|
|
import numpy as np |
|
|
import torch |
|
|
|
|
|
from .ctc_postprocess import BaseRecLabelDecode |
|
|
|
|
|
|
|
|
class ARLabelDecode(BaseRecLabelDecode): |
|
|
"""Convert between text-label and text-index.""" |
|
|
|
|
|
BOS = '<s>' |
|
|
EOS = '</s>' |
|
|
PAD = '<pad>' |
|
|
|
|
|
def __init__(self, |
|
|
character_dict_path=None, |
|
|
use_space_char=True, |
|
|
**kwargs): |
|
|
super(ARLabelDecode, self).__init__(character_dict_path, |
|
|
use_space_char) |
|
|
|
|
|
def __call__(self, preds, batch=None, *args, **kwargs): |
|
|
|
|
|
if isinstance(preds, list): |
|
|
preds = preds[-1] |
|
|
if isinstance(preds, torch.Tensor): |
|
|
preds = preds.detach().cpu().numpy() |
|
|
preds_idx = preds.argmax(axis=2) |
|
|
preds_prob = preds.max(axis=2) |
|
|
text = self.decode(preds_idx, preds_prob) |
|
|
if batch is None: |
|
|
return text |
|
|
label = batch[1] |
|
|
label = self.decode(label[:, 1:]) |
|
|
return text, label |
|
|
|
|
|
def add_special_char(self, dict_character): |
|
|
dict_character = [self.EOS] + dict_character + [self.BOS, self.PAD] |
|
|
return dict_character |
|
|
|
|
|
def decode(self, text_index, text_prob=None): |
|
|
"""convert text-index into text-label.""" |
|
|
result_list = [] |
|
|
batch_size = len(text_index) |
|
|
for batch_idx in range(batch_size): |
|
|
char_list = [] |
|
|
conf_list = [] |
|
|
for idx in range(len(text_index[batch_idx])): |
|
|
try: |
|
|
char_idx = self.character[int(text_index[batch_idx][idx])] |
|
|
except: |
|
|
continue |
|
|
if char_idx == self.EOS: |
|
|
break |
|
|
if char_idx == self.BOS or char_idx == self.PAD: |
|
|
continue |
|
|
char_list.append(char_idx) |
|
|
if text_prob is not None: |
|
|
conf_list.append(text_prob[batch_idx][idx]) |
|
|
else: |
|
|
conf_list.append(1) |
|
|
text = ''.join(char_list) |
|
|
result_list.append((text, np.mean(conf_list).tolist())) |
|
|
return result_list |
|
|
|
