823 lines
31 KiB
Python
823 lines
31 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 paddle
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from paddle.nn import functional as F
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import re
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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, character_dict_path=None, use_space_char=False):
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self.beg_str = "sos"
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self.end_str = "eos"
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self.character_str = []
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if character_dict_path is None:
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# self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
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self.character_str = "abcdefghijklmnopqrstuvwxyz1234567890"
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dict_character = list(self.character_str)
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else:
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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.append(line)
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if use_space_char:
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self.character_str.append(" ")
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dict_character = list(self.character_str)
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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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selection = np.ones(len(text_index[batch_idx]), dtype=bool)
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if is_remove_duplicate:
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selection[1:] = text_index[batch_idx][1:] != text_index[
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batch_idx][:-1]
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for ignored_token in ignored_tokens:
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selection &= text_index[batch_idx] != ignored_token
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char_list = [
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self.character[text_id]
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for text_id in text_index[batch_idx][selection]
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]
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if text_prob is not None:
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conf_list = text_prob[batch_idx][selection]
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else:
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conf_list = [1] * len(selection)
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if len(conf_list) == 0:
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conf_list = [0]
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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 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, character_dict_path=None, use_space_char=False,
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**kwargs):
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super(CTCLabelDecode, self).__init__(character_dict_path,
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use_space_char)
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def __call__(self, preds, label=None, *args, **kwargs):
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if isinstance(preds, tuple) or isinstance(preds, list):
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preds = preds[-1]
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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 DistillationCTCLabelDecode(CTCLabelDecode):
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"""
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Convert
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Convert between text-label and text-index
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"""
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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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model_name=["student"],
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key=None,
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multi_head=False,
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**kwargs):
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super(DistillationCTCLabelDecode, self).__init__(character_dict_path,
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use_space_char)
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if not isinstance(model_name, list):
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model_name = [model_name]
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self.model_name = model_name
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self.key = key
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self.multi_head = multi_head
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def __call__(self, preds, label=None, *args, **kwargs):
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output = dict()
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for name in self.model_name:
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pred = preds[name]
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if self.key is not None:
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pred = pred[self.key]
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if self.multi_head and isinstance(pred, dict):
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pred = pred['ctc']
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output[name] = super().__call__(pred, label=label, *args, **kwargs)
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return output
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class NRTRLabelDecode(BaseRecLabelDecode):
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""" Convert between text-label and text-index """
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def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
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super(NRTRLabelDecode, self).__init__(character_dict_path,
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use_space_char)
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def __call__(self, preds, label=None, *args, **kwargs):
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if len(preds) == 2:
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preds_id = preds[0]
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preds_prob = preds[1]
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if isinstance(preds_id, paddle.Tensor):
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preds_id = preds_id.numpy()
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if isinstance(preds_prob, paddle.Tensor):
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preds_prob = preds_prob.numpy()
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if preds_id[0][0] == 2:
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preds_idx = preds_id[:, 1:]
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preds_prob = preds_prob[:, 1:]
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else:
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preds_idx = preds_id
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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[:, 1:])
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else:
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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[:, 1:])
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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', '<unk>', '<s>', '</s>'] + 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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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] == 3: # end
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break
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try:
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char_list.append(self.character[int(text_index[batch_idx][
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idx])])
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except:
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continue
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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.lower(), np.mean(conf_list).tolist()))
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return result_list
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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, character_dict_path=None, use_space_char=False,
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**kwargs):
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super(AttnLabelDecode, self).__init__(character_dict_path,
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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).tolist()))
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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 SEEDLabelDecode(BaseRecLabelDecode):
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""" Convert between text-label and text-index """
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def __init__(self, character_dict_path=None, use_space_char=False,
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**kwargs):
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super(SEEDLabelDecode, self).__init__(character_dict_path,
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use_space_char)
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def add_special_char(self, dict_character):
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self.padding_str = "padding"
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self.end_str = "eos"
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self.unknown = "unknown"
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dict_character = dict_character + [
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self.end_str, self.padding_str, self.unknown
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]
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return dict_character
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def get_ignored_tokens(self):
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end_idx = self.get_beg_end_flag_idx("eos")
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return [end_idx]
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def get_beg_end_flag_idx(self, beg_or_end):
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if beg_or_end == "sos":
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idx = np.array(self.dict[self.beg_str])
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elif beg_or_end == "eos":
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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" % beg_or_end
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return idx
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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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[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 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).tolist()))
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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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preds_idx = preds["rec_pred"]
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if isinstance(preds_idx, paddle.Tensor):
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preds_idx = preds_idx.numpy()
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if "rec_pred_scores" in preds:
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preds_idx = preds["rec_pred"]
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preds_prob = preds["rec_pred_scores"]
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else:
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preds_idx = preds["rec_pred"].argmax(axis=2)
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preds_prob = preds["rec_pred"].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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class SRNLabelDecode(BaseRecLabelDecode):
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""" Convert between text-label and text-index """
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def __init__(self, character_dict_path=None, 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_text_length = kwargs.get('max_text_length', 25)
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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, self.max_text_length])
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preds_prob = np.reshape(preds_prob, [-1, self.max_text_length])
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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).tolist()))
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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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class TableLabelDecode(object):
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""" """
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def __init__(self, character_dict_path, **kwargs):
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list_character, list_elem = self.load_char_elem_dict(
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character_dict_path)
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list_character = self.add_special_char(list_character)
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list_elem = self.add_special_char(list_elem)
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self.dict_character = {}
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self.dict_idx_character = {}
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for i, char in enumerate(list_character):
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self.dict_idx_character[i] = char
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self.dict_character[char] = i
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self.dict_elem = {}
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self.dict_idx_elem = {}
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for i, elem in enumerate(list_elem):
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self.dict_idx_elem[i] = elem
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self.dict_elem[elem] = i
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def load_char_elem_dict(self, character_dict_path):
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list_character = []
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list_elem = []
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with open(character_dict_path, "rb") as fin:
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lines = fin.readlines()
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substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split(
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"\t")
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character_num = int(substr[0])
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elem_num = int(substr[1])
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for cno in range(1, 1 + character_num):
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character = lines[cno].decode('utf-8').strip("\n").strip("\r\n")
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list_character.append(character)
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for eno in range(1 + character_num, 1 + character_num + elem_num):
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elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n")
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list_elem.append(elem)
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return list_character, list_elem
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def add_special_char(self, list_character):
|
|
self.beg_str = "sos"
|
|
self.end_str = "eos"
|
|
list_character = [self.beg_str] + list_character + [self.end_str]
|
|
return list_character
|
|
|
|
def __call__(self, preds):
|
|
structure_probs = preds['structure_probs']
|
|
loc_preds = preds['loc_preds']
|
|
if isinstance(structure_probs, paddle.Tensor):
|
|
structure_probs = structure_probs.numpy()
|
|
if isinstance(loc_preds, paddle.Tensor):
|
|
loc_preds = loc_preds.numpy()
|
|
structure_idx = structure_probs.argmax(axis=2)
|
|
structure_probs = structure_probs.max(axis=2)
|
|
structure_str, structure_pos, result_score_list, result_elem_idx_list = self.decode(
|
|
structure_idx, structure_probs, 'elem')
|
|
res_html_code_list = []
|
|
res_loc_list = []
|
|
batch_num = len(structure_str)
|
|
for bno in range(batch_num):
|
|
res_loc = []
|
|
for sno in range(len(structure_str[bno])):
|
|
text = structure_str[bno][sno]
|
|
if text in ['<td>', '<td']:
|
|
pos = structure_pos[bno][sno]
|
|
res_loc.append(loc_preds[bno, pos])
|
|
res_html_code = ''.join(structure_str[bno])
|
|
res_loc = np.array(res_loc)
|
|
res_html_code_list.append(res_html_code)
|
|
res_loc_list.append(res_loc)
|
|
return {
|
|
'res_html_code': res_html_code_list,
|
|
'res_loc': res_loc_list,
|
|
'res_score_list': result_score_list,
|
|
'res_elem_idx_list': result_elem_idx_list,
|
|
'structure_str_list': structure_str
|
|
}
|
|
|
|
def decode(self, text_index, structure_probs, char_or_elem):
|
|
"""convert text-label into text-index.
|
|
"""
|
|
if char_or_elem == "char":
|
|
current_dict = self.dict_idx_character
|
|
else:
|
|
current_dict = self.dict_idx_elem
|
|
ignored_tokens = self.get_ignored_tokens('elem')
|
|
beg_idx, end_idx = ignored_tokens
|
|
|
|
result_list = []
|
|
result_pos_list = []
|
|
result_score_list = []
|
|
result_elem_idx_list = []
|
|
batch_size = len(text_index)
|
|
for batch_idx in range(batch_size):
|
|
char_list = []
|
|
elem_pos_list = []
|
|
elem_idx_list = []
|
|
score_list = []
|
|
for idx in range(len(text_index[batch_idx])):
|
|
tmp_elem_idx = int(text_index[batch_idx][idx])
|
|
if idx > 0 and tmp_elem_idx == end_idx:
|
|
break
|
|
if tmp_elem_idx in ignored_tokens:
|
|
continue
|
|
|
|
char_list.append(current_dict[tmp_elem_idx])
|
|
elem_pos_list.append(idx)
|
|
score_list.append(structure_probs[batch_idx, idx])
|
|
elem_idx_list.append(tmp_elem_idx)
|
|
result_list.append(char_list)
|
|
result_pos_list.append(elem_pos_list)
|
|
result_score_list.append(score_list)
|
|
result_elem_idx_list.append(elem_idx_list)
|
|
return result_list, result_pos_list, result_score_list, result_elem_idx_list
|
|
|
|
def get_ignored_tokens(self, char_or_elem):
|
|
beg_idx = self.get_beg_end_flag_idx("beg", char_or_elem)
|
|
end_idx = self.get_beg_end_flag_idx("end", char_or_elem)
|
|
return [beg_idx, end_idx]
|
|
|
|
def get_beg_end_flag_idx(self, beg_or_end, char_or_elem):
|
|
if char_or_elem == "char":
|
|
if beg_or_end == "beg":
|
|
idx = self.dict_character[self.beg_str]
|
|
elif beg_or_end == "end":
|
|
idx = self.dict_character[self.end_str]
|
|
else:
|
|
assert False, "Unsupport type %s in get_beg_end_flag_idx of char" \
|
|
% beg_or_end
|
|
elif char_or_elem == "elem":
|
|
if beg_or_end == "beg":
|
|
idx = self.dict_elem[self.beg_str]
|
|
elif beg_or_end == "end":
|
|
idx = self.dict_elem[self.end_str]
|
|
else:
|
|
assert False, "Unsupport type %s in get_beg_end_flag_idx of elem" \
|
|
% beg_or_end
|
|
else:
|
|
assert False, "Unsupport type %s in char_or_elem" \
|
|
% char_or_elem
|
|
return idx
|
|
|
|
|
|
class SARLabelDecode(BaseRecLabelDecode):
|
|
""" Convert between text-label and text-index """
|
|
|
|
def __init__(self, character_dict_path=None, use_space_char=False,
|
|
**kwargs):
|
|
super(SARLabelDecode, self).__init__(character_dict_path,
|
|
use_space_char)
|
|
|
|
self.rm_symbol = kwargs.get('rm_symbol', False)
|
|
|
|
def add_special_char(self, dict_character):
|
|
beg_end_str = "<BOS/EOS>"
|
|
unknown_str = "<UKN>"
|
|
padding_str = "<PAD>"
|
|
dict_character = dict_character + [unknown_str]
|
|
self.unknown_idx = len(dict_character) - 1
|
|
dict_character = dict_character + [beg_end_str]
|
|
self.start_idx = len(dict_character) - 1
|
|
self.end_idx = len(dict_character) - 1
|
|
dict_character = dict_character + [padding_str]
|
|
self.padding_idx = len(dict_character) - 1
|
|
return dict_character
|
|
|
|
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
|
|
""" convert text-index into text-label. """
|
|
result_list = []
|
|
ignored_tokens = self.get_ignored_tokens()
|
|
|
|
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])):
|
|
if text_index[batch_idx][idx] in ignored_tokens:
|
|
continue
|
|
if int(text_index[batch_idx][idx]) == int(self.end_idx):
|
|
if text_prob is None and idx == 0:
|
|
continue
|
|
else:
|
|
break
|
|
if is_remove_duplicate:
|
|
# only for predict
|
|
if idx > 0 and text_index[batch_idx][idx - 1] == text_index[
|
|
batch_idx][idx]:
|
|
continue
|
|
char_list.append(self.character[int(text_index[batch_idx][
|
|
idx])])
|
|
if text_prob is not None:
|
|
conf_list.append(text_prob[batch_idx][idx])
|
|
else:
|
|
conf_list.append(1)
|
|
text = ''.join(char_list)
|
|
if self.rm_symbol:
|
|
comp = re.compile('[^A-Z^a-z^0-9^\u4e00-\u9fa5]')
|
|
text = text.lower()
|
|
text = comp.sub('', text)
|
|
result_list.append((text, np.mean(conf_list).tolist()))
|
|
return result_list
|
|
|
|
def __call__(self, preds, label=None, *args, **kwargs):
|
|
if isinstance(preds, paddle.Tensor):
|
|
preds = preds.numpy()
|
|
preds_idx = preds.argmax(axis=2)
|
|
preds_prob = preds.max(axis=2)
|
|
|
|
text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
|
|
|
|
if label is None:
|
|
return text
|
|
label = self.decode(label, is_remove_duplicate=False)
|
|
return text, label
|
|
|
|
def get_ignored_tokens(self):
|
|
return [self.padding_idx]
|
|
|
|
|
|
class DistillationSARLabelDecode(SARLabelDecode):
|
|
"""
|
|
Convert
|
|
Convert between text-label and text-index
|
|
"""
|
|
|
|
def __init__(self,
|
|
character_dict_path=None,
|
|
use_space_char=False,
|
|
model_name=["student"],
|
|
key=None,
|
|
multi_head=False,
|
|
**kwargs):
|
|
super(DistillationSARLabelDecode, self).__init__(character_dict_path,
|
|
use_space_char)
|
|
if not isinstance(model_name, list):
|
|
model_name = [model_name]
|
|
self.model_name = model_name
|
|
|
|
self.key = key
|
|
self.multi_head = multi_head
|
|
|
|
def __call__(self, preds, label=None, *args, **kwargs):
|
|
output = dict()
|
|
for name in self.model_name:
|
|
pred = preds[name]
|
|
if self.key is not None:
|
|
pred = pred[self.key]
|
|
if self.multi_head and isinstance(pred, dict):
|
|
pred = pred['sar']
|
|
output[name] = super().__call__(pred, label=label, *args, **kwargs)
|
|
return output
|
|
|
|
|
|
class PRENLabelDecode(BaseRecLabelDecode):
|
|
""" Convert between text-label and text-index """
|
|
|
|
def __init__(self, character_dict_path=None, use_space_char=False,
|
|
**kwargs):
|
|
super(PRENLabelDecode, self).__init__(character_dict_path,
|
|
use_space_char)
|
|
|
|
def add_special_char(self, dict_character):
|
|
padding_str = '<PAD>' # 0
|
|
end_str = '<EOS>' # 1
|
|
unknown_str = '<UNK>' # 2
|
|
|
|
dict_character = [padding_str, end_str, unknown_str] + dict_character
|
|
self.padding_idx = 0
|
|
self.end_idx = 1
|
|
self.unknown_idx = 2
|
|
|
|
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])):
|
|
if text_index[batch_idx][idx] == self.end_idx:
|
|
break
|
|
if text_index[batch_idx][idx] in \
|
|
[self.padding_idx, self.unknown_idx]:
|
|
continue
|
|
char_list.append(self.character[int(text_index[batch_idx][
|
|
idx])])
|
|
if text_prob is not None:
|
|
conf_list.append(text_prob[batch_idx][idx])
|
|
else:
|
|
conf_list.append(1)
|
|
|
|
text = ''.join(char_list)
|
|
if len(text) > 0:
|
|
result_list.append((text, np.mean(conf_list).tolist()))
|
|
else:
|
|
# here confidence of empty recog result is 1
|
|
result_list.append(('', 1))
|
|
return result_list
|
|
|
|
def __call__(self, preds, label=None, *args, **kwargs):
|
|
preds = preds.numpy()
|
|
preds_idx = preds.argmax(axis=2)
|
|
preds_prob = preds.max(axis=2)
|
|
text = self.decode(preds_idx, preds_prob)
|
|
if label is None:
|
|
return text
|
|
label = self.decode(label)
|
|
return text, label
|
|
|
|
|
|
class VLLabelDecode(BaseRecLabelDecode):
|
|
""" Convert between text-label and text-index """
|
|
|
|
def __init__(self, character_dict_path=None, use_space_char=False,
|
|
**kwargs):
|
|
super(VLLabelDecode, self).__init__(character_dict_path, use_space_char)
|
|
|
|
def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
|
|
""" convert text-index into text-label. """
|
|
result_list = []
|
|
ignored_tokens = self.get_ignored_tokens()
|
|
batch_size = len(text_index)
|
|
for batch_idx in range(batch_size):
|
|
selection = np.ones(len(text_index[batch_idx]), dtype=bool)
|
|
if is_remove_duplicate:
|
|
selection[1:] = text_index[batch_idx][1:] != text_index[
|
|
batch_idx][:-1]
|
|
for ignored_token in ignored_tokens:
|
|
selection &= text_index[batch_idx] != ignored_token
|
|
|
|
char_list = [
|
|
self.character[text_id - 1]
|
|
for text_id in text_index[batch_idx][selection]
|
|
]
|
|
if text_prob is not None:
|
|
conf_list = text_prob[batch_idx][selection]
|
|
else:
|
|
conf_list = [1] * len(selection)
|
|
if len(conf_list) == 0:
|
|
conf_list = [0]
|
|
|
|
text = ''.join(char_list)
|
|
result_list.append((text, np.mean(conf_list).tolist()))
|
|
return result_list
|
|
|
|
def __call__(self, preds, label=None, length=None, *args, **kwargs):
|
|
if len(preds) == 2: # eval mode
|
|
net_out, length = preds
|
|
else: # train mode
|
|
net_out = preds[0]
|
|
length = length
|
|
net_out = paddle.concat([t[:l] for t, l in zip(net_out, length)])
|
|
text = []
|
|
if not isinstance(net_out, paddle.Tensor):
|
|
net_out = paddle.to_tensor(net_out, dtype='float32')
|
|
# import pdb
|
|
# pdb.set_trace()
|
|
net_out = F.softmax(net_out, axis=1)
|
|
for i in range(0, length.shape[0]):
|
|
preds_idx = net_out[int(length[:i].sum()):int(length[:i].sum(
|
|
) + length[i])].topk(1)[1][:, 0].tolist()
|
|
preds_text = ''.join([
|
|
self.character[idx - 1]
|
|
if idx > 0 and idx <= len(self.character) else ''
|
|
for idx in preds_idx
|
|
])
|
|
preds_prob = net_out[int(length[:i].sum()):int(length[:i].sum(
|
|
) + length[i])].topk(1)[0][:, 0]
|
|
preds_prob = paddle.exp(
|
|
paddle.log(preds_prob).sum() / (preds_prob.shape[0] + 1e-6))
|
|
text.append((preds_text, preds_prob))
|
|
if label is None:
|
|
return text
|
|
label = self.decode(label)
|
|
return text, label
|