基於Python的身份證驗證識別和數據處理

根據GB11643-1999公民身份證號碼是特征組合碼，由十七位數字本體碼和一位數字校驗碼組成，排列順序從左至右依次為：

1. 六位數字地址碼
2. 八位數字出生日期碼
3. 三位數字順序碼
4. 一位數字校驗碼（數字10用羅馬X表示）

校驗系統：

     校驗碼采用ISO7064:1983，MOD11-2校驗碼系統（圖為校驗規則樣例）

用身份證號的前17位的每一位號碼字符值分別乘上對應的加權因子值，得到的結果求和后對11進行取余，最后的結果放到表2檢驗碼字符值..換算關系表中得出最后的一位身份證號碼

代碼：

# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
"""Convert BERT checkpoint."""


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
    # Initialise PyTorch model
    config = BertConfig.from_json_file(bert_config_file)
    print("Building PyTorch model from configuration: {}".format(str(config)))
    model = BertForPreTraining(config)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(model, config, tf_checkpoint_path)

    # Save pytorch-model
    print("Save PyTorch model to {}".format(pytorch_dump_path))
    torch.save(model.state_dict(), pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--bert_config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained BERT model. \n"
        "This specifies the model architecture.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)