Source code for doctr.datasets.generator.pytorch

# Copyright (C) 2021-2025, Mindee.

# This program is licensed under the Apache License 2.0.
# See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details.

from torch.utils.data._utils.collate import default_collate

from .base import _CharacterGenerator, _WordGenerator

__all__ = ["CharacterGenerator", "WordGenerator"]




[docs]
class CharacterGenerator(_CharacterGenerator):
    """Implements a character image generation dataset

    >>> from doctr.datasets import CharacterGenerator
    >>> ds = CharacterGenerator(vocab='abdef', num_samples=100)
    >>> img, target = ds[0]

    Args:
        vocab: vocabulary to take the character from
        num_samples: number of samples that will be generated iterating over the dataset
        cache_samples: whether generated images should be cached firsthand
        font_family: font to use to generate the text images
        img_transforms: composable transformations that will be applied to each image
        sample_transforms: composable transformations that will be applied to both the image and the target
    """

    def __init__(self, *args, **kwargs) -> None:
        super().__init__(*args, **kwargs)
        setattr(self, "collate_fn", default_collate)






[docs]
class WordGenerator(_WordGenerator):
    """Implements a character image generation dataset

    >>> from doctr.datasets import WordGenerator
    >>> ds = WordGenerator(vocab='abdef', min_chars=1, max_chars=32, num_samples=100)
    >>> img, target = ds[0]

    Args:
        vocab: vocabulary to take the character from
        min_chars: minimum number of characters in a word
        max_chars: maximum number of characters in a word
        num_samples: number of samples that will be generated iterating over the dataset
        cache_samples: whether generated images should be cached firsthand
        font_family: font to use to generate the text images
        img_transforms: composable transformations that will be applied to each image
        sample_transforms: composable transformations that will be applied to both the image and the target
    """

    pass