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"""Target transform for word pieces."""
from pathlib import Path
from typing import Optional, Union, Set
import torch
from torch import Tensor
from text_recognizer.data.mappings.word_piece_mapping import WordPieceMapping
class WordPiece:
"""Converts EMNIST indices to Word Piece indices."""
def __init__(
self,
num_features: int = 1000,
tokens: str = "iamdb_1kwp_tokens_1000.txt",
lexicon: str = "iamdb_1kwp_lex_1000.txt",
data_dir: Optional[Union[str, Path]] = None,
use_words: bool = False,
prepend_wordsep: bool = False,
special_tokens: Set[str] = {"<s>", "<e>", "<p>"},
extra_symbols: Optional[Set[str]] = {"\n",},
max_len: int = 451,
) -> None:
self.mapping = WordPieceMapping(
data_dir=data_dir,
num_features=num_features,
tokens=tokens,
lexicon=lexicon,
use_words=use_words,
prepend_wordsep=prepend_wordsep,
special_tokens=special_tokens,
extra_symbols=extra_symbols,
)
self.max_len = max_len
def __call__(self, x: Tensor) -> Tensor:
"""Converts Emnist target tensor to Word piece target tensor."""
y = self.mapping.emnist_to_wordpiece_indices(x)
if len(y) < self.max_len:
pad_len = self.max_len - len(y)
y = torch.cat(
(y, torch.LongTensor([self.mapping.get_index("<p>")] * pad_len))
)
else:
y = y[: self.max_len]
return y
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