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from typing import Iterable, Union, Tuple |
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from collections import Counter |
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import argparse |
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import os |
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import yaml |
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from pyarabic.araby import tokenize, strip_tatweel |
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from tqdm import tqdm |
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import numpy as np |
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import torch as T |
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from torch.utils.data import DataLoader |
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from diac_utils import HARAKAT_MAP, shakkel_char, diac_ids_of_line |
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from model_partial import PartialDD |
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from data_utils import DatasetUtils |
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from dataloader import DataRetriever |
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from segment import segment |
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class Predictor: |
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def __init__(self, config, text): |
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self.data_utils = DatasetUtils(config) |
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vocab_size = len(self.data_utils.letter_list) |
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word_embeddings = self.data_utils.embeddings |
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stride = config["segment"]["stride"] |
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window = config["segment"]["window"] |
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min_window = config["segment"]["min-window"] |
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segments, mapping = segment([text], stride, window, min_window) |
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mapping_lines = [] |
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for sent_idx, seg_idx, word_idx, char_idx in mapping: |
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mapping_lines += [f"{sent_idx}, {seg_idx}, {word_idx}, {char_idx}"] |
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self.mapping = self.data_utils.load_mapping_v3_from_list(mapping_lines) |
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self.original_lines = [text] |
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self.segments = segments |
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self.device = T.device( |
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config['predictor'].get('device', 'cuda:0') |
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if T.cuda.is_available() else 'cpu' |
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) |
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self.model = PartialDD(config, d2=True) |
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self.model.sentence_diac.build(word_embeddings, vocab_size) |
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state_dict = T.load(config["paths"]["load"], map_location=T.device(self.device))['state_dict'] |
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self.model.load_state_dict(state_dict) |
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self.model.to(self.device) |
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self.model.eval() |
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self.data_loader = DataLoader( |
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DataRetriever(self.data_utils, segments), |
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batch_size=config["predictor"].get("batch-size", 32), |
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shuffle=False, |
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num_workers=config['loader'].get('num-workers', 0), |
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) |
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class PredictTri(Predictor): |
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def __init__(self, config, text): |
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super().__init__(config, text) |
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self.diacritics = { |
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"FATHA": 1, |
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"KASRA": 2, |
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"DAMMA": 3, |
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"SUKUN": 4 |
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} |
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self.votes: Union[Counter[int], Counter[bool]] = Counter() |
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def count_votes( |
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self, |
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things: Union[Iterable[int], Iterable[bool]] |
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): |
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self.votes.clear() |
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self.votes.update(things) |
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return self.votes.most_common(1)[0][0] |
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def predict_majority_vote(self): |
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y_gen_diac, y_gen_tanween, y_gen_shadda = self.model.predict(self.data_loader) |
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diacritized_lines = self.coalesce_votes_by_majority(y_gen_diac, y_gen_tanween, y_gen_shadda) |
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return diacritized_lines |
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def predict_majority_vote_context_contrastive(self, overwrite_cache=False): |
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assert isinstance(self.model, PartialDD) |
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if not os.path.exists("dataset/cache/y_gen_diac.npy") or overwrite_cache: |
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if not os.path.exists("dataset/cache"): |
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os.mkdir("dataset/cache") |
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segment_outputs = self.model.predict_partial(self.data_loader, return_extra=False, eval_only='ctxt') |
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T.save(segment_outputs, "dataset/cache/cache.pt") |
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else: |
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segment_outputs = T.load("dataset/cache/cache.pt") |
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y_gen_diac, y_gen_tanween, y_gen_shadda = segment_outputs['diacritics'] |
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diacritized_lines, extra_for_lines = self.coalesce_votes_by_majority( |
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y_gen_diac, y_gen_tanween, y_gen_shadda, |
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) |
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extra_out = { |
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'line_data': { |
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**extra_for_lines, |
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}, |
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'segment_data': { |
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**segment_outputs, |
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} |
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} |
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return diacritized_lines, extra_out |
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def coalesce_votes_by_majority( |
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self, |
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y_gen_diac: np.ndarray, |
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y_gen_tanween: np.ndarray, |
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y_gen_shadda: np.ndarray, |
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): |
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prepped_lines_og = [' '.join(tokenize(strip_tatweel(line))) for line in self.original_lines] |
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max_line_chars = max(len(line) for line in prepped_lines_og) |
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diacritics_pred = np.full((len(self.original_lines), max_line_chars), fill_value=-1, dtype=int) |
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count_processed_sents = 0 |
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do_break = False |
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diacritized_lines = [] |
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for sent_idx, line in enumerate(tqdm(prepped_lines_og)): |
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count_processed_sents = sent_idx + 1 |
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line = line.strip() |
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diacritized_line = "" |
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for char_idx, char in enumerate(line): |
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diacritized_line += char |
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char_vote_diacritic = [] |
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if sent_idx not in self.mapping: |
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continue |
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mapping_s_i = self.mapping[sent_idx] |
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for seg_idx in mapping_s_i: |
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if self.data_utils.debug and seg_idx >= 256: |
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do_break = True |
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break |
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mapping_g_i = mapping_s_i[seg_idx] |
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for t_idx in mapping_g_i: |
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mapping_t_i = mapping_g_i[t_idx] |
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if char_idx in mapping_t_i: |
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c_idx = mapping_t_i.index(char_idx) |
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output_idx = np.s_[seg_idx, t_idx, c_idx] |
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diac_h3 = (y_gen_diac[output_idx], y_gen_tanween[output_idx], y_gen_shadda[output_idx]) |
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diac_char_i = HARAKAT_MAP.index(diac_h3) |
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if c_idx < 13 and diac_char_i != 0: |
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char_vote_diacritic.append(diac_char_i) |
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if do_break: |
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break |
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if len(char_vote_diacritic) > 0: |
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char_mv_diac = self.count_votes(char_vote_diacritic) |
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diacritized_line += shakkel_char(*HARAKAT_MAP[char_mv_diac]) |
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diacritics_pred[sent_idx, char_idx] = char_mv_diac |
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else: |
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diacritics_pred[sent_idx, char_idx] = 0 |
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if do_break: |
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break |
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diacritized_lines += [diacritized_line.strip()] |
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print(f'[INFO] Cutting stats from {len(diacritics_pred)} to {count_processed_sents}') |
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extra = { |
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'diac_pred': diacritics_pred[:count_processed_sents], |
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} |
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return diacritized_lines, extra |
