neuralmonkey.readers package¶
Submodules¶
neuralmonkey.readers.image_reader module¶
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neuralmonkey.readers.image_reader.image_reader(prefix='', pad_w: typing.Union[int, NoneType] = None, pad_h: typing.Union[int, NoneType] = None, rescale: 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 – If true, bigger images will be rescaled to the pad_w x pad_h size. Otherwise, they will be cropped from the middle.
- 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.
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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¶
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neuralmonkey.readers.numpy_reader.numpy_reader(files: typing.List[str])¶
neuralmonkey.readers.plain_text_reader module¶
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neuralmonkey.readers.plain_text_reader.UtfPlainTextReader(files: typing.List[str]) → typing.Iterable[typing.List[str]]¶
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neuralmonkey.readers.plain_text_reader.get_plain_text_reader(encoding: str = 'utf-8')¶ Get reader for space-separated tokenized text.