# Copyright (c) 2025, NVIDIA CORPORATION.
# SPDX-License-Identifier: BSD-3-Clause
from typing import List, Optional, Union
import torch
from megatron.energon.edataclass import edataclass
from megatron.energon.epathlib.epath import EPath
from megatron.energon.errors import warn_deprecated
from megatron.energon.flavors.base_dataset import Sample
from megatron.energon.flavors.webdataset import DefaultDecoderWebdatasetFactory
[docs]
@edataclass
class OCRSample(Sample):
"""Sample type for optical character recognition."""
#: The input image tensor in the shape (C, H, W)
image: torch.Tensor
#: The text contained in the image
text: str
#: The bounding boxes of the blocks in the image float(N, 4|5<x, y, w, h>)
block_boxes: Optional[torch.Tensor] = None
#: The classes of the blocks in the image int(N, 1<block_class>)
block_classes: Optional[Union[torch.Tensor, List[str]]] = None
#: The text contained in each block (N,)
block_text: Optional[List[str]] = None
#: The bounding boxes of the lines in the image float(N, 4|5<x, y, w, h[, confidence]>)
lines_boxes: Optional[torch.Tensor] = None
#: The text contained in each line (N,)
lines_text: Optional[List[str]] = None
#: The bounding boxes of the words in the image float(N, 4|5<x, y, w, h[, confidence]>)
words_boxes: Optional[torch.Tensor] = None
#: The text contained in each word (N,)
words_text: Optional[List[str]] = None
#: The bounding boxes of the chars in the image float(N, 4|5<x, y, w, h[, confidence]>)
chars_boxes: Optional[torch.Tensor] = None
#: The character contained in each char (N,)
chars_text: Optional[List[str]] = None
[docs]
class OCRWebdataset(DefaultDecoderWebdatasetFactory[OCRSample]):
__sample_type__ = OCRSample
def __init__(self, path: EPath, **kwargs):
warn_deprecated(
f"{type(self)} is deprecated, use the default instead and set the sample_type:\n"
f"To convert, update your {path}/.nv-meta/dataset.yaml to:\n"
f"# remove top-level __module__ and __class__\n"
f"sample_type:\n"
f" __module__: megatron.energon\n"
f" __class__: {self.__sample_type__.__name__}\n"
f"# Keep the remaining content"
)
super().__init__(path, **kwargs)