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"""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, Set, Union
import click
from loguru import logger as log
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."""
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,
special_tokens: Optional[Set[str]] = None,
) -> None:
self.wordsep = "▁"
self._use_word = use_words
self._prepend_wordsep = prepend_wordsep
self.special_tokens = special_tokens if special_tokens is not None else None
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
if self.special_tokens is not None:
special_tokens_ = (*self.special_tokens, "#", "*")
self.tokens += special_tokens_
self.graphemes += special_tokens_
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:
if self.special_tokens is not None and line in self.special_tokens:
return torch.LongTensor([self.tokens_to_index[line]])
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.
tokens = [
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:
tokens = itertools.chain([self.wordsep], tokens)
return torch.LongTensor([token_to_index[t] for t in tokens])
def to_index(self, line: str) -> torch.LongTensor:
"""Converts text to a tensor of indices."""
if self.special_tokens is not None:
pattern = f"({'|'.join(self.special_tokens)})"
lines = list(filter(None, re.split(pattern, line)))
return torch.cat([self._to_index(line) for line in lines])
return self._to_index(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[2]
/ "data"
/ "downloaded"
/ "iam"
/ "iamdb"
)
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)
preprocessor = Preprocessor(data_dir, 64, use_words=use_words)
preprocessor.extract_train_text()
processed_dir = data_dir.parents[2] / "processed" / "iam_lines"
log.debug(f"Saving processed files at: {processed_dir}")
if save_text is not None:
log.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:
log.info("Saving tokens")
with open(processed_dir / save_tokens, "w") as f:
f.write("\n".join(preprocessor.tokens))
if __name__ == "__main__":
cli()
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