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from random import sample |
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import numpy as np |
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from .ctc_label_encode import BaseRecLabelEncode |
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class VisionLANLabelEncode(BaseRecLabelEncode): |
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"""Convert between text-label and text-index.""" |
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def __init__(self, |
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max_text_length, |
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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(VisionLANLabelEncode, |
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self).__init__(max_text_length, character_dict_path, |
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use_space_char) |
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self.dict = {} |
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for i, char in enumerate(self.character): |
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self.dict[char] = i |
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def __call__(self, data): |
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text = data['label'] |
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len_str = len(text) |
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if len_str <= 0: |
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return None |
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change_num = 1 |
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order = list(range(len_str)) |
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change_id = sample(order, change_num)[0] |
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label_sub = text[change_id] |
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if change_id == (len_str - 1): |
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label_res = text[:change_id] |
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elif change_id == 0: |
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label_res = text[1:] |
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else: |
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label_res = text[:change_id] + text[change_id + 1:] |
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data['label_res'] = label_res |
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data['label_sub'] = label_sub |
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data['label_id'] = change_id |
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text = self.encode(text) |
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if text is None: |
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return None |
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text = [i + 1 for i in text] |
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data['length'] = np.array(len(text)) |
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text = text + [0] * (self.max_text_len + 1 - len(text)) |
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data['label'] = np.array(text) |
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label_res = self.encode(label_res) |
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label_sub = self.encode(label_sub) |
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if label_res is None: |
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label_res = [] |
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else: |
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label_res = [i + 1 for i in label_res] |
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if label_sub is None: |
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label_sub = [] |
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else: |
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label_sub = [i + 1 for i in label_sub] |
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data['length_res'] = np.array(len(label_res)) |
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data['length_sub'] = np.array(len(label_sub)) |
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label_res = label_res + [0] * (self.max_text_len - len(label_res)) |
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label_sub = label_sub + [0] * (self.max_text_len - len(label_sub)) |
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data['label_res'] = np.array(label_res) |
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data['label_sub'] = np.array(label_sub) |
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return data |
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