290 lines
11 KiB
Python
290 lines
11 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import string
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import paddle
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from paddle.nn import functional as F
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class BaseRecLabelDecode(object):
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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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character_type='ch',
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use_space_char=False):
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support_character_type = [
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'ch', 'en', 'EN_symbol', 'french', 'german', 'japan', 'korean',
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'it', 'xi', 'pu', 'ru', 'ar', 'ta', 'ug', 'fa', 'ur', 'rs', 'oc',
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'rsc', 'bg', 'uk', 'be', 'te', 'ka', 'chinese_cht', 'hi', 'mr',
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'ne', 'EN'
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]
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assert character_type in support_character_type, "Only {} are supported now but get {}".format(
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support_character_type, character_type)
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self.beg_str = "sos"
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self.end_str = "eos"
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if character_type == "en":
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self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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dict_character = list(self.character_str)
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elif character_type == "EN_symbol":
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# same with ASTER setting (use 94 char).
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self.character_str = string.printable[:-6]
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dict_character = list(self.character_str)
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elif character_type in support_character_type:
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self.character_str = ""
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assert character_dict_path is not None, "character_dict_path should not be None when character_type is {}".format(
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character_type)
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with open(character_dict_path, "rb") as fin:
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lines = fin.readlines()
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for line in lines:
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line = line.decode('utf-8').strip("\n").strip("\r\n")
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self.character_str += line
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if use_space_char:
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self.character_str += " "
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dict_character = list(self.character_str)
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else:
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raise NotImplementedError
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self.character_type = character_type
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dict_character = self.add_special_char(dict_character)
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self.dict = {}
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for i, char in enumerate(dict_character):
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self.dict[char] = i
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self.character = dict_character
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def add_special_char(self, dict_character):
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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 is_remove_duplicate:
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# only for predict
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if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
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batch_idx][idx]:
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continue
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char_list.append(self.character[int(text_index[batch_idx][
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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)))
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return result_list
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def get_ignored_tokens(self):
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return [0] # for ctc blank
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class CTCLabelDecode(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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character_type='ch',
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use_space_char=False,
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**kwargs):
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super(CTCLabelDecode, self).__init__(character_dict_path,
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character_type, use_space_char)
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def __call__(self, preds, label=None, *args, **kwargs):
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if isinstance(preds, paddle.Tensor):
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preds = preds.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=True)
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if label is None:
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return text
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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 = ['blank'] + dict_character
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return dict_character
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class AttnLabelDecode(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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character_type='ch',
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use_space_char=False,
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**kwargs):
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super(AttnLabelDecode, self).__init__(character_dict_path,
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character_type, use_space_char)
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def add_special_char(self, dict_character):
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self.beg_str = "sos"
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self.end_str = "eos"
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dict_character = dict_character
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dict_character = [self.beg_str] + dict_character + [self.end_str]
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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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[beg_idx, end_idx] = 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(end_idx):
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break
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if is_remove_duplicate:
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# only for predict
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if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
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batch_idx][idx]:
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continue
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char_list.append(self.character[int(text_index[batch_idx][
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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)))
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return result_list
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def __call__(self, preds, label=None, *args, **kwargs):
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"""
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text = self.decode(text)
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if label is None:
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return text
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else:
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label = self.decode(label, is_remove_duplicate=False)
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return text, label
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"""
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if isinstance(preds, paddle.Tensor):
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preds = preds.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 label is None:
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return text
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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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beg_idx = self.get_beg_end_flag_idx("beg")
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end_idx = self.get_beg_end_flag_idx("end")
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return [beg_idx, end_idx]
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def get_beg_end_flag_idx(self, beg_or_end):
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if beg_or_end == "beg":
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idx = np.array(self.dict[self.beg_str])
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elif beg_or_end == "end":
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idx = np.array(self.dict[self.end_str])
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else:
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assert False, "unsupport type %s in get_beg_end_flag_idx" \
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% beg_or_end
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return idx
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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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character_type='en',
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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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character_type, use_space_char)
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def __call__(self, preds, label=None, *args, **kwargs):
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pred = preds['predict']
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char_num = len(self.character_str) + 2
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if isinstance(pred, paddle.Tensor):
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pred = pred.numpy()
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pred = np.reshape(pred, [-1, char_num])
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preds_idx = np.argmax(pred, axis=1)
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preds_prob = np.max(pred, axis=1)
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preds_idx = np.reshape(preds_idx, [-1, 25])
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preds_prob = np.reshape(preds_prob, [-1, 25])
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text = self.decode(preds_idx, preds_prob)
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if label is None:
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text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
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return text
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label = self.decode(label)
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return text, label
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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 is_remove_duplicate:
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# only for predict
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if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
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batch_idx][idx]:
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continue
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char_list.append(self.character[int(text_index[batch_idx][
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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)))
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return result_list
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def add_special_char(self, dict_character):
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dict_character = dict_character + [self.beg_str, self.end_str]
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return dict_character
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def get_ignored_tokens(self):
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beg_idx = self.get_beg_end_flag_idx("beg")
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end_idx = self.get_beg_end_flag_idx("end")
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return [beg_idx, end_idx]
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def get_beg_end_flag_idx(self, beg_or_end):
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if beg_or_end == "beg":
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idx = np.array(self.dict[self.beg_str])
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elif beg_or_end == "end":
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idx = np.array(self.dict[self.end_str])
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else:
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assert False, "unsupport type %s in get_beg_end_flag_idx" \
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% beg_or_end
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return idx
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