neuralmonkey.readers package

Submodules

neuralmonkey.readers.audio_reader module

class neuralmonkey.readers.audio_reader.Audio(rate, data)

Bases: tuple

data

Alias for field number 1

rate

Alias for field number 0

neuralmonkey.readers.audio_reader.audio_reader(prefix: str = '', audio_format: str = 'wav') → typing.Callable

Get a reader of audio files loading them from a list of pahts.

Parameters:prefix – Prefix of the paths to the audio files. Returns:The reader function that takes a list of audio file paths (relative to provided prefix) and returns a list of numpy arrays.

neuralmonkey.readers.image_reader module

neuralmonkey.readers.image_reader.image_reader(prefix='', pad_w: typing.Union[int, NoneType] = None, pad_h: typing.Union[int, NoneType] = None, rescale_w: bool = False, rescale_h: bool = False, keep_aspect_ratio: bool = False, mode: str = 'RGB') → typing.Callable

Get a reader of images loading them from a list of pahts.

Parameters:

• prefix – Prefix of the paths that are listed in a image files.
• pad_w – Width to which the images will be padded/cropped/resized.
• pad_h – Height to with the images will be padded/corpped/resized.
• rescale_w – If true, image is rescaled to have given width. It is cropped/padded otherwise.
• rescale_h – If true, image is rescaled to have given height. It is cropped/padded otherwise.
• keep_aspect_ratio – Flag whether the aspect ration should be kept during rescaling. Can only be used if both width and height are rescaled.
• mode – Scipy image loading mode, see scipy documentation for more details.

Returns:

The reader function that takes a list of image paths (relative to provided prefix) and returns a list of images as numpy arrays of shape pad_h x pad_w x number of channels.

neuralmonkey.readers.image_reader.imagenet_reader(prefix: str, target_width: int = 227, target_height: int = 227) → typing.Callable

Load and prepare image the same way as Caffe scripts.

neuralmonkey.readers.numpy_reader module

neuralmonkey.readers.numpy_reader.numpy_reader(files: typing.List[str])

neuralmonkey.readers.plain_text_reader module

neuralmonkey.readers.plain_text_reader.UtfPlainTextReader(files: typing.List[str]) → typing.Iterable[typing.List[str]]

neuralmonkey.readers.plain_text_reader.column_separated_reader(column: int, delimiter: str = '\t', quotechar: str = None, encoding: str = 'utf-8') → typing.Callable[[typing.List[str]], typing.Iterable[typing.List[str]]]

Get reader for delimiter-separated tokenized text.

Parameters:column – number of column to be returned. It starts with 1 for the first

neuralmonkey.readers.plain_text_reader.csv_reader(column: int)

neuralmonkey.readers.plain_text_reader.string_reader(encoding: str = 'utf-8') → typing.Callable[[typing.List[str]], typing.Iterable[str]]

neuralmonkey.readers.plain_text_reader.tokenized_text_reader(encoding: str = 'utf-8') → typing.Callable[[typing.List[str]], typing.Iterable[typing.List[str]]]

Get reader for space-separated tokenized text.

neuralmonkey.readers.plain_text_reader.tsv_reader(column: int)

neuralmonkey.readers.string_vector_reader module

neuralmonkey.readers.string_vector_reader.FloatVectorReader(files: typing.List[str]) → typing.Iterable[typing.List[numpy.ndarray]]

neuralmonkey.readers.string_vector_reader.IntVectorReader(files: typing.List[str]) → typing.Iterable[typing.List[numpy.ndarray]]

neuralmonkey.readers.string_vector_reader.get_string_vector_reader(dtype: typing.Type = <class 'numpy.float32'>, columns: int = None)

Get a reader for vectors encoded as whitespace-separated numbers

Module contents