Inital commit for refactoring to lightning

1 files changed, 0 insertions, 196 deletions

diff --git a/src/text_recognizer/datasets/iam_preprocessor.py b/src/text_recognizer/datasets/iam_preprocessor.py
deleted file mode 100644
index a93eb00..0000000
--- a/src/text_recognizer/datasets/iam_preprocessor.py
+++ /dev/null
@@ -1,196 +0,0 @@
-"""Preprocessor for extracting word letters from the IAM dataset.
-
-The code is mostly stolen from:
-
-    https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py
-
-"""
-
-import collections
-import itertools
-from pathlib import Path
-import re
-from typing import List, Optional, Union
-
-import click
-from loguru import logger
-import torch
-
-
-def load_metadata(
-    data_dir: Path, wordsep: str, use_words: bool = False
-) -> collections.defaultdict:
-    """Loads IAM metadata and returns it as a dictionary."""
-    forms = collections.defaultdict(list)
-    filename = "words.txt" if use_words else "lines.txt"
-
-    with open(data_dir / "ascii" / filename, "r") as f:
-        lines = (line.strip().split() for line in f if line[0] != "#")
-        for line in lines:
-            # Skip word segmentation errors.
-            if use_words and line[1] == "err":
-                continue
-            text = " ".join(line[8:])
-
-            # Remove garbage tokens:
-            text = text.replace("#", "")
-
-            # Swap word sep form | to wordsep
-            text = re.sub(r"\|+|\s", wordsep, text).strip(wordsep)
-            form_key = "-".join(line[0].split("-")[:2])
-            line_key = "-".join(line[0].split("-")[:3])
-            box_idx = 4 - use_words
-            box = tuple(int(val) for val in line[box_idx : box_idx + 4])
-            forms[form_key].append({"key": line_key, "box": box, "text": text})
-    return forms
-
-
-class Preprocessor:
-    """A preprocessor for the IAM dataset."""
-
-    # TODO: add lower case only to when generating...
-
-    def __init__(
-        self,
-        data_dir: Union[str, Path],
-        num_features: int,
-        tokens_path: Optional[Union[str, Path]] = None,
-        lexicon_path: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        self.wordsep = "▁"
-        self._use_word = use_words
-        self._prepend_wordsep = prepend_wordsep
-
-        self.data_dir = Path(data_dir)
-
-        self.forms = load_metadata(self.data_dir, self.wordsep, use_words=use_words)
-
-        # Load the set of graphemes:
-        graphemes = set()
-        for _, form in self.forms.items():
-            for line in form:
-                graphemes.update(line["text"].lower())
-        self.graphemes = sorted(graphemes)
-
-        # Build the token-to-index and index-to-token maps.
-        if tokens_path is not None:
-            with open(tokens_path, "r") as f:
-                self.tokens = [line.strip() for line in f]
-        else:
-            self.tokens = self.graphemes
-
-        if lexicon_path is not None:
-            with open(lexicon_path, "r") as f:
-                lexicon = (line.strip().split() for line in f)
-                lexicon = {line[0]: line[1:] for line in lexicon}
-                self.lexicon = lexicon
-        else:
-            self.lexicon = None
-
-        self.graphemes_to_index = {t: i for i, t in enumerate(self.graphemes)}
-        self.tokens_to_index = {t: i for i, t in enumerate(self.tokens)}
-        self.num_features = num_features
-        self.text = []
-
-    @property
-    def num_tokens(self) -> int:
-        """Returns the number or tokens."""
-        return len(self.tokens)
-
-    @property
-    def use_words(self) -> bool:
-        """If words are used."""
-        return self._use_word
-
-    def extract_train_text(self) -> None:
-        """Extracts training text."""
-        keys = []
-        with open(self.data_dir / "task" / "trainset.txt") as f:
-            keys.extend((line.strip() for line in f))
-
-        for _, examples in self.forms.items():
-            for example in examples:
-                if example["key"] not in keys:
-                    continue
-                self.text.append(example["text"].lower())
-
-    def to_index(self, line: str) -> torch.LongTensor:
-        """Converts text to a tensor of indices."""
-        token_to_index = self.graphemes_to_index
-        if self.lexicon is not None:
-            if len(line) > 0:
-                # If the word is not found in the lexicon, fall back to letters.
-                line = [
-                    t
-                    for w in line.split(self.wordsep)
-                    for t in self.lexicon.get(w, self.wordsep + w)
-                ]
-            token_to_index = self.tokens_to_index
-        if self._prepend_wordsep:
-            line = itertools.chain([self.wordsep], line)
-        return torch.LongTensor([token_to_index[t] for t in line])
-
-    def to_text(self, indices: List[int]) -> str:
-        """Converts indices to text."""
-        # Roughly the inverse of `to_index`
-        encoding = self.graphemes
-        if self.lexicon is not None:
-            encoding = self.tokens
-        return self._post_process(encoding[i] for i in indices)
-
-    def tokens_to_text(self, indices: List[int]) -> str:
-        """Converts tokens to text."""
-        return self._post_process(self.tokens[i] for i in indices)
-
-    def _post_process(self, indices: List[int]) -> str:
-        """A list join."""
-        return "".join(indices).strip(self.wordsep)
-
-
-@click.command()
-@click.option("--data_dir", type=str, default=None, help="Path to iam dataset")
-@click.option(
-    "--use_words", is_flag=True, help="Load word segmented dataset instead of lines"
-)
-@click.option(
-    "--save_text", type=str, default=None, help="Path to save parsed train text"
-)
-@click.option("--save_tokens", type=str, default=None, help="Path to save tokens")
-def cli(
-    data_dir: Optional[str],
-    use_words: bool,
-    save_text: Optional[str],
-    save_tokens: Optional[str],
-) -> None:
-    """CLI for extracting text data from the iam dataset."""
-    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)
-
-    preprocessor = Preprocessor(data_dir, 64, use_words=use_words)
-    preprocessor.extract_train_text()
-
-    processed_dir = data_dir.parents[2] / "processed" / "iam_lines"
-    logger.debug(f"Saving processed files at: {processed_dir}")
-
-    if save_text is not None:
-        logger.info("Saving training text")
-        with open(processed_dir / save_text, "w") as f:
-            f.write("\n".join(t for t in preprocessor.text))
-
-    if save_tokens is not None:
-        logger.info("Saving tokens")
-        with open(processed_dir / save_tokens, "w") as f:
-            f.write("\n".join(preprocessor.tokens))
-
-
-if __name__ == "__main__":
-    cli()