From b291781c3f4599e7320388ee6c26178e4e371dae Mon Sep 17 00:00:00 2001 From: Gustaf Rydholm Date: Wed, 27 Oct 2021 22:10:46 +0200 Subject: Remove build transitions --- text_recognizer/data/utils/build_transitions.py | 265 ------------------------ 1 file changed, 265 deletions(-) delete mode 100644 text_recognizer/data/utils/build_transitions.py (limited to 'text_recognizer') 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 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() -- cgit v1.2.3-70-g09d2