MinerU/mineru/model/utils/pytorchocr/postprocess/rec_postprocess.py

# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
import numpy as np
import torch


class BaseRecLabelDecode(object):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 use_space_char=False):

        self.beg_str = "sos"
        self.end_str = "eos"
        self.reverse = False
        self.character_str = []

        if character_dict_path is None:
            self.character_str = "0123456789abcdefghijklmnopqrstuvwxyz"
            dict_character = list(self.character_str)
        else:
            with open(character_dict_path, "rb") as fin:
                lines = fin.readlines()
                for line in lines:
                    line = line.decode('utf-8').strip("\n").strip("\r\n")
                    self.character_str.append(line)
            if use_space_char:
                self.character_str.append(" ")
            dict_character = list(self.character_str)
            if "arabic" in character_dict_path:
                self.reverse = True

        dict_character = self.add_special_char(dict_character)
        self.dict = {}
        for i, char in enumerate(dict_character):
            self.dict[char] = i
        self.character = np.array(dict_character)

    def pred_reverse(self, pred):
        pred_re = []
        c_current = ""
        for c in pred:
            if not bool(re.search("[a-zA-Z0-9 :*./%+-]", c)):
                if c_current != "":
                    pred_re.append(c_current)
                pred_re.append(c)
                c_current = ""
            else:
                c_current += c
        if c_current != "":
            pred_re.append(c_current)

        return "".join(pred_re[::-1])

    def add_special_char(self, dict_character):
        return dict_character

    def get_word_info(self, text, selection):
        """
        Group the decoded characters and record the corresponding decoded positions.

        Args:
            text: the decoded text
            selection: the bool array that identifies which columns of features are decoded as non-separated characters
        Returns:
            word_list: list of the grouped words
            word_col_list: list of decoding positions corresponding to each character in the grouped word
            state_list: list of marker to identify the type of grouping words, including two types of grouping words:
                        - 'cn': continuous chinese characters (e.g., 你好啊)
                        - 'en&num': continuous english characters (e.g., hello), number (e.g., 123, 1.123), or mixed of them connected by '-' (e.g., VGG-16)
                        The remaining characters in text are treated as separators between groups (e.g., space, '(', ')', etc.).
        """
        state = None
        word_content = []
        word_col_content = []
        word_list = []
        word_col_list = []
        state_list = []
        valid_col = np.where(selection == True)[0]

        for c_i, char in enumerate(text):
            if "\u4e00" <= char <= "\u9fff":
                c_state = "cn"
            elif bool(re.search("[a-zA-Z0-9]", char)):
                c_state = "en&num"
            else:
                c_state = "splitter"

            if (
                char == "."
                and state == "en&num"
                and c_i + 1 < len(text)
                and bool(re.search("[0-9]", text[c_i + 1]))
            ):  # grouping floating number
                c_state = "en&num"
            if (
                char == "-" and state == "en&num"
            ):  # grouping word with '-', such as 'state-of-the-art'
                c_state = "en&num"

            if state == None:
                state = c_state

            if state != c_state:
                if len(word_content) != 0:
                    word_list.append(word_content)
                    word_col_list.append(word_col_content)
                    state_list.append(state)
                    word_content = []
                    word_col_content = []
                state = c_state

            if state != "splitter":
                word_content.append(char)
                word_col_content.append(valid_col[c_i])

        if len(word_content) != 0:
            word_list.append(word_content)
            word_col_list.append(word_col_content)
            state_list.append(state)

        return word_list, word_col_list, state_list

    def decode(
            self,
            text_index,
            text_prob=None,
            is_remove_duplicate=False,
            return_word_box=False,
    ):
        """ convert text-index into text-label. """
        result_list = []
        batch_size = text_index.shape[0]
        blank_word = self.get_ignored_tokens()[0]
        for batch_idx in range(batch_size):
            probs = None if text_prob is None else np.array(text_prob[batch_idx])
            sequence = text_index[batch_idx]

            final_mask = sequence != blank_word
            if is_remove_duplicate:
                duplicate_mask = np.insert(sequence[1:] != sequence[:-1], 0, True)
                final_mask &= duplicate_mask

            sequence = sequence[final_mask]
            probs = None if probs is None else probs[final_mask]
            text = "".join(self.character[sequence])

            if text_prob is not None and probs is not None and len(probs) > 0:
                mean_conf = np.mean(probs)
            else:
                # 如果没有提供概率或最终结果为空，则默认置信度为1.0
                mean_conf = 1.0
            result_list.append((text, mean_conf))
        return result_list

    def get_ignored_tokens(self):
        return [0]  # for ctc blank


class CTCLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(CTCLabelDecode, self).__init__(character_dict_path,
                                             use_space_char)

    def __call__(self, preds, label=None, return_word_box=False, *args, **kwargs):
        preds_prob, preds_idx = preds.max(axis=2)
        text = self.decode(
            preds_idx.cpu().numpy(),
            preds_prob.float().cpu().numpy(),
            is_remove_duplicate=True,
            return_word_box=return_word_box,
        )
        if return_word_box:
            for rec_idx, rec in enumerate(text):
                wh_ratio = kwargs["wh_ratio_list"][rec_idx]
                max_wh_ratio = kwargs["max_wh_ratio"]
                rec[2][0] = rec[2][0] * (wh_ratio / max_wh_ratio)

        if label is None:
            return text
        label = self.decode(label.cpu().numpy())
        return text, label

    def add_special_char(self, dict_character):
        dict_character = ['blank'] + dict_character
        return dict_character


class NRTRLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self, character_dict_path=None, use_space_char=True, **kwargs):
        super(NRTRLabelDecode, self).__init__(character_dict_path,
                                              use_space_char)

    def __call__(self, preds, label=None, *args, **kwargs):

        if len(preds) == 2:
            preds_id = preds[0]
            preds_prob = preds[1]
            if isinstance(preds_id, torch.Tensor):
                preds_id = preds_id.numpy()
            if isinstance(preds_prob, torch.Tensor):
                preds_prob = preds_prob.numpy()
            if preds_id[0][0] == 2:
                preds_idx = preds_id[:, 1:]
                preds_prob = preds_prob[:, 1:]
            else:
                preds_idx = preds_id
            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
            if label is None:
                return text
            label = self.decode(label[:, 1:])
        else:
            if isinstance(preds, torch.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[:, 1:])
        return text, label

    def add_special_char(self, dict_character):
        dict_character = ['blank', '<unk>', '<s>', '</s>'] + dict_character
        return dict_character

    def decode(self, text_index, text_prob=None, is_remove_duplicate=False):
        """ 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 == '</s>':  # end
                    break
                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.lower(), np.mean(conf_list).tolist()))
        return result_list

class ViTSTRLabelDecode(NRTRLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self, character_dict_path=None, use_space_char=False,
                 **kwargs):
        super(ViTSTRLabelDecode, self).__init__(character_dict_path,
                                                use_space_char)

    def __call__(self, preds, label=None, *args, **kwargs):
        if isinstance(preds, torch.Tensor):
            preds = preds[:, 1:].numpy()
        else:
            preds = preds[:, 1:]
        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[:, 1:])
        return text, label

    def add_special_char(self, dict_character):
        dict_character = ['<s>', '</s>'] + dict_character
        return dict_character


class AttnLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        super(AttnLabelDecode, self).__init__(character_dict_path,
                                              use_space_char)

    def add_special_char(self, dict_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        dict_character = dict_character
        dict_character = [self.beg_str] + dict_character + [self.end_str]
        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()
        [beg_idx, end_idx] = 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(end_idx):
                    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)
            result_list.append((text, np.mean(conf_list)))
        return result_list

    def __call__(self, preds, label=None, *args, **kwargs):
        """
        text = self.decode(text)
        if label is None:
            return text
        else:
            label = self.decode(label, is_remove_duplicate=False)
            return text, label
        """
        if isinstance(preds, torch.Tensor):
            preds = preds.cpu().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):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "unsupport type %s in get_beg_end_flag_idx" \
                          % beg_or_end
        return idx


class RFLLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self, character_dict_path=None, use_space_char=False,
                 **kwargs):
        super(RFLLabelDecode, self).__init__(character_dict_path,
                                             use_space_char)

    def add_special_char(self, dict_character):
        self.beg_str = "sos"
        self.end_str = "eos"
        dict_character = dict_character
        dict_character = [self.beg_str] + dict_character + [self.end_str]
        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()
        [beg_idx, end_idx] = 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(end_idx):
                    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)
            result_list.append((text, np.mean(conf_list).tolist()))
        return result_list

    def __call__(self, preds, label=None, *args, **kwargs):
        # if seq_outputs is not None:
        if isinstance(preds, tuple) or isinstance(preds, list):
            cnt_outputs, seq_outputs = preds
            if isinstance(seq_outputs, torch.Tensor):
                seq_outputs = seq_outputs.numpy()
            preds_idx = seq_outputs.argmax(axis=2)
            preds_prob = seq_outputs.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

        else:
            cnt_outputs = preds
            if isinstance(cnt_outputs, torch.Tensor):
                cnt_outputs = cnt_outputs.numpy()
            cnt_length = []
            for lens in cnt_outputs:
                length = round(np.sum(lens))
                cnt_length.append(length)
            if label is None:
                return cnt_length
            label = self.decode(label, is_remove_duplicate=False)
            length = [len(res[0]) for res in label]
            return cnt_length, length

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "unsupport type %s in get_beg_end_flag_idx" \
                          % beg_or_end
        return idx


class SRNLabelDecode(BaseRecLabelDecode):
    """ Convert between text-label and text-index """

    def __init__(self,
                 character_dict_path=None,
                 use_space_char=False,
                 **kwargs):
        self.max_text_length = kwargs.get('max_text_length', 25)
        super(SRNLabelDecode, self).__init__(character_dict_path,
                                             use_space_char)

    def __call__(self, preds, label=None, *args, **kwargs):
        pred = preds['predict']
        char_num = len(self.character_str) + 2
        if isinstance(pred, torch.Tensor):
            pred = pred.numpy()
        pred = np.reshape(pred, [-1, char_num])

        preds_idx = np.argmax(pred, axis=1)
        preds_prob = np.max(pred, axis=1)

        preds_idx = np.reshape(preds_idx, [-1, self.max_text_length])

        preds_prob = np.reshape(preds_prob, [-1, self.max_text_length])

        text = self.decode(preds_idx, preds_prob)

        if label is None:
            text = self.decode(preds_idx, preds_prob, is_remove_duplicate=False)
            return text
        label = self.decode(label)
        return text, label

    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 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)
            result_list.append((text, np.mean(conf_list)))
        return result_list

    def add_special_char(self, dict_character):
        dict_character = dict_character + [self.beg_str, self.end_str]
        return dict_character

    def get_ignored_tokens(self):
        beg_idx = self.get_beg_end_flag_idx("beg")
        end_idx = self.get_beg_end_flag_idx("end")
        return [beg_idx, end_idx]

    def get_beg_end_flag_idx(self, beg_or_end):
        if beg_or_end == "beg":
            idx = np.array(self.dict[self.beg_str])
        elif beg_or_end == "end":
            idx = np.array(self.dict[self.end_str])
        else:
            assert False, "unsupport type %s in get_beg_end_flag_idx" \
                          % beg_or_end
        return idx


class TableLabelDecode(object):
    """  """

    def __init__(self,
                 character_dict_path,
                 **kwargs):
        list_character, list_elem = self.load_char_elem_dict(character_dict_path)
        list_character = self.add_special_char(list_character)
        list_elem = self.add_special_char(list_elem)
        self.dict_character = {}
        self.dict_idx_character = {}
        for i, char in enumerate(list_character):
            self.dict_idx_character[i] = char
            self.dict_character[char] = i
        self.dict_elem = {}
        self.dict_idx_elem = {}
        for i, elem in enumerate(list_elem):
            self.dict_idx_elem[i] = elem
            self.dict_elem[elem] = i

    def load_char_elem_dict(self, character_dict_path):
        list_character = []
        list_elem = []
        with open(character_dict_path, "rb") as fin:
            lines = fin.readlines()
            substr = lines[0].decode('utf-8').strip("\n").strip("\r\n").split("\t")
            character_num = int(substr[0])
            elem_num = int(substr[1])
            for cno in range(1, 1 + character_num):
                character = lines[cno].decode('utf-8').strip("\n").strip("\r\n")
                list_character.append(character)
            for eno in range(1 + character_num, 1 + character_num + elem_num):
                elem = lines[eno].decode('utf-8').strip("\n").strip("\r\n")
                list_elem.append(elem)
        return list_character, list_elem

    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,torch.Tensor):
            structure_probs = structure_probs.numpy()
        if isinstance(loc_preds,torch.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, torch.Tensor):
            preds = preds.cpu().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 CANLabelDecode(BaseRecLabelDecode):
    """ Convert between latex-symbol and symbol-index """

    def __init__(self, character_dict_path=None, use_space_char=False,
                 **kwargs):
        super(CANLabelDecode, self).__init__(character_dict_path,
                                             use_space_char)

    def decode(self, text_index, preds_prob=None):
        result_list = []
        batch_size = len(text_index)
        for batch_idx in range(batch_size):
            seq_end = text_index[batch_idx].argmin(0)
            idx_list = text_index[batch_idx][:seq_end].tolist()
            symbol_list = [self.character[idx] for idx in idx_list]
            probs = []
            if preds_prob is not None:
                probs = preds_prob[batch_idx][:len(symbol_list)].tolist()

            result_list.append([' '.join(symbol_list), probs])
        return result_list

    def __call__(self, preds, label=None, *args, **kwargs):
        pred_prob, _, _, _ = preds
        preds_idx = pred_prob.argmax(axis=2)

        text = self.decode(preds_idx)
        if label is None:
            return text
        label = self.decode(label)
        return text, label