Inital commit for refactoring to lightning

1 files changed, 0 insertions, 114 deletions

diff --git a/src/tasks/make_wordpieces.py b/src/tasks/make_wordpieces.py
deleted file mode 100644
index 2ac0e2c..0000000
--- a/src/tasks/make_wordpieces.py
+++ /dev/null
@@ -1,114 +0,0 @@
-"""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()