|
|
''' |
|
|
This code is refer from: |
|
|
https://github.com/AlibabaResearch/AdvancedLiterateMachinery/blob/main/OCR/MGP-STR |
|
|
''' |
|
|
import numpy as np |
|
|
|
|
|
from openrec.preprocess.ctc_label_encode import BaseRecLabelEncode |
|
|
|
|
|
|
|
|
class MGPLabelEncode(BaseRecLabelEncode): |
|
|
""" Convert between text-label and text-index """ |
|
|
SPACE = '[s]' |
|
|
GO = '[GO]' |
|
|
list_token = [GO, SPACE] |
|
|
|
|
|
def __init__(self, |
|
|
max_text_length, |
|
|
character_dict_path=None, |
|
|
use_space_char=False, |
|
|
only_char=False, |
|
|
**kwargs): |
|
|
super(MGPLabelEncode, |
|
|
self).__init__(max_text_length, character_dict_path, |
|
|
use_space_char) |
|
|
|
|
|
|
|
|
|
|
|
self.batch_max_length = max_text_length + len(self.list_token) |
|
|
self.only_char = only_char |
|
|
if not only_char: |
|
|
|
|
|
from transformers import BertTokenizer, GPT2Tokenizer |
|
|
self.bpe_tokenizer = GPT2Tokenizer.from_pretrained('gpt2') |
|
|
self.wp_tokenizer = BertTokenizer.from_pretrained( |
|
|
'bert-base-uncased') |
|
|
|
|
|
def __call__(self, data): |
|
|
text = data['label'] |
|
|
char_text, char_len = self.encode(text) |
|
|
if char_text is None: |
|
|
return None |
|
|
data['length'] = np.array(char_len) |
|
|
data['char_label'] = np.array(char_text) |
|
|
if self.only_char: |
|
|
return data |
|
|
bpe_text = self.bpe_encode(text) |
|
|
if bpe_text is None: |
|
|
return None |
|
|
wp_text = self.wp_encode(text) |
|
|
data['bpe_label'] = np.array(bpe_text) |
|
|
data['wp_label'] = wp_text |
|
|
return data |
|
|
|
|
|
def add_special_char(self, dict_character): |
|
|
dict_character = self.list_token + dict_character |
|
|
return dict_character |
|
|
|
|
|
def encode(self, text): |
|
|
""" convert text-label into text-index. |
|
|
""" |
|
|
if len(text) == 0: |
|
|
return None, None |
|
|
if self.lower: |
|
|
text = text.lower() |
|
|
length = len(text) |
|
|
text = [self.GO] + list(text) + [self.SPACE] |
|
|
text_list = [] |
|
|
for char in text: |
|
|
if char not in self.dict: |
|
|
continue |
|
|
text_list.append(self.dict[char]) |
|
|
if len(text_list) == 0 or len(text_list) > self.batch_max_length: |
|
|
return None, None |
|
|
text_list = text_list + [self.dict[self.GO] |
|
|
] * (self.batch_max_length - len(text_list)) |
|
|
return text_list, length |
|
|
|
|
|
def bpe_encode(self, text): |
|
|
if len(text) == 0: |
|
|
return None |
|
|
token = self.bpe_tokenizer(text)['input_ids'] |
|
|
text_list = [1] + token + [2] |
|
|
if len(text_list) == 0 or len(text_list) > self.batch_max_length: |
|
|
return None |
|
|
text_list = text_list + [self.dict[self.GO] |
|
|
] * (self.batch_max_length - len(text_list)) |
|
|
return text_list |
|
|
|
|
|
def wp_encode(self, text): |
|
|
wp_target = self.wp_tokenizer([text], |
|
|
padding='max_length', |
|
|
max_length=self.batch_max_length, |
|
|
truncation=True, |
|
|
return_tensors='np') |
|
|
return wp_target['input_ids'][0] |
|
|
|
