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| import numpy as np | |
| import torch | |
| from .nrtr_postprocess import NRTRLabelDecode | |
| class IGTRLabelDecode(NRTRLabelDecode): | |
| """Convert between text-label and text-index.""" | |
| def __init__(self, | |
| character_dict_path=None, | |
| use_space_char=False, | |
| **kwargs): | |
| super(IGTRLabelDecode, self).__init__(character_dict_path, | |
| use_space_char) | |
| def __call__(self, preds, batch=None, *args, **kwargs): | |
| if isinstance(preds, list): | |
| if isinstance(preds[0], dict): | |
| preds = preds[-1].detach().cpu().numpy() | |
| if isinstance(preds, torch.Tensor): | |
| preds = preds.detach().cpu().numpy() | |
| elif isinstance(preds, dict): | |
| preds = preds['align'][-1].detach().cpu().numpy() | |
| else: | |
| preds = preds | |
| preds_idx = preds.argmax(axis=2) | |
| preds_prob = preds.max(axis=2) | |
| text = self.decode(preds_idx, | |
| preds_prob, | |
| is_remove_duplicate=False) | |
| else: | |
| preds_idx = preds[0].detach().cpu().numpy() | |
| preds_prob = preds[1].detach().cpu().numpy() | |
| text = self.decode(preds_idx, | |
| preds_prob, | |
| is_remove_duplicate=False) | |
| else: | |
| if isinstance(preds, torch.Tensor): | |
| preds = preds.detach().cpu().numpy() | |
| elif isinstance(preds, dict): | |
| preds = preds['align'][-1].detach().cpu().numpy() | |
| else: | |
| preds = preds | |
| preds_idx = preds.argmax(axis=2) | |
| preds_idx_top5 = preds.argsort(axis=2)[:, :, -5:] | |
| preds_prob = preds.max(axis=2) | |
| text = self.decode(preds_idx, | |
| preds_prob, | |
| is_remove_duplicate=False, | |
| idx_top5=preds_idx_top5) | |
| if batch is None: | |
| return text | |
| label = batch[1] | |
| label = self.decode(label.detach().cpu().numpy()) | |
| return text, label | |
| def add_special_char(self, dict_character): | |
| dict_character = ['</s>'] + dict_character + ['<s>', '<pad>'] | |
| return dict_character | |
| def decode(self, | |
| text_index, | |
| text_prob=None, | |
| is_remove_duplicate=False, | |
| idx_top5=None): | |
| """convert text-index into text-label.""" | |
| result_list = [] | |
| batch_size = len(text_index) | |
| for batch_idx in range(batch_size): | |
| char_list = [] | |
| char_list_top5 = [] | |
| conf_list = [] | |
| for idx in range(len(text_index[batch_idx])): | |
| char_idx_top5 = [] | |
| try: | |
| char_idx = self.character[int(text_index[batch_idx][idx])] | |
| if idx_top5 is not None: | |
| for top5_i in idx_top5[batch_idx][idx]: | |
| char_idx_top5.append(self.character[top5_i]) | |
| except: | |
| continue | |
| if char_idx == '</s>': # end | |
| break | |
| if char_idx == '<s>' or char_idx == '<pad>': | |
| continue | |
| char_list.append(char_idx) | |
| char_list_top5.append(char_idx_top5) | |
| if text_prob is not None: | |
| conf_list.append(text_prob[batch_idx][idx]) | |
| else: | |
| conf_list.append(1) | |
| text = ''.join(char_list) | |
| if idx_top5 is not None: | |
| result_list.append( | |
| (text, [np.mean(conf_list).tolist(), char_list_top5])) | |
| else: | |
| result_list.append((text, np.mean(conf_list).tolist())) | |
| return result_list | |