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"""Transforms for PyTorch datasets."""
from abc import abstractmethod
from pathlib import Path
from typing import Any, Optional, Union
from loguru import logger
import torch
from torch import Tensor
from text_recognizer.datasets.iam_preprocessor import Preprocessor
from text_recognizer.data.emnist import emnist_mapping
class ToLower:
"""Converts target to lower case."""
def __call__(self, target: Tensor) -> Tensor:
"""Corrects index value in target tensor."""
device = target.device
return torch.stack([x - 26 if x > 35 else x for x in target]).to(device)
class ToCharcters:
"""Converts integers to characters."""
def __init__(self) -> None:
self.mapping, _, _ = emnist_mapping()
def __call__(self, y: Tensor) -> str:
"""Converts a Tensor to a str."""
return "".join([self.mapping(int(i)) for i in y]).strip("<p>").replace(" ", "▁")
class WordPieces:
"""Abstract transform for word pieces."""
def __init__(
self,
num_features: int,
data_dir: Optional[Union[str, Path]] = None,
tokens: Optional[Union[str, Path]] = None,
lexicon: Optional[Union[str, Path]] = None,
use_words: bool = False,
prepend_wordsep: bool = False,
) -> None:
if data_dir is None:
data_dir = (
Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb"
)
logger.debug(f"Using data dir: {data_dir}")
if not data_dir.exists():
raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
else:
data_dir = Path(data_dir)
processed_path = (
Path(__file__).resolve().parents[3] / "data" / "processed" / "iam_lines"
)
tokens_path = processed_path / tokens
lexicon_path = processed_path / lexicon
self.preprocessor = Preprocessor(
data_dir,
num_features,
tokens_path,
lexicon_path,
use_words,
prepend_wordsep,
)
@abstractmethod
def __call__(self, *args, **kwargs) -> Any:
"""Transforms input."""
...
class ToWordPieces(WordPieces):
"""Transforms str to word pieces."""
def __init__(
self,
num_features: int,
data_dir: Optional[Union[str, Path]] = None,
tokens: Optional[Union[str, Path]] = None,
lexicon: Optional[Union[str, Path]] = None,
use_words: bool = False,
prepend_wordsep: bool = False,
) -> None:
super().__init__(
num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep
)
def __call__(self, line: str) -> Tensor:
"""Transforms str to word pieces."""
return self.preprocessor.to_index(line)
class ToText(WordPieces):
"""Takes word pieces and converts them to text."""
def __init__(
self,
num_features: int,
data_dir: Optional[Union[str, Path]] = None,
tokens: Optional[Union[str, Path]] = None,
lexicon: Optional[Union[str, Path]] = None,
use_words: bool = False,
prepend_wordsep: bool = False,
) -> None:
super().__init__(
num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep
)
def __call__(self, x: Tensor) -> str:
"""Converts tensor to text."""
return self.preprocessor.to_text(x.tolist())
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