chore: remove word pieces

3 files changed, 0 insertions, 229 deletions

diff --git a/text_recognizer/data/mappings/word_piece.py b/text_recognizer/data/mappings/word_piece.py
deleted file mode 100644
index 861c3bd..0000000
--- a/text_recognizer/data/mappings/word_piece.py
+++ /dev/null
@@ -1,72 +0,0 @@
-"""Word piece mapping."""
-from typing import List, Set, Union
-
-import torch
-from torch import Tensor
-
-from text_recognizer.data.mappings.emnist import EmnistMapping
-from text_recognizer.data.utils.iam_preprocessor import Preprocessor
-
-
-class WordPieceMapping(EmnistMapping):
-    """Word piece mapping."""
-
-    def __init__(
-        self,
-        num_features: int = 1000,
-        tokens: str = "iamdb_1kwp_tokens_1000.txt",
-        lexicon: str = "iamdb_1kwp_lex_1000.txt",
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-        special_tokens: Set[str] = {"<s>", "<e>", "<p>"},
-        extra_symbols: Set[str] = {"\n"},
-    ) -> None:
-        super().__init__(extra_symbols=extra_symbols)
-        special_tokens = set(special_tokens)
-        if self.extra_symbols is not None:
-            special_tokens = special_tokens | set(extra_symbols)
-
-        self.wordpiece_processor = Preprocessor(
-            num_features=num_features,
-            tokens=tokens,
-            lexicon=lexicon,
-            use_words=use_words,
-            prepend_wordsep=prepend_wordsep,
-            special_tokens=special_tokens,
-        )
-
-    def __len__(self) -> int:
-        """Return number of word pieces."""
-        return len(self.wordpiece_processor.tokens)
-
-    def get_token(self, index: Union[int, Tensor]) -> str:
-        """Returns token for index."""
-        if (index := int(index)) <= self.wordpiece_processor.num_tokens:
-            return self.wordpiece_processor.tokens[index]
-        raise KeyError(f"Index ({index}) not in mapping.")
-
-    def get_index(self, token: str) -> Tensor:
-        """Returns index of token."""
-        if token in self.wordpiece_processor.tokens:
-            return torch.LongTensor([self.wordpiece_processor.tokens_to_index[token]])
-        raise KeyError(f"Token ({token}) not found in inverse mapping.")
-
-    def get_text(self, indices: Union[List[int], Tensor]) -> str:
-        """Returns text from indices."""
-        if isinstance(indices, Tensor):
-            indices = indices.tolist()
-        return self.wordpiece_processor.to_text(indices)
-
-    def get_indices(self, text: str) -> Tensor:
-        """Returns indices of text."""
-        return self.wordpiece_processor.to_index(text)
-
-    def emnist_to_wordpiece_indices(self, x: Tensor) -> Tensor:
-        """Returns word pieces indices from emnist indices."""
-        text = "".join([self.mapping[i] for i in x])
-        text = text.lower().replace(" ", "▁")
-        return torch.LongTensor(self.wordpiece_processor.to_index(text))
-
-    def __getitem__(self, x: Union[int, Tensor]) -> str:
-        """Returns token for word piece index."""
-        return self.get_token(x)
diff --git a/text_recognizer/data/transforms/word_piece.py b/text_recognizer/data/transforms/word_piece.py
deleted file mode 100644
index d805c7e..0000000
--- a/text_recognizer/data/transforms/word_piece.py
+++ /dev/null
@@ -1,45 +0,0 @@
-"""Target transform for word pieces."""
-from typing import Optional, Sequence
-
-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",
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-        special_tokens: Sequence[str] = ("<s>", "<e>", "<p>"),
-        extra_symbols: Optional[Sequence[str]] = ("\n",),
-        max_len: int = 451,
-    ) -> None:
-        self.mapping = WordPieceMapping(
-            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
diff --git a/text_recognizer/data/utils/make_wordpieces.py b/text_recognizer/data/utils/make_wordpieces.py
deleted file mode 100644
index 8e53815..0000000
--- a/text_recognizer/data/utils/make_wordpieces.py
+++ /dev/null
@@ -1,112 +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 as log
-import sentencepiece as spm
-
-
-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."""
-    log.info(f"Generating word piece list of size {num_pieces}.")
-    pieces = [sp.id_to_piece(i) for i in range(1, num_pieces + 1)]
-    log.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"
-        )
-        log.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()