaders

Data Readers

exception onmt.inputters.datareader_base.MissingDependencyException

Bases: Exception

class onmt.inputters.DataReaderBase

Bases: object

Read data from file system and yield as dicts.

Raises

onmt.inputters.datareader_base.MissingDependencyException – A number of DataReaders need specific additional packages. If any are missing, this will be raised.

classmethod from_opt(opt)

Alternative constructor.

Parameters

opt (argparse.Namespace) – The parsed arguments.

read(data, side, src_dir)

Read data from file system and yield as dicts.

class onmt.inputters.TextDataReader

Bases: onmt.inputters.datareader_base.DataReaderBase

read(sequences, side, _dir=None)

Read text data from disk.

Parameters

• sequences (str or Iterable[str]) – path to text file or iterable of the actual text data.

• side (str) – Prefix used in return dict. Usually "src" or "tgt".

• _dir (NoneType) – Leave as None. This parameter exists to conform with the DataReaderBase.read() signature.

Yields

dictionaries whose keys are the names of fields and whose values are more or less the result of tokenizing with those fields.

class onmt.inputters.ImageDataReader(truncate=None, channel_size=3)

Bases: onmt.inputters.datareader_base.DataReaderBase

Read image data from disk.

Parameters

• truncate (tuple[int] or NoneType) – maximum img size. Use (0,0) or None for unlimited.

• channel_size (int) – Number of channels per image.

Raises

onmt.inputters.datareader_base.MissingDependencyException – If importing any of PIL, torchvision, or cv2 fail.

classmethod from_opt(opt)

Alternative constructor.

Parameters

opt (argparse.Namespace) – The parsed arguments.

read(images, side, img_dir=None)

Read data into dicts.

Parameters

• images (str or Iterable[str]) – Sequence of image paths or path to file containing audio paths. In either case, the filenames may be relative to src_dir (default behavior) or absolute.

• side (str) – Prefix used in return dict. Usually "src" or "tgt".

• img_dir (str) – Location of source image files. See images.

Yields

a dictionary containing image data, path and index for each line.

class onmt.inputters.AudioDataReader(sample_rate=0, window_size=0, window_stride=0, window=None, normalize_audio=True, truncate=None)

Bases: onmt.inputters.datareader_base.DataReaderBase

Read audio data from disk.

Parameters

• sample_rate (int) – sample_rate.

• window_size (float) – window size for spectrogram in seconds.

• window_stride (float) – window stride for spectrogram in seconds.

• window (str) – window type for spectrogram generation. See librosa.stft() window for more details.

• normalize_audio (bool) – subtract spectrogram by mean and divide by std or not.

• truncate (int or NoneType) – maximum audio length (0 or None for unlimited).

Raises

onmt.inputters.datareader_base.MissingDependencyException – If importing any of torchaudio, librosa, or numpy fail.

classmethod from_opt(opt)

Alternative constructor.

Parameters

opt (argparse.Namespace) – The parsed arguments.

read(data, side, src_dir=None)

Read data into dicts.

Parameters

• data (str or Iterable[str]) – Sequence of audio paths or path to file containing audio paths. In either case, the filenames may be relative to src_dir (default behavior) or absolute.

• side (str) – Prefix used in return dict. Usually "src" or "tgt".

• src_dir (str) – Location of source audio files. See data.

Yields

A dictionary containing audio data for each line.

Dataset

class onmt.inputters.Dataset(fields, readers, data, dirs, sort_key, filter_pred=None)

Bases: torchtext.data.dataset.Dataset

Contain data and process it.

A dataset is an object that accepts sequences of raw data (sentence pairs in the case of machine translation) and fields which describe how this raw data should be processed to produce tensors. When a dataset is instantiated, it applies the fields’ preprocessing pipeline (but not the bit that numericalizes it or turns it into batch tensors) to the raw data, producing a list of torchtext.data.Example objects. torchtext’s iterators then know how to use these examples to make batches.

Parameters

• fields (dict[str, Field]) – a dict with the structure returned by onmt.inputters.get_fields(). Usually that means the dataset side, "src" or "tgt". Keys match the keys of items yielded by the readers, while values are lists of (name, Field) pairs. An attribute with this name will be created for each torchtext.data.Example object and its value will be the result of applying the Field to the data that matches the key. The advantage of having sequences of fields for each piece of raw input is that it allows the dataset to store multiple “views” of each input, which allows for easy implementation of token-level features, mixed word- and character-level models, and so on. (See also onmt.inputters.TextMultiField.)

• readers (Iterable[onmt.inputters.DataReaderBase]) – Reader objects for disk-to-dict. The yielded dicts are then processed according to fields.

• data (Iterable[Tuple[str, Any]]) – (name, data_arg) pairs where data_arg is passed to the read() method of the reader in readers at that position. (See the reader object for details on the Any type.)

• dirs (Iterable[str or NoneType]) – A list of directories where data is contained. See the reader object for more details.

• sort_key (Callable[[torchtext.data.Example], Any]) – A function for determining the value on which data is sorted (i.e. length).

• filter_pred (Callable[[torchtext.data.Example], bool]) – A function that accepts Example objects and returns a boolean value indicating whether to include that example in the dataset.

Variables

src_vocabs (List[torchtext.data.Vocab]) – Used with dynamic dict/copy attention. There is a very short vocab for each src example. It contains just the source words, e.g. so that the generator can predict to copy them.