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-rw-r--r--text_recognizer/data/utils/build_transitions.py265
1 files changed, 0 insertions, 265 deletions
diff --git a/text_recognizer/data/utils/build_transitions.py b/text_recognizer/data/utils/build_transitions.py
deleted file mode 100644
index 65485f0..0000000
--- a/text_recognizer/data/utils/build_transitions.py
+++ /dev/null
@@ -1,265 +0,0 @@
-"""Builds transition graph.
-
-Most code stolen from here:
- https://github.com/facebookresearch/gtn_applications/blob/master/scripts/build_transitions.py
-"""
-
-import collections
-import itertools
-from pathlib import Path
-from typing import Any, Dict, List, Optional
-
-import click
-import gtn
-from loguru import logger
-
-
-START_IDX = -1
-END_IDX = -2
-WORDSEP = "▁"
-
-
-def build_graph(ngrams: List, disable_backoff: bool = False) -> gtn.Graph:
- """Returns a gtn Graph based on the ngrams."""
- graph = gtn.Graph(False)
- ngram = len(ngrams)
- state_to_node = {}
-
- def get_node(state: Optional[List]) -> Any:
- node = state_to_node.get(state, None)
-
- if node is not None:
- return node
-
- start = state == tuple([START_IDX]) if ngram > 1 else True
- end = state == tuple([END_IDX]) if ngram > 1 else True
- node = graph.add_node(start, end)
- state_to_node[state] = node
-
- if not disable_backoff and not end:
- # Add back off when adding node.
- for n in range(1, len(state) + 1):
- backoff_node = state_to_node.get(state[n:], None)
-
- # Epsilon transition to the back-off state.
- if backoff_node is not None:
- graph.add_arc(node, backoff_node, gtn.epsilon)
- break
- return node
-
- for grams in ngrams:
- for gram in grams:
- istate, ostate = gram[:-1], gram[len(gram) - ngram + 1 :]
- inode = get_node(istate)
-
- if END_IDX not in gram[1:] and gram[1:] not in state_to_node:
- raise ValueError(
- "Ill formed counts: if (x, y_1, ..., y_{n-1}) is above"
- "the n-gram threshold, then (y_1, ..., y_{n-1}) must be"
- "above the (n-1)-gram threshold"
- )
-
- if END_IDX in ostate:
- # Merge all state having </s> into one as final graph generated
- # will be similar.
- ostate = tuple([END_IDX])
-
- onode = get_node(ostate)
- # p(gram[-1] | gram[:-1])
- graph.add_arc(
- inode, onode, gtn.epsilon if gram[-1] == END_IDX else gram[-1]
- )
- return graph
-
-
-def count_ngrams(lines: List, ngram: List, tokens_to_index: Dict) -> List:
- """Counts the number of ngrams."""
- counts = [collections.Counter() for _ in range(ngram)]
- for line in lines:
- # Prepend implicit start token.
- token_line = [START_IDX]
- for t in line:
- token_line.append(tokens_to_index[t])
- token_line.append(END_IDX)
- for n, counter in enumerate(counts):
- start_offset = n == 0
- end_offset = ngram == 1
- for e in range(n + start_offset, len(token_line) - end_offset):
- counter[tuple(token_line[e - n : e + 1])] += 1
-
- return counts
-
-
-def prune_ngrams(ngrams: List, prune: List) -> List:
- """Prunes ngrams."""
- pruned_ngrams = []
- for n, grams in enumerate(ngrams):
- grams = grams.most_common()
- pruned_grams = [gram for gram, c in grams if c > prune[n]]
- pruned_ngrams.append(pruned_grams)
- return pruned_ngrams
-
-
-def add_blank_grams(pruned_ngrams: List, num_tokens: int, blank: str) -> List:
- """Adds blank token to grams."""
- all_grams = [gram for grams in pruned_ngrams for gram in grams]
- maxorder = len(pruned_ngrams)
- blank_grams = {}
- if blank == "forced":
- pruned_ngrams = [pruned_ngrams[0] if i == 0 else [] for i in range(maxorder)]
- pruned_ngrams[0].append(tuple([num_tokens]))
- blank_grams[tuple([num_tokens])] = True
-
- for gram in all_grams:
- # Iterate over all possibilities by using a vector of 0s, 1s to
- # denote whether a blank is being used at each position.
- if blank == "optional":
- # Given a gram ab.. if order n, we have n + 1 positions
- # available whether to use blank or not.
- onehot_vectors = itertools.product([0, 1], repeat=len(gram) + 1)
- elif blank == "forced":
- # Must include a blank token in between.
- onehot_vectors = [[1] * (len(gram) + 1)]
- else:
- raise ValueError(
- "Invalid value specificed for blank. Must be in |optional|forced|none|"
- )
-
- for j in onehot_vectors:
- new_array = []
- for idx, oz in enumerate(j[:-1]):
- if oz == 1 and gram[idx] != START_IDX:
- new_array.append(num_tokens)
- new_array.append(gram[idx])
- if j[-1] == 1 and gram[-1] != END_IDX:
- new_array.append(num_tokens)
- for n in range(maxorder):
- for e in range(n, len(new_array)):
- cur_gram = tuple(new_array[e - n : e + 1])
- if num_tokens in cur_gram and cur_gram not in blank_grams:
- pruned_ngrams[n].append(cur_gram)
- blank_grams[cur_gram] = True
-
- return pruned_ngrams
-
-
-def add_self_loops(pruned_ngrams: List) -> List:
- """Adds self loops to the ngrams."""
- maxorder = len(pruned_ngrams)
-
- # Use dict for fast search.
- all_grams = set([gram for grams in pruned_ngrams for gram in grams])
- for o in range(1, maxorder):
- for gram in pruned_ngrams[o - 1]:
- # Repeat one of the tokens.
- for pos in range(len(gram)):
- if gram[pos] == START_IDX or gram[pos] == END_IDX:
- continue
- new_gram = gram[:pos] + (gram[pos],) + gram[pos:]
-
- if new_gram not in all_grams:
- pruned_ngrams[o].append(new_gram)
- all_grams.add(new_gram)
- return pruned_ngrams
-
-
-def parse_lines(lines: List, lexicon: Path) -> List:
- """Parses lines with a lexicon."""
- with (lexicon).open("r") as f:
- lex = (line.strip().split() for line in f)
- lex = {line[0]: line[1:] for line in lex}
- return [[t for w in line.split(WORDSEP) for t in lex[w]] for line in lines if line]
-
-
-@click.command()
-@click.option("--data_dir", type=str, default=None, help="Path to dataset root.")
-@click.option(
- "--tokens",
- type=str,
- default="iamdb_1kwp_tokens_1000.txt",
- help="Path to token list (in order used with training).",
-)
-@click.option(
- "--lexicon", type=str, default="iamdb_1kwp_lex_1000.txt", help="Path to lexicon"
-)
-@click.option(
- "--prune",
- nargs=2,
- type=int,
- help="Threshold values for prune unigrams, bigrams, etc.",
-)
-@click.option(
- "--blank",
- default=click.Choice(["none", "optional", "forced"]),
- help="Specifies the usage of blank token"
- "'none' - do not use blank token "
- "'optional' - allow an optional blank inbetween tokens"
- "'forced' - force a blank inbetween tokens (also referred to as garbage token)",
-)
-@click.option("--self_loops", is_flag=True, help="Add self loops for tokens")
-@click.option("--disable_backoff", is_flag=True, help="Disable backoff transitions")
-@click.option("--save_path", default=None, help="Path to save transition graph.")
-def cli(
- data_dir: str,
- tokens: str,
- lexicon: str,
- prune: List[int],
- blank: str,
- self_loops: bool,
- disable_backoff: bool,
- save_path: str,
-) -> None:
- """CLI for creating the transitions."""
- logger.info(f"Building {len(prune)}-gram transition models.")
-
- if data_dir is None:
- data_dir = (
- Path(__file__).resolve().parents[3] / "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)
-
- # Build table of counts and the back-off if below threshold.
- with (data_dir / "train.txt").open("r") as f:
- lines = [line.strip() for line in f]
-
- with (data_dir / tokens).open("r") as f:
- tokens = [line.strip() for line in f]
-
- if lexicon is not None:
- lexicon = data_dir / lexicon
- lines = parse_lines(lines, lexicon)
-
- tokens_to_idx = {t: e for e, t in enumerate(tokens)}
-
- ngram = len(prune)
-
- logger.info("Counting data...")
- ngrams = count_ngrams(lines, ngram, tokens_to_idx)
-
- pruned_ngrams = prune_ngrams(ngrams, prune)
-
- for n in range(ngram):
- logger.info(f"Kept {len(pruned_ngrams[n])} of {len(ngrams[n])} {n + 1}-grams")
-
- if blank == "none":
- pruned_ngrams = add_blank_grams(pruned_ngrams, len(tokens_to_idx), blank)
-
- if self_loops:
- pruned_ngrams = add_self_loops(pruned_ngrams)
-
- logger.info("Building graph from pruned ngrams...")
- graph = build_graph(pruned_ngrams, disable_backoff)
- logger.info(f"Graph has {graph.num_arcs()} arcs and {graph.num_nodes()} nodes.")
-
- save_path = str(data_dir / save_path)
-
- logger.info(f"Saving graph to {save_path}")
- gtn.save(save_path, graph)
-
-
-if __name__ == "__main__":
- cli()