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import numpy as np |
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import torch |
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from .ctc_postprocess import BaseRecLabelDecode |
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class SRNLabelDecode(BaseRecLabelDecode): |
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"""Convert between text-label and text-index.""" |
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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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**kwargs): |
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super(SRNLabelDecode, self).__init__(character_dict_path, |
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use_space_char) |
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self.max_len = 25 |
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def add_special_char(self, dict_character): |
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dict_character = dict_character + ['<BOS>', '<EOS>'] |
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self.start_idx = len(dict_character) - 2 |
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self.end_idx = len(dict_character) - 1 |
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return dict_character |
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def decode(self, text_index, text_prob=None, is_remove_duplicate=False): |
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"""convert text-index into text-label.""" |
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result_list = [] |
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ignored_tokens = self.get_ignored_tokens() |
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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 = [] |
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for idx in range(len(text_index[batch_idx])): |
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if text_index[batch_idx][idx] in ignored_tokens: |
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continue |
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if int(text_index[batch_idx][idx]) == int(self.end_idx): |
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if text_prob is None and idx == 0: |
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continue |
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else: |
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break |
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if is_remove_duplicate: |
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if idx > 0 and text_index[batch_idx][ |
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idx - 1] == text_index[batch_idx][idx]: |
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continue |
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char_list.append(self.character[int( |
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text_index[batch_idx][idx])]) |
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if text_prob is not None: |
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conf_list.append(text_prob[batch_idx][idx]) |
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else: |
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conf_list.append(1) |
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text = ''.join(char_list) |
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result_list.append((text, np.mean(conf_list).tolist())) |
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return result_list |
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def __call__(self, preds, batch=None, *args, **kwargs): |
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if isinstance(preds, torch.Tensor): |
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preds = preds.reshape([-1, self.max_len, preds.shape[-1]]) |
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preds = preds.detach().cpu().numpy() |
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else: |
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preds = preds[-1] |
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preds = preds.reshape([-1, self.max_len, |
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preds.shape[-1]]).detach().cpu().numpy() |
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preds_idx = preds.argmax(axis=2) |
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preds_prob = preds.max(axis=2) |
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False) |
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if batch is None: |
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return text |
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label = batch[1] |
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label = self.decode(label, is_remove_duplicate=False) |
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return text, label |
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def get_ignored_tokens(self): |
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return [self.start_idx, self.end_idx] |
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