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from .ctc_postprocess import BaseRecLabelDecode |
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class MPGLabelDecode(BaseRecLabelDecode): |
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"""Convert between text-label and text-index.""" |
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SPACE = '[s]' |
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GO = '[GO]' |
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list_token = [GO, SPACE] |
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def __init__(self, |
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character_dict_path=None, |
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use_space_char=False, |
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only_char=False, |
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**kwargs): |
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super(MPGLabelDecode, self).__init__(character_dict_path, |
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use_space_char) |
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self.only_char = only_char |
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self.EOS = '[s]' |
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self.PAD = '[GO]' |
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if not only_char: |
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from transformers import BertTokenizer, GPT2Tokenizer |
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self.bpe_tokenizer = GPT2Tokenizer.from_pretrained('gpt2') |
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self.wp_tokenizer = BertTokenizer.from_pretrained( |
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'bert-base-uncased') |
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def __call__(self, preds, batch=None, *args, **kwargs): |
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if isinstance(preds, list): |
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char_preds = preds[0].detach().cpu().numpy() |
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else: |
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char_preds = preds.detach().cpu().numpy() |
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preds_idx = char_preds.argmax(axis=2) |
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preds_prob = char_preds.max(axis=2) |
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char_text = self.char_decode(preds_idx[:, 1:], preds_prob[:, 1:]) |
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if batch is None: |
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return char_text |
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label = batch[1] |
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label = self.char_decode(label[:, 1:]) |
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if self.only_char: |
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return char_text, label |
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else: |
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bpe_preds = preds[1].detach().cpu().numpy() |
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wp_preds = preds[2] |
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bpe_preds_idx = bpe_preds.argmax(axis=2) |
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bpe_preds_prob = bpe_preds.max(axis=2) |
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bpe_text = self.bpe_decode(bpe_preds_idx[:, 1:], |
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bpe_preds_prob[:, 1:]) |
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wp_preds = wp_preds.detach() |
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wp_preds_prob, wp_preds_idx = wp_preds.max(-1) |
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wp_text = self.wp_decode(wp_preds_idx[:, 1:], wp_preds_prob[:, 1:]) |
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final_text = self.final_decode(char_text, bpe_text, wp_text) |
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return char_text, bpe_text, wp_text, final_text, label |
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def add_special_char(self, dict_character): |
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dict_character = self.list_token + dict_character |
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return dict_character |
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def final_decode(self, char_text, bpe_text, wp_text): |
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result_list = [] |
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for (char_pred, |
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char_pred_conf), (bpe_pred, |
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bpe_pred_conf), (wp_pred, wp_pred_conf) in zip( |
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char_text, bpe_text, wp_text): |
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final_text = char_pred |
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final_prob = char_pred_conf |
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if bpe_pred_conf > final_prob: |
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final_text = bpe_pred |
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final_prob = bpe_pred_conf |
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if wp_pred_conf > final_prob: |
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final_text = wp_pred |
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final_prob = wp_pred_conf |
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result_list.append((final_text, final_prob)) |
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return result_list |
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def char_decode(self, text_index, text_prob=None): |
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""" convert text-index into text-label. """ |
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result_list = [] |
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batch_size = len(text_index) |
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for batch_idx in range(batch_size): |
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char_list = [] |
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conf_list = 1.0 |
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for idx in range(len(text_index[batch_idx])): |
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try: |
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char_idx = self.character[int(text_index[batch_idx][idx])] |
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except: |
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continue |
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if text_prob is not None: |
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conf_list *= text_prob[batch_idx][idx] |
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if char_idx == self.EOS: |
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break |
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if char_idx == self.PAD: |
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continue |
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char_list.append(char_idx) |
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text = ''.join(char_list) |
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result_list.append((text, conf_list)) |
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return result_list |
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def bpe_decode(self, text_index, text_prob): |
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""" convert text-index into text-label. """ |
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result_list = [] |
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for text, probs in zip(text_index, text_prob): |
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text_decoded = [] |
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conf_list = 1.0 |
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for bpeindx, prob in zip(text, probs): |
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tokenstr = self.bpe_tokenizer.decode([bpeindx]) |
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if tokenstr == '#': |
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break |
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text_decoded.append(tokenstr) |
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conf_list *= prob |
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text = ''.join(text_decoded) |
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result_list.append((text, conf_list)) |
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return result_list |
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def wp_decode(self, text_index, text_prob=None): |
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""" convert text-index into text-label. """ |
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result_list = [] |
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for batch_idx, text in enumerate(text_index): |
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wp_pred = self.wp_tokenizer.decode(text) |
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wp_pred_EOS = wp_pred.find('[SEP]') |
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wp_pred = wp_pred[:wp_pred_EOS] |
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if text_prob is not None: |
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try: |
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wp_pred_EOS_index = text.cpu().tolist().index(102) + 1 |
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except: |
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wp_pred_EOS_index = -1 |
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wp_pred_max_prob = text_prob[batch_idx][:wp_pred_EOS_index] |
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try: |
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wp_confidence_score = wp_pred_max_prob.cumprod( |
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dim=0)[-1].cpu().numpy().sum() |
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except: |
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wp_confidence_score = 0.0 |
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else: |
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wp_confidence_score = 1.0 |
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result_list.append((wp_pred, wp_confidence_score)) |
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return result_list |
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