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-rw-r--r--tasks/make_wordpieces.py114
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diff --git a/tasks/make_wordpieces.py b/tasks/make_wordpieces.py
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-"""Creates word pieces from a text file.
-
-Most code stolen from:
-
- https://github.com/facebookresearch/gtn_applications/blob/master/scripts/make_wordpieces.py
-
-"""
-import io
-from pathlib import Path
-from typing import List, Optional, Union
-
-import click
-from loguru import logger
-import sentencepiece as spm
-
-from text_recognizer.datasets.iam_preprocessor import load_metadata
-
-
-def iamdb_pieces(
- data_dir: Path, text_file: str, num_pieces: int, output_prefix: str
-) -> None:
- """Creates word pieces from the iamdb train text."""
- # Load training text.
- with open(data_dir / text_file, "r") as f:
- text = [line.strip() for line in f]
-
- sp = train_spm_model(
- iter(text),
- num_pieces + 1, # To account for <unk>
- user_symbols=["/"], # added so token is in the output set
- )
-
- vocab = sorted(set(w for t in text for w in t.split("▁") if w))
- if "move" not in vocab:
- raise RuntimeError("`MOVE` not in vocab")
-
- save_pieces(sp, num_pieces, data_dir, output_prefix, vocab)
-
-
-def train_spm_model(
- sentences: iter, vocab_size: int, user_symbols: Union[str, List[str]] = ""
-) -> spm.SentencePieceProcessor:
- """Trains the sentence piece model."""
- model = io.BytesIO()
- spm.SentencePieceTrainer.train(
- sentence_iterator=sentences,
- model_writer=model,
- vocab_size=vocab_size,
- bos_id=-1,
- eos_id=-1,
- character_coverage=1.0,
- user_defined_symbols=user_symbols,
- )
- sp = spm.SentencePieceProcessor(model_proto=model.getvalue())
- return sp
-
-
-def save_pieces(
- sp: spm.SentencePieceProcessor,
- num_pieces: int,
- data_dir: Path,
- output_prefix: str,
- vocab: set,
-) -> None:
- """Saves word pieces to disk."""
- logger.info(f"Generating word piece list of size {num_pieces}.")
- pieces = [sp.id_to_piece(i) for i in range(1, num_pieces + 1)]
- logger.info(f"Encoding vocabulary of size {len(vocab)}.")
- encoded_vocab = [sp.encode_as_pieces(v) for v in vocab]
-
- # Save pieces to file.
- with open(data_dir / f"{output_prefix}_tokens_{num_pieces}.txt", "w") as f:
- f.write("\n".join(pieces))
-
- # Save lexicon to a file.
- with open(data_dir / f"{output_prefix}_lex_{num_pieces}.txt", "w") as f:
- for v, p in zip(vocab, encoded_vocab):
- f.write(f"{v} {' '.join(p)}\n")
-
-
-@click.command()
-@click.option("--data_dir", type=str, default=None, help="Path to processed iam dir.")
-@click.option(
- "--text_file", type=str, default=None, help="Name of sentence piece training text."
-)
-@click.option(
- "--output_prefix",
- type=str,
- default="word_pieces",
- help="Prefix name to store tokens and lexicon.",
-)
-@click.option("--num_pieces", type=int, default=1000, help="Number of word pieces.")
-def cli(
- data_dir: Optional[str],
- text_file: Optional[str],
- output_prefix: Optional[str],
- num_pieces: Optional[int],
-) -> None:
- """CLI for training the sentence piece model."""
- if data_dir is None:
- data_dir = (
- Path(__file__).resolve().parents[2] / "data" / "processed" / "iam_lines"
- )
- 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)
-
- iamdb_pieces(data_dir, text_file, num_pieces, output_prefix)
-
-
-if __name__ == "__main__":
- cli()