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